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A

abs(Column) - Static method in class org.apache.spark.sql.functions
Computes the absolute value.
abs() - Method in class org.apache.spark.sql.types.Decimal
 
AbsoluteError - Class in org.apache.spark.mllib.tree.loss
:: DeveloperApi :: Class for absolute error loss calculation (for regression).
AbsoluteError() - Constructor for class org.apache.spark.mllib.tree.loss.AbsoluteError
 
accessTime() - Method in class org.apache.spark.sql.sources.HadoopFsRelation.FakeFileStatus
 
accId() - Method in class org.apache.spark.CleanAccum
 
Accumulable<R,T> - Class in org.apache.spark
A data type that can be accumulated, ie has an commutative and associative "add" operation, but where the result type, R, may be different from the element type being added, T.
Accumulable(R, AccumulableParam<R, T>, Option<String>) - Constructor for class org.apache.spark.Accumulable
 
Accumulable(R, AccumulableParam<R, T>) - Constructor for class org.apache.spark.Accumulable
 
accumulable(T, AccumulableParam<T, R>) - Method in class org.apache.spark.api.java.JavaSparkContext
Create an Accumulable shared variable of the given type, to which tasks can "add" values with add.
accumulable(T, String, AccumulableParam<T, R>) - Method in class org.apache.spark.api.java.JavaSparkContext
Create an Accumulable shared variable of the given type, to which tasks can "add" values with add.
accumulable(R, AccumulableParam<R, T>) - Method in class org.apache.spark.SparkContext
Create an Accumulable shared variable, to which tasks can add values with +=.
accumulable(R, String, AccumulableParam<R, T>) - Method in class org.apache.spark.SparkContext
Create an Accumulable shared variable, with a name for display in the Spark UI.
accumulableCollection(R, Function1<R, Growable<T>>, ClassTag<R>) - Method in class org.apache.spark.SparkContext
Create an accumulator from a "mutable collection" type.
AccumulableInfo - Class in org.apache.spark.scheduler
:: DeveloperApi :: Information about an Accumulable modified during a task or stage.
AccumulableInfo - Class in org.apache.spark.status.api.v1
 
AccumulableParam<R,T> - Interface in org.apache.spark
Helper object defining how to accumulate values of a particular type.
accumulables() - Method in class org.apache.spark.scheduler.StageInfo
Terminal values of accumulables updated during this stage.
accumulables() - Method in class org.apache.spark.scheduler.TaskInfo
Intermediate updates to accumulables during this task.
Accumulator<T> - Class in org.apache.spark
A simpler value of Accumulable where the result type being accumulated is the same as the types of elements being merged, i.e.
Accumulator(T, AccumulatorParam<T>, Option<String>) - Constructor for class org.apache.spark.Accumulator
 
Accumulator(T, AccumulatorParam<T>) - Constructor for class org.apache.spark.Accumulator
 
accumulator(int) - Method in class org.apache.spark.api.java.JavaSparkContext
Create an Accumulator integer variable, which tasks can "add" values to using the add method.
accumulator(int, String) - Method in class org.apache.spark.api.java.JavaSparkContext
Create an Accumulator integer variable, which tasks can "add" values to using the add method.
accumulator(double) - Method in class org.apache.spark.api.java.JavaSparkContext
Create an Accumulator double variable, which tasks can "add" values to using the add method.
accumulator(double, String) - Method in class org.apache.spark.api.java.JavaSparkContext
Create an Accumulator double variable, which tasks can "add" values to using the add method.
accumulator(T, AccumulatorParam<T>) - Method in class org.apache.spark.api.java.JavaSparkContext
Create an Accumulator variable of a given type, which tasks can "add" values to using the add method.
accumulator(T, String, AccumulatorParam<T>) - Method in class org.apache.spark.api.java.JavaSparkContext
Create an Accumulator variable of a given type, which tasks can "add" values to using the add method.
accumulator(T, AccumulatorParam<T>) - Method in class org.apache.spark.SparkContext
Create an Accumulator variable of a given type, which tasks can "add" values to using the += method.
accumulator(T, String, AccumulatorParam<T>) - Method in class org.apache.spark.SparkContext
Create an Accumulator variable of a given type, with a name for display in the Spark UI.
AccumulatorParam<T> - Interface in org.apache.spark
A simpler version of AccumulableParam where the only data type you can add in is the same type as the accumulated value.
AccumulatorParam.DoubleAccumulatorParam$ - Class in org.apache.spark
 
AccumulatorParam.DoubleAccumulatorParam$() - Constructor for class org.apache.spark.AccumulatorParam.DoubleAccumulatorParam$
 
AccumulatorParam.FloatAccumulatorParam$ - Class in org.apache.spark
 
AccumulatorParam.FloatAccumulatorParam$() - Constructor for class org.apache.spark.AccumulatorParam.FloatAccumulatorParam$
 
AccumulatorParam.IntAccumulatorParam$ - Class in org.apache.spark
 
AccumulatorParam.IntAccumulatorParam$() - Constructor for class org.apache.spark.AccumulatorParam.IntAccumulatorParam$
 
AccumulatorParam.LongAccumulatorParam$ - Class in org.apache.spark
 
AccumulatorParam.LongAccumulatorParam$() - Constructor for class org.apache.spark.AccumulatorParam.LongAccumulatorParam$
 
accumulatorUpdates() - Method in class org.apache.spark.status.api.v1.StageData
 
accumulatorUpdates() - Method in class org.apache.spark.status.api.v1.TaskData
 
accuracy() - Method in class org.apache.spark.mllib.evaluation.MultilabelMetrics
Returns accuracy
acos(Column) - Static method in class org.apache.spark.sql.functions
Computes the cosine inverse of the given value; the returned angle is in the range 0.0 through pi.
acos(String) - Static method in class org.apache.spark.sql.functions
Computes the cosine inverse of the given column; the returned angle is in the range 0.0 through pi.
active() - Method in class org.apache.spark.streaming.scheduler.ReceiverInfo
 
activeJobs() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
activeStages() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
activeTasks() - Method in class org.apache.spark.status.api.v1.ExecutorSummary
 
ActorHelper - Interface in org.apache.spark.streaming.receiver
:: DeveloperApi :: A receiver trait to be mixed in with your Actor to gain access to the API for pushing received data into Spark Streaming for being processed.
actorStream(Props, String, StorageLevel, SupervisorStrategy) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream with any arbitrary user implemented actor receiver.
actorStream(Props, String, StorageLevel) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream with any arbitrary user implemented actor receiver.
actorStream(Props, String) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream with any arbitrary user implemented actor receiver.
actorStream(Props, String, StorageLevel, SupervisorStrategy, ClassTag<T>) - Method in class org.apache.spark.streaming.StreamingContext
Create an input stream with any arbitrary user implemented actor receiver.
ActorSupervisorStrategy - Class in org.apache.spark.streaming.receiver
:: DeveloperApi :: A helper with set of defaults for supervisor strategy
ActorSupervisorStrategy() - Constructor for class org.apache.spark.streaming.receiver.ActorSupervisorStrategy
 
actorSystem() - Method in class org.apache.spark.SparkEnv
 
add(T) - Method in class org.apache.spark.Accumulable
Add more data to this accumulator / accumulable
add(org.apache.spark.ml.feature.Instance) - Method in class org.apache.spark.ml.classification.LogisticAggregator
Add a new training instance to this LogisticAggregator, and update the loss and gradient of the objective function.
add(AFTPoint) - Method in class org.apache.spark.ml.regression.AFTAggregator
 
add(org.apache.spark.ml.feature.Instance) - Method in class org.apache.spark.ml.regression.LeastSquaresAggregator
Add a new training instance to this LeastSquaresAggregator, and update the loss and gradient of the objective function.
add(double[], MultivariateGaussian[], ExpectationSum, Vector<Object>) - Static method in class org.apache.spark.mllib.clustering.ExpectationSum
 
add(Vector) - Method in class org.apache.spark.mllib.feature.IDF.DocumentFrequencyAggregator
Adds a new document.
add(BlockMatrix) - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
Adds two block matrices together.
add(Vector) - Method in class org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
Add a new sample to this summarizer, and update the statistical summary.
add(StructField) - Method in class org.apache.spark.sql.types.StructType
Creates a new StructType by adding a new field.
add(String, DataType) - Method in class org.apache.spark.sql.types.StructType
Creates a new StructType by adding a new nullable field with no metadata.
add(String, DataType, boolean) - Method in class org.apache.spark.sql.types.StructType
Creates a new StructType by adding a new field with no metadata.
add(String, DataType, boolean, Metadata) - Method in class org.apache.spark.sql.types.StructType
Creates a new StructType by adding a new field and specifying metadata.
add(String, String) - Method in class org.apache.spark.sql.types.StructType
Creates a new StructType by adding a new nullable field with no metadata where the dataType is specified as a String.
add(String, String, boolean) - Method in class org.apache.spark.sql.types.StructType
Creates a new StructType by adding a new field with no metadata where the dataType is specified as a String.
add(String, String, boolean, Metadata) - Method in class org.apache.spark.sql.types.StructType
Creates a new StructType by adding a new field and specifying metadata where the dataType is specified as a String.
add(Vector) - Method in class org.apache.spark.util.Vector
 
add_months(Column, int) - Static method in class org.apache.spark.sql.functions
Returns the date that is numMonths after startDate.
addAccumulator(R, T) - Method in interface org.apache.spark.AccumulableParam
Add additional data to the accumulator value.
addAccumulator(T, T) - Method in interface org.apache.spark.AccumulatorParam
 
addAppArgs(String...) - Method in class org.apache.spark.launcher.SparkLauncher
Adds command line arguments for the application.
addedFiles() - Method in class org.apache.spark.SparkContext
 
addedJars() - Method in class org.apache.spark.SparkContext
 
addFile(String) - Method in class org.apache.spark.api.java.JavaSparkContext
Add a file to be downloaded with this Spark job on every node.
addFile(String) - Method in class org.apache.spark.launcher.SparkLauncher
Adds a file to be submitted with the application.
addFile(String) - Method in class org.apache.spark.SparkContext
Add a file to be downloaded with this Spark job on every node.
addFile(String, boolean) - Method in class org.apache.spark.SparkContext
Add a file to be downloaded with this Spark job on every node.
addGrid(Param<T>, Iterable<T>) - Method in class org.apache.spark.ml.tuning.ParamGridBuilder
Adds a param with multiple values (overwrites if the input param exists).
addGrid(DoubleParam, double[]) - Method in class org.apache.spark.ml.tuning.ParamGridBuilder
Adds a double param with multiple values.
addGrid(IntParam, int[]) - Method in class org.apache.spark.ml.tuning.ParamGridBuilder
Adds a int param with multiple values.
addGrid(FloatParam, float[]) - Method in class org.apache.spark.ml.tuning.ParamGridBuilder
Adds a float param with multiple values.
addGrid(LongParam, long[]) - Method in class org.apache.spark.ml.tuning.ParamGridBuilder
Adds a long param with multiple values.
addGrid(BooleanParam) - Method in class org.apache.spark.ml.tuning.ParamGridBuilder
Adds a boolean param with true and false.
addInPlace(R, R) - Method in interface org.apache.spark.AccumulableParam
Merge two accumulated values together.
addInPlace(double, double) - Method in class org.apache.spark.AccumulatorParam.DoubleAccumulatorParam$
 
addInPlace(float, float) - Method in class org.apache.spark.AccumulatorParam.FloatAccumulatorParam$
 
addInPlace(int, int) - Method in class org.apache.spark.AccumulatorParam.IntAccumulatorParam$
 
addInPlace(long, long) - Method in class org.apache.spark.AccumulatorParam.LongAccumulatorParam$
 
addInPlace(double, double) - Method in class org.apache.spark.SparkContext.DoubleAccumulatorParam$
 
addInPlace(float, float) - Method in class org.apache.spark.SparkContext.FloatAccumulatorParam$
 
addInPlace(int, int) - Method in class org.apache.spark.SparkContext.IntAccumulatorParam$
 
addInPlace(long, long) - Method in class org.apache.spark.SparkContext.LongAccumulatorParam$
 
addInPlace(Vector) - Method in class org.apache.spark.util.Vector
 
addInPlace(Vector, Vector) - Method in class org.apache.spark.util.Vector.VectorAccumParam$
 
addIntercept() - Method in class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
Whether to add intercept (default: false).
addJar(String) - Method in class org.apache.spark.api.java.JavaSparkContext
Adds a JAR dependency for all tasks to be executed on this SparkContext in the future.
addJar(String) - Method in class org.apache.spark.launcher.SparkLauncher
Adds a jar file to be submitted with the application.
addJar(String) - Method in class org.apache.spark.SparkContext
Adds a JAR dependency for all tasks to be executed on this SparkContext in the future.
addJar(String) - Method in class org.apache.spark.sql.hive.HiveContext
 
addJar(String) - Method in class org.apache.spark.sql.SQLContext
Add a jar to SQLContext
addListener(SparkAppHandle.Listener) - Method in interface org.apache.spark.launcher.SparkAppHandle
Adds a listener to be notified of changes to the handle's information.
addLocalConfiguration(String, int, int, int, JobConf) - Static method in class org.apache.spark.rdd.HadoopRDD
Add Hadoop configuration specific to a single partition and attempt.
addOnCompleteCallback(Function0<BoxedUnit>) - Method in class org.apache.spark.TaskContext
Adds a callback function to be executed on task completion.
addPartToPGroup(Partition, PartitionGroup) - Method in class org.apache.spark.rdd.PartitionCoalescer
 
addPyFile(String) - Method in class org.apache.spark.launcher.SparkLauncher
Adds a python file / zip / egg to be submitted with the application.
address() - Method in class org.apache.spark.status.api.v1.RDDDataDistribution
 
addSparkArg(String) - Method in class org.apache.spark.launcher.SparkLauncher
Adds a no-value argument to the Spark invocation.
addSparkArg(String, String) - Method in class org.apache.spark.launcher.SparkLauncher
Adds an argument with a value to the Spark invocation.
addSparkListener(SparkListener) - Method in class org.apache.spark.SparkContext
:: DeveloperApi :: Register a listener to receive up-calls from events that happen during execution.
addStreamingListener(StreamingListener) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Add a StreamingListener object for receiving system events related to streaming.
addStreamingListener(StreamingListener) - Method in class org.apache.spark.streaming.StreamingContext
Add a StreamingListener object for receiving system events related to streaming.
addTaskCompletionListener(TaskCompletionListener) - Method in class org.apache.spark.TaskContext
Adds a (Java friendly) listener to be executed on task completion.
addTaskCompletionListener(Function1<TaskContext, BoxedUnit>) - Method in class org.apache.spark.TaskContext
Adds a listener in the form of a Scala closure to be executed on task completion.
AFTAggregator - Class in org.apache.spark.ml.regression
 
AFTAggregator(DenseVector<Object>, boolean) - Constructor for class org.apache.spark.ml.regression.AFTAggregator
 
AFTCostFun - Class in org.apache.spark.ml.regression
 
AFTCostFun(RDD<AFTPoint>, boolean) - Constructor for class org.apache.spark.ml.regression.AFTCostFun
 
AFTSurvivalRegression - Class in org.apache.spark.ml.regression
:: Experimental :: Fit a parametric survival regression model named accelerated failure time (AFT) model (https://en.wikipedia.org/wiki/Accelerated_failure_time_model) based on the Weibull distribution of the survival time.
AFTSurvivalRegression(String) - Constructor for class org.apache.spark.ml.regression.AFTSurvivalRegression
 
AFTSurvivalRegression() - Constructor for class org.apache.spark.ml.regression.AFTSurvivalRegression
 
AFTSurvivalRegressionModel - Class in org.apache.spark.ml.regression
:: Experimental :: Model produced by AFTSurvivalRegression.
agg(Column, Column...) - Method in class org.apache.spark.sql.DataFrame
Aggregates on the entire DataFrame without groups.
agg(Tuple2<String, String>, Seq<Tuple2<String, String>>) - Method in class org.apache.spark.sql.DataFrame
(Scala-specific) Aggregates on the entire DataFrame without groups.
agg(Map<String, String>) - Method in class org.apache.spark.sql.DataFrame
(Scala-specific) Aggregates on the entire DataFrame without groups.
agg(Map<String, String>) - Method in class org.apache.spark.sql.DataFrame
(Java-specific) Aggregates on the entire DataFrame without groups.
agg(Column, Seq<Column>) - Method in class org.apache.spark.sql.DataFrame
Aggregates on the entire DataFrame without groups.
agg(Column, Column...) - Method in class org.apache.spark.sql.GroupedData
Compute aggregates by specifying a series of aggregate columns.
agg(Tuple2<String, String>, Seq<Tuple2<String, String>>) - Method in class org.apache.spark.sql.GroupedData
(Scala-specific) Compute aggregates by specifying a map from column name to aggregate methods.
agg(Map<String, String>) - Method in class org.apache.spark.sql.GroupedData
(Scala-specific) Compute aggregates by specifying a map from column name to aggregate methods.
agg(Map<String, String>) - Method in class org.apache.spark.sql.GroupedData
(Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.
agg(Column, Seq<Column>) - Method in class org.apache.spark.sql.GroupedData
Compute aggregates by specifying a series of aggregate columns.
agg(TypedColumn<V, U1>) - Method in class org.apache.spark.sql.GroupedDataset
Computes the given aggregation, returning a Dataset of tuples for each unique key and the result of computing this aggregation over all elements in the group.
agg(TypedColumn<V, U1>, TypedColumn<V, U2>) - Method in class org.apache.spark.sql.GroupedDataset
Computes the given aggregations, returning a Dataset of tuples for each unique key and the result of computing these aggregations over all elements in the group.
agg(TypedColumn<V, U1>, TypedColumn<V, U2>, TypedColumn<V, U3>) - Method in class org.apache.spark.sql.GroupedDataset
Computes the given aggregations, returning a Dataset of tuples for each unique key and the result of computing these aggregations over all elements in the group.
agg(TypedColumn<V, U1>, TypedColumn<V, U2>, TypedColumn<V, U3>, TypedColumn<V, U4>) - Method in class org.apache.spark.sql.GroupedDataset
Computes the given aggregations, returning a Dataset of tuples for each unique key and the result of computing these aggregations over all elements in the group.
aggregate(U, Function2<U, T, U>, Function2<U, U, U>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral "zero value".
aggregate(U, Function2<U, T, U>, Function2<U, U, U>, ClassTag<U>) - Method in class org.apache.spark.rdd.RDD
Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral "zero value".
aggregateByKey(U, Partitioner, Function2<U, V, U>, Function2<U, U, U>) - Method in class org.apache.spark.api.java.JavaPairRDD
Aggregate the values of each key, using given combine functions and a neutral "zero value".
aggregateByKey(U, int, Function2<U, V, U>, Function2<U, U, U>) - Method in class org.apache.spark.api.java.JavaPairRDD
Aggregate the values of each key, using given combine functions and a neutral "zero value".
aggregateByKey(U, Function2<U, V, U>, Function2<U, U, U>) - Method in class org.apache.spark.api.java.JavaPairRDD
Aggregate the values of each key, using given combine functions and a neutral "zero value".
aggregateByKey(U, Partitioner, Function2<U, V, U>, Function2<U, U, U>, ClassTag<U>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Aggregate the values of each key, using given combine functions and a neutral "zero value".
aggregateByKey(U, int, Function2<U, V, U>, Function2<U, U, U>, ClassTag<U>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Aggregate the values of each key, using given combine functions and a neutral "zero value".
aggregateByKey(U, Function2<U, V, U>, Function2<U, U, U>, ClassTag<U>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Aggregate the values of each key, using given combine functions and a neutral "zero value".
AggregatedDialect - Class in org.apache.spark.sql.jdbc
AggregatedDialect can unify multiple dialects into one virtual Dialect.
AggregatedDialect(List<JdbcDialect>) - Constructor for class org.apache.spark.sql.jdbc.AggregatedDialect
 
aggregateMessages(Function1<EdgeContext<VD, ED, A>, BoxedUnit>, Function2<A, A, A>, TripletFields, ClassTag<A>) - Method in class org.apache.spark.graphx.Graph
Aggregates values from the neighboring edges and vertices of each vertex.
aggregateMessagesWithActiveSet(Function1<EdgeContext<VD, ED, A>, BoxedUnit>, Function2<A, A, A>, TripletFields, Option<Tuple2<VertexRDD<?>, EdgeDirection>>, ClassTag<A>) - Method in class org.apache.spark.graphx.impl.GraphImpl
 
aggregateUsingIndex(RDD<Tuple2<Object, VD2>>, Function2<VD2, VD2, VD2>, ClassTag<VD2>) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
aggregateUsingIndex(RDD<Tuple2<Object, VD2>>, Function2<VD2, VD2, VD2>, ClassTag<VD2>) - Method in class org.apache.spark.graphx.VertexRDD
Aggregates vertices in messages that have the same ids using reduceFunc, returning a VertexRDD co-indexed with this.
AggregatingEdgeContext<VD,ED,A> - Class in org.apache.spark.graphx.impl
 
AggregatingEdgeContext(Function2<A, A, A>, Object, BitSet) - Constructor for class org.apache.spark.graphx.impl.AggregatingEdgeContext
 
Aggregator<K,V,C> - Class in org.apache.spark
:: DeveloperApi :: A set of functions used to aggregate data.
Aggregator(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>) - Constructor for class org.apache.spark.Aggregator
 
aggregator() - Method in class org.apache.spark.ShuffleDependency
 
Aggregator<I,B,O> - Class in org.apache.spark.sql.expressions
A base class for user-defined aggregations, which can be used in DataFrame and Dataset operations to take all of the elements of a group and reduce them to a single value.
Aggregator() - Constructor for class org.apache.spark.sql.expressions.Aggregator
 
aggUntyped(Seq<TypedColumn<?, ?>>) - Method in class org.apache.spark.sql.GroupedDataset
Internal helper function for building typed aggregations that return tuples.
Algo - Class in org.apache.spark.mllib.tree.configuration
:: Experimental :: Enum to select the algorithm for the decision tree
Algo() - Constructor for class org.apache.spark.mllib.tree.configuration.Algo
 
algo() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
algo() - Method in class org.apache.spark.mllib.tree.model.DecisionTreeModel
 
algo() - Method in class org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
 
algo() - Method in class org.apache.spark.mllib.tree.model.RandomForestModel
 
algorithm() - Method in class org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD
 
algorithm() - Method in class org.apache.spark.mllib.regression.StreamingLinearAlgorithm
The algorithm to use for updating.
algorithm() - Method in class org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
 
alias(String) - Method in class org.apache.spark.sql.Column
Gives the column an alias.
alias(String) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame with an alias set.
alias(Symbol) - Method in class org.apache.spark.sql.DataFrame
(Scala-specific) Returns a new DataFrame with an alias set.
All - Static variable in class org.apache.spark.graphx.TripletFields
Expose all the fields (source, edge, and destination).
alpha() - Method in class org.apache.spark.mllib.random.WeibullGenerator
 
AlphaComponent - Annotation Type in org.apache.spark.annotation
A new component of Spark which may have unstable API's.
ALS - Class in org.apache.spark.ml.recommendation
:: Experimental :: Alternating Least Squares (ALS) matrix factorization.
ALS(String) - Constructor for class org.apache.spark.ml.recommendation.ALS
 
ALS() - Constructor for class org.apache.spark.ml.recommendation.ALS
 
ALS - Class in org.apache.spark.mllib.recommendation
 
ALS() - Constructor for class org.apache.spark.mllib.recommendation.ALS
 
ALS.Rating<ID> - Class in org.apache.spark.ml.recommendation
:: DeveloperApi :: Rating class for better code readability.
ALS.Rating(ID, ID, float) - Constructor for class org.apache.spark.ml.recommendation.ALS.Rating
 
ALS.Rating$ - Class in org.apache.spark.ml.recommendation
 
ALS.Rating$() - Constructor for class org.apache.spark.ml.recommendation.ALS.Rating$
 
ALSModel - Class in org.apache.spark.ml.recommendation
:: Experimental :: Model fitted by ALS.
AnalysisException - Exception in org.apache.spark.sql
:: DeveloperApi :: Thrown when a query fails to analyze, usually because the query itself is invalid.
AnalysisException(String, Option<Object>, Option<Object>) - Constructor for exception org.apache.spark.sql.AnalysisException
 
analyze(String) - Method in class org.apache.spark.sql.hive.HiveContext
Analyzes the given table in the current database to generate statistics, which will be used in query optimizations.
analyzer() - Method in class org.apache.spark.sql.hive.HiveContext
 
analyzer() - Method in class org.apache.spark.sql.SQLContext
 
and(Column) - Method in class org.apache.spark.sql.Column
Boolean AND.
And - Class in org.apache.spark.sql.sources
A filter that evaluates to true iff both left or right evaluate to true.
And(Filter, Filter) - Constructor for class org.apache.spark.sql.sources.And
 
antecedent() - Method in class org.apache.spark.mllib.fpm.AssociationRules.Rule
 
ANY() - Static method in class org.apache.spark.scheduler.TaskLocality
 
anyNull() - Method in interface org.apache.spark.sql.Row
Returns true if there are any NULL values in this row.
appAttemptId() - Method in class org.apache.spark.scheduler.SparkListenerApplicationStart
 
appendBias(Vector) - Static method in class org.apache.spark.mllib.util.MLUtils
Returns a new vector with 1.0 (bias) appended to the input vector.
appId() - Method in class org.apache.spark.scheduler.SparkListenerApplicationStart
 
applicationAttemptId() - Method in class org.apache.spark.SparkContext
 
ApplicationAttemptInfo - Class in org.apache.spark.status.api.v1
 
applicationId() - Method in class org.apache.spark.SparkContext
A unique identifier for the Spark application.
ApplicationInfo - Class in org.apache.spark.status.api.v1
 
ApplicationStatus - Enum in org.apache.spark.status.api.v1
 
apply(RDD<Tuple2<Object, VD>>, RDD<Edge<ED>>, VD, StorageLevel, StorageLevel, ClassTag<VD>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.Graph
Construct a graph from a collection of vertices and edges with attributes.
apply(RDD<Edge<ED>>, VD, StorageLevel, StorageLevel, ClassTag<VD>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.impl.GraphImpl
Create a graph from edges, setting referenced vertices to `defaultVertexAttr`.
apply(RDD<Tuple2<Object, VD>>, RDD<Edge<ED>>, VD, StorageLevel, StorageLevel, ClassTag<VD>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.impl.GraphImpl
Create a graph from vertices and edges, setting missing vertices to `defaultVertexAttr`.
apply(VertexRDD<VD>, EdgeRDD<ED>, ClassTag<VD>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.impl.GraphImpl
Create a graph from a VertexRDD and an EdgeRDD with arbitrary replicated vertices.
apply(Graph<VD, ED>, A, int, EdgeDirection, Function3<Object, VD, A, VD>, Function1<EdgeTriplet<VD, ED>, Iterator<Tuple2<Object, A>>>, Function2<A, A, A>, ClassTag<VD>, ClassTag<ED>, ClassTag<A>) - Static method in class org.apache.spark.graphx.Pregel
Execute a Pregel-like iterative vertex-parallel abstraction.
apply(RDD<Tuple2<Object, VD>>, ClassTag<VD>) - Static method in class org.apache.spark.graphx.VertexRDD
Constructs a standalone VertexRDD (one that is not set up for efficient joins with an EdgeRDD) from an RDD of vertex-attribute pairs.
apply(RDD<Tuple2<Object, VD>>, EdgeRDD<?>, VD, ClassTag<VD>) - Static method in class org.apache.spark.graphx.VertexRDD
Constructs a VertexRDD from an RDD of vertex-attribute pairs.
apply(RDD<Tuple2<Object, VD>>, EdgeRDD<?>, VD, Function2<VD, VD, VD>, ClassTag<VD>) - Static method in class org.apache.spark.graphx.VertexRDD
Constructs a VertexRDD from an RDD of vertex-attribute pairs.
apply(String) - Method in class org.apache.spark.ml.attribute.AttributeGroup
Gets an attribute by its name.
apply(int) - Method in class org.apache.spark.ml.attribute.AttributeGroup
Gets an attribute by its index.
apply(Param<T>) - Method in class org.apache.spark.ml.param.ParamMap
Gets the value of the input param or its default value if it does not exist.
apply(int, int) - Method in class org.apache.spark.mllib.linalg.DenseMatrix
 
apply(int) - Method in class org.apache.spark.mllib.linalg.DenseVector
 
apply(int, int) - Method in interface org.apache.spark.mllib.linalg.Matrix
Gets the (i, j)-th element.
apply(int, int) - Method in class org.apache.spark.mllib.linalg.SparseMatrix
 
apply(int) - Method in interface org.apache.spark.mllib.linalg.Vector
Gets the value of the ith element.
apply(int, Predict, double, boolean) - Static method in class org.apache.spark.mllib.tree.model.Node
Construct a node with nodeIndex, predict, impurity and isLeaf parameters.
apply(String) - Static method in class org.apache.spark.rdd.PartitionGroup
 
apply(long, String, Option<String>, String, boolean) - Static method in class org.apache.spark.scheduler.AccumulableInfo
 
apply(long, String, Option<String>, String) - Static method in class org.apache.spark.scheduler.AccumulableInfo
 
apply(long, String, String) - Static method in class org.apache.spark.scheduler.AccumulableInfo
 
apply(long, TaskMetrics) - Static method in class org.apache.spark.scheduler.RuntimePercentage
 
apply(Object) - Method in class org.apache.spark.sql.Column
Extracts a value or values from a complex type.
apply(String) - Method in class org.apache.spark.sql.DataFrame
Selects column based on the column name and return it as a Column.
apply(Column...) - Method in class org.apache.spark.sql.expressions.UserDefinedAggregateFunction
Creates a Column for this UDAF using given Columns as input arguments.
apply(Seq<Column>) - Method in class org.apache.spark.sql.expressions.UserDefinedAggregateFunction
Creates a Column for this UDAF using given Columns as input arguments.
apply(DataFrame, Seq<Expression>, GroupedData.GroupType) - Static method in class org.apache.spark.sql.GroupedData
 
apply(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i.
apply(DataType) - Static method in class org.apache.spark.sql.types.ArrayType
Construct a ArrayType object with the given element type.
apply(double) - Static method in class org.apache.spark.sql.types.Decimal
 
apply(long) - Static method in class org.apache.spark.sql.types.Decimal
 
apply(int) - Static method in class org.apache.spark.sql.types.Decimal
 
apply(BigDecimal) - Static method in class org.apache.spark.sql.types.Decimal
 
apply(BigDecimal) - Static method in class org.apache.spark.sql.types.Decimal
 
apply(BigDecimal, int, int) - Static method in class org.apache.spark.sql.types.Decimal
 
apply(BigDecimal, int, int) - Static method in class org.apache.spark.sql.types.Decimal
 
apply(long, int, int) - Static method in class org.apache.spark.sql.types.Decimal
 
apply(String) - Static method in class org.apache.spark.sql.types.Decimal
 
apply() - Static method in class org.apache.spark.sql.types.DecimalType
 
apply(Option<PrecisionInfo>) - Static method in class org.apache.spark.sql.types.DecimalType
 
apply(DataType, DataType) - Static method in class org.apache.spark.sql.types.MapType
Construct a MapType object with the given key type and value type.
apply(String) - Method in class org.apache.spark.sql.types.StructType
Extracts a StructField of the given name.
apply(Set<String>) - Method in class org.apache.spark.sql.types.StructType
Returns a StructType containing StructFields of the given names, preserving the original order of fields.
apply(int) - Method in class org.apache.spark.sql.types.StructType
 
apply(Seq<Column>) - Method in class org.apache.spark.sql.UserDefinedFunction
 
apply(String) - Static method in class org.apache.spark.storage.BlockId
Converts a BlockId "name" String back into a BlockId.
apply(String, String, int) - Static method in class org.apache.spark.storage.BlockManagerId
Returns a BlockManagerId for the given configuration.
apply(ObjectInput) - Static method in class org.apache.spark.storage.BlockManagerId
 
apply(boolean, boolean, boolean, boolean, int) - Static method in class org.apache.spark.storage.StorageLevel
:: DeveloperApi :: Create a new StorageLevel object without setting useOffHeap.
apply(boolean, boolean, boolean, int) - Static method in class org.apache.spark.storage.StorageLevel
:: DeveloperApi :: Create a new StorageLevel object.
apply(int, int) - Static method in class org.apache.spark.storage.StorageLevel
:: DeveloperApi :: Create a new StorageLevel object from its integer representation.
apply(ObjectInput) - Static method in class org.apache.spark.storage.StorageLevel
:: DeveloperApi :: Read StorageLevel object from ObjectInput stream.
apply(String, int) - Static method in class org.apache.spark.streaming.kafka.Broker
 
apply(String, int, long, long) - Static method in class org.apache.spark.streaming.kafka.OffsetRange
 
apply(TopicAndPartition, long, long) - Static method in class org.apache.spark.streaming.kafka.OffsetRange
 
apply(long) - Static method in class org.apache.spark.streaming.Milliseconds
 
apply(long) - Static method in class org.apache.spark.streaming.Minutes
 
apply(long) - Static method in class org.apache.spark.streaming.Seconds
 
apply(TraversableOnce<Object>) - Static method in class org.apache.spark.util.StatCounter
Build a StatCounter from a list of values.
apply(Seq<Object>) - Static method in class org.apache.spark.util.StatCounter
Build a StatCounter from a list of values passed as variable-length arguments.
apply(int) - Method in class org.apache.spark.util.Vector
 
applySchema(RDD<Row>, StructType) - Method in class org.apache.spark.sql.SQLContext
 
applySchema(JavaRDD<Row>, StructType) - Method in class org.apache.spark.sql.SQLContext
 
applySchema(RDD<?>, Class<?>) - Method in class org.apache.spark.sql.SQLContext
 
applySchema(JavaRDD<?>, Class<?>) - Method in class org.apache.spark.sql.SQLContext
 
applySchemaToPythonRDD(RDD<Object[]>, String) - Method in class org.apache.spark.sql.SQLContext
 
applySchemaToPythonRDD(RDD<Object[]>, StructType) - Method in class org.apache.spark.sql.SQLContext
 
appName() - Method in class org.apache.spark.api.java.JavaSparkContext
 
appName() - Method in class org.apache.spark.scheduler.SparkListenerApplicationStart
 
appName() - Method in class org.apache.spark.SparkContext
 
approxCountDistinct(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the approximate number of distinct items in a group.
approxCountDistinct(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the approximate number of distinct items in a group.
approxCountDistinct(Column, double) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the approximate number of distinct items in a group.
approxCountDistinct(String, double) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the approximate number of distinct items in a group.
ApproxHist() - Static method in class org.apache.spark.mllib.tree.configuration.QuantileStrategy
 
areaUnderPR() - Method in class org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
Computes the area under the precision-recall curve.
areaUnderROC() - Method in class org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
Computes the area under the receiver operating characteristic (ROC) curve.
areaUnderROC() - Method in class org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
Computes the area under the receiver operating characteristic (ROC) curve.
argmax() - Method in class org.apache.spark.mllib.linalg.DenseVector
 
argmax() - Method in class org.apache.spark.mllib.linalg.SparseVector
 
argmax() - Method in interface org.apache.spark.mllib.linalg.Vector
Find the index of a maximal element.
arr() - Method in class org.apache.spark.rdd.PartitionGroup
 
array(DataType) - Method in class org.apache.spark.sql.ColumnName
Creates a new StructField of type array.
array(Column...) - Static method in class org.apache.spark.sql.functions
Creates a new array column.
array(String, String...) - Static method in class org.apache.spark.sql.functions
Creates a new array column.
array(Seq<Column>) - Static method in class org.apache.spark.sql.functions
Creates a new array column.
array(String, Seq<String>) - Static method in class org.apache.spark.sql.functions
Creates a new array column.
array_contains(Column, Object) - Static method in class org.apache.spark.sql.functions
Returns true if the array contain the value
arrayLengthGt(double) - Static method in class org.apache.spark.ml.param.ParamValidators
Check that the array length is greater than lowerBound.
ArrayType - Class in org.apache.spark.sql.types
 
ArrayType(DataType, boolean) - Constructor for class org.apache.spark.sql.types.ArrayType
 
ArrayType() - Constructor for class org.apache.spark.sql.types.ArrayType
No-arg constructor for kryo.
as(Encoder<U>) - Method in class org.apache.spark.sql.Column
Provides a type hint about the expected return value of this column.
as(String) - Method in class org.apache.spark.sql.Column
Gives the column an alias.
as(Seq<String>) - Method in class org.apache.spark.sql.Column
(Scala-specific) Assigns the given aliases to the results of a table generating function.
as(String[]) - Method in class org.apache.spark.sql.Column
Assigns the given aliases to the results of a table generating function.
as(Symbol) - Method in class org.apache.spark.sql.Column
Gives the column an alias.
as(String, Metadata) - Method in class org.apache.spark.sql.Column
Gives the column an alias with metadata.
as(Encoder<U>) - Method in class org.apache.spark.sql.DataFrame
:: Experimental :: Converts this DataFrame to a strongly-typed Dataset containing objects of the specified type, U.
as(String) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame with an alias set.
as(Symbol) - Method in class org.apache.spark.sql.DataFrame
(Scala-specific) Returns a new DataFrame with an alias set.
as(Encoder<U>) - Method in class org.apache.spark.sql.Dataset
Returns a new Dataset where each record has been mapped on to the specified type.
as(String) - Method in class org.apache.spark.sql.Dataset
Applies a logical alias to this Dataset that can be used to disambiguate columns that have the same name after two Datasets have been joined.
asc() - Method in class org.apache.spark.sql.Column
Returns an ordering used in sorting.
asc(String) - Static method in class org.apache.spark.sql.functions
Returns a sort expression based on ascending order of the column.
ascii(Column) - Static method in class org.apache.spark.sql.functions
Computes the numeric value of the first character of the string column, and returns the result as a int column.
asin(Column) - Static method in class org.apache.spark.sql.functions
Computes the sine inverse of the given value; the returned angle is in the range -pi/2 through pi/2.
asin(String) - Static method in class org.apache.spark.sql.functions
Computes the sine inverse of the given column; the returned angle is in the range -pi/2 through pi/2.
asIntegral() - Method in class org.apache.spark.sql.types.DecimalType
 
asIntegral() - Method in class org.apache.spark.sql.types.DoubleType
 
asIntegral() - Method in class org.apache.spark.sql.types.FloatType
 
asIterator() - Method in class org.apache.spark.serializer.DeserializationStream
Read the elements of this stream through an iterator.
asJavaPairRDD() - Method in class org.apache.spark.api.r.PairwiseRRDD
 
asJavaRDD() - Method in class org.apache.spark.api.r.RRDD
 
asJavaRDD() - Method in class org.apache.spark.api.r.StringRRDD
 
asKeyValueIterator() - Method in class org.apache.spark.serializer.DeserializationStream
Read the elements of this stream through an iterator over key-value pairs.
AskPermissionToCommitOutput - Class in org.apache.spark.scheduler
 
AskPermissionToCommitOutput(int, int, int) - Constructor for class org.apache.spark.scheduler.AskPermissionToCommitOutput
 
askTimeout(SparkConf) - Static method in class org.apache.spark.util.RpcUtils
 
asRDDId() - Method in class org.apache.spark.storage.BlockId
 
assertValid() - Method in class org.apache.spark.broadcast.Broadcast
Check if this broadcast is valid.
assignments() - Method in class org.apache.spark.mllib.clustering.PowerIterationClusteringModel
 
AssociationRules - Class in org.apache.spark.mllib.fpm
:: Experimental ::
AssociationRules() - Constructor for class org.apache.spark.mllib.fpm.AssociationRules
Constructs a default instance with default parameters {minConfidence = 0.8}.
AssociationRules.Rule<Item> - Class in org.apache.spark.mllib.fpm
:: Experimental ::
AsyncRDDActions<T> - Class in org.apache.spark.rdd
A set of asynchronous RDD actions available through an implicit conversion.
AsyncRDDActions(RDD<T>, ClassTag<T>) - Constructor for class org.apache.spark.rdd.AsyncRDDActions
 
atan(Column) - Static method in class org.apache.spark.sql.functions
Computes the tangent inverse of the given value.
atan(String) - Static method in class org.apache.spark.sql.functions
Computes the tangent inverse of the given column.
atan2(Column, Column) - Static method in class org.apache.spark.sql.functions
Returns the angle theta from the conversion of rectangular coordinates (x, y) to polar coordinates (r, theta).
atan2(Column, String) - Static method in class org.apache.spark.sql.functions
Returns the angle theta from the conversion of rectangular coordinates (x, y) to polar coordinates (r, theta).
atan2(String, Column) - Static method in class org.apache.spark.sql.functions
Returns the angle theta from the conversion of rectangular coordinates (x, y) to polar coordinates (r, theta).
atan2(String, String) - Static method in class org.apache.spark.sql.functions
Returns the angle theta from the conversion of rectangular coordinates (x, y) to polar coordinates (r, theta).
atan2(Column, double) - Static method in class org.apache.spark.sql.functions
Returns the angle theta from the conversion of rectangular coordinates (x, y) to polar coordinates (r, theta).
atan2(String, double) - Static method in class org.apache.spark.sql.functions
Returns the angle theta from the conversion of rectangular coordinates (x, y) to polar coordinates (r, theta).
atan2(double, Column) - Static method in class org.apache.spark.sql.functions
Returns the angle theta from the conversion of rectangular coordinates (x, y) to polar coordinates (r, theta).
atan2(double, String) - Static method in class org.apache.spark.sql.functions
Returns the angle theta from the conversion of rectangular coordinates (x, y) to polar coordinates (r, theta).
attempt() - Method in class org.apache.spark.scheduler.TaskInfo
 
attempt() - Method in class org.apache.spark.status.api.v1.TaskData
 
attemptId() - Method in class org.apache.spark.scheduler.StageInfo
 
attemptId() - Method in class org.apache.spark.status.api.v1.ApplicationAttemptInfo
 
attemptId() - Method in class org.apache.spark.status.api.v1.StageData
 
attemptId() - Method in class org.apache.spark.TaskContext
 
attemptNumber() - Method in class org.apache.spark.scheduler.AskPermissionToCommitOutput
 
attemptNumber() - Method in class org.apache.spark.scheduler.TaskInfo
 
attemptNumber() - Method in class org.apache.spark.TaskCommitDenied
 
attemptNumber() - Method in class org.apache.spark.TaskContext
How many times this task has been attempted.
attempts() - Method in class org.apache.spark.status.api.v1.ApplicationInfo
 
attr() - Method in class org.apache.spark.graphx.Edge
 
attr() - Method in class org.apache.spark.graphx.EdgeContext
The attribute associated with the edge.
attr() - Method in class org.apache.spark.graphx.impl.AggregatingEdgeContext
 
Attribute - Class in org.apache.spark.ml.attribute
:: DeveloperApi :: Abstract class for ML attributes.
Attribute() - Constructor for class org.apache.spark.ml.attribute.Attribute
 
attribute() - Method in class org.apache.spark.sql.sources.EqualNullSafe
 
attribute() - Method in class org.apache.spark.sql.sources.EqualTo
 
attribute() - Method in class org.apache.spark.sql.sources.GreaterThan
 
attribute() - Method in class org.apache.spark.sql.sources.GreaterThanOrEqual
 
attribute() - Method in class org.apache.spark.sql.sources.In
 
attribute() - Method in class org.apache.spark.sql.sources.IsNotNull
 
attribute() - Method in class org.apache.spark.sql.sources.IsNull
 
attribute() - Method in class org.apache.spark.sql.sources.LessThan
 
attribute() - Method in class org.apache.spark.sql.sources.LessThanOrEqual
 
attribute() - Method in class org.apache.spark.sql.sources.StringContains
 
attribute() - Method in class org.apache.spark.sql.sources.StringEndsWith
 
attribute() - Method in class org.apache.spark.sql.sources.StringStartsWith
 
AttributeGroup - Class in org.apache.spark.ml.attribute
:: DeveloperApi :: Attributes that describe a vector ML column.
AttributeGroup(String) - Constructor for class org.apache.spark.ml.attribute.AttributeGroup
Creates an attribute group without attribute info.
AttributeGroup(String, int) - Constructor for class org.apache.spark.ml.attribute.AttributeGroup
Creates an attribute group knowing only the number of attributes.
AttributeGroup(String, Attribute[]) - Constructor for class org.apache.spark.ml.attribute.AttributeGroup
Creates an attribute group with attributes.
attributes() - Method in class org.apache.spark.ml.attribute.AttributeGroup
Optional array of attributes.
AttributeType - Class in org.apache.spark.ml.attribute
:: DeveloperApi :: An enum-like type for attribute types: AttributeType$.Numeric, AttributeType$.Nominal, and AttributeType$.Binary.
AttributeType(String) - Constructor for class org.apache.spark.ml.attribute.AttributeType
 
attrType() - Method in class org.apache.spark.ml.attribute.Attribute
Attribute type.
attrType() - Method in class org.apache.spark.ml.attribute.BinaryAttribute
 
attrType() - Method in class org.apache.spark.ml.attribute.NominalAttribute
 
attrType() - Method in class org.apache.spark.ml.attribute.NumericAttribute
 
attrType() - Static method in class org.apache.spark.ml.attribute.UnresolvedAttribute
 
available() - Method in class org.apache.spark.storage.BufferReleasingInputStream
 
avg(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the average of the values in a group.
avg(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the average of the values in a group.
avg(String...) - Method in class org.apache.spark.sql.GroupedData
Compute the mean value for each numeric columns for each group.
avg(Seq<String>) - Method in class org.apache.spark.sql.GroupedData
Compute the mean value for each numeric columns for each group.
avgMetrics() - Method in class org.apache.spark.ml.tuning.CrossValidatorModel
 
awaitTermination() - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Wait for the execution to stop.
awaitTermination(long) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Deprecated.
As of 1.3.0, replaced by awaitTerminationOrTimeout(Long).
awaitTermination() - Method in class org.apache.spark.streaming.StreamingContext
Wait for the execution to stop.
awaitTermination(long) - Method in class org.apache.spark.streaming.StreamingContext
Deprecated.
As of 1.3.0, replaced by awaitTerminationOrTimeout(Long).
awaitTerminationOrTimeout(long) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Wait for the execution to stop.
awaitTerminationOrTimeout(long) - Method in class org.apache.spark.streaming.StreamingContext
Wait for the execution to stop.

B

base64(Column) - Static method in class org.apache.spark.sql.functions
Computes the BASE64 encoding of a binary column and returns it as a string column.
baseOn(ParamPair<?>...) - Method in class org.apache.spark.ml.tuning.ParamGridBuilder
Sets the given parameters in this grid to fixed values.
baseOn(ParamMap) - Method in class org.apache.spark.ml.tuning.ParamGridBuilder
Sets the given parameters in this grid to fixed values.
baseOn(Seq<ParamPair<?>>) - Method in class org.apache.spark.ml.tuning.ParamGridBuilder
Sets the given parameters in this grid to fixed values.
BaseRelation - Class in org.apache.spark.sql.sources
::DeveloperApi:: Represents a collection of tuples with a known schema.
BaseRelation() - Constructor for class org.apache.spark.sql.sources.BaseRelation
 
baseRelationToDataFrame(BaseRelation) - Method in class org.apache.spark.sql.SQLContext
 
BaseRRDD<T,U> - Class in org.apache.spark.api.r
 
BaseRRDD(RDD<T>, int, byte[], String, String, byte[], Broadcast<Object>[], ClassTag<T>, ClassTag<U>) - Constructor for class org.apache.spark.api.r.BaseRRDD
 
baseScope() - Method in class org.apache.spark.streaming.dstream.DStream
The base scope associated with the operation that created this DStream.
baseScope() - Method in class org.apache.spark.streaming.dstream.InputDStream
The base scope associated with the operation that created this DStream.
BATCHES() - Static method in class org.apache.spark.mllib.clustering.StreamingKMeans
 
BatchInfo - Class in org.apache.spark.streaming.scheduler
:: DeveloperApi :: Class having information on completed batches.
BatchInfo(Time, Map<Object, StreamInputInfo>, long, Option<Object>, Option<Object>, Map<Object, OutputOperationInfo>) - Constructor for class org.apache.spark.streaming.scheduler.BatchInfo
 
batchInfo() - Method in class org.apache.spark.streaming.scheduler.StreamingListenerBatchCompleted
 
batchInfo() - Method in class org.apache.spark.streaming.scheduler.StreamingListenerBatchStarted
 
batchInfo() - Method in class org.apache.spark.streaming.scheduler.StreamingListenerBatchSubmitted
 
batchInfos() - Method in class org.apache.spark.streaming.scheduler.StatsReportListener
 
batchTime() - Method in class org.apache.spark.streaming.scheduler.BatchInfo
 
batchTime() - Method in class org.apache.spark.streaming.scheduler.OutputOperationInfo
 
bean(Class<T>) - Static method in class org.apache.spark.sql.Encoders
Creates an encoder for Java Bean of type T.
beforeFetch(Connection, Map<String, String>) - Method in class org.apache.spark.sql.jdbc.JdbcDialect
Override connection specific properties to run before a select is made.
beforeFetch(Connection, Map<String, String>) - Static method in class org.apache.spark.sql.jdbc.PostgresDialect
 
Bernoulli() - Static method in class org.apache.spark.mllib.classification.NaiveBayes
 
BernoulliCellSampler<T> - Class in org.apache.spark.util.random
:: DeveloperApi :: A sampler based on Bernoulli trials for partitioning a data sequence.
BernoulliCellSampler(double, double, boolean) - Constructor for class org.apache.spark.util.random.BernoulliCellSampler
 
BernoulliSampler<T> - Class in org.apache.spark.util.random
:: DeveloperApi :: A sampler based on Bernoulli trials.
BernoulliSampler(double, ClassTag<T>) - Constructor for class org.apache.spark.util.random.BernoulliSampler
 
bestModel() - Method in class org.apache.spark.ml.tuning.CrossValidatorModel
 
bestModel() - Method in class org.apache.spark.ml.tuning.TrainValidationSplitModel
 
beta() - Method in class org.apache.spark.mllib.random.WeibullGenerator
 
between(Object, Object) - Method in class org.apache.spark.sql.Column
True if the current column is between the lower bound and upper bound, inclusive.
bin(Column) - Static method in class org.apache.spark.sql.functions
An expression that returns the string representation of the binary value of the given long column.
bin(String) - Static method in class org.apache.spark.sql.functions
An expression that returns the string representation of the binary value of the given long column.
Binarizer - Class in org.apache.spark.ml.feature
:: Experimental :: Binarize a column of continuous features given a threshold.
Binarizer(String) - Constructor for class org.apache.spark.ml.feature.Binarizer
 
Binarizer() - Constructor for class org.apache.spark.ml.feature.Binarizer
 
Binary() - Static method in class org.apache.spark.ml.attribute.AttributeType
Binary type.
binary() - Method in class org.apache.spark.sql.ColumnName
Creates a new StructField of type binary.
BinaryAttribute - Class in org.apache.spark.ml.attribute
:: DeveloperApi :: A binary attribute.
BinaryClassificationEvaluator - Class in org.apache.spark.ml.evaluation
:: Experimental :: Evaluator for binary classification, which expects two input columns: rawPrediction and label.
BinaryClassificationEvaluator(String) - Constructor for class org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
 
BinaryClassificationEvaluator() - Constructor for class org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
 
BinaryClassificationMetrics - Class in org.apache.spark.mllib.evaluation
Evaluator for binary classification.
BinaryClassificationMetrics(RDD<Tuple2<Object, Object>>, int) - Constructor for class org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
 
BinaryClassificationMetrics(RDD<Tuple2<Object, Object>>) - Constructor for class org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
Defaults numBins to 0.
binaryFiles(String, int) - Method in class org.apache.spark.api.java.JavaSparkContext
Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI as a byte array.
binaryFiles(String) - Method in class org.apache.spark.api.java.JavaSparkContext
Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI as a byte array.
binaryFiles(String, int) - Method in class org.apache.spark.SparkContext
Get an RDD for a Hadoop-readable dataset as PortableDataStream for each file (useful for binary data)
binaryLabelValidator() - Static method in class org.apache.spark.mllib.util.DataValidators
Function to check if labels used for classification are either zero or one.
BinaryLogisticRegressionSummary - Class in org.apache.spark.ml.classification
:: Experimental :: Binary Logistic regression results for a given model.
BinaryLogisticRegressionTrainingSummary - Class in org.apache.spark.ml.classification
:: Experimental :: Logistic regression training results.
binaryRecords(String, int) - Method in class org.apache.spark.api.java.JavaSparkContext
Load data from a flat binary file, assuming the length of each record is constant.
binaryRecords(String, int, Configuration) - Method in class org.apache.spark.SparkContext
Load data from a flat binary file, assuming the length of each record is constant.
binaryRecordsStream(String, int) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them as flat binary files with fixed record lengths, yielding byte arrays
binaryRecordsStream(String, int) - Method in class org.apache.spark.streaming.StreamingContext
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them as flat binary files, assuming a fixed length per record, generating one byte array per record.
BinarySample - Class in org.apache.spark.mllib.stat.test
Class that represents the group and value of a sample.
BinarySample(boolean, double) - Constructor for class org.apache.spark.mllib.stat.test.BinarySample
 
BinaryType - Class in org.apache.spark.sql.types
:: DeveloperApi :: The data type representing Array[Byte] values.
BinaryType - Static variable in class org.apache.spark.sql.types.DataTypes
Gets the BinaryType object.
BisectingKMeans - Class in org.apache.spark.mllib.clustering
A bisecting k-means algorithm based on the paper "A comparison of document clustering techniques" by Steinbach, Karypis, and Kumar, with modification to fit Spark.
BisectingKMeans() - Constructor for class org.apache.spark.mllib.clustering.BisectingKMeans
Constructs with the default configuration
BisectingKMeansModel - Class in org.apache.spark.mllib.clustering
Clustering model produced by BisectingKMeans.
bitwiseAND(Object) - Method in class org.apache.spark.sql.Column
Compute bitwise AND of this expression with another expression.
bitwiseNOT(Column) - Static method in class org.apache.spark.sql.functions
Computes bitwise NOT.
bitwiseOR(Object) - Method in class org.apache.spark.sql.Column
Compute bitwise OR of this expression with another expression.
bitwiseXOR(Object) - Method in class org.apache.spark.sql.Column
Compute bitwise XOR of this expression with another expression.
BlockId - Class in org.apache.spark.storage
:: DeveloperApi :: Identifies a particular Block of data, usually associated with a single file.
BlockId() - Constructor for class org.apache.spark.storage.BlockId
 
blockId() - Method in class org.apache.spark.storage.BlockUpdatedInfo
 
blockManager() - Method in class org.apache.spark.SparkEnv
 
blockManagerId() - Method in class org.apache.spark.scheduler.SparkListenerBlockManagerAdded
 
blockManagerId() - Method in class org.apache.spark.scheduler.SparkListenerBlockManagerRemoved
 
BlockManagerId - Class in org.apache.spark.storage
:: DeveloperApi :: This class represent an unique identifier for a BlockManager.
blockManagerId() - Method in class org.apache.spark.storage.BlockUpdatedInfo
 
blockManagerId() - Method in class org.apache.spark.storage.StorageStatus
 
blockManagerIdCache() - Static method in class org.apache.spark.storage.BlockManagerId
 
blockManagerIds() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
BlockMatrix - Class in org.apache.spark.mllib.linalg.distributed
Represents a distributed matrix in blocks of local matrices.
BlockMatrix(RDD<Tuple2<Tuple2<Object, Object>, Matrix>>, int, int, long, long) - Constructor for class org.apache.spark.mllib.linalg.distributed.BlockMatrix
 
BlockMatrix(RDD<Tuple2<Tuple2<Object, Object>, Matrix>>, int, int) - Constructor for class org.apache.spark.mllib.linalg.distributed.BlockMatrix
Alternate constructor for BlockMatrix without the input of the number of rows and columns.
blockName() - Method in class org.apache.spark.status.api.v1.RDDPartitionInfo
 
BlockNotFoundException - Exception in org.apache.spark.storage
 
BlockNotFoundException(String) - Constructor for exception org.apache.spark.storage.BlockNotFoundException
 
blockReplication() - Method in class org.apache.spark.sql.sources.HadoopFsRelation.FakeFileStatus
 
blocks() - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
 
blocks() - Method in class org.apache.spark.storage.StorageStatus
Return the blocks stored in this block manager.
blockSize() - Method in class org.apache.spark.sql.sources.HadoopFsRelation.FakeFileStatus
 
BlockStatus - Class in org.apache.spark.storage
 
BlockStatus(StorageLevel, long, long, long) - Constructor for class org.apache.spark.storage.BlockStatus
 
blockTransferService() - Method in class org.apache.spark.SparkEnv
 
blockUpdatedInfo() - Method in class org.apache.spark.scheduler.SparkListenerBlockUpdated
 
BlockUpdatedInfo - Class in org.apache.spark.storage
:: DeveloperApi :: Stores information about a block status in a block manager.
BlockUpdatedInfo(BlockManagerId, BlockId, StorageLevel, long, long, long) - Constructor for class org.apache.spark.storage.BlockUpdatedInfo
 
bmAddress() - Method in class org.apache.spark.FetchFailed
 
BOOLEAN() - Static method in class org.apache.spark.sql.Encoders
An encoder for nullable boolean type.
BooleanParam - Class in org.apache.spark.ml.param
:: DeveloperApi :: Specialized version of Param[Boolean] for Java.
BooleanParam(String, String, String) - Constructor for class org.apache.spark.ml.param.BooleanParam
 
BooleanParam(Identifiable, String, String) - Constructor for class org.apache.spark.ml.param.BooleanParam
 
BooleanType - Class in org.apache.spark.sql.types
:: DeveloperApi :: The data type representing Boolean values.
BooleanType - Static variable in class org.apache.spark.sql.types.DataTypes
Gets the BooleanType object.
booleanWritableConverter() - Static method in class org.apache.spark.SparkContext
 
boolToBoolWritable(boolean) - Static method in class org.apache.spark.SparkContext
 
BoostingStrategy - Class in org.apache.spark.mllib.tree.configuration
Configuration options for GradientBoostedTrees.
BoostingStrategy(Strategy, Loss, int, double, double) - Constructor for class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
Both() - Static method in class org.apache.spark.graphx.EdgeDirection
Edges originating from *and* arriving at a vertex of interest.
boundaries() - Method in class org.apache.spark.ml.regression.IsotonicRegressionModel
Boundaries in increasing order for which predictions are known.
boundaries() - Method in class org.apache.spark.mllib.regression.IsotonicRegressionModel
 
BoundedDouble - Class in org.apache.spark.partial
A Double value with error bars and associated confidence.
BoundedDouble(double, double, double, double) - Constructor for class org.apache.spark.partial.BoundedDouble
 
boundTEncoder() - Method in class org.apache.spark.sql.Dataset
The encoder where the expressions used to construct an object from an input row have been bound to the ordinals of the given schema.
broadcast(T) - Method in class org.apache.spark.api.java.JavaSparkContext
Broadcast a read-only variable to the cluster, returning a Broadcast object for reading it in distributed functions.
Broadcast<T> - Class in org.apache.spark.broadcast
A broadcast variable.
Broadcast(long, ClassTag<T>) - Constructor for class org.apache.spark.broadcast.Broadcast
 
broadcast(T, ClassTag<T>) - Method in class org.apache.spark.SparkContext
Broadcast a read-only variable to the cluster, returning a Broadcast object for reading it in distributed functions.
broadcast(DataFrame) - Static method in class org.apache.spark.sql.functions
Marks a DataFrame as small enough for use in broadcast joins.
BROADCAST() - Static method in class org.apache.spark.storage.BlockId
 
BroadcastBlockId - Class in org.apache.spark.storage
 
BroadcastBlockId(long, String) - Constructor for class org.apache.spark.storage.BroadcastBlockId
 
BroadcastFactory - Interface in org.apache.spark.broadcast
:: DeveloperApi :: An interface for all the broadcast implementations in Spark (to allow multiple broadcast implementations).
broadcastId() - Method in class org.apache.spark.CleanBroadcast
 
broadcastId() - Method in class org.apache.spark.storage.BroadcastBlockId
 
broadcastManager() - Method in class org.apache.spark.SparkEnv
 
Broker - Class in org.apache.spark.streaming.kafka
Represents the host and port info for a Kafka broker.
Bucketizer - Class in org.apache.spark.ml.feature
:: Experimental :: Bucketizer maps a column of continuous features to a column of feature buckets.
Bucketizer(String) - Constructor for class org.apache.spark.ml.feature.Bucketizer
 
Bucketizer() - Constructor for class org.apache.spark.ml.feature.Bucketizer
 
BufferReleasingInputStream - Class in org.apache.spark.storage
Helper class that ensures a ManagedBuffer is release upon InputStream.close()
BufferReleasingInputStream(InputStream, ShuffleBlockFetcherIterator) - Constructor for class org.apache.spark.storage.BufferReleasingInputStream
 
bufferSchema() - Method in class org.apache.spark.sql.expressions.UserDefinedAggregateFunction
A StructType represents data types of values in the aggregation buffer.
build() - Method in class org.apache.spark.ml.tuning.ParamGridBuilder
Builds and returns all combinations of parameters specified by the param grid.
build(Node[]) - Method in class org.apache.spark.mllib.tree.model.Node
build the left node and right nodes if not leaf
build() - Method in class org.apache.spark.sql.types.MetadataBuilder
Builds the Metadata instance.
buildFormattedString(DataType, String, StringBuilder) - Static method in class org.apache.spark.sql.types.DataType
 
buildJobStageDependencies(int, Seq<Object>) - Method in class org.apache.spark.scheduler.JobLogger
Build up the maps that represent stage-job relationships
buildScan(Seq<Attribute>, Seq<Expression>) - Method in interface org.apache.spark.sql.sources.CatalystScan
 
buildScan(FileStatus[]) - Method in class org.apache.spark.sql.sources.HadoopFsRelation
For a non-partitioned relation, this method builds an RDD[Row] containing all rows within this relation.
buildScan(String[], FileStatus[]) - Method in class org.apache.spark.sql.sources.HadoopFsRelation
For a non-partitioned relation, this method builds an RDD[Row] containing all rows within this relation.
buildScan(String[], Filter[], FileStatus[]) - Method in class org.apache.spark.sql.sources.HadoopFsRelation
For a non-partitioned relation, this method builds an RDD[Row] containing all rows within this relation.
buildScan(String[], Filter[]) - Method in interface org.apache.spark.sql.sources.PrunedFilteredScan
 
buildScan(String[]) - Method in interface org.apache.spark.sql.sources.PrunedScan
 
buildScan() - Method in interface org.apache.spark.sql.sources.TableScan
 
BYTE() - Static method in class org.apache.spark.sql.Encoders
An encoder for nullable byte type.
ByteDecimal() - Static method in class org.apache.spark.sql.types.DecimalType
 
bytesRead() - Method in class org.apache.spark.status.api.v1.InputMetricDistributions
 
bytesRead() - Method in class org.apache.spark.status.api.v1.InputMetrics
 
bytesToBytesWritable(byte[]) - Static method in class org.apache.spark.SparkContext
 
bytesWritableConverter() - Static method in class org.apache.spark.SparkContext
 
bytesWritten() - Method in class org.apache.spark.status.api.v1.OutputMetricDistributions
 
bytesWritten() - Method in class org.apache.spark.status.api.v1.OutputMetrics
 
bytesWritten() - Method in class org.apache.spark.status.api.v1.ShuffleWriteMetrics
 
ByteType - Class in org.apache.spark.sql.types
:: DeveloperApi :: The data type representing Byte values.
ByteType - Static variable in class org.apache.spark.sql.types.DataTypes
Gets the ByteType object.

C

cache() - Method in class org.apache.spark.api.java.JavaDoubleRDD
Persist this RDD with the default storage level (`MEMORY_ONLY`).
cache() - Method in class org.apache.spark.api.java.JavaPairRDD
Persist this RDD with the default storage level (`MEMORY_ONLY`).
cache() - Method in class org.apache.spark.api.java.JavaRDD
Persist this RDD with the default storage level (`MEMORY_ONLY`).
cache() - Method in class org.apache.spark.graphx.Graph
Caches the vertices and edges associated with this graph at the previously-specified target storage levels, which default to MEMORY_ONLY.
cache() - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
Persists the edge partitions using `targetStorageLevel`, which defaults to MEMORY_ONLY.
cache() - Method in class org.apache.spark.graphx.impl.GraphImpl
 
cache() - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
Persists the vertex partitions at `targetStorageLevel`, which defaults to MEMORY_ONLY.
cache() - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
Caches the underlying RDD.
cache() - Method in class org.apache.spark.rdd.RDD
Persist this RDD with the default storage level (`MEMORY_ONLY`).
cache() - Method in class org.apache.spark.sql.DataFrame
Persist this DataFrame with the default storage level (MEMORY_AND_DISK).
cache() - Method in class org.apache.spark.sql.Dataset
Persist this Dataset with the default storage level (MEMORY_AND_DISK).
cache() - Method in class org.apache.spark.streaming.api.java.JavaDStream
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
cache() - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
cache() - Method in class org.apache.spark.streaming.dstream.DStream
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
cachedLeafStatuses() - Method in class org.apache.spark.sql.sources.HadoopFsRelation
 
cacheManager() - Method in class org.apache.spark.SparkEnv
 
cacheManager() - Method in class org.apache.spark.sql.SQLContext
 
cacheTable(String) - Method in class org.apache.spark.sql.SQLContext
Caches the specified table in-memory.
calculate(DenseVector<Object>) - Method in class org.apache.spark.ml.classification.LogisticCostFun
 
calculate(DenseVector<Object>) - Method in class org.apache.spark.ml.regression.AFTCostFun
 
calculate(DenseVector<Object>) - Method in class org.apache.spark.ml.regression.LeastSquaresCostFun
 
calculate(double[], double) - Static method in class org.apache.spark.mllib.tree.impurity.Entropy
:: DeveloperApi :: information calculation for multiclass classification
calculate(double, double, double) - Static method in class org.apache.spark.mllib.tree.impurity.Entropy
:: DeveloperApi :: variance calculation
calculate(double[], double) - Static method in class org.apache.spark.mllib.tree.impurity.Gini
:: DeveloperApi :: information calculation for multiclass classification
calculate(double, double, double) - Static method in class org.apache.spark.mllib.tree.impurity.Gini
:: DeveloperApi :: variance calculation
calculate(double[], double) - Method in interface org.apache.spark.mllib.tree.impurity.Impurity
:: DeveloperApi :: information calculation for multiclass classification
calculate(double, double, double) - Method in interface org.apache.spark.mllib.tree.impurity.Impurity
:: DeveloperApi :: information calculation for regression
calculate(double[], double) - Static method in class org.apache.spark.mllib.tree.impurity.Variance
:: DeveloperApi :: information calculation for multiclass classification
calculate(double, double, double) - Static method in class org.apache.spark.mllib.tree.impurity.Variance
:: DeveloperApi :: variance calculation
CalendarIntervalType - Class in org.apache.spark.sql.types
:: DeveloperApi :: The data type representing calendar time intervals.
CalendarIntervalType - Static variable in class org.apache.spark.sql.types.DataTypes
Gets the CalendarIntervalType object.
call(K, Iterator<V1>, Iterator<V2>) - Method in interface org.apache.spark.api.java.function.CoGroupFunction
 
call(T) - Method in interface org.apache.spark.api.java.function.DoubleFlatMapFunction
 
call(T) - Method in interface org.apache.spark.api.java.function.DoubleFunction
 
call(T) - Method in interface org.apache.spark.api.java.function.FilterFunction
 
call(T) - Method in interface org.apache.spark.api.java.function.FlatMapFunction
 
call(T1, T2) - Method in interface org.apache.spark.api.java.function.FlatMapFunction2
 
call(K, Iterator<V>) - Method in interface org.apache.spark.api.java.function.FlatMapGroupsFunction
 
call(T) - Method in interface org.apache.spark.api.java.function.ForeachFunction
 
call(Iterator<T>) - Method in interface org.apache.spark.api.java.function.ForeachPartitionFunction
 
call(T1) - Method in interface org.apache.spark.api.java.function.Function
 
call() - Method in interface org.apache.spark.api.java.function.Function0
 
call(T1, T2) - Method in interface org.apache.spark.api.java.function.Function2
 
call(T1, T2, T3) - Method in interface org.apache.spark.api.java.function.Function3
 
call(T1, T2, T3, T4) - Method in interface org.apache.spark.api.java.function.Function4
 
call(T) - Method in interface org.apache.spark.api.java.function.MapFunction
 
call(K, Iterator<V>) - Method in interface org.apache.spark.api.java.function.MapGroupsFunction
 
call(Iterator<T>) - Method in interface org.apache.spark.api.java.function.MapPartitionsFunction
 
call(T) - Method in interface org.apache.spark.api.java.function.PairFlatMapFunction
 
call(T) - Method in interface org.apache.spark.api.java.function.PairFunction
 
call(T, T) - Method in interface org.apache.spark.api.java.function.ReduceFunction
 
call(T) - Method in interface org.apache.spark.api.java.function.VoidFunction
 
call(T1, T2) - Method in interface org.apache.spark.api.java.function.VoidFunction2
 
call(T1) - Method in interface org.apache.spark.sql.api.java.UDF1
 
call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10) - Method in interface org.apache.spark.sql.api.java.UDF10
 
call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11) - Method in interface org.apache.spark.sql.api.java.UDF11
 
call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12) - Method in interface org.apache.spark.sql.api.java.UDF12
 
call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13) - Method in interface org.apache.spark.sql.api.java.UDF13
 
call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14) - Method in interface org.apache.spark.sql.api.java.UDF14
 
call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15) - Method in interface org.apache.spark.sql.api.java.UDF15
 
call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16) - Method in interface org.apache.spark.sql.api.java.UDF16
 
call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16, T17) - Method in interface org.apache.spark.sql.api.java.UDF17
 
call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16, T17, T18) - Method in interface org.apache.spark.sql.api.java.UDF18
 
call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16, T17, T18, T19) - Method in interface org.apache.spark.sql.api.java.UDF19
 
call(T1, T2) - Method in interface org.apache.spark.sql.api.java.UDF2
 
call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16, T17, T18, T19, T20) - Method in interface org.apache.spark.sql.api.java.UDF20
 
call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16, T17, T18, T19, T20, T21) - Method in interface org.apache.spark.sql.api.java.UDF21
 
call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16, T17, T18, T19, T20, T21, T22) - Method in interface org.apache.spark.sql.api.java.UDF22
 
call(T1, T2, T3) - Method in interface org.apache.spark.sql.api.java.UDF3
 
call(T1, T2, T3, T4) - Method in interface org.apache.spark.sql.api.java.UDF4
 
call(T1, T2, T3, T4, T5) - Method in interface org.apache.spark.sql.api.java.UDF5
 
call(T1, T2, T3, T4, T5, T6) - Method in interface org.apache.spark.sql.api.java.UDF6
 
call(T1, T2, T3, T4, T5, T6, T7) - Method in interface org.apache.spark.sql.api.java.UDF7
 
call(T1, T2, T3, T4, T5, T6, T7, T8) - Method in interface org.apache.spark.sql.api.java.UDF8
 
call(T1, T2, T3, T4, T5, T6, T7, T8, T9) - Method in interface org.apache.spark.sql.api.java.UDF9
 
callSite() - Method in class org.apache.spark.storage.RDDInfo
 
callUDF(String, Column...) - Static method in class org.apache.spark.sql.functions
Call an user-defined function.
callUDF(Function0<?>, DataType) - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.5.0, since it's redundant with udf() This will be removed in Spark 2.0.
callUDF(Function1<?, ?>, DataType, Column) - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.5.0, since it's redundant with udf() This will be removed in Spark 2.0.
callUDF(Function2<?, ?, ?>, DataType, Column, Column) - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.5.0, since it's redundant with udf() This will be removed in Spark 2.0.
callUDF(Function3<?, ?, ?, ?>, DataType, Column, Column, Column) - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.5.0, since it's redundant with udf() This will be removed in Spark 2.0.
callUDF(Function4<?, ?, ?, ?, ?>, DataType, Column, Column, Column, Column) - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.5.0, since it's redundant with udf() This will be removed in Spark 2.0.
callUDF(Function5<?, ?, ?, ?, ?, ?>, DataType, Column, Column, Column, Column, Column) - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.5.0, since it's redundant with udf() This will be removed in Spark 2.0.
callUDF(Function6<?, ?, ?, ?, ?, ?, ?>, DataType, Column, Column, Column, Column, Column, Column) - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.5.0, since it's redundant with udf() This will be removed in Spark 2.0.
callUDF(Function7<?, ?, ?, ?, ?, ?, ?, ?>, DataType, Column, Column, Column, Column, Column, Column, Column) - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.5.0, since it's redundant with udf() This will be removed in Spark 2.0.
callUDF(Function8<?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType, Column, Column, Column, Column, Column, Column, Column, Column) - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.5.0, since it's redundant with udf() This will be removed in Spark 2.0.
callUDF(Function9<?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType, Column, Column, Column, Column, Column, Column, Column, Column, Column) - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.5.0, since it's redundant with udf(). This will be removed in Spark 2.0.
callUDF(Function10<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType, Column, Column, Column, Column, Column, Column, Column, Column, Column, Column) - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.5.0, since it's redundant with udf(). This will be removed in Spark 2.0.
callUDF(String, Seq<Column>) - Static method in class org.apache.spark.sql.functions
Call an user-defined function.
callUdf(String, Seq<Column>) - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.5.0, since it was not coherent to have two functions callUdf and callUDF. This will be removed in Spark 2.0.
cancel() - Method in class org.apache.spark.ComplexFutureAction
 
cancel() - Method in interface org.apache.spark.FutureAction
Cancels the execution of this action.
cancel() - Method in class org.apache.spark.SimpleFutureAction
 
cancelAllJobs() - Method in class org.apache.spark.api.java.JavaSparkContext
Cancel all jobs that have been scheduled or are running.
cancelAllJobs() - Method in class org.apache.spark.SparkContext
Cancel all jobs that have been scheduled or are running.
cancelJobGroup(String) - Method in class org.apache.spark.api.java.JavaSparkContext
Cancel active jobs for the specified group.
cancelJobGroup(String) - Method in class org.apache.spark.SparkContext
Cancel active jobs for the specified group.
canEqual(Object) - Method in class org.apache.spark.scheduler.cluster.ExecutorInfo
 
canEqual(Object) - Method in class org.apache.spark.util.MutablePair
 
canHandle(String) - Method in class org.apache.spark.sql.jdbc.AggregatedDialect
 
canHandle(String) - Static method in class org.apache.spark.sql.jdbc.DB2Dialect
 
canHandle(String) - Static method in class org.apache.spark.sql.jdbc.DerbyDialect
 
canHandle(String) - Method in class org.apache.spark.sql.jdbc.JdbcDialect
Check if this dialect instance can handle a certain jdbc url.
canHandle(String) - Static method in class org.apache.spark.sql.jdbc.MsSqlServerDialect
 
canHandle(String) - Static method in class org.apache.spark.sql.jdbc.MySQLDialect
 
canHandle(String) - Static method in class org.apache.spark.sql.jdbc.NoopDialect
 
canHandle(String) - Static method in class org.apache.spark.sql.jdbc.OracleDialect
 
canHandle(String) - Static method in class org.apache.spark.sql.jdbc.PostgresDialect
 
cartesian(JavaRDDLike<U, ?>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of elements (a, b) where a is in this and b is in other.
cartesian(RDD<U>, ClassTag<U>) - Method in class org.apache.spark.rdd.RDD
Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of elements (a, b) where a is in this and b is in other.
caseSensitive() - Method in class org.apache.spark.ml.feature.StopWordsRemover
whether to do a case sensitive comparison over the stop words Default: false
cast(DataType) - Method in class org.apache.spark.sql.Column
Casts the column to a different data type.
cast(String) - Method in class org.apache.spark.sql.Column
Casts the column to a different data type, using the canonical string representation of the type.
catalog() - Method in class org.apache.spark.sql.hive.HiveContext
 
catalog() - Method in class org.apache.spark.sql.SQLContext
 
CatalystScan - Interface in org.apache.spark.sql.sources
::Experimental:: An interface for experimenting with a more direct connection to the query planner.
Categorical() - Static method in class org.apache.spark.mllib.tree.configuration.FeatureType
 
categoricalFeaturesInfo() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
CategoricalSplit - Class in org.apache.spark.ml.tree
:: DeveloperApi :: Split which tests a categorical feature.
categories() - Method in class org.apache.spark.mllib.tree.model.Split
 
categoryMaps() - Method in class org.apache.spark.ml.feature.VectorIndexerModel
 
cbrt(Column) - Static method in class org.apache.spark.sql.functions
Computes the cube-root of the given value.
cbrt(String) - Static method in class org.apache.spark.sql.functions
Computes the cube-root of the given column.
ceil(Column) - Static method in class org.apache.spark.sql.functions
Computes the ceiling of the given value.
ceil(String) - Static method in class org.apache.spark.sql.functions
Computes the ceiling of the given column.
ceil() - Method in class org.apache.spark.sql.types.Decimal
 
changePrecision(int, int) - Method in class org.apache.spark.sql.types.Decimal
Update precision and scale while keeping our value the same, and return true if successful.
checkpoint() - Method in interface org.apache.spark.api.java.JavaRDDLike
Mark this RDD for checkpointing.
checkpoint() - Method in class org.apache.spark.graphx.Graph
Mark this Graph for checkpointing.
checkpoint() - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
 
checkpoint() - Method in class org.apache.spark.graphx.impl.GraphImpl
 
checkpoint() - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
checkpoint() - Method in class org.apache.spark.rdd.HadoopRDD
 
checkpoint() - Method in class org.apache.spark.rdd.RDD
Mark this RDD for checkpointing.
checkpoint(Duration) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Enable periodic checkpointing of RDDs of this DStream.
checkpoint(String) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Sets the context to periodically checkpoint the DStream operations for master fault-tolerance.
checkpoint(Duration) - Method in class org.apache.spark.streaming.dstream.DStream
Enable periodic checkpointing of RDDs of this DStream
checkpoint(String) - Method in class org.apache.spark.streaming.StreamingContext
Set the context to periodically checkpoint the DStream operations for driver fault-tolerance.
checkpointData() - Method in class org.apache.spark.rdd.RDD
 
checkpointData() - Method in class org.apache.spark.streaming.dstream.DStream
 
checkpointDir() - Method in class org.apache.spark.SparkContext
 
checkpointDir() - Method in class org.apache.spark.streaming.StreamingContext
 
checkpointDuration() - Method in class org.apache.spark.streaming.dstream.DStream
 
checkpointDuration() - Method in class org.apache.spark.streaming.StreamingContext
 
checkpointFile(String) - Method in class org.apache.spark.api.java.JavaSparkContext
 
checkpointFile(String, ClassTag<T>) - Method in class org.apache.spark.SparkContext
 
checkpointInterval() - Method in class org.apache.spark.mllib.clustering.EMLDAOptimizer
 
checkpointInterval() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
child() - Method in class org.apache.spark.sql.sources.Not
 
CHILD_CONNECTION_TIMEOUT - Static variable in class org.apache.spark.launcher.SparkLauncher
Maximum time (in ms) to wait for a child process to connect back to the launcher server when using @link{#start()}.
CHILD_PROCESS_LOGGER_NAME - Static variable in class org.apache.spark.launcher.SparkLauncher
Logger name to use when launching a child process.
ChiSqSelector - Class in org.apache.spark.ml.feature
:: Experimental :: Chi-Squared feature selection, which selects categorical features to use for predicting a categorical label.
ChiSqSelector(String) - Constructor for class org.apache.spark.ml.feature.ChiSqSelector
 
ChiSqSelector() - Constructor for class org.apache.spark.ml.feature.ChiSqSelector
 
ChiSqSelector - Class in org.apache.spark.mllib.feature
 
ChiSqSelector(int) - Constructor for class org.apache.spark.mllib.feature.ChiSqSelector
 
ChiSqSelectorModel - Class in org.apache.spark.ml.feature
 
ChiSqSelectorModel - Class in org.apache.spark.mllib.feature
Chi Squared selector model.
ChiSqSelectorModel(int[]) - Constructor for class org.apache.spark.mllib.feature.ChiSqSelectorModel
 
chiSqTest(Vector, Vector) - Static method in class org.apache.spark.mllib.stat.Statistics
Conduct Pearson's chi-squared goodness of fit test of the observed data against the expected distribution.
chiSqTest(Vector) - Static method in class org.apache.spark.mllib.stat.Statistics
Conduct Pearson's chi-squared goodness of fit test of the observed data against the uniform distribution, with each category having an expected frequency of 1 / observed.size.
chiSqTest(Matrix) - Static method in class org.apache.spark.mllib.stat.Statistics
Conduct Pearson's independence test on the input contingency matrix, which cannot contain negative entries or columns or rows that sum up to 0.
chiSqTest(RDD<LabeledPoint>) - Static method in class org.apache.spark.mllib.stat.Statistics
Conduct Pearson's independence test for every feature against the label across the input RDD.
chiSqTest(JavaRDD<LabeledPoint>) - Static method in class org.apache.spark.mllib.stat.Statistics
Java-friendly version of chiSqTest()
ChiSqTestResult - Class in org.apache.spark.mllib.stat.test
Object containing the test results for the chi-squared hypothesis test.
Classification() - Static method in class org.apache.spark.mllib.tree.configuration.Algo
 
ClassificationModel<FeaturesType,M extends ClassificationModel<FeaturesType,M>> - Class in org.apache.spark.ml.classification
:: DeveloperApi ::
ClassificationModel() - Constructor for class org.apache.spark.ml.classification.ClassificationModel
 
ClassificationModel - Interface in org.apache.spark.mllib.classification
Represents a classification model that predicts to which of a set of categories an example belongs.
Classifier<FeaturesType,E extends Classifier<FeaturesType,E,M>,M extends ClassificationModel<FeaturesType,M>> - Class in org.apache.spark.ml.classification
:: DeveloperApi ::
Classifier() - Constructor for class org.apache.spark.ml.classification.Classifier
 
className() - Method in class org.apache.spark.ExceptionFailure
 
classpathEntries() - Method in class org.apache.spark.ui.env.EnvironmentListener
 
classTag() - Method in class org.apache.spark.api.java.JavaDoubleRDD
 
classTag() - Method in class org.apache.spark.api.java.JavaPairRDD
 
classTag() - Method in class org.apache.spark.api.java.JavaRDD
 
classTag() - Method in interface org.apache.spark.api.java.JavaRDDLike
 
classTag() - Method in class org.apache.spark.streaming.api.java.JavaDStream
 
classTag() - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
 
classTag() - Method in class org.apache.spark.streaming.api.java.JavaInputDStream
 
classTag() - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
 
classTag() - Method in class org.apache.spark.streaming.api.java.JavaReceiverInputDStream
 
clean(long, boolean) - Method in class org.apache.spark.streaming.util.WriteAheadLog
Clean all the records that are older than the threshold time.
CleanAccum - Class in org.apache.spark
 
CleanAccum(long) - Constructor for class org.apache.spark.CleanAccum
 
CleanBroadcast - Class in org.apache.spark
 
CleanBroadcast(long) - Constructor for class org.apache.spark.CleanBroadcast
 
CleanCheckpoint - Class in org.apache.spark
 
CleanCheckpoint(int) - Constructor for class org.apache.spark.CleanCheckpoint
 
CleanRDD - Class in org.apache.spark
 
CleanRDD(int) - Constructor for class org.apache.spark.CleanRDD
 
CleanShuffle - Class in org.apache.spark
 
CleanShuffle(int) - Constructor for class org.apache.spark.CleanShuffle
 
CleanupTask - Interface in org.apache.spark
Classes that represent cleaning tasks.
CleanupTaskWeakReference - Class in org.apache.spark
A WeakReference associated with a CleanupTask.
CleanupTaskWeakReference(CleanupTask, Object, ReferenceQueue<Object>) - Constructor for class org.apache.spark.CleanupTaskWeakReference
 
clear(Param<?>) - Method in interface org.apache.spark.ml.param.Params
 
clear() - Method in class org.apache.spark.sql.util.ExecutionListenerManager
Removes all the registered QueryExecutionListener.
clearActive() - Static method in class org.apache.spark.sql.SQLContext
Clears the active SQLContext for current thread.
clearCache() - Method in class org.apache.spark.sql.SQLContext
Removes all cached tables from the in-memory cache.
clearCallSite() - Method in class org.apache.spark.api.java.JavaSparkContext
Pass-through to SparkContext.setCallSite.
clearCallSite() - Method in class org.apache.spark.SparkContext
Clear the thread-local property for overriding the call sites of actions and RDDs.
clearDependencies() - Method in class org.apache.spark.rdd.CoGroupedRDD
 
clearDependencies() - Method in class org.apache.spark.rdd.RDD
Clears the dependencies of this RDD.
clearDependencies() - Method in class org.apache.spark.rdd.ShuffledRDD
 
clearDependencies() - Method in class org.apache.spark.rdd.UnionRDD
 
clearFiles() - Method in class org.apache.spark.api.java.JavaSparkContext
Clear the job's list of files added by addFile so that they do not get downloaded to any new nodes.
clearFiles() - Method in class org.apache.spark.SparkContext
Clear the job's list of files added by addFile so that they do not get downloaded to any new nodes.
clearJars() - Method in class org.apache.spark.api.java.JavaSparkContext
Clear the job's list of JARs added by addJar so that they do not get downloaded to any new nodes.
clearJars() - Method in class org.apache.spark.SparkContext
Clear the job's list of JARs added by addJar so that they do not get downloaded to any new nodes.
clearJobGroup() - Method in class org.apache.spark.api.java.JavaSparkContext
Clear the current thread's job group ID and its description.
clearJobGroup() - Method in class org.apache.spark.SparkContext
Clear the current thread's job group ID and its description.
clearThreshold() - Method in class org.apache.spark.mllib.classification.LogisticRegressionModel
Clears the threshold so that predict will output raw prediction scores.
clearThreshold() - Method in class org.apache.spark.mllib.classification.SVMModel
Clears the threshold so that predict will output raw prediction scores.
clone() - Method in class org.apache.spark.SparkConf
Copy this object
clone() - Method in class org.apache.spark.sql.types.Decimal
 
clone() - Method in class org.apache.spark.storage.StorageLevel
 
clone() - Method in class org.apache.spark.util.random.BernoulliCellSampler
 
clone() - Method in class org.apache.spark.util.random.BernoulliSampler
 
clone() - Method in class org.apache.spark.util.random.PoissonSampler
 
clone() - Method in interface org.apache.spark.util.random.RandomSampler
return a copy of the RandomSampler object
cloneComplement() - Method in class org.apache.spark.util.random.BernoulliCellSampler
Return a sampler that is the complement of the range specified of the current sampler.
close() - Method in class org.apache.spark.api.java.JavaSparkContext
 
close() - Method in class org.apache.spark.input.PortableDataStream
Closing the PortableDataStream is not needed anymore.
close() - Method in class org.apache.spark.io.SnappyOutputStreamWrapper
 
close() - Method in class org.apache.spark.serializer.DeserializationStream
 
close() - Method in class org.apache.spark.serializer.SerializationStream
 
close() - Method in class org.apache.spark.sql.sources.OutputWriter
Closes the OutputWriter.
close() - Method in class org.apache.spark.storage.BufferReleasingInputStream
 
close() - Method in class org.apache.spark.storage.TimeTrackingOutputStream
 
close() - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
 
close() - Method in class org.apache.spark.streaming.util.WriteAheadLog
Close this log and release any resources.
closeLogWriter(int) - Method in class org.apache.spark.scheduler.JobLogger
Close log file, and clean the stage relationship in stageIdToJobId
closureSerializer() - Method in class org.apache.spark.SparkEnv
 
cls() - Method in class org.apache.spark.util.MethodIdentifier
 
clsTag() - Method in interface org.apache.spark.sql.Encoder
A ClassTag that can be used to construct and Array to contain a collection of `T`.
cluster() - Method in class org.apache.spark.mllib.clustering.PowerIterationClustering.Assignment
 
clusterCenters() - Method in class org.apache.spark.ml.clustering.KMeansModel
 
clusterCenters() - Method in class org.apache.spark.mllib.clustering.BisectingKMeansModel
Leaf cluster centers.
clusterCenters() - Method in class org.apache.spark.mllib.clustering.KMeansModel
 
clusterCenters() - Method in class org.apache.spark.mllib.clustering.StreamingKMeansModel
 
clusterWeights() - Method in class org.apache.spark.mllib.clustering.StreamingKMeansModel
 
cn() - Method in class org.apache.spark.mllib.feature.VocabWord
 
coalesce(int) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Return a new RDD that is reduced into numPartitions partitions.
coalesce(int, boolean) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Return a new RDD that is reduced into numPartitions partitions.
coalesce(int) - Method in class org.apache.spark.api.java.JavaPairRDD
Return a new RDD that is reduced into numPartitions partitions.
coalesce(int, boolean) - Method in class org.apache.spark.api.java.JavaPairRDD
Return a new RDD that is reduced into numPartitions partitions.
coalesce(int) - Method in class org.apache.spark.api.java.JavaRDD
Return a new RDD that is reduced into numPartitions partitions.
coalesce(int, boolean) - Method in class org.apache.spark.api.java.JavaRDD
Return a new RDD that is reduced into numPartitions partitions.
coalesce(int, boolean, Ordering<T>) - Method in class org.apache.spark.rdd.RDD
Return a new RDD that is reduced into numPartitions partitions.
coalesce(int) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame that has exactly numPartitions partitions.
coalesce(int) - Method in class org.apache.spark.sql.Dataset
Returns a new Dataset that has exactly numPartitions partitions.
coalesce(Column...) - Static method in class org.apache.spark.sql.functions
Returns the first column that is not null, or null if all inputs are null.
coalesce(Seq<Column>) - Static method in class org.apache.spark.sql.functions
Returns the first column that is not null, or null if all inputs are null.
code() - Method in class org.apache.spark.mllib.feature.VocabWord
 
codeLen() - Method in class org.apache.spark.mllib.feature.VocabWord
 
coefficients() - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
coefficients() - Method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
coefficients() - Method in class org.apache.spark.ml.regression.LinearRegressionModel
 
coefficientStandardErrors() - Method in class org.apache.spark.ml.regression.LinearRegressionSummary
Standard error of estimated coefficients and intercept.
cogroup(JavaPairRDD<K, W>, Partitioner) - Method in class org.apache.spark.api.java.JavaPairRDD
For each key k in this or other, return a resulting RDD that contains a tuple with the list of values for that key in this as well as other.
cogroup(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>, Partitioner) - Method in class org.apache.spark.api.java.JavaPairRDD
For each key k in this or other1 or other2, return a resulting RDD that contains a tuple with the list of values for that key in this, other1 and other2.
cogroup(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>, JavaPairRDD<K, W3>, Partitioner) - Method in class org.apache.spark.api.java.JavaPairRDD
For each key k in this or other1 or other2 or other3, return a resulting RDD that contains a tuple with the list of values for that key in this, other1, other2 and other3.
cogroup(JavaPairRDD<K, W>) - Method in class org.apache.spark.api.java.JavaPairRDD
For each key k in this or other, return a resulting RDD that contains a tuple with the list of values for that key in this as well as other.
cogroup(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>) - Method in class org.apache.spark.api.java.JavaPairRDD
For each key k in this or other1 or other2, return a resulting RDD that contains a tuple with the list of values for that key in this, other1 and other2.
cogroup(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>, JavaPairRDD<K, W3>) - Method in class org.apache.spark.api.java.JavaPairRDD
For each key k in this or other1 or other2 or other3, return a resulting RDD that contains a tuple with the list of values for that key in this, other1, other2 and other3.
cogroup(JavaPairRDD<K, W>, int) - Method in class org.apache.spark.api.java.JavaPairRDD
For each key k in this or other, return a resulting RDD that contains a tuple with the list of values for that key in this as well as other.
cogroup(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>, int) - Method in class org.apache.spark.api.java.JavaPairRDD
For each key k in this or other1 or other2, return a resulting RDD that contains a tuple with the list of values for that key in this, other1 and other2.
cogroup(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>, JavaPairRDD<K, W3>, int) - Method in class org.apache.spark.api.java.JavaPairRDD
For each key k in this or other1 or other2 or other3, return a resulting RDD that contains a tuple with the list of values for that key in this, other1, other2 and other3.
cogroup(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>, RDD<Tuple2<K, W3>>, Partitioner) - Method in class org.apache.spark.rdd.PairRDDFunctions
For each key k in this or other1 or other2 or other3, return a resulting RDD that contains a tuple with the list of values for that key in this, other1, other2 and other3.
cogroup(RDD<Tuple2<K, W>>, Partitioner) - Method in class org.apache.spark.rdd.PairRDDFunctions
 
cogroup(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>, Partitioner) - Method in class org.apache.spark.rdd.PairRDDFunctions
 
cogroup(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>, RDD<Tuple2<K, W3>>) - Method in class org.apache.spark.rdd.PairRDDFunctions
 
cogroup(RDD<Tuple2<K, W>>) - Method in class org.apache.spark.rdd.PairRDDFunctions
For each key k in this or other, return a resulting RDD that contains a tuple with the list of values for that key in this as well as other.
cogroup(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>) - Method in class org.apache.spark.rdd.PairRDDFunctions
For each key k in this or other1 or other2, return a resulting RDD that contains a tuple with the list of values for that key in this, other1 and other2.
cogroup(RDD<Tuple2<K, W>>, int) - Method in class org.apache.spark.rdd.PairRDDFunctions
For each key k in this or other, return a resulting RDD that contains a tuple with the list of values for that key in this as well as other.
cogroup(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>, int) - Method in class org.apache.spark.rdd.PairRDDFunctions
For each key k in this or other1 or other2, return a resulting RDD that contains a tuple with the list of values for that key in this, other1 and other2.
cogroup(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>, RDD<Tuple2<K, W3>>, int) - Method in class org.apache.spark.rdd.PairRDDFunctions
For each key k in this or other1 or other2 or other3, return a resulting RDD that contains a tuple with the list of values for that key in this, other1, other2 and other3.
cogroup(GroupedDataset<K, U>, Function3<K, Iterator<V>, Iterator<U>, TraversableOnce<R>>, Encoder<R>) - Method in class org.apache.spark.sql.GroupedDataset
Applies the given function to each cogrouped data.
cogroup(GroupedDataset<K, U>, CoGroupFunction<K, V, U, R>, Encoder<R>) - Method in class org.apache.spark.sql.GroupedDataset
Applies the given function to each cogrouped data.
cogroup(JavaPairDStream<K, W>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.
cogroup(JavaPairDStream<K, W>, int) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.
cogroup(JavaPairDStream<K, W>, Partitioner) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.
cogroup(DStream<Tuple2<K, W>>, ClassTag<W>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.
cogroup(DStream<Tuple2<K, W>>, int, ClassTag<W>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.
cogroup(DStream<Tuple2<K, W>>, Partitioner, ClassTag<W>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.
CoGroupedRDD<K> - Class in org.apache.spark.rdd
:: DeveloperApi :: A RDD that cogroups its parents.
CoGroupedRDD(Seq<RDD<? extends Product2<K, ?>>>, Partitioner, ClassTag<K>) - Constructor for class org.apache.spark.rdd.CoGroupedRDD
 
CoGroupFunction<K,V1,V2,R> - Interface in org.apache.spark.api.java.function
A function that returns zero or more output records from each grouping key and its values from 2 Datasets.
col(String) - Method in class org.apache.spark.sql.DataFrame
Selects column based on the column name and return it as a Column.
col(String) - Static method in class org.apache.spark.sql.functions
Returns a Column based on the given column name.
collect() - Method in interface org.apache.spark.api.java.JavaRDDLike
Return an array that contains all of the elements in this RDD.
collect() - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
 
collect() - Method in class org.apache.spark.rdd.RDD
Return an array that contains all of the elements in this RDD.
collect(PartialFunction<T, U>, ClassTag<U>) - Method in class org.apache.spark.rdd.RDD
Return an RDD that contains all matching values by applying f.
collect() - Method in class org.apache.spark.sql.DataFrame
Returns an array that contains all of Rows in this DataFrame.
collect() - Method in class org.apache.spark.sql.Dataset
Returns an array that contains all the elements in this Dataset.
collect_list(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns a list of objects with duplicates.
collect_list(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns a list of objects with duplicates.
collect_set(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns a set of objects with duplicate elements eliminated.
collect_set(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns a set of objects with duplicate elements eliminated.
collectAsList() - Method in class org.apache.spark.sql.DataFrame
Returns a Java list that contains all of Rows in this DataFrame.
collectAsList() - Method in class org.apache.spark.sql.Dataset
Returns an array that contains all the elements in this Dataset.
collectAsMap() - Method in class org.apache.spark.api.java.JavaPairRDD
Return the key-value pairs in this RDD to the master as a Map.
collectAsMap() - Method in class org.apache.spark.rdd.PairRDDFunctions
Return the key-value pairs in this RDD to the master as a Map.
collectAsync() - Method in interface org.apache.spark.api.java.JavaRDDLike
The asynchronous version of collect, which returns a future for retrieving an array containing all of the elements in this RDD.
collectAsync() - Method in class org.apache.spark.rdd.AsyncRDDActions
Returns a future for retrieving all elements of this RDD.
collectEdges(EdgeDirection) - Method in class org.apache.spark.graphx.GraphOps
Returns an RDD that contains for each vertex v its local edges, i.e., the edges that are incident on v, in the user-specified direction.
collectNeighborIds(EdgeDirection) - Method in class org.apache.spark.graphx.GraphOps
Collect the neighbor vertex ids for each vertex.
collectNeighbors(EdgeDirection) - Method in class org.apache.spark.graphx.GraphOps
Collect the neighbor vertex attributes for each vertex.
collectPartitions(int[]) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return an array that contains all of the elements in a specific partition of this RDD.
collectToPython() - Method in class org.apache.spark.sql.DataFrame
 
colPtrs() - Method in class org.apache.spark.mllib.linalg.SparseMatrix
 
colsPerBlock() - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
 
colStats(RDD<Vector>) - Static method in class org.apache.spark.mllib.stat.Statistics
Computes column-wise summary statistics for the input RDD[Vector].
Column - Class in org.apache.spark.sql
:: Experimental :: A column that will be computed based on the data in a DataFrame.
Column(Expression) - Constructor for class org.apache.spark.sql.Column
 
Column(String) - Constructor for class org.apache.spark.sql.Column
 
column(String) - Static method in class org.apache.spark.sql.functions
Returns a Column based on the given column name.
ColumnName - Class in org.apache.spark.sql
:: Experimental :: A convenient class used for constructing schema.
ColumnName(String) - Constructor for class org.apache.spark.sql.ColumnName
 
ColumnPruner - Class in org.apache.spark.ml.feature
Utility transformer for removing temporary columns from a DataFrame.
ColumnPruner(Set<String>) - Constructor for class org.apache.spark.ml.feature.ColumnPruner
 
columns() - Method in class org.apache.spark.sql.DataFrame
Returns all column names as an array.
columnSimilarities() - Method in class org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
 
columnSimilarities() - Method in class org.apache.spark.mllib.linalg.distributed.RowMatrix
Compute all cosine similarities between columns of this matrix using the brute-force approach of computing normalized dot products.
columnSimilarities(double) - Method in class org.apache.spark.mllib.linalg.distributed.RowMatrix
Compute similarities between columns of this matrix using a sampling approach.
combineByKey(Function<V, C>, Function2<C, V, C>, Function2<C, C, C>, Partitioner, boolean, Serializer) - Method in class org.apache.spark.api.java.JavaPairRDD
Generic function to combine the elements for each key using a custom set of aggregation functions.
combineByKey(Function<V, C>, Function2<C, V, C>, Function2<C, C, C>, Partitioner) - Method in class org.apache.spark.api.java.JavaPairRDD
Generic function to combine the elements for each key using a custom set of aggregation functions.
combineByKey(Function<V, C>, Function2<C, V, C>, Function2<C, C, C>, int) - Method in class org.apache.spark.api.java.JavaPairRDD
Simplified version of combineByKey that hash-partitions the output RDD and uses map-side aggregation.
combineByKey(Function<V, C>, Function2<C, V, C>, Function2<C, C, C>) - Method in class org.apache.spark.api.java.JavaPairRDD
Simplified version of combineByKey that hash-partitions the resulting RDD using the existing partitioner/parallelism level and using map-side aggregation.
combineByKey(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>, Partitioner, boolean, Serializer) - Method in class org.apache.spark.rdd.PairRDDFunctions
Generic function to combine the elements for each key using a custom set of aggregation functions.
combineByKey(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>, int) - Method in class org.apache.spark.rdd.PairRDDFunctions
Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD.
combineByKey(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>) - Method in class org.apache.spark.rdd.PairRDDFunctions
 
combineByKey(Function<V, C>, Function2<C, V, C>, Function2<C, C, C>, Partitioner) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Combine elements of each key in DStream's RDDs using custom function.
combineByKey(Function<V, C>, Function2<C, V, C>, Function2<C, C, C>, Partitioner, boolean) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Combine elements of each key in DStream's RDDs using custom function.
combineByKey(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>, Partitioner, boolean, ClassTag<C>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Combine elements of each key in DStream's RDDs using custom functions.
combineByKeyWithClassTag(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>, Partitioner, boolean, Serializer, ClassTag<C>) - Method in class org.apache.spark.rdd.PairRDDFunctions
:: Experimental :: Generic function to combine the elements for each key using a custom set of aggregation functions.
combineByKeyWithClassTag(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>, int, ClassTag<C>) - Method in class org.apache.spark.rdd.PairRDDFunctions
:: Experimental :: Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD.
combineByKeyWithClassTag(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>, ClassTag<C>) - Method in class org.apache.spark.rdd.PairRDDFunctions
:: Experimental :: Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the existing partitioner/parallelism level.
combineCombinersByKey(Iterator<Product2<K, C>>) - Method in class org.apache.spark.Aggregator
 
combineCombinersByKey(Iterator<Product2<K, C>>, TaskContext) - Method in class org.apache.spark.Aggregator
 
combinerClassName() - Method in class org.apache.spark.ShuffleDependency
 
combineValuesByKey(Iterator<Product2<K, V>>) - Method in class org.apache.spark.Aggregator
 
combineValuesByKey(Iterator<Product2<K, V>>, TaskContext) - Method in class org.apache.spark.Aggregator
 
compare(PartitionGroup, PartitionGroup) - Method in class org.apache.spark.rdd.PartitionCoalescer
 
compare(Option<PartitionGroup>, Option<PartitionGroup>) - Method in class org.apache.spark.rdd.PartitionCoalescer
 
compare(Decimal) - Method in class org.apache.spark.sql.types.Decimal
 
compare(RDDInfo) - Method in class org.apache.spark.storage.RDDInfo
 
compareTo(SparkShutdownHook) - Method in class org.apache.spark.util.SparkShutdownHook
 
completed() - Method in class org.apache.spark.status.api.v1.ApplicationAttemptInfo
 
completedJobs() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
completedStages() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
completedTasks() - Method in class org.apache.spark.status.api.v1.ExecutorSummary
 
completionTime() - Method in class org.apache.spark.scheduler.StageInfo
Time when all tasks in the stage completed or when the stage was cancelled.
completionTime() - Method in class org.apache.spark.status.api.v1.JobData
 
ComplexFutureAction<T> - Class in org.apache.spark
A FutureAction for actions that could trigger multiple Spark jobs.
ComplexFutureAction() - Constructor for class org.apache.spark.ComplexFutureAction
 
compressed() - Method in interface org.apache.spark.mllib.linalg.Vector
Returns a vector in either dense or sparse format, whichever uses less storage.
compressedInputStream(InputStream) - Method in interface org.apache.spark.io.CompressionCodec
 
compressedInputStream(InputStream) - Method in class org.apache.spark.io.LZ4CompressionCodec
 
compressedInputStream(InputStream) - Method in class org.apache.spark.io.LZFCompressionCodec
 
compressedInputStream(InputStream) - Method in class org.apache.spark.io.SnappyCompressionCodec
 
compressedOutputStream(OutputStream) - Method in interface org.apache.spark.io.CompressionCodec
 
compressedOutputStream(OutputStream) - Method in class org.apache.spark.io.LZ4CompressionCodec
 
compressedOutputStream(OutputStream) - Method in class org.apache.spark.io.LZFCompressionCodec
 
compressedOutputStream(OutputStream) - Method in class org.apache.spark.io.SnappyCompressionCodec
 
CompressionCodec - Interface in org.apache.spark.io
:: DeveloperApi :: CompressionCodec allows the customization of choosing different compression implementations to be used in block storage.
compute(Partition, TaskContext) - Method in class org.apache.spark.api.r.BaseRRDD
 
compute(Partition, TaskContext) - Method in class org.apache.spark.graphx.EdgeRDD
 
compute(Partition, TaskContext) - Method in class org.apache.spark.graphx.VertexRDD
Provides the RDD[(VertexId, VD)] equivalent output.
compute(Vector, double, Vector) - Method in class org.apache.spark.mllib.optimization.Gradient
Compute the gradient and loss given the features of a single data point.
compute(Vector, double, Vector, Vector) - Method in class org.apache.spark.mllib.optimization.Gradient
Compute the gradient and loss given the features of a single data point, add the gradient to a provided vector to avoid creating new objects, and return loss.
compute(Vector, double, Vector) - Method in class org.apache.spark.mllib.optimization.HingeGradient
 
compute(Vector, double, Vector, Vector) - Method in class org.apache.spark.mllib.optimization.HingeGradient
 
compute(Vector, Vector, double, int, double) - Method in class org.apache.spark.mllib.optimization.L1Updater
 
compute(Vector, double, Vector) - Method in class org.apache.spark.mllib.optimization.LeastSquaresGradient
 
compute(Vector, double, Vector, Vector) - Method in class org.apache.spark.mllib.optimization.LeastSquaresGradient
 
compute(Vector, double, Vector) - Method in class org.apache.spark.mllib.optimization.LogisticGradient
 
compute(Vector, double, Vector, Vector) - Method in class org.apache.spark.mllib.optimization.LogisticGradient
 
compute(Vector, Vector, double, int, double) - Method in class org.apache.spark.mllib.optimization.SimpleUpdater
 
compute(Vector, Vector, double, int, double) - Method in class org.apache.spark.mllib.optimization.SquaredL2Updater
 
compute(Vector, Vector, double, int, double) - Method in class org.apache.spark.mllib.optimization.Updater
Compute an updated value for weights given the gradient, stepSize, iteration number and regularization parameter.
compute(Partition, TaskContext) - Method in class org.apache.spark.rdd.CoGroupedRDD
 
compute(Partition, TaskContext) - Method in class org.apache.spark.rdd.HadoopRDD
 
compute(Partition, TaskContext) - Method in class org.apache.spark.rdd.JdbcRDD
 
compute(Partition, TaskContext) - Method in class org.apache.spark.rdd.NewHadoopRDD
 
compute(Partition, TaskContext) - Method in class org.apache.spark.rdd.PartitionPruningRDD
 
compute(Partition, TaskContext) - Method in class org.apache.spark.rdd.RDD
:: DeveloperApi :: Implemented by subclasses to compute a given partition.
compute(Partition, TaskContext) - Method in class org.apache.spark.rdd.ShuffledRDD
 
compute(Partition, TaskContext) - Method in class org.apache.spark.rdd.UnionRDD
 
compute(Time) - Method in class org.apache.spark.streaming.api.java.JavaDStream
Generate an RDD for the given duration
compute(Time) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Method that generates a RDD for the given Duration
compute(Time) - Method in class org.apache.spark.streaming.dstream.ConstantInputDStream
 
compute(Time) - Method in class org.apache.spark.streaming.dstream.DStream
Method that generates a RDD for the given time
compute(Time) - Method in class org.apache.spark.streaming.dstream.ReceiverInputDStream
 
computeColumnSummaryStatistics() - Method in class org.apache.spark.mllib.linalg.distributed.RowMatrix
Computes column-wise summary statistics.
computeCost(DataFrame) - Method in class org.apache.spark.ml.clustering.KMeansModel
Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.
computeCost(Vector) - Method in class org.apache.spark.mllib.clustering.BisectingKMeansModel
Computes the squared distance between the input point and the cluster center it belongs to.
computeCost(RDD<Vector>) - Method in class org.apache.spark.mllib.clustering.BisectingKMeansModel
Computes the sum of squared distances between the input points and their corresponding cluster centers.
computeCost(JavaRDD<Vector>) - Method in class org.apache.spark.mllib.clustering.BisectingKMeansModel
Java-friendly version of computeCost().
computeCost(RDD<Vector>) - Method in class org.apache.spark.mllib.clustering.KMeansModel
Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.
computeCovariance() - Method in class org.apache.spark.mllib.linalg.distributed.RowMatrix
Computes the covariance matrix, treating each row as an observation.
computeError(org.apache.spark.mllib.tree.model.TreeEnsembleModel, RDD<LabeledPoint>) - Method in interface org.apache.spark.mllib.tree.loss.Loss
Method to calculate error of the base learner for the gradient boosting calculation.
computeError(double, double) - Method in interface org.apache.spark.mllib.tree.loss.Loss
Method to calculate loss when the predictions are already known.
computeGramianMatrix() - Method in class org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
 
computeGramianMatrix() - Method in class org.apache.spark.mllib.linalg.distributed.RowMatrix
Computes the Gramian matrix A^T A.
computeInitialPredictionAndError(RDD<LabeledPoint>, double, DecisionTreeModel, Loss) - Static method in class org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
:: DeveloperApi :: Compute the initial predictions and errors for a dataset for the first iteration of gradient boosting.
computePreferredLocations(Seq<InputFormatInfo>) - Static method in class org.apache.spark.scheduler.InputFormatInfo
Computes the preferred locations based on input(s) and returned a location to block map.
computePrincipalComponents(int) - Method in class org.apache.spark.mllib.linalg.distributed.RowMatrix
Computes the top k principal components.
computeSVD(int, boolean, double) - Method in class org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
 
computeSVD(int, boolean, double) - Method in class org.apache.spark.mllib.linalg.distributed.RowMatrix
Computes singular value decomposition of this matrix.
concat(Column...) - Static method in class org.apache.spark.sql.functions
Concatenates multiple input string columns together into a single string column.
concat(Seq<Column>) - Static method in class org.apache.spark.sql.functions
Concatenates multiple input string columns together into a single string column.
concat_ws(String, Column...) - Static method in class org.apache.spark.sql.functions
Concatenates multiple input string columns together into a single string column, using the given separator.
concat_ws(String, Seq<Column>) - Static method in class org.apache.spark.sql.functions
Concatenates multiple input string columns together into a single string column, using the given separator.
conf() - Method in class org.apache.spark.SparkEnv
 
conf() - Method in class org.apache.spark.sql.hive.HiveContext
 
conf() - Method in class org.apache.spark.sql.SQLContext
 
conf() - Method in class org.apache.spark.streaming.StreamingContext
 
confidence() - Method in class org.apache.spark.mllib.fpm.AssociationRules.Rule
Returns the confidence of the rule.
confidence() - Method in class org.apache.spark.partial.BoundedDouble
 
configuration() - Method in class org.apache.spark.scheduler.InputFormatInfo
 
CONFIGURATION_INSTANTIATION_LOCK() - Static method in class org.apache.spark.rdd.HadoopRDD
Configuration's constructor is not threadsafe (see SPARK-1097 and HADOOP-10456).
CONFIGURATION_INSTANTIATION_LOCK() - Static method in class org.apache.spark.rdd.NewHadoopRDD
Configuration's constructor is not threadsafe (see SPARK-1097 and HADOOP-10456).
configure() - Method in class org.apache.spark.sql.hive.HiveContext
Overridden by child classes that need to set configuration before the client init.
confusionMatrix() - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns confusion matrix: predicted classes are in columns, they are ordered by class label ascending, as in "labels"
connectedComponents() - Method in class org.apache.spark.graphx.GraphOps
Compute the connected component membership of each vertex and return a graph with the vertex value containing the lowest vertex id in the connected component containing that vertex.
ConnectedComponents - Class in org.apache.spark.graphx.lib
Connected components algorithm.
ConnectedComponents() - Constructor for class org.apache.spark.graphx.lib.ConnectedComponents
 
consequent() - Method in class org.apache.spark.mllib.fpm.AssociationRules.Rule
 
ConstantInputDStream<T> - Class in org.apache.spark.streaming.dstream
An input stream that always returns the same RDD on each timestep.
ConstantInputDStream(StreamingContext, RDD<T>, ClassTag<T>) - Constructor for class org.apache.spark.streaming.dstream.ConstantInputDStream
 
contains(Param<?>) - Method in class org.apache.spark.ml.param.ParamMap
Checks whether a parameter is explicitly specified.
contains(String) - Method in class org.apache.spark.SparkConf
Does the configuration contain a given parameter?
contains(Object) - Method in class org.apache.spark.sql.Column
Contains the other element.
contains(String) - Method in class org.apache.spark.sql.types.Metadata
Tests whether this Metadata contains a binding for a key.
containsBlock(BlockId) - Method in class org.apache.spark.storage.StorageStatus
Return whether the given block is stored in this block manager in O(1) time.
containsCachedMetadata(String) - Static method in class org.apache.spark.rdd.HadoopRDD
 
containsNull() - Method in class org.apache.spark.sql.types.ArrayType
 
context() - Method in interface org.apache.spark.api.java.JavaRDDLike
The SparkContext that this RDD was created on.
context() - Method in class org.apache.spark.InterruptibleIterator
 
context(SQLContext) - Method in class org.apache.spark.ml.util.MLReader
 
context(SQLContext) - Method in class org.apache.spark.ml.util.MLWriter
 
context() - Method in class org.apache.spark.rdd.RDD
The SparkContext that this RDD was created on.
context() - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return the StreamingContext associated with this DStream
context() - Method in class org.apache.spark.streaming.dstream.DStream
Return the StreamingContext associated with this DStream
Continuous() - Static method in class org.apache.spark.mllib.tree.configuration.FeatureType
 
ContinuousSplit - Class in org.apache.spark.ml.tree
:: DeveloperApi :: Split which tests a continuous feature.
conv(Column, int, int) - Static method in class org.apache.spark.sql.functions
Convert a number in a string column from one base to another.
CONVERT_CTAS() - Static method in class org.apache.spark.sql.hive.HiveContext
 
CONVERT_METASTORE_PARQUET() - Static method in class org.apache.spark.sql.hive.HiveContext
 
CONVERT_METASTORE_PARQUET_WITH_SCHEMA_MERGING() - Static method in class org.apache.spark.sql.hive.HiveContext
 
convertCTAS() - Method in class org.apache.spark.sql.hive.HiveContext
When true, a table created by a Hive CTAS statement (no USING clause) will be converted to a data source table, using the data source set by spark.sql.sources.default.
convertMetastoreParquet() - Method in class org.apache.spark.sql.hive.HiveContext
When true, enables an experimental feature where metastore tables that use the parquet SerDe are automatically converted to use the Spark SQL parquet table scan, instead of the Hive SerDe.
convertMetastoreParquetWithSchemaMerging() - Method in class org.apache.spark.sql.hive.HiveContext
When true, also tries to merge possibly different but compatible Parquet schemas in different Parquet data files.
convertToCanonicalEdges(Function2<ED, ED, ED>) - Method in class org.apache.spark.graphx.GraphOps
Convert bi-directional edges into uni-directional ones.
CoordinateMatrix - Class in org.apache.spark.mllib.linalg.distributed
 
CoordinateMatrix(RDD<MatrixEntry>, long, long) - Constructor for class org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
 
CoordinateMatrix(RDD<MatrixEntry>) - Constructor for class org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
 
copy(ParamMap) - Method in class org.apache.spark.ml.classification.DecisionTreeClassificationModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.classification.DecisionTreeClassifier
 
copy(ParamMap) - Method in class org.apache.spark.ml.classification.GBTClassificationModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.classification.GBTClassifier
 
copy(ParamMap) - Method in class org.apache.spark.ml.classification.LogisticRegression
 
copy(ParamMap) - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.classification.MultilayerPerceptronClassifier
 
copy(ParamMap) - Method in class org.apache.spark.ml.classification.NaiveBayes
 
copy(ParamMap) - Method in class org.apache.spark.ml.classification.NaiveBayesModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.classification.OneVsRest
 
copy(ParamMap) - Method in class org.apache.spark.ml.classification.OneVsRestModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.classification.RandomForestClassificationModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.classification.RandomForestClassifier
 
copy(ParamMap) - Method in class org.apache.spark.ml.clustering.DistributedLDAModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.clustering.KMeans
 
copy(ParamMap) - Method in class org.apache.spark.ml.clustering.KMeansModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.clustering.LDA
 
copy(ParamMap) - Method in class org.apache.spark.ml.clustering.LocalLDAModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.Estimator
 
copy(ParamMap) - Method in class org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
 
copy(ParamMap) - Method in class org.apache.spark.ml.evaluation.Evaluator
 
copy(ParamMap) - Method in class org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
 
copy(ParamMap) - Method in class org.apache.spark.ml.evaluation.RegressionEvaluator
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.Binarizer
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.Bucketizer
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.ChiSqSelector
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.ChiSqSelectorModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.ColumnPruner
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.CountVectorizer
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.CountVectorizerModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.HashingTF
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.IDF
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.IDFModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.IndexToString
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.Interaction
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.MinMaxScaler
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.MinMaxScalerModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.OneHotEncoder
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.PCA
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.PCAModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.PolynomialExpansion
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.QuantileDiscretizer
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.RegexTokenizer
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.RFormula
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.RFormulaModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.SQLTransformer
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.StandardScaler
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.StandardScalerModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.StopWordsRemover
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.StringIndexer
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.StringIndexerModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.Tokenizer
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.VectorAssembler
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.VectorAttributeRewriter
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.VectorIndexer
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.VectorIndexerModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.VectorSlicer
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.Word2Vec
 
copy(ParamMap) - Method in class org.apache.spark.ml.feature.Word2VecModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.Model
 
copy() - Method in class org.apache.spark.ml.param.ParamMap
Creates a copy of this param map.
copy(ParamMap) - Method in interface org.apache.spark.ml.param.Params
Creates a copy of this instance with the same UID and some extra params.
copy(ParamMap) - Method in class org.apache.spark.ml.Pipeline
 
copy(ParamMap) - Method in class org.apache.spark.ml.PipelineModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.PipelineStage
 
copy(ParamMap) - Method in class org.apache.spark.ml.Predictor
 
copy(ParamMap) - Method in class org.apache.spark.ml.recommendation.ALS
 
copy(ParamMap) - Method in class org.apache.spark.ml.recommendation.ALSModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegression
 
copy(ParamMap) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.regression.DecisionTreeRegressionModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.regression.DecisionTreeRegressor
 
copy(ParamMap) - Method in class org.apache.spark.ml.regression.GBTRegressionModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.regression.GBTRegressor
 
copy(ParamMap) - Method in class org.apache.spark.ml.regression.IsotonicRegression
 
copy(ParamMap) - Method in class org.apache.spark.ml.regression.IsotonicRegressionModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.regression.LinearRegression
 
copy(ParamMap) - Method in class org.apache.spark.ml.regression.LinearRegressionModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.regression.RandomForestRegressionModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.regression.RandomForestRegressor
 
copy(ParamMap) - Method in class org.apache.spark.ml.Transformer
 
copy(ParamMap) - Method in class org.apache.spark.ml.tuning.CrossValidator
 
copy(ParamMap) - Method in class org.apache.spark.ml.tuning.CrossValidatorModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.tuning.TrainValidationSplit
 
copy(ParamMap) - Method in class org.apache.spark.ml.tuning.TrainValidationSplitModel
 
copy(ParamMap) - Method in class org.apache.spark.ml.UnaryTransformer
 
copy() - Method in class org.apache.spark.mllib.linalg.DenseMatrix
 
copy() - Method in class org.apache.spark.mllib.linalg.DenseVector
 
copy() - Method in interface org.apache.spark.mllib.linalg.Matrix
Get a deep copy of the matrix.
copy() - Method in class org.apache.spark.mllib.linalg.SparseMatrix
 
copy() - Method in class org.apache.spark.mllib.linalg.SparseVector
 
copy() - Method in interface org.apache.spark.mllib.linalg.Vector
Makes a deep copy of this vector.
copy() - Method in class org.apache.spark.mllib.random.ExponentialGenerator
 
copy() - Method in class org.apache.spark.mllib.random.GammaGenerator
 
copy() - Method in class org.apache.spark.mllib.random.LogNormalGenerator
 
copy() - Method in class org.apache.spark.mllib.random.PoissonGenerator
 
copy() - Method in interface org.apache.spark.mllib.random.RandomDataGenerator
Returns a copy of the RandomDataGenerator with a new instance of the rng object used in the class when applicable for non-locking concurrent usage.
copy() - Method in class org.apache.spark.mllib.random.StandardNormalGenerator
 
copy() - Method in class org.apache.spark.mllib.random.UniformGenerator
 
copy() - Method in class org.apache.spark.mllib.random.WeibullGenerator
 
copy() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
Returns a shallow copy of this instance.
copy() - Method in interface org.apache.spark.sql.Row
Make a copy of the current Row object.
copy() - Method in class org.apache.spark.util.StatCounter
Clone this StatCounter
copyValues(T, ParamMap) - Method in interface org.apache.spark.ml.param.Params
Copies param values from this instance to another instance for params shared by them.
coresGranted() - Method in class org.apache.spark.status.api.v1.ApplicationInfo
 
coresPerExecutor() - Method in class org.apache.spark.status.api.v1.ApplicationInfo
 
corr(RDD<Vector>) - Static method in class org.apache.spark.mllib.stat.Statistics
Compute the Pearson correlation matrix for the input RDD of Vectors.
corr(RDD<Vector>, String) - Static method in class org.apache.spark.mllib.stat.Statistics
Compute the correlation matrix for the input RDD of Vectors using the specified method.
corr(RDD<Object>, RDD<Object>) - Static method in class org.apache.spark.mllib.stat.Statistics
Compute the Pearson correlation for the input RDDs.
corr(JavaRDD<Double>, JavaRDD<Double>) - Static method in class org.apache.spark.mllib.stat.Statistics
Java-friendly version of corr()
corr(RDD<Object>, RDD<Object>, String) - Static method in class org.apache.spark.mllib.stat.Statistics
Compute the correlation for the input RDDs using the specified method.
corr(JavaRDD<Double>, JavaRDD<Double>, String) - Static method in class org.apache.spark.mllib.stat.Statistics
Java-friendly version of corr()
corr(String, String, String) - Method in class org.apache.spark.sql.DataFrameStatFunctions
Calculates the correlation of two columns of a DataFrame.
corr(String, String) - Method in class org.apache.spark.sql.DataFrameStatFunctions
Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
corr(Column, Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the Pearson Correlation Coefficient for two columns.
corr(String, String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the Pearson Correlation Coefficient for two columns.
cos(Column) - Static method in class org.apache.spark.sql.functions
Computes the cosine of the given value.
cos(String) - Static method in class org.apache.spark.sql.functions
Computes the cosine of the given column.
cosh(Column) - Static method in class org.apache.spark.sql.functions
Computes the hyperbolic cosine of the given value.
cosh(String) - Static method in class org.apache.spark.sql.functions
Computes the hyperbolic cosine of the given column.
count() - Method in interface org.apache.spark.api.java.JavaRDDLike
Return the number of elements in the RDD.
count() - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
The number of edges in the RDD.
count() - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
The number of vertices in the RDD.
count() - Method in class org.apache.spark.ml.regression.AFTAggregator
 
count() - Method in class org.apache.spark.ml.regression.LeastSquaresAggregator
 
count() - Method in class org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
Sample size.
count() - Method in interface org.apache.spark.mllib.stat.MultivariateStatisticalSummary
Sample size.
count() - Method in class org.apache.spark.rdd.RDD
Return the number of elements in the RDD.
count() - Method in class org.apache.spark.sql.DataFrame
Returns the number of rows in the DataFrame.
count() - Method in class org.apache.spark.sql.Dataset
Returns the number of elements in the Dataset.
count(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the number of items in a group.
count(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the number of items in a group.
count() - Method in class org.apache.spark.sql.GroupedData
Count the number of rows for each group.
count() - Method in class org.apache.spark.sql.GroupedDataset
Returns a Dataset that contains a tuple with each key and the number of items present for that key.
count() - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD has a single element generated by counting each RDD of this DStream.
count() - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream in which each RDD has a single element generated by counting each RDD of this DStream.
count() - Method in class org.apache.spark.streaming.kafka.OffsetRange
Number of messages this OffsetRange refers to
count() - Method in class org.apache.spark.util.StatCounter
 
countApprox(long, double) - Method in interface org.apache.spark.api.java.JavaRDDLike
Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
countApprox(long) - Method in interface org.apache.spark.api.java.JavaRDDLike
Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
countApprox(long, double) - Method in class org.apache.spark.rdd.RDD
Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
countApproxDistinct(double) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return approximate number of distinct elements in the RDD.
countApproxDistinct(int, int) - Method in class org.apache.spark.rdd.RDD
Return approximate number of distinct elements in the RDD.
countApproxDistinct(double) - Method in class org.apache.spark.rdd.RDD
Return approximate number of distinct elements in the RDD.
countApproxDistinctByKey(double, Partitioner) - Method in class org.apache.spark.api.java.JavaPairRDD
Return approximate number of distinct values for each key in this RDD.
countApproxDistinctByKey(double, int) - Method in class org.apache.spark.api.java.JavaPairRDD
Return approximate number of distinct values for each key in this RDD.
countApproxDistinctByKey(double) - Method in class org.apache.spark.api.java.JavaPairRDD
Return approximate number of distinct values for each key in this RDD.
countApproxDistinctByKey(int, int, Partitioner) - Method in class org.apache.spark.rdd.PairRDDFunctions
Return approximate number of distinct values for each key in this RDD.
countApproxDistinctByKey(double, Partitioner) - Method in class org.apache.spark.rdd.PairRDDFunctions
Return approximate number of distinct values for each key in this RDD.
countApproxDistinctByKey(double, int) - Method in class org.apache.spark.rdd.PairRDDFunctions
Return approximate number of distinct values for each key in this RDD.
countApproxDistinctByKey(double) - Method in class org.apache.spark.rdd.PairRDDFunctions
Return approximate number of distinct values for each key in this RDD.
countAsync() - Method in interface org.apache.spark.api.java.JavaRDDLike
The asynchronous version of count, which returns a future for counting the number of elements in this RDD.
countAsync() - Method in class org.apache.spark.rdd.AsyncRDDActions
Returns a future for counting the number of elements in the RDD.
countByKey() - Method in class org.apache.spark.api.java.JavaPairRDD
Count the number of elements for each key, and return the result to the master as a Map.
countByKey() - Method in class org.apache.spark.rdd.PairRDDFunctions
Count the number of elements for each key, collecting the results to a local Map.
countByKeyApprox(long) - Method in class org.apache.spark.api.java.JavaPairRDD
Approximate version of countByKey that can return a partial result if it does not finish within a timeout.
countByKeyApprox(long, double) - Method in class org.apache.spark.api.java.JavaPairRDD
Approximate version of countByKey that can return a partial result if it does not finish within a timeout.
countByKeyApprox(long, double) - Method in class org.apache.spark.rdd.PairRDDFunctions
Approximate version of countByKey that can return a partial result if it does not finish within a timeout.
countByValue() - Method in interface org.apache.spark.api.java.JavaRDDLike
Return the count of each unique value in this RDD as a map of (value, count) pairs.
countByValue(Ordering<T>) - Method in class org.apache.spark.rdd.RDD
Return the count of each unique value in this RDD as a local map of (value, count) pairs.
countByValue() - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream.
countByValue(int) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream.
countByValue(int, Ordering<T>) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream in which each RDD contains the counts of each distinct value in each RDD of this DStream.
countByValueAndWindow(Duration, Duration) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream.
countByValueAndWindow(Duration, Duration, int) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream.
countByValueAndWindow(Duration, Duration, int, Ordering<T>) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream in which each RDD contains the count of distinct elements in RDDs in a sliding window over this DStream.
countByValueApprox(long, double) - Method in interface org.apache.spark.api.java.JavaRDDLike
(Experimental) Approximate version of countByValue().
countByValueApprox(long) - Method in interface org.apache.spark.api.java.JavaRDDLike
(Experimental) Approximate version of countByValue().
countByValueApprox(long, double, Ordering<T>) - Method in class org.apache.spark.rdd.RDD
Approximate version of countByValue().
countByWindow(Duration, Duration) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD has a single element generated by counting the number of elements in a window over this DStream.
countByWindow(Duration, Duration) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream in which each RDD has a single element generated by counting the number of elements in a sliding window over this DStream.
countDistinct(Column, Column...) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the number of distinct items in a group.
countDistinct(String, String...) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the number of distinct items in a group.
countDistinct(Column, Seq<Column>) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the number of distinct items in a group.
countDistinct(String, Seq<String>) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the number of distinct items in a group.
countTowardsTaskFailures() - Method in class org.apache.spark.ExecutorLostFailure
 
countTowardsTaskFailures() - Method in class org.apache.spark.TaskCommitDenied
If a task failed because its attempt to commit was denied, do not count this failure towards failing the stage.
countTowardsTaskFailures() - Method in interface org.apache.spark.TaskFailedReason
Whether this task failure should be counted towards the maximum number of times the task is allowed to fail before the stage is aborted.
CountVectorizer - Class in org.apache.spark.ml.feature
:: Experimental :: Extracts a vocabulary from document collections and generates a CountVectorizerModel.
CountVectorizer(String) - Constructor for class org.apache.spark.ml.feature.CountVectorizer
 
CountVectorizer() - Constructor for class org.apache.spark.ml.feature.CountVectorizer
 
CountVectorizerModel - Class in org.apache.spark.ml.feature
:: Experimental :: Converts a text document to a sparse vector of token counts.
CountVectorizerModel(String, String[]) - Constructor for class org.apache.spark.ml.feature.CountVectorizerModel
 
CountVectorizerModel(String[]) - Constructor for class org.apache.spark.ml.feature.CountVectorizerModel
 
cov(String, String) - Method in class org.apache.spark.sql.DataFrameStatFunctions
Calculate the sample covariance of two numerical columns of a DataFrame.
crc32(Column) - Static method in class org.apache.spark.sql.functions
Calculates the cyclic redundancy check value (CRC32) of a binary column and returns the value as a bigint.
CreatableRelationProvider - Interface in org.apache.spark.sql.sources
 
create(boolean, boolean, boolean, int) - Static method in class org.apache.spark.api.java.StorageLevels
Deprecated.
create(boolean, boolean, boolean, boolean, int) - Static method in class org.apache.spark.api.java.StorageLevels
Create a new StorageLevel object.
create(JavaSparkContext, JdbcRDD.ConnectionFactory, String, long, long, int, Function<ResultSet, T>) - Static method in class org.apache.spark.rdd.JdbcRDD
Create an RDD that executes an SQL query on a JDBC connection and reads results.
create(JavaSparkContext, JdbcRDD.ConnectionFactory, String, long, long, int) - Static method in class org.apache.spark.rdd.JdbcRDD
Create an RDD that executes an SQL query on a JDBC connection and reads results.
create(RDD<T>, Function1<Object, Object>) - Static method in class org.apache.spark.rdd.PartitionPruningRDD
Create a PartitionPruningRDD.
create(Object...) - Static method in class org.apache.spark.sql.RowFactory
Create a Row from the given arguments.
create() - Method in interface org.apache.spark.streaming.api.java.JavaStreamingContextFactory
 
create(String, int) - Static method in class org.apache.spark.streaming.kafka.Broker
 
create(String, int, long, long) - Static method in class org.apache.spark.streaming.kafka.OffsetRange
 
create(TopicAndPartition, long, long) - Static method in class org.apache.spark.streaming.kafka.OffsetRange
 
createArrayType(DataType) - Static method in class org.apache.spark.sql.types.DataTypes
Creates an ArrayType by specifying the data type of elements (elementType).
createArrayType(DataType, boolean) - Static method in class org.apache.spark.sql.types.DataTypes
Creates an ArrayType by specifying the data type of elements (elementType) and whether the array contains null values (containsNull).
createCombiner() - Method in class org.apache.spark.Aggregator
 
createDataFrame(RDD<A>, TypeTags.TypeTag<A>) - Method in class org.apache.spark.sql.SQLContext
 
createDataFrame(Seq<A>, TypeTags.TypeTag<A>) - Method in class org.apache.spark.sql.SQLContext
 
createDataFrame(RDD<Row>, StructType) - Method in class org.apache.spark.sql.SQLContext
 
createDataFrame(JavaRDD<Row>, StructType) - Method in class org.apache.spark.sql.SQLContext
 
createDataFrame(List<Row>, StructType) - Method in class org.apache.spark.sql.SQLContext
 
createDataFrame(RDD<?>, Class<?>) - Method in class org.apache.spark.sql.SQLContext
 
createDataFrame(JavaRDD<?>, Class<?>) - Method in class org.apache.spark.sql.SQLContext
 
createDataFrame(List<?>, Class<?>) - Method in class org.apache.spark.sql.SQLContext
 
createDataset(Seq<T>, Encoder<T>) - Method in class org.apache.spark.sql.SQLContext
 
createDataset(RDD<T>, Encoder<T>) - Method in class org.apache.spark.sql.SQLContext
 
createDataset(List<T>, Encoder<T>) - Method in class org.apache.spark.sql.SQLContext
 
createDecimalType(int, int) - Static method in class org.apache.spark.sql.types.DataTypes
Creates a DecimalType by specifying the precision and scale.
createDecimalType() - Static method in class org.apache.spark.sql.types.DataTypes
Creates a DecimalType with default precision and scale, which are 10 and 0.
createDirectStream(StreamingContext, Map<String, String>, Map<TopicAndPartition, Object>, Function1<MessageAndMetadata<K, V>, R>, ClassTag<K>, ClassTag<V>, ClassTag<KD>, ClassTag<VD>, ClassTag<R>) - Static method in class org.apache.spark.streaming.kafka.KafkaUtils
Create an input stream that directly pulls messages from Kafka Brokers without using any receiver.
createDirectStream(StreamingContext, Map<String, String>, Set<String>, ClassTag<K>, ClassTag<V>, ClassTag<KD>, ClassTag<VD>) - Static method in class org.apache.spark.streaming.kafka.KafkaUtils
Create an input stream that directly pulls messages from Kafka Brokers without using any receiver.
createDirectStream(JavaStreamingContext, Class<K>, Class<V>, Class<KD>, Class<VD>, Class<R>, Map<String, String>, Map<TopicAndPartition, Long>, Function<MessageAndMetadata<K, V>, R>) - Static method in class org.apache.spark.streaming.kafka.KafkaUtils
Create an input stream that directly pulls messages from Kafka Brokers without using any receiver.
createDirectStream(JavaStreamingContext, Class<K>, Class<V>, Class<KD>, Class<VD>, Map<String, String>, Set<String>) - Static method in class org.apache.spark.streaming.kafka.KafkaUtils
Create an input stream that directly pulls messages from Kafka Brokers without using any receiver.
createExternalTable(String, String) - Method in class org.apache.spark.sql.SQLContext
 
createExternalTable(String, String, String) - Method in class org.apache.spark.sql.SQLContext
 
createExternalTable(String, String, Map<String, String>) - Method in class org.apache.spark.sql.SQLContext
 
createExternalTable(String, String, Map<String, String>) - Method in class org.apache.spark.sql.SQLContext
 
createExternalTable(String, String, StructType, Map<String, String>) - Method in class org.apache.spark.sql.SQLContext
 
createExternalTable(String, String, StructType, Map<String, String>) - Method in class org.apache.spark.sql.SQLContext
 
createJDBCTable(String, String, boolean) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.340, replaced by write().jdbc(). This will be removed in Spark 2.0.
createLogDir() - Method in class org.apache.spark.scheduler.JobLogger
Create a folder for log files, the folder's name is the creation time of jobLogger
createLogWriter(int) - Method in class org.apache.spark.scheduler.JobLogger
Create a log file for one job
createMapType(DataType, DataType) - Static method in class org.apache.spark.sql.types.DataTypes
Creates a MapType by specifying the data type of keys (keyType) and values (keyType).
createMapType(DataType, DataType, boolean) - Static method in class org.apache.spark.sql.types.DataTypes
Creates a MapType by specifying the data type of keys (keyType), the data type of values (keyType), and whether values contain any null value (valueContainsNull).
createModel(Vector, double) - Method in class org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
 
createModel(Vector, double) - Method in class org.apache.spark.mllib.classification.LogisticRegressionWithSGD
 
createModel(Vector, double) - Method in class org.apache.spark.mllib.classification.SVMWithSGD
 
createModel(Vector, double) - Method in class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
Create a model given the weights and intercept
createModel(Vector, double) - Method in class org.apache.spark.mllib.regression.LassoWithSGD
 
createModel(Vector, double) - Method in class org.apache.spark.mllib.regression.LinearRegressionWithSGD
 
createModel(Vector, double) - Method in class org.apache.spark.mllib.regression.RidgeRegressionWithSGD
 
createPollingStream(StreamingContext, String, int, StorageLevel) - Static method in class org.apache.spark.streaming.flume.FlumeUtils
Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.
createPollingStream(StreamingContext, Seq<InetSocketAddress>, StorageLevel) - Static method in class org.apache.spark.streaming.flume.FlumeUtils
Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.
createPollingStream(StreamingContext, Seq<InetSocketAddress>, StorageLevel, int, int) - Static method in class org.apache.spark.streaming.flume.FlumeUtils
Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.
createPollingStream(JavaStreamingContext, String, int) - Static method in class org.apache.spark.streaming.flume.FlumeUtils
Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.
createPollingStream(JavaStreamingContext, String, int, StorageLevel) - Static method in class org.apache.spark.streaming.flume.FlumeUtils
Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.
createPollingStream(JavaStreamingContext, InetSocketAddress[], StorageLevel) - Static method in class org.apache.spark.streaming.flume.FlumeUtils
Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.
createPollingStream(JavaStreamingContext, InetSocketAddress[], StorageLevel, int, int) - Static method in class org.apache.spark.streaming.flume.FlumeUtils
Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.
createRDD(SparkContext, Map<String, String>, OffsetRange[], ClassTag<K>, ClassTag<V>, ClassTag<KD>, ClassTag<VD>) - Static method in class org.apache.spark.streaming.kafka.KafkaUtils
Create a RDD from Kafka using offset ranges for each topic and partition.
createRDD(SparkContext, Map<String, String>, OffsetRange[], Map<TopicAndPartition, Broker>, Function1<MessageAndMetadata<K, V>, R>, ClassTag<K>, ClassTag<V>, ClassTag<KD>, ClassTag<VD>, ClassTag<R>) - Static method in class org.apache.spark.streaming.kafka.KafkaUtils
Create a RDD from Kafka using offset ranges for each topic and partition.
createRDD(JavaSparkContext, Class<K>, Class<V>, Class<KD>, Class<VD>, Map<String, String>, OffsetRange[]) - Static method in class org.apache.spark.streaming.kafka.KafkaUtils
Create a RDD from Kafka using offset ranges for each topic and partition.
createRDD(JavaSparkContext, Class<K>, Class<V>, Class<KD>, Class<VD>, Class<R>, Map<String, String>, OffsetRange[], Map<TopicAndPartition, Broker>, Function<MessageAndMetadata<K, V>, R>) - Static method in class org.apache.spark.streaming.kafka.KafkaUtils
Create a RDD from Kafka using offset ranges for each topic and partition.
createRDDFromArray(JavaSparkContext, byte[][]) - Static method in class org.apache.spark.api.r.RRDD
Create an RRDD given a sequence of byte arrays.
createRDDWithLocalProperties(Time, boolean, Function0<U>) - Method in class org.apache.spark.streaming.dstream.DStream
Wrap a body of code such that the call site and operation scope information are passed to the RDDs created in this body properly.
createRelation(SQLContext, Map<String, String>) - Method in class org.apache.spark.ml.source.libsvm.DefaultSource
 
createRelation(SQLContext, SaveMode, Map<String, String>, DataFrame) - Method in interface org.apache.spark.sql.sources.CreatableRelationProvider
Creates a relation with the given parameters based on the contents of the given DataFrame.
createRelation(SQLContext, String[], Option<StructType>, Option<StructType>, Map<String, String>) - Method in interface org.apache.spark.sql.sources.HadoopFsRelationProvider
Returns a new base relation with the given parameters, a user defined schema, and a list of partition columns.
createRelation(SQLContext, Map<String, String>) - Method in interface org.apache.spark.sql.sources.RelationProvider
Returns a new base relation with the given parameters.
createRelation(SQLContext, Map<String, String>, StructType) - Method in interface org.apache.spark.sql.sources.SchemaRelationProvider
Returns a new base relation with the given parameters and user defined schema.
createRWorker(int) - Static method in class org.apache.spark.api.r.RRDD
ProcessBuilder used to launch worker R processes.
createSparkContext(String, String, String, String[], Map<Object, Object>, Map<Object, Object>) - Static method in class org.apache.spark.api.r.RRDD
 
createStream(StreamingContext, String, int, StorageLevel) - Static method in class org.apache.spark.streaming.flume.FlumeUtils
Create a input stream from a Flume source.
createStream(StreamingContext, String, int, StorageLevel, boolean) - Static method in class org.apache.spark.streaming.flume.FlumeUtils
Create a input stream from a Flume source.
createStream(JavaStreamingContext, String, int) - Static method in class org.apache.spark.streaming.flume.FlumeUtils
Creates a input stream from a Flume source.
createStream(JavaStreamingContext, String, int, StorageLevel) - Static method in class org.apache.spark.streaming.flume.FlumeUtils
Creates a input stream from a Flume source.
createStream(JavaStreamingContext, String, int, StorageLevel, boolean) - Static method in class org.apache.spark.streaming.flume.FlumeUtils
Creates a input stream from a Flume source.
createStream(StreamingContext, String, String, Map<String, Object>, StorageLevel) - Static method in class org.apache.spark.streaming.kafka.KafkaUtils
Create an input stream that pulls messages from Kafka Brokers.
createStream(StreamingContext, Map<String, String>, Map<String, Object>, StorageLevel, ClassTag<K>, ClassTag<V>, ClassTag<U>, ClassTag<T>) - Static method in class org.apache.spark.streaming.kafka.KafkaUtils
Create an input stream that pulls messages from Kafka Brokers.
createStream(JavaStreamingContext, String, String, Map<String, Integer>) - Static method in class org.apache.spark.streaming.kafka.KafkaUtils
Create an input stream that pulls messages from Kafka Brokers.
createStream(JavaStreamingContext, String, String, Map<String, Integer>, StorageLevel) - Static method in class org.apache.spark.streaming.kafka.KafkaUtils
Create an input stream that pulls messages from Kafka Brokers.
createStream(JavaStreamingContext, Class<K>, Class<V>, Class<U>, Class<T>, Map<String, String>, Map<String, Integer>, StorageLevel) - Static method in class org.apache.spark.streaming.kafka.KafkaUtils
Create an input stream that pulls messages from Kafka Brokers.
createStream(StreamingContext, String, String, String, String, InitialPositionInStream, Duration, StorageLevel, Function1<Record, T>, ClassTag<T>) - Static method in class org.apache.spark.streaming.kinesis.KinesisUtils
Create an input stream that pulls messages from a Kinesis stream.
createStream(StreamingContext, String, String, String, String, InitialPositionInStream, Duration, StorageLevel, Function1<Record, T>, String, String, ClassTag<T>) - Static method in class org.apache.spark.streaming.kinesis.KinesisUtils
Create an input stream that pulls messages from a Kinesis stream.
createStream(StreamingContext, String, String, String, String, InitialPositionInStream, Duration, StorageLevel) - Static method in class org.apache.spark.streaming.kinesis.KinesisUtils
Create an input stream that pulls messages from a Kinesis stream.
createStream(StreamingContext, String, String, String, String, InitialPositionInStream, Duration, StorageLevel, String, String) - Static method in class org.apache.spark.streaming.kinesis.KinesisUtils
Create an input stream that pulls messages from a Kinesis stream.
createStream(StreamingContext, String, String, Duration, InitialPositionInStream, StorageLevel) - Static method in class org.apache.spark.streaming.kinesis.KinesisUtils
Create an input stream that pulls messages from a Kinesis stream.
createStream(JavaStreamingContext, String, String, String, String, InitialPositionInStream, Duration, StorageLevel, Function<Record, T>, Class<T>) - Static method in class org.apache.spark.streaming.kinesis.KinesisUtils
Create an input stream that pulls messages from a Kinesis stream.
createStream(JavaStreamingContext, String, String, String, String, InitialPositionInStream, Duration, StorageLevel, Function<Record, T>, Class<T>, String, String) - Static method in class org.apache.spark.streaming.kinesis.KinesisUtils
Create an input stream that pulls messages from a Kinesis stream.
createStream(JavaStreamingContext, String, String, String, String, InitialPositionInStream, Duration, StorageLevel) - Static method in class org.apache.spark.streaming.kinesis.KinesisUtils
Create an input stream that pulls messages from a Kinesis stream.
createStream(JavaStreamingContext, String, String, String, String, InitialPositionInStream, Duration, StorageLevel, String, String) - Static method in class org.apache.spark.streaming.kinesis.KinesisUtils
Create an input stream that pulls messages from a Kinesis stream.
createStream(JavaStreamingContext, String, String, Duration, InitialPositionInStream, StorageLevel) - Static method in class org.apache.spark.streaming.kinesis.KinesisUtils
Create an input stream that pulls messages from a Kinesis stream.
createStream(JavaStreamingContext, String, String, String, String, int, Duration, StorageLevel, String, String) - Method in class org.apache.spark.streaming.kinesis.KinesisUtilsPythonHelper
 
createStream(StreamingContext, String, String, StorageLevel) - Static method in class org.apache.spark.streaming.mqtt.MQTTUtils
Create an input stream that receives messages pushed by a MQTT publisher.
createStream(JavaStreamingContext, String, String) - Static method in class org.apache.spark.streaming.mqtt.MQTTUtils
Create an input stream that receives messages pushed by a MQTT publisher.
createStream(JavaStreamingContext, String, String, StorageLevel) - Static method in class org.apache.spark.streaming.mqtt.MQTTUtils
Create an input stream that receives messages pushed by a MQTT publisher.
createStream(StreamingContext, Option<Authorization>, Seq<String>, StorageLevel) - Static method in class org.apache.spark.streaming.twitter.TwitterUtils
Create a input stream that returns tweets received from Twitter.
createStream(JavaStreamingContext) - Static method in class org.apache.spark.streaming.twitter.TwitterUtils
Create a input stream that returns tweets received from Twitter using Twitter4J's default OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey, twitter4j.oauth.consumerSecret, twitter4j.oauth.accessToken and twitter4j.oauth.accessTokenSecret.
createStream(JavaStreamingContext, String[]) - Static method in class org.apache.spark.streaming.twitter.TwitterUtils
Create a input stream that returns tweets received from Twitter using Twitter4J's default OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey, twitter4j.oauth.consumerSecret, twitter4j.oauth.accessToken and twitter4j.oauth.accessTokenSecret.
createStream(JavaStreamingContext, String[], StorageLevel) - Static method in class org.apache.spark.streaming.twitter.TwitterUtils
Create a input stream that returns tweets received from Twitter using Twitter4J's default OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey, twitter4j.oauth.consumerSecret, twitter4j.oauth.accessToken and twitter4j.oauth.accessTokenSecret.
createStream(JavaStreamingContext, Authorization) - Static method in class org.apache.spark.streaming.twitter.TwitterUtils
Create a input stream that returns tweets received from Twitter.
createStream(JavaStreamingContext, Authorization, String[]) - Static method in class org.apache.spark.streaming.twitter.TwitterUtils
Create a input stream that returns tweets received from Twitter.
createStream(JavaStreamingContext, Authorization, String[], StorageLevel) - Static method in class org.apache.spark.streaming.twitter.TwitterUtils
Create a input stream that returns tweets received from Twitter.
createStream(StreamingContext, String, Subscribe, Function1<Seq<ByteString>, Iterator<T>>, StorageLevel, SupervisorStrategy, ClassTag<T>) - Static method in class org.apache.spark.streaming.zeromq.ZeroMQUtils
Create an input stream that receives messages pushed by a zeromq publisher.
createStream(JavaStreamingContext, String, Subscribe, Function<byte[][], Iterable<T>>, StorageLevel, SupervisorStrategy) - Static method in class org.apache.spark.streaming.zeromq.ZeroMQUtils
Create an input stream that receives messages pushed by a zeromq publisher.
createStream(JavaStreamingContext, String, Subscribe, Function<byte[][], Iterable<T>>, StorageLevel) - Static method in class org.apache.spark.streaming.zeromq.ZeroMQUtils
Create an input stream that receives messages pushed by a zeromq publisher.
createStream(JavaStreamingContext, String, Subscribe, Function<byte[][], Iterable<T>>) - Static method in class org.apache.spark.streaming.zeromq.ZeroMQUtils
Create an input stream that receives messages pushed by a zeromq publisher.
createStructField(String, DataType, boolean, Metadata) - Static method in class org.apache.spark.sql.types.DataTypes
Creates a StructField by specifying the name (name), data type (dataType) and whether values of this field can be null values (nullable).
createStructField(String, DataType, boolean) - Static method in class org.apache.spark.sql.types.DataTypes
Creates a StructField with empty metadata.
createStructType(List<StructField>) - Static method in class org.apache.spark.sql.types.DataTypes
Creates a StructType with the given list of StructFields (fields).
createStructType(StructField[]) - Static method in class org.apache.spark.sql.types.DataTypes
Creates a StructType with the given StructField array (fields).
createTransformFunc() - Method in class org.apache.spark.ml.feature.DCT
 
createTransformFunc() - Method in class org.apache.spark.ml.feature.ElementwiseProduct
 
createTransformFunc() - Method in class org.apache.spark.ml.feature.NGram
 
createTransformFunc() - Method in class org.apache.spark.ml.feature.Normalizer
 
createTransformFunc() - Method in class org.apache.spark.ml.feature.PolynomialExpansion
 
createTransformFunc() - Method in class org.apache.spark.ml.feature.RegexTokenizer
 
createTransformFunc() - Method in class org.apache.spark.ml.feature.Tokenizer
 
createTransformFunc() - Method in class org.apache.spark.ml.UnaryTransformer
Creates the transform function using the given param map.
creationSite() - Method in class org.apache.spark.rdd.RDD
User code that created this RDD (e.g.
creationSite() - Method in class org.apache.spark.streaming.dstream.DStream
 
crosstab(String, String) - Method in class org.apache.spark.sql.DataFrameStatFunctions
Computes a pair-wise frequency table of the given columns.
CrossValidator - Class in org.apache.spark.ml.tuning
:: Experimental :: K-fold cross validation.
CrossValidator(String) - Constructor for class org.apache.spark.ml.tuning.CrossValidator
 
CrossValidator() - Constructor for class org.apache.spark.ml.tuning.CrossValidator
 
CrossValidatorModel - Class in org.apache.spark.ml.tuning
:: Experimental :: Model from k-fold cross validation.
cube(Column...) - Method in class org.apache.spark.sql.DataFrame
Create a multi-dimensional cube for the current DataFrame using the specified columns, so we can run aggregation on them.
cube(String, String...) - Method in class org.apache.spark.sql.DataFrame
Create a multi-dimensional cube for the current DataFrame using the specified columns, so we can run aggregation on them.
cube(Seq<Column>) - Method in class org.apache.spark.sql.DataFrame
Create a multi-dimensional cube for the current DataFrame using the specified columns, so we can run aggregation on them.
cube(String, Seq<String>) - Method in class org.apache.spark.sql.DataFrame
Create a multi-dimensional cube for the current DataFrame using the specified columns, so we can run aggregation on them.
cume_dist() - Static method in class org.apache.spark.sql.functions
Window function: returns the cumulative distribution of values within a window partition, i.e.
cumeDist() - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.6.0, replaced by cume_dist. This will be removed in Spark 2.0.
current_date() - Static method in class org.apache.spark.sql.functions
Returns the current date as a date column.
current_timestamp() - Static method in class org.apache.spark.sql.functions
Returns the current timestamp as a timestamp column.
currentAttemptId() - Method in interface org.apache.spark.SparkStageInfo
 
currentAttemptId() - Method in class org.apache.spark.SparkStageInfoImpl
 
currPrefLocs(Partition) - Method in class org.apache.spark.rdd.PartitionCoalescer
 

D

databaseTypeDefinition() - Method in class org.apache.spark.sql.jdbc.JdbcType
 
dataDistribution() - Method in class org.apache.spark.status.api.v1.RDDStorageInfo
 
DataFrame - Class in org.apache.spark.sql
:: Experimental :: A distributed collection of data organized into named columns.
DataFrame(SQLContext, LogicalPlan) - Constructor for class org.apache.spark.sql.DataFrame
A constructor that automatically analyzes the logical plan.
DataFrameHolder - Class in org.apache.spark.sql
A container for a DataFrame, used for implicit conversions.
DataFrameNaFunctions - Class in org.apache.spark.sql
:: Experimental :: Functionality for working with missing data in DataFrames.
DataFrameReader - Class in org.apache.spark.sql
:: Experimental :: Interface used to load a DataFrame from external storage systems (e.g.
DataFrameStatFunctions - Class in org.apache.spark.sql
:: Experimental :: Statistic functions for DataFrames.
DataFrameWriter - Class in org.apache.spark.sql
:: Experimental :: Interface used to write a DataFrame to external storage systems (e.g.
dataSchema() - Method in class org.apache.spark.sql.sources.HadoopFsRelation
Specifies schema of actual data files.
Dataset<T> - Class in org.apache.spark.sql
:: Experimental :: A Dataset is a strongly typed collection of objects that can be transformed in parallel using functional or relational operations.
DatasetHolder<T> - Class in org.apache.spark.sql
A container for a Dataset, used for implicit conversions.
DataSourceRegister - Interface in org.apache.spark.sql.sources
::DeveloperApi:: Data sources should implement this trait so that they can register an alias to their data source.
dataStream() - Method in class org.apache.spark.api.r.BaseRRDD
 
dataType() - Method in class org.apache.spark.sql.expressions.UserDefinedAggregateFunction
The DataType of the returned value of this UserDefinedAggregateFunction.
DataType - Class in org.apache.spark.sql.types
:: DeveloperApi :: The base type of all Spark SQL data types.
DataType() - Constructor for class org.apache.spark.sql.types.DataType
 
dataType() - Method in class org.apache.spark.sql.types.StructField
 
dataType() - Method in class org.apache.spark.sql.UserDefinedFunction
 
DataTypes - Class in org.apache.spark.sql.types
To get/create specific data type, users should use singleton objects and factory methods provided by this class.
DataTypes() - Constructor for class org.apache.spark.sql.types.DataTypes
 
DataValidators - Class in org.apache.spark.mllib.util
:: DeveloperApi :: A collection of methods used to validate data before applying ML algorithms.
DataValidators() - Constructor for class org.apache.spark.mllib.util.DataValidators
 
date() - Method in class org.apache.spark.sql.ColumnName
Creates a new StructField of type date.
DATE() - Static method in class org.apache.spark.sql.Encoders
An encoder for nullable date type.
date_add(Column, int) - Static method in class org.apache.spark.sql.functions
Returns the date that is days days after start
date_format(Column, String) - Static method in class org.apache.spark.sql.functions
Converts a date/timestamp/string to a value of string in the format specified by the date format given by the second argument.
date_sub(Column, int) - Static method in class org.apache.spark.sql.functions
Returns the date that is days days before start
datediff(Column, Column) - Static method in class org.apache.spark.sql.functions
Returns the number of days from start to end.
DateType - Static variable in class org.apache.spark.sql.types.DataTypes
Gets the DateType object.
DateType - Class in org.apache.spark.sql.types
:: DeveloperApi :: A date type, supporting "0001-01-01" through "9999-12-31".
dayofmonth(Column) - Static method in class org.apache.spark.sql.functions
Extracts the day of the month as an integer from a given date/timestamp/string.
dayofyear(Column) - Static method in class org.apache.spark.sql.functions
Extracts the day of the year as an integer from a given date/timestamp/string.
DB2Dialect - Class in org.apache.spark.sql.jdbc
 
DB2Dialect() - Constructor for class org.apache.spark.sql.jdbc.DB2Dialect
 
DCT - Class in org.apache.spark.ml.feature
:: Experimental :: A feature transformer that takes the 1D discrete cosine transform of a real vector.
DCT(String) - Constructor for class org.apache.spark.ml.feature.DCT
 
DCT() - Constructor for class org.apache.spark.ml.feature.DCT
 
ddlParser() - Method in class org.apache.spark.sql.SQLContext
 
decayFactor() - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
 
decimal() - Method in class org.apache.spark.sql.ColumnName
Creates a new StructField of type decimal.
decimal(int, int) - Method in class org.apache.spark.sql.ColumnName
Creates a new StructField of type decimal.
DECIMAL() - Static method in class org.apache.spark.sql.Encoders
An encoder for nullable decimal type.
Decimal - Class in org.apache.spark.sql.types
A mutable implementation of BigDecimal that can hold a Long if values are small enough.
Decimal() - Constructor for class org.apache.spark.sql.types.Decimal
 
DecimalType - Class in org.apache.spark.sql.types
 
DecimalType(int, int) - Constructor for class org.apache.spark.sql.types.DecimalType
 
DecimalType(int) - Constructor for class org.apache.spark.sql.types.DecimalType
 
DecimalType() - Constructor for class org.apache.spark.sql.types.DecimalType
 
DecimalType(Option<PrecisionInfo>) - Constructor for class org.apache.spark.sql.types.DecimalType
 
DecisionTree - Class in org.apache.spark.mllib.tree
A class which implements a decision tree learning algorithm for classification and regression.
DecisionTree(Strategy) - Constructor for class org.apache.spark.mllib.tree.DecisionTree
 
DecisionTreeClassificationModel - Class in org.apache.spark.ml.classification
:: Experimental :: Decision tree model for classification.
DecisionTreeClassifier - Class in org.apache.spark.ml.classification
:: Experimental :: Decision tree learning algorithm for classification.
DecisionTreeClassifier(String) - Constructor for class org.apache.spark.ml.classification.DecisionTreeClassifier
 
DecisionTreeClassifier() - Constructor for class org.apache.spark.ml.classification.DecisionTreeClassifier
 
DecisionTreeModel - Class in org.apache.spark.mllib.tree.model
Decision tree model for classification or regression.
DecisionTreeModel(Node, Enumeration.Value) - Constructor for class org.apache.spark.mllib.tree.model.DecisionTreeModel
 
DecisionTreeRegressionModel - Class in org.apache.spark.ml.regression
:: Experimental :: Decision tree model for regression.
DecisionTreeRegressor - Class in org.apache.spark.ml.regression
:: Experimental :: Decision tree learning algorithm for regression.
DecisionTreeRegressor(String) - Constructor for class org.apache.spark.ml.regression.DecisionTreeRegressor
 
DecisionTreeRegressor() - Constructor for class org.apache.spark.ml.regression.DecisionTreeRegressor
 
decode(Column, String) - Static method in class org.apache.spark.sql.functions
Computes the first argument into a string from a binary using the provided character set (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
decodeLabel(Vector) - Static method in class org.apache.spark.ml.classification.LabelConverter
Converts a vector to a label.
defaultAttr() - Static method in class org.apache.spark.ml.attribute.BinaryAttribute
The default binary attribute.
defaultAttr() - Static method in class org.apache.spark.ml.attribute.NominalAttribute
The default nominal attribute.
defaultAttr() - Static method in class org.apache.spark.ml.attribute.NumericAttribute
The default numeric attribute.
defaultClassLoader() - Method in class org.apache.spark.serializer.Serializer
Default ClassLoader to use in deserialization.
defaultCopy(ParamMap) - Method in interface org.apache.spark.ml.param.Params
Default implementation of copy with extra params.
defaultMinPartitions() - Method in class org.apache.spark.api.java.JavaSparkContext
Default min number of partitions for Hadoop RDDs when not given by user
defaultMinPartitions() - Method in class org.apache.spark.SparkContext
Default min number of partitions for Hadoop RDDs when not given by user Notice that we use math.min so the "defaultMinPartitions" cannot be higher than 2.
defaultMinSplits() - Method in class org.apache.spark.api.java.JavaSparkContext
Deprecated.
As of Spark 1.0.0, defaultMinSplits is deprecated, use JavaSparkContext.defaultMinPartitions() instead
defaultMinSplits() - Method in class org.apache.spark.SparkContext
Default min number of partitions for Hadoop RDDs when not given by user
defaultParallelism() - Method in class org.apache.spark.api.java.JavaSparkContext
Default level of parallelism to use when not given by user (e.g.
defaultParallelism() - Method in class org.apache.spark.SparkContext
Default level of parallelism to use when not given by user (e.g.
defaultParamMap() - Method in interface org.apache.spark.ml.param.Params
Internal param map for default values.
defaultParams(String) - Static method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
defaultParams(Enumeration.Value) - Static method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
defaultPartitioner(RDD<?>, Seq<RDD<?>>) - Static method in class org.apache.spark.Partitioner
Choose a partitioner to use for a cogroup-like operation between a number of RDDs.
defaultSize() - Method in class org.apache.spark.sql.types.ArrayType
The default size of a value of the ArrayType is 100 * the default size of the element type.
defaultSize() - Method in class org.apache.spark.sql.types.BinaryType
The default size of a value of the BinaryType is 4096 bytes.
defaultSize() - Method in class org.apache.spark.sql.types.BooleanType
The default size of a value of the BooleanType is 1 byte.
defaultSize() - Method in class org.apache.spark.sql.types.ByteType
The default size of a value of the ByteType is 1 byte.
defaultSize() - Method in class org.apache.spark.sql.types.CalendarIntervalType
 
defaultSize() - Method in class org.apache.spark.sql.types.DataType
The default size of a value of this data type, used internally for size estimation.
defaultSize() - Method in class org.apache.spark.sql.types.DateType
The default size of a value of the DateType is 4 bytes.
defaultSize() - Method in class org.apache.spark.sql.types.DecimalType
The default size of a value of the DecimalType is 4096 bytes.
defaultSize() - Method in class org.apache.spark.sql.types.DoubleType
The default size of a value of the DoubleType is 8 bytes.
defaultSize() - Method in class org.apache.spark.sql.types.FloatType
The default size of a value of the FloatType is 4 bytes.
defaultSize() - Method in class org.apache.spark.sql.types.IntegerType
The default size of a value of the IntegerType is 4 bytes.
defaultSize() - Method in class org.apache.spark.sql.types.LongType
The default size of a value of the LongType is 8 bytes.
defaultSize() - Method in class org.apache.spark.sql.types.MapType
The default size of a value of the MapType is 100 * (the default size of the key type + the default size of the value type).
defaultSize() - Method in class org.apache.spark.sql.types.NullType
 
defaultSize() - Method in class org.apache.spark.sql.types.ShortType
The default size of a value of the ShortType is 2 bytes.
defaultSize() - Method in class org.apache.spark.sql.types.StringType
The default size of a value of the StringType is 4096 bytes.
defaultSize() - Method in class org.apache.spark.sql.types.StructType
The default size of a value of the StructType is the total default sizes of all field types.
defaultSize() - Method in class org.apache.spark.sql.types.TimestampType
The default size of a value of the TimestampType is 8 bytes.
defaultSize() - Method in class org.apache.spark.sql.types.UserDefinedType
The default size of a value of the UserDefinedType is 4096 bytes.
DefaultSource - Class in org.apache.spark.ml.source.libsvm
libsvm package implements Spark SQL data source API for loading LIBSVM data as DataFrame.
DefaultSource() - Constructor for class org.apache.spark.ml.source.libsvm.DefaultSource
 
defaultStategy(Enumeration.Value) - Static method in class org.apache.spark.mllib.tree.configuration.Strategy
 
defaultStrategy(String) - Static method in class org.apache.spark.mllib.tree.configuration.Strategy
Construct a default set of parameters for DecisionTree
defaultStrategy(Enumeration.Value) - Static method in class org.apache.spark.mllib.tree.configuration.Strategy
Construct a default set of parameters for DecisionTree
defaultStrategy() - Static method in class org.apache.spark.streaming.receiver.ActorSupervisorStrategy
 
degree() - Method in class org.apache.spark.ml.feature.PolynomialExpansion
The polynomial degree to expand, which should be >= 1.
degrees() - Method in class org.apache.spark.graphx.GraphOps
The degree of each vertex in the graph.
degreesOfFreedom() - Method in class org.apache.spark.mllib.stat.test.ChiSqTestResult
 
degreesOfFreedom() - Method in class org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult
 
degreesOfFreedom() - Method in interface org.apache.spark.mllib.stat.test.TestResult
Returns the degree(s) of freedom of the hypothesis test.
delegate() - Method in class org.apache.spark.InterruptibleIterator
 
dense(int, int, double[]) - Static method in class org.apache.spark.mllib.linalg.Matrices
Creates a column-major dense matrix.
dense(double, double...) - Static method in class org.apache.spark.mllib.linalg.Vectors
Creates a dense vector from its values.
dense(double, Seq<Object>) - Static method in class org.apache.spark.mllib.linalg.Vectors
Creates a dense vector from its values.
dense(double[]) - Static method in class org.apache.spark.mllib.linalg.Vectors
Creates a dense vector from a double array.
dense_rank() - Static method in class org.apache.spark.sql.functions
Window function: returns the rank of rows within a window partition, without any gaps.
DenseMatrix - Class in org.apache.spark.mllib.linalg
Column-major dense matrix.
DenseMatrix(int, int, double[], boolean) - Constructor for class org.apache.spark.mllib.linalg.DenseMatrix
 
DenseMatrix(int, int, double[]) - Constructor for class org.apache.spark.mllib.linalg.DenseMatrix
Column-major dense matrix.
denseRank() - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.6.0, replaced by dense_rank. This will be removed in Spark 2.0.
DenseVector - Class in org.apache.spark.mllib.linalg
A dense vector represented by a value array.
DenseVector(double[]) - Constructor for class org.apache.spark.mllib.linalg.DenseVector
 
dependencies() - Method in class org.apache.spark.rdd.RDD
Get the list of dependencies of this RDD, taking into account whether the RDD is checkpointed or not.
dependencies() - Method in class org.apache.spark.streaming.dstream.DStream
List of parent DStreams on which this DStream depends on
dependencies() - Method in class org.apache.spark.streaming.dstream.InputDStream
 
Dependency<T> - Class in org.apache.spark
:: DeveloperApi :: Base class for dependencies.
Dependency() - Constructor for class org.apache.spark.Dependency
 
depth() - Method in class org.apache.spark.mllib.tree.model.DecisionTreeModel
Get depth of tree.
DerbyDialect - Class in org.apache.spark.sql.jdbc
 
DerbyDialect() - Constructor for class org.apache.spark.sql.jdbc.DerbyDialect
 
desc() - Method in class org.apache.spark.sql.Column
Returns an ordering used in sorting.
desc(String) - Static method in class org.apache.spark.sql.functions
Returns a sort expression based on the descending order of the column.
desc() - Method in class org.apache.spark.util.MethodIdentifier
 
describe(String...) - Method in class org.apache.spark.sql.DataFrame
Computes statistics for numeric columns, including count, mean, stddev, min, and max.
describe(Seq<String>) - Method in class org.apache.spark.sql.DataFrame
Computes statistics for numeric columns, including count, mean, stddev, min, and max.
describeTopics(int) - Method in class org.apache.spark.ml.clustering.LDAModel
Return the topics described by their top-weighted terms.
describeTopics() - Method in class org.apache.spark.ml.clustering.LDAModel
 
describeTopics(int) - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
 
describeTopics(int) - Method in class org.apache.spark.mllib.clustering.LDAModel
Return the topics described by weighted terms.
describeTopics() - Method in class org.apache.spark.mllib.clustering.LDAModel
Return the topics described by weighted terms.
describeTopics(int) - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
 
description() - Method in class org.apache.spark.ExceptionFailure
 
description() - Method in class org.apache.spark.status.api.v1.JobData
 
description() - Method in class org.apache.spark.storage.StorageLevel
 
description() - Method in class org.apache.spark.streaming.scheduler.OutputOperationInfo
 
DeserializationStream - Class in org.apache.spark.serializer
:: DeveloperApi :: A stream for reading serialized objects.
DeserializationStream() - Constructor for class org.apache.spark.serializer.DeserializationStream
 
deserialize(Object) - Method in class org.apache.spark.mllib.linalg.VectorUDT
 
deserialize(ByteBuffer, ClassLoader, ClassTag<T>) - Method in class org.apache.spark.serializer.DummySerializerInstance
 
deserialize(ByteBuffer, ClassTag<T>) - Method in class org.apache.spark.serializer.DummySerializerInstance
 
deserialize(ByteBuffer, ClassTag<T>) - Method in class org.apache.spark.serializer.SerializerInstance
 
deserialize(ByteBuffer, ClassLoader, ClassTag<T>) - Method in class org.apache.spark.serializer.SerializerInstance
 
deserialize(Object) - Method in class org.apache.spark.sql.types.UserDefinedType
Convert a SQL datum to the user type
deserialized() - Method in class org.apache.spark.storage.MemoryEntry
 
deserialized() - Method in class org.apache.spark.storage.StorageLevel
 
deserializeStream(InputStream) - Method in class org.apache.spark.serializer.DummySerializerInstance
 
deserializeStream(InputStream) - Method in class org.apache.spark.serializer.SerializerInstance
 
destroy() - Method in class org.apache.spark.broadcast.Broadcast
Destroy all data and metadata related to this broadcast variable.
details() - Method in class org.apache.spark.scheduler.StageInfo
 
details() - Method in class org.apache.spark.status.api.v1.StageData
 
determineBounds(ArrayBuffer<Tuple2<K, Object>>, int, Ordering<K>, ClassTag<K>) - Static method in class org.apache.spark.RangePartitioner
Determines the bounds for range partitioning from candidates with weights indicating how many items each represents.
deterministic() - Method in class org.apache.spark.sql.expressions.UserDefinedAggregateFunction
Returns true iff this function is deterministic, i.e.
DeveloperApi - Annotation Type in org.apache.spark.annotation
A lower-level, unstable API intended for developers.
devianceResiduals() - Method in class org.apache.spark.ml.regression.LinearRegressionSummary
The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
diag(Vector) - Static method in class org.apache.spark.mllib.linalg.DenseMatrix
Generate a diagonal matrix in DenseMatrix format from the supplied values.
diag(Vector) - Static method in class org.apache.spark.mllib.linalg.Matrices
Generate a diagonal matrix in Matrix format from the supplied values.
dialectClassName() - Method in class org.apache.spark.sql.SQLContext
 
diff(RDD<Tuple2<Object, VD>>) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
diff(VertexRDD<VD>) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
diff(RDD<Tuple2<Object, VD>>) - Method in class org.apache.spark.graphx.VertexRDD
For each vertex present in both this and other, diff returns only those vertices with differing values; for values that are different, keeps the values from other.
diff(VertexRDD<VD>) - Method in class org.apache.spark.graphx.VertexRDD
For each vertex present in both this and other, diff returns only those vertices with differing values; for values that are different, keeps the values from other.
disableOutputSpecValidation() - Static method in class org.apache.spark.rdd.PairRDDFunctions
 
disconnect() - Method in interface org.apache.spark.launcher.SparkAppHandle
Disconnects the handle from the application, without stopping it.
DISK_ONLY - Static variable in class org.apache.spark.api.java.StorageLevels
 
DISK_ONLY() - Static method in class org.apache.spark.storage.StorageLevel
 
DISK_ONLY_2 - Static variable in class org.apache.spark.api.java.StorageLevels
 
DISK_ONLY_2() - Static method in class org.apache.spark.storage.StorageLevel
 
diskBytesSpilled() - Method in class org.apache.spark.status.api.v1.ExecutorStageSummary
 
diskBytesSpilled() - Method in class org.apache.spark.status.api.v1.StageData
 
diskBytesSpilled() - Method in class org.apache.spark.status.api.v1.TaskMetricDistributions
 
diskBytesSpilled() - Method in class org.apache.spark.status.api.v1.TaskMetrics
 
diskSize() - Method in class org.apache.spark.storage.BlockStatus
 
diskSize() - Method in class org.apache.spark.storage.BlockUpdatedInfo
 
diskSize() - Method in class org.apache.spark.storage.RDDInfo
 
diskUsed() - Method in class org.apache.spark.status.api.v1.ExecutorSummary
 
diskUsed() - Method in class org.apache.spark.status.api.v1.RDDDataDistribution
 
diskUsed() - Method in class org.apache.spark.status.api.v1.RDDPartitionInfo
 
diskUsed() - Method in class org.apache.spark.status.api.v1.RDDStorageInfo
 
diskUsed() - Method in class org.apache.spark.storage.StorageStatus
Return the disk space used by this block manager.
diskUsedByRdd(int) - Method in class org.apache.spark.storage.StorageStatus
Return the disk space used by the given RDD in this block manager in O(1) time.
dist(Vector) - Method in class org.apache.spark.util.Vector
 
distinct() - Method in class org.apache.spark.api.java.JavaDoubleRDD
Return a new RDD containing the distinct elements in this RDD.
distinct(int) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Return a new RDD containing the distinct elements in this RDD.
distinct() - Method in class org.apache.spark.api.java.JavaPairRDD
Return a new RDD containing the distinct elements in this RDD.
distinct(int) - Method in class org.apache.spark.api.java.JavaPairRDD
Return a new RDD containing the distinct elements in this RDD.
distinct() - Method in class org.apache.spark.api.java.JavaRDD
Return a new RDD containing the distinct elements in this RDD.
distinct(int) - Method in class org.apache.spark.api.java.JavaRDD
Return a new RDD containing the distinct elements in this RDD.
distinct(int, Ordering<T>) - Method in class org.apache.spark.rdd.RDD
Return a new RDD containing the distinct elements in this RDD.
distinct() - Method in class org.apache.spark.rdd.RDD
Return a new RDD containing the distinct elements in this RDD.
distinct() - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame that contains only the unique rows from this DataFrame.
distinct() - Method in class org.apache.spark.sql.Dataset
Returns a new Dataset that contains only the unique elements of this Dataset.
distinct(Column...) - Method in class org.apache.spark.sql.expressions.UserDefinedAggregateFunction
Creates a Column for this UDAF using the distinct values of the given Columns as input arguments.
distinct(Seq<Column>) - Method in class org.apache.spark.sql.expressions.UserDefinedAggregateFunction
Creates a Column for this UDAF using the distinct values of the given Columns as input arguments.
DistributedLDAModel - Class in org.apache.spark.ml.clustering
:: Experimental ::
DistributedLDAModel - Class in org.apache.spark.mllib.clustering
 
DistributedMatrix - Interface in org.apache.spark.mllib.linalg.distributed
Represents a distributively stored matrix backed by one or more RDDs.
div(Duration) - Method in class org.apache.spark.streaming.Duration
 
divide(Object) - Method in class org.apache.spark.sql.Column
Division this expression by another expression.
divide(double) - Method in class org.apache.spark.util.Vector
 
doc() - Method in class org.apache.spark.ml.param.Param
 
docConcentration() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
 
docConcentration() - Method in class org.apache.spark.mllib.clustering.EMLDAOptimizer
 
docConcentration() - Method in class org.apache.spark.mllib.clustering.LDAModel
Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").
docConcentration() - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
 
doDestroy(boolean) - Method in class org.apache.spark.broadcast.Broadcast
Actually destroy all data and metadata related to this broadcast variable.
dot(Vector) - Method in class org.apache.spark.util.Vector
 
DOUBLE() - Static method in class org.apache.spark.sql.Encoders
An encoder for nullable double type.
doubleAccumulator(double) - Method in class org.apache.spark.api.java.JavaSparkContext
Create an Accumulator double variable, which tasks can "add" values to using the add method.
doubleAccumulator(double, String) - Method in class org.apache.spark.api.java.JavaSparkContext
Create an Accumulator double variable, which tasks can "add" values to using the add method.
DoubleArrayParam - Class in org.apache.spark.ml.param
:: DeveloperApi :: Specialized version of Param[Array[Double} for Java.
DoubleArrayParam(Params, String, String, Function1<double[], Object>) - Constructor for class org.apache.spark.ml.param.DoubleArrayParam
 
DoubleArrayParam(Params, String, String) - Constructor for class org.apache.spark.ml.param.DoubleArrayParam
 
DoubleDecimal() - Static method in class org.apache.spark.sql.types.DecimalType
 
DoubleFlatMapFunction<T> - Interface in org.apache.spark.api.java.function
A function that returns zero or more records of type Double from each input record.
DoubleFunction<T> - Interface in org.apache.spark.api.java.function
A function that returns Doubles, and can be used to construct DoubleRDDs.
DoubleParam - Class in org.apache.spark.ml.param
:: DeveloperApi :: Specialized version of Param[Double] for Java.
DoubleParam(String, String, String, Function1<Object, Object>) - Constructor for class org.apache.spark.ml.param.DoubleParam
 
DoubleParam(String, String, String) - Constructor for class org.apache.spark.ml.param.DoubleParam
 
DoubleParam(Identifiable, String, String, Function1<Object, Object>) - Constructor for class org.apache.spark.ml.param.DoubleParam
 
DoubleParam(Identifiable, String, String) - Constructor for class org.apache.spark.ml.param.DoubleParam
 
DoubleRDDFunctions - Class in org.apache.spark.rdd
Extra functions available on RDDs of Doubles through an implicit conversion.
DoubleRDDFunctions(RDD<Object>) - Constructor for class org.apache.spark.rdd.DoubleRDDFunctions
 
doubleRDDToDoubleRDDFunctions(RDD<Object>) - Static method in class org.apache.spark.rdd.RDD
 
doubleRDDToDoubleRDDFunctions(RDD<Object>) - Static method in class org.apache.spark.SparkContext
 
doubleToDoubleWritable(double) - Static method in class org.apache.spark.SparkContext
 
doubleToMultiplier(double) - Static method in class org.apache.spark.util.Vector
 
DoubleType - Static variable in class org.apache.spark.sql.types.DataTypes
Gets the DoubleType object.
DoubleType - Class in org.apache.spark.sql.types
:: DeveloperApi :: The data type representing Double values.
doubleWritableConverter() - Static method in class org.apache.spark.SparkContext
 
doUnpersist(boolean) - Method in class org.apache.spark.broadcast.Broadcast
Actually unpersist the broadcasted value on the executors.
DRIVER_EXTRA_CLASSPATH - Static variable in class org.apache.spark.launcher.SparkLauncher
Configuration key for the driver class path.
DRIVER_EXTRA_JAVA_OPTIONS - Static variable in class org.apache.spark.launcher.SparkLauncher
Configuration key for the driver VM options.
DRIVER_EXTRA_LIBRARY_PATH - Static variable in class org.apache.spark.launcher.SparkLauncher
Configuration key for the driver native library path.
DRIVER_IDENTIFIER() - Static method in class org.apache.spark.SparkContext
Executor id for the driver.
DRIVER_MEMORY - Static variable in class org.apache.spark.launcher.SparkLauncher
Configuration key for the driver memory.
driverActorSystemName() - Static method in class org.apache.spark.SparkEnv
 
driverLogs() - Method in class org.apache.spark.scheduler.SparkListenerApplicationStart
 
drop(String) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame with a column dropped.
drop(Column) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame with a column dropped.
drop() - Method in class org.apache.spark.sql.DataFrameNaFunctions
Returns a new DataFrame that drops rows containing any null or NaN values.
drop(String) - Method in class org.apache.spark.sql.DataFrameNaFunctions
Returns a new DataFrame that drops rows containing null or NaN values.
drop(String[]) - Method in class org.apache.spark.sql.DataFrameNaFunctions
Returns a new DataFrame that drops rows containing any null or NaN values in the specified columns.
drop(Seq<String>) - Method in class org.apache.spark.sql.DataFrameNaFunctions
(Scala-specific) Returns a new DataFrame that drops rows containing any null or NaN values in the specified columns.
drop(String, String[]) - Method in class org.apache.spark.sql.DataFrameNaFunctions
Returns a new DataFrame that drops rows containing null or NaN values in the specified columns.
drop(String, Seq<String>) - Method in class org.apache.spark.sql.DataFrameNaFunctions
(Scala-specific) Returns a new DataFrame that drops rows containing null or NaN values in the specified columns.
drop(int) - Method in class org.apache.spark.sql.DataFrameNaFunctions
Returns a new DataFrame that drops rows containing less than minNonNulls non-null and non-NaN values.
drop(int, String[]) - Method in class org.apache.spark.sql.DataFrameNaFunctions
Returns a new DataFrame that drops rows containing less than minNonNulls non-null and non-NaN values in the specified columns.
drop(int, Seq<String>) - Method in class org.apache.spark.sql.DataFrameNaFunctions
(Scala-specific) Returns a new DataFrame that drops rows containing less than minNonNulls non-null and non-NaN values in the specified columns.
dropDuplicates() - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame that contains only the unique rows from this DataFrame.
dropDuplicates(Seq<String>) - Method in class org.apache.spark.sql.DataFrame
(Scala-specific) Returns a new DataFrame with duplicate rows removed, considering only the subset of columns.
dropDuplicates(String[]) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame with duplicate rows removed, considering only the subset of columns.
dropLast() - Method in class org.apache.spark.ml.feature.OneHotEncoder
Whether to drop the last category in the encoded vector (default: true)
dropTempTable(String) - Method in class org.apache.spark.sql.SQLContext
 
Dst - Static variable in class org.apache.spark.graphx.TripletFields
Expose the destination and edge fields but not the source field.
dstAttr() - Method in class org.apache.spark.graphx.EdgeContext
The vertex attribute of the edge's destination vertex.
dstAttr() - Method in class org.apache.spark.graphx.EdgeTriplet
The destination vertex attribute
dstAttr() - Method in class org.apache.spark.graphx.impl.AggregatingEdgeContext
 
dstId() - Method in class org.apache.spark.graphx.Edge
 
dstId() - Method in class org.apache.spark.graphx.EdgeContext
The vertex id of the edge's destination vertex.
dstId() - Method in class org.apache.spark.graphx.impl.AggregatingEdgeContext
 
dstream() - Method in class org.apache.spark.streaming.api.java.JavaDStream
 
dstream() - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
 
dstream() - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
 
DStream<T> - Class in org.apache.spark.streaming.dstream
A Discretized Stream (DStream), the basic abstraction in Spark Streaming, is a continuous sequence of RDDs (of the same type) representing a continuous stream of data (see org.apache.spark.rdd.RDD in the Spark core documentation for more details on RDDs).
DStream(StreamingContext, ClassTag<T>) - Constructor for class org.apache.spark.streaming.dstream.DStream
 
dtypes() - Method in class org.apache.spark.sql.DataFrame
Returns all column names and their data types as an array.
DummySerializerInstance - Class in org.apache.spark.serializer
Unfortunately, we need a serializer instance in order to construct a DiskBlockObjectWriter.
duration() - Method in class org.apache.spark.scheduler.TaskInfo
 
Duration - Class in org.apache.spark.streaming
 
Duration(long) - Constructor for class org.apache.spark.streaming.Duration
 
duration() - Method in class org.apache.spark.streaming.scheduler.OutputOperationInfo
Return the duration of this output operation.
Durations - Class in org.apache.spark.streaming
 
Durations() - Constructor for class org.apache.spark.streaming.Durations
 

E

Edge<ED> - Class in org.apache.spark.graphx
A single directed edge consisting of a source id, target id, and the data associated with the edge.
Edge(long, long, ED) - Constructor for class org.apache.spark.graphx.Edge
 
EdgeActiveness - Enum in org.apache.spark.graphx.impl
Criteria for filtering edges based on activeness.
EdgeContext<VD,ED,A> - Class in org.apache.spark.graphx
Represents an edge along with its neighboring vertices and allows sending messages along the edge.
EdgeContext() - Constructor for class org.apache.spark.graphx.EdgeContext
 
EdgeDirection - Class in org.apache.spark.graphx
The direction of a directed edge relative to a vertex.
edgeListFile(SparkContext, String, boolean, int, StorageLevel, StorageLevel) - Static method in class org.apache.spark.graphx.GraphLoader
Loads a graph from an edge list formatted file where each line contains two integers: a source id and a target id.
EdgeOnly - Static variable in class org.apache.spark.graphx.TripletFields
Expose only the edge field and not the source or destination field.
EdgeRDD<ED> - Class in org.apache.spark.graphx
EdgeRDD[ED, VD] extends RDD[Edge[ED} by storing the edges in columnar format on each partition for performance.
EdgeRDD(SparkContext, Seq<Dependency<?>>) - Constructor for class org.apache.spark.graphx.EdgeRDD
 
EdgeRDDImpl<ED,VD> - Class in org.apache.spark.graphx.impl
 
edges() - Method in class org.apache.spark.graphx.Graph
An RDD containing the edges and their associated attributes.
edges() - Method in class org.apache.spark.graphx.impl.GraphImpl
 
EdgeTriplet<VD,ED> - Class in org.apache.spark.graphx
An edge triplet represents an edge along with the vertex attributes of its neighboring vertices.
EdgeTriplet() - Constructor for class org.apache.spark.graphx.EdgeTriplet
 
Either() - Static method in class org.apache.spark.graphx.EdgeDirection
Edges originating from *or* arriving at a vertex of interest.
elements() - Method in class org.apache.spark.util.Vector
 
elementType() - Method in class org.apache.spark.sql.types.ArrayType
 
ElementwiseProduct - Class in org.apache.spark.ml.feature
:: Experimental :: Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a provided "weight" vector.
ElementwiseProduct(String) - Constructor for class org.apache.spark.ml.feature.ElementwiseProduct
 
ElementwiseProduct() - Constructor for class org.apache.spark.ml.feature.ElementwiseProduct
 
ElementwiseProduct - Class in org.apache.spark.mllib.feature
Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a provided "weight" vector.
ElementwiseProduct(Vector) - Constructor for class org.apache.spark.mllib.feature.ElementwiseProduct
 
EMLDAOptimizer - Class in org.apache.spark.mllib.clustering
:: DeveloperApi ::
EMLDAOptimizer() - Constructor for class org.apache.spark.mllib.clustering.EMLDAOptimizer
 
empty() - Static method in class org.apache.spark.ml.param.ParamMap
Returns an empty param map.
empty() - Static method in class org.apache.spark.sql.types.Metadata
Returns an empty Metadata.
empty() - Static method in class org.apache.spark.storage.BlockStatus
 
emptyDataFrame() - Method in class org.apache.spark.sql.SQLContext
:: Experimental :: Returns a DataFrame with no rows or columns.
emptyNode(int) - Static method in class org.apache.spark.mllib.tree.model.Node
Return a node with the given node id (but nothing else set).
emptyRDD() - Method in class org.apache.spark.api.java.JavaSparkContext
Get an RDD that has no partitions or elements.
emptyRDD(ClassTag<T>) - Method in class org.apache.spark.SparkContext
Get an RDD that has no partitions or elements.
emptyResult() - Method in class org.apache.spark.sql.SQLContext
 
encode(Column, String) - Static method in class org.apache.spark.sql.functions
Computes the first argument into a binary from a string using the provided character set (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
encodeLabeledPoint(LabeledPoint, int) - Static method in class org.apache.spark.ml.classification.LabelConverter
Encodes a label as a vector.
Encoder<T> - Interface in org.apache.spark.sql
:: Experimental :: Used to convert a JVM object of type T to and from the internal Spark SQL representation.
encoder() - Method in class org.apache.spark.sql.TypedColumn
 
Encoders - Class in org.apache.spark.sql
:: Experimental :: Methods for creating an Encoder.
Encoders() - Constructor for class org.apache.spark.sql.Encoders
 
endsWith(Column) - Method in class org.apache.spark.sql.Column
String ends with.
endsWith(String) - Method in class org.apache.spark.sql.Column
String ends with another string literal.
endTime() - Method in class org.apache.spark.status.api.v1.ApplicationAttemptInfo
 
endTime() - Method in class org.apache.spark.streaming.scheduler.OutputOperationInfo
 
endTime() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
entries() - Method in class org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
 
Entropy - Class in org.apache.spark.mllib.tree.impurity
:: Experimental :: Class for calculating entropy during binary classification.
Entropy() - Constructor for class org.apache.spark.mllib.tree.impurity.Entropy
 
EnumUtil - Class in org.apache.spark.util
 
EnumUtil() - Constructor for class org.apache.spark.util.EnumUtil
 
env() - Method in class org.apache.spark.api.java.JavaSparkContext
 
env() - Method in class org.apache.spark.streaming.StreamingContext
 
environmentDetails() - Method in class org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
 
EnvironmentListener - Class in org.apache.spark.ui.env
:: DeveloperApi :: A SparkListener that prepares information to be displayed on the EnvironmentTab
EnvironmentListener() - Constructor for class org.apache.spark.ui.env.EnvironmentListener
 
EPSILON() - Static method in class org.apache.spark.mllib.util.MLUtils
 
eqNullSafe(Object) - Method in class org.apache.spark.sql.Column
Equality test that is safe for null values.
EqualNullSafe - Class in org.apache.spark.sql.sources
Performs equality comparison, similar to EqualTo.
EqualNullSafe(String, Object) - Constructor for class org.apache.spark.sql.sources.EqualNullSafe
 
equals(Object) - Method in class org.apache.spark.graphx.EdgeDirection
 
equals(Object) - Method in class org.apache.spark.HashPartitioner
 
equals(Object) - Method in class org.apache.spark.ml.attribute.AttributeGroup
 
equals(Object) - Method in class org.apache.spark.ml.attribute.BinaryAttribute
 
equals(Object) - Method in class org.apache.spark.ml.attribute.NominalAttribute
 
equals(Object) - Method in class org.apache.spark.ml.attribute.NumericAttribute
 
equals(Object) - Method in class org.apache.spark.ml.param.Param
 
equals(Object) - Method in class org.apache.spark.ml.tree.CategoricalSplit
 
equals(Object) - Method in class org.apache.spark.ml.tree.ContinuousSplit
 
equals(Object) - Method in class org.apache.spark.mllib.linalg.DenseMatrix
 
equals(Object) - Method in class org.apache.spark.mllib.linalg.SparseMatrix
 
equals(Object) - Method in interface org.apache.spark.mllib.linalg.Vector
 
equals(Object) - Method in class org.apache.spark.mllib.linalg.VectorUDT
 
equals(Object) - Method in class org.apache.spark.mllib.tree.model.InformationGainStats
 
equals(Object) - Method in class org.apache.spark.mllib.tree.model.Predict
 
equals(Object) - Method in class org.apache.spark.RangePartitioner
 
equals(Object) - Method in class org.apache.spark.scheduler.AccumulableInfo
 
equals(Object) - Method in class org.apache.spark.scheduler.cluster.ExecutorInfo
 
equals(Object) - Method in class org.apache.spark.scheduler.InputFormatInfo
 
equals(Object) - Method in class org.apache.spark.scheduler.SplitInfo
 
equals(Object) - Method in class org.apache.spark.sql.Column
 
equals(Object) - Method in interface org.apache.spark.sql.Row
 
equals(Object) - Method in class org.apache.spark.sql.types.Decimal
 
equals(Object) - Method in class org.apache.spark.sql.types.Metadata
 
equals(Object) - Method in class org.apache.spark.storage.BlockId
 
equals(Object) - Method in class org.apache.spark.storage.BlockManagerId
 
equals(Object) - Method in class org.apache.spark.storage.StorageLevel
 
equals(Object) - Method in class org.apache.spark.streaming.kafka.Broker
Broker's port
equals(Object) - Method in class org.apache.spark.streaming.kafka.OffsetRange
 
equalTo(Object) - Method in class org.apache.spark.sql.Column
Equality test.
EqualTo - Class in org.apache.spark.sql.sources
A filter that evaluates to true iff the attribute evaluates to a value equal to value.
EqualTo(String, Object) - Constructor for class org.apache.spark.sql.sources.EqualTo
 
errorMessage() - Method in class org.apache.spark.status.api.v1.TaskData
 
estimate(double[]) - Method in class org.apache.spark.mllib.stat.KernelDensity
Estimates probability density function at the given array of points.
estimate(Object) - Static method in class org.apache.spark.util.SizeEstimator
Estimate the number of bytes that the given object takes up on the JVM heap.
estimatedDocConcentration() - Method in class org.apache.spark.ml.clustering.LDAModel
Value for docConcentration estimated from data.
Estimator<M extends Model<M>> - Class in org.apache.spark.ml
:: DeveloperApi :: Abstract class for estimators that fit models to data.
Estimator() - Constructor for class org.apache.spark.ml.Estimator
 
evaluate(DataFrame) - Method in class org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
 
evaluate(DataFrame, ParamMap) - Method in class org.apache.spark.ml.evaluation.Evaluator
Evaluates model output and returns a scalar metric (larger is better).
evaluate(DataFrame) - Method in class org.apache.spark.ml.evaluation.Evaluator
Evaluates the output.
evaluate(DataFrame) - Method in class org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
 
evaluate(DataFrame) - Method in class org.apache.spark.ml.evaluation.RegressionEvaluator
 
evaluate(Row) - Method in class org.apache.spark.sql.expressions.UserDefinedAggregateFunction
Calculates the final result of this UserDefinedAggregateFunction based on the given aggregation buffer.
evaluateEachIteration(RDD<LabeledPoint>, Loss) - Method in class org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
Method to compute error or loss for every iteration of gradient boosting.
Evaluator - Class in org.apache.spark.ml.evaluation
:: DeveloperApi :: Abstract class for evaluators that compute metrics from predictions.
Evaluator() - Constructor for class org.apache.spark.ml.evaluation.Evaluator
 
event() - Method in class org.apache.spark.streaming.flume.SparkFlumeEvent
 
except(DataFrame) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame containing rows in this frame but not in another frame.
exception() - Method in class org.apache.spark.ExceptionFailure
 
exception() - Method in class org.apache.spark.sql.hive.execution.ScriptTransformationWriterThread
Contains the exception thrown while writing the parent iterator to the external process.
ExceptionFailure - Class in org.apache.spark
:: DeveloperApi :: Task failed due to a runtime exception.
ExceptionFailure(String, String, StackTraceElement[], String, Option<TaskMetrics>, Option<ThrowableSerializationWrapper>) - Constructor for class org.apache.spark.ExceptionFailure
 
execId() - Method in class org.apache.spark.ExecutorLostFailure
 
execId() - Method in class org.apache.spark.scheduler.SparkListenerExecutorMetricsUpdate
 
executePlan(LogicalPlan) - Method in class org.apache.spark.sql.hive.HiveContext
 
executePlan(LogicalPlan) - Method in class org.apache.spark.sql.SQLContext
 
executeSql(String) - Method in class org.apache.spark.sql.SQLContext
 
executionHive() - Method in class org.apache.spark.sql.hive.HiveContext
The copy of the hive client that is used for execution.
ExecutionListenerManager - Class in org.apache.spark.sql.util
:: Experimental ::
EXECUTOR_CORES - Static variable in class org.apache.spark.launcher.SparkLauncher
Configuration key for the number of executor CPU cores.
EXECUTOR_EXTRA_CLASSPATH - Static variable in class org.apache.spark.launcher.SparkLauncher
Configuration key for the executor class path.
EXECUTOR_EXTRA_JAVA_OPTIONS - Static variable in class org.apache.spark.launcher.SparkLauncher
Configuration key for the executor VM options.
EXECUTOR_EXTRA_LIBRARY_PATH - Static variable in class org.apache.spark.launcher.SparkLauncher
Configuration key for the executor native library path.
EXECUTOR_MEMORY - Static variable in class org.apache.spark.launcher.SparkLauncher
Configuration key for the executor memory.
executorActorSystemName() - Static method in class org.apache.spark.SparkEnv
 
executorDeserializeTime() - Method in class org.apache.spark.status.api.v1.TaskMetricDistributions
 
executorDeserializeTime() - Method in class org.apache.spark.status.api.v1.TaskMetrics
 
executorEnvs() - Method in class org.apache.spark.SparkContext
 
executorHost() - Method in class org.apache.spark.scheduler.cluster.ExecutorInfo
 
executorId() - Method in class org.apache.spark.ExecutorRegistered
 
executorId() - Method in class org.apache.spark.ExecutorRemoved
 
executorId() - Method in class org.apache.spark.scheduler.SparkListenerExecutorAdded
 
executorId() - Method in class org.apache.spark.scheduler.SparkListenerExecutorRemoved
 
executorId() - Method in class org.apache.spark.scheduler.TaskInfo
 
executorId() - Method in class org.apache.spark.SparkEnv
 
executorId() - Method in class org.apache.spark.status.api.v1.TaskData
 
executorId() - Method in class org.apache.spark.storage.BlockManagerId
 
executorId() - Method in class org.apache.spark.streaming.scheduler.ReceiverInfo
 
executorIdToBlockManagerId() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
executorIdToData() - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
executorIdToStorageStatus() - Method in class org.apache.spark.storage.StorageStatusListener
 
ExecutorInfo - Class in org.apache.spark.scheduler.cluster
:: DeveloperApi :: Stores information about an executor to pass from the scheduler to SparkListeners.
ExecutorInfo(String, int, Map<String, String>) - Constructor for class org.apache.spark.scheduler.cluster.ExecutorInfo
 
executorInfo() - Method in class org.apache.spark.scheduler.SparkListenerExecutorAdded
 
executorLogs() - Method in class org.apache.spark.status.api.v1.ExecutorSummary
 
ExecutorLostFailure - Class in org.apache.spark
:: DeveloperApi :: The task failed because the executor that it was running on was lost.
ExecutorLostFailure(String, boolean, Option<String>) - Constructor for class org.apache.spark.ExecutorLostFailure
 
executorPct() - Method in class org.apache.spark.scheduler.RuntimePercentage
 
ExecutorRegistered - Class in org.apache.spark
 
ExecutorRegistered(String) - Constructor for class org.apache.spark.ExecutorRegistered
 
ExecutorRemoved - Class in org.apache.spark
 
ExecutorRemoved(String) - Constructor for class org.apache.spark.ExecutorRemoved
 
executorRunTime() - Method in class org.apache.spark.status.api.v1.StageData
 
executorRunTime() - Method in class org.apache.spark.status.api.v1.TaskMetricDistributions
 
executorRunTime() - Method in class org.apache.spark.status.api.v1.TaskMetrics
 
executors() - Method in class org.apache.spark.status.api.v1.RDDPartitionInfo
 
ExecutorsListener - Class in org.apache.spark.ui.exec
:: DeveloperApi :: A SparkListener that prepares information to be displayed on the ExecutorsTab
ExecutorsListener(StorageStatusListener) - Constructor for class org.apache.spark.ui.exec.ExecutorsListener
 
ExecutorStageSummary - Class in org.apache.spark.status.api.v1
 
ExecutorSummary - Class in org.apache.spark.status.api.v1
 
executorSummary() - Method in class org.apache.spark.status.api.v1.StageData
 
executorToDuration() - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
executorToInputBytes() - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
executorToInputRecords() - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
executorToLogUrls() - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
executorToOutputBytes() - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
executorToOutputRecords() - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
executorToShuffleRead() - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
executorToShuffleWrite() - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
executorToTasksActive() - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
executorToTasksComplete() - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
executorToTasksFailed() - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
exists() - Method in class org.apache.spark.streaming.State
Whether the state already exists
exitCausedByApp() - Method in class org.apache.spark.ExecutorLostFailure
 
exp(Column) - Static method in class org.apache.spark.sql.functions
Computes the exponential of the given value.
exp(String) - Static method in class org.apache.spark.sql.functions
Computes the exponential of the given column.
ExpectationSum - Class in org.apache.spark.mllib.clustering
 
ExpectationSum(double, double[], DenseVector<Object>[], DenseMatrix<Object>[]) - Constructor for class org.apache.spark.mllib.clustering.ExpectationSum
 
Experimental - Annotation Type in org.apache.spark.annotation
An experimental user-facing API.
experimental() - Method in class org.apache.spark.sql.SQLContext
:: Experimental :: A collection of methods that are considered experimental, but can be used to hook into the query planner for advanced functionality.
ExperimentalMethods - Class in org.apache.spark.sql
:: Experimental :: Holder for experimental methods for the bravest.
ExperimentalMethods(SQLContext) - Constructor for class org.apache.spark.sql.ExperimentalMethods
 
explain(boolean) - Method in class org.apache.spark.sql.Column
Prints the expression to the console for debugging purpose.
explain(boolean) - Method in class org.apache.spark.sql.DataFrame
Prints the plans (logical and physical) to the console for debugging purposes.
explain() - Method in class org.apache.spark.sql.DataFrame
Prints the physical plan to the console for debugging purposes.
explain(boolean) - Method in class org.apache.spark.sql.Dataset
Prints the plans (logical and physical) to the console for debugging purposes.
explain() - Method in class org.apache.spark.sql.Dataset
Prints the physical plan to the console for debugging purposes.
explainedVariance() - Method in class org.apache.spark.ml.regression.LinearRegressionSummary
 
explainedVariance() - Method in class org.apache.spark.mllib.evaluation.RegressionMetrics
Returns the variance explained by regression.
explainParam(Param<?>) - Method in interface org.apache.spark.ml.param.Params
 
explainParams() - Method in interface org.apache.spark.ml.param.Params
 
explode(Seq<Column>, Function1<Row, TraversableOnce<A>>, TypeTags.TypeTag<A>) - Method in class org.apache.spark.sql.DataFrame
(Scala-specific) Returns a new DataFrame where each row has been expanded to zero or more rows by the provided function.
explode(String, String, Function1<A, TraversableOnce<B>>, TypeTags.TypeTag<B>) - Method in class org.apache.spark.sql.DataFrame
(Scala-specific) Returns a new DataFrame where a single column has been expanded to zero or more rows by the provided function.
explode(Column) - Static method in class org.apache.spark.sql.functions
Creates a new row for each element in the given array or map column.
expm1(Column) - Static method in class org.apache.spark.sql.functions
Computes the exponential of the given value minus one.
expm1(String) - Static method in class org.apache.spark.sql.functions
Computes the exponential of the given column.
ExponentialGenerator - Class in org.apache.spark.mllib.random
:: DeveloperApi :: Generates i.i.d.
ExponentialGenerator(double) - Constructor for class org.apache.spark.mllib.random.ExponentialGenerator
 
exponentialJavaRDD(JavaSparkContext, double, long, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
exponentialJavaRDD(JavaSparkContext, double, long, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
exponentialJavaRDD(JavaSparkContext, double, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
exponentialJavaVectorRDD(JavaSparkContext, double, long, int, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
exponentialJavaVectorRDD(JavaSparkContext, double, long, int, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
exponentialJavaVectorRDD(JavaSparkContext, double, long, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
exponentialRDD(SparkContext, double, long, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
Generates an RDD comprised of i.i.d. samples from the exponential distribution with the input mean.
exponentialVectorRDD(SparkContext, double, long, int, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the exponential distribution with the input mean.
expr() - Method in class org.apache.spark.sql.Column
 
expr(String) - Static method in class org.apache.spark.sql.functions
Parses the expression string into the column that it represents, similar to DataFrame.selectExpr
externalBlockStoreFolderName() - Method in class org.apache.spark.SparkContext
 
externalBlockStoreSize() - Method in class org.apache.spark.storage.BlockStatus
 
externalBlockStoreSize() - Method in class org.apache.spark.storage.BlockUpdatedInfo
 
externalBlockStoreSize() - Method in class org.apache.spark.storage.RDDInfo
 
extractAFTPoints(DataFrame) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegression
Extract featuresCol, labelCol and censorCol from input dataset, and put it in an RDD with strong types.
extractDistribution(Function1<BatchInfo, Option<Object>>) - Method in class org.apache.spark.streaming.scheduler.StatsReportListener
 
extractDoubleDistribution(Seq<Tuple2<TaskInfo, TaskMetrics>>, Function2<TaskInfo, TaskMetrics, Option<Object>>) - Static method in class org.apache.spark.scheduler.StatsReportListener
 
extractLabeledPoints(DataFrame) - Method in class org.apache.spark.ml.Predictor
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
extractLongDistribution(Seq<Tuple2<TaskInfo, TaskMetrics>>, Function2<TaskInfo, TaskMetrics, Option<Object>>) - Static method in class org.apache.spark.scheduler.StatsReportListener
 
extractParamMap(ParamMap) - Method in interface org.apache.spark.ml.param.Params
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
extractParamMap() - Method in interface org.apache.spark.ml.param.Params
extractParamMap with no extra values.
extraStrategies() - Method in class org.apache.spark.sql.ExperimentalMethods
Allows extra strategies to be injected into the query planner at runtime.
eye(int) - Static method in class org.apache.spark.mllib.linalg.DenseMatrix
Generate an Identity Matrix in DenseMatrix format.
eye(int) - Static method in class org.apache.spark.mllib.linalg.Matrices
Generate a dense Identity Matrix in Matrix format.

F

f() - Method in class org.apache.spark.sql.UserDefinedFunction
 
f1Measure() - Method in class org.apache.spark.mllib.evaluation.MultilabelMetrics
Returns document-based f1-measure averaged by the number of documents
f1Measure(double) - Method in class org.apache.spark.mllib.evaluation.MultilabelMetrics
Returns f1-measure for a given label (category)
factorial(Column) - Static method in class org.apache.spark.sql.functions
Computes the factorial of the given value.
failed() - Method in class org.apache.spark.scheduler.TaskInfo
 
failedJobs() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
failedStages() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
failedTasks() - Method in class org.apache.spark.status.api.v1.ExecutorStageSummary
 
failedTasks() - Method in class org.apache.spark.status.api.v1.ExecutorSummary
 
failureReason() - Method in class org.apache.spark.scheduler.StageInfo
If the stage failed, the reason why.
failureReason() - Method in class org.apache.spark.streaming.scheduler.OutputOperationInfo
 
FAIR() - Static method in class org.apache.spark.scheduler.SchedulingMode
 
falsePositiveRate(double) - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns false positive rate for a given label (category)
feature() - Method in class org.apache.spark.mllib.tree.model.Split
 
featureImportances() - Method in class org.apache.spark.ml.classification.RandomForestClassificationModel
Estimate of the importance of each feature.
featureImportances() - Method in class org.apache.spark.ml.regression.RandomForestRegressionModel
Estimate of the importance of each feature.
featureIndex() - Method in class org.apache.spark.ml.tree.CategoricalSplit
 
featureIndex() - Method in class org.apache.spark.ml.tree.ContinuousSplit
 
featureIndex() - Method in interface org.apache.spark.ml.tree.Split
Index of feature which this split tests
features() - Method in class org.apache.spark.mllib.regression.LabeledPoint
 
featuresCol() - Method in class org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
 
featuresCol() - Method in interface org.apache.spark.ml.classification.LogisticRegressionSummary
Field in "predictions" which gives the features of each instance as a vector.
featuresCol() - Method in class org.apache.spark.ml.regression.LinearRegressionTrainingSummary
 
featuresDataType() - Method in class org.apache.spark.ml.PredictionModel
Returns the SQL DataType corresponding to the FeaturesType type parameter.
FeatureType - Class in org.apache.spark.mllib.tree.configuration
Enum to describe whether a feature is "continuous" or "categorical"
FeatureType() - Constructor for class org.apache.spark.mllib.tree.configuration.FeatureType
 
featureType() - Method in class org.apache.spark.mllib.tree.model.Split
 
FetchFailed - Class in org.apache.spark
:: DeveloperApi :: Task failed to fetch shuffle data from a remote node.
FetchFailed(BlockManagerId, int, int, int, String) - Constructor for class org.apache.spark.FetchFailed
 
fetchPct() - Method in class org.apache.spark.scheduler.RuntimePercentage
 
fetchWaitTime() - Method in class org.apache.spark.status.api.v1.ShuffleReadMetricDistributions
 
fetchWaitTime() - Method in class org.apache.spark.status.api.v1.ShuffleReadMetrics
 
field() - Method in class org.apache.spark.storage.BroadcastBlockId
 
fieldIndex(String) - Method in interface org.apache.spark.sql.Row
Returns the index of a given field name.
fieldIndex(String) - Method in class org.apache.spark.sql.types.StructType
Returns index of a given field
fieldNames() - Method in class org.apache.spark.sql.types.StructType
Returns all field names in an array.
fields() - Method in class org.apache.spark.sql.types.StructType
 
FIFO() - Static method in class org.apache.spark.scheduler.SchedulingMode
 
files() - Method in class org.apache.spark.SparkContext
 
fileStream(String, Class<K>, Class<V>, Class<F>) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format.
fileStream(String, Class<K>, Class<V>, Class<F>, Function<Path, Boolean>, boolean) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format.
fileStream(String, Class<K>, Class<V>, Class<F>, Function<Path, Boolean>, boolean, Configuration) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format.
fileStream(String, ClassTag<K>, ClassTag<V>, ClassTag<F>) - Method in class org.apache.spark.streaming.StreamingContext
Create a input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format.
fileStream(String, Function1<Path, Object>, boolean, ClassTag<K>, ClassTag<V>, ClassTag<F>) - Method in class org.apache.spark.streaming.StreamingContext
Create a input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format.
fileStream(String, Function1<Path, Object>, boolean, Configuration, ClassTag<K>, ClassTag<V>, ClassTag<F>) - Method in class org.apache.spark.streaming.StreamingContext
Create a input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format.
fill(double) - Method in class org.apache.spark.sql.DataFrameNaFunctions
Returns a new DataFrame that replaces null or NaN values in numeric columns with value.
fill(String) - Method in class org.apache.spark.sql.DataFrameNaFunctions
Returns a new DataFrame that replaces null values in string columns with value.
fill(double, String[]) - Method in class org.apache.spark.sql.DataFrameNaFunctions
Returns a new DataFrame that replaces null or NaN values in specified numeric columns.
fill(double, Seq<String>) - Method in class org.apache.spark.sql.DataFrameNaFunctions
(Scala-specific) Returns a new DataFrame that replaces null or NaN values in specified numeric columns.
fill(String, String[]) - Method in class org.apache.spark.sql.DataFrameNaFunctions
Returns a new DataFrame that replaces null values in specified string columns.
fill(String, Seq<String>) - Method in class org.apache.spark.sql.DataFrameNaFunctions
(Scala-specific) Returns a new DataFrame that replaces null values in specified string columns.
fill(Map<String, Object>) - Method in class org.apache.spark.sql.DataFrameNaFunctions
Returns a new DataFrame that replaces null values.
fill(Map<String, Object>) - Method in class org.apache.spark.sql.DataFrameNaFunctions
(Scala-specific) Returns a new DataFrame that replaces null values.
filter(Function<Double, Boolean>) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Return a new RDD containing only the elements that satisfy a predicate.
filter(Function<Tuple2<K, V>, Boolean>) - Method in class org.apache.spark.api.java.JavaPairRDD
Return a new RDD containing only the elements that satisfy a predicate.
filter(Function<T, Boolean>) - Method in class org.apache.spark.api.java.JavaRDD
Return a new RDD containing only the elements that satisfy a predicate.
filter(Function1<Graph<VD, ED>, Graph<VD2, ED2>>, Function1<EdgeTriplet<VD2, ED2>, Object>, Function2<Object, VD2, Object>, ClassTag<VD2>, ClassTag<ED2>) - Method in class org.apache.spark.graphx.GraphOps
Filter the graph by computing some values to filter on, and applying the predicates.
filter(Function1<EdgeTriplet<VD, ED>, Object>, Function2<Object, VD, Object>) - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
 
filter(Function1<Tuple2<Object, VD>, Object>) - Method in class org.apache.spark.graphx.VertexRDD
Restricts the vertex set to the set of vertices satisfying the given predicate.
filter(Params) - Method in class org.apache.spark.ml.param.ParamMap
Filters this param map for the given parent.
filter(Function1<T, Object>) - Method in class org.apache.spark.rdd.RDD
Return a new RDD containing only the elements that satisfy a predicate.
filter(Column) - Method in class org.apache.spark.sql.DataFrame
Filters rows using the given condition.
filter(String) - Method in class org.apache.spark.sql.DataFrame
Filters rows using the given SQL expression.
filter(Function1<T, Object>) - Method in class org.apache.spark.sql.Dataset
(Scala-specific) Returns a new Dataset that only contains elements where func returns true.
filter(FilterFunction<T>) - Method in class org.apache.spark.sql.Dataset
(Java-specific) Returns a new Dataset that only contains elements where func returns true.
Filter - Class in org.apache.spark.sql.sources
A filter predicate for data sources.
Filter() - Constructor for class org.apache.spark.sql.sources.Filter
 
filter(Function<T, Boolean>) - Method in class org.apache.spark.streaming.api.java.JavaDStream
Return a new DStream containing only the elements that satisfy a predicate.
filter(Function<Tuple2<K, V>, Boolean>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream containing only the elements that satisfy a predicate.
filter(Function1<T, Object>) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream containing only the elements that satisfy a predicate.
filterByRange(K, K) - Method in class org.apache.spark.rdd.OrderedRDDFunctions
Returns an RDD containing only the elements in the the inclusive range lower to upper.
FilterFunction<T> - Interface in org.apache.spark.api.java.function
Base interface for a function used in Dataset's filter function.
filterWith(Function1<Object, A>, Function2<T, A, Object>) - Method in class org.apache.spark.rdd.RDD
Filters this RDD with p, where p takes an additional parameter of type A.
findSplitsBins(RDD<LabeledPoint>, org.apache.spark.mllib.tree.impl.DecisionTreeMetadata) - Static method in class org.apache.spark.mllib.tree.DecisionTree
Returns splits and bins for decision tree calculation.
findSynonyms(String, int) - Method in class org.apache.spark.ml.feature.Word2VecModel
Find "num" number of words closest in similarity to the given word.
findSynonyms(Vector, int) - Method in class org.apache.spark.ml.feature.Word2VecModel
Find "num" number of words closest to similarity to the given vector representation of the word.
findSynonyms(String, int) - Method in class org.apache.spark.mllib.feature.Word2VecModel
 
findSynonyms(Vector, int) - Method in class org.apache.spark.mllib.feature.Word2VecModel
 
finish(B) - Method in class org.apache.spark.sql.expressions.Aggregator
Transform the output of the reduction.
finished() - Method in class org.apache.spark.scheduler.TaskInfo
 
finishTime() - Method in class org.apache.spark.scheduler.TaskInfo
The time when the task has completed successfully (including the time to remotely fetch results, if necessary).
first() - Method in class org.apache.spark.api.java.JavaDoubleRDD
 
first() - Method in class org.apache.spark.api.java.JavaPairRDD
 
first() - Method in interface org.apache.spark.api.java.JavaRDDLike
Return the first element in this RDD.
first() - Method in class org.apache.spark.rdd.RDD
Return the first element in this RDD.
first() - Method in class org.apache.spark.sql.DataFrame
Returns the first row.
first() - Method in class org.apache.spark.sql.Dataset
Returns the first element in this Dataset.
first(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the first value in a group.
first(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the first value of a column in a group.
firstParent(ClassTag<U>) - Method in class org.apache.spark.rdd.RDD
Returns the first parent RDD
fit(DataFrame) - Method in class org.apache.spark.ml.classification.OneVsRest
 
fit(DataFrame) - Method in class org.apache.spark.ml.clustering.KMeans
 
fit(DataFrame) - Method in class org.apache.spark.ml.clustering.LDA
 
fit(DataFrame, ParamPair<?>, ParamPair<?>...) - Method in class org.apache.spark.ml.Estimator
Fits a single model to the input data with optional parameters.
fit(DataFrame, ParamPair<?>, Seq<ParamPair<?>>) - Method in class org.apache.spark.ml.Estimator
Fits a single model to the input data with optional parameters.
fit(DataFrame, ParamMap) - Method in class org.apache.spark.ml.Estimator
Fits a single model to the input data with provided parameter map.
fit(DataFrame) - Method in class org.apache.spark.ml.Estimator
Fits a model to the input data.
fit(DataFrame, ParamMap[]) - Method in class org.apache.spark.ml.Estimator
Fits multiple models to the input data with multiple sets of parameters.
fit(DataFrame) - Method in class org.apache.spark.ml.feature.ChiSqSelector
 
fit(DataFrame) - Method in class org.apache.spark.ml.feature.CountVectorizer
 
fit(DataFrame) - Method in class org.apache.spark.ml.feature.IDF
 
fit(DataFrame) - Method in class org.apache.spark.ml.feature.MinMaxScaler
 
fit(DataFrame) - Method in class org.apache.spark.ml.feature.PCA
Computes a PCAModel that contains the principal components of the input vectors.
fit(DataFrame) - Method in class org.apache.spark.ml.feature.QuantileDiscretizer
 
fit(DataFrame) - Method in class org.apache.spark.ml.feature.RFormula
 
fit(DataFrame) - Method in class org.apache.spark.ml.feature.StandardScaler
 
fit(DataFrame) - Method in class org.apache.spark.ml.feature.StringIndexer
 
fit(DataFrame) - Method in class org.apache.spark.ml.feature.VectorIndexer
 
fit(DataFrame) - Method in class org.apache.spark.ml.feature.Word2Vec
 
fit(DataFrame) - Method in class org.apache.spark.ml.Pipeline
Fits the pipeline to the input dataset with additional parameters.
fit(DataFrame) - Method in class org.apache.spark.ml.Predictor
 
fit(DataFrame) - Method in class org.apache.spark.ml.recommendation.ALS
 
fit(DataFrame) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegression
 
fit(DataFrame) - Method in class org.apache.spark.ml.regression.IsotonicRegression
 
fit(DataFrame) - Method in class org.apache.spark.ml.tuning.CrossValidator
 
fit(DataFrame) - Method in class org.apache.spark.ml.tuning.TrainValidationSplit
 
fit(RDD<LabeledPoint>) - Method in class org.apache.spark.mllib.feature.ChiSqSelector
 
fit(RDD<Vector>) - Method in class org.apache.spark.mllib.feature.IDF
Computes the inverse document frequency.
fit(JavaRDD<Vector>) - Method in class org.apache.spark.mllib.feature.IDF
Computes the inverse document frequency.
fit(RDD<Vector>) - Method in class org.apache.spark.mllib.feature.PCA
Computes a PCAModel that contains the principal components of the input vectors.
fit(JavaRDD<Vector>) - Method in class org.apache.spark.mllib.feature.PCA
Java-friendly version of fit()
fit(RDD<Vector>) - Method in class org.apache.spark.mllib.feature.StandardScaler
Computes the mean and variance and stores as a model to be used for later scaling.
fit(RDD<S>) - Method in class org.apache.spark.mllib.feature.Word2Vec
 
fit(JavaRDD<S>) - Method in class org.apache.spark.mllib.feature.Word2Vec
 
flatMap(FlatMapFunction<T, U>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
flatMap(Function1<T, TraversableOnce<U>>, ClassTag<U>) - Method in class org.apache.spark.rdd.RDD
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
flatMap(Function1<Row, TraversableOnce<R>>, ClassTag<R>) - Method in class org.apache.spark.sql.DataFrame
Returns a new RDD by first applying a function to all rows of this DataFrame, and then flattening the results.
flatMap(Function1<T, TraversableOnce<U>>, Encoder<U>) - Method in class org.apache.spark.sql.Dataset
(Scala-specific) Returns a new Dataset by first applying a function to all elements of this Dataset, and then flattening the results.
flatMap(FlatMapFunction<T, U>, Encoder<U>) - Method in class org.apache.spark.sql.Dataset
(Java-specific) Returns a new Dataset by first applying a function to all elements of this Dataset, and then flattening the results.
flatMap(FlatMapFunction<T, U>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream by applying a function to all elements of this DStream, and then flattening the results
flatMap(Function1<T, Traversable<U>>, ClassTag<U>) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream by applying a function to all elements of this DStream, and then flattening the results
FlatMapFunction<T,R> - Interface in org.apache.spark.api.java.function
A function that returns zero or more output records from each input record.
FlatMapFunction2<T1,T2,R> - Interface in org.apache.spark.api.java.function
A function that takes two inputs and returns zero or more output records.
flatMapGroups(Function2<K, Iterator<V>, TraversableOnce<U>>, Encoder<U>) - Method in class org.apache.spark.sql.GroupedDataset
Applies the given function to each group of data.
flatMapGroups(FlatMapGroupsFunction<K, V, U>, Encoder<U>) - Method in class org.apache.spark.sql.GroupedDataset
Applies the given function to each group of data.
FlatMapGroupsFunction<K,V,R> - Interface in org.apache.spark.api.java.function
A function that returns zero or more output records from each grouping key and its values.
flatMapToDouble(DoubleFlatMapFunction<T>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
flatMapToPair(PairFlatMapFunction<T, K2, V2>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
flatMapToPair(PairFlatMapFunction<T, K2, V2>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream by applying a function to all elements of this DStream, and then flattening the results
flatMapValues(Function<V, Iterable<U>>) - Method in class org.apache.spark.api.java.JavaPairRDD
Pass each value in the key-value pair RDD through a flatMap function without changing the keys; this also retains the original RDD's partitioning.
flatMapValues(Function1<V, TraversableOnce<U>>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Pass each value in the key-value pair RDD through a flatMap function without changing the keys; this also retains the original RDD's partitioning.
flatMapValues(Function<V, Iterable<U>>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying a flatmap function to the value of each key-value pairs in 'this' DStream without changing the key.
flatMapValues(Function1<V, TraversableOnce<U>>, ClassTag<U>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying a flatmap function to the value of each key-value pairs in 'this' DStream without changing the key.
flatMapWith(Function1<Object, A>, boolean, Function2<T, A, Seq<U>>, ClassTag<U>) - Method in class org.apache.spark.rdd.RDD
FlatMaps f over this RDD, where f takes an additional parameter of type A.
FLOAT() - Static method in class org.apache.spark.sql.Encoders
An encoder for nullable float type.
FloatDecimal() - Static method in class org.apache.spark.sql.types.DecimalType
 
FloatParam - Class in org.apache.spark.ml.param
:: DeveloperApi :: Specialized version of Param[Float] for Java.
FloatParam(String, String, String, Function1<Object, Object>) - Constructor for class org.apache.spark.ml.param.FloatParam
 
FloatParam(String, String, String) - Constructor for class org.apache.spark.ml.param.FloatParam
 
FloatParam(Identifiable, String, String, Function1<Object, Object>) - Constructor for class org.apache.spark.ml.param.FloatParam
 
FloatParam(Identifiable, String, String) - Constructor for class org.apache.spark.ml.param.FloatParam
 
floatToFloatWritable(float) - Static method in class org.apache.spark.SparkContext
 
FloatType - Static variable in class org.apache.spark.sql.types.DataTypes
Gets the FloatType object.
FloatType - Class in org.apache.spark.sql.types
:: DeveloperApi :: The data type representing Float values.
floatWritableConverter() - Static method in class org.apache.spark.SparkContext
 
floor(Column) - Static method in class org.apache.spark.sql.functions
Computes the floor of the given value.
floor(String) - Static method in class org.apache.spark.sql.functions
Computes the floor of the given column.
floor() - Method in class org.apache.spark.sql.types.Decimal
 
floor(Duration) - Method in class org.apache.spark.streaming.Time
 
floor(Duration, Time) - Method in class org.apache.spark.streaming.Time
 
FlumeUtils - Class in org.apache.spark.streaming.flume
 
FlumeUtils() - Constructor for class org.apache.spark.streaming.flume.FlumeUtils
 
flush() - Method in class org.apache.spark.io.SnappyOutputStreamWrapper
 
flush() - Method in class org.apache.spark.serializer.SerializationStream
 
flush() - Method in class org.apache.spark.storage.TimeTrackingOutputStream
 
fMeasure(double, double) - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns f-measure for a given label (category)
fMeasure(double) - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns f1-measure for a given label (category)
fMeasure() - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns f-measure (equals to precision and recall because precision equals recall)
fMeasureByThreshold() - Method in class org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
fMeasureByThreshold(double) - Method in class org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
Returns the (threshold, F-Measure) curve.
fMeasureByThreshold() - Method in class org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
Returns the (threshold, F-Measure) curve with beta = 1.0.
fold(T, Function2<T, T, T>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Aggregate the elements of each partition, and then the results for all the partitions, using a given associative and commutative function and a neutral "zero value".
fold(T, Function2<T, T, T>) - Method in class org.apache.spark.rdd.RDD
Aggregate the elements of each partition, and then the results for all the partitions, using a given associative and commutative function and a neutral "zero value".
foldByKey(V, Partitioner, Function2<V, V, V>) - Method in class org.apache.spark.api.java.JavaPairRDD
Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
foldByKey(V, int, Function2<V, V, V>) - Method in class org.apache.spark.api.java.JavaPairRDD
Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
foldByKey(V, Function2<V, V, V>) - Method in class org.apache.spark.api.java.JavaPairRDD
Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
foldByKey(V, Partitioner, Function2<V, V, V>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
foldByKey(V, int, Function2<V, V, V>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
foldByKey(V, Function2<V, V, V>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Merge the values for each key using an associative function and a neutral "zero value" which may be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
foreach(VoidFunction<T>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Applies a function f to all elements of this RDD.
foreach(Function1<T, BoxedUnit>) - Method in class org.apache.spark.rdd.RDD
Applies a function f to all elements of this RDD.
foreach(Function1<Row, BoxedUnit>) - Method in class org.apache.spark.sql.DataFrame
Applies a function f to all rows.
foreach(Function1<T, BoxedUnit>) - Method in class org.apache.spark.sql.Dataset
(Scala-specific) Runs func on each element of this Dataset.
foreach(ForeachFunction<T>) - Method in class org.apache.spark.sql.Dataset
(Java-specific) Runs func on each element of this Dataset.
foreach(Function<R, Void>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Deprecated.
As of release 0.9.0, replaced by foreachRDD
foreach(Function2<R, Time, Void>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Deprecated.
As of release 0.9.0, replaced by foreachRDD
foreach(Function1<RDD<T>, BoxedUnit>) - Method in class org.apache.spark.streaming.dstream.DStream
Deprecated.
As of 0.9.0, replaced by foreachRDD.
foreach(Function2<RDD<T>, Time, BoxedUnit>) - Method in class org.apache.spark.streaming.dstream.DStream
Deprecated.
As of 0.9.0, replaced by foreachRDD.
foreachActive(Function2<Object, Object, BoxedUnit>) - Method in class org.apache.spark.mllib.linalg.DenseVector
 
foreachActive(Function3<Object, Object, Object, BoxedUnit>) - Method in interface org.apache.spark.mllib.linalg.Matrix
Applies a function f to all the active elements of dense and sparse matrix.
foreachActive(Function2<Object, Object, BoxedUnit>) - Method in class org.apache.spark.mllib.linalg.SparseVector
 
foreachActive(Function2<Object, Object, BoxedUnit>) - Method in interface org.apache.spark.mllib.linalg.Vector
Applies a function f to all the active elements of dense and sparse vector.
foreachAsync(VoidFunction<T>) - Method in interface org.apache.spark.api.java.JavaRDDLike
The asynchronous version of the foreach action, which applies a function f to all the elements of this RDD.
foreachAsync(Function1<T, BoxedUnit>) - Method in class org.apache.spark.rdd.AsyncRDDActions
Applies a function f to all elements of this RDD.
ForeachFunction<T> - Interface in org.apache.spark.api.java.function
Base interface for a function used in Dataset's foreach function.
foreachPartition(VoidFunction<Iterator<T>>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Applies a function f to each partition of this RDD.
foreachPartition(Function1<Iterator<T>, BoxedUnit>) - Method in class org.apache.spark.rdd.RDD
Applies a function f to each partition of this RDD.
foreachPartition(Function1<Iterator<Row>, BoxedUnit>) - Method in class org.apache.spark.sql.DataFrame
Applies a function f to each partition of this DataFrame.
foreachPartition(Function1<Iterator<T>, BoxedUnit>) - Method in class org.apache.spark.sql.Dataset
(Scala-specific) Runs func on each partition of this Dataset.
foreachPartition(ForeachPartitionFunction<T>) - Method in class org.apache.spark.sql.Dataset
(Java-specific) Runs func on each partition of this Dataset.
foreachPartitionAsync(VoidFunction<Iterator<T>>) - Method in interface org.apache.spark.api.java.JavaRDDLike
The asynchronous version of the foreachPartition action, which applies a function f to each partition of this RDD.
foreachPartitionAsync(Function1<Iterator<T>, BoxedUnit>) - Method in class org.apache.spark.rdd.AsyncRDDActions
Applies a function f to each partition of this RDD.
ForeachPartitionFunction<T> - Interface in org.apache.spark.api.java.function
Base interface for a function used in Dataset's foreachPartition function.
foreachRDD(Function<R, Void>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Deprecated.
As of release 1.6.0, replaced by foreachRDD(JVoidFunction)
foreachRDD(Function2<R, Time, Void>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Deprecated.
As of release 1.6.0, replaced by foreachRDD(JVoidFunction2)
foreachRDD(VoidFunction<R>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Apply a function to each RDD in this DStream.
foreachRDD(VoidFunction2<R, Time>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Apply a function to each RDD in this DStream.
foreachRDD(Function1<RDD<T>, BoxedUnit>) - Method in class org.apache.spark.streaming.dstream.DStream
Apply a function to each RDD in this DStream.
foreachRDD(Function2<RDD<T>, Time, BoxedUnit>) - Method in class org.apache.spark.streaming.dstream.DStream
Apply a function to each RDD in this DStream.
foreachWith(Function1<Object, A>, Function2<T, A, BoxedUnit>) - Method in class org.apache.spark.rdd.RDD
Applies f to each element of this RDD, where f takes an additional parameter of type A.
format(String) - Method in class org.apache.spark.sql.DataFrameReader
Specifies the input data source format.
format(String) - Method in class org.apache.spark.sql.DataFrameWriter
Specifies the underlying output data source.
format_number(Column, int) - Static method in class org.apache.spark.sql.functions
Formats numeric column x to a format like '#,###,###.##', rounded to d decimal places, and returns the result as a string column.
format_string(String, Column...) - Static method in class org.apache.spark.sql.functions
Formats the arguments in printf-style and returns the result as a string column.
format_string(String, Seq<Column>) - Static method in class org.apache.spark.sql.functions
Formats the arguments in printf-style and returns the result as a string column.
formatVersion() - Method in class org.apache.spark.mllib.classification.LogisticRegressionModel
 
formatVersion() - Method in class org.apache.spark.mllib.classification.NaiveBayesModel
 
formatVersion() - Method in class org.apache.spark.mllib.classification.SVMModel
 
formatVersion() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
 
formatVersion() - Method in class org.apache.spark.mllib.clustering.GaussianMixtureModel
 
formatVersion() - Method in class org.apache.spark.mllib.clustering.KMeansModel
 
formatVersion() - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
 
formatVersion() - Method in class org.apache.spark.mllib.clustering.PowerIterationClusteringModel
 
formatVersion() - Method in class org.apache.spark.mllib.feature.ChiSqSelectorModel
 
formatVersion() - Method in class org.apache.spark.mllib.feature.Word2VecModel
 
formatVersion() - Method in class org.apache.spark.mllib.recommendation.MatrixFactorizationModel
 
formatVersion() - Method in class org.apache.spark.mllib.regression.IsotonicRegressionModel
 
formatVersion() - Method in class org.apache.spark.mllib.regression.LassoModel
 
formatVersion() - Method in class org.apache.spark.mllib.regression.LinearRegressionModel
 
formatVersion() - Method in class org.apache.spark.mllib.regression.RidgeRegressionModel
 
formatVersion() - Method in class org.apache.spark.mllib.tree.model.DecisionTreeModel
 
formatVersion() - Method in class org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
 
formatVersion() - Method in class org.apache.spark.mllib.tree.model.RandomForestModel
 
formatVersion() - Method in interface org.apache.spark.mllib.util.Saveable
Current version of model save/load format.
formula() - Method in class org.apache.spark.ml.feature.RFormula
R formula parameter.
FPGrowth - Class in org.apache.spark.mllib.fpm
A parallel FP-growth algorithm to mine frequent itemsets.
FPGrowth() - Constructor for class org.apache.spark.mllib.fpm.FPGrowth
Constructs a default instance with default parameters {minSupport: 0.3, numPartitions: same as the input data}.
FPGrowth.FreqItemset<Item> - Class in org.apache.spark.mllib.fpm
Frequent itemset.
FPGrowth.FreqItemset(Object, long) - Constructor for class org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
 
FPGrowthModel<Item> - Class in org.apache.spark.mllib.fpm
Model trained by FPGrowth, which holds frequent itemsets.
FPGrowthModel(RDD<FPGrowth.FreqItemset<Item>>, ClassTag<Item>) - Constructor for class org.apache.spark.mllib.fpm.FPGrowthModel
 
fractional() - Method in class org.apache.spark.sql.types.DecimalType
 
fractional() - Method in class org.apache.spark.sql.types.DoubleType
 
fractional() - Method in class org.apache.spark.sql.types.FloatType
 
freq() - Method in class org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
 
freq() - Method in class org.apache.spark.mllib.fpm.PrefixSpan.FreqSequence
 
freqItems(String[], double) - Method in class org.apache.spark.sql.DataFrameStatFunctions
Finding frequent items for columns, possibly with false positives.
freqItems(String[]) - Method in class org.apache.spark.sql.DataFrameStatFunctions
Finding frequent items for columns, possibly with false positives.
freqItems(Seq<String>, double) - Method in class org.apache.spark.sql.DataFrameStatFunctions
(Scala-specific) Finding frequent items for columns, possibly with false positives.
freqItems(Seq<String>) - Method in class org.apache.spark.sql.DataFrameStatFunctions
(Scala-specific) Finding frequent items for columns, possibly with false positives.
freqItemsets() - Method in class org.apache.spark.mllib.fpm.FPGrowthModel
 
freqSequences() - Method in class org.apache.spark.mllib.fpm.PrefixSpanModel
 
from_unixtime(Column) - Static method in class org.apache.spark.sql.functions
Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string representing the timestamp of that moment in the current system time zone in the given format.
from_unixtime(Column, String) - Static method in class org.apache.spark.sql.functions
Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string representing the timestamp of that moment in the current system time zone in the given format.
from_utc_timestamp(Column, String) - Static method in class org.apache.spark.sql.functions
Assumes given timestamp is UTC and converts to given timezone.
fromAttributes(Seq<Attribute>) - Static method in class org.apache.spark.sql.types.StructType
 
fromAvroFlumeEvent(AvroFlumeEvent) - Static method in class org.apache.spark.streaming.flume.SparkFlumeEvent
 
fromCaseClassString(String) - Static method in class org.apache.spark.sql.types.DataType
Deprecated.
As of 1.2.0, replaced by DataType.fromJson()
fromCOO(int, int, Iterable<Tuple3<Object, Object, Object>>) - Static method in class org.apache.spark.mllib.linalg.SparseMatrix
Generate a SparseMatrix from Coordinate List (COO) format.
fromDStream(DStream<T>, ClassTag<T>) - Static method in class org.apache.spark.streaming.api.java.JavaDStream
Convert a scala DStream to a Java-friendly JavaDStream.
fromEdgePartitions(RDD<Tuple2<Object, EdgePartition<ED, VD>>>, VD, StorageLevel, StorageLevel, ClassTag<VD>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.impl.GraphImpl
Create a graph from EdgePartitions, setting referenced vertices to `defaultVertexAttr`.
fromEdges(RDD<Edge<ED>>, ClassTag<ED>, ClassTag<VD>) - Static method in class org.apache.spark.graphx.EdgeRDD
Creates an EdgeRDD from a set of edges.
fromEdges(RDD<Edge<ED>>, VD, StorageLevel, StorageLevel, ClassTag<VD>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.Graph
Construct a graph from a collection of edges.
fromEdges(EdgeRDD<?>, int, VD, ClassTag<VD>) - Static method in class org.apache.spark.graphx.VertexRDD
Constructs a VertexRDD containing all vertices referred to in edges.
fromEdgeTuples(RDD<Tuple2<Object, Object>>, VD, Option<PartitionStrategy>, StorageLevel, StorageLevel, ClassTag<VD>) - Static method in class org.apache.spark.graphx.Graph
Construct a graph from a collection of edges encoded as vertex id pairs.
fromExistingRDDs(VertexRDD<VD>, EdgeRDD<ED>, ClassTag<VD>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.impl.GraphImpl
Create a graph from a VertexRDD and an EdgeRDD with the same replicated vertex type as the vertices.
fromInputDStream(InputDStream<T>, ClassTag<T>) - Static method in class org.apache.spark.streaming.api.java.JavaInputDStream
Convert a scala InputDStream to a Java-friendly JavaInputDStream.
fromInputDStream(InputDStream<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - Static method in class org.apache.spark.streaming.api.java.JavaPairInputDStream
Convert a scala InputDStream of pairs to a Java-friendly JavaPairInputDStream.
fromJavaDStream(JavaDStream<Tuple2<K, V>>) - Static method in class org.apache.spark.streaming.api.java.JavaPairDStream
 
fromJavaRDD(JavaRDD<Tuple2<K, V>>) - Static method in class org.apache.spark.api.java.JavaPairRDD
Convert a JavaRDD of key-value pairs to JavaPairRDD.
fromJson(String) - Static method in class org.apache.spark.mllib.linalg.Vectors
Parses the JSON representation of a vector into a Vector.
fromJson(String) - Static method in class org.apache.spark.sql.types.DataType
 
fromJson(String) - Static method in class org.apache.spark.sql.types.Metadata
Creates a Metadata instance from JSON.
fromName(String) - Static method in class org.apache.spark.ml.attribute.AttributeType
Gets the AttributeType object from its name.
fromOffset() - Method in class org.apache.spark.streaming.kafka.OffsetRange
 
fromOld(DecisionTreeModel, DecisionTreeClassifier, Map<Object, Object>, int) - Static method in class org.apache.spark.ml.classification.DecisionTreeClassificationModel
(private[ml]) Convert a model from the old API
fromOld(GradientBoostedTreesModel, GBTClassifier, Map<Object, Object>, int) - Static method in class org.apache.spark.ml.classification.GBTClassificationModel
(private[ml]) Convert a model from the old API
fromOld(RandomForestModel, RandomForestClassifier, Map<Object, Object>, int, int) - Static method in class org.apache.spark.ml.classification.RandomForestClassificationModel
(private[ml]) Convert a model from the old API
fromOld(DecisionTreeModel, DecisionTreeRegressor, Map<Object, Object>, int) - Static method in class org.apache.spark.ml.regression.DecisionTreeRegressionModel
(private[ml]) Convert a model from the old API
fromOld(GradientBoostedTreesModel, GBTRegressor, Map<Object, Object>, int) - Static method in class org.apache.spark.ml.regression.GBTRegressionModel
(private[ml]) Convert a model from the old API
fromOld(RandomForestModel, RandomForestRegressor, Map<Object, Object>, int) - Static method in class org.apache.spark.ml.regression.RandomForestRegressionModel
(private[ml]) Convert a model from the old API
fromOld(Node, Map<Object, Object>) - Static method in class org.apache.spark.ml.tree.Node
Create a new Node from the old Node format, recursively creating child nodes as needed.
fromPairDStream(DStream<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - Static method in class org.apache.spark.streaming.api.java.JavaPairDStream
 
fromPairRDD(RDD<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - Static method in class org.apache.spark.mllib.rdd.MLPairRDDFunctions
Implicit conversion from a pair RDD to MLPairRDDFunctions.
fromRDD(RDD<Object>) - Static method in class org.apache.spark.api.java.JavaDoubleRDD
 
fromRDD(RDD<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - Static method in class org.apache.spark.api.java.JavaPairRDD
 
fromRDD(RDD<T>, ClassTag<T>) - Static method in class org.apache.spark.api.java.JavaRDD
 
fromRDD(RDD<T>, ClassTag<T>) - Static method in class org.apache.spark.mllib.rdd.RDDFunctions
Implicit conversion from an RDD to RDDFunctions.
fromRdd(RDD<?>) - Static method in class org.apache.spark.storage.RDDInfo
 
fromReceiverInputDStream(ReceiverInputDStream<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - Static method in class org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream
Convert a scala ReceiverInputDStream to a Java-friendly JavaReceiverInputDStream.
fromReceiverInputDStream(ReceiverInputDStream<T>, ClassTag<T>) - Static method in class org.apache.spark.streaming.api.java.JavaReceiverInputDStream
Convert a scala ReceiverInputDStream to a Java-friendly JavaReceiverInputDStream.
fromSparkContext(SparkContext) - Static method in class org.apache.spark.api.java.JavaSparkContext
 
fromStage(Stage, int, Option<Object>, Seq<Seq<TaskLocation>>) - Static method in class org.apache.spark.scheduler.StageInfo
Construct a StageInfo from a Stage.
fromString(String) - Static method in enum org.apache.spark.JobExecutionStatus
 
fromString(String) - Static method in class org.apache.spark.mllib.tree.loss.Losses
 
fromString(String) - Static method in enum org.apache.spark.status.api.v1.ApplicationStatus
 
fromString(String) - Static method in enum org.apache.spark.status.api.v1.StageStatus
 
fromString(String) - Static method in enum org.apache.spark.status.api.v1.TaskSorting
 
fromString(String) - Static method in class org.apache.spark.storage.StorageLevel
:: DeveloperApi :: Return the StorageLevel object with the specified name.
fromStructField(StructField) - Static method in class org.apache.spark.ml.attribute.AttributeGroup
Creates an attribute group from a StructField instance.
fullOuterJoin(JavaPairRDD<K, W>, Partitioner) - Method in class org.apache.spark.api.java.JavaPairRDD
Perform a full outer join of this and other.
fullOuterJoin(JavaPairRDD<K, W>) - Method in class org.apache.spark.api.java.JavaPairRDD
Perform a full outer join of this and other.
fullOuterJoin(JavaPairRDD<K, W>, int) - Method in class org.apache.spark.api.java.JavaPairRDD
Perform a full outer join of this and other.
fullOuterJoin(RDD<Tuple2<K, W>>, Partitioner) - Method in class org.apache.spark.rdd.PairRDDFunctions
Perform a full outer join of this and other.
fullOuterJoin(RDD<Tuple2<K, W>>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Perform a full outer join of this and other.
fullOuterJoin(RDD<Tuple2<K, W>>, int) - Method in class org.apache.spark.rdd.PairRDDFunctions
Perform a full outer join of this and other.
fullOuterJoin(JavaPairDStream<K, W>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream.
fullOuterJoin(JavaPairDStream<K, W>, int) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream.
fullOuterJoin(JavaPairDStream<K, W>, Partitioner) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream.
fullOuterJoin(DStream<Tuple2<K, W>>, ClassTag<W>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream.
fullOuterJoin(DStream<Tuple2<K, W>>, int, ClassTag<W>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream.
fullOuterJoin(DStream<Tuple2<K, W>>, Partitioner, ClassTag<W>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream.
fullStackTrace() - Method in class org.apache.spark.ExceptionFailure
 
Function<T1,R> - Interface in org.apache.spark.api.java.function
Base interface for functions whose return types do not create special RDDs.
function(Function4<Time, KeyType, Option<ValueType>, State<StateType>, Option<MappedType>>) - Static method in class org.apache.spark.streaming.StateSpec
Create a StateSpec for setting all the specifications of the mapWithState operation on a pair DStream.
function(Function3<KeyType, Option<ValueType>, State<StateType>, MappedType>) - Static method in class org.apache.spark.streaming.StateSpec
Create a StateSpec for setting all the specifications of the mapWithState operation on a pair DStream.
function(Function4<Time, KeyType, Optional<ValueType>, State<StateType>, Optional<MappedType>>) - Static method in class org.apache.spark.streaming.StateSpec
Create a StateSpec for setting all the specifications of the mapWithState operation on a JavaPairDStream.
function(Function3<KeyType, Optional<ValueType>, State<StateType>, MappedType>) - Static method in class org.apache.spark.streaming.StateSpec
Create a StateSpec for setting all the specifications of the mapWithState operation on a JavaPairDStream.
Function0<R> - Interface in org.apache.spark.api.java.function
A zero-argument function that returns an R.
Function2<T1,T2,R> - Interface in org.apache.spark.api.java.function
A two-argument function that takes arguments of type T1 and T2 and returns an R.
Function3<T1,T2,T3,R> - Interface in org.apache.spark.api.java.function
A three-argument function that takes arguments of type T1, T2 and T3 and returns an R.
Function4<T1,T2,T3,T4,R> - Interface in org.apache.spark.api.java.function
A four-argument function that takes arguments of type T1, T2, T3 and T4 and returns an R.
functionRegistry() - Method in class org.apache.spark.sql.hive.HiveContext
 
functionRegistry() - Method in class org.apache.spark.sql.SQLContext
 
functions - Class in org.apache.spark.sql
 
functions() - Constructor for class org.apache.spark.sql.functions
 
FutureAction<T> - Interface in org.apache.spark
A future for the result of an action to support cancellation.
futureExecutionContext() - Static method in class org.apache.spark.rdd.AsyncRDDActions
 

G

gain() - Method in class org.apache.spark.ml.tree.InternalNode
 
gain() - Method in class org.apache.spark.mllib.tree.model.InformationGainStats
 
gamma1() - Method in class org.apache.spark.graphx.lib.SVDPlusPlus.Conf
 
gamma2() - Method in class org.apache.spark.graphx.lib.SVDPlusPlus.Conf
 
gamma6() - Method in class org.apache.spark.graphx.lib.SVDPlusPlus.Conf
 
gamma7() - Method in class org.apache.spark.graphx.lib.SVDPlusPlus.Conf
 
GammaGenerator - Class in org.apache.spark.mllib.random
:: DeveloperApi :: Generates i.i.d.
GammaGenerator(double, double) - Constructor for class org.apache.spark.mllib.random.GammaGenerator
 
gammaJavaRDD(JavaSparkContext, double, double, long, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
gammaJavaRDD(JavaSparkContext, double, double, long, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
gammaJavaRDD(JavaSparkContext, double, double, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
gammaJavaVectorRDD(JavaSparkContext, double, double, long, int, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
gammaJavaVectorRDD(JavaSparkContext, double, double, long, int, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
gammaJavaVectorRDD(JavaSparkContext, double, double, long, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
gammaRDD(SparkContext, double, double, long, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
Generates an RDD comprised of i.i.d. samples from the gamma distribution with the input shape and scale.
gammaShape() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
 
gammaShape() - Method in class org.apache.spark.mllib.clustering.LDAModel
Shape parameter for random initialization of variational parameter gamma.
gammaShape() - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
 
gammaVectorRDD(SparkContext, double, double, long, int, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the gamma distribution with the input shape and scale.
gaps() - Method in class org.apache.spark.ml.feature.RegexTokenizer
Indicates whether regex splits on gaps (true) or matches tokens (false).
GaussianMixture - Class in org.apache.spark.mllib.clustering
This class performs expectation maximization for multivariate Gaussian Mixture Models (GMMs).
GaussianMixture() - Constructor for class org.apache.spark.mllib.clustering.GaussianMixture
Constructs a default instance.
GaussianMixtureModel - Class in org.apache.spark.mllib.clustering
Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points are drawn from each Gaussian i=1..k with probability w(i); mu(i) and sigma(i) are the respective mean and covariance for each Gaussian distribution i=1..k.
GaussianMixtureModel(double[], MultivariateGaussian[]) - Constructor for class org.apache.spark.mllib.clustering.GaussianMixtureModel
 
gaussians() - Method in class org.apache.spark.mllib.clustering.GaussianMixtureModel
 
GBTClassificationModel - Class in org.apache.spark.ml.classification
:: Experimental :: Gradient-Boosted Trees (GBTs) model for classification.
GBTClassificationModel(String, DecisionTreeRegressionModel[], double[]) - Constructor for class org.apache.spark.ml.classification.GBTClassificationModel
Construct a GBTClassificationModel
GBTClassifier - Class in org.apache.spark.ml.classification
:: Experimental :: Gradient-Boosted Trees (GBTs) learning algorithm for classification.
GBTClassifier(String) - Constructor for class org.apache.spark.ml.classification.GBTClassifier
 
GBTClassifier() - Constructor for class org.apache.spark.ml.classification.GBTClassifier
 
GBTRegressionModel - Class in org.apache.spark.ml.regression
:: Experimental ::
GBTRegressionModel(String, DecisionTreeRegressionModel[], double[]) - Constructor for class org.apache.spark.ml.regression.GBTRegressionModel
Construct a GBTRegressionModel
GBTRegressor - Class in org.apache.spark.ml.regression
:: Experimental :: Gradient-Boosted Trees (GBTs) learning algorithm for regression.
GBTRegressor(String) - Constructor for class org.apache.spark.ml.regression.GBTRegressor
 
GBTRegressor() - Constructor for class org.apache.spark.ml.regression.GBTRegressor
 
GeneralizedLinearAlgorithm<M extends GeneralizedLinearModel> - Class in org.apache.spark.mllib.regression
:: DeveloperApi :: GeneralizedLinearAlgorithm implements methods to train a Generalized Linear Model (GLM).
GeneralizedLinearAlgorithm() - Constructor for class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
 
GeneralizedLinearModel - Class in org.apache.spark.mllib.regression
:: DeveloperApi :: GeneralizedLinearModel (GLM) represents a model trained using GeneralizedLinearAlgorithm.
GeneralizedLinearModel(Vector, double) - Constructor for class org.apache.spark.mllib.regression.GeneralizedLinearModel
 
generateAssociationRules(double) - Method in class org.apache.spark.mllib.fpm.FPGrowthModel
Generates association rules for the Items in freqItemsets.
generatedRDDs() - Method in class org.apache.spark.streaming.dstream.DStream
 
generateKMeansRDD(SparkContext, int, int, int, double, int) - Static method in class org.apache.spark.mllib.util.KMeansDataGenerator
Generate an RDD containing test data for KMeans.
generateLinearInput(double, double[], int, int, double) - Static method in class org.apache.spark.mllib.util.LinearDataGenerator
For compatibility, the generated data without specifying the mean and variance will have zero mean and variance of (1.0/3.0) since the original output range is [-1, 1] with uniform distribution, and the variance of uniform distribution is (b - a)^2^ / 12 which will be (1.0/3.0)
generateLinearInput(double, double[], double[], double[], int, int, double) - Static method in class org.apache.spark.mllib.util.LinearDataGenerator
 
generateLinearInput(double, double[], double[], double[], int, int, double, double) - Static method in class org.apache.spark.mllib.util.LinearDataGenerator
 
generateLinearInputAsList(double, double[], int, int, double) - Static method in class org.apache.spark.mllib.util.LinearDataGenerator
Return a Java List of synthetic data randomly generated according to a multi collinear model.
generateLinearRDD(SparkContext, int, int, double, int, double) - Static method in class org.apache.spark.mllib.util.LinearDataGenerator
Generate an RDD containing sample data for Linear Regression models - including Ridge, Lasso, and uregularized variants.
generateLogisticRDD(SparkContext, int, int, double, int, double) - Static method in class org.apache.spark.mllib.util.LogisticRegressionDataGenerator
Generate an RDD containing test data for LogisticRegression.
generateRandomEdges(int, int, int, long) - Static method in class org.apache.spark.graphx.util.GraphGenerators
 
geq(Object) - Method in class org.apache.spark.sql.Column
Greater than or equal to an expression.
get() - Method in interface org.apache.spark.FutureAction
Blocks and returns the result of this job.
get(Param<T>) - Method in class org.apache.spark.ml.param.ParamMap
Optionally returns the value associated with a param.
get(Param<T>) - Method in interface org.apache.spark.ml.param.Params
 
get(String) - Method in class org.apache.spark.SparkConf
Get a parameter; throws a NoSuchElementException if it's not set
get(String, String) - Method in class org.apache.spark.SparkConf
Get a parameter, falling back to a default if not set
get() - Static method in class org.apache.spark.SparkEnv
Returns the SparkEnv.
get(String) - Static method in class org.apache.spark.SparkFiles
Get the absolute path of a file added through SparkContext.addFile().
get(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i.
get() - Method in class org.apache.spark.streaming.State
Get the state if it exists, otherwise it will throw java.util.NoSuchElementException.
get() - Static method in class org.apache.spark.TaskContext
Return the currently active TaskContext.
get_json_object(Column, String) - Static method in class org.apache.spark.sql.functions
Extracts json object from a json string based on json path specified, and returns json string of the extracted json object.
getActive() - Static method in class org.apache.spark.streaming.StreamingContext
:: Experimental ::
getActiveJobIds() - Method in class org.apache.spark.api.java.JavaSparkStatusTracker
Returns an array containing the ids of all active jobs.
getActiveJobIds() - Method in class org.apache.spark.SparkStatusTracker
Returns an array containing the ids of all active jobs.
getActiveOrCreate(Function0<StreamingContext>) - Static method in class org.apache.spark.streaming.StreamingContext
:: Experimental ::
getActiveOrCreate(String, Function0<StreamingContext>, Configuration, boolean) - Static method in class org.apache.spark.streaming.StreamingContext
:: Experimental ::
getActiveStageIds() - Method in class org.apache.spark.api.java.JavaSparkStatusTracker
Returns an array containing the ids of all active stages.
getActiveStageIds() - Method in class org.apache.spark.SparkStatusTracker
Returns an array containing the ids of all active stages.
getAkkaConf() - Method in class org.apache.spark.SparkConf
Get all akka conf variables set on this SparkConf
getAlgo() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
getAll() - Method in class org.apache.spark.SparkConf
Get all parameters as a list of pairs
getAllConfs() - Method in class org.apache.spark.sql.SQLContext
Return all the configuration properties that have been set (i.e.
getAllPools() - Method in class org.apache.spark.SparkContext
:: DeveloperApi :: Return pools for fair scheduler
getAlpha() - Method in class org.apache.spark.mllib.clustering.LDA
Alias for getDocConcentration
getAnyValAs(int) - Method in interface org.apache.spark.sql.Row
Returns the value of a given fieldName.
getAppId() - Method in interface org.apache.spark.launcher.SparkAppHandle
Returns the application ID, or null if not yet known.
getAppId() - Method in class org.apache.spark.SparkConf
Returns the Spark application id, valid in the Driver after TaskScheduler registration and from the start in the Executor.
getAs(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i.
getAs(String) - Method in interface org.apache.spark.sql.Row
Returns the value of a given fieldName.
getAsymmetricAlpha() - Method in class org.apache.spark.mllib.clustering.LDA
Alias for getAsymmetricDocConcentration
getAsymmetricDocConcentration() - Method in class org.apache.spark.mllib.clustering.LDA
Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").
getAttr(String) - Method in class org.apache.spark.ml.attribute.AttributeGroup
Gets an attribute by its name.
getAttr(int) - Method in class org.apache.spark.ml.attribute.AttributeGroup
Gets an attribute by its index.
getAvroSchema() - Method in class org.apache.spark.SparkConf
Gets all the avro schemas in the configuration used in the generic Avro record serializer
getBeta() - Method in class org.apache.spark.mllib.clustering.LDA
Alias for getTopicConcentration
getBlock(BlockId) - Method in class org.apache.spark.storage.StorageStatus
Return the given block stored in this block manager in O(1) time.
getBoolean(String, boolean) - Method in class org.apache.spark.SparkConf
Get a parameter as a boolean, falling back to a default if not set
getBoolean(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i as a primitive boolean.
getBoolean(String) - Method in class org.apache.spark.sql.types.Metadata
Gets a Boolean.
getBooleanArray(String) - Method in class org.apache.spark.sql.types.Metadata
Gets a Boolean array.
getByte(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i as a primitive byte.
getCachedBlockManagerId(BlockManagerId) - Static method in class org.apache.spark.storage.BlockManagerId
 
getCachedMetadata(String) - Static method in class org.apache.spark.rdd.HadoopRDD
The three methods below are helpers for accessing the local map, a property of the SparkEnv of the local process.
getCaseSensitive() - Method in class org.apache.spark.ml.feature.StopWordsRemover
 
getCatalystType(int, String, int, MetadataBuilder) - Method in class org.apache.spark.sql.jdbc.AggregatedDialect
 
getCatalystType(int, String, int, MetadataBuilder) - Static method in class org.apache.spark.sql.jdbc.DerbyDialect
 
getCatalystType(int, String, int, MetadataBuilder) - Method in class org.apache.spark.sql.jdbc.JdbcDialect
Get the custom datatype mapping for the given jdbc meta information.
getCatalystType(int, String, int, MetadataBuilder) - Static method in class org.apache.spark.sql.jdbc.MsSqlServerDialect
 
getCatalystType(int, String, int, MetadataBuilder) - Static method in class org.apache.spark.sql.jdbc.MySQLDialect
 
getCatalystType(int, String, int, MetadataBuilder) - Static method in class org.apache.spark.sql.jdbc.OracleDialect
 
getCatalystType(int, String, int, MetadataBuilder) - Static method in class org.apache.spark.sql.jdbc.PostgresDialect
 
getCategoricalFeaturesInfo() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
getCheckpointDir() - Method in class org.apache.spark.api.java.JavaSparkContext
 
getCheckpointDir() - Method in class org.apache.spark.SparkContext
 
getCheckpointFile() - Method in interface org.apache.spark.api.java.JavaRDDLike
Gets the name of the file to which this RDD was checkpointed
getCheckpointFile() - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
 
getCheckpointFile() - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
getCheckpointFile() - Method in class org.apache.spark.rdd.RDD
Gets the name of the directory to which this RDD was checkpointed.
getCheckpointFiles() - Method in class org.apache.spark.graphx.Graph
Gets the name of the files to which this Graph was checkpointed.
getCheckpointFiles() - Method in class org.apache.spark.graphx.impl.GraphImpl
 
getCheckpointInterval() - Method in class org.apache.spark.mllib.clustering.LDA
Period (in iterations) between checkpoints.
getCheckpointInterval() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
getConf() - Method in class org.apache.spark.api.java.JavaSparkContext
Return a copy of this JavaSparkContext's configuration.
getConf() - Method in class org.apache.spark.rdd.HadoopRDD
 
getConf() - Method in class org.apache.spark.rdd.NewHadoopRDD
 
getConf() - Method in class org.apache.spark.SparkContext
Return a copy of this SparkContext's configuration.
getConf(String) - Method in class org.apache.spark.sql.SQLContext
Return the value of Spark SQL configuration property for the given key.
getConf(String, String) - Method in class org.apache.spark.sql.SQLContext
Return the value of Spark SQL configuration property for the given key.
getConnection() - Method in interface org.apache.spark.rdd.JdbcRDD.ConnectionFactory
 
getConvergenceTol() - Method in class org.apache.spark.mllib.clustering.GaussianMixture
Return the largest change in log-likelihood at which convergence is considered to have occurred.
getDate(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i of date type as java.sql.Date.
getDecimal(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i of decimal type as java.math.BigDecimal.
getDefault(Param<T>) - Method in interface org.apache.spark.ml.param.Params
Gets the default value of a parameter.
getDegree() - Method in class org.apache.spark.ml.feature.PolynomialExpansion
 
getDependencies() - Method in class org.apache.spark.rdd.CoGroupedRDD
 
getDependencies() - Method in class org.apache.spark.rdd.RDD
Implemented by subclasses to return how this RDD depends on parent RDDs.
getDependencies() - Method in class org.apache.spark.rdd.ShuffledRDD
 
getDependencies() - Method in class org.apache.spark.rdd.UnionRDD
 
getDeprecatedConfig(String, SparkConf) - Static method in class org.apache.spark.SparkConf
Looks for available deprecated keys for the given config option, and return the first value available.
getDocConcentration() - Method in class org.apache.spark.mllib.clustering.LDA
Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").
getDouble(String, double) - Method in class org.apache.spark.SparkConf
Get a parameter as a double, falling back to a default if not set
getDouble(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i as a primitive double.
getDouble(String) - Method in class org.apache.spark.sql.types.Metadata
Gets a Double.
getDoubleArray(String) - Method in class org.apache.spark.sql.types.Metadata
Gets a Double array.
getEpsilon() - Method in class org.apache.spark.mllib.clustering.KMeans
The distance threshold within which we've consider centers to have converged.
getExecutorEnv() - Method in class org.apache.spark.SparkConf
Get all executor environment variables set on this SparkConf
getExecutorMemoryStatus() - Method in class org.apache.spark.SparkContext
Return a map from the slave to the max memory available for caching and the remaining memory available for caching.
getExecutorStorageStatus() - Method in class org.apache.spark.SparkContext
:: DeveloperApi :: Return information about blocks stored in all of the slaves
getField(String) - Method in class org.apache.spark.sql.Column
An expression that gets a field by name in a StructType.
getFinalValue() - Method in class org.apache.spark.partial.PartialResult
Blocking method to wait for and return the final value.
getFloat(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i as a primitive float.
getFormula() - Method in class org.apache.spark.ml.feature.RFormula
 
getGaps() - Method in class org.apache.spark.ml.feature.RegexTokenizer
 
getImpurity() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
getIndices() - Method in class org.apache.spark.ml.feature.VectorSlicer
 
getInitializationMode() - Method in class org.apache.spark.mllib.clustering.KMeans
The initialization algorithm.
getInitializationSteps() - Method in class org.apache.spark.mllib.clustering.KMeans
Number of steps for the k-means|| initialization mode
getInitialModel() - Method in class org.apache.spark.mllib.clustering.GaussianMixture
Return the user supplied initial GMM, if supplied
getInitialPositionInStream(int) - Method in class org.apache.spark.streaming.kinesis.KinesisUtilsPythonHelper
 
getInputFormat(JobConf) - Method in class org.apache.spark.rdd.HadoopRDD
 
getInt(String, int) - Method in class org.apache.spark.SparkConf
Get a parameter as an integer, falling back to a default if not set
getInt(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i as a primitive int.
getInverse() - Method in class org.apache.spark.ml.feature.DCT
 
getItem(Object) - Method in class org.apache.spark.sql.Column
An expression that gets an item at position ordinal out of an array, or gets a value by key key in a MapType.
getJavaMap(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i of array type as a Map.
getJDBCType(DataType) - Method in class org.apache.spark.sql.jdbc.AggregatedDialect
 
getJDBCType(DataType) - Static method in class org.apache.spark.sql.jdbc.DB2Dialect
 
getJDBCType(DataType) - Static method in class org.apache.spark.sql.jdbc.DerbyDialect
 
getJDBCType(DataType) - Method in class org.apache.spark.sql.jdbc.JdbcDialect
Retrieve the jdbc / sql type for a given datatype.
getJDBCType(DataType) - Static method in class org.apache.spark.sql.jdbc.MsSqlServerDialect
 
getJDBCType(DataType) - Static method in class org.apache.spark.sql.jdbc.PostgresDialect
 
getJobConf() - Method in class org.apache.spark.rdd.HadoopRDD
 
getJobIdsForGroup(String) - Method in class org.apache.spark.api.java.JavaSparkStatusTracker
Return a list of all known jobs in a particular job group.
getJobIdsForGroup(String) - Method in class org.apache.spark.SparkStatusTracker
Return a list of all known jobs in a particular job group.
getJobInfo(int) - Method in class org.apache.spark.api.java.JavaSparkStatusTracker
Returns job information, or null if the job info could not be found or was garbage collected.
getJobInfo(int) - Method in class org.apache.spark.SparkStatusTracker
Returns job information, or None if the job info could not be found or was garbage collected.
getK() - Method in class org.apache.spark.mllib.clustering.BisectingKMeans
Gets the desired number of leaf clusters.
getK() - Method in class org.apache.spark.mllib.clustering.GaussianMixture
Return the number of Gaussians in the mixture model
getK() - Method in class org.apache.spark.mllib.clustering.KMeans
Number of clusters to create (k).
getK() - Method in class org.apache.spark.mllib.clustering.LDA
Number of topics to infer.
getKappa() - Method in class org.apache.spark.mllib.clustering.OnlineLDAOptimizer
Learning rate: exponential decay rate
getLabels() - Method in class org.apache.spark.ml.feature.IndexToString
 
getLambda() - Method in class org.apache.spark.mllib.classification.NaiveBayes
 
getLDAModel(double[]) - Method in interface org.apache.spark.mllib.clustering.LDAOptimizer
 
getLearningRate() - Method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
getLeastGroupHash(String) - Method in class org.apache.spark.rdd.PartitionCoalescer
Sorts and gets the least element of the list associated with key in groupHash The returned PartitionGroup is the least loaded of all groups that represent the machine "key"
getList(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i of array type as List.
getLocalProperty(String) - Method in class org.apache.spark.api.java.JavaSparkContext
Get a local property set in this thread, or null if it is missing.
getLocalProperty(String) - Method in class org.apache.spark.SparkContext
Get a local property set in this thread, or null if it is missing.
getLong(String, long) - Method in class org.apache.spark.SparkConf
Get a parameter as a long, falling back to a default if not set
getLong(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i as a primitive long.
getLong(String) - Method in class org.apache.spark.sql.types.Metadata
Gets a Long.
getLongArray(String) - Method in class org.apache.spark.sql.types.Metadata
Gets a Long array.
getLoss() - Method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
getLossType() - Method in class org.apache.spark.ml.classification.GBTClassifier
 
getLossType() - Method in class org.apache.spark.ml.regression.GBTRegressor
 
getMap(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i of map type as a Scala Map.
getMap() - Method in class org.apache.spark.sql.types.MetadataBuilder
Returns the immutable version of this map.
getMaxBins() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
getMaxDepth() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
getMaxIterations() - Method in class org.apache.spark.mllib.clustering.BisectingKMeans
Gets the max number of k-means iterations to split clusters.
getMaxIterations() - Method in class org.apache.spark.mllib.clustering.GaussianMixture
Return the maximum number of iterations to run
getMaxIterations() - Method in class org.apache.spark.mllib.clustering.KMeans
Maximum number of iterations to run.
getMaxIterations() - Method in class org.apache.spark.mllib.clustering.LDA
Maximum number of iterations for learning.
getMaxLocalProjDBSize() - Method in class org.apache.spark.mllib.fpm.PrefixSpan
Gets the maximum number of items allowed in a projected database before local processing.
getMaxMemoryInMB() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
getMaxPatternLength() - Method in class org.apache.spark.mllib.fpm.PrefixSpan
Gets the maximal pattern length (i.e.
getMessage() - Method in exception org.apache.spark.sql.AnalysisException
 
getMetadata(String) - Method in class org.apache.spark.sql.types.Metadata
Gets a Metadata.
getMetadataArray(String) - Method in class org.apache.spark.sql.types.Metadata
Gets a Metadata array.
getMetricName() - Method in class org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
 
getMetricName() - Method in class org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
 
getMetricName() - Method in class org.apache.spark.ml.evaluation.RegressionEvaluator
 
getMetricsSources(String) - Method in class org.apache.spark.TaskContext
::DeveloperApi:: Returns all metrics sources with the given name which are associated with the instance which runs the task.
getMinDivisibleClusterSize() - Method in class org.apache.spark.mllib.clustering.BisectingKMeans
Gets the minimum number of points (if >= 1.0) or the minimum proportion of points (if < 1.0) of a divisible cluster.
getMiniBatchFraction() - Method in class org.apache.spark.mllib.clustering.OnlineLDAOptimizer
Mini-batch fraction, which sets the fraction of document sampled and used in each iteration
getMinInfoGain() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
getMinInstancesPerNode() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
getMinSupport() - Method in class org.apache.spark.mllib.fpm.PrefixSpan
Get the minimal support (i.e.
getMinTokenLength() - Method in class org.apache.spark.ml.feature.RegexTokenizer
 
getModel() - Method in class org.apache.spark.ml.clustering.DistributedLDAModel
 
getModel() - Method in class org.apache.spark.ml.clustering.LDAModel
Returns underlying spark.mllib model, which may be local or distributed
getModel() - Method in class org.apache.spark.ml.clustering.LocalLDAModel
 
getModelType() - Method in class org.apache.spark.mllib.classification.NaiveBayes
 
getN() - Method in class org.apache.spark.ml.feature.NGram
 
getNames() - Method in class org.apache.spark.ml.feature.VectorSlicer
 
getNode(int, Node) - Static method in class org.apache.spark.mllib.tree.model.Node
Traces down from a root node to get the node with the given node index.
getNumClasses() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
getNumFeatures() - Method in class org.apache.spark.ml.feature.HashingTF
 
getNumFeatures() - Method in class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
The dimension of training features.
getNumIterations() - Method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
getNumPartitions() - Method in interface org.apache.spark.api.java.JavaRDDLike
Return the number of partitions in this RDD.
getNumPartitions() - Method in class org.apache.spark.rdd.RDD
Returns the number of partitions of this RDD.
getNumValues() - Method in class org.apache.spark.ml.attribute.NominalAttribute
Get the number of values, either from numValues or from values.
getOldDataset(DataFrame, String) - Static method in class org.apache.spark.ml.clustering.LDA
Get dataset for spark.mllib LDA
getOptimizeDocConcentration() - Method in class org.apache.spark.mllib.clustering.OnlineLDAOptimizer
Optimize docConcentration, indicates whether docConcentration (Dirichlet parameter for document-topic distribution) will be optimized during training.
getOptimizer() - Method in class org.apache.spark.mllib.clustering.LDA
:: DeveloperApi ::
getOption(String) - Method in class org.apache.spark.SparkConf
Get a parameter as an Option
getOption() - Method in class org.apache.spark.streaming.State
Get the state as an Option.
getOrCreate(SparkConf) - Static method in class org.apache.spark.SparkContext
This function may be used to get or instantiate a SparkContext and register it as a singleton object.
getOrCreate() - Static method in class org.apache.spark.SparkContext
This function may be used to get or instantiate a SparkContext and register it as a singleton object.
getOrCreate(SparkContext) - Static method in class org.apache.spark.sql.SQLContext
Get the singleton SQLContext if it exists or create a new one using the given SparkContext.
getOrCreate(String, JavaStreamingContextFactory) - Static method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Deprecated.
As of 1.4.0, replaced by getOrCreate without JavaStreamingContextFactor.
getOrCreate(String, Configuration, JavaStreamingContextFactory) - Static method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Deprecated.
As of 1.4.0, replaced by getOrCreate without JavaStreamingContextFactor.
getOrCreate(String, Configuration, JavaStreamingContextFactory, boolean) - Static method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Deprecated.
As of 1.4.0, replaced by getOrCreate without JavaStreamingContextFactor.
getOrCreate(String, Function0<JavaStreamingContext>) - Static method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.
getOrCreate(String, Function0<JavaStreamingContext>, Configuration) - Static method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.
getOrCreate(String, Function0<JavaStreamingContext>, Configuration, boolean) - Static method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.
getOrCreate(String, Function0<StreamingContext>, Configuration, boolean) - Static method in class org.apache.spark.streaming.StreamingContext
Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.
getOrDefault(Param<T>) - Method in interface org.apache.spark.ml.param.Params
Gets the value of a param in the embedded param map or its default value.
getOrElse(Param<T>, T) - Method in class org.apache.spark.ml.param.ParamMap
Returns the value associated with a param or a default value.
getP() - Method in class org.apache.spark.ml.feature.Normalizer
 
getParam(String) - Method in interface org.apache.spark.ml.param.Params
 
getParents(int) - Method in class org.apache.spark.NarrowDependency
Get the parent partitions for a child partition.
getParents(int) - Method in class org.apache.spark.OneToOneDependency
 
getParents(int) - Method in class org.apache.spark.RangeDependency
 
getPartition(long, long, int) - Method in class org.apache.spark.graphx.PartitionStrategy.CanonicalRandomVertexCut$
 
getPartition(long, long, int) - Method in class org.apache.spark.graphx.PartitionStrategy.EdgePartition1D$
 
getPartition(long, long, int) - Method in class org.apache.spark.graphx.PartitionStrategy.EdgePartition2D$
 
getPartition(long, long, int) - Method in interface org.apache.spark.graphx.PartitionStrategy
Returns the partition number for a given edge.
getPartition(long, long, int) - Method in class org.apache.spark.graphx.PartitionStrategy.RandomVertexCut$
 
getPartition(Object) - Method in class org.apache.spark.HashPartitioner
 
getPartition(Object) - Method in class org.apache.spark.Partitioner
 
getPartition(Object) - Method in class org.apache.spark.RangePartitioner
 
getPartitionId() - Static method in class org.apache.spark.TaskContext
Returns the partition id of currently active TaskContext.
getPartitions() - Method in class org.apache.spark.api.r.BaseRRDD
 
getPartitions() - Method in class org.apache.spark.graphx.EdgeRDD
 
getPartitions() - Method in class org.apache.spark.graphx.VertexRDD
 
getPartitions() - Method in class org.apache.spark.rdd.CoGroupedRDD
 
getPartitions() - Method in class org.apache.spark.rdd.HadoopRDD
 
getPartitions() - Method in class org.apache.spark.rdd.JdbcRDD
 
getPartitions() - Method in class org.apache.spark.rdd.NewHadoopRDD
 
getPartitions() - Method in class org.apache.spark.rdd.PartitionCoalescer
 
getPartitions() - Method in class org.apache.spark.rdd.PartitionPruningRDD
 
getPartitions() - Method in class org.apache.spark.rdd.RDD
Implemented by subclasses to return the set of partitions in this RDD.
getPartitions() - Method in class org.apache.spark.rdd.ShuffledRDD
 
getPartitions() - Method in class org.apache.spark.rdd.UnionRDD
 
getPath() - Method in class org.apache.spark.input.PortableDataStream
 
getPattern() - Method in class org.apache.spark.ml.feature.RegexTokenizer
 
getPersistentRDDs() - Method in class org.apache.spark.SparkContext
Returns an immutable map of RDDs that have marked themselves as persistent via cache() call.
getPoolForName(String) - Method in class org.apache.spark.SparkContext
:: DeveloperApi :: Return the pool associated with the given name, if one exists
getPreferredLocations(Partition) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
getPreferredLocations(Partition) - Method in class org.apache.spark.rdd.HadoopRDD
 
getPreferredLocations(Partition) - Method in class org.apache.spark.rdd.NewHadoopRDD
 
getPreferredLocations(Partition) - Method in class org.apache.spark.rdd.RDD
Optionally overridden by subclasses to specify placement preferences.
getPreferredLocations(Partition) - Method in class org.apache.spark.rdd.ShuffledRDD
 
getPreferredLocations(Partition) - Method in class org.apache.spark.rdd.UnionRDD
 
getQuantileCalculationStrategy() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
getRDDStorageInfo() - Method in class org.apache.spark.SparkContext
:: DeveloperApi :: Return information about what RDDs are cached, if they are in mem or on disk, how much space they take, etc.
getReceiver() - Method in class org.apache.spark.streaming.dstream.ReceiverInputDStream
Gets the receiver object that will be sent to the worker nodes to receive data.
getRootDirectory() - Static method in class org.apache.spark.SparkFiles
Get the root directory that contains files added through SparkContext.addFile().
getRuns() - Method in class org.apache.spark.mllib.clustering.KMeans
:: Experimental :: Number of runs of the algorithm to execute in parallel.
getScalingVec() - Method in class org.apache.spark.ml.feature.ElementwiseProduct
 
getSchedulingMode() - Method in class org.apache.spark.SparkContext
Return current scheduling mode
getSchema(Class<?>) - Method in class org.apache.spark.sql.SQLContext
 
getSeed() - Method in class org.apache.spark.mllib.clustering.BisectingKMeans
Gets the random seed.
getSeed() - Method in class org.apache.spark.mllib.clustering.GaussianMixture
Return the random seed
getSeed() - Method in class org.apache.spark.mllib.clustering.KMeans
The random seed for cluster initialization.
getSeed() - Method in class org.apache.spark.mllib.clustering.LDA
Random seed
getSeq(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i of array type as a Scala Seq.
getSerializer(Serializer) - Static method in class org.apache.spark.serializer.Serializer
 
getSerializer(Option<Serializer>) - Static method in class org.apache.spark.serializer.Serializer
 
getShort(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i as a primitive short.
getSizeAsBytes(String) - Method in class org.apache.spark.SparkConf
Get a size parameter as bytes; throws a NoSuchElementException if it's not set.
getSizeAsBytes(String, String) - Method in class org.apache.spark.SparkConf
Get a size parameter as bytes, falling back to a default if not set.
getSizeAsBytes(String, long) - Method in class org.apache.spark.SparkConf
Get a size parameter as bytes, falling back to a default if not set.
getSizeAsGb(String) - Method in class org.apache.spark.SparkConf
Get a size parameter as Gibibytes; throws a NoSuchElementException if it's not set.
getSizeAsGb(String, String) - Method in class org.apache.spark.SparkConf
Get a size parameter as Gibibytes, falling back to a default if not set.
getSizeAsKb(String) - Method in class org.apache.spark.SparkConf
Get a size parameter as Kibibytes; throws a NoSuchElementException if it's not set.
getSizeAsKb(String, String) - Method in class org.apache.spark.SparkConf
Get a size parameter as Kibibytes, falling back to a default if not set.
getSizeAsMb(String) - Method in class org.apache.spark.SparkConf
Get a size parameter as Mebibytes; throws a NoSuchElementException if it's not set.
getSizeAsMb(String, String) - Method in class org.apache.spark.SparkConf
Get a size parameter as Mebibytes, falling back to a default if not set.
getSparkHome() - Method in class org.apache.spark.api.java.JavaSparkContext
Get Spark's home location from either a value set through the constructor, or the spark.home Java property, or the SPARK_HOME environment variable (in that order of preference).
getSplits() - Method in class org.apache.spark.ml.feature.Bucketizer
 
getSQLDialect() - Method in class org.apache.spark.sql.hive.HiveContext
 
getSQLDialect() - Method in class org.apache.spark.sql.SQLContext
 
getStageInfo(int) - Method in class org.apache.spark.api.java.JavaSparkStatusTracker
Returns stage information, or null if the stage info could not be found or was garbage collected.
getStageInfo(int) - Method in class org.apache.spark.SparkStatusTracker
Returns stage information, or None if the stage info could not be found or was garbage collected.
getStages() - Method in class org.apache.spark.ml.Pipeline
 
getState() - Method in interface org.apache.spark.launcher.SparkAppHandle
Returns the current application state.
getState() - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
:: DeveloperApi ::
getState() - Method in class org.apache.spark.streaming.StreamingContext
:: DeveloperApi ::
getStatement() - Method in class org.apache.spark.ml.feature.SQLTransformer
 
getStopWords() - Method in class org.apache.spark.ml.feature.StopWordsRemover
 
getStorageLevel() - Method in interface org.apache.spark.api.java.JavaRDDLike
Get the RDD's current storage level, or StorageLevel.NONE if none is set.
getStorageLevel() - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
 
getStorageLevel() - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
getStorageLevel() - Method in class org.apache.spark.rdd.RDD
Get the RDD's current storage level, or StorageLevel.NONE if none is set.
getString(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i as a String object.
getString(String) - Method in class org.apache.spark.sql.types.Metadata
Gets a String.
getStringArray(String) - Method in class org.apache.spark.sql.types.Metadata
Gets a String array.
getStruct(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i of struct type as an Row object.
getSubsamplingRate() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
getTableExistsQuery(String) - Method in class org.apache.spark.sql.jdbc.JdbcDialect
Get the SQL query that should be used to find if the given table exists.
getTableExistsQuery(String) - Static method in class org.apache.spark.sql.jdbc.MySQLDialect
 
getTableExistsQuery(String) - Static method in class org.apache.spark.sql.jdbc.PostgresDialect
 
getTau0() - Method in class org.apache.spark.mllib.clustering.OnlineLDAOptimizer
A (positive) learning parameter that downweights early iterations.
getThreadLocal() - Static method in class org.apache.spark.SparkEnv
Returns the ThreadLocal SparkEnv.
getThreshold() - Method in class org.apache.spark.ml.classification.LogisticRegression
 
getThreshold() - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
getThreshold() - Method in class org.apache.spark.ml.feature.Binarizer
 
getThreshold() - Method in class org.apache.spark.mllib.classification.LogisticRegressionModel
Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
getThreshold() - Method in class org.apache.spark.mllib.classification.SVMModel
Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
getThresholds() - Method in class org.apache.spark.ml.classification.LogisticRegression
 
getThresholds() - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
getTimeAsMs(String) - Method in class org.apache.spark.SparkConf
Get a time parameter as milliseconds; throws a NoSuchElementException if it's not set.
getTimeAsMs(String, String) - Method in class org.apache.spark.SparkConf
Get a time parameter as milliseconds, falling back to a default if not set.
getTimeAsSeconds(String) - Method in class org.apache.spark.SparkConf
Get a time parameter as seconds; throws a NoSuchElementException if it's not set.
getTimeAsSeconds(String, String) - Method in class org.apache.spark.SparkConf
Get a time parameter as seconds, falling back to a default if not set.
getTimestamp(int) - Method in interface org.apache.spark.sql.Row
Returns the value at position i of date type as java.sql.Timestamp.
gettingResult() - Method in class org.apache.spark.scheduler.TaskInfo
 
gettingResultTime() - Method in class org.apache.spark.scheduler.TaskInfo
The time when the task started remotely getting the result.
getToLowercase() - Method in class org.apache.spark.ml.feature.RegexTokenizer
 
getTopicConcentration() - Method in class org.apache.spark.mllib.clustering.LDA
Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms.
getTreeStrategy() - Method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
getUseNodeIdCache() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
getValidationTol() - Method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
getValue() - Method in class org.apache.spark.broadcast.Broadcast
Actually get the broadcasted value.
getValue(int) - Method in class org.apache.spark.ml.attribute.NominalAttribute
Gets a value given its index.
getValuesMap(Seq<String>) - Method in interface org.apache.spark.sql.Row
Returns a Map(name -> value) for the requested fieldNames For primitive types if value is null it returns 'zero value' specific for primitive ie.
getVectors() - Method in class org.apache.spark.ml.feature.Word2VecModel
Returns a dataframe with two fields, "word" and "vector", with "word" being a String and and the vector the DenseVector that it is mapped to.
getVectors() - Method in class org.apache.spark.mllib.feature.Word2VecModel
 
Gini - Class in org.apache.spark.mllib.tree.impurity
:: Experimental :: Class for calculating the Gini impurity during binary classification.
Gini() - Constructor for class org.apache.spark.mllib.tree.impurity.Gini
 
globalTopicTotals() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
 
globalTopicTotals() - Method in class org.apache.spark.mllib.clustering.EMLDAOptimizer
Aggregate distributions over topics from all term vertices.
glom() - Method in interface org.apache.spark.api.java.JavaRDDLike
Return an RDD created by coalescing all elements within each partition into an array.
glom() - Method in class org.apache.spark.rdd.RDD
Return an RDD created by coalescing all elements within each partition into an array.
glom() - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD is generated by applying glom() to each RDD of this DStream.
glom() - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream in which each RDD is generated by applying glom() to each RDD of this DStream.
gradient() - Method in class org.apache.spark.ml.classification.LogisticAggregator
 
gradient() - Method in class org.apache.spark.ml.regression.AFTAggregator
 
gradient() - Method in class org.apache.spark.ml.regression.LeastSquaresAggregator
 
Gradient - Class in org.apache.spark.mllib.optimization
:: DeveloperApi :: Class used to compute the gradient for a loss function, given a single data point.
Gradient() - Constructor for class org.apache.spark.mllib.optimization.Gradient
 
gradient(double, double) - Static method in class org.apache.spark.mllib.tree.loss.AbsoluteError
Method to calculate the gradients for the gradient boosting calculation for least absolute error calculation.
gradient(double, double) - Static method in class org.apache.spark.mllib.tree.loss.LogLoss
Method to calculate the loss gradients for the gradient boosting calculation for binary classification The gradient with respect to F(x) is: - 4 y / (1 + exp(2 y F(x)))
gradient(double, double) - Method in interface org.apache.spark.mllib.tree.loss.Loss
Method to calculate the gradients for the gradient boosting calculation.
gradient(double, double) - Static method in class org.apache.spark.mllib.tree.loss.SquaredError
Method to calculate the gradients for the gradient boosting calculation for least squares error calculation.
GradientBoostedTrees - Class in org.apache.spark.mllib.tree
A class that implements Stochastic Gradient Boosting for regression and binary classification.
GradientBoostedTrees(BoostingStrategy) - Constructor for class org.apache.spark.mllib.tree.GradientBoostedTrees
 
GradientBoostedTreesModel - Class in org.apache.spark.mllib.tree.model
Represents a gradient boosted trees model.
GradientBoostedTreesModel(Enumeration.Value, DecisionTreeModel[], double[]) - Constructor for class org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
 
GradientDescent - Class in org.apache.spark.mllib.optimization
Class used to solve an optimization problem using Gradient Descent.
Graph<VD,ED> - Class in org.apache.spark.graphx
The Graph abstractly represents a graph with arbitrary objects associated with vertices and edges.
Graph(ClassTag<VD>, ClassTag<ED>) - Constructor for class org.apache.spark.graphx.Graph
 
graph() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
 
graph() - Method in class org.apache.spark.mllib.clustering.EMLDAOptimizer
The following fields will only be initialized through the initialize() method
graph() - Method in class org.apache.spark.streaming.dstream.DStream
 
graph() - Method in class org.apache.spark.streaming.StreamingContext
 
GraphGenerators - Class in org.apache.spark.graphx.util
A collection of graph generating functions.
GraphGenerators() - Constructor for class org.apache.spark.graphx.util.GraphGenerators
 
GraphImpl<VD,ED> - Class in org.apache.spark.graphx.impl
An implementation of Graph to support computation on graphs.
GraphImpl(VertexRDD<VD>, ReplicatedVertexView<VD, ED>, ClassTag<VD>, ClassTag<ED>) - Constructor for class org.apache.spark.graphx.impl.GraphImpl
 
GraphImpl(ClassTag<VD>, ClassTag<ED>) - Constructor for class org.apache.spark.graphx.impl.GraphImpl
Default constructor is provided to support serialization
GraphKryoRegistrator - Class in org.apache.spark.graphx
Registers GraphX classes with Kryo for improved performance.
GraphKryoRegistrator() - Constructor for class org.apache.spark.graphx.GraphKryoRegistrator
 
GraphLoader - Class in org.apache.spark.graphx
Provides utilities for loading Graphs from files.
GraphLoader() - Constructor for class org.apache.spark.graphx.GraphLoader
 
GraphOps<VD,ED> - Class in org.apache.spark.graphx
Contains additional functionality for Graph.
GraphOps(Graph<VD, ED>, ClassTag<VD>, ClassTag<ED>) - Constructor for class org.apache.spark.graphx.GraphOps
 
graphToGraphOps(Graph<VD, ED>, ClassTag<VD>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.Graph
Implicitly extracts the GraphOps member from a graph.
GraphXUtils - Class in org.apache.spark.graphx
 
GraphXUtils() - Constructor for class org.apache.spark.graphx.GraphXUtils
 
greater(Duration) - Method in class org.apache.spark.streaming.Duration
 
greater(Time) - Method in class org.apache.spark.streaming.Time
 
greaterEq(Duration) - Method in class org.apache.spark.streaming.Duration
 
greaterEq(Time) - Method in class org.apache.spark.streaming.Time
 
GreaterThan - Class in org.apache.spark.sql.sources
A filter that evaluates to true iff the attribute evaluates to a value greater than value.
GreaterThan(String, Object) - Constructor for class org.apache.spark.sql.sources.GreaterThan
 
GreaterThanOrEqual - Class in org.apache.spark.sql.sources
A filter that evaluates to true iff the attribute evaluates to a value greater than or equal to value.
GreaterThanOrEqual(String, Object) - Constructor for class org.apache.spark.sql.sources.GreaterThanOrEqual
 
greatest(Column...) - Static method in class org.apache.spark.sql.functions
Returns the greatest value of the list of values, skipping null values.
greatest(String, String...) - Static method in class org.apache.spark.sql.functions
Returns the greatest value of the list of column names, skipping null values.
greatest(Seq<Column>) - Static method in class org.apache.spark.sql.functions
Returns the greatest value of the list of values, skipping null values.
greatest(String, Seq<String>) - Static method in class org.apache.spark.sql.functions
Returns the greatest value of the list of column names, skipping null values.
gridGraph(SparkContext, int, int) - Static method in class org.apache.spark.graphx.util.GraphGenerators
Create rows by cols grid graph with each vertex connected to its row+1 and col+1 neighbors.
groupArr() - Method in class org.apache.spark.rdd.PartitionCoalescer
 
groupBy(Function<T, U>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return an RDD of grouped elements.
groupBy(Function<T, U>, int) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return an RDD of grouped elements.
groupBy(Function1<T, K>, ClassTag<K>) - Method in class org.apache.spark.rdd.RDD
Return an RDD of grouped items.
groupBy(Function1<T, K>, int, ClassTag<K>) - Method in class org.apache.spark.rdd.RDD
Return an RDD of grouped elements.
groupBy(Function1<T, K>, Partitioner, ClassTag<K>, Ordering<K>) - Method in class org.apache.spark.rdd.RDD
Return an RDD of grouped items.
groupBy(Column...) - Method in class org.apache.spark.sql.DataFrame
Groups the DataFrame using the specified columns, so we can run aggregation on them.
groupBy(String, String...) - Method in class org.apache.spark.sql.DataFrame
Groups the DataFrame using the specified columns, so we can run aggregation on them.
groupBy(Seq<Column>) - Method in class org.apache.spark.sql.DataFrame
Groups the DataFrame using the specified columns, so we can run aggregation on them.
groupBy(String, Seq<String>) - Method in class org.apache.spark.sql.DataFrame
Groups the DataFrame using the specified columns, so we can run aggregation on them.
groupBy(Column...) - Method in class org.apache.spark.sql.Dataset
Returns a GroupedDataset where the data is grouped by the given Column expressions.
groupBy(Function1<T, K>, Encoder<K>) - Method in class org.apache.spark.sql.Dataset
(Scala-specific) Returns a GroupedDataset where the data is grouped by the given key func.
groupBy(Seq<Column>) - Method in class org.apache.spark.sql.Dataset
Returns a GroupedDataset where the data is grouped by the given Column expressions.
groupBy(MapFunction<T, K>, Encoder<K>) - Method in class org.apache.spark.sql.Dataset
(Java-specific) Returns a GroupedDataset where the data is grouped by the given key func.
groupByKey(Partitioner) - Method in class org.apache.spark.api.java.JavaPairRDD
Group the values for each key in the RDD into a single sequence.
groupByKey(int) - Method in class org.apache.spark.api.java.JavaPairRDD
Group the values for each key in the RDD into a single sequence.
groupByKey() - Method in class org.apache.spark.api.java.JavaPairRDD
Group the values for each key in the RDD into a single sequence.
groupByKey(Partitioner) - Method in class org.apache.spark.rdd.PairRDDFunctions
Group the values for each key in the RDD into a single sequence.
groupByKey(int) - Method in class org.apache.spark.rdd.PairRDDFunctions
Group the values for each key in the RDD into a single sequence.
groupByKey() - Method in class org.apache.spark.rdd.PairRDDFunctions
Group the values for each key in the RDD into a single sequence.
groupByKey() - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying groupByKey to each RDD.
groupByKey(int) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying groupByKey to each RDD.
groupByKey(Partitioner) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying groupByKey on each RDD of this DStream.
groupByKey() - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying groupByKey to each RDD.
groupByKey(int) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying groupByKey to each RDD.
groupByKey(Partitioner) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying groupByKey on each RDD.
groupByKeyAndWindow(Duration) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying groupByKey over a sliding window.
groupByKeyAndWindow(Duration, Duration) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying groupByKey over a sliding window.
groupByKeyAndWindow(Duration, Duration, int) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying groupByKey over a sliding window on this DStream.
groupByKeyAndWindow(Duration, Duration, Partitioner) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying groupByKey over a sliding window on this DStream.
groupByKeyAndWindow(Duration) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying groupByKey over a sliding window.
groupByKeyAndWindow(Duration, Duration) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying groupByKey over a sliding window.
groupByKeyAndWindow(Duration, Duration, int) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying groupByKey over a sliding window on this DStream.
groupByKeyAndWindow(Duration, Duration, Partitioner) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Create a new DStream by applying groupByKey over a sliding window on this DStream.
GroupedData - Class in org.apache.spark.sql
:: Experimental :: A set of methods for aggregations on a DataFrame, created by DataFrame.groupBy.
GroupedData(DataFrame, Seq<Expression>, GroupedData.GroupType) - Constructor for class org.apache.spark.sql.GroupedData
 
GroupedDataset<K,V> - Class in org.apache.spark.sql
:: Experimental :: A Dataset has been logically grouped by a user specified grouping key.
groupEdges(Function2<ED, ED, ED>) - Method in class org.apache.spark.graphx.Graph
Merges multiple edges between two vertices into a single edge.
groupEdges(Function2<ED, ED, ED>) - Method in class org.apache.spark.graphx.impl.GraphImpl
 
groupHash() - Method in class org.apache.spark.rdd.PartitionCoalescer
 
groupWith(JavaPairRDD<K, W>) - Method in class org.apache.spark.api.java.JavaPairRDD
Alias for cogroup.
groupWith(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>) - Method in class org.apache.spark.api.java.JavaPairRDD
Alias for cogroup.
groupWith(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>, JavaPairRDD<K, W3>) - Method in class org.apache.spark.api.java.JavaPairRDD
Alias for cogroup.
groupWith(RDD<Tuple2<K, W>>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Alias for cogroup.
groupWith(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Alias for cogroup.
groupWith(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>, RDD<Tuple2<K, W3>>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Alias for cogroup.
gt(double) - Static method in class org.apache.spark.ml.param.ParamValidators
Check if value > lowerBound
gt(Object) - Method in class org.apache.spark.sql.Column
Greater than.
gtEq(double) - Static method in class org.apache.spark.ml.param.ParamValidators
Check if value >= lowerBound

H

hadoopConfiguration() - Method in class org.apache.spark.api.java.JavaSparkContext
Returns the Hadoop configuration used for the Hadoop code (e.g.
hadoopConfiguration() - Method in class org.apache.spark.SparkContext
A default Hadoop Configuration for the Hadoop code (e.g.
hadoopFile(String, Class<F>, Class<K>, Class<V>, int) - Method in class org.apache.spark.api.java.JavaSparkContext
Get an RDD for a Hadoop file with an arbitrary InputFormat.
hadoopFile(String, Class<F>, Class<K>, Class<V>) - Method in class org.apache.spark.api.java.JavaSparkContext
Get an RDD for a Hadoop file with an arbitrary InputFormat
hadoopFile(String, Class<? extends InputFormat<K, V>>, Class<K>, Class<V>, int) - Method in class org.apache.spark.SparkContext
Get an RDD for a Hadoop file with an arbitrary InputFormat
hadoopFile(String, int, ClassTag<K>, ClassTag<V>, ClassTag<F>) - Method in class org.apache.spark.SparkContext
Smarter version of hadoopFile() that uses class tags to figure out the classes of keys, values and the InputFormat so that users don't need to pass them directly.
hadoopFile(String, ClassTag<K>, ClassTag<V>, ClassTag<F>) - Method in class org.apache.spark.SparkContext
Smarter version of hadoopFile() that uses class tags to figure out the classes of keys, values and the InputFormat so that users don't need to pass them directly.
HadoopFsRelation - Class in org.apache.spark.sql.sources
::Experimental:: A BaseRelation that provides much of the common code required for relations that store their data to an HDFS compatible filesystem.
HadoopFsRelation() - Constructor for class org.apache.spark.sql.sources.HadoopFsRelation
 
HadoopFsRelation(Map<String, String>) - Constructor for class org.apache.spark.sql.sources.HadoopFsRelation
 
HadoopFsRelation.FakeFileStatus - Class in org.apache.spark.sql.sources
 
HadoopFsRelation.FakeFileStatus(String, long, boolean, short, long, long, long) - Constructor for class org.apache.spark.sql.sources.HadoopFsRelation.FakeFileStatus
 
HadoopFsRelation.FakeFileStatus$ - Class in org.apache.spark.sql.sources
 
HadoopFsRelation.FakeFileStatus$() - Constructor for class org.apache.spark.sql.sources.HadoopFsRelation.FakeFileStatus$
 
HadoopFsRelationProvider - Interface in org.apache.spark.sql.sources
::Experimental:: Implemented by objects that produce relations for a specific kind of data source with a given schema and partitioned columns.
hadoopJobMetadata() - Method in class org.apache.spark.SparkEnv
 
hadoopRDD(JobConf, Class<F>, Class<K>, Class<V>, int) - Method in class org.apache.spark.api.java.JavaSparkContext
Get an RDD for a Hadoop-readable dataset from a Hadooop JobConf giving its InputFormat and any other necessary info (e.g.
hadoopRDD(JobConf, Class<F>, Class<K>, Class<V>) - Method in class org.apache.spark.api.java.JavaSparkContext
Get an RDD for a Hadoop-readable dataset from a Hadooop JobConf giving its InputFormat and any other necessary info (e.g.
HadoopRDD<K,V> - Class in org.apache.spark.rdd
:: DeveloperApi :: An RDD that provides core functionality for reading data stored in Hadoop (e.g., files in HDFS, sources in HBase, or S3), using the older MapReduce API (org.apache.hadoop.mapred).
HadoopRDD(SparkContext, Broadcast<org.apache.spark.util.SerializableConfiguration>, Option<Function1<JobConf, BoxedUnit>>, Class<? extends InputFormat<K, V>>, Class<K>, Class<V>, int) - Constructor for class org.apache.spark.rdd.HadoopRDD
 
HadoopRDD(SparkContext, JobConf, Class<? extends InputFormat<K, V>>, Class<K>, Class<V>, int) - Constructor for class org.apache.spark.rdd.HadoopRDD
 
hadoopRDD(JobConf, Class<? extends InputFormat<K, V>>, Class<K>, Class<V>, int) - Method in class org.apache.spark.SparkContext
Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf given its InputFormat and other necessary info (e.g.
hammingLoss() - Method in class org.apache.spark.mllib.evaluation.MultilabelMetrics
Returns Hamming-loss
handle(Signal) - Method in class org.apache.spark.util.SignalLoggerHandler
 
hasAttr(String) - Method in class org.apache.spark.ml.attribute.AttributeGroup
Test whether this attribute group contains a specific attribute.
hasDefault(Param<T>) - Method in interface org.apache.spark.ml.param.Params
Tests whether the input param has a default value set.
hashCode() - Method in class org.apache.spark.graphx.EdgeDirection
 
hashCode() - Method in class org.apache.spark.HashPartitioner
 
hashCode() - Method in class org.apache.spark.ml.attribute.AttributeGroup
 
hashCode() - Method in class org.apache.spark.ml.attribute.BinaryAttribute
 
hashCode() - Method in class org.apache.spark.ml.attribute.NominalAttribute
 
hashCode() - Method in class org.apache.spark.ml.attribute.NumericAttribute
 
hashCode() - Method in class org.apache.spark.ml.param.Param
 
hashCode() - Method in class org.apache.spark.mllib.linalg.DenseMatrix
 
hashCode() - Method in class org.apache.spark.mllib.linalg.DenseVector
 
hashCode() - Method in class org.apache.spark.mllib.linalg.SparseVector
 
hashCode() - Method in interface org.apache.spark.mllib.linalg.Vector
Returns a hash code value for the vector.
hashCode() - Method in class org.apache.spark.mllib.linalg.VectorUDT
 
hashCode() - Method in class org.apache.spark.mllib.tree.model.InformationGainStats
 
hashCode() - Method in class org.apache.spark.mllib.tree.model.Predict
 
hashCode() - Method in interface org.apache.spark.Partition
 
hashCode() - Method in class org.apache.spark.RangePartitioner
 
hashCode() - Method in class org.apache.spark.scheduler.AccumulableInfo
 
hashCode() - Method in class org.apache.spark.scheduler.cluster.ExecutorInfo
 
hashCode() - Method in class org.apache.spark.scheduler.InputFormatInfo
 
hashCode() - Method in class org.apache.spark.scheduler.SplitInfo
 
hashCode() - Method in class org.apache.spark.sql.Column
 
hashCode() - Method in interface org.apache.spark.sql.Row
 
hashCode() - Method in class org.apache.spark.sql.types.Decimal
 
hashCode() - Method in class org.apache.spark.sql.types.Metadata
 
hashCode() - Method in class org.apache.spark.storage.BlockId
 
hashCode() - Method in class org.apache.spark.storage.BlockManagerId
 
hashCode() - Method in class org.apache.spark.storage.StorageLevel
 
hashCode() - Method in class org.apache.spark.streaming.kafka.Broker
 
hashCode() - Method in class org.apache.spark.streaming.kafka.OffsetRange
 
HashingTF - Class in org.apache.spark.ml.feature
:: Experimental :: Maps a sequence of terms to their term frequencies using the hashing trick.
HashingTF(String) - Constructor for class org.apache.spark.ml.feature.HashingTF
 
HashingTF() - Constructor for class org.apache.spark.ml.feature.HashingTF
 
HashingTF - Class in org.apache.spark.mllib.feature
Maps a sequence of terms to their term frequencies using the hashing trick.
HashingTF(int) - Constructor for class org.apache.spark.mllib.feature.HashingTF
 
HashingTF() - Constructor for class org.apache.spark.mllib.feature.HashingTF
 
HashPartitioner - Class in org.apache.spark
A Partitioner that implements hash-based partitioning using Java's Object.hashCode.
HashPartitioner(int) - Constructor for class org.apache.spark.HashPartitioner
 
hasNext() - Method in class org.apache.spark.InterruptibleIterator
 
hasNext() - Method in class org.apache.spark.rdd.PartitionCoalescer.LocationIterator
 
HasOffsetRanges - Interface in org.apache.spark.streaming.kafka
Represents any object that has a collection of OffsetRanges.
hasParam(String) - Method in interface org.apache.spark.ml.param.Params
 
hasParent() - Method in class org.apache.spark.ml.Model
Indicates whether this Model has a corresponding parent.
hasSummary() - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
Indicates whether a training summary exists for this model instance.
hasSummary() - Method in class org.apache.spark.ml.regression.LinearRegressionModel
Indicates whether a training summary exists for this model instance.
hasValue(String) - Method in class org.apache.spark.ml.attribute.NominalAttribute
Tests whether this attribute contains a specific value.
head(int) - Method in class org.apache.spark.sql.DataFrame
Returns the first n rows.
head() - Method in class org.apache.spark.sql.DataFrame
Returns the first row.
hex(Column) - Static method in class org.apache.spark.sql.functions
Computes hex value of the given column.
high() - Method in class org.apache.spark.partial.BoundedDouble
 
HingeGradient - Class in org.apache.spark.mllib.optimization
:: DeveloperApi :: Compute gradient and loss for a Hinge loss function, as used in SVM binary classification.
HingeGradient() - Constructor for class org.apache.spark.mllib.optimization.HingeGradient
 
histogram(int) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Compute a histogram of the data using bucketCount number of buckets evenly spaced between the minimum and maximum of the RDD.
histogram(double[]) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Compute a histogram using the provided buckets.
histogram(Double[], boolean) - Method in class org.apache.spark.api.java.JavaDoubleRDD
 
histogram(int) - Method in class org.apache.spark.rdd.DoubleRDDFunctions
Compute a histogram of the data using bucketCount number of buckets evenly spaced between the minimum and maximum of the RDD.
histogram(double[], boolean) - Method in class org.apache.spark.rdd.DoubleRDDFunctions
Compute a histogram using the provided buckets.
HIVE_EXECUTION_VERSION() - Static method in class org.apache.spark.sql.hive.HiveContext
 
HIVE_METASTORE_BARRIER_PREFIXES() - Static method in class org.apache.spark.sql.hive.HiveContext
 
HIVE_METASTORE_JARS() - Static method in class org.apache.spark.sql.hive.HiveContext
 
HIVE_METASTORE_SHARED_PREFIXES() - Static method in class org.apache.spark.sql.hive.HiveContext
 
HIVE_METASTORE_VERSION() - Static method in class org.apache.spark.sql.hive.HiveContext
 
HIVE_THRIFT_SERVER_ASYNC() - Static method in class org.apache.spark.sql.hive.HiveContext
 
hiveconf() - Method in class org.apache.spark.sql.hive.HiveContext
SQLConf and HiveConf contracts:
HiveContext - Class in org.apache.spark.sql.hive
An instance of the Spark SQL execution engine that integrates with data stored in Hive.
HiveContext(SparkContext) - Constructor for class org.apache.spark.sql.hive.HiveContext
 
HiveContext(JavaSparkContext) - Constructor for class org.apache.spark.sql.hive.HiveContext
 
HiveContext.QueryExecution - Class in org.apache.spark.sql.hive
Extends QueryExecution with hive specific features.
HiveContext.QueryExecution(LogicalPlan) - Constructor for class org.apache.spark.sql.hive.HiveContext.QueryExecution
 
hiveExecutionVersion() - Static method in class org.apache.spark.sql.hive.HiveContext
The version of hive used internally by Spark SQL.
hiveMetastoreBarrierPrefixes() - Method in class org.apache.spark.sql.hive.HiveContext
A comma separated list of class prefixes that should explicitly be reloaded for each version of Hive that Spark SQL is communicating with.
hiveMetastoreJars() - Method in class org.apache.spark.sql.hive.HiveContext
The location of the jars that should be used to instantiate the HiveMetastoreClient.
hiveMetastoreSharedPrefixes() - Method in class org.apache.spark.sql.hive.HiveContext
A comma separated list of class prefixes that should be loaded using the classloader that is shared between Spark SQL and a specific version of Hive.
hiveMetastoreVersion() - Method in class org.apache.spark.sql.hive.HiveContext
The version of the hive client that will be used to communicate with the metastore.
hiveThriftServerAsync() - Method in class org.apache.spark.sql.hive.HiveContext
 
hiveThriftServerSingleSession() - Method in class org.apache.spark.sql.hive.HiveContext
 
horzcat(Matrix[]) - Static method in class org.apache.spark.mllib.linalg.Matrices
Horizontally concatenate a sequence of matrices.
host() - Method in class org.apache.spark.scheduler.TaskInfo
 
host() - Method in class org.apache.spark.status.api.v1.TaskData
 
host() - Method in class org.apache.spark.storage.BlockManagerId
 
host() - Method in class org.apache.spark.streaming.kafka.Broker
Broker's hostname
hostLocation() - Method in class org.apache.spark.scheduler.SplitInfo
 
hostPort() - Method in class org.apache.spark.status.api.v1.ExecutorSummary
 
hostPort() - Method in class org.apache.spark.storage.BlockManagerId
 
hour(Column) - Static method in class org.apache.spark.sql.functions
Extracts the hours as an integer from a given date/timestamp/string.
hours() - Static method in class org.apache.spark.scheduler.StatsReportListener
 
HttpBroadcastFactory - Class in org.apache.spark.broadcast
A BroadcastFactory implementation that uses a HTTP server as the broadcast mechanism.
HttpBroadcastFactory() - Constructor for class org.apache.spark.broadcast.HttpBroadcastFactory
 
hypot(Column, Column) - Static method in class org.apache.spark.sql.functions
Computes sqrt(a^2^ + b^2^) without intermediate overflow or underflow.
hypot(Column, String) - Static method in class org.apache.spark.sql.functions
Computes sqrt(a^2^ + b^2^) without intermediate overflow or underflow.
hypot(String, Column) - Static method in class org.apache.spark.sql.functions
Computes sqrt(a^2^ + b^2^) without intermediate overflow or underflow.
hypot(String, String) - Static method in class org.apache.spark.sql.functions
Computes sqrt(a^2^ + b^2^) without intermediate overflow or underflow.
hypot(Column, double) - Static method in class org.apache.spark.sql.functions
Computes sqrt(a^2^ + b^2^) without intermediate overflow or underflow.
hypot(String, double) - Static method in class org.apache.spark.sql.functions
Computes sqrt(a^2^ + b^2^) without intermediate overflow or underflow.
hypot(double, Column) - Static method in class org.apache.spark.sql.functions
Computes sqrt(a^2^ + b^2^) without intermediate overflow or underflow.
hypot(double, String) - Static method in class org.apache.spark.sql.functions
Computes sqrt(a^2^ + b^2^) without intermediate overflow or underflow.

I

i() - Method in class org.apache.spark.mllib.linalg.distributed.MatrixEntry
 
id() - Method in class org.apache.spark.Accumulable
 
id() - Method in interface org.apache.spark.api.java.JavaRDDLike
A unique ID for this RDD (within its SparkContext).
id() - Method in class org.apache.spark.broadcast.Broadcast
 
id() - Method in class org.apache.spark.mllib.clustering.PowerIterationClustering.Assignment
 
id() - Method in class org.apache.spark.mllib.tree.model.Node
 
id() - Method in class org.apache.spark.rdd.RDD
A unique ID for this RDD (within its SparkContext).
id() - Method in class org.apache.spark.scheduler.AccumulableInfo
 
id() - Method in class org.apache.spark.scheduler.TaskInfo
 
id() - Method in class org.apache.spark.status.api.v1.AccumulableInfo
 
id() - Method in class org.apache.spark.status.api.v1.ApplicationInfo
 
id() - Method in class org.apache.spark.status.api.v1.ExecutorSummary
 
id() - Method in class org.apache.spark.status.api.v1.RDDStorageInfo
 
id() - Method in class org.apache.spark.storage.RDDInfo
 
id() - Method in class org.apache.spark.streaming.dstream.InputDStream
This is an unique identifier for the input stream.
id() - Method in class org.apache.spark.streaming.scheduler.OutputOperationInfo
 
Identifiable - Interface in org.apache.spark.ml.util
:: DeveloperApi ::
IDF - Class in org.apache.spark.ml.feature
:: Experimental :: Compute the Inverse Document Frequency (IDF) given a collection of documents.
IDF(String) - Constructor for class org.apache.spark.ml.feature.IDF
 
IDF() - Constructor for class org.apache.spark.ml.feature.IDF
 
idf() - Method in class org.apache.spark.ml.feature.IDFModel
 
IDF - Class in org.apache.spark.mllib.feature
Inverse document frequency (IDF).
IDF(int) - Constructor for class org.apache.spark.mllib.feature.IDF
 
IDF() - Constructor for class org.apache.spark.mllib.feature.IDF
 
idf() - Method in class org.apache.spark.mllib.feature.IDF.DocumentFrequencyAggregator
Returns the current IDF vector.
idf() - Method in class org.apache.spark.mllib.feature.IDFModel
 
IDF.DocumentFrequencyAggregator - Class in org.apache.spark.mllib.feature
Document frequency aggregator.
IDF.DocumentFrequencyAggregator(int) - Constructor for class org.apache.spark.mllib.feature.IDF.DocumentFrequencyAggregator
 
IDF.DocumentFrequencyAggregator() - Constructor for class org.apache.spark.mllib.feature.IDF.DocumentFrequencyAggregator
 
IDFModel - Class in org.apache.spark.ml.feature
 
IDFModel - Class in org.apache.spark.mllib.feature
Represents an IDF model that can transform term frequency vectors.
implicits() - Method in class org.apache.spark.sql.SQLContext
Accessor for nested Scala object
impurity() - Method in class org.apache.spark.ml.tree.InternalNode
 
impurity() - Method in class org.apache.spark.ml.tree.LeafNode
 
impurity() - Method in class org.apache.spark.ml.tree.Node
Impurity measure at this node (for training data)
impurity() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
Impurity - Interface in org.apache.spark.mllib.tree.impurity
:: Experimental :: Trait for calculating information gain.
impurity() - Method in class org.apache.spark.mllib.tree.model.InformationGainStats
 
impurity() - Method in class org.apache.spark.mllib.tree.model.Node
 
impurityStats() - Method in class org.apache.spark.ml.tree.InternalNode
 
impurityStats() - Method in class org.apache.spark.ml.tree.LeafNode
 
In() - Static method in class org.apache.spark.graphx.EdgeDirection
Edges arriving at a vertex.
in(Object...) - Method in class org.apache.spark.sql.Column
Deprecated.
As of 1.5.0. Use isin. This will be removed in Spark 2.0.
in(Seq<Object>) - Method in class org.apache.spark.sql.Column
Deprecated.
As of 1.5.0. Use isin. This will be removed in Spark 2.0.
In - Class in org.apache.spark.sql.sources
A filter that evaluates to true iff the attribute evaluates to one of the values in the array.
In(String, Object[]) - Constructor for class org.apache.spark.sql.sources.In
 
inArray(Object) - Static method in class org.apache.spark.ml.param.ParamValidators
Check for value in an allowed set of values.
inArray(List<T>) - Static method in class org.apache.spark.ml.param.ParamValidators
Check for value in an allowed set of values.
inDegrees() - Method in class org.apache.spark.graphx.GraphOps
The in-degree of each vertex in the graph.
index() - Method in class org.apache.spark.ml.attribute.Attribute
Index of the attribute.
index() - Method in class org.apache.spark.ml.attribute.BinaryAttribute
 
index() - Method in class org.apache.spark.ml.attribute.NominalAttribute
 
index() - Method in class org.apache.spark.ml.attribute.NumericAttribute
 
index() - Static method in class org.apache.spark.ml.attribute.UnresolvedAttribute
 
index() - Method in class org.apache.spark.mllib.linalg.distributed.IndexedRow
 
index(int, int) - Method in interface org.apache.spark.mllib.linalg.Matrix
Return the index for the (i, j)-th element in the backing array.
index() - Method in interface org.apache.spark.Partition
Get the partition's index within its parent RDD
index() - Method in class org.apache.spark.scheduler.TaskInfo
 
index() - Method in class org.apache.spark.status.api.v1.TaskData
 
IndexedRow - Class in org.apache.spark.mllib.linalg.distributed
Represents a row of IndexedRowMatrix.
IndexedRow(long, Vector) - Constructor for class org.apache.spark.mllib.linalg.distributed.IndexedRow
 
IndexedRowMatrix - Class in org.apache.spark.mllib.linalg.distributed
 
IndexedRowMatrix(RDD<IndexedRow>, long, int) - Constructor for class org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
 
IndexedRowMatrix(RDD<IndexedRow>) - Constructor for class org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
 
indexOf(String) - Method in class org.apache.spark.ml.attribute.AttributeGroup
Index of an attribute specified by name.
indexOf(String) - Method in class org.apache.spark.ml.attribute.NominalAttribute
Index of a specific value.
indexOf(Object) - Method in class org.apache.spark.mllib.feature.HashingTF
Returns the index of the input term.
indexToLevel(int) - Static method in class org.apache.spark.mllib.tree.model.Node
Return the level of a tree which the given node is in.
IndexToString - Class in org.apache.spark.ml.feature
 
IndexToString() - Constructor for class org.apache.spark.ml.feature.IndexToString
 
indices() - Method in class org.apache.spark.ml.feature.VectorSlicer
An array of indices to select features from a vector column.
indices() - Method in class org.apache.spark.mllib.linalg.SparseVector
 
infoChanged(SparkAppHandle) - Method in interface org.apache.spark.launcher.SparkAppHandle.Listener
Callback for changes in any information that is not the handle's state.
InformationGainStats - Class in org.apache.spark.mllib.tree.model
:: DeveloperApi :: Information gain statistics for each split param: gain information gain value param: impurity current node impurity param: leftImpurity left node impurity param: rightImpurity right node impurity param: leftPredict left node predict param: rightPredict right node predict
InformationGainStats(double, double, double, double, Predict, Predict) - Constructor for class org.apache.spark.mllib.tree.model.InformationGainStats
 
initcap(Column) - Static method in class org.apache.spark.sql.functions
Returns a new string column by converting the first letter of each word to uppercase.
initConverter(StructType) - Method in class org.apache.spark.sql.sources.OutputWriter
 
initialHash() - Method in class org.apache.spark.rdd.PartitionCoalescer
 
initialize(boolean, SparkConf, org.apache.spark.SecurityManager) - Method in interface org.apache.spark.broadcast.BroadcastFactory
 
initialize(boolean, SparkConf, org.apache.spark.SecurityManager) - Method in class org.apache.spark.broadcast.HttpBroadcastFactory
 
initialize(boolean, SparkConf, org.apache.spark.SecurityManager) - Method in class org.apache.spark.broadcast.TorrentBroadcastFactory
 
initialize(RDD<Tuple2<Object, Vector>>, LDA) - Method in interface org.apache.spark.mllib.clustering.LDAOptimizer
Initializer for the optimizer.
initialize(MutableAggregationBuffer) - Method in class org.apache.spark.sql.expressions.UserDefinedAggregateFunction
Initializes the given aggregation buffer, i.e.
initializeIfNecessary() - Method in interface org.apache.spark.Logging
 
initializeLogging() - Method in interface org.apache.spark.Logging
 
initialState(RDD<Tuple2<KeyType, StateType>>) - Method in class org.apache.spark.streaming.StateSpec
Set the RDD containing the initial states that will be used by `mapWithState`
initialState(JavaPairRDD<KeyType, StateType>) - Method in class org.apache.spark.streaming.StateSpec
Set the RDD containing the initial states that will be used by `mapWithState`
initialValue() - Method in class org.apache.spark.Accumulator
 
initialValue() - Method in class org.apache.spark.partial.PartialResult
 
initLocalProperties() - Method in class org.apache.spark.SparkContext
 
InnerClosureFinder - Class in org.apache.spark.util
 
InnerClosureFinder(Set<Class<?>>) - Constructor for class org.apache.spark.util.InnerClosureFinder
 
innerJoin(EdgeRDD<ED2>, Function4<Object, Object, ED, ED2, ED3>, ClassTag<ED2>, ClassTag<ED3>) - Method in class org.apache.spark.graphx.EdgeRDD
Inner joins this EdgeRDD with another EdgeRDD, assuming both are partitioned using the same PartitionStrategy.
innerJoin(EdgeRDD<ED2>, Function4<Object, Object, ED, ED2, ED3>, ClassTag<ED2>, ClassTag<ED3>) - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
 
innerJoin(RDD<Tuple2<Object, U>>, Function3<Object, VD, U, VD2>, ClassTag<U>, ClassTag<VD2>) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
innerJoin(RDD<Tuple2<Object, U>>, Function3<Object, VD, U, VD2>, ClassTag<U>, ClassTag<VD2>) - Method in class org.apache.spark.graphx.VertexRDD
Inner joins this VertexRDD with an RDD containing vertex attribute pairs.
innerZipJoin(VertexRDD<U>, Function3<Object, VD, U, VD2>, ClassTag<U>, ClassTag<VD2>) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
innerZipJoin(VertexRDD<U>, Function3<Object, VD, U, VD2>, ClassTag<U>, ClassTag<VD2>) - Method in class org.apache.spark.graphx.VertexRDD
Efficiently inner joins this VertexRDD with another VertexRDD sharing the same index.
input_file_name() - Static method in class org.apache.spark.sql.functions
Creates a string column for the file name of the current Spark task.
inputBytes() - Method in class org.apache.spark.status.api.v1.ExecutorStageSummary
 
inputBytes() - Method in class org.apache.spark.status.api.v1.StageData
 
inputDStream() - Method in class org.apache.spark.streaming.api.java.JavaInputDStream
 
inputDStream() - Method in class org.apache.spark.streaming.api.java.JavaPairInputDStream
 
InputDStream<T> - Class in org.apache.spark.streaming.dstream
This is the abstract base class for all input streams.
InputDStream(StreamingContext, ClassTag<T>) - Constructor for class org.apache.spark.streaming.dstream.InputDStream
 
inputFileName() - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.6.0, replaced by input_file_name. This will be removed in Spark 2.0.
inputFiles() - Method in class org.apache.spark.sql.DataFrame
Returns a best-effort snapshot of the files that compose this DataFrame.
inputFiles() - Method in class org.apache.spark.sql.sources.HadoopFsRelation
 
inputFormatCacheKey() - Method in class org.apache.spark.rdd.HadoopRDD
 
inputFormatClazz() - Method in class org.apache.spark.scheduler.InputFormatInfo
 
inputFormatClazz() - Method in class org.apache.spark.scheduler.SplitInfo
 
InputFormatInfo - Class in org.apache.spark.scheduler
:: DeveloperApi :: Parses and holds information about inputFormat (and files) specified as a parameter.
InputFormatInfo(Configuration, Class<?>, String) - Constructor for class org.apache.spark.scheduler.InputFormatInfo
 
InputMetricDistributions - Class in org.apache.spark.status.api.v1
 
InputMetrics - Class in org.apache.spark.status.api.v1
 
inputMetrics() - Method in class org.apache.spark.status.api.v1.TaskMetricDistributions
 
inputMetrics() - Method in class org.apache.spark.status.api.v1.TaskMetrics
 
inputRecords() - Method in class org.apache.spark.status.api.v1.StageData
 
inputSchema() - Method in class org.apache.spark.sql.expressions.UserDefinedAggregateFunction
A StructType represents data types of input arguments of this aggregate function.
inputStreamId() - Method in class org.apache.spark.streaming.scheduler.StreamInputInfo
 
inputTypes() - Method in class org.apache.spark.sql.UserDefinedFunction
 
inRange(double, double, boolean, boolean) - Static method in class org.apache.spark.ml.param.ParamValidators
Check for value in range lowerBound to upperBound.
inRange(double, double) - Static method in class org.apache.spark.ml.param.ParamValidators
Version of inRange() which uses inclusive be default: [lowerBound, upperBound]
insert(DataFrame, boolean) - Method in interface org.apache.spark.sql.sources.InsertableRelation
 
InsertableRelation - Interface in org.apache.spark.sql.sources
::DeveloperApi:: A BaseRelation that can be used to insert data into it through the insert method.
insertInto(String, boolean) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().mode(SaveMode.Append|SaveMode.Overwrite).saveAsTable(tableName). This will be removed in Spark 2.0.
insertInto(String) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().mode(SaveMode.Append).saveAsTable(tableName). This will be removed in Spark 2.0.
insertInto(String) - Method in class org.apache.spark.sql.DataFrameWriter
Inserts the content of the DataFrame to the specified table.
insertIntoJDBC(String, String, boolean) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().jdbc(). This will be removed in Spark 2.0.
instance() - Static method in class org.apache.spark.mllib.tree.impurity.Entropy
Get this impurity instance.
instance() - Static method in class org.apache.spark.mllib.tree.impurity.Gini
Get this impurity instance.
instance() - Static method in class org.apache.spark.mllib.tree.impurity.Variance
Get this impurity instance.
INSTANCE - Static variable in class org.apache.spark.serializer.DummySerializerInstance
 
instr(Column, String) - Static method in class org.apache.spark.sql.functions
Locate the position of the first occurrence of substr column in the given string.
INT() - Static method in class org.apache.spark.sql.Encoders
An encoder for nullable int type.
intAccumulator(int) - Method in class org.apache.spark.api.java.JavaSparkContext
Create an Accumulator integer variable, which tasks can "add" values to using the add method.
intAccumulator(int, String) - Method in class org.apache.spark.api.java.JavaSparkContext
Create an Accumulator integer variable, which tasks can "add" values to using the add method.
IntArrayParam - Class in org.apache.spark.ml.param
:: DeveloperApi :: Specialized version of Param[Array[Int} for Java.
IntArrayParam(Params, String, String, Function1<int[], Object>) - Constructor for class org.apache.spark.ml.param.IntArrayParam
 
IntArrayParam(Params, String, String) - Constructor for class org.apache.spark.ml.param.IntArrayParam
 
IntDecimal() - Static method in class org.apache.spark.sql.types.DecimalType
 
IntegerType - Static variable in class org.apache.spark.sql.types.DataTypes
Gets the IntegerType object.
IntegerType - Class in org.apache.spark.sql.types
:: DeveloperApi :: The data type representing Int values.
integral() - Method in class org.apache.spark.sql.types.ByteType
 
integral() - Method in class org.apache.spark.sql.types.IntegerType
 
integral() - Method in class org.apache.spark.sql.types.LongType
 
integral() - Method in class org.apache.spark.sql.types.ShortType
 
Interaction - Class in org.apache.spark.ml.feature
:: Experimental :: Implements the feature interaction transform.
Interaction(String) - Constructor for class org.apache.spark.ml.feature.Interaction
 
Interaction() - Constructor for class org.apache.spark.ml.feature.Interaction
 
intercept() - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
intercept() - Method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
intercept() - Method in class org.apache.spark.ml.regression.LinearRegressionModel
 
intercept() - Method in class org.apache.spark.mllib.classification.LogisticRegressionModel
 
intercept() - Method in class org.apache.spark.mllib.classification.SVMModel
 
intercept() - Method in class org.apache.spark.mllib.regression.GeneralizedLinearModel
 
intercept() - Method in class org.apache.spark.mllib.regression.LassoModel
 
intercept() - Method in class org.apache.spark.mllib.regression.LinearRegressionModel
 
intercept() - Method in class org.apache.spark.mllib.regression.RidgeRegressionModel
 
internal() - Method in class org.apache.spark.scheduler.AccumulableInfo
 
internalMetricsToAccumulators() - Method in class org.apache.spark.TaskContext
Accumulators for tracking internal metrics indexed by the name.
InternalNode - Class in org.apache.spark.ml.tree
:: DeveloperApi :: Internal Decision Tree node.
interpretedOrdering() - Method in class org.apache.spark.sql.types.ArrayType
 
interpretedOrdering() - Method in class org.apache.spark.sql.types.StructType
 
InterruptibleIterator<T> - Class in org.apache.spark
:: DeveloperApi :: An iterator that wraps around an existing iterator to provide task killing functionality.
InterruptibleIterator(TaskContext, Iterator<T>) - Constructor for class org.apache.spark.InterruptibleIterator
 
interruptThread() - Method in class org.apache.spark.scheduler.local.KillTask
 
intersect(DataFrame) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame containing rows only in both this frame and another frame.
intersect(Dataset<T>) - Method in class org.apache.spark.sql.Dataset
Returns a new Dataset that contains only the elements of this Dataset that are also present in other.
intersection(JavaDoubleRDD) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Return the intersection of this RDD and another one.
intersection(JavaPairRDD<K, V>) - Method in class org.apache.spark.api.java.JavaPairRDD
Return the intersection of this RDD and another one.
intersection(JavaRDD<T>) - Method in class org.apache.spark.api.java.JavaRDD
Return the intersection of this RDD and another one.
intersection(RDD<T>) - Method in class org.apache.spark.rdd.RDD
Return the intersection of this RDD and another one.
intersection(RDD<T>, Partitioner, Ordering<T>) - Method in class org.apache.spark.rdd.RDD
Return the intersection of this RDD and another one.
intersection(RDD<T>, int) - Method in class org.apache.spark.rdd.RDD
Return the intersection of this RDD and another one.
IntParam - Class in org.apache.spark.ml.param
:: DeveloperApi :: Specialized version of Param[Int] for Java.
IntParam(String, String, String, Function1<Object, Object>) - Constructor for class org.apache.spark.ml.param.IntParam
 
IntParam(String, String, String) - Constructor for class org.apache.spark.ml.param.IntParam
 
IntParam(Identifiable, String, String, Function1<Object, Object>) - Constructor for class org.apache.spark.ml.param.IntParam
 
IntParam(Identifiable, String, String) - Constructor for class org.apache.spark.ml.param.IntParam
 
intRddToDataFrameHolder(RDD<Object>) - Method in class org.apache.spark.sql.SQLImplicits
Creates a single column DataFrame from an RDD[Int].
intToIntWritable(int) - Static method in class org.apache.spark.SparkContext
 
intWritableConverter() - Static method in class org.apache.spark.SparkContext
 
invalidateTable(String) - Method in class org.apache.spark.sql.hive.HiveContext
 
invalidInformationGainStats() - Static method in class org.apache.spark.mllib.tree.model.InformationGainStats
An InformationGainStats object to denote that current split doesn't satisfies minimum info gain or minimum number of instances per node.
inverse() - Method in class org.apache.spark.ml.feature.DCT
Indicates whether to perform the inverse DCT (true) or forward DCT (false).
isAddIntercept() - Method in class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
Get if the algorithm uses addIntercept
isAkkaConf(String) - Static method in class org.apache.spark.SparkConf
Return whether the given config is an akka config (e.g.
isAllowed(Enumeration.Value, Enumeration.Value) - Static method in class org.apache.spark.scheduler.TaskLocality
 
isBroadcast() - Method in class org.apache.spark.storage.BlockId
 
isCached(String) - Method in class org.apache.spark.sql.SQLContext
Returns true if the table is currently cached in-memory.
isCached() - Method in class org.apache.spark.storage.BlockStatus
 
isCached() - Method in class org.apache.spark.storage.RDDInfo
 
isCancelled() - Method in class org.apache.spark.ComplexFutureAction
 
isCancelled() - Method in interface org.apache.spark.FutureAction
Returns whether the action has been cancelled.
isCancelled() - Method in class org.apache.spark.SimpleFutureAction
 
isCheckpointed() - Method in interface org.apache.spark.api.java.JavaRDDLike
Return whether this RDD has been checkpointed or not
isCheckpointed() - Method in class org.apache.spark.graphx.Graph
Return whether this Graph has been checkpointed or not.
isCheckpointed() - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
 
isCheckpointed() - Method in class org.apache.spark.graphx.impl.GraphImpl
 
isCheckpointed() - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
isCheckpointed() - Method in class org.apache.spark.rdd.RDD
Return whether this RDD is checkpointed and materialized, either reliably or locally.
isCheckpointPresent() - Method in class org.apache.spark.streaming.StreamingContext
 
isCompleted() - Method in class org.apache.spark.ComplexFutureAction
 
isCompleted() - Method in interface org.apache.spark.FutureAction
Returns whether the action has already been completed with a value or an exception.
isCompleted() - Method in class org.apache.spark.SimpleFutureAction
 
isCompleted() - Method in class org.apache.spark.TaskContext
Returns true if the task has completed.
isDefined(Param<?>) - Method in interface org.apache.spark.ml.param.Params
 
isDir() - Method in class org.apache.spark.sql.sources.HadoopFsRelation.FakeFileStatus
 
isDistributed() - Method in class org.apache.spark.ml.clustering.DistributedLDAModel
 
isDistributed() - Method in class org.apache.spark.ml.clustering.LDAModel
Indicates whether this instance is of type DistributedLDAModel
isDistributed() - Method in class org.apache.spark.ml.clustering.LocalLDAModel
 
isDriver() - Method in class org.apache.spark.storage.BlockManagerId
 
isEmpty() - Method in interface org.apache.spark.api.java.JavaRDDLike
 
isEmpty() - Method in class org.apache.spark.rdd.PartitionCoalescer.LocationIterator
 
isEmpty() - Method in class org.apache.spark.rdd.RDD
 
isExecutorStartupConf(String) - Static method in class org.apache.spark.SparkConf
Return whether the given config should be passed to an executor on start-up.
isExperiment() - Method in class org.apache.spark.mllib.stat.test.BinarySample
 
isFinal() - Method in enum org.apache.spark.launcher.SparkAppHandle.State
Whether this state is a final state, meaning the application is not running anymore once it's reached.
isin(Object...) - Method in class org.apache.spark.sql.Column
A boolean expression that is evaluated to true if the value of this expression is contained by the evaluated values of the arguments.
isin(Seq<Object>) - Method in class org.apache.spark.sql.Column
A boolean expression that is evaluated to true if the value of this expression is contained by the evaluated values of the arguments.
isInitialValueFinal() - Method in class org.apache.spark.partial.PartialResult
 
isInterrupted() - Method in class org.apache.spark.TaskContext
Returns true if the task has been killed.
isLargerBetter() - Method in class org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
 
isLargerBetter() - Method in class org.apache.spark.ml.evaluation.Evaluator
Indicates whether the metric returned by evaluate() should be maximized (true, default) or minimized (false).
isLargerBetter() - Method in class org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
 
isLargerBetter() - Method in class org.apache.spark.ml.evaluation.RegressionEvaluator
 
isLeaf() - Method in class org.apache.spark.mllib.tree.model.Node
 
isLeftChild(int) - Static method in class org.apache.spark.mllib.tree.model.Node
Returns true if this is a left child.
isLocal() - Method in class org.apache.spark.api.java.JavaSparkContext
 
isLocal() - Method in class org.apache.spark.SparkContext
 
isLocal() - Method in class org.apache.spark.sql.DataFrame
Returns true if the collect and take methods can be run locally (without any Spark executors).
isMulticlassClassification() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
isMulticlassWithCategoricalFeatures() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
isMultipleOf(Duration) - Method in class org.apache.spark.streaming.Duration
 
isMultipleOf(Duration) - Method in class org.apache.spark.streaming.Time
 
isNaN() - Method in class org.apache.spark.sql.Column
True if the current expression is NaN.
isNaN(Column) - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.6.0, replaced by isnan. This will be removed in Spark 2.0.
isnan(Column) - Static method in class org.apache.spark.sql.functions
Return true iff the column is NaN.
isNominal() - Method in class org.apache.spark.ml.attribute.Attribute
Tests whether this attribute is nominal, true for NominalAttribute and BinaryAttribute.
isNominal() - Method in class org.apache.spark.ml.attribute.BinaryAttribute
 
isNominal() - Method in class org.apache.spark.ml.attribute.NominalAttribute
 
isNominal() - Method in class org.apache.spark.ml.attribute.NumericAttribute
 
isNominal() - Static method in class org.apache.spark.ml.attribute.UnresolvedAttribute
 
isNotNull() - Method in class org.apache.spark.sql.Column
True if the current expression is NOT null.
IsNotNull - Class in org.apache.spark.sql.sources
A filter that evaluates to true iff the attribute evaluates to a non-null value.
IsNotNull(String) - Constructor for class org.apache.spark.sql.sources.IsNotNull
 
isNull() - Method in class org.apache.spark.sql.Column
True if the current expression is null.
isnull(Column) - Static method in class org.apache.spark.sql.functions
Return true iff the column is null.
IsNull - Class in org.apache.spark.sql.sources
A filter that evaluates to true iff the attribute evaluates to null.
IsNull(String) - Constructor for class org.apache.spark.sql.sources.IsNull
 
isNullAt(int) - Method in interface org.apache.spark.sql.Row
Checks whether the value at position i is null.
isNumeric() - Method in class org.apache.spark.ml.attribute.Attribute
Tests whether this attribute is numeric, true for NumericAttribute and BinaryAttribute.
isNumeric() - Method in class org.apache.spark.ml.attribute.BinaryAttribute
 
isNumeric() - Method in class org.apache.spark.ml.attribute.NominalAttribute
 
isNumeric() - Method in class org.apache.spark.ml.attribute.NumericAttribute
 
isNumeric() - Static method in class org.apache.spark.ml.attribute.UnresolvedAttribute
 
isOrdinal() - Method in class org.apache.spark.ml.attribute.NominalAttribute
 
isotonic() - Method in class org.apache.spark.mllib.regression.IsotonicRegressionModel
 
IsotonicRegression - Class in org.apache.spark.ml.regression
 
IsotonicRegression(String) - Constructor for class org.apache.spark.ml.regression.IsotonicRegression
 
IsotonicRegression() - Constructor for class org.apache.spark.ml.regression.IsotonicRegression
 
IsotonicRegression - Class in org.apache.spark.mllib.regression
 
IsotonicRegression() - Constructor for class org.apache.spark.mllib.regression.IsotonicRegression
 
IsotonicRegressionModel - Class in org.apache.spark.ml.regression
:: Experimental :: Model fitted by IsotonicRegression.
IsotonicRegressionModel - Class in org.apache.spark.mllib.regression
Regression model for isotonic regression.
IsotonicRegressionModel(double[], double[], boolean) - Constructor for class org.apache.spark.mllib.regression.IsotonicRegressionModel
 
IsotonicRegressionModel(Iterable<Object>, Iterable<Object>, Boolean) - Constructor for class org.apache.spark.mllib.regression.IsotonicRegressionModel
A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter.
isRDD() - Method in class org.apache.spark.storage.BlockId
 
isRootContext() - Method in class org.apache.spark.sql.SQLContext
 
isRunningLocally() - Method in class org.apache.spark.TaskContext
Returns true if the task is running locally in the driver program.
isSet(Param<?>) - Method in interface org.apache.spark.ml.param.Params
 
isShuffle() - Method in class org.apache.spark.storage.BlockId
 
isSorted(int[]) - Method in class org.apache.spark.mllib.feature.ChiSqSelectorModel
 
isSparkPortConf(String) - Static method in class org.apache.spark.SparkConf
Return true if the given config matches either spark.*.port or spark.port.*.
isStarted() - Method in class org.apache.spark.streaming.receiver.Receiver
Check if the receiver has started or not.
isStopped() - Method in class org.apache.spark.SparkContext
 
isStopped() - Method in class org.apache.spark.SparkEnv
 
isStopped() - Method in class org.apache.spark.streaming.receiver.Receiver
Check if receiver has been marked for stopping.
isTimingOut() - Method in class org.apache.spark.streaming.State
Whether the state is timing out and going to be removed by the system after the current batch.
isTraceEnabled() - Method in interface org.apache.spark.Logging
 
isTransposed() - Method in class org.apache.spark.mllib.linalg.DenseMatrix
 
isTransposed() - Method in interface org.apache.spark.mllib.linalg.Matrix
Flag that keeps track whether the matrix is transposed or not.
isTransposed() - Method in class org.apache.spark.mllib.linalg.SparseMatrix
 
isValid() - Method in class org.apache.spark.ml.param.Param
 
isValid() - Method in class org.apache.spark.storage.StorageLevel
 
isZero() - Method in class org.apache.spark.sql.types.Decimal
 
isZero() - Method in class org.apache.spark.streaming.Duration
 
it() - Method in class org.apache.spark.rdd.PartitionCoalescer.LocationIterator
 
item() - Method in class org.apache.spark.ml.recommendation.ALS.Rating
 
itemFactors() - Method in class org.apache.spark.ml.recommendation.ALSModel
 
items() - Method in class org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
 
iterationTimes() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
 
iterator(Partition, TaskContext) - Method in interface org.apache.spark.api.java.JavaRDDLike
Internal method to this RDD; will read from cache if applicable, or otherwise compute it.
iterator(Partition, TaskContext) - Method in class org.apache.spark.rdd.RDD
Internal method to this RDD; will read from cache if applicable, or otherwise compute it.
iterator() - Method in class org.apache.spark.sql.types.StructType
 

J

j() - Method in class org.apache.spark.mllib.linalg.distributed.MatrixEntry
 
jarOfClass(Class<?>) - Static method in class org.apache.spark.api.java.JavaSparkContext
Find the JAR from which a given class was loaded, to make it easy for users to pass their JARs to SparkContext.
jarOfClass(Class<?>) - Static method in class org.apache.spark.SparkContext
Find the JAR from which a given class was loaded, to make it easy for users to pass their JARs to SparkContext.
jarOfClass(Class<?>) - Static method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Find the JAR from which a given class was loaded, to make it easy for users to pass their JARs to StreamingContext.
jarOfClass(Class<?>) - Static method in class org.apache.spark.streaming.StreamingContext
Find the JAR from which a given class was loaded, to make it easy for users to pass their JARs to StreamingContext.
jarOfObject(Object) - Static method in class org.apache.spark.api.java.JavaSparkContext
Find the JAR that contains the class of a particular object, to make it easy for users to pass their JARs to SparkContext.
jarOfObject(Object) - Static method in class org.apache.spark.SparkContext
Find the JAR that contains the class of a particular object, to make it easy for users to pass their JARs to SparkContext.
jars() - Method in class org.apache.spark.api.java.JavaSparkContext
 
jars() - Method in class org.apache.spark.SparkContext
 
javaAntecedent() - Method in class org.apache.spark.mllib.fpm.AssociationRules.Rule
Returns antecedent in a Java List.
javaCategoryMaps() - Method in class org.apache.spark.ml.feature.VectorIndexerModel
Java-friendly version of categoryMaps
javaConsequent() - Method in class org.apache.spark.mllib.fpm.AssociationRules.Rule
Returns consequent in a Java List.
JavaDoubleRDD - Class in org.apache.spark.api.java
 
JavaDoubleRDD(RDD<Object>) - Constructor for class org.apache.spark.api.java.JavaDoubleRDD
 
JavaDStream<T> - Class in org.apache.spark.streaming.api.java
A Java-friendly interface to DStream, the basic abstraction in Spark Streaming that represents a continuous stream of data.
JavaDStream(DStream<T>, ClassTag<T>) - Constructor for class org.apache.spark.streaming.api.java.JavaDStream
 
JavaDStreamLike<T,This extends JavaDStreamLike<T,This,R>,R extends JavaRDDLike<T,R>> - Interface in org.apache.spark.streaming.api.java
 
JavaFutureAction<T> - Interface in org.apache.spark.api.java
 
JavaHadoopRDD<K,V> - Class in org.apache.spark.api.java
 
JavaHadoopRDD(HadoopRDD<K, V>, ClassTag<K>, ClassTag<V>) - Constructor for class org.apache.spark.api.java.JavaHadoopRDD
 
JavaInputDStream<T> - Class in org.apache.spark.streaming.api.java
A Java-friendly interface to InputDStream.
JavaInputDStream(InputDStream<T>, ClassTag<T>) - Constructor for class org.apache.spark.streaming.api.java.JavaInputDStream
 
javaItems() - Method in class org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
Returns items in a Java List.
JavaIterableWrapperSerializer - Class in org.apache.spark.serializer
A Kryo serializer for serializing results returned by asJavaIterable.
JavaIterableWrapperSerializer() - Constructor for class org.apache.spark.serializer.JavaIterableWrapperSerializer
 
JavaMapWithStateDStream<KeyType,ValueType,StateType,MappedType> - Class in org.apache.spark.streaming.api.java
:: Experimental :: DStream representing the stream of data generated by mapWithState operation on a JavaPairDStream.
JavaNewHadoopRDD<K,V> - Class in org.apache.spark.api.java
 
JavaNewHadoopRDD(NewHadoopRDD<K, V>, ClassTag<K>, ClassTag<V>) - Constructor for class org.apache.spark.api.java.JavaNewHadoopRDD
 
JavaPairDStream<K,V> - Class in org.apache.spark.streaming.api.java
A Java-friendly interface to a DStream of key-value pairs, which provides extra methods like reduceByKey and join.
JavaPairDStream(DStream<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - Constructor for class org.apache.spark.streaming.api.java.JavaPairDStream
 
JavaPairInputDStream<K,V> - Class in org.apache.spark.streaming.api.java
A Java-friendly interface to InputDStream of key-value pairs.
JavaPairInputDStream(InputDStream<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - Constructor for class org.apache.spark.streaming.api.java.JavaPairInputDStream
 
JavaPairRDD<K,V> - Class in org.apache.spark.api.java
 
JavaPairRDD(RDD<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - Constructor for class org.apache.spark.api.java.JavaPairRDD
 
JavaPairReceiverInputDStream<K,V> - Class in org.apache.spark.streaming.api.java
A Java-friendly interface to ReceiverInputDStream, the abstract class for defining any input stream that receives data over the network.
JavaPairReceiverInputDStream(ReceiverInputDStream<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - Constructor for class org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream
 
JavaParams - Class in org.apache.spark.ml.param
:: DeveloperApi :: Java-friendly wrapper for Params.
JavaParams() - Constructor for class org.apache.spark.ml.param.JavaParams
 
JavaRDD<T> - Class in org.apache.spark.api.java
 
JavaRDD(RDD<T>, ClassTag<T>) - Constructor for class org.apache.spark.api.java.JavaRDD
 
javaRDD() - Method in class org.apache.spark.sql.DataFrame
Returns the content of the DataFrame as a JavaRDD of Rows.
JavaRDDLike<T,This extends JavaRDDLike<T,This>> - Interface in org.apache.spark.api.java
Defines operations common to several Java RDD implementations.
JavaReceiverInputDStream<T> - Class in org.apache.spark.streaming.api.java
A Java-friendly interface to ReceiverInputDStream, the abstract class for defining any input stream that receives data over the network.
JavaReceiverInputDStream(ReceiverInputDStream<T>, ClassTag<T>) - Constructor for class org.apache.spark.streaming.api.java.JavaReceiverInputDStream
 
javaSequence() - Method in class org.apache.spark.mllib.fpm.PrefixSpan.FreqSequence
Returns sequence as a Java List of lists for Java users.
javaSerialization(ClassTag<T>) - Static method in class org.apache.spark.sql.Encoders
(Scala-specific) Creates an encoder that serializes objects of type T using generic Java serialization.
javaSerialization(Class<T>) - Static method in class org.apache.spark.sql.Encoders
Creates an encoder that serializes objects of type T using generic Java serialization.
JavaSerializer - Class in org.apache.spark.serializer
:: DeveloperApi :: A Spark serializer that uses Java's built-in serialization.
JavaSerializer(SparkConf) - Constructor for class org.apache.spark.serializer.JavaSerializer
 
JavaSerializer() - Constructor for class org.apache.spark.serializer.JavaSerializer
 
JavaSparkContext - Class in org.apache.spark.api.java
A Java-friendly version of SparkContext that returns JavaRDDs and works with Java collections instead of Scala ones.
JavaSparkContext(SparkContext) - Constructor for class org.apache.spark.api.java.JavaSparkContext
 
JavaSparkContext() - Constructor for class org.apache.spark.api.java.JavaSparkContext
Create a JavaSparkContext that loads settings from system properties (for instance, when launching with ./bin/spark-submit).
JavaSparkContext(SparkConf) - Constructor for class org.apache.spark.api.java.JavaSparkContext
 
JavaSparkContext(String, String) - Constructor for class org.apache.spark.api.java.JavaSparkContext
 
JavaSparkContext(String, String, SparkConf) - Constructor for class org.apache.spark.api.java.JavaSparkContext
 
JavaSparkContext(String, String, String, String) - Constructor for class org.apache.spark.api.java.JavaSparkContext
 
JavaSparkContext(String, String, String, String[]) - Constructor for class org.apache.spark.api.java.JavaSparkContext
 
JavaSparkContext(String, String, String, String[], Map<String, String>) - Constructor for class org.apache.spark.api.java.JavaSparkContext
 
JavaSparkListener - Class in org.apache.spark
Java clients should extend this class instead of implementing SparkListener directly.
JavaSparkListener() - Constructor for class org.apache.spark.JavaSparkListener
 
JavaSparkStatusTracker - Class in org.apache.spark.api.java
Low-level status reporting APIs for monitoring job and stage progress.
JavaStreamingContext - Class in org.apache.spark.streaming.api.java
A Java-friendly version of StreamingContext which is the main entry point for Spark Streaming functionality.
JavaStreamingContext(StreamingContext) - Constructor for class org.apache.spark.streaming.api.java.JavaStreamingContext
 
JavaStreamingContext(String, String, Duration) - Constructor for class org.apache.spark.streaming.api.java.JavaStreamingContext
Create a StreamingContext.
JavaStreamingContext(String, String, Duration, String, String) - Constructor for class org.apache.spark.streaming.api.java.JavaStreamingContext
Create a StreamingContext.
JavaStreamingContext(String, String, Duration, String, String[]) - Constructor for class org.apache.spark.streaming.api.java.JavaStreamingContext
Create a StreamingContext.
JavaStreamingContext(String, String, Duration, String, String[], Map<String, String>) - Constructor for class org.apache.spark.streaming.api.java.JavaStreamingContext
Create a StreamingContext.
JavaStreamingContext(JavaSparkContext, Duration) - Constructor for class org.apache.spark.streaming.api.java.JavaStreamingContext
Create a JavaStreamingContext using an existing JavaSparkContext.
JavaStreamingContext(SparkConf, Duration) - Constructor for class org.apache.spark.streaming.api.java.JavaStreamingContext
Create a JavaStreamingContext using a SparkConf configuration.
JavaStreamingContext(String) - Constructor for class org.apache.spark.streaming.api.java.JavaStreamingContext
Recreate a JavaStreamingContext from a checkpoint file.
JavaStreamingContext(String, Configuration) - Constructor for class org.apache.spark.streaming.api.java.JavaStreamingContext
Re-creates a JavaStreamingContext from a checkpoint file.
JavaStreamingContextFactory - Interface in org.apache.spark.streaming.api.java
Factory interface for creating a new JavaStreamingContext
javaTopicAssignments() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
Java-friendly version of topicAssignments
javaTopicDistributions() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
Java-friendly version of topicDistributions
javaTopTopicsPerDocument(int) - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
Java-friendly version of topTopicsPerDocument
javaToPython() - Method in class org.apache.spark.sql.DataFrame
Converts a JavaRDD to a PythonRDD.
jdbc(String, String, Properties) - Method in class org.apache.spark.sql.DataFrameReader
Construct a DataFrame representing the database table accessible via JDBC URL url named table and connection properties.
jdbc(String, String, String, long, long, int, Properties) - Method in class org.apache.spark.sql.DataFrameReader
Construct a DataFrame representing the database table accessible via JDBC URL url named table.
jdbc(String, String, String[], Properties) - Method in class org.apache.spark.sql.DataFrameReader
Construct a DataFrame representing the database table accessible via JDBC URL url named table using connection properties.
jdbc(String, String, Properties) - Method in class org.apache.spark.sql.DataFrameWriter
Saves the content of the DataFrame to a external database table via JDBC.
jdbc(String, String) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().jdbc(). This will be removed in Spark 2.0.
jdbc(String, String, String, long, long, int) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().jdbc(). This will be removed in Spark 2.0.
jdbc(String, String, String[]) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().jdbc(). This will be removed in Spark 2.0.
JdbcDialect - Class in org.apache.spark.sql.jdbc
:: DeveloperApi :: Encapsulates everything (extensions, workarounds, quirks) to handle the SQL dialect of a certain database or jdbc driver.
JdbcDialect() - Constructor for class org.apache.spark.sql.jdbc.JdbcDialect
 
JdbcDialects - Class in org.apache.spark.sql.jdbc
:: DeveloperApi :: Registry of dialects that apply to every new jdbc DataFrame.
JdbcDialects() - Constructor for class org.apache.spark.sql.jdbc.JdbcDialects
 
jdbcNullType() - Method in class org.apache.spark.sql.jdbc.JdbcType
 
JdbcRDD<T> - Class in org.apache.spark.rdd
An RDD that executes an SQL query on a JDBC connection and reads results.
JdbcRDD(SparkContext, Function0<Connection>, String, long, long, int, Function1<ResultSet, T>, ClassTag<T>) - Constructor for class org.apache.spark.rdd.JdbcRDD
 
JdbcRDD.ConnectionFactory - Interface in org.apache.spark.rdd
 
JdbcType - Class in org.apache.spark.sql.jdbc
:: DeveloperApi :: A database type definition coupled with the jdbc type needed to send null values to the database.
JdbcType(String, int) - Constructor for class org.apache.spark.sql.jdbc.JdbcType
 
jobConfCacheKey() - Method in class org.apache.spark.rdd.HadoopRDD
 
JobData - Class in org.apache.spark.status.api.v1
 
JobExecutionStatus - Enum in org.apache.spark
 
jobGroup() - Method in class org.apache.spark.status.api.v1.JobData
 
jobGroupToJobIds() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
jobId() - Method in class org.apache.spark.rdd.NewHadoopRDD
 
jobId() - Method in class org.apache.spark.scheduler.SparkListenerJobEnd
 
jobId() - Method in class org.apache.spark.scheduler.SparkListenerJobStart
 
jobId() - Method in interface org.apache.spark.SparkJobInfo
 
jobId() - Method in class org.apache.spark.SparkJobInfoImpl
 
jobId() - Method in class org.apache.spark.status.api.v1.JobData
 
jobID() - Method in class org.apache.spark.TaskCommitDenied
 
jobIds() - Method in interface org.apache.spark.api.java.JavaFutureAction
Returns the job IDs run by the underlying async operation.
jobIds() - Method in class org.apache.spark.ComplexFutureAction
 
jobIds() - Method in interface org.apache.spark.FutureAction
Returns the job IDs run by the underlying async operation.
jobIds() - Method in class org.apache.spark.SimpleFutureAction
 
jobIdToData() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
JobLogger - Class in org.apache.spark.scheduler
:: DeveloperApi :: A logger class to record runtime information for jobs in Spark.
JobLogger(String, String) - Constructor for class org.apache.spark.scheduler.JobLogger
 
JobLogger() - Constructor for class org.apache.spark.scheduler.JobLogger
 
jobLogInfo(int, String, boolean) - Method in class org.apache.spark.scheduler.JobLogger
Write info into log file
JobProgressListener - Class in org.apache.spark.ui.jobs
:: DeveloperApi :: Tracks task-level information to be displayed in the UI.
JobProgressListener(SparkConf) - Constructor for class org.apache.spark.ui.jobs.JobProgressListener
 
JobResult - Interface in org.apache.spark.scheduler
:: DeveloperApi :: A result of a job in the DAGScheduler.
jobResult() - Method in class org.apache.spark.scheduler.SparkListenerJobEnd
 
JobSucceeded - Class in org.apache.spark.scheduler
 
JobSucceeded() - Constructor for class org.apache.spark.scheduler.JobSucceeded
 
join(JavaPairRDD<K, W>, Partitioner) - Method in class org.apache.spark.api.java.JavaPairRDD
Merge the values for each key using an associative reduce function.
join(JavaPairRDD<K, W>) - Method in class org.apache.spark.api.java.JavaPairRDD
Return an RDD containing all pairs of elements with matching keys in this and other.
join(JavaPairRDD<K, W>, int) - Method in class org.apache.spark.api.java.JavaPairRDD
Return an RDD containing all pairs of elements with matching keys in this and other.
join(RDD<Tuple2<K, W>>, Partitioner) - Method in class org.apache.spark.rdd.PairRDDFunctions
Return an RDD containing all pairs of elements with matching keys in this and other.
join(RDD<Tuple2<K, W>>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Return an RDD containing all pairs of elements with matching keys in this and other.
join(RDD<Tuple2<K, W>>, int) - Method in class org.apache.spark.rdd.PairRDDFunctions
Return an RDD containing all pairs of elements with matching keys in this and other.
join(DataFrame) - Method in class org.apache.spark.sql.DataFrame
Cartesian join with another DataFrame.
join(DataFrame, String) - Method in class org.apache.spark.sql.DataFrame
Inner equi-join with another DataFrame using the given column.
join(DataFrame, Seq<String>) - Method in class org.apache.spark.sql.DataFrame
Inner equi-join with another DataFrame using the given columns.
join(DataFrame, Seq<String>, String) - Method in class org.apache.spark.sql.DataFrame
Equi-join with another DataFrame using the given columns.
join(DataFrame, Column) - Method in class org.apache.spark.sql.DataFrame
Inner join with another DataFrame, using the given join expression.
join(DataFrame, Column, String) - Method in class org.apache.spark.sql.DataFrame
Join with another DataFrame, using the given join expression.
join(JavaPairDStream<K, W>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying 'join' between RDDs of this DStream and other DStream.
join(JavaPairDStream<K, W>, int) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying 'join' between RDDs of this DStream and other DStream.
join(JavaPairDStream<K, W>, Partitioner) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying 'join' between RDDs of this DStream and other DStream.
join(DStream<Tuple2<K, W>>, ClassTag<W>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying 'join' between RDDs of this DStream and other DStream.
join(DStream<Tuple2<K, W>>, int, ClassTag<W>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying 'join' between RDDs of this DStream and other DStream.
join(DStream<Tuple2<K, W>>, Partitioner, ClassTag<W>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying 'join' between RDDs of this DStream and other DStream.
joinVertices(RDD<Tuple2<Object, U>>, Function3<Object, VD, U, VD>, ClassTag<U>) - Method in class org.apache.spark.graphx.GraphOps
Join the vertices with an RDD and then apply a function from the vertex and RDD entry to a new vertex value.
joinWith(Dataset<U>, Column, String) - Method in class org.apache.spark.sql.Dataset
Joins this Dataset returning a Tuple2 for each pair where condition evaluates to true.
joinWith(Dataset<U>, Column) - Method in class org.apache.spark.sql.Dataset
Using inner equi-join to join this Dataset returning a Tuple2 for each pair where condition evaluates to true.
json(String) - Method in class org.apache.spark.sql.DataFrameReader
Loads a JSON file (one object per line) and returns the result as a DataFrame.
json(Seq<String>) - Method in class org.apache.spark.sql.DataFrameReader
Loads a JSON file (one object per line) and returns the result as a DataFrame.
json(JavaRDD<String>) - Method in class org.apache.spark.sql.DataFrameReader
Loads an JavaRDD[String] storing JSON objects (one object per record) and returns the result as a DataFrame.
json(RDD<String>) - Method in class org.apache.spark.sql.DataFrameReader
Loads an RDD[String] storing JSON objects (one object per record) and returns the result as a DataFrame.
json(String) - Method in class org.apache.spark.sql.DataFrameWriter
Saves the content of the DataFrame in JSON format at the specified path.
json() - Method in class org.apache.spark.sql.types.DataType
The compact JSON representation of this data type.
json() - Method in class org.apache.spark.sql.types.Metadata
Converts to its JSON representation.
json_tuple(Column, String...) - Static method in class org.apache.spark.sql.functions
Creates a new row for a json column according to the given field names.
json_tuple(Column, Seq<String>) - Static method in class org.apache.spark.sql.functions
Creates a new row for a json column according to the given field names.
jsonDecode(String) - Method in class org.apache.spark.ml.param.BooleanParam
 
jsonDecode(String) - Method in class org.apache.spark.ml.param.DoubleArrayParam
 
jsonDecode(String) - Method in class org.apache.spark.ml.param.DoubleParam
 
jsonDecode(String) - Method in class org.apache.spark.ml.param.FloatParam
 
jsonDecode(String) - Method in class org.apache.spark.ml.param.IntArrayParam
 
jsonDecode(String) - Method in class org.apache.spark.ml.param.IntParam
 
jsonDecode(String) - Method in class org.apache.spark.ml.param.LongParam
 
jsonDecode(String) - Method in class org.apache.spark.ml.param.Param
Decodes a param value from JSON.
jsonDecode(String) - Method in class org.apache.spark.ml.param.StringArrayParam
 
jsonEncode(boolean) - Method in class org.apache.spark.ml.param.BooleanParam
 
jsonEncode(double[]) - Method in class org.apache.spark.ml.param.DoubleArrayParam
 
jsonEncode(double) - Method in class org.apache.spark.ml.param.DoubleParam
 
jsonEncode(float) - Method in class org.apache.spark.ml.param.FloatParam
 
jsonEncode(int[]) - Method in class org.apache.spark.ml.param.IntArrayParam
 
jsonEncode(int) - Method in class org.apache.spark.ml.param.IntParam
 
jsonEncode(long) - Method in class org.apache.spark.ml.param.LongParam
 
jsonEncode(T) - Method in class org.apache.spark.ml.param.Param
Encodes a param value into JSON, which can be decoded by jsonDecode().
jsonEncode(String[]) - Method in class org.apache.spark.ml.param.StringArrayParam
 
jsonFile(String) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().json(). This will be removed in Spark 2.0.
jsonFile(String, StructType) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().json(). This will be removed in Spark 2.0.
jsonFile(String, double) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().json(). This will be removed in Spark 2.0.
jsonRDD(RDD<String>) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().json(). This will be removed in Spark 2.0.
jsonRDD(JavaRDD<String>) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().json(). This will be removed in Spark 2.0.
jsonRDD(RDD<String>, StructType) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().json(). This will be removed in Spark 2.0.
jsonRDD(JavaRDD<String>, StructType) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().json(). This will be removed in Spark 2.0.
jsonRDD(RDD<String>, double) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().json(). This will be removed in Spark 2.0.
jsonRDD(JavaRDD<String>, double) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().json(). This will be removed in Spark 2.0.
jValueDecode(JsonAST.JValue) - Static method in class org.apache.spark.ml.param.DoubleParam
Decodes a param value from JValue.
jValueDecode(JsonAST.JValue) - Static method in class org.apache.spark.ml.param.FloatParam
Decodes a param value from JValue.
jValueEncode(double) - Static method in class org.apache.spark.ml.param.DoubleParam
Encodes a param value into JValue.
jValueEncode(float) - Static method in class org.apache.spark.ml.param.FloatParam
Encodes a param value into JValue.
jvmGcTime() - Method in class org.apache.spark.status.api.v1.TaskMetricDistributions
 
jvmGcTime() - Method in class org.apache.spark.status.api.v1.TaskMetrics
 
jvmInformation() - Method in class org.apache.spark.ui.env.EnvironmentListener
 

K

k() - Method in class org.apache.spark.mllib.clustering.BisectingKMeansModel
Number of leaf clusters.
k() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
 
k() - Method in class org.apache.spark.mllib.clustering.EMLDAOptimizer
 
k() - Method in class org.apache.spark.mllib.clustering.ExpectationSum
 
k() - Method in class org.apache.spark.mllib.clustering.GaussianMixtureModel
Number of gaussians in mixture
k() - Method in class org.apache.spark.mllib.clustering.KMeansModel
Total number of clusters.
k() - Method in class org.apache.spark.mllib.clustering.LDAModel
Number of topics
k() - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
 
k() - Method in class org.apache.spark.mllib.clustering.PowerIterationClusteringModel
 
k() - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
 
k() - Method in class org.apache.spark.mllib.feature.PCA
 
k() - Method in class org.apache.spark.mllib.feature.PCAModel
 
K_MEANS_PARALLEL() - Static method in class org.apache.spark.mllib.clustering.KMeans
 
KafkaUtils - Class in org.apache.spark.streaming.kafka
 
KafkaUtils() - Constructor for class org.apache.spark.streaming.kafka.KafkaUtils
 
kClassTag() - Method in class org.apache.spark.api.java.JavaHadoopRDD
 
kClassTag() - Method in class org.apache.spark.api.java.JavaNewHadoopRDD
 
kClassTag() - Method in class org.apache.spark.api.java.JavaPairRDD
 
kClassTag() - Method in class org.apache.spark.streaming.api.java.JavaPairInputDStream
 
kClassTag() - Method in class org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream
 
KernelDensity - Class in org.apache.spark.mllib.stat
Kernel density estimation.
KernelDensity() - Constructor for class org.apache.spark.mllib.stat.KernelDensity
 
keyAs(Encoder<L>) - Method in class org.apache.spark.sql.GroupedDataset
Returns a new GroupedDataset where the type of the key has been mapped to the specified type.
keyBy(Function<T, U>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Creates tuples of the elements in this RDD by applying f.
keyBy(Function1<T, K>) - Method in class org.apache.spark.rdd.RDD
Creates tuples of the elements in this RDD by applying f.
keyClassName() - Method in class org.apache.spark.ShuffleDependency
 
keyOrdering() - Method in class org.apache.spark.ShuffleDependency
 
keys() - Method in class org.apache.spark.api.java.JavaPairRDD
Return an RDD with the keys of each tuple.
keys() - Method in class org.apache.spark.rdd.PairRDDFunctions
Return an RDD with the keys of each tuple.
keys() - Method in class org.apache.spark.sql.GroupedDataset
Returns a Dataset that contains each unique key.
keyType() - Method in class org.apache.spark.sql.types.MapType
 
kFold(RDD<T>, int, int, ClassTag<T>) - Static method in class org.apache.spark.mllib.util.MLUtils
Return a k element array of pairs of RDDs with the first element of each pair containing the training data, a complement of the validation data and the second element, the validation data, containing a unique 1/kth of the data.
kill() - Method in interface org.apache.spark.launcher.SparkAppHandle
Tries to kill the underlying application.
killExecutor(String) - Method in class org.apache.spark.SparkContext
:: DeveloperApi :: Request that the cluster manager kill the specified executor.
killExecutors(Seq<String>) - Method in class org.apache.spark.SparkContext
:: DeveloperApi :: Request that the cluster manager kill the specified executors.
KillTask - Class in org.apache.spark.scheduler.local
 
KillTask(long, boolean) - Constructor for class org.apache.spark.scheduler.local.KillTask
 
KinesisUtils - Class in org.apache.spark.streaming.kinesis
 
KinesisUtils() - Constructor for class org.apache.spark.streaming.kinesis.KinesisUtils
 
KinesisUtilsPythonHelper - Class in org.apache.spark.streaming.kinesis
This is a helper class that wraps the methods in KinesisUtils into more Python-friendly class and function so that it can be easily instantiated and called from Python's KinesisUtils.
KinesisUtilsPythonHelper() - Constructor for class org.apache.spark.streaming.kinesis.KinesisUtilsPythonHelper
 
kManifest() - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
 
KMeans - Class in org.apache.spark.ml.clustering
 
KMeans(String) - Constructor for class org.apache.spark.ml.clustering.KMeans
 
KMeans() - Constructor for class org.apache.spark.ml.clustering.KMeans
 
KMeans - Class in org.apache.spark.mllib.clustering
K-means clustering with support for multiple parallel runs and a k-means++ like initialization mode (the k-means|| algorithm by Bahmani et al).
KMeans() - Constructor for class org.apache.spark.mllib.clustering.KMeans
Constructs a KMeans instance with default parameters: {k: 2, maxIterations: 20, runs: 1, initializationMode: "k-means||", initializationSteps: 5, epsilon: 1e-4, seed: random}.
KMeansDataGenerator - Class in org.apache.spark.mllib.util
:: DeveloperApi :: Generate test data for KMeans.
KMeansDataGenerator() - Constructor for class org.apache.spark.mllib.util.KMeansDataGenerator
 
KMeansModel - Class in org.apache.spark.ml.clustering
:: Experimental :: Model fitted by KMeans.
KMeansModel - Class in org.apache.spark.mllib.clustering
A clustering model for K-means.
KMeansModel(Vector[]) - Constructor for class org.apache.spark.mllib.clustering.KMeansModel
 
KMeansModel(Iterable<Vector>) - Constructor for class org.apache.spark.mllib.clustering.KMeansModel
A Java-friendly constructor that takes an Iterable of Vectors.
kolmogorovSmirnovTest(RDD<Object>, String, double...) - Static method in class org.apache.spark.mllib.stat.Statistics
Convenience function to conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability distribution equality.
kolmogorovSmirnovTest(JavaDoubleRDD, String, double...) - Static method in class org.apache.spark.mllib.stat.Statistics
Java-friendly version of kolmogorovSmirnovTest()
kolmogorovSmirnovTest(RDD<Object>, Function1<Object, Object>) - Static method in class org.apache.spark.mllib.stat.Statistics
Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a continuous distribution.
kolmogorovSmirnovTest(RDD<Object>, String, Seq<Object>) - Static method in class org.apache.spark.mllib.stat.Statistics
Convenience function to conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability distribution equality.
kolmogorovSmirnovTest(JavaDoubleRDD, String, Seq<Object>) - Static method in class org.apache.spark.mllib.stat.Statistics
Java-friendly version of kolmogorovSmirnovTest()
KolmogorovSmirnovTestResult - Class in org.apache.spark.mllib.stat.test
:: Experimental :: Object containing the test results for the Kolmogorov-Smirnov test.
kryo(ClassTag<T>) - Static method in class org.apache.spark.sql.Encoders
(Scala-specific) Creates an encoder that serializes objects of type T using Kryo.
kryo(Class<T>) - Static method in class org.apache.spark.sql.Encoders
Creates an encoder that serializes objects of type T using Kryo.
KryoRegistrator - Interface in org.apache.spark.serializer
Interface implemented by clients to register their classes with Kryo when using Kryo serialization.
KryoSerializer - Class in org.apache.spark.serializer
A Spark serializer that uses the Kryo serialization library.
KryoSerializer(SparkConf) - Constructor for class org.apache.spark.serializer.KryoSerializer
 
kurtosis(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the kurtosis of the values in a group.
kurtosis(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the kurtosis of the values in a group.

L

L1Updater - Class in org.apache.spark.mllib.optimization
:: DeveloperApi :: Updater for L1 regularized problems.
L1Updater() - Constructor for class org.apache.spark.mllib.optimization.L1Updater
 
label() - Method in class org.apache.spark.mllib.regression.LabeledPoint
 
labelCol() - Method in class org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
 
labelCol() - Method in interface org.apache.spark.ml.classification.LogisticRegressionSummary
Field in "predictions" which gives the true label of each instance.
labelCol() - Method in class org.apache.spark.ml.regression.LinearRegressionSummary
 
LabelConverter - Class in org.apache.spark.ml.classification
Label to vector converter.
LabelConverter() - Constructor for class org.apache.spark.ml.classification.LabelConverter
 
LabeledPoint - Class in org.apache.spark.mllib.regression
Class that represents the features and labels of a data point.
LabeledPoint(double, Vector) - Constructor for class org.apache.spark.mllib.regression.LabeledPoint
 
LabelPropagation - Class in org.apache.spark.graphx.lib
Label Propagation algorithm.
LabelPropagation() - Constructor for class org.apache.spark.graphx.lib.LabelPropagation
 
labels() - Method in class org.apache.spark.ml.feature.IndexToString
 
labels() - Method in class org.apache.spark.ml.feature.StringIndexerModel
 
labels() - Method in class org.apache.spark.mllib.classification.NaiveBayesModel
 
labels() - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns the sequence of labels in ascending order
labels() - Method in class org.apache.spark.mllib.evaluation.MultilabelMetrics
Returns the sequence of labels in ascending order
lag(Column, int) - Static method in class org.apache.spark.sql.functions
Window function: returns the value that is offset rows before the current row, and null if there is less than offset rows before the current row.
lag(String, int) - Static method in class org.apache.spark.sql.functions
Window function: returns the value that is offset rows before the current row, and null if there is less than offset rows before the current row.
lag(String, int, Object) - Static method in class org.apache.spark.sql.functions
Window function: returns the value that is offset rows before the current row, and defaultValue if there is less than offset rows before the current row.
lag(Column, int, Object) - Static method in class org.apache.spark.sql.functions
Window function: returns the value that is offset rows before the current row, and defaultValue if there is less than offset rows before the current row.
LassoModel - Class in org.apache.spark.mllib.regression
Regression model trained using Lasso.
LassoModel(Vector, double) - Constructor for class org.apache.spark.mllib.regression.LassoModel
 
LassoWithSGD - Class in org.apache.spark.mllib.regression
Train a regression model with L1-regularization using Stochastic Gradient Descent.
LassoWithSGD() - Constructor for class org.apache.spark.mllib.regression.LassoWithSGD
Construct a Lasso object with default parameters: {stepSize: 1.0, numIterations: 100, regParam: 0.01, miniBatchFraction: 1.0}.
last(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the last value in a group.
last(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the last value of the column in a group.
last_day(Column) - Static method in class org.apache.spark.sql.functions
Given a date column, returns the last day of the month which the given date belongs to.
lastError() - Method in class org.apache.spark.streaming.scheduler.ReceiverInfo
 
lastErrorMessage() - Method in class org.apache.spark.streaming.scheduler.ReceiverInfo
 
lastErrorTime() - Method in class org.apache.spark.streaming.scheduler.ReceiverInfo
 
lastValidTime() - Method in class org.apache.spark.streaming.dstream.InputDStream
 
latestModel() - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
Return the latest model.
latestModel() - Method in class org.apache.spark.mllib.regression.StreamingLinearAlgorithm
Return the latest model.
launch() - Method in class org.apache.spark.launcher.SparkLauncher
Launches a sub-process that will start the configured Spark application.
launchTime() - Method in class org.apache.spark.scheduler.TaskInfo
 
launchTime() - Method in class org.apache.spark.status.api.v1.TaskData
 
layers() - Method in class org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel
 
LBFGS - Class in org.apache.spark.mllib.optimization
:: DeveloperApi :: Class used to solve an optimization problem using Limited-memory BFGS.
LBFGS(Gradient, Updater) - Constructor for class org.apache.spark.mllib.optimization.LBFGS
 
LDA - Class in org.apache.spark.ml.clustering
:: Experimental ::
LDA(String) - Constructor for class org.apache.spark.ml.clustering.LDA
 
LDA() - Constructor for class org.apache.spark.ml.clustering.LDA
 
LDA - Class in org.apache.spark.mllib.clustering
Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
LDA() - Constructor for class org.apache.spark.mllib.clustering.LDA
Constructs a LDA instance with default parameters.
LDAModel - Class in org.apache.spark.ml.clustering
:: Experimental :: Model fitted by LDA.
LDAModel - Class in org.apache.spark.mllib.clustering
Latent Dirichlet Allocation (LDA) model.
LDAOptimizer - Interface in org.apache.spark.mllib.clustering
:: DeveloperApi ::
lead(String, int) - Static method in class org.apache.spark.sql.functions
Window function: returns the value that is offset rows after the current row, and null if there is less than offset rows after the current row.
lead(Column, int) - Static method in class org.apache.spark.sql.functions
Window function: returns the value that is offset rows after the current row, and null if there is less than offset rows after the current row.
lead(String, int, Object) - Static method in class org.apache.spark.sql.functions
Window function: returns the value that is offset rows after the current row, and defaultValue if there is less than offset rows after the current row.
lead(Column, int, Object) - Static method in class org.apache.spark.sql.functions
Window function: returns the value that is offset rows after the current row, and defaultValue if there is less than offset rows after the current row.
LeafNode - Class in org.apache.spark.ml.tree
:: DeveloperApi :: Decision tree leaf node.
learningRate() - Method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
least(Column...) - Static method in class org.apache.spark.sql.functions
Returns the least value of the list of values, skipping null values.
least(String, String...) - Static method in class org.apache.spark.sql.functions
Returns the least value of the list of column names, skipping null values.
least(Seq<Column>) - Static method in class org.apache.spark.sql.functions
Returns the least value of the list of values, skipping null values.
least(String, Seq<String>) - Static method in class org.apache.spark.sql.functions
Returns the least value of the list of column names, skipping null values.
LeastSquaresAggregator - Class in org.apache.spark.ml.regression
LeastSquaresAggregator computes the gradient and loss for a Least-squared loss function, as used in linear regression for samples in sparse or dense vector in a online fashion.
LeastSquaresAggregator(Vector, double, double, boolean, double[], double[]) - Constructor for class org.apache.spark.ml.regression.LeastSquaresAggregator
 
LeastSquaresCostFun - Class in org.apache.spark.ml.regression
LeastSquaresCostFun implements Breeze's DiffFunction[T] for Least Squares cost.
LeastSquaresCostFun(RDD<org.apache.spark.ml.feature.Instance>, double, double, boolean, boolean, double[], double[], double) - Constructor for class org.apache.spark.ml.regression.LeastSquaresCostFun
 
LeastSquaresGradient - Class in org.apache.spark.mllib.optimization
:: DeveloperApi :: Compute gradient and loss for a Least-squared loss function, as used in linear regression.
LeastSquaresGradient() - Constructor for class org.apache.spark.mllib.optimization.LeastSquaresGradient
 
left() - Method in class org.apache.spark.sql.sources.And
 
left() - Method in class org.apache.spark.sql.sources.Or
 
leftCategories() - Method in class org.apache.spark.ml.tree.CategoricalSplit
Get sorted categories which split to the left
leftChild() - Method in class org.apache.spark.ml.tree.InternalNode
 
leftChildIndex(int) - Static method in class org.apache.spark.mllib.tree.model.Node
Return the index of the left child of this node.
leftImpurity() - Method in class org.apache.spark.mllib.tree.model.InformationGainStats
 
leftJoin(RDD<Tuple2<Object, VD2>>, Function3<Object, VD, Option<VD2>, VD3>, ClassTag<VD2>, ClassTag<VD3>) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
leftJoin(RDD<Tuple2<Object, VD2>>, Function3<Object, VD, Option<VD2>, VD3>, ClassTag<VD2>, ClassTag<VD3>) - Method in class org.apache.spark.graphx.VertexRDD
Left joins this VertexRDD with an RDD containing vertex attribute pairs.
leftNode() - Method in class org.apache.spark.mllib.tree.model.Node
 
leftOuterJoin(JavaPairRDD<K, W>, Partitioner) - Method in class org.apache.spark.api.java.JavaPairRDD
Perform a left outer join of this and other.
leftOuterJoin(JavaPairRDD<K, W>) - Method in class org.apache.spark.api.java.JavaPairRDD
Perform a left outer join of this and other.
leftOuterJoin(JavaPairRDD<K, W>, int) - Method in class org.apache.spark.api.java.JavaPairRDD
Perform a left outer join of this and other.
leftOuterJoin(RDD<Tuple2<K, W>>, Partitioner) - Method in class org.apache.spark.rdd.PairRDDFunctions
Perform a left outer join of this and other.
leftOuterJoin(RDD<Tuple2<K, W>>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Perform a left outer join of this and other.
leftOuterJoin(RDD<Tuple2<K, W>>, int) - Method in class org.apache.spark.rdd.PairRDDFunctions
Perform a left outer join of this and other.
leftOuterJoin(JavaPairDStream<K, W>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream.
leftOuterJoin(JavaPairDStream<K, W>, int) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream.
leftOuterJoin(JavaPairDStream<K, W>, Partitioner) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream.
leftOuterJoin(DStream<Tuple2<K, W>>, ClassTag<W>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream.
leftOuterJoin(DStream<Tuple2<K, W>>, int, ClassTag<W>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream.
leftOuterJoin(DStream<Tuple2<K, W>>, Partitioner, ClassTag<W>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream.
leftPredict() - Method in class org.apache.spark.mllib.tree.model.InformationGainStats
 
leftZipJoin(VertexRDD<VD2>, Function3<Object, VD, Option<VD2>, VD3>, ClassTag<VD2>, ClassTag<VD3>) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
leftZipJoin(VertexRDD<VD2>, Function3<Object, VD, Option<VD2>, VD3>, ClassTag<VD2>, ClassTag<VD3>) - Method in class org.apache.spark.graphx.VertexRDD
Left joins this RDD with another VertexRDD with the same index.
LEGACY_DRIVER_IDENTIFIER() - Static method in class org.apache.spark.SparkContext
Legacy version of DRIVER_IDENTIFIER, retained for backwards-compatibility.
length() - Method in class org.apache.spark.scheduler.SplitInfo
 
length(Column) - Static method in class org.apache.spark.sql.functions
Computes the length of a given string or binary column.
length() - Method in interface org.apache.spark.sql.Row
Number of elements in the Row.
length() - Method in class org.apache.spark.sql.sources.HadoopFsRelation.FakeFileStatus
 
length() - Method in class org.apache.spark.sql.types.StructType
 
length() - Method in class org.apache.spark.util.Vector
 
leq(Object) - Method in class org.apache.spark.sql.Column
Less than or equal to.
less(Duration) - Method in class org.apache.spark.streaming.Duration
 
less(Time) - Method in class org.apache.spark.streaming.Time
 
lessEq(Duration) - Method in class org.apache.spark.streaming.Duration
 
lessEq(Time) - Method in class org.apache.spark.streaming.Time
 
LessThan - Class in org.apache.spark.sql.sources
A filter that evaluates to true iff the attribute evaluates to a value less than value.
LessThan(String, Object) - Constructor for class org.apache.spark.sql.sources.LessThan
 
LessThanOrEqual - Class in org.apache.spark.sql.sources
A filter that evaluates to true iff the attribute evaluates to a value less than or equal to value.
LessThanOrEqual(String, Object) - Constructor for class org.apache.spark.sql.sources.LessThanOrEqual
 
levenshtein(Column, Column) - Static method in class org.apache.spark.sql.functions
Computes the Levenshtein distance of the two given string columns.
like(String) - Method in class org.apache.spark.sql.Column
SQL like expression.
limit(int) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame by taking the first n rows.
line() - Method in exception org.apache.spark.sql.AnalysisException
 
LinearDataGenerator - Class in org.apache.spark.mllib.util
:: DeveloperApi :: Generate sample data used for Linear Data.
LinearDataGenerator() - Constructor for class org.apache.spark.mllib.util.LinearDataGenerator
 
LinearRegression - Class in org.apache.spark.ml.regression
:: Experimental :: Linear regression.
LinearRegression(String) - Constructor for class org.apache.spark.ml.regression.LinearRegression
 
LinearRegression() - Constructor for class org.apache.spark.ml.regression.LinearRegression
 
LinearRegressionModel - Class in org.apache.spark.ml.regression
:: Experimental :: Model produced by LinearRegression.
LinearRegressionModel - Class in org.apache.spark.mllib.regression
Regression model trained using LinearRegression.
LinearRegressionModel(Vector, double) - Constructor for class org.apache.spark.mllib.regression.LinearRegressionModel
 
LinearRegressionSummary - Class in org.apache.spark.ml.regression
 
LinearRegressionTrainingSummary - Class in org.apache.spark.ml.regression
 
LinearRegressionWithSGD - Class in org.apache.spark.mllib.regression
Train a linear regression model with no regularization using Stochastic Gradient Descent.
LinearRegressionWithSGD() - Constructor for class org.apache.spark.mllib.regression.LinearRegressionWithSGD
Construct a LinearRegression object with default parameters: {stepSize: 1.0, numIterations: 100, miniBatchFraction: 1.0}.
listener() - Method in class org.apache.spark.sql.SQLContext
 
listenerBus() - Method in class org.apache.spark.SparkContext
 
listenerManager() - Method in class org.apache.spark.sql.SQLContext
 
listLeafFiles(FileSystem, FileStatus) - Static method in class org.apache.spark.sql.sources.HadoopFsRelation
 
listLeafFilesInParallel(String[], Configuration, SparkContext) - Static method in class org.apache.spark.sql.sources.HadoopFsRelation
 
lit(Object) - Static method in class org.apache.spark.sql.functions
Creates a Column of literal value.
load(String) - Static method in class org.apache.spark.ml.classification.LogisticRegression
 
load(String) - Static method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
load(String) - Static method in class org.apache.spark.ml.classification.NaiveBayes
 
load(String) - Static method in class org.apache.spark.ml.classification.NaiveBayesModel
 
load(String) - Static method in class org.apache.spark.ml.clustering.DistributedLDAModel
 
load(String) - Static method in class org.apache.spark.ml.clustering.KMeans
 
load(String) - Static method in class org.apache.spark.ml.clustering.KMeansModel
 
load(String) - Static method in class org.apache.spark.ml.clustering.LDA
 
load(String) - Static method in class org.apache.spark.ml.clustering.LocalLDAModel
 
load(String) - Static method in class org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
 
load(String) - Static method in class org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
 
load(String) - Static method in class org.apache.spark.ml.evaluation.RegressionEvaluator
 
load(String) - Static method in class org.apache.spark.ml.feature.Binarizer
 
load(String) - Static method in class org.apache.spark.ml.feature.Bucketizer
 
load(String) - Static method in class org.apache.spark.ml.feature.ChiSqSelector
 
load(String) - Static method in class org.apache.spark.ml.feature.ChiSqSelectorModel
 
load(String) - Static method in class org.apache.spark.ml.feature.CountVectorizer
 
load(String) - Static method in class org.apache.spark.ml.feature.CountVectorizerModel
 
load(String) - Static method in class org.apache.spark.ml.feature.DCT
 
load(String) - Static method in class org.apache.spark.ml.feature.HashingTF
 
load(String) - Static method in class org.apache.spark.ml.feature.IDF
 
load(String) - Static method in class org.apache.spark.ml.feature.IDFModel
 
load(String) - Static method in class org.apache.spark.ml.feature.IndexToString
 
load(String) - Static method in class org.apache.spark.ml.feature.Interaction
 
load(String) - Static method in class org.apache.spark.ml.feature.MinMaxScaler
 
load(String) - Static method in class org.apache.spark.ml.feature.MinMaxScalerModel
 
load(String) - Static method in class org.apache.spark.ml.feature.NGram
 
load(String) - Static method in class org.apache.spark.ml.feature.Normalizer
 
load(String) - Static method in class org.apache.spark.ml.feature.OneHotEncoder
 
load(String) - Static method in class org.apache.spark.ml.feature.PCA
 
load(String) - Static method in class org.apache.spark.ml.feature.PCAModel
 
load(String) - Static method in class org.apache.spark.ml.feature.PolynomialExpansion
 
load(String) - Static method in class org.apache.spark.ml.feature.QuantileDiscretizer
 
load(String) - Static method in class org.apache.spark.ml.feature.RegexTokenizer
 
load(String) - Static method in class org.apache.spark.ml.feature.SQLTransformer
 
load(String) - Static method in class org.apache.spark.ml.feature.StandardScaler
 
load(String) - Static method in class org.apache.spark.ml.feature.StandardScalerModel
 
load(String) - Static method in class org.apache.spark.ml.feature.StopWordsRemover
 
load(String) - Static method in class org.apache.spark.ml.feature.StringIndexer
 
load(String) - Static method in class org.apache.spark.ml.feature.StringIndexerModel
 
load(String) - Static method in class org.apache.spark.ml.feature.Tokenizer
 
load(String) - Static method in class org.apache.spark.ml.feature.VectorAssembler
 
load(String) - Static method in class org.apache.spark.ml.feature.VectorIndexer
 
load(String) - Static method in class org.apache.spark.ml.feature.VectorIndexerModel
 
load(String) - Static method in class org.apache.spark.ml.feature.VectorSlicer
 
load(String) - Static method in class org.apache.spark.ml.feature.Word2Vec
 
load(String) - Static method in class org.apache.spark.ml.feature.Word2VecModel
 
load(String) - Static method in class org.apache.spark.ml.Pipeline
 
load(String) - Static method in class org.apache.spark.ml.PipelineModel
 
load(String) - Static method in class org.apache.spark.ml.recommendation.ALS
 
load(String) - Static method in class org.apache.spark.ml.recommendation.ALSModel
 
load(String) - Static method in class org.apache.spark.ml.regression.AFTSurvivalRegression
 
load(String) - Static method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
load(String) - Static method in class org.apache.spark.ml.regression.IsotonicRegression
 
load(String) - Static method in class org.apache.spark.ml.regression.IsotonicRegressionModel
 
load(String) - Static method in class org.apache.spark.ml.regression.LinearRegression
 
load(String) - Static method in class org.apache.spark.ml.regression.LinearRegressionModel
 
load(String) - Static method in class org.apache.spark.ml.tuning.CrossValidator
 
load(String) - Static method in class org.apache.spark.ml.tuning.CrossValidatorModel
 
load(String) - Method in interface org.apache.spark.ml.util.MLReadable
Reads an ML instance from the input path, a shortcut of read.load(path).
load(String) - Method in class org.apache.spark.ml.util.MLReader
Loads the ML component from the input path.
load(SparkContext, String) - Static method in class org.apache.spark.mllib.classification.LogisticRegressionModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.classification.NaiveBayesModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.classification.SVMModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.clustering.DistributedLDAModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.clustering.GaussianMixtureModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.clustering.KMeansModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.clustering.LocalLDAModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.clustering.PowerIterationClusteringModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.feature.ChiSqSelectorModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.feature.Word2VecModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.recommendation.MatrixFactorizationModel
Load a model from the given path.
load(SparkContext, String) - Static method in class org.apache.spark.mllib.regression.IsotonicRegressionModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.regression.LassoModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.regression.LinearRegressionModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.regression.RidgeRegressionModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.tree.model.DecisionTreeModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
 
load(SparkContext, String) - Static method in class org.apache.spark.mllib.tree.model.RandomForestModel
 
load(SparkContext, String) - Method in interface org.apache.spark.mllib.util.Loader
Load a model from the given path.
load(String...) - Method in class org.apache.spark.sql.DataFrameReader
Loads input in as a DataFrame, for data sources that support multiple paths.
load(String) - Method in class org.apache.spark.sql.DataFrameReader
Loads input in as a DataFrame, for data sources that require a path (e.g.
load() - Method in class org.apache.spark.sql.DataFrameReader
Loads input in as a DataFrame, for data sources that don't require a path (e.g.
load(Seq<String>) - Method in class org.apache.spark.sql.DataFrameReader
Loads input in as a DataFrame, for data sources that support multiple paths.
load(String) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().load(path). This will be removed in Spark 2.0.
load(String, String) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().format(source).load(path). This will be removed in Spark 2.0.
load(String, Map<String, String>) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().format(source).options(options).load(). This will be removed in Spark 2.0.
load(String, Map<String, String>) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().format(source).options(options).load().
load(String, StructType, Map<String, String>) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().format(source).schema(schema).options(options).load().
load(String, StructType, Map<String, String>) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().format(source).schema(schema).options(options).load().
Loader<M extends Saveable> - Interface in org.apache.spark.mllib.util
:: DeveloperApi ::
loadLabeledData(SparkContext, String) - Static method in class org.apache.spark.mllib.util.MLUtils
loadLabeledPoints(SparkContext, String, int) - Static method in class org.apache.spark.mllib.util.MLUtils
Loads labeled points saved using RDD[LabeledPoint].saveAsTextFile.
loadLabeledPoints(SparkContext, String) - Static method in class org.apache.spark.mllib.util.MLUtils
Loads labeled points saved using RDD[LabeledPoint].saveAsTextFile with the default number of partitions.
loadLibSVMFile(SparkContext, String, int, int) - Static method in class org.apache.spark.mllib.util.MLUtils
Loads labeled data in the LIBSVM format into an RDD[LabeledPoint].
loadLibSVMFile(SparkContext, String, boolean, int, int) - Static method in class org.apache.spark.mllib.util.MLUtils
 
loadLibSVMFile(SparkContext, String, int) - Static method in class org.apache.spark.mllib.util.MLUtils
Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], with the default number of partitions.
loadLibSVMFile(SparkContext, String, boolean, int) - Static method in class org.apache.spark.mllib.util.MLUtils
 
loadLibSVMFile(SparkContext, String, boolean) - Static method in class org.apache.spark.mllib.util.MLUtils
 
loadLibSVMFile(SparkContext, String) - Static method in class org.apache.spark.mllib.util.MLUtils
Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint], with number of features determined automatically and the default number of partitions.
loadVectors(SparkContext, String, int) - Static method in class org.apache.spark.mllib.util.MLUtils
Loads vectors saved using RDD[Vector].saveAsTextFile.
loadVectors(SparkContext, String) - Static method in class org.apache.spark.mllib.util.MLUtils
Loads vectors saved using RDD[Vector].saveAsTextFile with the default number of partitions.
LOCAL_CLUSTER_REGEX() - Static method in class org.apache.spark.SparkMasterRegex
 
LOCAL_N_FAILURES_REGEX() - Static method in class org.apache.spark.SparkMasterRegex
 
LOCAL_N_REGEX() - Static method in class org.apache.spark.SparkMasterRegex
 
localBlocksFetched() - Method in class org.apache.spark.status.api.v1.ShuffleReadMetricDistributions
 
localBlocksFetched() - Method in class org.apache.spark.status.api.v1.ShuffleReadMetrics
 
localCheckpoint() - Method in class org.apache.spark.rdd.RDD
Mark this RDD for local checkpointing using Spark's existing caching layer.
LocalLDAModel - Class in org.apache.spark.ml.clustering
:: Experimental ::
LocalLDAModel - Class in org.apache.spark.mllib.clustering
Local LDA model.
localProperties() - Method in class org.apache.spark.SparkContext
 
localSeqToDataFrameHolder(Seq<A>, TypeTags.TypeTag<A>) - Method in class org.apache.spark.sql.SQLImplicits
Creates a DataFrame from a local Seq of Product.
localSeqToDatasetHolder(Seq<T>, Encoder<T>) - Method in class org.apache.spark.sql.SQLImplicits
Creates a Dataset from a local Seq.
localValue() - Method in class org.apache.spark.Accumulable
Get the current value of this accumulator from within a task.
locate(String, Column) - Static method in class org.apache.spark.sql.functions
Locate the position of the first occurrence of substr.
locate(String, Column, int) - Static method in class org.apache.spark.sql.functions
Locate the position of the first occurrence of substr in a string column, after position pos.
location() - Method in class org.apache.spark.streaming.scheduler.ReceiverInfo
 
log() - Method in interface org.apache.spark.Logging
 
log(Column) - Static method in class org.apache.spark.sql.functions
Computes the natural logarithm of the given value.
log(String) - Static method in class org.apache.spark.sql.functions
Computes the natural logarithm of the given column.
log(double, Column) - Static method in class org.apache.spark.sql.functions
Returns the first argument-base logarithm of the second argument.
log(double, String) - Static method in class org.apache.spark.sql.functions
Returns the first argument-base logarithm of the second argument.
log10(Column) - Static method in class org.apache.spark.sql.functions
Computes the logarithm of the given value in base 10.
log10(String) - Static method in class org.apache.spark.sql.functions
Computes the logarithm of the given value in base 10.
log1p(Column) - Static method in class org.apache.spark.sql.functions
Computes the natural logarithm of the given value plus one.
log1p(String) - Static method in class org.apache.spark.sql.functions
Computes the natural logarithm of the given column plus one.
log2(Column) - Static method in class org.apache.spark.sql.functions
Computes the logarithm of the given column in base 2.
log2(String) - Static method in class org.apache.spark.sql.functions
Computes the logarithm of the given value in base 2.
log_() - Method in interface org.apache.spark.Logging
 
logDebug(Function0<String>) - Method in interface org.apache.spark.Logging
 
logDebug(Function0<String>, Throwable) - Method in interface org.apache.spark.Logging
 
logDeprecationWarning(String) - Static method in class org.apache.spark.SparkConf
Logs a warning message if the given config key is deprecated.
logDirName() - Method in class org.apache.spark.scheduler.JobLogger
 
logError(Function0<String>) - Method in interface org.apache.spark.Logging
 
logError(Function0<String>, Throwable) - Method in interface org.apache.spark.Logging
 
Logging - Interface in org.apache.spark
Utility trait for classes that want to log data.
logicalPlan() - Method in class org.apache.spark.sql.DataFrame
 
logInfo(Function0<String>) - Method in interface org.apache.spark.Logging
 
logInfo(Function0<String>, Throwable) - Method in interface org.apache.spark.Logging
 
LogisticAggregator - Class in org.apache.spark.ml.classification
LogisticAggregator computes the gradient and loss for binary logistic loss function, as used in binary classification for instances in sparse or dense vector in a online fashion.
LogisticAggregator(Vector, int, boolean, double[], double[]) - Constructor for class org.apache.spark.ml.classification.LogisticAggregator
 
LogisticCostFun - Class in org.apache.spark.ml.classification
LogisticCostFun implements Breeze's DiffFunction[T] for a multinomial logistic loss function, as used in multi-class classification (it is also used in binary logistic regression).
LogisticCostFun(RDD<org.apache.spark.ml.feature.Instance>, int, boolean, boolean, double[], double[], double) - Constructor for class org.apache.spark.ml.classification.LogisticCostFun
 
LogisticGradient - Class in org.apache.spark.mllib.optimization
:: DeveloperApi :: Compute gradient and loss for a multinomial logistic loss function, as used in multi-class classification (it is also used in binary logistic regression).
LogisticGradient(int) - Constructor for class org.apache.spark.mllib.optimization.LogisticGradient
 
LogisticGradient() - Constructor for class org.apache.spark.mllib.optimization.LogisticGradient
 
LogisticRegression - Class in org.apache.spark.ml.classification
:: Experimental :: Logistic regression.
LogisticRegression(String) - Constructor for class org.apache.spark.ml.classification.LogisticRegression
 
LogisticRegression() - Constructor for class org.apache.spark.ml.classification.LogisticRegression
 
LogisticRegressionDataGenerator - Class in org.apache.spark.mllib.util
:: DeveloperApi :: Generate test data for LogisticRegression.
LogisticRegressionDataGenerator() - Constructor for class org.apache.spark.mllib.util.LogisticRegressionDataGenerator
 
LogisticRegressionModel - Class in org.apache.spark.ml.classification
:: Experimental :: Model produced by LogisticRegression.
LogisticRegressionModel - Class in org.apache.spark.mllib.classification
Classification model trained using Multinomial/Binary Logistic Regression.
LogisticRegressionModel(Vector, double, int, int) - Constructor for class org.apache.spark.mllib.classification.LogisticRegressionModel
 
LogisticRegressionModel(Vector, double) - Constructor for class org.apache.spark.mllib.classification.LogisticRegressionModel
Constructs a LogisticRegressionModel with weights and intercept for binary classification.
LogisticRegressionSummary - Interface in org.apache.spark.ml.classification
Abstraction for Logistic Regression Results for a given model.
LogisticRegressionTrainingSummary - Interface in org.apache.spark.ml.classification
Abstraction for multinomial Logistic Regression Training results.
LogisticRegressionWithLBFGS - Class in org.apache.spark.mllib.classification
Train a classification model for Multinomial/Binary Logistic Regression using Limited-memory BFGS.
LogisticRegressionWithLBFGS() - Constructor for class org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
 
LogisticRegressionWithSGD - Class in org.apache.spark.mllib.classification
Train a classification model for Binary Logistic Regression using Stochastic Gradient Descent.
LogisticRegressionWithSGD() - Constructor for class org.apache.spark.mllib.classification.LogisticRegressionWithSGD
Construct a LogisticRegression object with default parameters: {stepSize: 1.0, numIterations: 100, regParm: 0.01, miniBatchFraction: 1.0}.
logLikelihood(DataFrame) - Method in class org.apache.spark.ml.clustering.LDAModel
Calculates a lower bound on the log likelihood of the entire corpus.
logLikelihood() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
Log likelihood of the observed tokens in the training set, given the current parameter estimates: log P(docs | topics, topic distributions for docs, alpha, eta)
logLikelihood() - Method in class org.apache.spark.mllib.clustering.ExpectationSum
 
logLikelihood(RDD<Tuple2<Object, Vector>>) - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
Calculates a lower bound on the log likelihood of the entire corpus.
logLikelihood(JavaPairRDD<Long, Vector>) - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
Java-friendly version of logLikelihood
LogLoss - Class in org.apache.spark.mllib.tree.loss
:: DeveloperApi :: Class for log loss calculation (for classification).
LogLoss() - Constructor for class org.apache.spark.mllib.tree.loss.LogLoss
 
logName() - Method in interface org.apache.spark.Logging
 
LogNormalGenerator - Class in org.apache.spark.mllib.random
:: DeveloperApi :: Generates i.i.d.
LogNormalGenerator(double, double) - Constructor for class org.apache.spark.mllib.random.LogNormalGenerator
 
logNormalGraph(SparkContext, int, int, double, double, long) - Static method in class org.apache.spark.graphx.util.GraphGenerators
Generate a graph whose vertex out degree distribution is log normal.
logNormalJavaRDD(JavaSparkContext, double, double, long, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
logNormalJavaRDD(JavaSparkContext, double, double, long, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
logNormalJavaRDD(JavaSparkContext, double, double, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
logNormalJavaVectorRDD(JavaSparkContext, double, double, long, int, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
logNormalJavaVectorRDD(JavaSparkContext, double, double, long, int, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
logNormalJavaVectorRDD(JavaSparkContext, double, double, long, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
logNormalRDD(SparkContext, double, double, long, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
Generates an RDD comprised of i.i.d. samples from the log normal distribution with the input mean and standard deviation
logNormalVectorRDD(SparkContext, double, double, long, int, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from a log normal distribution.
logpdf(Vector) - Method in class org.apache.spark.mllib.stat.distribution.MultivariateGaussian
Returns the log-density of this multivariate Gaussian at given point, x
logPerplexity(DataFrame) - Method in class org.apache.spark.ml.clustering.LDAModel
Calculate an upper bound bound on perplexity.
logPerplexity(RDD<Tuple2<Object, Vector>>) - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
Calculate an upper bound bound on perplexity.
logPerplexity(JavaPairRDD<Long, Vector>) - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
Java-friendly version of logPerplexity
logPrior() - Method in class org.apache.spark.ml.clustering.DistributedLDAModel
Log probability of the current parameter estimate: log P(topics, topic distributions for docs | Dirichlet hyperparameters)
logPrior() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
Log probability of the current parameter estimate: log P(topics, topic distributions for docs | alpha, eta)
logTrace(Function0<String>) - Method in interface org.apache.spark.Logging
 
logTrace(Function0<String>, Throwable) - Method in interface org.apache.spark.Logging
 
logUrlMap() - Method in class org.apache.spark.scheduler.cluster.ExecutorInfo
 
logWarning(Function0<String>) - Method in interface org.apache.spark.Logging
 
logWarning(Function0<String>, Throwable) - Method in interface org.apache.spark.Logging
 
LONG() - Static method in class org.apache.spark.sql.Encoders
An encoder for nullable long type.
LongDecimal() - Static method in class org.apache.spark.sql.types.DecimalType
 
LongParam - Class in org.apache.spark.ml.param
:: DeveloperApi :: Specialized version of Param[Long] for Java.
LongParam(String, String, String, Function1<Object, Object>) - Constructor for class org.apache.spark.ml.param.LongParam
 
LongParam(String, String, String) - Constructor for class org.apache.spark.ml.param.LongParam
 
LongParam(Identifiable, String, String, Function1<Object, Object>) - Constructor for class org.apache.spark.ml.param.LongParam
 
LongParam(Identifiable, String, String) - Constructor for class org.apache.spark.ml.param.LongParam
 
longRddToDataFrameHolder(RDD<Object>) - Method in class org.apache.spark.sql.SQLImplicits
Creates a single column DataFrame from an RDD[Long].
longToLongWritable(long) - Static method in class org.apache.spark.SparkContext
 
LongType - Static variable in class org.apache.spark.sql.types.DataTypes
Gets the LongType object.
LongType - Class in org.apache.spark.sql.types
:: DeveloperApi :: The data type representing Long values.
longWritableConverter() - Static method in class org.apache.spark.SparkContext
 
lookup(K) - Method in class org.apache.spark.api.java.JavaPairRDD
Return the list of values in the RDD for key key.
lookup(K) - Method in class org.apache.spark.rdd.PairRDDFunctions
Return the list of values in the RDD for key key.
lookupTimeout(SparkConf) - Static method in class org.apache.spark.util.RpcUtils
 
loss() - Method in class org.apache.spark.ml.classification.LogisticAggregator
 
loss() - Method in class org.apache.spark.ml.regression.AFTAggregator
 
loss() - Method in class org.apache.spark.ml.regression.LeastSquaresAggregator
 
loss() - Method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
Loss - Interface in org.apache.spark.mllib.tree.loss
:: DeveloperApi :: Trait for adding "pluggable" loss functions for the gradient boosting algorithm.
Losses - Class in org.apache.spark.mllib.tree.loss
 
Losses() - Constructor for class org.apache.spark.mllib.tree.loss.Losses
 
lossType() - Method in class org.apache.spark.ml.classification.GBTClassifier
Loss function which GBT tries to minimize.
lossType() - Method in class org.apache.spark.ml.regression.GBTRegressor
Loss function which GBT tries to minimize.
low() - Method in class org.apache.spark.partial.BoundedDouble
 
lower(Column) - Static method in class org.apache.spark.sql.functions
Converts a string column to lower case.
lpad(Column, int, String) - Static method in class org.apache.spark.sql.functions
Left-pad the string column with
lt(double) - Static method in class org.apache.spark.ml.param.ParamValidators
Check if value < upperBound
lt(Object) - Method in class org.apache.spark.sql.Column
Less than.
ltEq(double) - Static method in class org.apache.spark.ml.param.ParamValidators
Check if value <= upperBound
ltrim(Column) - Static method in class org.apache.spark.sql.functions
Trim the spaces from left end for the specified string value.
LZ4CompressionCodec - Class in org.apache.spark.io
:: DeveloperApi :: LZ4 implementation of CompressionCodec.
LZ4CompressionCodec(SparkConf) - Constructor for class org.apache.spark.io.LZ4CompressionCodec
 
LZFCompressionCodec - Class in org.apache.spark.io
:: DeveloperApi :: LZF implementation of CompressionCodec.
LZFCompressionCodec(SparkConf) - Constructor for class org.apache.spark.io.LZFCompressionCodec
 

M

main(String[]) - Static method in class org.apache.spark.mllib.util.KMeansDataGenerator
 
main(String[]) - Static method in class org.apache.spark.mllib.util.LinearDataGenerator
 
main(String[]) - Static method in class org.apache.spark.mllib.util.LogisticRegressionDataGenerator
 
main(String[]) - Static method in class org.apache.spark.mllib.util.MFDataGenerator
 
main(String[]) - Static method in class org.apache.spark.mllib.util.SVMDataGenerator
 
makeDriverRef(String, SparkConf, org.apache.spark.rpc.RpcEnv) - Static method in class org.apache.spark.util.RpcUtils
Retrieve a RpcEndpointRef which is located in the driver via its name.
makeRDD(Seq<T>, int, ClassTag<T>) - Method in class org.apache.spark.SparkContext
Distribute a local Scala collection to form an RDD.
makeRDD(Seq<Tuple2<T, Seq<String>>>, ClassTag<T>) - Method in class org.apache.spark.SparkContext
Distribute a local Scala collection to form an RDD, with one or more location preferences (hostnames of Spark nodes) for each object.
map(Function<T, R>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return a new RDD by applying a function to all elements of this RDD.
map(Function1<Object, Object>) - Method in interface org.apache.spark.mllib.linalg.Matrix
Map the values of this matrix using a function.
map(Function1<R, T>) - Method in class org.apache.spark.partial.PartialResult
Transform this PartialResult into a PartialResult of type T.
map(Function1<T, U>, ClassTag<U>) - Method in class org.apache.spark.rdd.RDD
Return a new RDD by applying a function to all elements of this RDD.
map(DataType, DataType) - Method in class org.apache.spark.sql.ColumnName
Creates a new StructField of type map.
map(MapType) - Method in class org.apache.spark.sql.ColumnName
 
map(Function1<Row, R>, ClassTag<R>) - Method in class org.apache.spark.sql.DataFrame
Returns a new RDD by applying a function to all rows of this DataFrame.
map(Function1<T, U>, Encoder<U>) - Method in class org.apache.spark.sql.Dataset
(Scala-specific) Returns a new Dataset that contains the result of applying func to each element.
map(MapFunction<T, U>, Encoder<U>) - Method in class org.apache.spark.sql.Dataset
(Java-specific) Returns a new Dataset that contains the result of applying func to each element.
map() - Method in class org.apache.spark.sql.types.Metadata
 
map(Function<T, R>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream by applying a function to all elements of this DStream.
map(Function1<T, U>, ClassTag<U>) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream by applying a function to all elements of this DStream.
mapEdgePartitions(Function2<Object, EdgePartition<ED, VD>, EdgePartition<ED2, VD2>>, ClassTag<ED2>, ClassTag<VD2>) - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
 
mapEdges(Function1<Edge<ED>, ED2>, ClassTag<ED2>) - Method in class org.apache.spark.graphx.Graph
Transforms each edge attribute in the graph using the map function.
mapEdges(Function2<Object, Iterator<Edge<ED>>, Iterator<ED2>>, ClassTag<ED2>) - Method in class org.apache.spark.graphx.Graph
Transforms each edge attribute using the map function, passing it a whole partition at a time.
mapEdges(Function2<Object, Iterator<Edge<ED>>, Iterator<ED2>>, ClassTag<ED2>) - Method in class org.apache.spark.graphx.impl.GraphImpl
 
MapFunction<T,U> - Interface in org.apache.spark.api.java.function
Base interface for a map function used in Dataset's map function.
mapGroups(Function2<K, Iterator<V>, U>, Encoder<U>) - Method in class org.apache.spark.sql.GroupedDataset
Applies the given function to each group of data.
mapGroups(MapGroupsFunction<K, V, U>, Encoder<U>) - Method in class org.apache.spark.sql.GroupedDataset
Applies the given function to each group of data.
MapGroupsFunction<K,V,R> - Interface in org.apache.spark.api.java.function
Base interface for a map function used in GroupedDataset's mapGroup function.
mapId() - Method in class org.apache.spark.FetchFailed
 
mapId() - Method in class org.apache.spark.storage.ShuffleBlockId
 
mapId() - Method in class org.apache.spark.storage.ShuffleDataBlockId
 
mapId() - Method in class org.apache.spark.storage.ShuffleIndexBlockId
 
mapOutputTracker() - Method in class org.apache.spark.SparkEnv
 
mapPartitions(FlatMapFunction<Iterator<T>, U>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return a new RDD by applying a function to each partition of this RDD.
mapPartitions(FlatMapFunction<Iterator<T>, U>, boolean) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return a new RDD by applying a function to each partition of this RDD.
mapPartitions(Function1<Iterator<T>, Iterator<U>>, boolean, ClassTag<U>) - Method in class org.apache.spark.rdd.RDD
Return a new RDD by applying a function to each partition of this RDD.
mapPartitions(Function1<Iterator<Row>, Iterator<R>>, ClassTag<R>) - Method in class org.apache.spark.sql.DataFrame
Returns a new RDD by applying a function to each partition of this DataFrame.
mapPartitions(Function1<Iterator<T>, Iterator<U>>, Encoder<U>) - Method in class org.apache.spark.sql.Dataset
(Scala-specific) Returns a new Dataset that contains the result of applying func to each partition.
mapPartitions(MapPartitionsFunction<T, U>, Encoder<U>) - Method in class org.apache.spark.sql.Dataset
(Java-specific) Returns a new Dataset that contains the result of applying func to each partition.
mapPartitions(FlatMapFunction<Iterator<T>, U>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream.
mapPartitions(Function1<Iterator<T>, Iterator<U>>, boolean, ClassTag<U>) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream.
MapPartitionsFunction<T,U> - Interface in org.apache.spark.api.java.function
Base interface for function used in Dataset's mapPartitions.
mapPartitionsToDouble(DoubleFlatMapFunction<Iterator<T>>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return a new RDD by applying a function to each partition of this RDD.
mapPartitionsToDouble(DoubleFlatMapFunction<Iterator<T>>, boolean) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return a new RDD by applying a function to each partition of this RDD.
mapPartitionsToPair(PairFlatMapFunction<Iterator<T>, K2, V2>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return a new RDD by applying a function to each partition of this RDD.
mapPartitionsToPair(PairFlatMapFunction<Iterator<T>, K2, V2>, boolean) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return a new RDD by applying a function to each partition of this RDD.
mapPartitionsToPair(PairFlatMapFunction<Iterator<T>, K2, V2>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs of this DStream.
mapPartitionsWithContext(Function2<TaskContext, Iterator<T>, Iterator<U>>, boolean, ClassTag<U>) - Method in class org.apache.spark.rdd.RDD
:: DeveloperApi :: Return a new RDD by applying a function to each partition of this RDD.
mapPartitionsWithIndex(Function2<Integer, Iterator<T>, Iterator<R>>, boolean) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.
mapPartitionsWithIndex(Function2<Object, Iterator<T>, Iterator<U>>, boolean, ClassTag<U>) - Method in class org.apache.spark.rdd.RDD
Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.
mapPartitionsWithInputSplit(Function2<InputSplit, Iterator<Tuple2<K, V>>, Iterator<R>>, boolean) - Method in class org.apache.spark.api.java.JavaHadoopRDD
Maps over a partition, providing the InputSplit that was used as the base of the partition.
mapPartitionsWithInputSplit(Function2<InputSplit, Iterator<Tuple2<K, V>>, Iterator<R>>, boolean) - Method in class org.apache.spark.api.java.JavaNewHadoopRDD
Maps over a partition, providing the InputSplit that was used as the base of the partition.
mapPartitionsWithInputSplit(Function2<InputSplit, Iterator<Tuple2<K, V>>, Iterator<U>>, boolean, ClassTag<U>) - Method in class org.apache.spark.rdd.HadoopRDD
Maps over a partition, providing the InputSplit that was used as the base of the partition.
mapPartitionsWithInputSplit(Function2<InputSplit, Iterator<Tuple2<K, V>>, Iterator<U>>, boolean, ClassTag<U>) - Method in class org.apache.spark.rdd.NewHadoopRDD
Maps over a partition, providing the InputSplit that was used as the base of the partition.
mapPartitionsWithSplit(Function2<Object, Iterator<T>, Iterator<U>>, boolean, ClassTag<U>) - Method in class org.apache.spark.rdd.RDD
Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.
mapredInputFormat() - Method in class org.apache.spark.scheduler.InputFormatInfo
 
mapreduceInputFormat() - Method in class org.apache.spark.scheduler.InputFormatInfo
 
mapReduceTriplets(Function1<EdgeTriplet<VD, ED>, Iterator<Tuple2<Object, A>>>, Function2<A, A, A>, Option<Tuple2<VertexRDD<?>, EdgeDirection>>, ClassTag<A>) - Method in class org.apache.spark.graphx.Graph
Aggregates values from the neighboring edges and vertices of each vertex.
mapReduceTriplets(Function1<EdgeTriplet<VD, ED>, Iterator<Tuple2<Object, A>>>, Function2<A, A, A>, Option<Tuple2<VertexRDD<?>, EdgeDirection>>, ClassTag<A>) - Method in class org.apache.spark.graphx.impl.GraphImpl
 
mapSideCombine() - Method in class org.apache.spark.ShuffleDependency
 
mapToDouble(DoubleFunction<T>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return a new RDD by applying a function to all elements of this RDD.
mapToPair(PairFunction<T, K2, V2>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return a new RDD by applying a function to all elements of this RDD.
mapToPair(PairFunction<T, K2, V2>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream by applying a function to all elements of this DStream.
mapTriplets(Function1<EdgeTriplet<VD, ED>, ED2>, ClassTag<ED2>) - Method in class org.apache.spark.graphx.Graph
Transforms each edge attribute using the map function, passing it the adjacent vertex attributes as well.
mapTriplets(Function1<EdgeTriplet<VD, ED>, ED2>, TripletFields, ClassTag<ED2>) - Method in class org.apache.spark.graphx.Graph
Transforms each edge attribute using the map function, passing it the adjacent vertex attributes as well.
mapTriplets(Function2<Object, Iterator<EdgeTriplet<VD, ED>>, Iterator<ED2>>, TripletFields, ClassTag<ED2>) - Method in class org.apache.spark.graphx.Graph
Transforms each edge attribute a partition at a time using the map function, passing it the adjacent vertex attributes as well.
mapTriplets(Function2<Object, Iterator<EdgeTriplet<VD, ED>>, Iterator<ED2>>, TripletFields, ClassTag<ED2>) - Method in class org.apache.spark.graphx.impl.GraphImpl
 
MapType - Class in org.apache.spark.sql.types
:: DeveloperApi :: The data type for Maps.
MapType(DataType, DataType, boolean) - Constructor for class org.apache.spark.sql.types.MapType
 
MapType() - Constructor for class org.apache.spark.sql.types.MapType
No-arg constructor for kryo.
mapValues(Function<V, U>) - Method in class org.apache.spark.api.java.JavaPairRDD
Pass each value in the key-value pair RDD through a map function without changing the keys; this also retains the original RDD's partitioning.
mapValues(Function1<Edge<ED>, ED2>, ClassTag<ED2>) - Method in class org.apache.spark.graphx.EdgeRDD
Map the values in an edge partitioning preserving the structure but changing the values.
mapValues(Function1<Edge<ED>, ED2>, ClassTag<ED2>) - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
 
mapValues(Function1<VD, VD2>, ClassTag<VD2>) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
mapValues(Function2<Object, VD, VD2>, ClassTag<VD2>) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
mapValues(Function1<VD, VD2>, ClassTag<VD2>) - Method in class org.apache.spark.graphx.VertexRDD
Maps each vertex attribute, preserving the index.
mapValues(Function2<Object, VD, VD2>, ClassTag<VD2>) - Method in class org.apache.spark.graphx.VertexRDD
Maps each vertex attribute, additionally supplying the vertex ID.
mapValues(Function1<V, U>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Pass each value in the key-value pair RDD through a map function without changing the keys; this also retains the original RDD's partitioning.
mapValues(Function<V, U>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying a map function to the value of each key-value pairs in 'this' DStream without changing the key.
mapValues(Function1<V, U>, ClassTag<U>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying a map function to the value of each key-value pairs in 'this' DStream without changing the key.
mapVertices(Function2<Object, VD, VD2>, ClassTag<VD2>, Predef.$eq$colon$eq<VD, VD2>) - Method in class org.apache.spark.graphx.Graph
Transforms each vertex attribute in the graph using the map function.
mapVertices(Function2<Object, VD, VD2>, ClassTag<VD2>, Predef.$eq$colon$eq<VD, VD2>) - Method in class org.apache.spark.graphx.impl.GraphImpl
 
mapWith(Function1<Object, A>, boolean, Function2<T, A, U>, ClassTag<U>) - Method in class org.apache.spark.rdd.RDD
Maps f over this RDD, where f takes an additional parameter of type A.
mapWithState(StateSpec<K, V, StateType, MappedType>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
:: Experimental :: Return a JavaMapWithStateDStream by applying a function to every key-value element of this stream, while maintaining some state data for each unique key.
mapWithState(StateSpec<K, V, StateType, MappedType>, ClassTag<StateType>, ClassTag<MappedType>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
:: Experimental :: Return a MapWithStateDStream by applying a function to every key-value element of this stream, while maintaining some state data for each unique key.
MapWithStateDStream<KeyType,ValueType,StateType,MappedType> - Class in org.apache.spark.streaming.dstream
:: Experimental :: DStream representing the stream of data generated by mapWithState operation on a pair DStream.
MapWithStateDStream(StreamingContext, ClassTag<MappedType>) - Constructor for class org.apache.spark.streaming.dstream.MapWithStateDStream
 
mark(int) - Method in class org.apache.spark.storage.BufferReleasingInputStream
 
markSupported() - Method in class org.apache.spark.storage.BufferReleasingInputStream
 
mask(Graph<VD2, ED2>, ClassTag<VD2>, ClassTag<ED2>) - Method in class org.apache.spark.graphx.Graph
Restricts the graph to only the vertices and edges that are also in other, but keeps the attributes from this graph.
mask(Graph<VD2, ED2>, ClassTag<VD2>, ClassTag<ED2>) - Method in class org.apache.spark.graphx.impl.GraphImpl
 
master() - Method in class org.apache.spark.api.java.JavaSparkContext
 
master() - Method in class org.apache.spark.SparkContext
 
Matrices - Class in org.apache.spark.mllib.linalg
Factory methods for Matrix.
Matrices() - Constructor for class org.apache.spark.mllib.linalg.Matrices
 
Matrix - Interface in org.apache.spark.mllib.linalg
Trait for a local matrix.
MatrixEntry - Class in org.apache.spark.mllib.linalg.distributed
Represents an entry in an distributed matrix.
MatrixEntry(long, long, double) - Constructor for class org.apache.spark.mllib.linalg.distributed.MatrixEntry
 
MatrixFactorizationModel - Class in org.apache.spark.mllib.recommendation
Model representing the result of matrix factorization.
MatrixFactorizationModel(int, RDD<Tuple2<Object, double[]>>, RDD<Tuple2<Object, double[]>>) - Constructor for class org.apache.spark.mllib.recommendation.MatrixFactorizationModel
 
max() - Method in class org.apache.spark.api.java.JavaDoubleRDD
Returns the maximum element from this RDD as defined by the default comparator natural order.
max(Comparator<T>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Returns the maximum element from this RDD as defined by the specified Comparator[T].
max() - Method in class org.apache.spark.ml.attribute.NumericAttribute
 
max() - Method in class org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
Maximum value of each dimension.
max() - Method in interface org.apache.spark.mllib.stat.MultivariateStatisticalSummary
Maximum value of each column.
max(Ordering<T>) - Method in class org.apache.spark.rdd.RDD
Returns the max of this RDD as defined by the implicit Ordering[T].
max(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the maximum value of the expression in a group.
max(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the maximum value of the column in a group.
max(String...) - Method in class org.apache.spark.sql.GroupedData
Compute the max value for each numeric columns for each group.
max(Seq<String>) - Method in class org.apache.spark.sql.GroupedData
Compute the max value for each numeric columns for each group.
max(Duration) - Method in class org.apache.spark.streaming.Duration
 
max(Time) - Method in class org.apache.spark.streaming.Time
 
max() - Method in class org.apache.spark.util.StatCounter
 
MAX_HASH_NNZ() - Static method in class org.apache.spark.mllib.linalg.Vectors
Max number of nonzero entries used in computing hash code.
MAX_LONG_DIGITS() - Static method in class org.apache.spark.sql.types.Decimal
Maximum number of decimal digits a Long can represent
MAX_PRECISION() - Static method in class org.apache.spark.sql.types.DecimalType
 
MAX_SCALE() - Static method in class org.apache.spark.sql.types.DecimalType
 
maxBins() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
maxBufferSizeMb() - Method in class org.apache.spark.serializer.KryoSerializer
 
maxCores() - Method in class org.apache.spark.status.api.v1.ApplicationInfo
 
maxDepth() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
maxIters() - Method in class org.apache.spark.graphx.lib.SVDPlusPlus.Conf
 
maxMem() - Method in class org.apache.spark.scheduler.SparkListenerBlockManagerAdded
 
maxMem() - Method in class org.apache.spark.storage.StorageStatus
 
maxMemory() - Method in class org.apache.spark.status.api.v1.ExecutorSummary
 
maxMemoryInMB() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
maxNodesInLevel(int) - Static method in class org.apache.spark.mllib.tree.model.Node
Return the maximum number of nodes which can be in the given level of the tree.
maxVal() - Method in class org.apache.spark.graphx.lib.SVDPlusPlus.Conf
 
md5(Column) - Static method in class org.apache.spark.sql.functions
Calculates the MD5 digest of a binary column and returns the value as a 32 character hex string.
mean() - Method in class org.apache.spark.api.java.JavaDoubleRDD
Compute the mean of this RDD's elements.
mean() - Method in class org.apache.spark.ml.feature.StandardScalerModel
 
mean() - Method in class org.apache.spark.mllib.feature.StandardScalerModel
 
mean() - Method in class org.apache.spark.mllib.random.ExponentialGenerator
 
mean() - Method in class org.apache.spark.mllib.random.LogNormalGenerator
 
mean() - Method in class org.apache.spark.mllib.random.PoissonGenerator
 
mean() - Method in class org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
Sample mean of each dimension.
mean() - Method in interface org.apache.spark.mllib.stat.MultivariateStatisticalSummary
Sample mean vector.
mean() - Method in class org.apache.spark.partial.BoundedDouble
 
mean() - Method in class org.apache.spark.rdd.DoubleRDDFunctions
Compute the mean of this RDD's elements.
mean(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the average of the values in a group.
mean(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the average of the values in a group.
mean(String...) - Method in class org.apache.spark.sql.GroupedData
Compute the average value for each numeric columns for each group.
mean(Seq<String>) - Method in class org.apache.spark.sql.GroupedData
Compute the average value for each numeric columns for each group.
mean() - Method in class org.apache.spark.util.StatCounter
 
meanAbsoluteError() - Method in class org.apache.spark.ml.regression.LinearRegressionSummary
Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.
meanAbsoluteError() - Method in class org.apache.spark.mllib.evaluation.RegressionMetrics
Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.
meanApprox(long, Double) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Return the approximate mean of the elements in this RDD.
meanApprox(long) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Approximate operation to return the mean within a timeout.
meanApprox(long, double) - Method in class org.apache.spark.rdd.DoubleRDDFunctions
Approximate operation to return the mean within a timeout.
meanAveragePrecision() - Method in class org.apache.spark.mllib.evaluation.RankingMetrics
Returns the mean average precision (MAP) of all the queries.
means() - Method in class org.apache.spark.mllib.clustering.ExpectationSum
 
meanSquaredError() - Method in class org.apache.spark.ml.regression.LinearRegressionSummary
Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
meanSquaredError() - Method in class org.apache.spark.mllib.evaluation.RegressionMetrics
Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
MEMORY_AND_DISK - Static variable in class org.apache.spark.api.java.StorageLevels
 
MEMORY_AND_DISK() - Static method in class org.apache.spark.storage.StorageLevel
 
MEMORY_AND_DISK_2 - Static variable in class org.apache.spark.api.java.StorageLevels
 
MEMORY_AND_DISK_2() - Static method in class org.apache.spark.storage.StorageLevel
 
MEMORY_AND_DISK_SER - Static variable in class org.apache.spark.api.java.StorageLevels
 
MEMORY_AND_DISK_SER() - Static method in class org.apache.spark.storage.StorageLevel
 
MEMORY_AND_DISK_SER_2 - Static variable in class org.apache.spark.api.java.StorageLevels
 
MEMORY_AND_DISK_SER_2() - Static method in class org.apache.spark.storage.StorageLevel
 
MEMORY_ONLY - Static variable in class org.apache.spark.api.java.StorageLevels
 
MEMORY_ONLY() - Static method in class org.apache.spark.storage.StorageLevel
 
MEMORY_ONLY_2 - Static variable in class org.apache.spark.api.java.StorageLevels
 
MEMORY_ONLY_2() - Static method in class org.apache.spark.storage.StorageLevel
 
MEMORY_ONLY_SER - Static variable in class org.apache.spark.api.java.StorageLevels
 
MEMORY_ONLY_SER() - Static method in class org.apache.spark.storage.StorageLevel
 
MEMORY_ONLY_SER_2 - Static variable in class org.apache.spark.api.java.StorageLevels
 
MEMORY_ONLY_SER_2() - Static method in class org.apache.spark.storage.StorageLevel
 
memoryBytesSpilled() - Method in class org.apache.spark.status.api.v1.ExecutorStageSummary
 
memoryBytesSpilled() - Method in class org.apache.spark.status.api.v1.StageData
 
memoryBytesSpilled() - Method in class org.apache.spark.status.api.v1.TaskMetricDistributions
 
memoryBytesSpilled() - Method in class org.apache.spark.status.api.v1.TaskMetrics
 
MemoryEntry - Class in org.apache.spark.storage
 
MemoryEntry(Object, long, boolean) - Constructor for class org.apache.spark.storage.MemoryEntry
 
memoryManager() - Method in class org.apache.spark.SparkEnv
 
memoryPerExecutorMB() - Method in class org.apache.spark.status.api.v1.ApplicationInfo
 
memoryRemaining() - Method in class org.apache.spark.status.api.v1.RDDDataDistribution
 
memoryUsed() - Method in class org.apache.spark.status.api.v1.ExecutorSummary
 
memoryUsed() - Method in class org.apache.spark.status.api.v1.RDDDataDistribution
 
memoryUsed() - Method in class org.apache.spark.status.api.v1.RDDPartitionInfo
 
memoryUsed() - Method in class org.apache.spark.status.api.v1.RDDStorageInfo
 
memRemaining() - Method in class org.apache.spark.storage.StorageStatus
Return the memory remaining in this block manager.
memSize() - Method in class org.apache.spark.storage.BlockStatus
 
memSize() - Method in class org.apache.spark.storage.BlockUpdatedInfo
 
memSize() - Method in class org.apache.spark.storage.RDDInfo
 
memUsed() - Method in class org.apache.spark.storage.StorageStatus
Return the memory used by this block manager.
memUsedByRdd(int) - Method in class org.apache.spark.storage.StorageStatus
Return the memory used by the given RDD in this block manager in O(1) time.
merge(R) - Method in class org.apache.spark.Accumulable
Merge two accumulable objects together
merge(LogisticAggregator) - Method in class org.apache.spark.ml.classification.LogisticAggregator
Merge another LogisticAggregator, and update the loss and gradient of the objective function.
merge(AFTAggregator) - Method in class org.apache.spark.ml.regression.AFTAggregator
 
merge(LeastSquaresAggregator) - Method in class org.apache.spark.ml.regression.LeastSquaresAggregator
Merge another LeastSquaresAggregator, and update the loss and gradient of the objective function.
merge(IDF.DocumentFrequencyAggregator) - Method in class org.apache.spark.mllib.feature.IDF.DocumentFrequencyAggregator
Merges another.
merge(MultivariateOnlineSummarizer) - Method in class org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
Merge another MultivariateOnlineSummarizer, and update the statistical summary.
merge(B, B) - Method in class org.apache.spark.sql.expressions.Aggregator
Merge two intermediate values.
merge(MutableAggregationBuffer, Row) - Method in class org.apache.spark.sql.expressions.UserDefinedAggregateFunction
Merges two aggregation buffers and stores the updated buffer values back to buffer1.
merge(double) - Method in class org.apache.spark.util.StatCounter
Add a value into this StatCounter, updating the internal statistics.
merge(TraversableOnce<Object>) - Method in class org.apache.spark.util.StatCounter
Add multiple values into this StatCounter, updating the internal statistics.
merge(StatCounter) - Method in class org.apache.spark.util.StatCounter
Merge another StatCounter into this one, adding up the internal statistics.
mergeCombiners() - Method in class org.apache.spark.Aggregator
 
mergeValue() - Method in class org.apache.spark.Aggregator
 
MESOS_REGEX() - Static method in class org.apache.spark.SparkMasterRegex
 
message() - Method in class org.apache.spark.FetchFailed
 
message() - Method in exception org.apache.spark.sql.AnalysisException
 
Metadata - Class in org.apache.spark.sql.types
:: DeveloperApi ::
Metadata() - Constructor for class org.apache.spark.sql.types.Metadata
No-arg constructor for kryo.
metadata() - Method in class org.apache.spark.sql.types.StructField
 
metadata() - Method in class org.apache.spark.streaming.scheduler.StreamInputInfo
 
METADATA_KEY_DESCRIPTION() - Static method in class org.apache.spark.streaming.scheduler.StreamInputInfo
The key for description in StreamInputInfo.metadata.
MetadataBuilder - Class in org.apache.spark.sql.types
:: DeveloperApi ::
MetadataBuilder() - Constructor for class org.apache.spark.sql.types.MetadataBuilder
 
metadataDescription() - Method in class org.apache.spark.streaming.scheduler.StreamInputInfo
 
metadataHive() - Method in class org.apache.spark.sql.hive.HiveContext
The copy of the Hive client that is used to retrieve metadata from the Hive MetaStore.
method() - Method in class org.apache.spark.mllib.stat.test.ChiSqTestResult
 
MethodIdentifier<T> - Class in org.apache.spark.util
Helper class to identify a method.
MethodIdentifier(Class<T>, String, String) - Constructor for class org.apache.spark.util.MethodIdentifier
 
metricName() - Method in class org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
param for metric name in evaluation Default: areaUnderROC
metricName() - Method in class org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
param for metric name in evaluation (supports "f1" (default), "precision", "recall", "weightedPrecision", "weightedRecall")
metricName() - Method in class org.apache.spark.ml.evaluation.RegressionEvaluator
param for metric name in evaluation (supports "rmse" (default), "mse", "r2", and "mae")
metrics() - Method in class org.apache.spark.ExceptionFailure
 
metricsSystem() - Method in class org.apache.spark.SparkContext
 
metricsSystem() - Method in class org.apache.spark.SparkEnv
 
MFDataGenerator - Class in org.apache.spark.mllib.util
:: DeveloperApi :: Generate RDD(s) containing data for Matrix Factorization.
MFDataGenerator() - Constructor for class org.apache.spark.mllib.util.MFDataGenerator
 
microF1Measure() - Method in class org.apache.spark.mllib.evaluation.MultilabelMetrics
Returns micro-averaged label-based f1-measure (equals to micro-averaged document-based f1-measure)
microPrecision() - Method in class org.apache.spark.mllib.evaluation.MultilabelMetrics
Returns micro-averaged label-based precision (equals to micro-averaged document-based precision)
microRecall() - Method in class org.apache.spark.mllib.evaluation.MultilabelMetrics
Returns micro-averaged label-based recall (equals to micro-averaged document-based recall)
milliseconds() - Method in class org.apache.spark.streaming.Duration
 
milliseconds(long) - Static method in class org.apache.spark.streaming.Durations
 
Milliseconds - Class in org.apache.spark.streaming
Helper object that creates instance of Duration representing a given number of milliseconds.
Milliseconds() - Constructor for class org.apache.spark.streaming.Milliseconds
 
milliseconds() - Method in class org.apache.spark.streaming.Time
 
millisToString(long) - Static method in class org.apache.spark.scheduler.StatsReportListener
Reformat a time interval in milliseconds to a prettier format for output
min() - Method in class org.apache.spark.api.java.JavaDoubleRDD
Returns the minimum element from this RDD as defined by the default comparator natural order.
min(Comparator<T>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Returns the minimum element from this RDD as defined by the specified Comparator[T].
min() - Method in class org.apache.spark.ml.attribute.NumericAttribute
 
min() - Method in class org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
Minimum value of each dimension.
min() - Method in interface org.apache.spark.mllib.stat.MultivariateStatisticalSummary
Minimum value of each column.
min(Ordering<T>) - Method in class org.apache.spark.rdd.RDD
Returns the min of this RDD as defined by the implicit Ordering[T].
min(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the minimum value of the expression in a group.
min(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the minimum value of the column in a group.
min(String...) - Method in class org.apache.spark.sql.GroupedData
Compute the min value for each numeric column for each group.
min(Seq<String>) - Method in class org.apache.spark.sql.GroupedData
Compute the min value for each numeric column for each group.
min(Duration) - Method in class org.apache.spark.streaming.Duration
 
min(Time) - Method in class org.apache.spark.streaming.Time
 
min() - Method in class org.apache.spark.util.StatCounter
 
minDocFreq() - Method in class org.apache.spark.mllib.feature.IDF.DocumentFrequencyAggregator
 
minDocFreq() - Method in class org.apache.spark.mllib.feature.IDF
 
minInfoGain() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
minInstancesPerNode() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
MinMax() - Static method in class org.apache.spark.mllib.tree.configuration.QuantileStrategy
 
MinMaxScaler - Class in org.apache.spark.ml.feature
:: Experimental :: Rescale each feature individually to a common range [min, max] linearly using column summary statistics, which is also known as min-max normalization or Rescaling.
MinMaxScaler(String) - Constructor for class org.apache.spark.ml.feature.MinMaxScaler
 
MinMaxScaler() - Constructor for class org.apache.spark.ml.feature.MinMaxScaler
 
MinMaxScalerModel - Class in org.apache.spark.ml.feature
 
minTokenLength() - Method in class org.apache.spark.ml.feature.RegexTokenizer
Minimum token length, >= 0.
minus(RDD<Tuple2<Object, VD>>) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
minus(VertexRDD<VD>) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
minus(RDD<Tuple2<Object, VD>>) - Method in class org.apache.spark.graphx.VertexRDD
For each VertexId present in both this and other, minus will act as a set difference operation returning only those unique VertexId's present in this.
minus(VertexRDD<VD>) - Method in class org.apache.spark.graphx.VertexRDD
For each VertexId present in both this and other, minus will act as a set difference operation returning only those unique VertexId's present in this.
minus(Object) - Method in class org.apache.spark.sql.Column
Subtraction.
minus(Duration) - Method in class org.apache.spark.streaming.Duration
 
minus(Time) - Method in class org.apache.spark.streaming.Time
 
minus(Duration) - Method in class org.apache.spark.streaming.Time
 
minute(Column) - Static method in class org.apache.spark.sql.functions
Extracts the minutes as an integer from a given date/timestamp/string.
minutes() - Static method in class org.apache.spark.scheduler.StatsReportListener
 
minutes(long) - Static method in class org.apache.spark.streaming.Durations
 
Minutes - Class in org.apache.spark.streaming
Helper object that creates instance of Duration representing a given number of minutes.
Minutes() - Constructor for class org.apache.spark.streaming.Minutes
 
minVal() - Method in class org.apache.spark.graphx.lib.SVDPlusPlus.Conf
 
mkString() - Method in interface org.apache.spark.sql.Row
Displays all elements of this sequence in a string (without a separator).
mkString(String) - Method in interface org.apache.spark.sql.Row
Displays all elements of this sequence in a string using a separator string.
mkString(String, String, String) - Method in interface org.apache.spark.sql.Row
Displays all elements of this traversable or iterator in a string using start, end, and separator strings.
MLPairRDDFunctions<K,V> - Class in org.apache.spark.mllib.rdd
Machine learning specific Pair RDD functions.
MLPairRDDFunctions(RDD<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - Constructor for class org.apache.spark.mllib.rdd.MLPairRDDFunctions
 
MLReadable<T> - Interface in org.apache.spark.ml.util
Trait for objects that provide MLReader.
MLReader<T> - Class in org.apache.spark.ml.util
Abstract class for utility classes that can load ML instances.
MLReader() - Constructor for class org.apache.spark.ml.util.MLReader
 
MLUtils - Class in org.apache.spark.mllib.util
Helper methods to load, save and pre-process data used in ML Lib.
MLUtils() - Constructor for class org.apache.spark.mllib.util.MLUtils
 
MLWritable - Interface in org.apache.spark.ml.util
Trait for classes that provide MLWriter.
MLWriter - Class in org.apache.spark.ml.util
Abstract class for utility classes that can save ML instances.
MLWriter() - Constructor for class org.apache.spark.ml.util.MLWriter
 
mod(Object) - Method in class org.apache.spark.sql.Column
Modulo (a.k.a.
mode(SaveMode) - Method in class org.apache.spark.sql.DataFrameWriter
Specifies the behavior when data or table already exists.
mode(String) - Method in class org.apache.spark.sql.DataFrameWriter
Specifies the behavior when data or table already exists.
Model<M extends Model<M>> - Class in org.apache.spark.ml
:: DeveloperApi :: A fitted model, i.e., a Transformer produced by an Estimator.
Model() - Constructor for class org.apache.spark.ml.Model
 
model() - Method in class org.apache.spark.ml.regression.LinearRegressionSummary
 
model() - Method in class org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD
 
model() - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
 
model() - Method in class org.apache.spark.mllib.regression.StreamingLinearAlgorithm
The model to be updated and used for prediction.
model() - Method in class org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
 
models() - Method in class org.apache.spark.ml.classification.OneVsRestModel
 
modelType() - Method in class org.apache.spark.mllib.classification.NaiveBayesModel
 
modificationTime() - Method in class org.apache.spark.sql.sources.HadoopFsRelation.FakeFileStatus
 
MODULE$ - Static variable in class org.apache.spark.AccumulatorParam.DoubleAccumulatorParam$
Static reference to the singleton instance of this Scala object.
MODULE$ - Static variable in class org.apache.spark.AccumulatorParam.FloatAccumulatorParam$
Static reference to the singleton instance of this Scala object.
MODULE$ - Static variable in class org.apache.spark.AccumulatorParam.IntAccumulatorParam$
Static reference to the singleton instance of this Scala object.
MODULE$ - Static variable in class org.apache.spark.AccumulatorParam.LongAccumulatorParam$
Static reference to the singleton instance of this Scala object.
MODULE$ - Static variable in class org.apache.spark.graphx.PartitionStrategy.CanonicalRandomVertexCut$
Static reference to the singleton instance of this Scala object.
MODULE$ - Static variable in class org.apache.spark.graphx.PartitionStrategy.EdgePartition1D$
Static reference to the singleton instance of this Scala object.
MODULE$ - Static variable in class org.apache.spark.graphx.PartitionStrategy.EdgePartition2D$
Static reference to the singleton instance of this Scala object.
MODULE$ - Static variable in class org.apache.spark.graphx.PartitionStrategy.RandomVertexCut$
Static reference to the singleton instance of this Scala object.
MODULE$ - Static variable in class org.apache.spark.ml.recommendation.ALS.Rating$
Static reference to the singleton instance of this Scala object.
MODULE$ - Static variable in class org.apache.spark.mllib.clustering.PowerIterationClustering.Assignment$
Static reference to the singleton instance of this Scala object.
MODULE$ - Static variable in class org.apache.spark.SparkContext.DoubleAccumulatorParam$
Static reference to the singleton instance of this Scala object.
MODULE$ - Static variable in class org.apache.spark.SparkContext.FloatAccumulatorParam$
Static reference to the singleton instance of this Scala object.
MODULE$ - Static variable in class org.apache.spark.SparkContext.IntAccumulatorParam$
Static reference to the singleton instance of this Scala object.
MODULE$ - Static variable in class org.apache.spark.SparkContext.LongAccumulatorParam$
Static reference to the singleton instance of this Scala object.
MODULE$ - Static variable in class org.apache.spark.sql.sources.HadoopFsRelation.FakeFileStatus$
Static reference to the singleton instance of this Scala object.
MODULE$ - Static variable in class org.apache.spark.util.Vector.VectorAccumParam$
Static reference to the singleton instance of this Scala object.
monotonically_increasing_id() - Static method in class org.apache.spark.sql.functions
A column expression that generates monotonically increasing 64-bit integers.
monotonicallyIncreasingId() - Static method in class org.apache.spark.sql.functions
A column expression that generates monotonically increasing 64-bit integers.
month(Column) - Static method in class org.apache.spark.sql.functions
Extracts the month as an integer from a given date/timestamp/string.
months_between(Column, Column) - Static method in class org.apache.spark.sql.functions
 
MQTTUtils - Class in org.apache.spark.streaming.mqtt
 
MQTTUtils() - Constructor for class org.apache.spark.streaming.mqtt.MQTTUtils
 
MsSqlServerDialect - Class in org.apache.spark.sql.jdbc
 
MsSqlServerDialect() - Constructor for class org.apache.spark.sql.jdbc.MsSqlServerDialect
 
mu() - Method in class org.apache.spark.mllib.stat.distribution.MultivariateGaussian
 
MulticlassClassificationEvaluator - Class in org.apache.spark.ml.evaluation
:: Experimental :: Evaluator for multiclass classification, which expects two input columns: score and label.
MulticlassClassificationEvaluator(String) - Constructor for class org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
 
MulticlassClassificationEvaluator() - Constructor for class org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
 
MulticlassMetrics - Class in org.apache.spark.mllib.evaluation
::Experimental:: Evaluator for multiclass classification.
MulticlassMetrics(RDD<Tuple2<Object, Object>>) - Constructor for class org.apache.spark.mllib.evaluation.MulticlassMetrics
 
MultilabelMetrics - Class in org.apache.spark.mllib.evaluation
Evaluator for multilabel classification.
MultilabelMetrics(RDD<Tuple2<double[], double[]>>) - Constructor for class org.apache.spark.mllib.evaluation.MultilabelMetrics
 
multiLabelValidator(int) - Static method in class org.apache.spark.mllib.util.DataValidators
Function to check if labels used for k class multi-label classification are in the range of {0, 1, ..., k - 1}.
MultilayerPerceptronClassificationModel - Class in org.apache.spark.ml.classification
:: Experimental :: Classification model based on the Multilayer Perceptron.
MultilayerPerceptronClassifier - Class in org.apache.spark.ml.classification
:: Experimental :: Classifier trainer based on the Multilayer Perceptron.
MultilayerPerceptronClassifier(String) - Constructor for class org.apache.spark.ml.classification.MultilayerPerceptronClassifier
 
MultilayerPerceptronClassifier() - Constructor for class org.apache.spark.ml.classification.MultilayerPerceptronClassifier
 
Multinomial() - Static method in class org.apache.spark.mllib.classification.NaiveBayes
 
multiply(BlockMatrix) - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
Left multiplies this BlockMatrix to other, another BlockMatrix.
multiply(Matrix) - Method in class org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
 
multiply(Matrix) - Method in class org.apache.spark.mllib.linalg.distributed.RowMatrix
Multiply this matrix by a local matrix on the right.
multiply(DenseMatrix) - Method in interface org.apache.spark.mllib.linalg.Matrix
Convenience method for `Matrix`-`DenseMatrix` multiplication.
multiply(DenseVector) - Method in interface org.apache.spark.mllib.linalg.Matrix
Convenience method for `Matrix`-`DenseVector` multiplication.
multiply(Vector) - Method in interface org.apache.spark.mllib.linalg.Matrix
Convenience method for `Matrix`-`Vector` multiplication.
multiply(Object) - Method in class org.apache.spark.sql.Column
Multiplication of this expression and another expression.
multiply(double) - Method in class org.apache.spark.util.Vector
 
MultivariateGaussian - Class in org.apache.spark.mllib.stat.distribution
:: DeveloperApi :: This class provides basic functionality for a Multivariate Gaussian (Normal) Distribution.
MultivariateGaussian(Vector, Matrix) - Constructor for class org.apache.spark.mllib.stat.distribution.MultivariateGaussian
 
MultivariateOnlineSummarizer - Class in org.apache.spark.mllib.stat
:: DeveloperApi :: MultivariateOnlineSummarizer implements MultivariateStatisticalSummary to compute the mean, variance, minimum, maximum, counts, and nonzero counts for instances in sparse or dense vector format in a online fashion.
MultivariateOnlineSummarizer() - Constructor for class org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 
MultivariateStatisticalSummary - Interface in org.apache.spark.mllib.stat
Trait for multivariate statistical summary of a data matrix.
mustCheckpoint() - Method in class org.apache.spark.streaming.dstream.DStream
 
MutableAggregationBuffer - Class in org.apache.spark.sql.expressions
:: Experimental :: A Row representing an mutable aggregation buffer.
MutableAggregationBuffer() - Constructor for class org.apache.spark.sql.expressions.MutableAggregationBuffer
 
MutablePair<T1,T2> - Class in org.apache.spark.util
:: DeveloperApi :: A tuple of 2 elements.
MutablePair(T1, T2) - Constructor for class org.apache.spark.util.MutablePair
 
MutablePair() - Constructor for class org.apache.spark.util.MutablePair
No-arg constructor for serialization
myName() - Method in class org.apache.spark.util.InnerClosureFinder
 
MySQLDialect - Class in org.apache.spark.sql.jdbc
 
MySQLDialect() - Constructor for class org.apache.spark.sql.jdbc.MySQLDialect
 

N

n() - Method in class org.apache.spark.ml.feature.NGram
Minimum n-gram length, >= 1.
na() - Method in class org.apache.spark.sql.DataFrame
Returns a DataFrameNaFunctions for working with missing data.
NaiveBayes - Class in org.apache.spark.ml.classification
:: Experimental :: Naive Bayes Classifiers.
NaiveBayes(String) - Constructor for class org.apache.spark.ml.classification.NaiveBayes
 
NaiveBayes() - Constructor for class org.apache.spark.ml.classification.NaiveBayes
 
NaiveBayes - Class in org.apache.spark.mllib.classification
 
NaiveBayes(double) - Constructor for class org.apache.spark.mllib.classification.NaiveBayes
 
NaiveBayes() - Constructor for class org.apache.spark.mllib.classification.NaiveBayes
 
NaiveBayesModel - Class in org.apache.spark.ml.classification
:: Experimental :: Model produced by NaiveBayes param: pi log of class priors, whose dimension is C (number of classes) param: theta log of class conditional probabilities, whose dimension is C (number of classes) by D (number of features)
NaiveBayesModel - Class in org.apache.spark.mllib.classification
Model for Naive Bayes Classifiers.
name() - Method in class org.apache.spark.Accumulable
 
name() - Method in interface org.apache.spark.api.java.JavaRDDLike
 
name() - Method in class org.apache.spark.ml.attribute.Attribute
Name of the attribute.
name() - Method in class org.apache.spark.ml.attribute.AttributeGroup
 
name() - Method in class org.apache.spark.ml.attribute.AttributeType
 
name() - Method in class org.apache.spark.ml.attribute.BinaryAttribute
 
name() - Method in class org.apache.spark.ml.attribute.NominalAttribute
 
name() - Method in class org.apache.spark.ml.attribute.NumericAttribute
 
name() - Static method in class org.apache.spark.ml.attribute.UnresolvedAttribute
 
name() - Method in class org.apache.spark.ml.param.Param
 
name() - Method in class org.apache.spark.rdd.RDD
A friendly name for this RDD
name() - Method in class org.apache.spark.scheduler.AccumulableInfo
 
name() - Method in class org.apache.spark.scheduler.StageInfo
 
name() - Method in interface org.apache.spark.SparkStageInfo
 
name() - Method in class org.apache.spark.SparkStageInfoImpl
 
name() - Method in class org.apache.spark.sql.types.StructField
 
name() - Method in class org.apache.spark.status.api.v1.AccumulableInfo
 
name() - Method in class org.apache.spark.status.api.v1.ApplicationInfo
 
name() - Method in class org.apache.spark.status.api.v1.JobData
 
name() - Method in class org.apache.spark.status.api.v1.RDDStorageInfo
 
name() - Method in class org.apache.spark.status.api.v1.StageData
 
name() - Method in class org.apache.spark.storage.BlockId
A globally unique identifier for this Block.
name() - Method in class org.apache.spark.storage.BroadcastBlockId
 
name() - Method in class org.apache.spark.storage.RDDBlockId
 
name() - Method in class org.apache.spark.storage.RDDInfo
 
name() - Method in class org.apache.spark.storage.ShuffleBlockId
 
name() - Method in class org.apache.spark.storage.ShuffleDataBlockId
 
name() - Method in class org.apache.spark.storage.ShuffleIndexBlockId
 
name() - Method in class org.apache.spark.storage.StreamBlockId
 
name() - Method in class org.apache.spark.storage.TaskResultBlockId
 
name() - Method in class org.apache.spark.streaming.scheduler.OutputOperationInfo
 
name() - Method in class org.apache.spark.streaming.scheduler.ReceiverInfo
 
name() - Method in class org.apache.spark.util.MethodIdentifier
 
names() - Method in class org.apache.spark.ml.feature.VectorSlicer
An array of feature names to select features from a vector column.
nanvl(Column, Column) - Static method in class org.apache.spark.sql.functions
Returns col1 if it is not NaN, or col2 if col1 is NaN.
NarrowDependency<T> - Class in org.apache.spark
:: DeveloperApi :: Base class for dependencies where each partition of the child RDD depends on a small number of partitions of the parent RDD.
NarrowDependency(RDD<T>) - Constructor for class org.apache.spark.NarrowDependency
 
ndcgAt(int) - Method in class org.apache.spark.mllib.evaluation.RankingMetrics
Compute the average NDCG value of all the queries, truncated at ranking position k.
needConversion() - Method in class org.apache.spark.sql.sources.BaseRelation
Whether does it need to convert the objects in Row to internal representation, for example: java.lang.String -> UTF8String java.lang.Decimal -> Decimal
negate(Column) - Static method in class org.apache.spark.sql.functions
Unary minus, i.e.
networkStream(Receiver<T>, ClassTag<T>) - Method in class org.apache.spark.streaming.StreamingContext
Deprecated.
As of 1.0.0", replaced by receiverStream.
newAPIHadoopFile(String, Class<F>, Class<K>, Class<V>, Configuration) - Method in class org.apache.spark.api.java.JavaSparkContext
Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.
newAPIHadoopFile(String, ClassTag<K>, ClassTag<V>, ClassTag<F>) - Method in class org.apache.spark.SparkContext
Get an RDD for a Hadoop file with an arbitrary new API InputFormat.
newAPIHadoopFile(String, Class<F>, Class<K>, Class<V>, Configuration) - Method in class org.apache.spark.SparkContext
Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.
newAPIHadoopRDD(Configuration, Class<F>, Class<K>, Class<V>) - Method in class org.apache.spark.api.java.JavaSparkContext
Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.
newAPIHadoopRDD(Configuration, Class<F>, Class<K>, Class<V>) - Method in class org.apache.spark.SparkContext
Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.
newBooleanEncoder() - Method in class org.apache.spark.sql.SQLImplicits
 
newBroadcast(T, boolean, long, ClassTag<T>) - Method in interface org.apache.spark.broadcast.BroadcastFactory
Creates a new broadcast variable.
newBroadcast(T, boolean, long, ClassTag<T>) - Method in class org.apache.spark.broadcast.HttpBroadcastFactory
 
newBroadcast(T, boolean, long, ClassTag<T>) - Method in class org.apache.spark.broadcast.TorrentBroadcastFactory
 
newByteEncoder() - Method in class org.apache.spark.sql.SQLImplicits
 
newDoubleEncoder() - Method in class org.apache.spark.sql.SQLImplicits
 
newFloatEncoder() - Method in class org.apache.spark.sql.SQLImplicits
 
NewHadoopRDD<K,V> - Class in org.apache.spark.rdd
:: DeveloperApi :: An RDD that provides core functionality for reading data stored in Hadoop (e.g., files in HDFS, sources in HBase, or S3), using the new MapReduce API (org.apache.hadoop.mapreduce).
NewHadoopRDD(SparkContext, Class<? extends InputFormat<K, V>>, Class<K>, Class<V>, Configuration) - Constructor for class org.apache.spark.rdd.NewHadoopRDD
 
newInstance() - Method in class org.apache.spark.serializer.JavaSerializer
 
newInstance() - Method in class org.apache.spark.serializer.KryoSerializer
 
newInstance() - Method in class org.apache.spark.serializer.Serializer
Creates a new SerializerInstance.
newInstance(String, StructType, TaskAttemptContext) - Method in class org.apache.spark.sql.sources.OutputWriterFactory
When writing to a HadoopFsRelation, this method gets called by each task on executor side to instantiate new OutputWriters.
newIntEncoder() - Method in class org.apache.spark.sql.SQLImplicits
 
newKryo() - Method in class org.apache.spark.serializer.KryoSerializer
 
newKryoOutput() - Method in class org.apache.spark.serializer.KryoSerializer
 
newLongEncoder() - Method in class org.apache.spark.sql.SQLImplicits
 
newProductEncoder(TypeTags.TypeTag<T>) - Method in class org.apache.spark.sql.SQLImplicits
 
newSession() - Method in class org.apache.spark.sql.hive.HiveContext
Returns a new HiveContext as new session, which will have separated SQLConf, UDF/UDAF, temporary tables and SessionState, but sharing the same CacheManager, IsolatedClientLoader and Hive client (both of execution and metadata) with existing HiveContext.
newSession() - Method in class org.apache.spark.sql.SQLContext
Returns a SQLContext as new session, with separated SQL configurations, temporary tables, registered functions, but sharing the same SparkContext, CacheManager, SQLListener and SQLTab.
newShortEncoder() - Method in class org.apache.spark.sql.SQLImplicits
 
newStringEncoder() - Method in class org.apache.spark.sql.SQLImplicits
 
newTemporaryConfiguration() - Static method in class org.apache.spark.sql.hive.HiveContext
Constructs a configuration for hive, where the metastore is located in a temp directory.
next() - Method in class org.apache.spark.InterruptibleIterator
 
next() - Method in interface org.apache.spark.mllib.clustering.LDAOptimizer
 
next() - Method in class org.apache.spark.rdd.PartitionCoalescer.LocationIterator
 
next_day(Column, String) - Static method in class org.apache.spark.sql.functions
Given a date column, returns the first date which is later than the value of the date column that is on the specified day of the week.
nextValue() - Method in class org.apache.spark.mllib.random.ExponentialGenerator
 
nextValue() - Method in class org.apache.spark.mllib.random.GammaGenerator
 
nextValue() - Method in class org.apache.spark.mllib.random.LogNormalGenerator
 
nextValue() - Method in class org.apache.spark.mllib.random.PoissonGenerator
 
nextValue() - Method in interface org.apache.spark.mllib.random.RandomDataGenerator
Returns an i.i.d.
nextValue() - Method in class org.apache.spark.mllib.random.StandardNormalGenerator
 
nextValue() - Method in class org.apache.spark.mllib.random.UniformGenerator
 
nextValue() - Method in class org.apache.spark.mllib.random.WeibullGenerator
 
NGram - Class in org.apache.spark.ml.feature
:: Experimental :: A feature transformer that converts the input array of strings into an array of n-grams.
NGram(String) - Constructor for class org.apache.spark.ml.feature.NGram
 
NGram() - Constructor for class org.apache.spark.ml.feature.NGram
 
NO_PREF() - Static method in class org.apache.spark.scheduler.TaskLocality
 
Node - Class in org.apache.spark.ml.tree
:: DeveloperApi :: Decision tree node interface.
Node() - Constructor for class org.apache.spark.ml.tree.Node
 
Node - Class in org.apache.spark.mllib.tree.model
:: DeveloperApi :: Node in a decision tree.
Node(int, Predict, double, boolean, Option<Split>, Option<Node>, Option<Node>, Option<InformationGainStats>) - Constructor for class org.apache.spark.mllib.tree.model.Node
 
NODE_LOCAL() - Static method in class org.apache.spark.scheduler.TaskLocality
 
noLocality() - Method in class org.apache.spark.rdd.PartitionCoalescer
 
Nominal() - Static method in class org.apache.spark.ml.attribute.AttributeType
Nominal type.
NominalAttribute - Class in org.apache.spark.ml.attribute
:: DeveloperApi :: A nominal attribute.
NONE - Static variable in class org.apache.spark.api.java.StorageLevels
 
None - Static variable in class org.apache.spark.graphx.TripletFields
None of the triplet fields are exposed.
NONE() - Static method in class org.apache.spark.scheduler.SchedulingMode
 
NONE() - Static method in class org.apache.spark.storage.StorageLevel
 
NoopDialect - Class in org.apache.spark.sql.jdbc
NOOP dialect object, always returning the neutral element.
NoopDialect() - Constructor for class org.apache.spark.sql.jdbc.NoopDialect
 
norm(Vector, double) - Static method in class org.apache.spark.mllib.linalg.Vectors
Returns the p-norm of this vector.
Normalizer - Class in org.apache.spark.ml.feature
:: Experimental :: Normalize a vector to have unit norm using the given p-norm.
Normalizer(String) - Constructor for class org.apache.spark.ml.feature.Normalizer
 
Normalizer() - Constructor for class org.apache.spark.ml.feature.Normalizer
 
Normalizer - Class in org.apache.spark.mllib.feature
Normalizes samples individually to unit L^p^ norm
Normalizer(double) - Constructor for class org.apache.spark.mllib.feature.Normalizer
 
Normalizer() - Constructor for class org.apache.spark.mllib.feature.Normalizer
 
normalizeToProbabilitiesInPlace(DenseVector) - Static method in class org.apache.spark.ml.classification.ProbabilisticClassificationModel
Normalize a vector of raw predictions to be a multinomial probability vector, in place.
normalJavaRDD(JavaSparkContext, long, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
normalJavaRDD(JavaSparkContext, long, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
normalJavaRDD(JavaSparkContext, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
RandomRDDs.normalJavaRDD(org.apache.spark.api.java.JavaSparkContext, long, int, long) with the default number of partitions and the default seed.
normalJavaVectorRDD(JavaSparkContext, long, int, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
normalJavaVectorRDD(JavaSparkContext, long, int, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
normalJavaVectorRDD(JavaSparkContext, long, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
normalRDD(SparkContext, long, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
Generates an RDD comprised of i.i.d. samples from the standard normal distribution.
normalVectorRDD(SparkContext, long, int, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the standard normal distribution.
normL1() - Method in class org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
L1 norm of each dimension.
normL1() - Method in interface org.apache.spark.mllib.stat.MultivariateStatisticalSummary
L1 norm of each column
normL2() - Method in class org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
L2 (Euclidian) norm of each dimension.
normL2() - Method in interface org.apache.spark.mllib.stat.MultivariateStatisticalSummary
Euclidean magnitude of each column
normPdf(double, double, double, double) - Static method in class org.apache.spark.mllib.stat.KernelDensity
Evaluates the PDF of a normal distribution.
not(Column) - Static method in class org.apache.spark.sql.functions
Inversion of boolean expression, i.e.
Not - Class in org.apache.spark.sql.sources
A filter that evaluates to true iff child is evaluated to false.
Not(Filter) - Constructor for class org.apache.spark.sql.sources.Not
 
notEqual(Object) - Method in class org.apache.spark.sql.Column
Inequality test.
ntile(int) - Static method in class org.apache.spark.sql.functions
Window function: returns the ntile group id (from 1 to n inclusive) in an ordered window partition.
nullable() - Method in class org.apache.spark.sql.types.StructField
 
nullHypothesis() - Method in class org.apache.spark.mllib.stat.test.ChiSqTestResult
 
nullHypothesis() - Method in class org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult
 
nullHypothesis() - Method in interface org.apache.spark.mllib.stat.test.TestResult
Null hypothesis of the test.
NullType - Static variable in class org.apache.spark.sql.types.DataTypes
Gets the NullType object.
NullType - Class in org.apache.spark.sql.types
:: DeveloperApi :: The data type representing NULL values.
numActives() - Method in class org.apache.spark.mllib.linalg.DenseMatrix
 
numActives() - Method in class org.apache.spark.mllib.linalg.DenseVector
 
numActives() - Method in interface org.apache.spark.mllib.linalg.Matrix
Find the number of values stored explicitly.
numActives() - Method in class org.apache.spark.mllib.linalg.SparseMatrix
 
numActives() - Method in class org.apache.spark.mllib.linalg.SparseVector
 
numActives() - Method in interface org.apache.spark.mllib.linalg.Vector
Number of active entries.
numActiveStages() - Method in class org.apache.spark.status.api.v1.JobData
 
numActiveTasks() - Method in interface org.apache.spark.SparkStageInfo
 
numActiveTasks() - Method in class org.apache.spark.SparkStageInfoImpl
 
numActiveTasks() - Method in class org.apache.spark.status.api.v1.JobData
 
numActiveTasks() - Method in class org.apache.spark.status.api.v1.StageData
 
numAttributes() - Method in class org.apache.spark.ml.attribute.AttributeGroup
 
numberOfHiccups() - Method in class org.apache.spark.streaming.receiver.Statistics
 
numberOfMsgs() - Method in class org.apache.spark.streaming.receiver.Statistics
 
numberOfWorkers() - Method in class org.apache.spark.streaming.receiver.Statistics
 
numBins() - Method in class org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
 
numBlocks() - Method in class org.apache.spark.storage.StorageStatus
Return the number of blocks stored in this block manager in O(RDDs) time.
numCachedPartitions() - Method in class org.apache.spark.status.api.v1.RDDStorageInfo
 
numCachedPartitions() - Method in class org.apache.spark.storage.RDDInfo
 
numClasses() - Method in class org.apache.spark.ml.classification.ClassificationModel
Number of classes (values which the label can take).
numClasses() - Method in class org.apache.spark.ml.classification.DecisionTreeClassificationModel
 
numClasses() - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
numClasses() - Method in class org.apache.spark.ml.classification.NaiveBayesModel
 
numClasses() - Method in class org.apache.spark.ml.classification.RandomForestClassificationModel
 
numClasses() - Method in class org.apache.spark.mllib.classification.LogisticRegressionModel
 
numClasses() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
numColBlocks() - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
 
numCols() - Method in class org.apache.spark.mllib.linalg.DenseMatrix
 
numCols() - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
 
numCols() - Method in class org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
 
numCols() - Method in interface org.apache.spark.mllib.linalg.distributed.DistributedMatrix
Gets or computes the number of columns.
numCols() - Method in class org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
 
numCols() - Method in class org.apache.spark.mllib.linalg.distributed.RowMatrix
Gets or computes the number of columns.
numCols() - Method in interface org.apache.spark.mllib.linalg.Matrix
Number of columns.
numCols() - Method in class org.apache.spark.mllib.linalg.SparseMatrix
 
numCompletedJobs() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
numCompletedStages() - Method in class org.apache.spark.status.api.v1.JobData
 
numCompletedStages() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
numCompletedTasks() - Method in interface org.apache.spark.SparkStageInfo
 
numCompletedTasks() - Method in class org.apache.spark.SparkStageInfoImpl
 
numCompletedTasks() - Method in class org.apache.spark.status.api.v1.JobData
 
numCompleteTasks() - Method in class org.apache.spark.status.api.v1.StageData
 
numEdges() - Method in class org.apache.spark.graphx.GraphOps
The number of edges in the graph.
Numeric() - Static method in class org.apache.spark.ml.attribute.AttributeType
Numeric type.
numeric() - Method in class org.apache.spark.sql.types.ByteType
 
numeric() - Method in class org.apache.spark.sql.types.DecimalType
 
numeric() - Method in class org.apache.spark.sql.types.DoubleType
 
numeric() - Method in class org.apache.spark.sql.types.FloatType
 
numeric() - Method in class org.apache.spark.sql.types.IntegerType
 
numeric() - Method in class org.apache.spark.sql.types.LongType
 
numeric() - Method in class org.apache.spark.sql.types.NumericType
 
numeric() - Method in class org.apache.spark.sql.types.ShortType
 
NumericAttribute - Class in org.apache.spark.ml.attribute
:: DeveloperApi :: A numeric attribute with optional summary statistics.
numericColumns() - Method in class org.apache.spark.sql.DataFrame
 
numericRDDToDoubleRDDFunctions(RDD<T>, Numeric<T>) - Static method in class org.apache.spark.rdd.RDD
 
numericRDDToDoubleRDDFunctions(RDD<T>, Numeric<T>) - Static method in class org.apache.spark.SparkContext
 
NumericType - Class in org.apache.spark.sql.types
:: DeveloperApi :: Numeric data types.
NumericType() - Constructor for class org.apache.spark.sql.types.NumericType
 
numFailedJobs() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
numFailedStages() - Method in class org.apache.spark.status.api.v1.JobData
 
numFailedStages() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
numFailedTasks() - Method in interface org.apache.spark.SparkStageInfo
 
numFailedTasks() - Method in class org.apache.spark.SparkStageInfoImpl
 
numFailedTasks() - Method in class org.apache.spark.status.api.v1.JobData
 
numFailedTasks() - Method in class org.apache.spark.status.api.v1.StageData
 
numFeatures() - Method in class org.apache.spark.ml.classification.DecisionTreeClassificationModel
 
numFeatures() - Method in class org.apache.spark.ml.classification.GBTClassificationModel
 
numFeatures() - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
numFeatures() - Method in class org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel
 
numFeatures() - Method in class org.apache.spark.ml.classification.NaiveBayesModel
 
numFeatures() - Method in class org.apache.spark.ml.classification.RandomForestClassificationModel
 
numFeatures() - Method in class org.apache.spark.ml.feature.HashingTF
Number of features.
numFeatures() - Method in class org.apache.spark.ml.feature.VectorIndexerModel
 
numFeatures() - Method in class org.apache.spark.ml.PredictionModel
Returns the number of features the model was trained on.
numFeatures() - Method in class org.apache.spark.ml.regression.DecisionTreeRegressionModel
 
numFeatures() - Method in class org.apache.spark.ml.regression.GBTRegressionModel
 
numFeatures() - Method in class org.apache.spark.ml.regression.LinearRegressionModel
 
numFeatures() - Method in class org.apache.spark.ml.regression.RandomForestRegressionModel
 
numFeatures() - Method in class org.apache.spark.mllib.classification.LogisticRegressionModel
 
numFeatures() - Method in class org.apache.spark.mllib.feature.HashingTF
 
numFeatures() - Method in class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
The dimension of training features.
numInstances() - Method in class org.apache.spark.ml.regression.LinearRegressionSummary
Number of instances in DataFrame predictions
numIterations() - Method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
numNodes() - Method in class org.apache.spark.mllib.tree.model.DecisionTreeModel
Get number of nodes in tree, including leaf nodes.
numNonzeros() - Method in class org.apache.spark.mllib.linalg.DenseMatrix
 
numNonzeros() - Method in class org.apache.spark.mllib.linalg.DenseVector
 
numNonzeros() - Method in interface org.apache.spark.mllib.linalg.Matrix
Find the number of non-zero active values.
numNonzeros() - Method in class org.apache.spark.mllib.linalg.SparseMatrix
 
numNonzeros() - Method in class org.apache.spark.mllib.linalg.SparseVector
 
numNonzeros() - Method in interface org.apache.spark.mllib.linalg.Vector
Number of nonzero elements.
numNonzeros() - Method in class org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
Number of nonzero elements in each dimension.
numNonzeros() - Method in interface org.apache.spark.mllib.stat.MultivariateStatisticalSummary
Number of nonzero elements (including explicitly presented zero values) in each column.
numOfLinearPredictor() - Method in class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
In GeneralizedLinearModel, only single linear predictor is allowed for both weights and intercept.
numPartitions() - Method in class org.apache.spark.HashPartitioner
 
numPartitions() - Method in class org.apache.spark.Partitioner
 
numPartitions() - Method in class org.apache.spark.RangePartitioner
 
numPartitions() - Method in class org.apache.spark.status.api.v1.RDDStorageInfo
 
numPartitions() - Method in class org.apache.spark.storage.RDDInfo
 
numPartitions(int) - Method in class org.apache.spark.streaming.StateSpec
Set the number of partitions by which the state RDDs generated by mapWithState will be partitioned.
numRddBlocks() - Method in class org.apache.spark.storage.StorageStatus
Return the number of RDD blocks stored in this block manager in O(RDDs) time.
numRddBlocksById(int) - Method in class org.apache.spark.storage.StorageStatus
Return the number of blocks that belong to the given RDD in O(1) time.
numRecords() - Method in class org.apache.spark.streaming.scheduler.BatchInfo
The number of recorders received by the receivers in this batch.
numRecords() - Method in class org.apache.spark.streaming.scheduler.StreamInputInfo
 
numRetries(SparkConf) - Static method in class org.apache.spark.util.RpcUtils
Returns the configured number of times to retry connecting
numRowBlocks() - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
 
numRows() - Method in class org.apache.spark.mllib.linalg.DenseMatrix
 
numRows() - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
 
numRows() - Method in class org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
 
numRows() - Method in interface org.apache.spark.mllib.linalg.distributed.DistributedMatrix
Gets or computes the number of rows.
numRows() - Method in class org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
 
numRows() - Method in class org.apache.spark.mllib.linalg.distributed.RowMatrix
Gets or computes the number of rows.
numRows() - Method in interface org.apache.spark.mllib.linalg.Matrix
Number of rows.
numRows() - Method in class org.apache.spark.mllib.linalg.SparseMatrix
 
numSkippedStages() - Method in class org.apache.spark.status.api.v1.JobData
 
numSkippedTasks() - Method in class org.apache.spark.status.api.v1.JobData
 
numSpilledStages() - Method in class org.apache.spark.SpillListener
 
numTasks() - Method in class org.apache.spark.scheduler.StageInfo
 
numTasks() - Method in interface org.apache.spark.SparkStageInfo
 
numTasks() - Method in class org.apache.spark.SparkStageInfoImpl
 
numTasks() - Method in class org.apache.spark.status.api.v1.JobData
 
numTopFeatures() - Method in class org.apache.spark.mllib.feature.ChiSqSelector
 
numValues() - Method in class org.apache.spark.ml.attribute.NominalAttribute
 
numVertices() - Method in class org.apache.spark.graphx.GraphOps
The number of vertices in the graph.

O

objectFile(String, int) - Method in class org.apache.spark.api.java.JavaSparkContext
Load an RDD saved as a SequenceFile containing serialized objects, with NullWritable keys and BytesWritable values that contain a serialized partition.
objectFile(String) - Method in class org.apache.spark.api.java.JavaSparkContext
Load an RDD saved as a SequenceFile containing serialized objects, with NullWritable keys and BytesWritable values that contain a serialized partition.
objectFile(String, int, ClassTag<T>) - Method in class org.apache.spark.SparkContext
Load an RDD saved as a SequenceFile containing serialized objects, with NullWritable keys and BytesWritable values that contain a serialized partition.
objectiveHistory() - Method in class org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary
 
objectiveHistory() - Method in interface org.apache.spark.ml.classification.LogisticRegressionTrainingSummary
objective function (scaled loss + regularization) at each iteration.
objectiveHistory() - Method in class org.apache.spark.ml.regression.LinearRegressionTrainingSummary
 
of(JavaRDD<Tuple2<T, T>>) - Static method in class org.apache.spark.mllib.evaluation.RankingMetrics
Creates a RankingMetrics instance (for Java users).
OFF_HEAP - Static variable in class org.apache.spark.api.java.StorageLevels
 
OFF_HEAP() - Static method in class org.apache.spark.storage.StorageLevel
 
offHeapUsed() - Method in class org.apache.spark.storage.StorageStatus
Return the off-heap space used by this block manager.
offHeapUsedByRdd(int) - Method in class org.apache.spark.storage.StorageStatus
Return the off-heap space used by the given RDD in this block manager in O(1) time.
OffsetRange - Class in org.apache.spark.streaming.kafka
Represents a range of offsets from a single Kafka TopicAndPartition.
offsetRanges() - Method in interface org.apache.spark.streaming.kafka.HasOffsetRanges
 
oldLocalModel() - Method in class org.apache.spark.ml.clustering.DistributedLDAModel
 
oldLocalModel() - Method in class org.apache.spark.ml.clustering.LDAModel
Underlying spark.mllib model.
oldLocalModel() - Method in class org.apache.spark.ml.clustering.LocalLDAModel
 
onApplicationEnd(SparkListenerApplicationEnd) - Method in class org.apache.spark.JavaSparkListener
 
onApplicationEnd(SparkListenerApplicationEnd) - Method in interface org.apache.spark.scheduler.SparkListener
Called when the application ends
onApplicationEnd(SparkListenerApplicationEnd) - Method in class org.apache.spark.SparkFirehoseListener
 
onApplicationEnd(SparkListenerApplicationEnd) - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
onApplicationStart(SparkListenerApplicationStart) - Method in class org.apache.spark.JavaSparkListener
 
onApplicationStart(SparkListenerApplicationStart) - Method in interface org.apache.spark.scheduler.SparkListener
Called when the application starts
onApplicationStart(SparkListenerApplicationStart) - Method in class org.apache.spark.SparkFirehoseListener
 
onApplicationStart(SparkListenerApplicationStart) - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
onApplicationStart(SparkListenerApplicationStart) - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
onBatchCompleted(StreamingListenerBatchCompleted) - Method in class org.apache.spark.streaming.scheduler.StatsReportListener
 
onBatchCompleted(StreamingListenerBatchCompleted) - Method in interface org.apache.spark.streaming.scheduler.StreamingListener
Called when processing of a batch of jobs has completed.
onBatchStarted(StreamingListenerBatchStarted) - Method in interface org.apache.spark.streaming.scheduler.StreamingListener
Called when processing of a batch of jobs has started.
onBatchSubmitted(StreamingListenerBatchSubmitted) - Method in interface org.apache.spark.streaming.scheduler.StreamingListener
Called when a batch of jobs has been submitted for processing.
onBlockManagerAdded(SparkListenerBlockManagerAdded) - Method in class org.apache.spark.JavaSparkListener
 
onBlockManagerAdded(SparkListenerBlockManagerAdded) - Method in interface org.apache.spark.scheduler.SparkListener
Called when a new block manager has joined
onBlockManagerAdded(SparkListenerBlockManagerAdded) - Method in class org.apache.spark.SparkFirehoseListener
 
onBlockManagerAdded(SparkListenerBlockManagerAdded) - Method in class org.apache.spark.storage.StorageStatusListener
 
onBlockManagerAdded(SparkListenerBlockManagerAdded) - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
onBlockManagerRemoved(SparkListenerBlockManagerRemoved) - Method in class org.apache.spark.JavaSparkListener
 
onBlockManagerRemoved(SparkListenerBlockManagerRemoved) - Method in interface org.apache.spark.scheduler.SparkListener
Called when an existing block manager has been removed
onBlockManagerRemoved(SparkListenerBlockManagerRemoved) - Method in class org.apache.spark.SparkFirehoseListener
 
onBlockManagerRemoved(SparkListenerBlockManagerRemoved) - Method in class org.apache.spark.storage.StorageStatusListener
 
onBlockManagerRemoved(SparkListenerBlockManagerRemoved) - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
onBlockUpdated(SparkListenerBlockUpdated) - Method in class org.apache.spark.JavaSparkListener
 
onBlockUpdated(SparkListenerBlockUpdated) - Method in interface org.apache.spark.scheduler.SparkListener
Called when the driver receives a block update info.
onBlockUpdated(SparkListenerBlockUpdated) - Method in class org.apache.spark.SparkFirehoseListener
 
onComplete(Function1<Try<T>, U>, ExecutionContext) - Method in class org.apache.spark.ComplexFutureAction
 
onComplete(Function1<Try<T>, U>, ExecutionContext) - Method in interface org.apache.spark.FutureAction
When this action is completed, either through an exception, or a value, applies the provided function.
onComplete(Function1<R, BoxedUnit>) - Method in class org.apache.spark.partial.PartialResult
Set a handler to be called when this PartialResult completes.
onComplete(Function1<Try<T>, U>, ExecutionContext) - Method in class org.apache.spark.SimpleFutureAction
 
ONE() - Static method in class org.apache.spark.sql.types.Decimal
 
OneHotEncoder - Class in org.apache.spark.ml.feature
:: Experimental :: A one-hot encoder that maps a column of category indices to a column of binary vectors, with at most a single one-value per row that indicates the input category index.
OneHotEncoder(String) - Constructor for class org.apache.spark.ml.feature.OneHotEncoder
 
OneHotEncoder() - Constructor for class org.apache.spark.ml.feature.OneHotEncoder
 
onEnvironmentUpdate(SparkListenerEnvironmentUpdate) - Method in class org.apache.spark.JavaSparkListener
 
onEnvironmentUpdate(SparkListenerEnvironmentUpdate) - Method in interface org.apache.spark.scheduler.SparkListener
Called when environment properties have been updated
onEnvironmentUpdate(SparkListenerEnvironmentUpdate) - Method in class org.apache.spark.SparkFirehoseListener
 
onEnvironmentUpdate(SparkListenerEnvironmentUpdate) - Method in class org.apache.spark.ui.env.EnvironmentListener
 
onEnvironmentUpdate(SparkListenerEnvironmentUpdate) - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
ones(int, int) - Static method in class org.apache.spark.mllib.linalg.DenseMatrix
Generate a DenseMatrix consisting of ones.
ones(int, int) - Static method in class org.apache.spark.mllib.linalg.Matrices
Generate a DenseMatrix consisting of ones.
ones(int) - Static method in class org.apache.spark.util.Vector
 
OneToOneDependency<T> - Class in org.apache.spark
:: DeveloperApi :: Represents a one-to-one dependency between partitions of the parent and child RDDs.
OneToOneDependency(RDD<T>) - Constructor for class org.apache.spark.OneToOneDependency
 
onEvent(SparkListenerEvent) - Method in class org.apache.spark.SparkFirehoseListener
 
OneVsRest - Class in org.apache.spark.ml.classification
:: Experimental ::
OneVsRest(String) - Constructor for class org.apache.spark.ml.classification.OneVsRest
 
OneVsRest() - Constructor for class org.apache.spark.ml.classification.OneVsRest
 
OneVsRestModel - Class in org.apache.spark.ml.classification
:: Experimental :: Model produced by OneVsRest.
onExecutorAdded(SparkListenerExecutorAdded) - Method in class org.apache.spark.JavaSparkListener
 
onExecutorAdded(SparkListenerExecutorAdded) - Method in interface org.apache.spark.scheduler.SparkListener
Called when the driver registers a new executor.
onExecutorAdded(SparkListenerExecutorAdded) - Method in class org.apache.spark.SparkFirehoseListener
 
onExecutorAdded(SparkListenerExecutorAdded) - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
onExecutorMetricsUpdate(SparkListenerExecutorMetricsUpdate) - Method in class org.apache.spark.JavaSparkListener
 
onExecutorMetricsUpdate(SparkListenerExecutorMetricsUpdate) - Method in interface org.apache.spark.scheduler.SparkListener
Called when the driver receives task metrics from an executor in a heartbeat.
onExecutorMetricsUpdate(SparkListenerExecutorMetricsUpdate) - Method in class org.apache.spark.SparkFirehoseListener
 
onExecutorMetricsUpdate(SparkListenerExecutorMetricsUpdate) - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
onExecutorRemoved(SparkListenerExecutorRemoved) - Method in class org.apache.spark.JavaSparkListener
 
onExecutorRemoved(SparkListenerExecutorRemoved) - Method in interface org.apache.spark.scheduler.SparkListener
Called when the driver removes an executor.
onExecutorRemoved(SparkListenerExecutorRemoved) - Method in class org.apache.spark.SparkFirehoseListener
 
onExecutorRemoved(SparkListenerExecutorRemoved) - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
onFail(Function1<Exception, BoxedUnit>) - Method in class org.apache.spark.partial.PartialResult
Set a handler to be called if this PartialResult's job fails.
onFailure(String, QueryExecution, Exception) - Method in interface org.apache.spark.sql.util.QueryExecutionListener
A callback function that will be called when a query execution failed.
onJobEnd(SparkListenerJobEnd) - Method in class org.apache.spark.JavaSparkListener
 
onJobEnd(SparkListenerJobEnd) - Method in class org.apache.spark.scheduler.JobLogger
When job ends, recording job completion status and close log file
onJobEnd(SparkListenerJobEnd) - Method in interface org.apache.spark.scheduler.SparkListener
Called when a job ends
onJobEnd(SparkListenerJobEnd) - Method in class org.apache.spark.SparkFirehoseListener
 
onJobEnd(SparkListenerJobEnd) - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
onJobStart(SparkListenerJobStart) - Method in class org.apache.spark.JavaSparkListener
 
onJobStart(SparkListenerJobStart) - Method in class org.apache.spark.scheduler.JobLogger
When job starts, record job property and stage graph
onJobStart(SparkListenerJobStart) - Method in interface org.apache.spark.scheduler.SparkListener
Called when a job starts
onJobStart(SparkListenerJobStart) - Method in class org.apache.spark.SparkFirehoseListener
 
onJobStart(SparkListenerJobStart) - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
OnlineLDAOptimizer - Class in org.apache.spark.mllib.clustering
:: DeveloperApi ::
OnlineLDAOptimizer() - Constructor for class org.apache.spark.mllib.clustering.OnlineLDAOptimizer
 
onOutputOperationCompleted(StreamingListenerOutputOperationCompleted) - Method in interface org.apache.spark.streaming.scheduler.StreamingListener
Called when processing of a job of a batch has completed.
onOutputOperationStarted(StreamingListenerOutputOperationStarted) - Method in interface org.apache.spark.streaming.scheduler.StreamingListener
Called when processing of a job of a batch has started.
onReceiverError(StreamingListenerReceiverError) - Method in interface org.apache.spark.streaming.scheduler.StreamingListener
Called when a receiver has reported an error
onReceiverStarted(StreamingListenerReceiverStarted) - Method in interface org.apache.spark.streaming.scheduler.StreamingListener
Called when a receiver has been started
onReceiverStopped(StreamingListenerReceiverStopped) - Method in interface org.apache.spark.streaming.scheduler.StreamingListener
Called when a receiver has been stopped
onStageCompleted(SparkListenerStageCompleted) - Method in class org.apache.spark.JavaSparkListener
 
onStageCompleted(SparkListenerStageCompleted) - Method in class org.apache.spark.scheduler.JobLogger
When stage is completed, record stage completion status
onStageCompleted(SparkListenerStageCompleted) - Method in interface org.apache.spark.scheduler.SparkListener
Called when a stage completes successfully or fails, with information on the completed stage.
onStageCompleted(SparkListenerStageCompleted) - Method in class org.apache.spark.scheduler.StatsReportListener
 
onStageCompleted(SparkListenerStageCompleted) - Method in class org.apache.spark.SparkFirehoseListener
 
onStageCompleted(SparkListenerStageCompleted) - Method in class org.apache.spark.SpillListener
 
onStageCompleted(SparkListenerStageCompleted) - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
onStageCompleted(SparkListenerStageCompleted) - Method in class org.apache.spark.ui.storage.StorageListener
 
onStageSubmitted(SparkListenerStageSubmitted) - Method in class org.apache.spark.JavaSparkListener
 
onStageSubmitted(SparkListenerStageSubmitted) - Method in class org.apache.spark.scheduler.JobLogger
When stage is submitted, record stage submit info
onStageSubmitted(SparkListenerStageSubmitted) - Method in interface org.apache.spark.scheduler.SparkListener
Called when a stage is submitted
onStageSubmitted(SparkListenerStageSubmitted) - Method in class org.apache.spark.SparkFirehoseListener
 
onStageSubmitted(SparkListenerStageSubmitted) - Method in class org.apache.spark.ui.jobs.JobProgressListener
For FIFO, all stages are contained by "default" pool but "default" pool here is meaningless
onStageSubmitted(SparkListenerStageSubmitted) - Method in class org.apache.spark.ui.storage.StorageListener
 
onStart() - Method in class org.apache.spark.streaming.receiver.Receiver
This method is called by the system when the receiver is started.
onStop() - Method in class org.apache.spark.streaming.receiver.Receiver
This method is called by the system when the receiver is stopped.
onSuccess(String, QueryExecution, long) - Method in interface org.apache.spark.sql.util.QueryExecutionListener
A callback function that will be called when a query executed successfully.
onTaskCompletion(TaskContext) - Method in interface org.apache.spark.util.TaskCompletionListener
 
onTaskEnd(SparkListenerTaskEnd) - Method in class org.apache.spark.JavaSparkListener
 
onTaskEnd(SparkListenerTaskEnd) - Method in class org.apache.spark.scheduler.JobLogger
When task ends, record task completion status and metrics
onTaskEnd(SparkListenerTaskEnd) - Method in interface org.apache.spark.scheduler.SparkListener
Called when a task ends
onTaskEnd(SparkListenerTaskEnd) - Method in class org.apache.spark.scheduler.StatsReportListener
 
onTaskEnd(SparkListenerTaskEnd) - Method in class org.apache.spark.SparkFirehoseListener
 
onTaskEnd(SparkListenerTaskEnd) - Method in class org.apache.spark.SpillListener
 
onTaskEnd(SparkListenerTaskEnd) - Method in class org.apache.spark.storage.StorageStatusListener
 
onTaskEnd(SparkListenerTaskEnd) - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
onTaskEnd(SparkListenerTaskEnd) - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
onTaskEnd(SparkListenerTaskEnd) - Method in class org.apache.spark.ui.storage.StorageListener
Assumes the storage status list is fully up-to-date.
onTaskGettingResult(SparkListenerTaskGettingResult) - Method in class org.apache.spark.JavaSparkListener
 
onTaskGettingResult(SparkListenerTaskGettingResult) - Method in interface org.apache.spark.scheduler.SparkListener
Called when a task begins remotely fetching its result (will not be called for tasks that do not need to fetch the result remotely).
onTaskGettingResult(SparkListenerTaskGettingResult) - Method in class org.apache.spark.SparkFirehoseListener
 
onTaskGettingResult(SparkListenerTaskGettingResult) - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
onTaskStart(SparkListenerTaskStart) - Method in class org.apache.spark.JavaSparkListener
 
onTaskStart(SparkListenerTaskStart) - Method in interface org.apache.spark.scheduler.SparkListener
Called when a task starts
onTaskStart(SparkListenerTaskStart) - Method in class org.apache.spark.SparkFirehoseListener
 
onTaskStart(SparkListenerTaskStart) - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
onTaskStart(SparkListenerTaskStart) - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
onUnpersistRDD(SparkListenerUnpersistRDD) - Method in class org.apache.spark.JavaSparkListener
 
onUnpersistRDD(SparkListenerUnpersistRDD) - Method in interface org.apache.spark.scheduler.SparkListener
Called when an RDD is manually unpersisted by the application
onUnpersistRDD(SparkListenerUnpersistRDD) - Method in class org.apache.spark.SparkFirehoseListener
 
onUnpersistRDD(SparkListenerUnpersistRDD) - Method in class org.apache.spark.storage.StorageStatusListener
 
onUnpersistRDD(SparkListenerUnpersistRDD) - Method in class org.apache.spark.ui.storage.StorageListener
 
open() - Method in class org.apache.spark.input.PortableDataStream
Create a new DataInputStream from the split and context.
ops() - Method in class org.apache.spark.graphx.Graph
The associated GraphOps object.
optimize(RDD<Tuple2<Object, Vector>>, Vector) - Method in class org.apache.spark.mllib.optimization.GradientDescent
:: DeveloperApi :: Runs gradient descent on the given training data.
optimize(RDD<Tuple2<Object, Vector>>, Vector) - Method in class org.apache.spark.mllib.optimization.LBFGS
 
optimize(RDD<Tuple2<Object, Vector>>, Vector) - Method in interface org.apache.spark.mllib.optimization.Optimizer
Solve the provided convex optimization problem.
optimizer() - Method in class org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
 
optimizer() - Method in class org.apache.spark.mllib.classification.LogisticRegressionWithSGD
 
optimizer() - Method in class org.apache.spark.mllib.classification.SVMWithSGD
 
Optimizer - Interface in org.apache.spark.mllib.optimization
:: DeveloperApi :: Trait for optimization problem solvers.
optimizer() - Method in class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
The optimizer to solve the problem.
optimizer() - Method in class org.apache.spark.mllib.regression.LassoWithSGD
 
optimizer() - Method in class org.apache.spark.mllib.regression.LinearRegressionWithSGD
 
optimizer() - Method in class org.apache.spark.mllib.regression.RidgeRegressionWithSGD
 
optimizer() - Method in class org.apache.spark.sql.SQLContext
 
option(String, String) - Method in class org.apache.spark.sql.DataFrameReader
Adds an input option for the underlying data source.
option(String, String) - Method in class org.apache.spark.sql.DataFrameWriter
Adds an output option for the underlying data source.
options(Map<String, String>) - Method in class org.apache.spark.sql.DataFrameReader
(Scala-specific) Adds input options for the underlying data source.
options(Map<String, String>) - Method in class org.apache.spark.sql.DataFrameReader
Adds input options for the underlying data source.
options(Map<String, String>) - Method in class org.apache.spark.sql.DataFrameWriter
(Scala-specific) Adds output options for the underlying data source.
options(Map<String, String>) - Method in class org.apache.spark.sql.DataFrameWriter
Adds output options for the underlying data source.
or(Column) - Method in class org.apache.spark.sql.Column
Boolean OR.
Or - Class in org.apache.spark.sql.sources
A filter that evaluates to true iff at least one of left or right evaluates to true.
Or(Filter, Filter) - Constructor for class org.apache.spark.sql.sources.Or
 
OracleDialect - Class in org.apache.spark.sql.jdbc
 
OracleDialect() - Constructor for class org.apache.spark.sql.jdbc.OracleDialect
 
orc(String) - Method in class org.apache.spark.sql.DataFrameReader
Loads an ORC file and returns the result as a DataFrame.
orc(String) - Method in class org.apache.spark.sql.DataFrameWriter
Saves the content of the DataFrame in ORC format at the specified path.
orderBy(String, String...) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame sorted by the given expressions.
orderBy(Column...) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame sorted by the given expressions.
orderBy(String, Seq<String>) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame sorted by the given expressions.
orderBy(Seq<Column>) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame sorted by the given expressions.
orderBy(String, String...) - Static method in class org.apache.spark.sql.expressions.Window
Creates a WindowSpec with the ordering defined.
orderBy(Column...) - Static method in class org.apache.spark.sql.expressions.Window
Creates a WindowSpec with the ordering defined.
orderBy(String, Seq<String>) - Static method in class org.apache.spark.sql.expressions.Window
Creates a WindowSpec with the ordering defined.
orderBy(Seq<Column>) - Static method in class org.apache.spark.sql.expressions.Window
Creates a WindowSpec with the ordering defined.
orderBy(String, String...) - Method in class org.apache.spark.sql.expressions.WindowSpec
Defines the ordering columns in a WindowSpec.
orderBy(Column...) - Method in class org.apache.spark.sql.expressions.WindowSpec
Defines the ordering columns in a WindowSpec.
orderBy(String, Seq<String>) - Method in class org.apache.spark.sql.expressions.WindowSpec
Defines the ordering columns in a WindowSpec.
orderBy(Seq<Column>) - Method in class org.apache.spark.sql.expressions.WindowSpec
Defines the ordering columns in a WindowSpec.
OrderedRDDFunctions<K,V,P extends scala.Product2<K,V>> - Class in org.apache.spark.rdd
Extra functions available on RDDs of (key, value) pairs where the key is sortable through an implicit conversion.
OrderedRDDFunctions(RDD<P>, Ordering<K>, ClassTag<K>, ClassTag<V>, ClassTag<P>) - Constructor for class org.apache.spark.rdd.OrderedRDDFunctions
 
ordering() - Method in class org.apache.spark.sql.types.BinaryType
 
ordering() - Method in class org.apache.spark.sql.types.BooleanType
 
ordering() - Method in class org.apache.spark.sql.types.ByteType
 
ordering() - Method in class org.apache.spark.sql.types.DateType
 
ordering() - Method in class org.apache.spark.sql.types.DecimalType
 
ordering() - Method in class org.apache.spark.sql.types.DoubleType
 
ordering() - Method in class org.apache.spark.sql.types.FloatType
 
ordering() - Method in class org.apache.spark.sql.types.IntegerType
 
ordering() - Method in class org.apache.spark.sql.types.LongType
 
ordering() - Method in class org.apache.spark.sql.types.ShortType
 
ordering() - Method in class org.apache.spark.sql.types.StringType
 
ordering() - Method in class org.apache.spark.sql.types.TimestampType
 
ordering() - Static method in class org.apache.spark.streaming.Time
 
org.apache.spark - package org.apache.spark
Core Spark classes in Scala.
org.apache.spark.annotation - package org.apache.spark.annotation
Spark annotations to mark an API experimental or intended only for advanced usages by developers.
org.apache.spark.api.java - package org.apache.spark.api.java
Spark Java programming APIs.
org.apache.spark.api.java.function - package org.apache.spark.api.java.function
Set of interfaces to represent functions in Spark's Java API.
org.apache.spark.api.r - package org.apache.spark.api.r
 
org.apache.spark.broadcast - package org.apache.spark.broadcast
Spark's broadcast variables, used to broadcast immutable datasets to all nodes.
org.apache.spark.graphx - package org.apache.spark.graphx
ALPHA COMPONENT GraphX is a graph processing framework built on top of Spark.
org.apache.spark.graphx.impl - package org.apache.spark.graphx.impl
 
org.apache.spark.graphx.lib - package org.apache.spark.graphx.lib
Various analytics functions for graphs.
org.apache.spark.graphx.util - package org.apache.spark.graphx.util
Collections of utilities used by graphx.
org.apache.spark.input - package org.apache.spark.input
 
org.apache.spark.io - package org.apache.spark.io
IO codecs used for compression.
org.apache.spark.launcher - package org.apache.spark.launcher
Library for launching Spark applications.
org.apache.spark.ml - package org.apache.spark.ml
Spark ML is a BETA component that adds a new set of machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.
org.apache.spark.ml.attribute - package org.apache.spark.ml.attribute
ML attributes
org.apache.spark.ml.classification - package org.apache.spark.ml.classification
 
org.apache.spark.ml.clustering - package org.apache.spark.ml.clustering
 
org.apache.spark.ml.evaluation - package org.apache.spark.ml.evaluation
 
org.apache.spark.ml.feature - package org.apache.spark.ml.feature
Feature transformers The `ml.feature` package provides common feature transformers that help convert raw data or features into more suitable forms for model fitting.
org.apache.spark.ml.param - package org.apache.spark.ml.param
 
org.apache.spark.ml.recommendation - package org.apache.spark.ml.recommendation
 
org.apache.spark.ml.regression - package org.apache.spark.ml.regression
 
org.apache.spark.ml.source.libsvm - package org.apache.spark.ml.source.libsvm
 
org.apache.spark.ml.tree - package org.apache.spark.ml.tree
 
org.apache.spark.ml.tuning - package org.apache.spark.ml.tuning
 
org.apache.spark.ml.util - package org.apache.spark.ml.util
 
org.apache.spark.mllib.classification - package org.apache.spark.mllib.classification
 
org.apache.spark.mllib.clustering - package org.apache.spark.mllib.clustering
 
org.apache.spark.mllib.evaluation - package org.apache.spark.mllib.evaluation
 
org.apache.spark.mllib.feature - package org.apache.spark.mllib.feature
 
org.apache.spark.mllib.fpm - package org.apache.spark.mllib.fpm
 
org.apache.spark.mllib.linalg - package org.apache.spark.mllib.linalg
 
org.apache.spark.mllib.linalg.distributed - package org.apache.spark.mllib.linalg.distributed
 
org.apache.spark.mllib.optimization - package org.apache.spark.mllib.optimization
 
org.apache.spark.mllib.pmml - package org.apache.spark.mllib.pmml
 
org.apache.spark.mllib.random - package org.apache.spark.mllib.random
 
org.apache.spark.mllib.rdd - package org.apache.spark.mllib.rdd
 
org.apache.spark.mllib.recommendation - package org.apache.spark.mllib.recommendation
 
org.apache.spark.mllib.regression - package org.apache.spark.mllib.regression
 
org.apache.spark.mllib.stat - package org.apache.spark.mllib.stat
 
org.apache.spark.mllib.stat.distribution - package org.apache.spark.mllib.stat.distribution
 
org.apache.spark.mllib.stat.test - package org.apache.spark.mllib.stat.test
 
org.apache.spark.mllib.tree - package org.apache.spark.mllib.tree
 
org.apache.spark.mllib.tree.configuration - package org.apache.spark.mllib.tree.configuration
 
org.apache.spark.mllib.tree.impurity - package org.apache.spark.mllib.tree.impurity
 
org.apache.spark.mllib.tree.loss - package org.apache.spark.mllib.tree.loss
 
org.apache.spark.mllib.tree.model - package org.apache.spark.mllib.tree.model
 
org.apache.spark.mllib.util - package org.apache.spark.mllib.util
 
org.apache.spark.partial - package org.apache.spark.partial
 
org.apache.spark.rdd - package org.apache.spark.rdd
Provides implementation's of various RDDs.
org.apache.spark.scheduler - package org.apache.spark.scheduler
Spark's DAG scheduler.
org.apache.spark.scheduler.cluster - package org.apache.spark.scheduler.cluster
 
org.apache.spark.scheduler.local - package org.apache.spark.scheduler.local
 
org.apache.spark.serializer - package org.apache.spark.serializer
Pluggable serializers for RDD and shuffle data.
org.apache.spark.sql - package org.apache.spark.sql
 
org.apache.spark.sql.api.java - package org.apache.spark.sql.api.java
Allows the execution of relational queries, including those expressed in SQL using Spark.
org.apache.spark.sql.expressions - package org.apache.spark.sql.expressions
 
org.apache.spark.sql.hive - package org.apache.spark.sql.hive
 
org.apache.spark.sql.hive.execution - package org.apache.spark.sql.hive.execution
 
org.apache.spark.sql.jdbc - package org.apache.spark.sql.jdbc
 
org.apache.spark.sql.sources - package org.apache.spark.sql.sources
 
org.apache.spark.sql.types - package org.apache.spark.sql.types
 
org.apache.spark.sql.util - package org.apache.spark.sql.util
 
org.apache.spark.status.api.v1 - package org.apache.spark.status.api.v1
 
org.apache.spark.storage - package org.apache.spark.storage
 
org.apache.spark.streaming - package org.apache.spark.streaming
 
org.apache.spark.streaming.api.java - package org.apache.spark.streaming.api.java
Java APIs for spark streaming.
org.apache.spark.streaming.dstream - package org.apache.spark.streaming.dstream
Various implementations of DStreams.
org.apache.spark.streaming.flume - package org.apache.spark.streaming.flume
Spark streaming receiver for Flume.
org.apache.spark.streaming.kafka - package org.apache.spark.streaming.kafka
Kafka receiver for spark streaming.
org.apache.spark.streaming.kinesis - package org.apache.spark.streaming.kinesis
 
org.apache.spark.streaming.mqtt - package org.apache.spark.streaming.mqtt
MQTT receiver for Spark Streaming.
org.apache.spark.streaming.receiver - package org.apache.spark.streaming.receiver
 
org.apache.spark.streaming.scheduler - package org.apache.spark.streaming.scheduler
 
org.apache.spark.streaming.twitter - package org.apache.spark.streaming.twitter
Twitter feed receiver for spark streaming.
org.apache.spark.streaming.util - package org.apache.spark.streaming.util
 
org.apache.spark.streaming.zeromq - package org.apache.spark.streaming.zeromq
Zeromq receiver for spark streaming.
org.apache.spark.ui.env - package org.apache.spark.ui.env
 
org.apache.spark.ui.exec - package org.apache.spark.ui.exec
 
org.apache.spark.ui.jobs - package org.apache.spark.ui.jobs
 
org.apache.spark.ui.storage - package org.apache.spark.ui.storage
 
org.apache.spark.util - package org.apache.spark.util
Spark utilities.
org.apache.spark.util.random - package org.apache.spark.util.random
Utilities for random number generation.
originalMax() - Method in class org.apache.spark.ml.feature.MinMaxScalerModel
 
originalMin() - Method in class org.apache.spark.ml.feature.MinMaxScalerModel
 
other() - Method in class org.apache.spark.scheduler.RuntimePercentage
 
otherInfo() - Method in class org.apache.spark.streaming.receiver.Statistics
 
otherVertexAttr(long) - Method in class org.apache.spark.graphx.EdgeTriplet
Given one vertex in the edge return the other vertex.
otherVertexId(long) - Method in class org.apache.spark.graphx.Edge
Given one vertex in the edge return the other vertex.
otherwise(Object) - Method in class org.apache.spark.sql.Column
Evaluates a list of conditions and returns one of multiple possible result expressions.
Out() - Static method in class org.apache.spark.graphx.EdgeDirection
Edges originating from a vertex.
outDegrees() - Method in class org.apache.spark.graphx.GraphOps
The out-degree of each vertex in the graph.
outerJoinVertices(RDD<Tuple2<Object, U>>, Function3<Object, VD, Option<U>, VD2>, ClassTag<U>, ClassTag<VD2>, Predef.$eq$colon$eq<VD, VD2>) - Method in class org.apache.spark.graphx.Graph
Joins the vertices with entries in the table RDD and merges the results using mapFunc.
outerJoinVertices(RDD<Tuple2<Object, U>>, Function3<Object, VD, Option<U>, VD2>, ClassTag<U>, ClassTag<VD2>, Predef.$eq$colon$eq<VD, VD2>) - Method in class org.apache.spark.graphx.impl.GraphImpl
 
outputBytes() - Method in class org.apache.spark.status.api.v1.ExecutorStageSummary
 
outputBytes() - Method in class org.apache.spark.status.api.v1.StageData
 
OutputCommitCoordinationMessage - Interface in org.apache.spark.scheduler
 
outputCommitCoordinator() - Method in class org.apache.spark.SparkEnv
 
outputDataType() - Method in class org.apache.spark.ml.feature.DCT
 
outputDataType() - Method in class org.apache.spark.ml.feature.ElementwiseProduct
 
outputDataType() - Method in class org.apache.spark.ml.feature.NGram
 
outputDataType() - Method in class org.apache.spark.ml.feature.Normalizer
 
outputDataType() - Method in class org.apache.spark.ml.feature.PolynomialExpansion
 
outputDataType() - Method in class org.apache.spark.ml.feature.RegexTokenizer
 
outputDataType() - Method in class org.apache.spark.ml.feature.Tokenizer
 
outputDataType() - Method in class org.apache.spark.ml.UnaryTransformer
Returns the data type of the output column.
OutputMetricDistributions - Class in org.apache.spark.status.api.v1
 
OutputMetrics - Class in org.apache.spark.status.api.v1
 
outputMetrics() - Method in class org.apache.spark.status.api.v1.TaskMetricDistributions
 
outputMetrics() - Method in class org.apache.spark.status.api.v1.TaskMetrics
 
OutputOperationInfo - Class in org.apache.spark.streaming.scheduler
:: DeveloperApi :: Class having information on output operations.
OutputOperationInfo(Time, int, String, String, Option<Object>, Option<Object>, Option<String>) - Constructor for class org.apache.spark.streaming.scheduler.OutputOperationInfo
 
outputOperationInfo() - Method in class org.apache.spark.streaming.scheduler.StreamingListenerOutputOperationCompleted
 
outputOperationInfo() - Method in class org.apache.spark.streaming.scheduler.StreamingListenerOutputOperationStarted
 
outputOperationInfos() - Method in class org.apache.spark.streaming.scheduler.BatchInfo
 
outputRecords() - Method in class org.apache.spark.status.api.v1.StageData
 
OutputWriter - Class in org.apache.spark.sql.sources
::Experimental:: OutputWriter is used together with HadoopFsRelation for persisting rows to the underlying file system.
OutputWriter() - Constructor for class org.apache.spark.sql.sources.OutputWriter
 
OutputWriterFactory - Class in org.apache.spark.sql.sources
::Experimental:: A factory that produces OutputWriters.
OutputWriterFactory() - Constructor for class org.apache.spark.sql.sources.OutputWriterFactory
 
over(WindowSpec) - Method in class org.apache.spark.sql.Column
Define a windowing column.
overwrite() - Method in class org.apache.spark.ml.util.MLWriter
Overwrites if the output path already exists.

P

p() - Method in class org.apache.spark.ml.feature.Normalizer
Normalization in L^p^ space.
pageRank(double, double) - Method in class org.apache.spark.graphx.GraphOps
Run a dynamic version of PageRank returning a graph with vertex attributes containing the PageRank and edge attributes containing the normalized edge weight.
PageRank - Class in org.apache.spark.graphx.lib
PageRank algorithm implementation.
PageRank() - Constructor for class org.apache.spark.graphx.lib.PageRank
 
PairDStreamFunctions<K,V> - Class in org.apache.spark.streaming.dstream
Extra functions available on DStream of (key, value) pairs through an implicit conversion.
PairDStreamFunctions(DStream<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>, Ordering<K>) - Constructor for class org.apache.spark.streaming.dstream.PairDStreamFunctions
 
PairFlatMapFunction<T,K,V> - Interface in org.apache.spark.api.java.function
A function that returns zero or more key-value pair records from each input record.
PairFunction<T,K,V> - Interface in org.apache.spark.api.java.function
A function that returns key-value pairs (Tuple2<K, V>), and can be used to construct PairRDDs.
PairRDDFunctions<K,V> - Class in org.apache.spark.rdd
Extra functions available on RDDs of (key, value) pairs through an implicit conversion.
PairRDDFunctions(RDD<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>, Ordering<K>) - Constructor for class org.apache.spark.rdd.PairRDDFunctions
 
PairwiseRRDD<T> - Class in org.apache.spark.api.r
Form an RDD[(Int, Array[Byte])] from key-value pairs returned from R.
PairwiseRRDD(RDD<T>, int, byte[], String, byte[], Object[], ClassTag<T>) - Constructor for class org.apache.spark.api.r.PairwiseRRDD
 
parallelize(List<T>, int) - Method in class org.apache.spark.api.java.JavaSparkContext
Distribute a local Scala collection to form an RDD.
parallelize(List<T>) - Method in class org.apache.spark.api.java.JavaSparkContext
Distribute a local Scala collection to form an RDD.
parallelize(Seq<T>, int, ClassTag<T>) - Method in class org.apache.spark.SparkContext
Distribute a local Scala collection to form an RDD.
parallelizeDoubles(List<Double>, int) - Method in class org.apache.spark.api.java.JavaSparkContext
Distribute a local Scala collection to form an RDD.
parallelizeDoubles(List<Double>) - Method in class org.apache.spark.api.java.JavaSparkContext
Distribute a local Scala collection to form an RDD.
parallelizePairs(List<Tuple2<K, V>>, int) - Method in class org.apache.spark.api.java.JavaSparkContext
Distribute a local Scala collection to form an RDD.
parallelizePairs(List<Tuple2<K, V>>) - Method in class org.apache.spark.api.java.JavaSparkContext
Distribute a local Scala collection to form an RDD.
Param<T> - Class in org.apache.spark.ml.param
:: DeveloperApi :: A param with self-contained documentation and optionally default value.
Param(String, String, String, Function1<T, Object>) - Constructor for class org.apache.spark.ml.param.Param
 
Param(Identifiable, String, String, Function1<T, Object>) - Constructor for class org.apache.spark.ml.param.Param
 
Param(String, String, String) - Constructor for class org.apache.spark.ml.param.Param
 
Param(Identifiable, String, String) - Constructor for class org.apache.spark.ml.param.Param
 
param() - Method in class org.apache.spark.ml.param.ParamPair
 
ParamGridBuilder - Class in org.apache.spark.ml.tuning
:: Experimental :: Builder for a param grid used in grid search-based model selection.
ParamGridBuilder() - Constructor for class org.apache.spark.ml.tuning.ParamGridBuilder
 
ParamMap - Class in org.apache.spark.ml.param
:: Experimental :: A param to value map.
ParamMap() - Constructor for class org.apache.spark.ml.param.ParamMap
Creates an empty param map.
paramMap() - Method in interface org.apache.spark.ml.param.Params
Internal param map for user-supplied values.
ParamPair<T> - Class in org.apache.spark.ml.param
:: Experimental :: A param and its value.
ParamPair(Param<T>, T) - Constructor for class org.apache.spark.ml.param.ParamPair
 
Params - Interface in org.apache.spark.ml.param
 
params() - Method in interface org.apache.spark.ml.param.Params
 
ParamValidators - Class in org.apache.spark.ml.param
:: DeveloperApi :: Factory methods for common validation functions for Param.isValid.
ParamValidators() - Constructor for class org.apache.spark.ml.param.ParamValidators
 
parent() - Method in class org.apache.spark.ml.Model
The parent estimator that produced this model.
parent() - Method in class org.apache.spark.ml.param.Param
 
parent(int, ClassTag<U>) - Method in class org.apache.spark.rdd.RDD
Returns the jth parent RDD: e.g.
parentIds() - Method in class org.apache.spark.scheduler.StageInfo
 
parentIds() - Method in class org.apache.spark.storage.RDDInfo
 
parentIndex(int) - Static method in class org.apache.spark.mllib.tree.model.Node
Get the parent index of the given node, or 0 if it is the root.
parquet(String...) - Method in class org.apache.spark.sql.DataFrameReader
Loads a Parquet file, returning the result as a DataFrame.
parquet(Seq<String>) - Method in class org.apache.spark.sql.DataFrameReader
Loads a Parquet file, returning the result as a DataFrame.
parquet(String) - Method in class org.apache.spark.sql.DataFrameWriter
Saves the content of the DataFrame in Parquet format at the specified path.
parquetFile(String...) - Method in class org.apache.spark.sql.SQLContext
Deprecated.
As of 1.4.0, replaced by read().parquet(). This will be removed in Spark 2.0.
parquetFile(Seq<String>) - Method in class org.apache.spark.sql.SQLContext
 
parse(String) - Static method in class org.apache.spark.mllib.linalg.Vectors
Parses a string resulted from Vector.toString into a Vector.
parse(String) - Static method in class org.apache.spark.mllib.regression.LabeledPoint
 
parseDataType(String) - Method in class org.apache.spark.sql.SQLContext
 
parseIgnoreCase(Class<E>, String) - Static method in class org.apache.spark.util.EnumUtil
 
parseSql(String) - Method in class org.apache.spark.sql.hive.HiveContext
 
parseSql(String) - Method in class org.apache.spark.sql.SQLContext
 
PartialResult<R> - Class in org.apache.spark.partial
 
PartialResult(R, boolean) - Constructor for class org.apache.spark.partial.PartialResult
 
Partition - Interface in org.apache.spark
An identifier for a partition in an RDD.
partition() - Method in class org.apache.spark.scheduler.AskPermissionToCommitOutput
 
partition() - Method in class org.apache.spark.streaming.kafka.OffsetRange
 
partitionBy(Partitioner) - Method in class org.apache.spark.api.java.JavaPairRDD
Return a copy of the RDD partitioned using the specified partitioner.
partitionBy(PartitionStrategy) - Method in class org.apache.spark.graphx.Graph
Repartitions the edges in the graph according to partitionStrategy.
partitionBy(PartitionStrategy, int) - Method in class org.apache.spark.graphx.Graph
Repartitions the edges in the graph according to partitionStrategy.
partitionBy(PartitionStrategy) - Method in class org.apache.spark.graphx.impl.GraphImpl
 
partitionBy(PartitionStrategy, int) - Method in class org.apache.spark.graphx.impl.GraphImpl
 
partitionBy(Partitioner) - Method in class org.apache.spark.rdd.PairRDDFunctions
Return a copy of the RDD partitioned using the specified partitioner.
partitionBy(String...) - Method in class org.apache.spark.sql.DataFrameWriter
Partitions the output by the given columns on the file system.
partitionBy(Seq<String>) - Method in class org.apache.spark.sql.DataFrameWriter
Partitions the output by the given columns on the file system.
partitionBy(String, String...) - Static method in class org.apache.spark.sql.expressions.Window
Creates a WindowSpec with the partitioning defined.
partitionBy(Column...) - Static method in class org.apache.spark.sql.expressions.Window
Creates a WindowSpec with the partitioning defined.
partitionBy(String, Seq<String>) - Static method in class org.apache.spark.sql.expressions.Window
Creates a WindowSpec with the partitioning defined.
partitionBy(Seq<Column>) - Static method in class org.apache.spark.sql.expressions.Window
Creates a WindowSpec with the partitioning defined.
partitionBy(String, String...) - Method in class org.apache.spark.sql.expressions.WindowSpec
Defines the partitioning columns in a WindowSpec.
partitionBy(Column...) - Method in class org.apache.spark.sql.expressions.WindowSpec
Defines the partitioning columns in a WindowSpec.
partitionBy(String, Seq<String>) - Method in class org.apache.spark.sql.expressions.WindowSpec
Defines the partitioning columns in a WindowSpec.
partitionBy(Seq<Column>) - Method in class org.apache.spark.sql.expressions.WindowSpec
Defines the partitioning columns in a WindowSpec.
PartitionCoalescer - Class in org.apache.spark.rdd
Coalesce the partitions of a parent RDD (prev) into fewer partitions, so that each partition of this RDD computes one or more of the parent ones.
PartitionCoalescer(int, RDD<?>, double) - Constructor for class org.apache.spark.rdd.PartitionCoalescer
 
PartitionCoalescer.LocationIterator - Class in org.apache.spark.rdd
 
PartitionCoalescer.LocationIterator(RDD<?>) - Constructor for class org.apache.spark.rdd.PartitionCoalescer.LocationIterator
 
partitionColumns() - Method in class org.apache.spark.sql.sources.HadoopFsRelation
Partition columns.
partitioner() - Method in interface org.apache.spark.api.java.JavaRDDLike
The partitioner of this RDD.
partitioner() - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
If partitionsRDD already has a partitioner, use it.
partitioner() - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
Partitioner - Class in org.apache.spark
An object that defines how the elements in a key-value pair RDD are partitioned by key.
Partitioner() - Constructor for class org.apache.spark.Partitioner
 
partitioner() - Method in class org.apache.spark.rdd.CoGroupedRDD
 
partitioner() - Method in class org.apache.spark.rdd.RDD
Optionally overridden by subclasses to specify how they are partitioned.
partitioner() - Method in class org.apache.spark.rdd.ShuffledRDD
 
partitioner() - Method in class org.apache.spark.ShuffleDependency
 
partitioner(Partitioner) - Method in class org.apache.spark.streaming.StateSpec
Set the partitioner by which the state RDDs generated by mapWithState will be be partitioned.
PartitionGroup - Class in org.apache.spark.rdd
 
PartitionGroup(Option<String>) - Constructor for class org.apache.spark.rdd.PartitionGroup
 
partitionID() - Method in class org.apache.spark.TaskCommitDenied
 
partitionId() - Method in class org.apache.spark.TaskContext
The ID of the RDD partition that is computed by this task.
PartitionPruningRDD<T> - Class in org.apache.spark.rdd
:: DeveloperApi :: A RDD used to prune RDD partitions/partitions so we can avoid launching tasks on all partitions.
PartitionPruningRDD(RDD<T>, Function1<Object, Object>, ClassTag<T>) - Constructor for class org.apache.spark.rdd.PartitionPruningRDD
 
partitions() - Method in interface org.apache.spark.api.java.JavaRDDLike
Set of partitions in this RDD.
partitions() - Method in class org.apache.spark.rdd.RDD
Get the array of partitions of this RDD, taking into account whether the RDD is checkpointed or not.
partitions() - Method in class org.apache.spark.status.api.v1.RDDStorageInfo
 
partitionsRDD() - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
 
partitionsRDD() - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
PartitionStrategy - Interface in org.apache.spark.graphx
Represents the way edges are assigned to edge partitions based on their source and destination vertex IDs.
PartitionStrategy.CanonicalRandomVertexCut$ - Class in org.apache.spark.graphx
Assigns edges to partitions by hashing the source and destination vertex IDs in a canonical direction, resulting in a random vertex cut that colocates all edges between two vertices, regardless of direction.
PartitionStrategy.CanonicalRandomVertexCut$() - Constructor for class org.apache.spark.graphx.PartitionStrategy.CanonicalRandomVertexCut$
 
PartitionStrategy.EdgePartition1D$ - Class in org.apache.spark.graphx
Assigns edges to partitions using only the source vertex ID, colocating edges with the same source.
PartitionStrategy.EdgePartition1D$() - Constructor for class org.apache.spark.graphx.PartitionStrategy.EdgePartition1D$
 
PartitionStrategy.EdgePartition2D$ - Class in org.apache.spark.graphx
Assigns edges to partitions using a 2D partitioning of the sparse edge adjacency matrix, guaranteeing a 2 * sqrt(numParts) bound on vertex replication.
PartitionStrategy.EdgePartition2D$() - Constructor for class org.apache.spark.graphx.PartitionStrategy.EdgePartition2D$
 
PartitionStrategy.RandomVertexCut$ - Class in org.apache.spark.graphx
Assigns edges to partitions by hashing the source and destination vertex IDs, resulting in a random vertex cut that colocates all same-direction edges between two vertices.
PartitionStrategy.RandomVertexCut$() - Constructor for class org.apache.spark.graphx.PartitionStrategy.RandomVertexCut$
 
path() - Method in class org.apache.spark.scheduler.InputFormatInfo
 
path() - Method in class org.apache.spark.scheduler.SplitInfo
 
path() - Method in class org.apache.spark.sql.sources.HadoopFsRelation.FakeFileStatus
 
paths() - Method in class org.apache.spark.sql.sources.HadoopFsRelation
Paths of this relation.
pattern() - Method in class org.apache.spark.ml.feature.RegexTokenizer
Regex pattern used to match delimiters if gaps is true or tokens if gaps is false.
pc() - Method in class org.apache.spark.ml.feature.PCAModel
 
pc() - Method in class org.apache.spark.mllib.feature.PCAModel
 
PCA - Class in org.apache.spark.ml.feature
:: Experimental :: PCA trains a model to project vectors to a low-dimensional space using PCA.
PCA(String) - Constructor for class org.apache.spark.ml.feature.PCA
 
PCA() - Constructor for class org.apache.spark.ml.feature.PCA
 
PCA - Class in org.apache.spark.mllib.feature
A feature transformer that projects vectors to a low-dimensional space using PCA.
PCA(int) - Constructor for class org.apache.spark.mllib.feature.PCA
 
PCAModel - Class in org.apache.spark.ml.feature
 
PCAModel - Class in org.apache.spark.mllib.feature
Model fitted by PCA that can project vectors to a low-dimensional space using PCA.
pdf(Vector) - Method in class org.apache.spark.mllib.stat.distribution.MultivariateGaussian
Returns density of this multivariate Gaussian at given point, x
pendingStages() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
percent_rank() - Static method in class org.apache.spark.sql.functions
Window function: returns the relative rank (i.e.
percentiles() - Static method in class org.apache.spark.scheduler.StatsReportListener
 
percentilesHeader() - Static method in class org.apache.spark.scheduler.StatsReportListener
 
percentRank() - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.6.0, replaced by percent_rank. This will be removed in Spark 2.0.
persist(StorageLevel) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Set this RDD's storage level to persist its values across operations after the first time it is computed.
persist(StorageLevel) - Method in class org.apache.spark.api.java.JavaPairRDD
Set this RDD's storage level to persist its values across operations after the first time it is computed.
persist(StorageLevel) - Method in class org.apache.spark.api.java.JavaRDD
Set this RDD's storage level to persist its values across operations after the first time it is computed.
persist(StorageLevel) - Method in class org.apache.spark.graphx.Graph
Caches the vertices and edges associated with this graph at the specified storage level, ignoring any target storage levels previously set.
persist(StorageLevel) - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
Persists the edge partitions at the specified storage level, ignoring any existing target storage level.
persist(StorageLevel) - Method in class org.apache.spark.graphx.impl.GraphImpl
 
persist(StorageLevel) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
Persists the vertex partitions at the specified storage level, ignoring any existing target storage level.
persist(StorageLevel) - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
Persists the underlying RDD with the specified storage level.
persist(StorageLevel) - Method in class org.apache.spark.rdd.HadoopRDD
 
persist(StorageLevel) - Method in class org.apache.spark.rdd.NewHadoopRDD
 
persist(StorageLevel) - Method in class org.apache.spark.rdd.RDD
Set this RDD's storage level to persist its values across operations after the first time it is computed.
persist() - Method in class org.apache.spark.rdd.RDD
Persist this RDD with the default storage level (`MEMORY_ONLY`).
persist() - Method in class org.apache.spark.sql.DataFrame
Persist this DataFrame with the default storage level (MEMORY_AND_DISK).
persist(StorageLevel) - Method in class org.apache.spark.sql.DataFrame
Persist this DataFrame with the given storage level.
persist() - Method in class org.apache.spark.sql.Dataset
Persist this Dataset with the default storage level (MEMORY_AND_DISK).
persist(StorageLevel) - Method in class org.apache.spark.sql.Dataset
Persist this Dataset with the given storage level.
persist() - Method in class org.apache.spark.streaming.api.java.JavaDStream
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
persist(StorageLevel) - Method in class org.apache.spark.streaming.api.java.JavaDStream
Persist the RDDs of this DStream with the given storage level
persist() - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
persist(StorageLevel) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Persist the RDDs of this DStream with the given storage level
persist(StorageLevel) - Method in class org.apache.spark.streaming.dstream.DStream
Persist the RDDs of this DStream with the given storage level
persist() - Method in class org.apache.spark.streaming.dstream.DStream
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
persistentRdds() - Method in class org.apache.spark.SparkContext
 
personalizedPageRank(long, double, double) - Method in class org.apache.spark.graphx.GraphOps
Run personalized PageRank for a given vertex, such that all random walks are started relative to the source node.
pi() - Method in class org.apache.spark.ml.classification.NaiveBayesModel
 
pi() - Method in class org.apache.spark.mllib.classification.NaiveBayesModel
 
pickBin(Partition) - Method in class org.apache.spark.rdd.PartitionCoalescer
Takes a parent RDD partition and decides which of the partition groups to put it in Takes locality into account, but also uses power of 2 choices to load balance It strikes a balance between the two use the balanceSlack variable
pickRandomVertex() - Method in class org.apache.spark.graphx.GraphOps
Picks a random vertex from the graph and returns its ID.
pipe(String) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return an RDD created by piping elements to a forked external process.
pipe(List<String>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return an RDD created by piping elements to a forked external process.
pipe(List<String>, Map<String, String>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Return an RDD created by piping elements to a forked external process.
pipe(String) - Method in class org.apache.spark.rdd.RDD
Return an RDD created by piping elements to a forked external process.
pipe(String, Map<String, String>) - Method in class org.apache.spark.rdd.RDD
Return an RDD created by piping elements to a forked external process.
pipe(Seq<String>, Map<String, String>, Function1<Function1<String, BoxedUnit>, BoxedUnit>, Function2<T, Function1<String, BoxedUnit>, BoxedUnit>, boolean) - Method in class org.apache.spark.rdd.RDD
Return an RDD created by piping elements to a forked external process.
Pipeline - Class in org.apache.spark.ml
:: Experimental :: A simple pipeline, which acts as an estimator.
Pipeline(String) - Constructor for class org.apache.spark.ml.Pipeline
 
Pipeline() - Constructor for class org.apache.spark.ml.Pipeline
 
PipelineModel - Class in org.apache.spark.ml
:: Experimental :: Represents a fitted pipeline.
PipelineStage - Class in org.apache.spark.ml
:: DeveloperApi :: A stage in a pipeline, either an Estimator or a Transformer.
PipelineStage() - Constructor for class org.apache.spark.ml.PipelineStage
 
pivot(String) - Method in class org.apache.spark.sql.GroupedData
Pivots a column of the current DataFrame and perform the specified aggregation.
pivot(String, Seq<Object>) - Method in class org.apache.spark.sql.GroupedData
Pivots a column of the current DataFrame and perform the specified aggregation.
pivot(String, List<Object>) - Method in class org.apache.spark.sql.GroupedData
Pivots a column of the current DataFrame and perform the specified aggregation.
planner() - Method in class org.apache.spark.sql.hive.HiveContext
 
planner() - Method in class org.apache.spark.sql.SQLContext
 
plus(Object) - Method in class org.apache.spark.sql.Column
Sum of this expression and another expression.
plus(Duration) - Method in class org.apache.spark.streaming.Duration
 
plus(Duration) - Method in class org.apache.spark.streaming.Time
 
plusDot(Vector, Vector) - Method in class org.apache.spark.util.Vector
return (this + plus) dot other, but without creating any intermediate storage
PMMLExportable - Interface in org.apache.spark.mllib.pmml
:: DeveloperApi :: Export model to the PMML format Predictive Model Markup Language (PMML) is an XML-based file format developed by the Data Mining Group (www.dmg.org).
pmod(Column, Column) - Static method in class org.apache.spark.sql.functions
Returns the positive value of dividend mod divisor.
point() - Method in class org.apache.spark.mllib.feature.VocabWord
 
POINTS() - Static method in class org.apache.spark.mllib.clustering.StreamingKMeans
 
PoissonGenerator - Class in org.apache.spark.mllib.random
:: DeveloperApi :: Generates i.i.d.
PoissonGenerator(double) - Constructor for class org.apache.spark.mllib.random.PoissonGenerator
 
poissonJavaRDD(JavaSparkContext, double, long, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
poissonJavaRDD(JavaSparkContext, double, long, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
poissonJavaRDD(JavaSparkContext, double, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
poissonJavaVectorRDD(JavaSparkContext, double, long, int, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
poissonJavaVectorRDD(JavaSparkContext, double, long, int, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
poissonJavaVectorRDD(JavaSparkContext, double, long, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
poissonRDD(SparkContext, double, long, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
Generates an RDD comprised of i.i.d. samples from the Poisson distribution with the input mean.
PoissonSampler<T> - Class in org.apache.spark.util.random
:: DeveloperApi :: A sampler for sampling with replacement, based on values drawn from Poisson distribution.
PoissonSampler(double, boolean, ClassTag<T>) - Constructor for class org.apache.spark.util.random.PoissonSampler
 
PoissonSampler(double, ClassTag<T>) - Constructor for class org.apache.spark.util.random.PoissonSampler
 
poissonVectorRDD(SparkContext, double, long, int, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the Poisson distribution with the input mean.
PolynomialExpansion - Class in org.apache.spark.ml.feature
:: Experimental :: Perform feature expansion in a polynomial space.
PolynomialExpansion(String) - Constructor for class org.apache.spark.ml.feature.PolynomialExpansion
 
PolynomialExpansion() - Constructor for class org.apache.spark.ml.feature.PolynomialExpansion
 
poolToActiveStages() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
port() - Method in class org.apache.spark.storage.BlockManagerId
 
port() - Method in class org.apache.spark.streaming.kafka.Broker
Broker's port
PortableDataStream - Class in org.apache.spark.input
A class that allows DataStreams to be serialized and moved around by not creating them until they need to be read
PortableDataStream(CombineFileSplit, TaskAttemptContext, Integer) - Constructor for class org.apache.spark.input.PortableDataStream
 
PostgresDialect - Class in org.apache.spark.sql.jdbc
 
PostgresDialect() - Constructor for class org.apache.spark.sql.jdbc.PostgresDialect
 
pow(Column, Column) - Static method in class org.apache.spark.sql.functions
Returns the value of the first argument raised to the power of the second argument.
pow(Column, String) - Static method in class org.apache.spark.sql.functions
Returns the value of the first argument raised to the power of the second argument.
pow(String, Column) - Static method in class org.apache.spark.sql.functions
Returns the value of the first argument raised to the power of the second argument.
pow(String, String) - Static method in class org.apache.spark.sql.functions
Returns the value of the first argument raised to the power of the second argument.
pow(Column, double) - Static method in class org.apache.spark.sql.functions
Returns the value of the first argument raised to the power of the second argument.
pow(String, double) - Static method in class org.apache.spark.sql.functions
Returns the value of the first argument raised to the power of the second argument.
pow(double, Column) - Static method in class org.apache.spark.sql.functions
Returns the value of the first argument raised to the power of the second argument.
pow(double, String) - Static method in class org.apache.spark.sql.functions
Returns the value of the first argument raised to the power of the second argument.
PowerIterationClustering - Class in org.apache.spark.mllib.clustering
 
PowerIterationClustering() - Constructor for class org.apache.spark.mllib.clustering.PowerIterationClustering
 
PowerIterationClustering.Assignment - Class in org.apache.spark.mllib.clustering
Cluster assignment.
PowerIterationClustering.Assignment(long, int) - Constructor for class org.apache.spark.mllib.clustering.PowerIterationClustering.Assignment
 
PowerIterationClustering.Assignment$ - Class in org.apache.spark.mllib.clustering
 
PowerIterationClustering.Assignment$() - Constructor for class org.apache.spark.mllib.clustering.PowerIterationClustering.Assignment$
 
PowerIterationClusteringModel - Class in org.apache.spark.mllib.clustering
Model produced by PowerIterationClustering.
PowerIterationClusteringModel(int, RDD<PowerIterationClustering.Assignment>) - Constructor for class org.apache.spark.mllib.clustering.PowerIterationClusteringModel
 
pr() - Method in class org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
Returns the precision-recall curve, which is an Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
pr() - Method in class org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
Returns the precision-recall curve, which is an RDD of (recall, precision), NOT (precision, recall), with (0.0, 1.0) prepended to it.
precision(double) - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns precision for a given label (category)
precision() - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns precision
precision() - Method in class org.apache.spark.mllib.evaluation.MultilabelMetrics
Returns document-based precision averaged by the number of documents
precision(double) - Method in class org.apache.spark.mllib.evaluation.MultilabelMetrics
Returns precision for a given label (category)
precision() - Method in class org.apache.spark.sql.types.Decimal
 
precision() - Method in class org.apache.spark.sql.types.DecimalType
 
precision() - Method in class org.apache.spark.sql.types.PrecisionInfo
 
precisionAt(int) - Method in class org.apache.spark.mllib.evaluation.RankingMetrics
Compute the average precision of all the queries, truncated at ranking position k.
precisionByThreshold() - Method in class org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
Returns a dataframe with two fields (threshold, precision) curve.
precisionByThreshold() - Method in class org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
Returns the (threshold, precision) curve.
precisionInfo() - Method in class org.apache.spark.sql.types.DecimalType
 
PrecisionInfo - Class in org.apache.spark.sql.types
Precision parameters for a Decimal
PrecisionInfo(int, int) - Constructor for class org.apache.spark.sql.types.PrecisionInfo
 
predict(FeaturesType) - Method in class org.apache.spark.ml.classification.ClassificationModel
Predict label for the given features.
predict(Vector) - Method in class org.apache.spark.ml.classification.DecisionTreeClassificationModel
 
predict(Vector) - Method in class org.apache.spark.ml.classification.GBTClassificationModel
 
predict(Vector) - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
Predict label for the given feature vector.
predict(Vector) - Method in class org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel
Predict label for the given features.
predict(FeaturesType) - Method in class org.apache.spark.ml.PredictionModel
Predict label for the given features.
predict(Vector) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
predict(Vector) - Method in class org.apache.spark.ml.regression.DecisionTreeRegressionModel
 
predict(Vector) - Method in class org.apache.spark.ml.regression.GBTRegressionModel
 
predict(Vector) - Method in class org.apache.spark.ml.regression.LinearRegressionModel
 
predict(Vector) - Method in class org.apache.spark.ml.regression.RandomForestRegressionModel
 
predict(RDD<Vector>) - Method in interface org.apache.spark.mllib.classification.ClassificationModel
Predict values for the given data set using the model trained.
predict(Vector) - Method in interface org.apache.spark.mllib.classification.ClassificationModel
Predict values for a single data point using the model trained.
predict(JavaRDD<Vector>) - Method in interface org.apache.spark.mllib.classification.ClassificationModel
Predict values for examples stored in a JavaRDD.
predict(RDD<Vector>) - Method in class org.apache.spark.mllib.classification.NaiveBayesModel
 
predict(Vector) - Method in class org.apache.spark.mllib.classification.NaiveBayesModel
 
predict(Vector) - Method in class org.apache.spark.mllib.clustering.BisectingKMeansModel
Predicts the index of the cluster that the input point belongs to.
predict(RDD<Vector>) - Method in class org.apache.spark.mllib.clustering.BisectingKMeansModel
Predicts the indices of the clusters that the input points belong to.
predict(JavaRDD<Vector>) - Method in class org.apache.spark.mllib.clustering.BisectingKMeansModel
Java-friendly version of predict().
predict(RDD<Vector>) - Method in class org.apache.spark.mllib.clustering.GaussianMixtureModel
Maps given points to their cluster indices.
predict(Vector) - Method in class org.apache.spark.mllib.clustering.GaussianMixtureModel
Maps given point to its cluster index.
predict(JavaRDD<Vector>) - Method in class org.apache.spark.mllib.clustering.GaussianMixtureModel
Java-friendly version of predict()
predict(Vector) - Method in class org.apache.spark.mllib.clustering.KMeansModel
Returns the cluster index that a given point belongs to.
predict(RDD<Vector>) - Method in class org.apache.spark.mllib.clustering.KMeansModel
Maps given points to their cluster indices.
predict(JavaRDD<Vector>) - Method in class org.apache.spark.mllib.clustering.KMeansModel
Maps given points to their cluster indices.
predict(int, int) - Method in class org.apache.spark.mllib.recommendation.MatrixFactorizationModel
Predict the rating of one user for one product.
predict(RDD<Tuple2<Object, Object>>) - Method in class org.apache.spark.mllib.recommendation.MatrixFactorizationModel
Predict the rating of many users for many products.
predict(JavaPairRDD<Integer, Integer>) - Method in class org.apache.spark.mllib.recommendation.MatrixFactorizationModel
Java-friendly version of MatrixFactorizationModel.predict.
predict(RDD<Vector>) - Method in class org.apache.spark.mllib.regression.GeneralizedLinearModel
Predict values for the given data set using the model trained.
predict(Vector) - Method in class org.apache.spark.mllib.regression.GeneralizedLinearModel
Predict values for a single data point using the model trained.
predict(RDD<Object>) - Method in class org.apache.spark.mllib.regression.IsotonicRegressionModel
Predict labels for provided features.
predict(JavaDoubleRDD) - Method in class org.apache.spark.mllib.regression.IsotonicRegressionModel
Predict labels for provided features.
predict(double) - Method in class org.apache.spark.mllib.regression.IsotonicRegressionModel
Predict a single label.
predict(RDD<Vector>) - Method in interface org.apache.spark.mllib.regression.RegressionModel
Predict values for the given data set using the model trained.
predict(Vector) - Method in interface org.apache.spark.mllib.regression.RegressionModel
Predict values for a single data point using the model trained.
predict(JavaRDD<Vector>) - Method in interface org.apache.spark.mllib.regression.RegressionModel
Predict values for examples stored in a JavaRDD.
predict(Vector) - Method in class org.apache.spark.mllib.tree.model.DecisionTreeModel
Predict values for a single data point using the model trained.
predict(RDD<Vector>) - Method in class org.apache.spark.mllib.tree.model.DecisionTreeModel
Predict values for the given data set using the model trained.
predict(JavaRDD<Vector>) - Method in class org.apache.spark.mllib.tree.model.DecisionTreeModel
Predict values for the given data set using the model trained.
predict() - Method in class org.apache.spark.mllib.tree.model.Node
 
predict(Vector) - Method in class org.apache.spark.mllib.tree.model.Node
predict value if node is not leaf
Predict - Class in org.apache.spark.mllib.tree.model
Predicted value for a node param: predict predicted value param: prob probability of the label (classification only)
Predict(double, double) - Constructor for class org.apache.spark.mllib.tree.model.Predict
 
predict() - Method in class org.apache.spark.mllib.tree.model.Predict
 
prediction() - Method in class org.apache.spark.ml.tree.InternalNode
 
prediction() - Method in class org.apache.spark.ml.tree.LeafNode
 
prediction() - Method in class org.apache.spark.ml.tree.Node
Prediction a leaf node makes, or which an internal node would make if it were a leaf node
predictionCol() - Method in class org.apache.spark.ml.regression.LinearRegressionSummary
 
PredictionModel<FeaturesType,M extends PredictionModel<FeaturesType,M>> - Class in org.apache.spark.ml
:: DeveloperApi :: Abstraction for a model for prediction tasks (regression and classification).
PredictionModel() - Constructor for class org.apache.spark.ml.PredictionModel
 
predictions() - Method in class org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
 
predictions() - Method in interface org.apache.spark.ml.classification.LogisticRegressionSummary
Dataframe outputted by the model's `transform` method.
predictions() - Method in class org.apache.spark.ml.regression.IsotonicRegressionModel
Predictions associated with the boundaries at the same index, monotone because of isotonic regression.
predictions() - Method in class org.apache.spark.ml.regression.LinearRegressionSummary
 
predictions() - Method in class org.apache.spark.mllib.regression.IsotonicRegressionModel
 
predictOn(DStream<Vector>) - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
Use the clustering model to make predictions on batches of data from a DStream.
predictOn(JavaDStream<Vector>) - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
Java-friendly version of predictOn.
predictOn(DStream<Vector>) - Method in class org.apache.spark.mllib.regression.StreamingLinearAlgorithm
Use the model to make predictions on batches of data from a DStream
predictOn(JavaDStream<Vector>) - Method in class org.apache.spark.mllib.regression.StreamingLinearAlgorithm
Java-friendly version of predictOn.
predictOnValues(DStream<Tuple2<K, Vector>>, ClassTag<K>) - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
Use the model to make predictions on the values of a DStream and carry over its keys.
predictOnValues(JavaPairDStream<K, Vector>) - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
Java-friendly version of predictOnValues.
predictOnValues(DStream<Tuple2<K, Vector>>, ClassTag<K>) - Method in class org.apache.spark.mllib.regression.StreamingLinearAlgorithm
Use the model to make predictions on the values of a DStream and carry over its keys.
predictOnValues(JavaPairDStream<K, Vector>) - Method in class org.apache.spark.mllib.regression.StreamingLinearAlgorithm
Java-friendly version of predictOnValues.
Predictor<FeaturesType,Learner extends Predictor<FeaturesType,Learner,M>,M extends PredictionModel<FeaturesType,M>> - Class in org.apache.spark.ml
:: DeveloperApi :: Abstraction for prediction problems (regression and classification).
Predictor() - Constructor for class org.apache.spark.ml.Predictor
 
predictPoint(Vector, Vector, double) - Method in class org.apache.spark.mllib.classification.LogisticRegressionModel
 
predictPoint(Vector, Vector, double) - Method in class org.apache.spark.mllib.classification.SVMModel
 
predictPoint(Vector, Vector, double) - Method in class org.apache.spark.mllib.regression.GeneralizedLinearModel
Predict the result given a data point and the weights learned.
predictPoint(Vector, Vector, double) - Method in class org.apache.spark.mllib.regression.LassoModel
 
predictPoint(Vector, Vector, double) - Method in class org.apache.spark.mllib.regression.LinearRegressionModel
 
predictPoint(Vector, Vector, double) - Method in class org.apache.spark.mllib.regression.RidgeRegressionModel
 
predictProbabilities(RDD<Vector>) - Method in class org.apache.spark.mllib.classification.NaiveBayesModel
Predict values for the given data set using the model trained.
predictProbabilities(Vector) - Method in class org.apache.spark.mllib.classification.NaiveBayesModel
Predict posterior class probabilities for a single data point using the model trained.
predictProbability(FeaturesType) - Method in class org.apache.spark.ml.classification.ProbabilisticClassificationModel
Predict the probability of each class given the features.
predictQuantiles(Vector) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
predictRaw(FeaturesType) - Method in class org.apache.spark.ml.classification.ClassificationModel
Raw prediction for each possible label.
predictRaw(Vector) - Method in class org.apache.spark.ml.classification.DecisionTreeClassificationModel
 
predictRaw(Vector) - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
predictRaw(Vector) - Method in class org.apache.spark.ml.classification.NaiveBayesModel
 
predictRaw(Vector) - Method in class org.apache.spark.ml.classification.RandomForestClassificationModel
 
predictSoft(RDD<Vector>) - Method in class org.apache.spark.mllib.clustering.GaussianMixtureModel
Given the input vectors, return the membership value of each vector to all mixture components.
predictSoft(Vector) - Method in class org.apache.spark.mllib.clustering.GaussianMixtureModel
Given the input vector, return the membership values to all mixture components.
preferredLocation() - Method in class org.apache.spark.streaming.receiver.Receiver
Override this to specify a preferred location (hostname).
preferredLocations(Partition) - Method in class org.apache.spark.rdd.RDD
Get the preferred locations of a partition, taking into account whether the RDD is checkpointed.
PrefixSpan - Class in org.apache.spark.mllib.fpm
:: Experimental ::
PrefixSpan() - Constructor for class org.apache.spark.mllib.fpm.PrefixSpan
Constructs a default instance with default parameters {minSupport: 0.1, maxPatternLength: 10, maxLocalProjDBSize: 32000000L}.
PrefixSpan.FreqSequence<Item> - Class in org.apache.spark.mllib.fpm
Represents a frequence sequence.
PrefixSpan.FreqSequence(Object[], long) - Constructor for class org.apache.spark.mllib.fpm.PrefixSpan.FreqSequence
 
PrefixSpanModel<Item> - Class in org.apache.spark.mllib.fpm
Model fitted by PrefixSpan param: freqSequences frequent sequences
PrefixSpanModel(RDD<PrefixSpan.FreqSequence<Item>>) - Constructor for class org.apache.spark.mllib.fpm.PrefixSpanModel
 
prefLoc() - Method in class org.apache.spark.rdd.PartitionGroup
 
pregel(A, int, EdgeDirection, Function3<Object, VD, A, VD>, Function1<EdgeTriplet<VD, ED>, Iterator<Tuple2<Object, A>>>, Function2<A, A, A>, ClassTag<A>) - Method in class org.apache.spark.graphx.GraphOps
Execute a Pregel-like iterative vertex-parallel abstraction.
Pregel - Class in org.apache.spark.graphx
Implements a Pregel-like bulk-synchronous message-passing API.
Pregel() - Constructor for class org.apache.spark.graphx.Pregel
 
prepareForExecution() - Method in class org.apache.spark.sql.SQLContext
 
prepareJobForWrite(Job) - Method in class org.apache.spark.sql.sources.HadoopFsRelation
Prepares a write job and returns an OutputWriterFactory.
prettyJson() - Method in class org.apache.spark.sql.types.DataType
The pretty (i.e.
prettyPrint() - Method in class org.apache.spark.streaming.Duration
 
prev() - Method in class org.apache.spark.rdd.ShuffledRDD
 
prevHandler() - Method in class org.apache.spark.util.SignalLoggerHandler
 
primitiveTypes() - Static method in class org.apache.spark.sql.hive.HiveContext
 
print() - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Print the first ten elements of each RDD generated in this DStream.
print(int) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Print the first num elements of each RDD generated in this DStream.
print() - Method in class org.apache.spark.streaming.dstream.DStream
Print the first ten elements of each RDD generated in this DStream.
print(int) - Method in class org.apache.spark.streaming.dstream.DStream
Print the first num elements of each RDD generated in this DStream.
printSchema() - Method in class org.apache.spark.sql.DataFrame
Prints the schema to the console in a nice tree format.
printSchema() - Method in class org.apache.spark.sql.Dataset
Prints the schema of the underlying Dataset to the console in a nice tree format.
printStats() - Method in class org.apache.spark.streaming.scheduler.StatsReportListener
 
printTreeString() - Method in class org.apache.spark.sql.types.StructType
 
Private - Annotation Type in org.apache.spark.annotation
A class that is considered private to the internals of Spark -- there is a high-likelihood they will be changed in future versions of Spark.
prob() - Method in class org.apache.spark.mllib.tree.model.Predict
 
ProbabilisticClassificationModel<FeaturesType,M extends ProbabilisticClassificationModel<FeaturesType,M>> - Class in org.apache.spark.ml.classification
:: DeveloperApi ::
ProbabilisticClassificationModel() - Constructor for class org.apache.spark.ml.classification.ProbabilisticClassificationModel
 
ProbabilisticClassifier<FeaturesType,E extends ProbabilisticClassifier<FeaturesType,E,M>,M extends ProbabilisticClassificationModel<FeaturesType,M>> - Class in org.apache.spark.ml.classification
:: DeveloperApi ::
ProbabilisticClassifier() - Constructor for class org.apache.spark.ml.classification.ProbabilisticClassifier
 
probabilities() - Static method in class org.apache.spark.scheduler.StatsReportListener
 
probability2prediction(Vector) - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
probability2prediction(Vector) - Method in class org.apache.spark.ml.classification.ProbabilisticClassificationModel
Given a vector of class conditional probabilities, select the predicted label.
probabilityCol() - Method in class org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
 
probabilityCol() - Method in interface org.apache.spark.ml.classification.LogisticRegressionSummary
Field in "predictions" which gives the calibrated probability of each instance as a vector.
PROCESS_LOCAL() - Static method in class org.apache.spark.scheduler.TaskLocality
 
processingDelay() - Method in class org.apache.spark.streaming.scheduler.BatchInfo
Time taken for the all jobs of this batch to finish processing from the time they started processing.
processingEndTime() - Method in class org.apache.spark.streaming.scheduler.BatchInfo
 
processingStartTime() - Method in class org.apache.spark.streaming.scheduler.BatchInfo
 
product() - Method in class org.apache.spark.mllib.recommendation.Rating
 
productFeatures() - Method in class org.apache.spark.mllib.recommendation.MatrixFactorizationModel
 
progressListener() - Method in class org.apache.spark.streaming.StreamingContext
 
properties() - Method in class org.apache.spark.scheduler.SparkListenerJobStart
 
properties() - Method in class org.apache.spark.scheduler.SparkListenerStageSubmitted
 
PrunedFilteredScan - Interface in org.apache.spark.sql.sources
::DeveloperApi:: A BaseRelation that can eliminate unneeded columns and filter using selected predicates before producing an RDD containing all matching tuples as Row objects.
PrunedScan - Interface in org.apache.spark.sql.sources
::DeveloperApi:: A BaseRelation that can eliminate unneeded columns before producing an RDD containing all of its tuples as Row objects.
Pseudorandom - Interface in org.apache.spark.util.random
:: DeveloperApi :: A class with pseudorandom behavior.
put(ParamPair<?>...) - Method in class org.apache.spark.ml.param.ParamMap
Puts a list of param pairs (overwrites if the input params exists).
put(Param<T>, T) - Method in class org.apache.spark.ml.param.ParamMap
Puts a (param, value) pair (overwrites if the input param exists).
put(Seq<ParamPair<?>>) - Method in class org.apache.spark.ml.param.ParamMap
Puts a list of param pairs (overwrites if the input params exists).
putBoolean(String, boolean) - Method in class org.apache.spark.sql.types.MetadataBuilder
Puts a Boolean.
putBooleanArray(String, boolean[]) - Method in class org.apache.spark.sql.types.MetadataBuilder
Puts a Boolean array.
putDouble(String, double) - Method in class org.apache.spark.sql.types.MetadataBuilder
Puts a Double.
putDoubleArray(String, double[]) - Method in class org.apache.spark.sql.types.MetadataBuilder
Puts a Double array.
putLong(String, long) - Method in class org.apache.spark.sql.types.MetadataBuilder
Puts a Long.
putLongArray(String, long[]) - Method in class org.apache.spark.sql.types.MetadataBuilder
Puts a Long array.
putMetadata(String, Metadata) - Method in class org.apache.spark.sql.types.MetadataBuilder
Puts a Metadata.
putMetadataArray(String, Metadata[]) - Method in class org.apache.spark.sql.types.MetadataBuilder
Puts a Metadata array.
putString(String, String) - Method in class org.apache.spark.sql.types.MetadataBuilder
Puts a String.
putStringArray(String, String[]) - Method in class org.apache.spark.sql.types.MetadataBuilder
Puts a String array.
pValue() - Method in class org.apache.spark.mllib.stat.test.ChiSqTestResult
 
pValue() - Method in class org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult
 
pValue() - Method in interface org.apache.spark.mllib.stat.test.TestResult
The probability of obtaining a test statistic result at least as extreme as the one that was actually observed, assuming that the null hypothesis is true.
pValues() - Method in class org.apache.spark.ml.regression.LinearRegressionSummary
Two-sided p-value of estimated coefficients and intercept.
pyUDT() - Method in class org.apache.spark.mllib.linalg.VectorUDT
 
pyUDT() - Method in class org.apache.spark.sql.types.UserDefinedType
Paired Python UDT class, if exists.

Q

Q() - Method in class org.apache.spark.mllib.linalg.QRDecomposition
 
QRDecomposition<QType,RType> - Class in org.apache.spark.mllib.linalg
 
QRDecomposition(QType, RType) - Constructor for class org.apache.spark.mllib.linalg.QRDecomposition
 
quantileCalculationStrategy() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
QuantileDiscretizer - Class in org.apache.spark.ml.feature
:: Experimental :: QuantileDiscretizer takes a column with continuous features and outputs a column with binned categorical features.
QuantileDiscretizer(String) - Constructor for class org.apache.spark.ml.feature.QuantileDiscretizer
 
QuantileDiscretizer() - Constructor for class org.apache.spark.ml.feature.QuantileDiscretizer
 
quantiles() - Method in class org.apache.spark.status.api.v1.TaskMetricDistributions
 
QuantileStrategy - Class in org.apache.spark.mllib.tree.configuration
Enum for selecting the quantile calculation strategy
QuantileStrategy() - Constructor for class org.apache.spark.mllib.tree.configuration.QuantileStrategy
 
quarter(Column) - Static method in class org.apache.spark.sql.functions
Extracts the quarter as an integer from a given date/timestamp/string.
queryExecution() - Method in class org.apache.spark.sql.DataFrame
 
queryExecution() - Method in class org.apache.spark.sql.Dataset
 
queryExecution() - Method in class org.apache.spark.sql.GroupedDataset
 
QueryExecutionListener - Interface in org.apache.spark.sql.util
:: Experimental :: The interface of query execution listener that can be used to analyze execution metrics.
queueStream(Queue<JavaRDD<T>>) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream from an queue of RDDs.
queueStream(Queue<JavaRDD<T>>, boolean) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream from an queue of RDDs.
queueStream(Queue<JavaRDD<T>>, boolean, JavaRDD<T>) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream from an queue of RDDs.
queueStream(Queue<RDD<T>>, boolean, ClassTag<T>) - Method in class org.apache.spark.streaming.StreamingContext
Create an input stream from a queue of RDDs.
queueStream(Queue<RDD<T>>, boolean, RDD<T>, ClassTag<T>) - Method in class org.apache.spark.streaming.StreamingContext
Create an input stream from a queue of RDDs.
quoteIdentifier(String) - Method in class org.apache.spark.sql.jdbc.JdbcDialect
Quotes the identifier.
quoteIdentifier(String) - Static method in class org.apache.spark.sql.jdbc.MySQLDialect
 

R

R() - Method in class org.apache.spark.mllib.linalg.QRDecomposition
 
r2() - Method in class org.apache.spark.ml.regression.LinearRegressionSummary
Returns R^2^, the coefficient of determination.
r2() - Method in class org.apache.spark.mllib.evaluation.RegressionMetrics
Returns R^2^, the unadjusted coefficient of determination.
RACK_LOCAL() - Static method in class org.apache.spark.scheduler.TaskLocality
 
rand(int, int, Random) - Static method in class org.apache.spark.mllib.linalg.DenseMatrix
Generate a DenseMatrix consisting of i.i.d. uniform random numbers.
rand(int, int, Random) - Static method in class org.apache.spark.mllib.linalg.Matrices
Generate a DenseMatrix consisting of i.i.d. uniform random numbers.
rand(long) - Static method in class org.apache.spark.sql.functions
Generate a random column with i.i.d.
rand() - Static method in class org.apache.spark.sql.functions
Generate a random column with i.i.d.
randn(int, int, Random) - Static method in class org.apache.spark.mllib.linalg.DenseMatrix
Generate a DenseMatrix consisting of i.i.d. gaussian random numbers.
randn(int, int, Random) - Static method in class org.apache.spark.mllib.linalg.Matrices
Generate a DenseMatrix consisting of i.i.d. gaussian random numbers.
randn(long) - Static method in class org.apache.spark.sql.functions
Generate a column with i.i.d.
randn() - Static method in class org.apache.spark.sql.functions
Generate a column with i.i.d.
RANDOM() - Static method in class org.apache.spark.mllib.clustering.KMeans
 
random(int, Random) - Static method in class org.apache.spark.util.Vector
Creates this Vector of given length containing random numbers between 0.0 and 1.0.
RandomDataGenerator<T> - Interface in org.apache.spark.mllib.random
:: DeveloperApi :: Trait for random data generators that generate i.i.d.
RandomForest - Class in org.apache.spark.mllib.tree
A class that implements a Random Forest learning algorithm for classification and regression.
RandomForest(Strategy, int, String, int) - Constructor for class org.apache.spark.mllib.tree.RandomForest
 
RandomForestClassificationModel - Class in org.apache.spark.ml.classification
:: Experimental :: Random Forest model for classification.
RandomForestClassifier - Class in org.apache.spark.ml.classification
:: Experimental :: Random Forest learning algorithm for classification.
RandomForestClassifier(String) - Constructor for class org.apache.spark.ml.classification.RandomForestClassifier
 
RandomForestClassifier() - Constructor for class org.apache.spark.ml.classification.RandomForestClassifier
 
RandomForestModel - Class in org.apache.spark.mllib.tree.model
Represents a random forest model.
RandomForestModel(Enumeration.Value, DecisionTreeModel[]) - Constructor for class org.apache.spark.mllib.tree.model.RandomForestModel
 
RandomForestRegressionModel - Class in org.apache.spark.ml.regression
:: Experimental :: Random Forest model for regression.
RandomForestRegressor - Class in org.apache.spark.ml.regression
:: Experimental :: Random Forest learning algorithm for regression.
RandomForestRegressor(String) - Constructor for class org.apache.spark.ml.regression.RandomForestRegressor
 
RandomForestRegressor() - Constructor for class org.apache.spark.ml.regression.RandomForestRegressor
 
randomJavaRDD(JavaSparkContext, RandomDataGenerator<T>, long, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
:: DeveloperApi :: Generates an RDD comprised of i.i.d. samples produced by the input RandomDataGenerator.
randomJavaRDD(JavaSparkContext, RandomDataGenerator<T>, long, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
randomJavaRDD(JavaSparkContext, RandomDataGenerator<T>, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
randomJavaVectorRDD(JavaSparkContext, RandomDataGenerator<Object>, long, int, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
randomJavaVectorRDD(JavaSparkContext, RandomDataGenerator<Object>, long, int, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
randomJavaVectorRDD(JavaSparkContext, RandomDataGenerator<Object>, long, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
randomRDD(SparkContext, RandomDataGenerator<T>, long, int, long, ClassTag<T>) - Static method in class org.apache.spark.mllib.random.RandomRDDs
:: DeveloperApi :: Generates an RDD comprised of i.i.d. samples produced by the input RandomDataGenerator.
RandomRDDs - Class in org.apache.spark.mllib.random
Generator methods for creating RDDs comprised of i.i.d. samples from some distribution.
RandomRDDs() - Constructor for class org.apache.spark.mllib.random.RandomRDDs
 
RandomSampler<T,U> - Interface in org.apache.spark.util.random
:: DeveloperApi :: A pseudorandom sampler.
randomSplit(double[]) - Method in class org.apache.spark.api.java.JavaRDD
Randomly splits this RDD with the provided weights.
randomSplit(double[], long) - Method in class org.apache.spark.api.java.JavaRDD
Randomly splits this RDD with the provided weights.
randomSplit(double[], long) - Method in class org.apache.spark.rdd.RDD
Randomly splits this RDD with the provided weights.
randomSplit(double[], long) - Method in class org.apache.spark.sql.DataFrame
Randomly splits this DataFrame with the provided weights.
randomSplit(double[]) - Method in class org.apache.spark.sql.DataFrame
Randomly splits this DataFrame with the provided weights.
randomVectorRDD(SparkContext, RandomDataGenerator<Object>, long, int, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
:: DeveloperApi :: Generates an RDD[Vector] with vectors containing i.i.d. samples produced by the input RandomDataGenerator.
range(long, long, long, int) - Method in class org.apache.spark.SparkContext
Creates a new RDD[Long] containing elements from start to end(exclusive), increased by step every element.
range(long) - Method in class org.apache.spark.sql.SQLContext
 
range(long, long) - Method in class org.apache.spark.sql.SQLContext
 
range(long, long, long, int) - Method in class org.apache.spark.sql.SQLContext
 
rangeBetween(long, long) - Method in class org.apache.spark.sql.expressions.WindowSpec
Defines the frame boundaries, from start (inclusive) to end (inclusive).
RangeDependency<T> - Class in org.apache.spark
:: DeveloperApi :: Represents a one-to-one dependency between ranges of partitions in the parent and child RDDs.
RangeDependency(RDD<T>, int, int, int) - Constructor for class org.apache.spark.RangeDependency
 
RangePartitioner<K,V> - Class in org.apache.spark
A Partitioner that partitions sortable records by range into roughly equal ranges.
RangePartitioner(int, RDD<? extends Product2<K, V>>, boolean, Ordering<K>, ClassTag<K>) - Constructor for class org.apache.spark.RangePartitioner
 
rank() - Method in class org.apache.spark.graphx.lib.SVDPlusPlus.Conf
 
rank() - Method in class org.apache.spark.ml.recommendation.ALSModel
 
rank() - Method in class org.apache.spark.mllib.recommendation.MatrixFactorizationModel
 
rank() - Static method in class org.apache.spark.sql.functions
Window function: returns the rank of rows within a window partition.
RankingMetrics<T> - Class in org.apache.spark.mllib.evaluation
::Experimental:: Evaluator for ranking algorithms.
RankingMetrics(RDD<Tuple2<Object, Object>>, ClassTag<T>) - Constructor for class org.apache.spark.mllib.evaluation.RankingMetrics
 
rateController() - Method in class org.apache.spark.streaming.dstream.InputDStream
 
rateController() - Method in class org.apache.spark.streaming.dstream.ReceiverInputDStream
Asynchronously maintains & sends new rate limits to the receiver through the receiver tracker.
rating() - Method in class org.apache.spark.ml.recommendation.ALS.Rating
 
Rating - Class in org.apache.spark.mllib.recommendation
A more compact class to represent a rating than Tuple3[Int, Int, Double].
Rating(int, int, double) - Constructor for class org.apache.spark.mllib.recommendation.Rating
 
rating() - Method in class org.apache.spark.mllib.recommendation.Rating
 
raw2prediction(Vector) - Method in class org.apache.spark.ml.classification.ClassificationModel
Given a vector of raw predictions, select the predicted label.
raw2prediction(Vector) - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
raw2prediction(Vector) - Method in class org.apache.spark.ml.classification.ProbabilisticClassificationModel
 
raw2probability(Vector) - Method in class org.apache.spark.ml.classification.ProbabilisticClassificationModel
Non-in-place version of raw2probabilityInPlace()
raw2probabilityInPlace(Vector) - Method in class org.apache.spark.ml.classification.DecisionTreeClassificationModel
 
raw2probabilityInPlace(Vector) - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
raw2probabilityInPlace(Vector) - Method in class org.apache.spark.ml.classification.NaiveBayesModel
 
raw2probabilityInPlace(Vector) - Method in class org.apache.spark.ml.classification.ProbabilisticClassificationModel
Estimate the probability of each class given the raw prediction, doing the computation in-place.
raw2probabilityInPlace(Vector) - Method in class org.apache.spark.ml.classification.RandomForestClassificationModel
 
rawSocketStream(String, int, StorageLevel) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream from network source hostname:port, where data is received as serialized blocks (serialized using the Spark's serializer) that can be directly pushed into the block manager without deserializing them.
rawSocketStream(String, int) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream from network source hostname:port, where data is received as serialized blocks (serialized using the Spark's serializer) that can be directly pushed into the block manager without deserializing them.
rawSocketStream(String, int, StorageLevel, ClassTag<T>) - Method in class org.apache.spark.streaming.StreamingContext
Create a input stream from network source hostname:port, where data is received as serialized blocks (serialized using the Spark's serializer) that can be directly pushed into the block manager without deserializing them.
rdd() - Method in class org.apache.spark.api.java.JavaDoubleRDD
 
rdd() - Method in class org.apache.spark.api.java.JavaPairRDD
 
rdd() - Method in class org.apache.spark.api.java.JavaRDD
 
rdd() - Method in interface org.apache.spark.api.java.JavaRDDLike
 
rdd() - Method in class org.apache.spark.Dependency
 
rdd() - Method in class org.apache.spark.NarrowDependency
 
RDD<T> - Class in org.apache.spark.rdd
A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.
RDD(SparkContext, Seq<Dependency<?>>, ClassTag<T>) - Constructor for class org.apache.spark.rdd.RDD
 
RDD(RDD<?>, ClassTag<T>) - Constructor for class org.apache.spark.rdd.RDD
Construct an RDD with just a one-to-one dependency on one parent
rdd() - Method in class org.apache.spark.ShuffleDependency
 
rdd() - Method in class org.apache.spark.sql.DataFrame
Represents the content of the DataFrame as an RDD of Rows.
rdd() - Method in class org.apache.spark.sql.Dataset
Converts this Dataset to an RDD.
RDD() - Static method in class org.apache.spark.storage.BlockId
 
RDD_SCOPE_KEY() - Static method in class org.apache.spark.SparkContext
 
RDD_SCOPE_NO_OVERRIDE_KEY() - Static method in class org.apache.spark.SparkContext
 
RDDBlockId - Class in org.apache.spark.storage
 
RDDBlockId(int, int) - Constructor for class org.apache.spark.storage.RDDBlockId
 
rddBlocks() - Method in class org.apache.spark.status.api.v1.ExecutorSummary
 
rddBlocks() - Method in class org.apache.spark.storage.StorageStatus
Return the RDD blocks stored in this block manager.
rddBlocksById(int) - Method in class org.apache.spark.storage.StorageStatus
Return the blocks that belong to the given RDD stored in this block manager.
RDDDataDistribution - Class in org.apache.spark.status.api.v1
 
RDDFunctions<T> - Class in org.apache.spark.mllib.rdd
Machine learning specific RDD functions.
RDDFunctions(RDD<T>, ClassTag<T>) - Constructor for class org.apache.spark.mllib.rdd.RDDFunctions
 
rddId() - Method in class org.apache.spark.CleanCheckpoint
 
rddId() - Method in class org.apache.spark.CleanRDD
 
rddId() - Method in class org.apache.spark.scheduler.SparkListenerUnpersistRDD
 
rddId() - Method in class org.apache.spark.storage.RDDBlockId
 
RDDInfo - Class in org.apache.spark.storage
 
RDDInfo(int, String, int, StorageLevel, Seq<Object>, String, Option<org.apache.spark.rdd.RDDOperationScope>) - Constructor for class org.apache.spark.storage.RDDInfo
 
rddInfoList() - Method in class org.apache.spark.ui.storage.StorageListener
Filter RDD info to include only those with cached partitions
rddInfos() - Method in class org.apache.spark.scheduler.StageInfo
 
RDDPartitionInfo - Class in org.apache.spark.status.api.v1
 
rdds() - Method in class org.apache.spark.rdd.CoGroupedRDD
 
rdds() - Method in class org.apache.spark.rdd.UnionRDD
 
RDDStorageInfo - Class in org.apache.spark.status.api.v1
 
rddStorageLevel(int) - Method in class org.apache.spark.storage.StorageStatus
Return the storage level, if any, used by the given RDD in this block manager.
rddToAsyncRDDActions(RDD<T>, ClassTag<T>) - Static method in class org.apache.spark.rdd.RDD
 
rddToAsyncRDDActions(RDD<T>, ClassTag<T>) - Static method in class org.apache.spark.SparkContext
 
rddToDataFrameHolder(RDD<A>, TypeTags.TypeTag<A>) - Method in class org.apache.spark.sql.SQLImplicits
Creates a DataFrame from an RDD of Product (e.g.
rddToDatasetHolder(RDD<T>, Encoder<T>) - Method in class org.apache.spark.sql.SQLImplicits
Creates a Dataset from an RDD.
rddToOrderedRDDFunctions(RDD<Tuple2<K, V>>, Ordering<K>, ClassTag<K>, ClassTag<V>) - Static method in class org.apache.spark.rdd.RDD
 
rddToOrderedRDDFunctions(RDD<Tuple2<K, V>>, Ordering<K>, ClassTag<K>, ClassTag<V>) - Static method in class org.apache.spark.SparkContext
 
rddToPairRDDFunctions(RDD<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>, Ordering<K>) - Static method in class org.apache.spark.rdd.RDD
 
rddToPairRDDFunctions(RDD<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>, Ordering<K>) - Static method in class org.apache.spark.SparkContext
 
rddToSequenceFileRDDFunctions(RDD<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>, <any>, <any>) - Static method in class org.apache.spark.rdd.RDD
 
rddToSequenceFileRDDFunctions(RDD<Tuple2<K, V>>, Function1<K, Writable>, ClassTag<K>, Function1<V, Writable>, ClassTag<V>) - Static method in class org.apache.spark.SparkContext
 
read() - Method in class org.apache.spark.api.r.BaseRRDD
 
read() - Static method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
read() - Static method in class org.apache.spark.ml.classification.NaiveBayesModel
 
read() - Static method in class org.apache.spark.ml.clustering.DistributedLDAModel
 
read() - Static method in class org.apache.spark.ml.clustering.KMeansModel
 
read() - Static method in class org.apache.spark.ml.clustering.LocalLDAModel
 
read() - Static method in class org.apache.spark.ml.feature.ChiSqSelectorModel
 
read() - Static method in class org.apache.spark.ml.feature.CountVectorizerModel
 
read() - Static method in class org.apache.spark.ml.feature.IDFModel
 
read() - Static method in class org.apache.spark.ml.feature.MinMaxScalerModel
 
read() - Static method in class org.apache.spark.ml.feature.PCAModel
 
read() - Static method in class org.apache.spark.ml.feature.StandardScalerModel
 
read() - Static method in class org.apache.spark.ml.feature.StringIndexerModel
 
read() - Static method in class org.apache.spark.ml.feature.VectorIndexerModel
 
read() - Static method in class org.apache.spark.ml.feature.Word2VecModel
 
read() - Static method in class org.apache.spark.ml.Pipeline
 
read() - Static method in class org.apache.spark.ml.PipelineModel
 
read() - Static method in class org.apache.spark.ml.recommendation.ALSModel
 
read() - Static method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
read() - Static method in class org.apache.spark.ml.regression.IsotonicRegressionModel
 
read() - Static method in class org.apache.spark.ml.regression.LinearRegressionModel
 
read() - Static method in class org.apache.spark.ml.tuning.CrossValidator
 
read() - Static method in class org.apache.spark.ml.tuning.CrossValidatorModel
 
read() - Method in interface org.apache.spark.ml.util.MLReadable
Returns an MLReader instance for this class.
read(Kryo, Input, Class<Iterable<?>>) - Method in class org.apache.spark.serializer.JavaIterableWrapperSerializer
 
read() - Method in class org.apache.spark.sql.SQLContext
 
read() - Method in class org.apache.spark.storage.BufferReleasingInputStream
 
read(byte[]) - Method in class org.apache.spark.storage.BufferReleasingInputStream
 
read(byte[], int, int) - Method in class org.apache.spark.storage.BufferReleasingInputStream
 
read(WriteAheadLogRecordHandle) - Method in class org.apache.spark.streaming.util.WriteAheadLog
Read a written record based on the given record handle.
readAll() - Method in class org.apache.spark.streaming.util.WriteAheadLog
Read and return an iterator of all the records that have been written but not yet cleaned up.
readBytes() - Method in class org.apache.spark.status.api.v1.ShuffleReadMetricDistributions
 
readData(int) - Method in class org.apache.spark.api.r.BaseRRDD
 
readData(int) - Method in class org.apache.spark.api.r.PairwiseRRDD
 
readData(int) - Method in class org.apache.spark.api.r.RRDD
 
readData(int) - Method in class org.apache.spark.api.r.StringRRDD
 
readExternal(ObjectInput) - Method in class org.apache.spark.serializer.JavaSerializer
 
readExternal(ObjectInput) - Method in class org.apache.spark.storage.BlockManagerId
 
readExternal(ObjectInput) - Method in class org.apache.spark.storage.StorageLevel
 
readExternal(ObjectInput) - Method in class org.apache.spark.streaming.flume.SparkFlumeEvent
 
readKey(ClassTag<T>) - Method in class org.apache.spark.serializer.DeserializationStream
Reads the object representing the key of a key-value pair.
readObject(ClassTag<T>) - Method in class org.apache.spark.serializer.DeserializationStream
The most general-purpose method to read an object.
readRecords() - Method in class org.apache.spark.status.api.v1.ShuffleReadMetricDistributions
 
readValue(ClassTag<T>) - Method in class org.apache.spark.serializer.DeserializationStream
Reads the object representing the value of a key-value pair.
ready(Duration, CanAwait) - Method in class org.apache.spark.ComplexFutureAction
 
ready(Duration, CanAwait) - Method in interface org.apache.spark.FutureAction
Blocks until this action completes.
ready(Duration, CanAwait) - Method in class org.apache.spark.SimpleFutureAction
 
reason() - Method in class org.apache.spark.ExecutorLostFailure
 
reason() - Method in class org.apache.spark.scheduler.SparkListenerExecutorRemoved
 
reason() - Method in class org.apache.spark.scheduler.SparkListenerTaskEnd
 
recall(double) - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns recall for a given label (category)
recall() - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns recall (equals to precision for multiclass classifier because sum of all false positives is equal to sum of all false negatives)
recall() - Method in class org.apache.spark.mllib.evaluation.MultilabelMetrics
Returns document-based recall averaged by the number of documents
recall(double) - Method in class org.apache.spark.mllib.evaluation.MultilabelMetrics
Returns recall for a given label (category)
recallByThreshold() - Method in class org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
Returns a dataframe with two fields (threshold, recall) curve.
recallByThreshold() - Method in class org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
Returns the (threshold, recall) curve.
Receiver<T> - Class in org.apache.spark.streaming.receiver
:: DeveloperApi :: Abstract class of a receiver that can be run on worker nodes to receive external data.
Receiver(StorageLevel) - Constructor for class org.apache.spark.streaming.receiver.Receiver
 
ReceiverInfo - Class in org.apache.spark.streaming.scheduler
:: DeveloperApi :: Class having information about a receiver
ReceiverInfo(int, String, boolean, String, String, String, String, long) - Constructor for class org.apache.spark.streaming.scheduler.ReceiverInfo
 
receiverInfo() - Method in class org.apache.spark.streaming.scheduler.StreamingListenerReceiverError
 
receiverInfo() - Method in class org.apache.spark.streaming.scheduler.StreamingListenerReceiverStarted
 
receiverInfo() - Method in class org.apache.spark.streaming.scheduler.StreamingListenerReceiverStopped
 
receiverInputDStream() - Method in class org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream
 
receiverInputDStream() - Method in class org.apache.spark.streaming.api.java.JavaReceiverInputDStream
 
ReceiverInputDStream<T> - Class in org.apache.spark.streaming.dstream
Abstract class for defining any InputDStream that has to start a receiver on worker nodes to receive external data.
ReceiverInputDStream(StreamingContext, ClassTag<T>) - Constructor for class org.apache.spark.streaming.dstream.ReceiverInputDStream
 
receiverStream(Receiver<T>) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream with any arbitrary user implemented receiver.
receiverStream(Receiver<T>, ClassTag<T>) - Method in class org.apache.spark.streaming.StreamingContext
Create an input stream with any arbitrary user implemented receiver.
recommendProducts(int, int) - Method in class org.apache.spark.mllib.recommendation.MatrixFactorizationModel
Recommends products to a user.
recommendProductsForUsers(int) - Method in class org.apache.spark.mllib.recommendation.MatrixFactorizationModel
Recommends topK products for all users.
recommendUsers(int, int) - Method in class org.apache.spark.mllib.recommendation.MatrixFactorizationModel
Recommends users to a product.
recommendUsersForProducts(int) - Method in class org.apache.spark.mllib.recommendation.MatrixFactorizationModel
Recommends topK users for all products.
recordJobProperties(int, Properties) - Method in class org.apache.spark.scheduler.JobLogger
Record job properties into job log file
RECORDS_BETWEEN_BYTES_READ_METRIC_UPDATES() - Static method in class org.apache.spark.rdd.HadoopRDD
Update the input bytes read metric each time this number of records has been read
RECORDS_BETWEEN_BYTES_WRITTEN_METRIC_UPDATES() - Static method in class org.apache.spark.rdd.PairRDDFunctions
 
recordsRead() - Method in class org.apache.spark.status.api.v1.InputMetricDistributions
 
recordsRead() - Method in class org.apache.spark.status.api.v1.InputMetrics
 
recordsRead() - Method in class org.apache.spark.status.api.v1.ShuffleReadMetrics
 
recordsWritten() - Method in class org.apache.spark.status.api.v1.OutputMetricDistributions
 
recordsWritten() - Method in class org.apache.spark.status.api.v1.OutputMetrics
 
recordsWritten() - Method in class org.apache.spark.status.api.v1.ShuffleWriteMetrics
 
recordTaskMetrics(int, String, TaskInfo, TaskMetrics) - Method in class org.apache.spark.scheduler.JobLogger
Record task metrics into job log files, including execution info and shuffle metrics
reduce(Function2<T, T, T>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Reduces the elements of this RDD using the specified commutative and associative binary operator.
reduce(Function2<T, T, T>) - Method in class org.apache.spark.rdd.RDD
Reduces the elements of this RDD using the specified commutative and associative binary operator.
reduce(Function2<T, T, T>) - Method in class org.apache.spark.sql.Dataset
(Scala-specific) Reduces the elements of this Dataset using the specified binary function.
reduce(ReduceFunction<T>) - Method in class org.apache.spark.sql.Dataset
(Java-specific) Reduces the elements of this Dataset using the specified binary function.
reduce(B, I) - Method in class org.apache.spark.sql.expressions.Aggregator
Combine two values to produce a new value.
reduce(Function2<V, V, V>) - Method in class org.apache.spark.sql.GroupedDataset
Reduces the elements of each group of data using the specified binary function.
reduce(ReduceFunction<V>) - Method in class org.apache.spark.sql.GroupedDataset
Reduces the elements of each group of data using the specified binary function.
reduce(Function2<T, T, T>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD has a single element generated by reducing each RDD of this DStream.
reduce(Function2<T, T, T>) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream in which each RDD has a single element generated by reducing each RDD of this DStream.
reduceByKey(Partitioner, Function2<V, V, V>) - Method in class org.apache.spark.api.java.JavaPairRDD
Merge the values for each key using an associative reduce function.
reduceByKey(Function2<V, V, V>, int) - Method in class org.apache.spark.api.java.JavaPairRDD
Merge the values for each key using an associative reduce function.
reduceByKey(Function2<V, V, V>) - Method in class org.apache.spark.api.java.JavaPairRDD
Merge the values for each key using an associative reduce function.
reduceByKey(Partitioner, Function2<V, V, V>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Merge the values for each key using an associative reduce function.
reduceByKey(Function2<V, V, V>, int) - Method in class org.apache.spark.rdd.PairRDDFunctions
Merge the values for each key using an associative reduce function.
reduceByKey(Function2<V, V, V>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Merge the values for each key using an associative reduce function.
reduceByKey(Function2<V, V, V>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying reduceByKey to each RDD.
reduceByKey(Function2<V, V, V>, int) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying reduceByKey to each RDD.
reduceByKey(Function2<V, V, V>, Partitioner) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying reduceByKey to each RDD.
reduceByKey(Function2<V, V, V>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying reduceByKey to each RDD.
reduceByKey(Function2<V, V, V>, int) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying reduceByKey to each RDD.
reduceByKey(Function2<V, V, V>, Partitioner) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying reduceByKey to each RDD.
reduceByKeyAndWindow(Function2<V, V, V>, Duration) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Create a new DStream by applying reduceByKey over a sliding window on this DStream.
reduceByKeyAndWindow(Function2<V, V, V>, Duration, Duration) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying reduceByKey over a sliding window.
reduceByKeyAndWindow(Function2<V, V, V>, Duration, Duration, int) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying reduceByKey over a sliding window.
reduceByKeyAndWindow(Function2<V, V, V>, Duration, Duration, Partitioner) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying reduceByKey over a sliding window.
reduceByKeyAndWindow(Function2<V, V, V>, Function2<V, V, V>, Duration, Duration) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by reducing over a using incremental computation.
reduceByKeyAndWindow(Function2<V, V, V>, Function2<V, V, V>, Duration, Duration, int, Function<Tuple2<K, V>, Boolean>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying incremental reduceByKey over a sliding window.
reduceByKeyAndWindow(Function2<V, V, V>, Function2<V, V, V>, Duration, Duration, Partitioner, Function<Tuple2<K, V>, Boolean>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying incremental reduceByKey over a sliding window.
reduceByKeyAndWindow(Function2<V, V, V>, Duration) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying reduceByKey over a sliding window on this DStream.
reduceByKeyAndWindow(Function2<V, V, V>, Duration, Duration) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying reduceByKey over a sliding window.
reduceByKeyAndWindow(Function2<V, V, V>, Duration, Duration, int) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying reduceByKey over a sliding window.
reduceByKeyAndWindow(Function2<V, V, V>, Duration, Duration, Partitioner) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying reduceByKey over a sliding window.
reduceByKeyAndWindow(Function2<V, V, V>, Function2<V, V, V>, Duration, Duration, int, Function1<Tuple2<K, V>, Object>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying incremental reduceByKey over a sliding window.
reduceByKeyAndWindow(Function2<V, V, V>, Function2<V, V, V>, Duration, Duration, Partitioner, Function1<Tuple2<K, V>, Object>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying incremental reduceByKey over a sliding window.
reduceByKeyLocally(Function2<V, V, V>) - Method in class org.apache.spark.api.java.JavaPairRDD
Merge the values for each key using an associative reduce function, but return the results immediately to the master as a Map.
reduceByKeyLocally(Function2<V, V, V>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Merge the values for each key using an associative reduce function, but return the results immediately to the master as a Map.
reduceByKeyToDriver(Function2<V, V, V>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Alias for reduceByKeyLocally
reduceByWindow(Function2<T, T, T>, Duration, Duration) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Deprecated.
As this API is not Java compatible.
reduceByWindow(Function2<T, T, T>, Duration, Duration) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.
reduceByWindow(Function2<T, T, T>, Function2<T, T, T>, Duration, Duration) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.
reduceByWindow(Function2<T, T, T>, Duration, Duration) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.
reduceByWindow(Function2<T, T, T>, Function2<T, T, T>, Duration, Duration) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream in which each RDD has a single element generated by reducing all elements in a sliding window over this DStream.
ReduceFunction<T> - Interface in org.apache.spark.api.java.function
Base interface for function used in Dataset's reduce.
reduceId() - Method in class org.apache.spark.FetchFailed
 
reduceId() - Method in class org.apache.spark.storage.ShuffleBlockId
 
reduceId() - Method in class org.apache.spark.storage.ShuffleDataBlockId
 
reduceId() - Method in class org.apache.spark.storage.ShuffleIndexBlockId
 
refreshTable(String) - Method in class org.apache.spark.sql.hive.HiveContext
Invalidate and refresh all the cached the metadata of the given table.
regexp_extract(Column, String, int) - Static method in class org.apache.spark.sql.functions
Extract a specific(idx) group identified by a java regex, from the specified string column.
regexp_replace(Column, String, String) - Static method in class org.apache.spark.sql.functions
Replace all substrings of the specified string value that match regexp with rep.
RegexTokenizer - Class in org.apache.spark.ml.feature
:: Experimental :: A regex based tokenizer that extracts tokens either by using the provided regex pattern to split the text (default) or repeatedly matching the regex (if gaps is false).
RegexTokenizer(String) - Constructor for class org.apache.spark.ml.feature.RegexTokenizer
 
RegexTokenizer() - Constructor for class org.apache.spark.ml.feature.RegexTokenizer
 
register(String, UserDefinedAggregateFunction) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined aggregate function (UDAF).
register(String, Function0<RT>, TypeTags.TypeTag<RT>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 0 arguments as user-defined function (UDF).
register(String, Function1<A1, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 1 arguments as user-defined function (UDF).
register(String, Function2<A1, A2, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 2 arguments as user-defined function (UDF).
register(String, Function3<A1, A2, A3, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 3 arguments as user-defined function (UDF).
register(String, Function4<A1, A2, A3, A4, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 4 arguments as user-defined function (UDF).
register(String, Function5<A1, A2, A3, A4, A5, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 5 arguments as user-defined function (UDF).
register(String, Function6<A1, A2, A3, A4, A5, A6, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 6 arguments as user-defined function (UDF).
register(String, Function7<A1, A2, A3, A4, A5, A6, A7, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 7 arguments as user-defined function (UDF).
register(String, Function8<A1, A2, A3, A4, A5, A6, A7, A8, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 8 arguments as user-defined function (UDF).
register(String, Function9<A1, A2, A3, A4, A5, A6, A7, A8, A9, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 9 arguments as user-defined function (UDF).
register(String, Function10<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 10 arguments as user-defined function (UDF).
register(String, Function11<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 11 arguments as user-defined function (UDF).
register(String, Function12<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 12 arguments as user-defined function (UDF).
register(String, Function13<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 13 arguments as user-defined function (UDF).
register(String, Function14<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 14 arguments as user-defined function (UDF).
register(String, Function15<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 15 arguments as user-defined function (UDF).
register(String, Function16<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>, TypeTags.TypeTag<A16>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 16 arguments as user-defined function (UDF).
register(String, Function17<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>, TypeTags.TypeTag<A16>, TypeTags.TypeTag<A17>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 17 arguments as user-defined function (UDF).
register(String, Function18<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>, TypeTags.TypeTag<A16>, TypeTags.TypeTag<A17>, TypeTags.TypeTag<A18>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 18 arguments as user-defined function (UDF).
register(String, Function19<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>, TypeTags.TypeTag<A16>, TypeTags.TypeTag<A17>, TypeTags.TypeTag<A18>, TypeTags.TypeTag<A19>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 19 arguments as user-defined function (UDF).
register(String, Function20<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>, TypeTags.TypeTag<A16>, TypeTags.TypeTag<A17>, TypeTags.TypeTag<A18>, TypeTags.TypeTag<A19>, TypeTags.TypeTag<A20>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 20 arguments as user-defined function (UDF).
register(String, Function21<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, A21, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>, TypeTags.TypeTag<A16>, TypeTags.TypeTag<A17>, TypeTags.TypeTag<A18>, TypeTags.TypeTag<A19>, TypeTags.TypeTag<A20>, TypeTags.TypeTag<A21>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 21 arguments as user-defined function (UDF).
register(String, Function22<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, A21, A22, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>, TypeTags.TypeTag<A16>, TypeTags.TypeTag<A17>, TypeTags.TypeTag<A18>, TypeTags.TypeTag<A19>, TypeTags.TypeTag<A20>, TypeTags.TypeTag<A21>, TypeTags.TypeTag<A22>) - Method in class org.apache.spark.sql.UDFRegistration
Register a Scala closure of 22 arguments as user-defined function (UDF).
register(String, UDF1<?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 1 arguments.
register(String, UDF2<?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 2 arguments.
register(String, UDF3<?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 3 arguments.
register(String, UDF4<?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 4 arguments.
register(String, UDF5<?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 5 arguments.
register(String, UDF6<?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 6 arguments.
register(String, UDF7<?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 7 arguments.
register(String, UDF8<?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 8 arguments.
register(String, UDF9<?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 9 arguments.
register(String, UDF10<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 10 arguments.
register(String, UDF11<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 11 arguments.
register(String, UDF12<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 12 arguments.
register(String, UDF13<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 13 arguments.
register(String, UDF14<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 14 arguments.
register(String, UDF15<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 15 arguments.
register(String, UDF16<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 16 arguments.
register(String, UDF17<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 17 arguments.
register(String, UDF18<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 18 arguments.
register(String, UDF19<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 19 arguments.
register(String, UDF20<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 20 arguments.
register(String, UDF21<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 21 arguments.
register(String, UDF22<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - Method in class org.apache.spark.sql.UDFRegistration
Register a user-defined function with 22 arguments.
register(QueryExecutionListener) - Method in class org.apache.spark.sql.util.ExecutionListenerManager
Registers the specified QueryExecutionListener.
registerAvroSchemas(Seq<Schema>) - Method in class org.apache.spark.SparkConf
Use Kryo serialization and register the given set of Avro schemas so that the generic record serializer can decrease network IO
registerClasses(Kryo) - Method in class org.apache.spark.graphx.GraphKryoRegistrator
 
registerClasses(Kryo) - Method in interface org.apache.spark.serializer.KryoRegistrator
 
registerDialect(JdbcDialect) - Static method in class org.apache.spark.sql.jdbc.JdbcDialects
Register a dialect for use on all new matching jdbc DataFrame.
registerKryoClasses(SparkConf) - Static method in class org.apache.spark.graphx.GraphXUtils
Registers classes that GraphX uses with Kryo.
registerKryoClasses(Class<?>[]) - Method in class org.apache.spark.SparkConf
Use Kryo serialization and register the given set of classes with Kryo.
registerPython(String, UserDefinedPythonFunction) - Method in class org.apache.spark.sql.UDFRegistration
 
registerStream(DStream<BinarySample>) - Method in class org.apache.spark.mllib.stat.test.StreamingTest
 
registerStream(JavaDStream<BinarySample>) - Method in class org.apache.spark.mllib.stat.test.StreamingTest
 
registerTempTable(String) - Method in class org.apache.spark.sql.DataFrame
Registers this DataFrame as a temporary table using the given name.
Regression() - Static method in class org.apache.spark.mllib.tree.configuration.Algo
 
RegressionEvaluator - Class in org.apache.spark.ml.evaluation
:: Experimental :: Evaluator for regression, which expects two input columns: prediction and label.
RegressionEvaluator(String) - Constructor for class org.apache.spark.ml.evaluation.RegressionEvaluator
 
RegressionEvaluator() - Constructor for class org.apache.spark.ml.evaluation.RegressionEvaluator
 
RegressionMetrics - Class in org.apache.spark.mllib.evaluation
Evaluator for regression.
RegressionMetrics(RDD<Tuple2<Object, Object>>) - Constructor for class org.apache.spark.mllib.evaluation.RegressionMetrics
 
RegressionModel<FeaturesType,M extends RegressionModel<FeaturesType,M>> - Class in org.apache.spark.ml.regression
:: DeveloperApi ::
RegressionModel() - Constructor for class org.apache.spark.ml.regression.RegressionModel
 
RegressionModel - Interface in org.apache.spark.mllib.regression
 
reindex() - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
reindex() - Method in class org.apache.spark.graphx.VertexRDD
Construct a new VertexRDD that is indexed by only the visible vertices.
RelationProvider - Interface in org.apache.spark.sql.sources
::DeveloperApi:: Implemented by objects that produce relations for a specific kind of data source.
relativeDirection(long) - Method in class org.apache.spark.graphx.Edge
Return the relative direction of the edge to the corresponding vertex.
remainder(Decimal) - Method in class org.apache.spark.sql.types.Decimal
 
remember(Duration) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Sets each DStreams in this context to remember RDDs it generated in the last given duration.
remember(Duration) - Method in class org.apache.spark.streaming.StreamingContext
Set each DStreams in this context to remember RDDs it generated in the last given duration.
rememberDuration() - Method in class org.apache.spark.streaming.dstream.DStream
 
remoteBlocksFetched() - Method in class org.apache.spark.status.api.v1.ShuffleReadMetricDistributions
 
remoteBlocksFetched() - Method in class org.apache.spark.status.api.v1.ShuffleReadMetrics
 
remoteBytesRead() - Method in class org.apache.spark.status.api.v1.ShuffleReadMetricDistributions
 
remoteBytesRead() - Method in class org.apache.spark.status.api.v1.ShuffleReadMetrics
 
remove(Param<T>) - Method in class org.apache.spark.ml.param.ParamMap
Removes a key from this map and returns its value associated previously as an option.
remove(String) - Method in class org.apache.spark.SparkConf
Remove a parameter from the configuration
remove() - Method in class org.apache.spark.streaming.State
Remove the state if it exists.
repartition(int) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Return a new RDD that has exactly numPartitions partitions.
repartition(int) - Method in class org.apache.spark.api.java.JavaPairRDD
Return a new RDD that has exactly numPartitions partitions.
repartition(int) - Method in class org.apache.spark.api.java.JavaRDD
Return a new RDD that has exactly numPartitions partitions.
repartition(int, Ordering<T>) - Method in class org.apache.spark.rdd.RDD
Return a new RDD that has exactly numPartitions partitions.
repartition(int, Column...) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame partitioned by the given partitioning expressions into numPartitions.
repartition(Column...) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame partitioned by the given partitioning expressions preserving the existing number of partitions.
repartition(int) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame that has exactly numPartitions partitions.
repartition(int, Seq<Column>) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame partitioned by the given partitioning expressions into numPartitions.
repartition(Seq<Column>) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame partitioned by the given partitioning expressions preserving the existing number of partitions.
repartition(int) - Method in class org.apache.spark.sql.Dataset
Returns a new Dataset that has exactly numPartitions partitions.
repartition(int) - Method in class org.apache.spark.streaming.api.java.JavaDStream
Return a new DStream with an increased or decreased level of parallelism.
repartition(int) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream with an increased or decreased level of parallelism.
repartition(int) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream with an increased or decreased level of parallelism.
repartitionAndSortWithinPartitions(Partitioner) - Method in class org.apache.spark.api.java.JavaPairRDD
Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.
repartitionAndSortWithinPartitions(Partitioner, Comparator<K>) - Method in class org.apache.spark.api.java.JavaPairRDD
Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.
repartitionAndSortWithinPartitions(Partitioner) - Method in class org.apache.spark.rdd.OrderedRDDFunctions
Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.
repeat(Column, int) - Static method in class org.apache.spark.sql.functions
Repeats a string column n times, and returns it as a new string column.
replace(String, Map<T, T>) - Method in class org.apache.spark.sql.DataFrameNaFunctions
Replaces values matching keys in replacement map with the corresponding values.
replace(String[], Map<T, T>) - Method in class org.apache.spark.sql.DataFrameNaFunctions
Replaces values matching keys in replacement map with the corresponding values.
replace(String, Map<T, T>) - Method in class org.apache.spark.sql.DataFrameNaFunctions
(Scala-specific) Replaces values matching keys in replacement map.
replace(Seq<String>, Map<T, T>) - Method in class org.apache.spark.sql.DataFrameNaFunctions
(Scala-specific) Replaces values matching keys in replacement map.
replicatedVertexView() - Method in class org.apache.spark.graphx.impl.GraphImpl
 
replication() - Method in class org.apache.spark.storage.StorageLevel
 
reportError(String, Throwable) - Method in class org.apache.spark.streaming.receiver.Receiver
Report exceptions in receiving data.
requestExecutors(int) - Method in class org.apache.spark.SparkContext
:: DeveloperApi :: Request an additional number of executors from the cluster manager.
reset() - Method in class org.apache.spark.storage.BufferReleasingInputStream
 
resetIterator() - Method in class org.apache.spark.rdd.PartitionCoalescer.LocationIterator
 
residuals() - Method in class org.apache.spark.ml.regression.LinearRegressionSummary
Residuals (label - predicted value)
resolve(String) - Method in class org.apache.spark.sql.DataFrame
 
resolvedTEncoder() - Method in class org.apache.spark.sql.Dataset
The encoder for this Dataset that has been resolved to its output schema.
restart(String) - Method in class org.apache.spark.streaming.receiver.Receiver
Restart the receiver.
restart(String, Throwable) - Method in class org.apache.spark.streaming.receiver.Receiver
Restart the receiver.
restart(String, Throwable, int) - Method in class org.apache.spark.streaming.receiver.Receiver
Restart the receiver.
Resubmitted - Class in org.apache.spark
:: DeveloperApi :: A ShuffleMapTask that completed successfully earlier, but we lost the executor before the stage completed.
Resubmitted() - Constructor for class org.apache.spark.Resubmitted
 
result(Duration, CanAwait) - Method in class org.apache.spark.ComplexFutureAction
 
result(Duration, CanAwait) - Method in interface org.apache.spark.FutureAction
Awaits and returns the result (of type T) of this action.
result(Duration, CanAwait) - Method in class org.apache.spark.SimpleFutureAction
 
resultSerializationTime() - Method in class org.apache.spark.status.api.v1.TaskMetricDistributions
 
resultSerializationTime() - Method in class org.apache.spark.status.api.v1.TaskMetrics
 
resultSetToObjectArray(ResultSet) - Static method in class org.apache.spark.rdd.JdbcRDD
 
resultSize() - Method in class org.apache.spark.status.api.v1.TaskMetricDistributions
 
resultSize() - Method in class org.apache.spark.status.api.v1.TaskMetrics
 
retainedJobs() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
retainedStages() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
retryWaitMs(SparkConf) - Static method in class org.apache.spark.util.RpcUtils
Returns the configured number of milliseconds to wait on each retry
ReturnStatementFinder - Class in org.apache.spark.util
 
ReturnStatementFinder() - Constructor for class org.apache.spark.util.ReturnStatementFinder
 
reverse() - Method in class org.apache.spark.graphx.EdgeDirection
Reverse the direction of an edge.
reverse() - Method in class org.apache.spark.graphx.EdgeRDD
Reverse all the edges in this RDD.
reverse() - Method in class org.apache.spark.graphx.Graph
Reverses all edges in the graph.
reverse() - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
 
reverse() - Method in class org.apache.spark.graphx.impl.GraphImpl
 
reverse(Column) - Static method in class org.apache.spark.sql.functions
Reverses the string column and returns it as a new string column.
reverseRoutingTables() - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
reverseRoutingTables() - Method in class org.apache.spark.graphx.VertexRDD
Returns a new VertexRDD reflecting a reversal of all edge directions in the corresponding EdgeRDD.
ReviveOffers - Class in org.apache.spark.scheduler.local
 
ReviveOffers() - Constructor for class org.apache.spark.scheduler.local.ReviveOffers
 
RFormula - Class in org.apache.spark.ml.feature
:: Experimental :: Implements the transforms required for fitting a dataset against an R model formula.
RFormula(String) - Constructor for class org.apache.spark.ml.feature.RFormula
 
RFormula() - Constructor for class org.apache.spark.ml.feature.RFormula
 
RFormulaModel - Class in org.apache.spark.ml.feature
:: Experimental :: A fitted RFormula.
RidgeRegressionModel - Class in org.apache.spark.mllib.regression
Regression model trained using RidgeRegression.
RidgeRegressionModel(Vector, double) - Constructor for class org.apache.spark.mllib.regression.RidgeRegressionModel
 
RidgeRegressionWithSGD - Class in org.apache.spark.mllib.regression
Train a regression model with L2-regularization using Stochastic Gradient Descent.
RidgeRegressionWithSGD() - Constructor for class org.apache.spark.mllib.regression.RidgeRegressionWithSGD
Construct a RidgeRegression object with default parameters: {stepSize: 1.0, numIterations: 100, regParam: 0.01, miniBatchFraction: 1.0}.
right() - Method in class org.apache.spark.sql.sources.And
 
right() - Method in class org.apache.spark.sql.sources.Or
 
rightCategories() - Method in class org.apache.spark.ml.tree.CategoricalSplit
Get sorted categories which split to the right
rightChild() - Method in class org.apache.spark.ml.tree.InternalNode
 
rightChildIndex(int) - Static method in class org.apache.spark.mllib.tree.model.Node
Return the index of the right child of this node.
rightImpurity() - Method in class org.apache.spark.mllib.tree.model.InformationGainStats
 
rightNode() - Method in class org.apache.spark.mllib.tree.model.Node
 
rightOuterJoin(JavaPairRDD<K, W>, Partitioner) - Method in class org.apache.spark.api.java.JavaPairRDD
Perform a right outer join of this and other.
rightOuterJoin(JavaPairRDD<K, W>) - Method in class org.apache.spark.api.java.JavaPairRDD
Perform a right outer join of this and other.
rightOuterJoin(JavaPairRDD<K, W>, int) - Method in class org.apache.spark.api.java.JavaPairRDD
Perform a right outer join of this and other.
rightOuterJoin(RDD<Tuple2<K, W>>, Partitioner) - Method in class org.apache.spark.rdd.PairRDDFunctions
Perform a right outer join of this and other.
rightOuterJoin(RDD<Tuple2<K, W>>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Perform a right outer join of this and other.
rightOuterJoin(RDD<Tuple2<K, W>>, int) - Method in class org.apache.spark.rdd.PairRDDFunctions
Perform a right outer join of this and other.
rightOuterJoin(JavaPairDStream<K, W>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream.
rightOuterJoin(JavaPairDStream<K, W>, int) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream.
rightOuterJoin(JavaPairDStream<K, W>, Partitioner) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream.
rightOuterJoin(DStream<Tuple2<K, W>>, ClassTag<W>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream.
rightOuterJoin(DStream<Tuple2<K, W>>, int, ClassTag<W>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream.
rightOuterJoin(DStream<Tuple2<K, W>>, Partitioner, ClassTag<W>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new DStream by applying 'right outer join' between RDDs of this DStream and other DStream.
rightPredict() - Method in class org.apache.spark.mllib.tree.model.InformationGainStats
 
rint(Column) - Static method in class org.apache.spark.sql.functions
Returns the double value that is closest in value to the argument and is equal to a mathematical integer.
rint(String) - Static method in class org.apache.spark.sql.functions
Returns the double value that is closest in value to the argument and is equal to a mathematical integer.
rlike(String) - Method in class org.apache.spark.sql.Column
SQL RLIKE expression (LIKE with Regex).
RMATa() - Static method in class org.apache.spark.graphx.util.GraphGenerators
 
RMATb() - Static method in class org.apache.spark.graphx.util.GraphGenerators
 
RMATc() - Static method in class org.apache.spark.graphx.util.GraphGenerators
 
RMATd() - Static method in class org.apache.spark.graphx.util.GraphGenerators
 
rmatGraph(SparkContext, int, int) - Static method in class org.apache.spark.graphx.util.GraphGenerators
A random graph generator using the R-MAT model, proposed in "R-MAT: A Recursive Model for Graph Mining" by Chakrabarti et al.
rnd() - Method in class org.apache.spark.rdd.PartitionCoalescer
 
roc() - Method in class org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
Returns the receiver operating characteristic (ROC) curve, which is an Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
roc() - Method in class org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
Returns the receiver operating characteristic (ROC) curve, which is an RDD of (false positive rate, true positive rate) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
rollup(Column...) - Method in class org.apache.spark.sql.DataFrame
Create a multi-dimensional rollup for the current DataFrame using the specified columns, so we can run aggregation on them.
rollup(String, String...) - Method in class org.apache.spark.sql.DataFrame
Create a multi-dimensional rollup for the current DataFrame using the specified columns, so we can run aggregation on them.
rollup(Seq<Column>) - Method in class org.apache.spark.sql.DataFrame
Create a multi-dimensional rollup for the current DataFrame using the specified columns, so we can run aggregation on them.
rollup(String, Seq<String>) - Method in class org.apache.spark.sql.DataFrame
Create a multi-dimensional rollup for the current DataFrame using the specified columns, so we can run aggregation on them.
root() - Method in class org.apache.spark.mllib.clustering.BisectingKMeansModel
 
rootMeanSquaredError() - Method in class org.apache.spark.ml.regression.LinearRegressionSummary
Returns the root mean squared error, which is defined as the square root of the mean squared error.
rootMeanSquaredError() - Method in class org.apache.spark.mllib.evaluation.RegressionMetrics
Returns the root mean squared error, which is defined as the square root of the mean squared error.
rootNode() - Method in class org.apache.spark.ml.classification.DecisionTreeClassificationModel
 
rootNode() - Method in class org.apache.spark.ml.regression.DecisionTreeRegressionModel
 
round(Column) - Static method in class org.apache.spark.sql.functions
Returns the value of the column e rounded to 0 decimal places.
round(Column, int) - Static method in class org.apache.spark.sql.functions
Round the value of e to scale decimal places if scale >= 0 or at integral part when scale < 0.
ROUND_CEILING() - Static method in class org.apache.spark.sql.types.Decimal
 
ROUND_FLOOR() - Static method in class org.apache.spark.sql.types.Decimal
 
ROUND_HALF_UP() - Static method in class org.apache.spark.sql.types.Decimal
 
Row - Interface in org.apache.spark.sql
Represents one row of output from a relational operator.
row_number() - Static method in class org.apache.spark.sql.functions
Window function: returns a sequential number starting at 1 within a window partition.
RowFactory - Class in org.apache.spark.sql
A factory class used to construct Row objects.
RowFactory() - Constructor for class org.apache.spark.sql.RowFactory
 
rowIndices() - Method in class org.apache.spark.mllib.linalg.SparseMatrix
 
RowMatrix - Class in org.apache.spark.mllib.linalg.distributed
Represents a row-oriented distributed Matrix with no meaningful row indices.
RowMatrix(RDD<Vector>, long, int) - Constructor for class org.apache.spark.mllib.linalg.distributed.RowMatrix
 
RowMatrix(RDD<Vector>) - Constructor for class org.apache.spark.mllib.linalg.distributed.RowMatrix
Alternative constructor leaving matrix dimensions to be determined automatically.
rowNumber() - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.6.0, replaced by row_number. This will be removed in Spark 2.0.
rows() - Method in class org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
 
rows() - Method in class org.apache.spark.mllib.linalg.distributed.RowMatrix
 
rowsBetween(long, long) - Method in class org.apache.spark.sql.expressions.WindowSpec
Defines the frame boundaries, from start (inclusive) to end (inclusive).
rowsPerBlock() - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
 
rpad(Column, int, String) - Static method in class org.apache.spark.sql.functions
Right-padded with pad to a length of len.
rpcEnv() - Method in class org.apache.spark.SparkEnv
 
RpcUtils - Class in org.apache.spark.util
 
RpcUtils() - Constructor for class org.apache.spark.util.RpcUtils
 
RRDD<T> - Class in org.apache.spark.api.r
An RDD that stores serialized R objects as Array[Byte].
RRDD(RDD<T>, byte[], String, String, byte[], Object[], ClassTag<T>) - Constructor for class org.apache.spark.api.r.RRDD
 
rtrim(Column) - Static method in class org.apache.spark.sql.functions
Trim the spaces from right end for the specified string value.
run(Function0<T>, ExecutionContext) - Method in class org.apache.spark.ComplexFutureAction
Executes some action enclosed in the closure.
run(Graph<VD, ED>, ClassTag<VD>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.lib.ConnectedComponents
Compute the connected component membership of each vertex and return a graph with the vertex value containing the lowest vertex id in the connected component containing that vertex.
run(Graph<VD, ED>, int, ClassTag<ED>) - Static method in class org.apache.spark.graphx.lib.LabelPropagation
Run static Label Propagation for detecting communities in networks.
run(Graph<VD, ED>, int, double, ClassTag<VD>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.lib.PageRank
Run PageRank for a fixed number of iterations returning a graph with vertex attributes containing the PageRank and edge attributes the normalized edge weight.
run(Graph<VD, ED>, Seq<Object>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.lib.ShortestPaths
Computes shortest paths to the given set of landmark vertices.
run(Graph<VD, ED>, int, ClassTag<VD>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.lib.StronglyConnectedComponents
Compute the strongly connected component (SCC) of each vertex and return a graph with the vertex value containing the lowest vertex id in the SCC containing that vertex.
run(RDD<Edge<Object>>, SVDPlusPlus.Conf) - Static method in class org.apache.spark.graphx.lib.SVDPlusPlus
Implement SVD++ based on "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model", available at http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf.
run(Graph<VD, ED>, ClassTag<VD>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.lib.TriangleCount
 
run(RDD<LabeledPoint>) - Method in class org.apache.spark.mllib.classification.NaiveBayes
 
run(RDD<Vector>) - Method in class org.apache.spark.mllib.clustering.BisectingKMeans
Runs the bisecting k-means algorithm.
run(JavaRDD<Vector>) - Method in class org.apache.spark.mllib.clustering.BisectingKMeans
Java-friendly version of run().
run(RDD<Vector>) - Method in class org.apache.spark.mllib.clustering.GaussianMixture
Perform expectation maximization
run(JavaRDD<Vector>) - Method in class org.apache.spark.mllib.clustering.GaussianMixture
Java-friendly version of run()
run(RDD<Vector>) - Method in class org.apache.spark.mllib.clustering.KMeans
Train a K-means model on the given set of points; data should be cached for high performance, because this is an iterative algorithm.
run(RDD<Tuple2<Object, Vector>>) - Method in class org.apache.spark.mllib.clustering.LDA
Learn an LDA model using the given dataset.
run(JavaPairRDD<Long, Vector>) - Method in class org.apache.spark.mllib.clustering.LDA
Java-friendly version of run()
run(Graph<Object, Object>) - Method in class org.apache.spark.mllib.clustering.PowerIterationClustering
Run the PIC algorithm on Graph.
run(RDD<Tuple3<Object, Object, Object>>) - Method in class org.apache.spark.mllib.clustering.PowerIterationClustering
Run the PIC algorithm.
run(JavaRDD<Tuple3<Long, Long, Double>>) - Method in class org.apache.spark.mllib.clustering.PowerIterationClustering
A Java-friendly version of PowerIterationClustering.run.
run(RDD<FPGrowth.FreqItemset<Item>>, ClassTag<Item>) - Method in class org.apache.spark.mllib.fpm.AssociationRules
Computes the association rules with confidence above minConfidence.
run(JavaRDD<FPGrowth.FreqItemset<Item>>) - Method in class org.apache.spark.mllib.fpm.AssociationRules
Java-friendly version of run.
run(RDD<Object>, ClassTag<Item>) - Method in class org.apache.spark.mllib.fpm.FPGrowth
Computes an FP-Growth model that contains frequent itemsets.
run(JavaRDD<Basket>) - Method in class org.apache.spark.mllib.fpm.FPGrowth
Java-friendly version of run.
run(RDD<Object[]>, ClassTag<Item>) - Method in class org.apache.spark.mllib.fpm.PrefixSpan
Finds the complete set of frequent sequential patterns in the input sequences of itemsets.
run(JavaRDD<Sequence>) - Method in class org.apache.spark.mllib.fpm.PrefixSpan
A Java-friendly version of run() that reads sequences from a JavaRDD and returns frequent sequences in a PrefixSpanModel.
run(RDD<Rating>) - Method in class org.apache.spark.mllib.recommendation.ALS
 
run(JavaRDD<Rating>) - Method in class org.apache.spark.mllib.recommendation.ALS
 
run(RDD<LabeledPoint>) - Method in class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.
run(RDD<LabeledPoint>, Vector) - Method in class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.
run(RDD<Tuple3<Object, Object, Object>>) - Method in class org.apache.spark.mllib.regression.IsotonicRegression
 
run(JavaRDD<Tuple3<Double, Double, Double>>) - Method in class org.apache.spark.mllib.regression.IsotonicRegression
 
run(RDD<LabeledPoint>) - Method in class org.apache.spark.mllib.tree.DecisionTree
Method to train a decision tree model over an RDD
run(RDD<LabeledPoint>) - Method in class org.apache.spark.mllib.tree.GradientBoostedTrees
Method to train a gradient boosting model
run(JavaRDD<LabeledPoint>) - Method in class org.apache.spark.mllib.tree.GradientBoostedTrees
Java-friendly API for org.apache.spark.mllib.tree.GradientBoostedTrees!#run.
run(RDD<LabeledPoint>) - Method in class org.apache.spark.mllib.tree.RandomForest
Method to train a decision tree model over an RDD
run() - Method in class org.apache.spark.rdd.PartitionCoalescer
Runs the packing algorithm and returns an array of PartitionGroups that if possible are load balanced and grouped by locality
run() - Method in class org.apache.spark.sql.hive.execution.ScriptTransformationWriterThread
 
run() - Method in class org.apache.spark.util.SparkShutdownHook
 
runApproximateJob(RDD<T>, Function2<TaskContext, Iterator<T>, U>, <any>, long) - Method in class org.apache.spark.SparkContext
:: DeveloperApi :: Run a job that can return approximate results.
runJob(RDD<T>, Function1<Iterator<T>, U>, Seq<Object>, Function2<Object, U, BoxedUnit>, Function0<R>) - Method in class org.apache.spark.ComplexFutureAction
Runs a Spark job.
runJob(RDD<T>, Function2<TaskContext, Iterator<T>, U>, Seq<Object>, Function2<Object, U, BoxedUnit>, ClassTag<U>) - Method in class org.apache.spark.SparkContext
Run a function on a given set of partitions in an RDD and pass the results to the given handler function.
runJob(RDD<T>, Function2<TaskContext, Iterator<T>, U>, Seq<Object>, ClassTag<U>) - Method in class org.apache.spark.SparkContext
Run a function on a given set of partitions in an RDD and return the results as an array.
runJob(RDD<T>, Function1<Iterator<T>, U>, Seq<Object>, ClassTag<U>) - Method in class org.apache.spark.SparkContext
Run a job on a given set of partitions of an RDD, but take a function of type Iterator[T] => U instead of (TaskContext, Iterator[T]) => U.
runJob(RDD<T>, Function2<TaskContext, Iterator<T>, U>, Seq<Object>, boolean, Function2<Object, U, BoxedUnit>, ClassTag<U>) - Method in class org.apache.spark.SparkContext
Run a function on a given set of partitions in an RDD and pass the results to the given handler function.
runJob(RDD<T>, Function2<TaskContext, Iterator<T>, U>, Seq<Object>, boolean, ClassTag<U>) - Method in class org.apache.spark.SparkContext
Run a function on a given set of partitions in an RDD and return the results as an array.
runJob(RDD<T>, Function1<Iterator<T>, U>, Seq<Object>, boolean, ClassTag<U>) - Method in class org.apache.spark.SparkContext
Run a job on a given set of partitions of an RDD, but take a function of type Iterator[T] => U instead of (TaskContext, Iterator[T]) => U.
runJob(RDD<T>, Function2<TaskContext, Iterator<T>, U>, ClassTag<U>) - Method in class org.apache.spark.SparkContext
Run a job on all partitions in an RDD and return the results in an array.
runJob(RDD<T>, Function1<Iterator<T>, U>, ClassTag<U>) - Method in class org.apache.spark.SparkContext
Run a job on all partitions in an RDD and return the results in an array.
runJob(RDD<T>, Function2<TaskContext, Iterator<T>, U>, Function2<Object, U, BoxedUnit>, ClassTag<U>) - Method in class org.apache.spark.SparkContext
Run a job on all partitions in an RDD and pass the results to a handler function.
runJob(RDD<T>, Function1<Iterator<T>, U>, Function2<Object, U, BoxedUnit>, ClassTag<U>) - Method in class org.apache.spark.SparkContext
Run a job on all partitions in an RDD and pass the results to a handler function.
runLBFGS(RDD<Tuple2<Object, Vector>>, Gradient, Updater, int, double, int, double, Vector) - Static method in class org.apache.spark.mllib.optimization.LBFGS
Run Limited-memory BFGS (L-BFGS) in parallel.
runMiniBatchSGD(RDD<Tuple2<Object, Vector>>, Gradient, Updater, double, int, double, double, Vector, double) - Static method in class org.apache.spark.mllib.optimization.GradientDescent
Run stochastic gradient descent (SGD) in parallel using mini batches.
runMiniBatchSGD(RDD<Tuple2<Object, Vector>>, Gradient, Updater, double, int, double, double, Vector) - Static method in class org.apache.spark.mllib.optimization.GradientDescent
Alias of runMiniBatchSGD with convergenceTol set to default value of 0.001.
running() - Method in class org.apache.spark.scheduler.TaskInfo
 
runningLocally() - Method in class org.apache.spark.TaskContext
 
runSqlHive(String) - Method in class org.apache.spark.sql.hive.HiveContext
 
runSVDPlusPlus(RDD<Edge<Object>>, SVDPlusPlus.Conf) - Static method in class org.apache.spark.graphx.lib.SVDPlusPlus
This method is now replaced by the updated version of run() and returns exactly the same result.
RuntimePercentage - Class in org.apache.spark.scheduler
 
RuntimePercentage(double, Option<Object>, double) - Constructor for class org.apache.spark.scheduler.RuntimePercentage
 
runUntilConvergence(Graph<VD, ED>, double, double, ClassTag<VD>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.lib.PageRank
Run a dynamic version of PageRank returning a graph with vertex attributes containing the PageRank and edge attributes containing the normalized edge weight.
runUntilConvergenceWithOptions(Graph<VD, ED>, double, double, Option<Object>, ClassTag<VD>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.lib.PageRank
Run a dynamic version of PageRank returning a graph with vertex attributes containing the PageRank and edge attributes containing the normalized edge weight.
runWithOptions(Graph<VD, ED>, int, double, Option<Object>, ClassTag<VD>, ClassTag<ED>) - Static method in class org.apache.spark.graphx.lib.PageRank
Run PageRank for a fixed number of iterations returning a graph with vertex attributes containing the PageRank and edge attributes the normalized edge weight.
runWithValidation(RDD<LabeledPoint>, RDD<LabeledPoint>) - Method in class org.apache.spark.mllib.tree.GradientBoostedTrees
Method to validate a gradient boosting model
runWithValidation(JavaRDD<LabeledPoint>, JavaRDD<LabeledPoint>) - Method in class org.apache.spark.mllib.tree.GradientBoostedTrees
Java-friendly API for org.apache.spark.mllib.tree.GradientBoostedTrees!#runWithValidation.

S

s() - Method in class org.apache.spark.mllib.linalg.SingularValueDecomposition
 
sample(boolean, Double) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Return a sampled subset of this RDD.
sample(boolean, Double, long) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Return a sampled subset of this RDD.
sample(boolean, double) - Method in class org.apache.spark.api.java.JavaPairRDD
Return a sampled subset of this RDD.
sample(boolean, double, long) - Method in class org.apache.spark.api.java.JavaPairRDD
Return a sampled subset of this RDD.
sample(boolean, double) - Method in class org.apache.spark.api.java.JavaRDD
Return a sampled subset of this RDD.
sample(boolean, double, long) - Method in class org.apache.spark.api.java.JavaRDD
Return a sampled subset of this RDD.
sample(boolean, double, long) - Method in class org.apache.spark.rdd.RDD
Return a sampled subset of this RDD.
sample(boolean, double, long) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame by sampling a fraction of rows.
sample(boolean, double) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame by sampling a fraction of rows, using a random seed.
sample(boolean, double, long) - Method in class org.apache.spark.sql.Dataset
Returns a new Dataset by sampling a fraction of records.
sample(boolean, double) - Method in class org.apache.spark.sql.Dataset
Returns a new Dataset by sampling a fraction of records, using a random seed.
sample(Iterator<T>) - Method in class org.apache.spark.util.random.BernoulliCellSampler
 
sample(Iterator<T>) - Method in class org.apache.spark.util.random.BernoulliSampler
 
sample(Iterator<T>) - Method in class org.apache.spark.util.random.PoissonSampler
 
sample(Iterator<T>) - Method in interface org.apache.spark.util.random.RandomSampler
take a random sample
sampleBy(String, Map<T, Object>, long) - Method in class org.apache.spark.sql.DataFrameStatFunctions
Returns a stratified sample without replacement based on the fraction given on each stratum.
sampleBy(String, Map<T, Double>, long) - Method in class org.apache.spark.sql.DataFrameStatFunctions
Returns a stratified sample without replacement based on the fraction given on each stratum.
sampleByKey(boolean, Map<K, Object>, long) - Method in class org.apache.spark.api.java.JavaPairRDD
Return a subset of this RDD sampled by key (via stratified sampling).
sampleByKey(boolean, Map<K, Object>) - Method in class org.apache.spark.api.java.JavaPairRDD
Return a subset of this RDD sampled by key (via stratified sampling).
sampleByKey(boolean, Map<K, Object>, long) - Method in class org.apache.spark.rdd.PairRDDFunctions
Return a subset of this RDD sampled by key (via stratified sampling).
sampleByKeyExact(boolean, Map<K, Object>, long) - Method in class org.apache.spark.api.java.JavaPairRDD
Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
sampleByKeyExact(boolean, Map<K, Object>) - Method in class org.apache.spark.api.java.JavaPairRDD
Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
sampleByKeyExact(boolean, Map<K, Object>, long) - Method in class org.apache.spark.rdd.PairRDDFunctions
Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
sampleStdev() - Method in class org.apache.spark.api.java.JavaDoubleRDD
Compute the sample standard deviation of this RDD's elements (which corrects for bias in estimating the standard deviation by dividing by N-1 instead of N).
sampleStdev() - Method in class org.apache.spark.rdd.DoubleRDDFunctions
Compute the sample standard deviation of this RDD's elements (which corrects for bias in estimating the standard deviation by dividing by N-1 instead of N).
sampleStdev() - Method in class org.apache.spark.util.StatCounter
Return the sample standard deviation of the values, which corrects for bias in estimating the variance by dividing by N-1 instead of N.
sampleVariance() - Method in class org.apache.spark.api.java.JavaDoubleRDD
Compute the sample variance of this RDD's elements (which corrects for bias in estimating the standard variance by dividing by N-1 instead of N).
sampleVariance() - Method in class org.apache.spark.rdd.DoubleRDDFunctions
Compute the sample variance of this RDD's elements (which corrects for bias in estimating the variance by dividing by N-1 instead of N).
sampleVariance() - Method in class org.apache.spark.util.StatCounter
Return the sample variance, which corrects for bias in estimating the variance by dividing by N-1 instead of N.
save(String) - Method in interface org.apache.spark.ml.util.MLWritable
Saves this ML instance to the input path, a shortcut of write.save(path).
save(String) - Method in class org.apache.spark.ml.util.MLWriter
Saves the ML instances to the input path.
save(SparkContext, String) - Method in class org.apache.spark.mllib.classification.LogisticRegressionModel
 
save(SparkContext, String) - Method in class org.apache.spark.mllib.classification.NaiveBayesModel
 
save(SparkContext, String) - Method in class org.apache.spark.mllib.classification.SVMModel
 
save(SparkContext, String) - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
Java-friendly version of topicDistributions
save(SparkContext, String) - Method in class org.apache.spark.mllib.clustering.GaussianMixtureModel
 
save(SparkContext, String) - Method in class org.apache.spark.mllib.clustering.KMeansModel
 
save(SparkContext, String) - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
 
save(SparkContext, String) - Method in class org.apache.spark.mllib.clustering.PowerIterationClusteringModel
 
save(SparkContext, String) - Method in class org.apache.spark.mllib.feature.ChiSqSelectorModel
 
save(SparkContext, String) - Method in class org.apache.spark.mllib.feature.Word2VecModel
 
save(SparkContext, String) - Method in class org.apache.spark.mllib.recommendation.MatrixFactorizationModel
Save this model to the given path.
save(SparkContext, String) - Method in class org.apache.spark.mllib.regression.IsotonicRegressionModel
 
save(SparkContext, String) - Method in class org.apache.spark.mllib.regression.LassoModel
 
save(SparkContext, String) - Method in class org.apache.spark.mllib.regression.LinearRegressionModel
 
save(SparkContext, String) - Method in class org.apache.spark.mllib.regression.RidgeRegressionModel
 
save(SparkContext, String) - Method in class org.apache.spark.mllib.tree.model.DecisionTreeModel
 
save(SparkContext, String) - Method in class org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
 
save(SparkContext, String) - Method in class org.apache.spark.mllib.tree.model.RandomForestModel
 
save(SparkContext, String) - Method in interface org.apache.spark.mllib.util.Saveable
Save this model to the given path.
save(String) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().save(path). This will be removed in Spark 2.0.
save(String, SaveMode) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().mode(mode).save(path). This will be removed in Spark 2.0.
save(String, String) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().format(source).save(path). This will be removed in Spark 2.0.
save(String, String, SaveMode) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().format(source).mode(mode).save(path). This will be removed in Spark 2.0.
save(String, SaveMode, Map<String, String>) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().format(source).mode(mode).options(options).save(path). This will be removed in Spark 2.0.
save(String, SaveMode, Map<String, String>) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().format(source).mode(mode).options(options).save(path). This will be removed in Spark 2.0.
save(String) - Method in class org.apache.spark.sql.DataFrameWriter
Saves the content of the DataFrame at the specified path.
save() - Method in class org.apache.spark.sql.DataFrameWriter
Saves the content of the DataFrame as the specified table.
Saveable - Interface in org.apache.spark.mllib.util
:: DeveloperApi ::
saveAsHadoopDataset(JobConf) - Method in class org.apache.spark.api.java.JavaPairRDD
Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for that storage system.
saveAsHadoopDataset(JobConf) - Method in class org.apache.spark.rdd.PairRDDFunctions
Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for that storage system.
saveAsHadoopFile(String, Class<?>, Class<?>, Class<F>, JobConf) - Method in class org.apache.spark.api.java.JavaPairRDD
Output the RDD to any Hadoop-supported file system.
saveAsHadoopFile(String, Class<?>, Class<?>, Class<F>) - Method in class org.apache.spark.api.java.JavaPairRDD
Output the RDD to any Hadoop-supported file system.
saveAsHadoopFile(String, Class<?>, Class<?>, Class<F>, Class<? extends CompressionCodec>) - Method in class org.apache.spark.api.java.JavaPairRDD
Output the RDD to any Hadoop-supported file system, compressing with the supplied codec.
saveAsHadoopFile(String, ClassTag<F>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD.
saveAsHadoopFile(String, Class<? extends CompressionCodec>, ClassTag<F>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD.
saveAsHadoopFile(String, Class<?>, Class<?>, Class<? extends OutputFormat<?, ?>>, Class<? extends CompressionCodec>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD.
saveAsHadoopFile(String, Class<?>, Class<?>, Class<? extends OutputFormat<?, ?>>, JobConf, Option<Class<? extends CompressionCodec>>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class supporting the key and value types K and V in this RDD.
saveAsHadoopFiles(String, String) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Save each RDD in this DStream as a Hadoop file.
saveAsHadoopFiles(String, String, Class<?>, Class<?>, Class<F>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Save each RDD in this DStream as a Hadoop file.
saveAsHadoopFiles(String, String, Class<?>, Class<?>, Class<F>, JobConf) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Save each RDD in this DStream as a Hadoop file.
saveAsHadoopFiles(String, String, ClassTag<F>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Save each RDD in this DStream as a Hadoop file.
saveAsHadoopFiles(String, String, Class<?>, Class<?>, Class<? extends OutputFormat<?, ?>>, JobConf) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Save each RDD in this DStream as a Hadoop file.
saveAsLibSVMFile(RDD<LabeledPoint>, String) - Static method in class org.apache.spark.mllib.util.MLUtils
Save labeled data in LIBSVM format.
saveAsNewAPIHadoopDataset(Configuration) - Method in class org.apache.spark.api.java.JavaPairRDD
Output the RDD to any Hadoop-supported storage system, using a Configuration object for that storage system.
saveAsNewAPIHadoopDataset(Configuration) - Method in class org.apache.spark.rdd.PairRDDFunctions
Output the RDD to any Hadoop-supported storage system with new Hadoop API, using a Hadoop Configuration object for that storage system.
saveAsNewAPIHadoopFile(String, Class<?>, Class<?>, Class<F>, Configuration) - Method in class org.apache.spark.api.java.JavaPairRDD
Output the RDD to any Hadoop-supported file system.
saveAsNewAPIHadoopFile(String, Class<?>, Class<?>, Class<F>) - Method in class org.apache.spark.api.java.JavaPairRDD
Output the RDD to any Hadoop-supported file system.
saveAsNewAPIHadoopFile(String, ClassTag<F>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Output the RDD to any Hadoop-supported file system, using a new Hadoop API OutputFormat (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.
saveAsNewAPIHadoopFile(String, Class<?>, Class<?>, Class<? extends OutputFormat<?, ?>>, Configuration) - Method in class org.apache.spark.rdd.PairRDDFunctions
Output the RDD to any Hadoop-supported file system, using a new Hadoop API OutputFormat (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.
saveAsNewAPIHadoopFiles(String, String) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Save each RDD in this DStream as a Hadoop file.
saveAsNewAPIHadoopFiles(String, String, Class<?>, Class<?>, Class<F>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Save each RDD in this DStream as a Hadoop file.
saveAsNewAPIHadoopFiles(String, String, Class<?>, Class<?>, Class<F>, Configuration) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Save each RDD in this DStream as a Hadoop file.
saveAsNewAPIHadoopFiles(String, String, ClassTag<F>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Save each RDD in this DStream as a Hadoop file.
saveAsNewAPIHadoopFiles(String, String, Class<?>, Class<?>, Class<? extends OutputFormat<?, ?>>, Configuration) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Save each RDD in this DStream as a Hadoop file.
saveAsObjectFile(String) - Method in interface org.apache.spark.api.java.JavaRDDLike
Save this RDD as a SequenceFile of serialized objects.
saveAsObjectFile(String) - Method in class org.apache.spark.rdd.RDD
Save this RDD as a SequenceFile of serialized objects.
saveAsObjectFiles(String, String) - Method in class org.apache.spark.streaming.dstream.DStream
Save each RDD in this DStream as a Sequence file of serialized objects.
saveAsParquetFile(String) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().parquet(). This will be removed in Spark 2.0.
saveAsSequenceFile(String, Option<Class<? extends CompressionCodec>>) - Method in class org.apache.spark.rdd.SequenceFileRDDFunctions
Output the RDD as a Hadoop SequenceFile using the Writable types we infer from the RDD's key and value types.
saveAsTable(String) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().saveAsTable(tableName). This will be removed in Spark 2.0.
saveAsTable(String, SaveMode) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().mode(mode).saveAsTable(tableName). This will be removed in Spark 2.0.
saveAsTable(String, String) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().format(source).saveAsTable(tableName). This will be removed in Spark 2.0.
saveAsTable(String, String, SaveMode) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().mode(mode).saveAsTable(tableName). This will be removed in Spark 2.0.
saveAsTable(String, String, SaveMode, Map<String, String>) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().format(source).mode(mode).options(options).saveAsTable(tableName). This will be removed in Spark 2.0.
saveAsTable(String, String, SaveMode, Map<String, String>) - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.4.0, replaced by write().format(source).mode(mode).options(options).saveAsTable(tableName). This will be removed in Spark 2.0.
saveAsTable(String) - Method in class org.apache.spark.sql.DataFrameWriter
Saves the content of the DataFrame as the specified table.
saveAsTextFile(String) - Method in interface org.apache.spark.api.java.JavaRDDLike
Save this RDD as a text file, using string representations of elements.
saveAsTextFile(String, Class<? extends CompressionCodec>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Save this RDD as a compressed text file, using string representations of elements.
saveAsTextFile(String) - Method in class org.apache.spark.rdd.RDD
Save this RDD as a text file, using string representations of elements.
saveAsTextFile(String, Class<? extends CompressionCodec>) - Method in class org.apache.spark.rdd.RDD
Save this RDD as a compressed text file, using string representations of elements.
saveAsTextFiles(String, String) - Method in class org.apache.spark.streaming.dstream.DStream
Save each RDD in this DStream as at text file, using string representation of elements.
saveImpl(String) - Method in class org.apache.spark.ml.util.MLWriter
save() handles overwriting and then calls this method.
saveLabeledData(RDD<LabeledPoint>, String) - Static method in class org.apache.spark.mllib.util.MLUtils
SaveMode - Enum in org.apache.spark.sql
SaveMode is used to specify the expected behavior of saving a DataFrame to a data source.
sc() - Method in class org.apache.spark.api.java.JavaSparkContext
 
sc() - Method in class org.apache.spark.sql.SQLContext.implicits$.StringToColumn
 
sc() - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Deprecated.
As of 0.9.0, replaced by sparkContext
sc() - Method in class org.apache.spark.streaming.StreamingContext
 
scalaIntToJavaLong(DStream<Object>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
 
scalaToJavaLong(JavaPairDStream<K, Object>, ClassTag<K>) - Static method in class org.apache.spark.streaming.api.java.JavaPairDStream
 
scale() - Method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
scale() - Method in class org.apache.spark.mllib.random.GammaGenerator
 
scale() - Method in class org.apache.spark.sql.types.Decimal
 
scale() - Method in class org.apache.spark.sql.types.DecimalType
 
scale() - Method in class org.apache.spark.sql.types.PrecisionInfo
 
scalingVec() - Method in class org.apache.spark.ml.feature.ElementwiseProduct
the vector to multiply with input vectors
scalingVec() - Method in class org.apache.spark.mllib.feature.ElementwiseProduct
 
scheduler() - Method in class org.apache.spark.streaming.StreamingContext
 
schedulingDelay() - Method in class org.apache.spark.streaming.scheduler.BatchInfo
Time taken for the first job of this batch to start processing from the time this batch was submitted to the streaming scheduler.
SchedulingMode - Class in org.apache.spark.scheduler
"FAIR" and "FIFO" determines which policy is used to order tasks amongst a Schedulable's sub-queues "NONE" is used when the a Schedulable has no sub-queues.
SchedulingMode() - Constructor for class org.apache.spark.scheduler.SchedulingMode
 
schedulingMode() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
schedulingPool() - Method in class org.apache.spark.status.api.v1.StageData
 
schema() - Method in class org.apache.spark.sql.DataFrame
Returns the schema of this DataFrame.
schema(StructType) - Method in class org.apache.spark.sql.DataFrameReader
Specifies the input schema.
schema() - Method in class org.apache.spark.sql.Dataset
Returns the schema of the encoded form of the objects in this Dataset.
schema() - Method in interface org.apache.spark.sql.Encoder
Returns the schema of encoding this type of object as a Row.
schema() - Method in interface org.apache.spark.sql.Row
Schema for the row.
schema() - Method in class org.apache.spark.sql.sources.BaseRelation
 
schema() - Method in class org.apache.spark.sql.sources.HadoopFsRelation
Schema of this relation.
SchemaRelationProvider - Interface in org.apache.spark.sql.sources
::DeveloperApi:: Implemented by objects that produce relations for a specific kind of data source with a given schema.
scope() - Method in class org.apache.spark.rdd.RDD
The scope associated with the operation that created this RDD.
scope() - Method in class org.apache.spark.storage.RDDInfo
 
scoreAndLabels() - Method in class org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
 
ScriptTransformationWriterThread - Class in org.apache.spark.sql.hive.execution
 
ScriptTransformationWriterThread(Iterator<InternalRow>, Seq<DataType>, org.apache.spark.sql.catalyst.expressions.Projection, AbstractSerDe, ObjectInspector, HiveScriptIOSchema, OutputStream, Process, org.apache.spark.util.CircularBuffer, TaskContext, Configuration) - Constructor for class org.apache.spark.sql.hive.execution.ScriptTransformationWriterThread
 
second(Column) - Static method in class org.apache.spark.sql.functions
Extracts the seconds as an integer from a given date/timestamp/string.
seconds() - Static method in class org.apache.spark.scheduler.StatsReportListener
 
seconds(long) - Static method in class org.apache.spark.streaming.Durations
 
Seconds - Class in org.apache.spark.streaming
Helper object that creates instance of Duration representing a given number of seconds.
Seconds() - Constructor for class org.apache.spark.streaming.Seconds
 
securityManager() - Method in class org.apache.spark.SparkEnv
 
select(Column...) - Method in class org.apache.spark.sql.DataFrame
Selects a set of column based expressions.
select(String, String...) - Method in class org.apache.spark.sql.DataFrame
Selects a set of columns.
select(Seq<Column>) - Method in class org.apache.spark.sql.DataFrame
Selects a set of column based expressions.
select(String, Seq<String>) - Method in class org.apache.spark.sql.DataFrame
Selects a set of columns.
select(Column...) - Method in class org.apache.spark.sql.Dataset
Returns a new DataFrame by selecting a set of column based expressions.
select(Seq<Column>) - Method in class org.apache.spark.sql.Dataset
Returns a new DataFrame by selecting a set of column based expressions.
select(TypedColumn<T, U1>, Encoder<U1>) - Method in class org.apache.spark.sql.Dataset
Returns a new Dataset by computing the given Column expression for each element.
select(TypedColumn<T, U1>, TypedColumn<T, U2>) - Method in class org.apache.spark.sql.Dataset
Returns a new Dataset by computing the given Column expressions for each element.
select(TypedColumn<T, U1>, TypedColumn<T, U2>, TypedColumn<T, U3>) - Method in class org.apache.spark.sql.Dataset
Returns a new Dataset by computing the given Column expressions for each element.
select(TypedColumn<T, U1>, TypedColumn<T, U2>, TypedColumn<T, U3>, TypedColumn<T, U4>) - Method in class org.apache.spark.sql.Dataset
Returns a new Dataset by computing the given Column expressions for each element.
select(TypedColumn<T, U1>, TypedColumn<T, U2>, TypedColumn<T, U3>, TypedColumn<T, U4>, TypedColumn<T, U5>) - Method in class org.apache.spark.sql.Dataset
Returns a new Dataset by computing the given Column expressions for each element.
selectedFeatures() - Method in class org.apache.spark.ml.feature.ChiSqSelectorModel
 
selectedFeatures() - Method in class org.apache.spark.mllib.feature.ChiSqSelectorModel
 
selectExpr(String...) - Method in class org.apache.spark.sql.DataFrame
Selects a set of SQL expressions.
selectExpr(Seq<String>) - Method in class org.apache.spark.sql.DataFrame
Selects a set of SQL expressions.
selectUntyped(Seq<TypedColumn<?, ?>>) - Method in class org.apache.spark.sql.Dataset
Internal helper function for building typed selects that return tuples.
sendToDst(A) - Method in class org.apache.spark.graphx.EdgeContext
Sends a message to the destination vertex.
sendToDst(A) - Method in class org.apache.spark.graphx.impl.AggregatingEdgeContext
 
sendToSrc(A) - Method in class org.apache.spark.graphx.EdgeContext
Sends a message to the source vertex.
sendToSrc(A) - Method in class org.apache.spark.graphx.impl.AggregatingEdgeContext
 
sequence() - Method in class org.apache.spark.mllib.fpm.PrefixSpan.FreqSequence
 
sequenceFile(String, Class<K>, Class<V>, int) - Method in class org.apache.spark.api.java.JavaSparkContext
Get an RDD for a Hadoop SequenceFile with given key and value types.
sequenceFile(String, Class<K>, Class<V>) - Method in class org.apache.spark.api.java.JavaSparkContext
Get an RDD for a Hadoop SequenceFile.
sequenceFile(String, Class<K>, Class<V>, int) - Method in class org.apache.spark.SparkContext
Get an RDD for a Hadoop SequenceFile with given key and value types.
sequenceFile(String, Class<K>, Class<V>) - Method in class org.apache.spark.SparkContext
Get an RDD for a Hadoop SequenceFile with given key and value types.
sequenceFile(String, int, ClassTag<K>, ClassTag<V>, Function0<WritableConverter<K>>, Function0<WritableConverter<V>>) - Method in class org.apache.spark.SparkContext
Version of sequenceFile() for types implicitly convertible to Writables through a WritableConverter.
SequenceFileRDDFunctions<K,V> - Class in org.apache.spark.rdd
Extra functions available on RDDs of (key, value) pairs to create a Hadoop SequenceFile, through an implicit conversion.
SequenceFileRDDFunctions(RDD<Tuple2<K, V>>, Class<? extends Writable>, Class<? extends Writable>, Function1<K, Writable>, ClassTag<K>, Function1<V, Writable>, ClassTag<V>) - Constructor for class org.apache.spark.rdd.SequenceFileRDDFunctions
 
SequenceFileRDDFunctions(RDD<Tuple2<K, V>>, Function1<K, Writable>, ClassTag<K>, Function1<V, Writable>, ClassTag<V>) - Constructor for class org.apache.spark.rdd.SequenceFileRDDFunctions
 
SerializableWritable<T extends org.apache.hadoop.io.Writable> - Class in org.apache.spark
 
SerializableWritable(T) - Constructor for class org.apache.spark.SerializableWritable
 
SerializationStream - Class in org.apache.spark.serializer
:: DeveloperApi :: A stream for writing serialized objects.
SerializationStream() - Constructor for class org.apache.spark.serializer.SerializationStream
 
serialize(Object) - Method in class org.apache.spark.mllib.linalg.VectorUDT
 
serialize(T, ClassTag<T>) - Method in class org.apache.spark.serializer.DummySerializerInstance
 
serialize(T, ClassTag<T>) - Method in class org.apache.spark.serializer.SerializerInstance
 
serialize(Object) - Method in class org.apache.spark.sql.types.UserDefinedType
Convert the user type to a SQL datum
serializedData() - Method in class org.apache.spark.scheduler.local.StatusUpdate
 
serializedPyClass() - Method in class org.apache.spark.sql.types.UserDefinedType
Serialized Python UDT class, if exists.
Serializer - Class in org.apache.spark.serializer
:: DeveloperApi :: A serializer.
Serializer() - Constructor for class org.apache.spark.serializer.Serializer
 
serializer() - Method in class org.apache.spark.ShuffleDependency
 
serializer() - Method in class org.apache.spark.SparkEnv
 
SerializerInstance - Class in org.apache.spark.serializer
:: DeveloperApi :: An instance of a serializer, for use by one thread at a time.
SerializerInstance() - Constructor for class org.apache.spark.serializer.SerializerInstance
 
serializeStream(OutputStream) - Method in class org.apache.spark.serializer.DummySerializerInstance
 
serializeStream(OutputStream) - Method in class org.apache.spark.serializer.SerializerInstance
 
set(Edge<ED>) - Method in class org.apache.spark.graphx.EdgeTriplet
Set the edge properties of this triplet.
set(long, long, int, int, VD, VD, ED) - Method in class org.apache.spark.graphx.impl.AggregatingEdgeContext
 
set(Param<T>, T) - Method in interface org.apache.spark.ml.param.Params
 
set(String, Object) - Method in interface org.apache.spark.ml.param.Params
 
set(ParamPair<?>) - Method in interface org.apache.spark.ml.param.Params
 
set(String, String) - Method in class org.apache.spark.SparkConf
Set a configuration variable.
set(SparkEnv) - Static method in class org.apache.spark.SparkEnv
 
set(long) - Method in class org.apache.spark.sql.types.Decimal
Set this Decimal to the given Long.
set(int) - Method in class org.apache.spark.sql.types.Decimal
Set this Decimal to the given Int.
set(long, int, int) - Method in class org.apache.spark.sql.types.Decimal
Set this Decimal to the given unscaled Long, with a given precision and scale.
set(BigDecimal, int, int) - Method in class org.apache.spark.sql.types.Decimal
Set this Decimal to the given BigDecimal value, with a given precision and scale.
set(BigDecimal) - Method in class org.apache.spark.sql.types.Decimal
Set this Decimal to the given BigDecimal value, inheriting its precision and scale.
set(Decimal) - Method in class org.apache.spark.sql.types.Decimal
Set this Decimal to the given Decimal value.
setActive(SQLContext) - Static method in class org.apache.spark.sql.SQLContext
Changes the SQLContext that will be returned in this thread and its children when SQLContext.getOrCreate() is called.
setAggregator(Aggregator<K, V, C>) - Method in class org.apache.spark.rdd.ShuffledRDD
Set aggregator for RDD's shuffle.
setAlgo(String) - Method in class org.apache.spark.mllib.tree.configuration.Strategy
Sets Algorithm using a String.
setAll(Traversable<Tuple2<String, String>>) - Method in class org.apache.spark.SparkConf
Set multiple parameters together
setAlpha(double) - Method in class org.apache.spark.ml.recommendation.ALS
 
setAlpha(Vector) - Method in class org.apache.spark.mllib.clustering.LDA
Alias for setDocConcentration()
setAlpha(double) - Method in class org.apache.spark.mllib.clustering.LDA
Alias for setDocConcentration()
setAlpha(double) - Method in class org.apache.spark.mllib.recommendation.ALS
 
setAppName(String) - Method in class org.apache.spark.launcher.SparkLauncher
Set the application name.
setAppName(String) - Method in class org.apache.spark.SparkConf
Set a name for your application.
setAppResource(String) - Method in class org.apache.spark.launcher.SparkLauncher
Set the main application resource.
setBandwidth(double) - Method in class org.apache.spark.mllib.stat.KernelDensity
Sets the bandwidth (standard deviation) of the Gaussian kernel (default: 1.0).
setBeta(double) - Method in class org.apache.spark.mllib.clustering.LDA
Alias for setTopicConcentration()
setBlocks(int) - Method in class org.apache.spark.mllib.recommendation.ALS
 
setBlockSize(int) - Method in class org.apache.spark.ml.classification.MultilayerPerceptronClassifier
 
setCacheNodeIds(boolean) - Method in class org.apache.spark.ml.classification.DecisionTreeClassifier
 
setCacheNodeIds(boolean) - Method in class org.apache.spark.ml.classification.GBTClassifier
 
setCacheNodeIds(boolean) - Method in class org.apache.spark.ml.classification.RandomForestClassifier
 
setCacheNodeIds(boolean) - Method in class org.apache.spark.ml.regression.DecisionTreeRegressor
 
setCacheNodeIds(boolean) - Method in class org.apache.spark.ml.regression.GBTRegressor
 
setCacheNodeIds(boolean) - Method in class org.apache.spark.ml.regression.RandomForestRegressor
 
setCallSite(String) - Method in class org.apache.spark.api.java.JavaSparkContext
Pass-through to SparkContext.setCallSite.
setCallSite(String) - Method in class org.apache.spark.SparkContext
Set the thread-local property for overriding the call sites of actions and RDDs.
setCaseSensitive(boolean) - Method in class org.apache.spark.ml.feature.StopWordsRemover
 
setCategoricalFeaturesInfo(Map<Integer, Integer>) - Method in class org.apache.spark.mllib.tree.configuration.Strategy
Sets categoricalFeaturesInfo using a Java Map.
setCensorCol(String) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegression
 
setCheckpointDir(String) - Method in class org.apache.spark.api.java.JavaSparkContext
Set the directory under which RDDs are going to be checkpointed.
setCheckpointDir(String) - Method in class org.apache.spark.SparkContext
Set the directory under which RDDs are going to be checkpointed.
setCheckpointInterval(int) - Method in class org.apache.spark.ml.classification.DecisionTreeClassifier
 
setCheckpointInterval(int) - Method in class org.apache.spark.ml.classification.GBTClassifier
 
setCheckpointInterval(int) - Method in class org.apache.spark.ml.classification.RandomForestClassifier
 
setCheckpointInterval(int) - Method in class org.apache.spark.ml.clustering.LDA
 
setCheckpointInterval(int) - Method in class org.apache.spark.ml.recommendation.ALS
 
setCheckpointInterval(int) - Method in class org.apache.spark.ml.regression.DecisionTreeRegressor
 
setCheckpointInterval(int) - Method in class org.apache.spark.ml.regression.GBTRegressor
 
setCheckpointInterval(int) - Method in class org.apache.spark.ml.regression.RandomForestRegressor
 
setCheckpointInterval(int) - Method in class org.apache.spark.mllib.clustering.LDA
Period (in iterations) between checkpoints (default = 10).
setCheckpointInterval(int) - Method in class org.apache.spark.mllib.recommendation.ALS
 
setCheckpointInterval(int) - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
setClassifier(Classifier<?, ?, ?>) - Method in class org.apache.spark.ml.classification.OneVsRest
 
setConf(String, String) - Method in class org.apache.spark.launcher.SparkLauncher
Set a single configuration value for the application.
setConf(String, String) - Method in class org.apache.spark.sql.hive.HiveContext
 
setConf(Properties) - Method in class org.apache.spark.sql.SQLContext
Set Spark SQL configuration properties.
setConf(String, String) - Method in class org.apache.spark.sql.SQLContext
Set the given Spark SQL configuration property.
setConfig(String, String) - Static method in class org.apache.spark.launcher.SparkLauncher
Set a configuration value for the launcher library.
setConvergenceTol(double) - Method in class org.apache.spark.mllib.clustering.GaussianMixture
Set the largest change in log-likelihood at which convergence is considered to have occurred.
setConvergenceTol(double) - Method in class org.apache.spark.mllib.optimization.GradientDescent
Set the convergence tolerance.
setConvergenceTol(double) - Method in class org.apache.spark.mllib.optimization.LBFGS
Set the convergence tolerance of iterations for L-BFGS.
setConvergenceTol(double) - Method in class org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
Set the convergence tolerance.
setDecayFactor(double) - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
Set the decay factor directly (for forgetful algorithms).
setDefault(Param<T>, T) - Method in interface org.apache.spark.ml.param.Params
Sets a default value for a param.
setDefault(Seq<ParamPair<?>>) - Method in interface org.apache.spark.ml.param.Params
Sets default values for a list of params.
setDefaultClassLoader(ClassLoader) - Method in class org.apache.spark.serializer.Serializer
Sets a class loader for the serializer to use in deserialization.
setDegree(int) - Method in class org.apache.spark.ml.feature.PolynomialExpansion
 
setDeployMode(String) - Method in class org.apache.spark.launcher.SparkLauncher
Set the deploy mode for the application.
setDocConcentration(double[]) - Method in class org.apache.spark.ml.clustering.LDA
 
setDocConcentration(double) - Method in class org.apache.spark.ml.clustering.LDA
 
setDocConcentration(Vector) - Method in class org.apache.spark.mllib.clustering.LDA
Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").
setDocConcentration(double) - Method in class org.apache.spark.mllib.clustering.LDA
Replicates a Double docConcentration to create a symmetric prior.
setDropLast(boolean) - Method in class org.apache.spark.ml.feature.OneHotEncoder
 
setElasticNetParam(double) - Method in class org.apache.spark.ml.classification.LogisticRegression
Set the ElasticNet mixing parameter.
setElasticNetParam(double) - Method in class org.apache.spark.ml.regression.LinearRegression
Set the ElasticNet mixing parameter.
setEpsilon(double) - Method in class org.apache.spark.mllib.clustering.KMeans
Set the distance threshold within which we've consider centers to have converged.
setEstimator(Estimator<?>) - Method in class org.apache.spark.ml.tuning.CrossValidator
 
setEstimator(Estimator<?>) - Method in class org.apache.spark.ml.tuning.TrainValidationSplit
 
setEstimatorParamMaps(ParamMap[]) - Method in class org.apache.spark.ml.tuning.CrossValidator
 
setEstimatorParamMaps(ParamMap[]) - Method in class org.apache.spark.ml.tuning.TrainValidationSplit
 
setEvaluator(Evaluator) - Method in class org.apache.spark.ml.tuning.CrossValidator
 
setEvaluator(Evaluator) - Method in class org.apache.spark.ml.tuning.TrainValidationSplit
 
setExecutorEnv(String, String) - Method in class org.apache.spark.SparkConf
Set an environment variable to be used when launching executors for this application.
setExecutorEnv(Seq<Tuple2<String, String>>) - Method in class org.apache.spark.SparkConf
Set multiple environment variables to be used when launching executors.
setExecutorEnv(Tuple2<String, String>[]) - Method in class org.apache.spark.SparkConf
Set multiple environment variables to be used when launching executors.
setFeatureIndex(int) - Method in class org.apache.spark.ml.regression.IsotonicRegression
 
setFeatureIndex(int) - Method in class org.apache.spark.ml.regression.IsotonicRegressionModel
 
setFeaturesCol(String) - Method in class org.apache.spark.ml.classification.OneVsRest
 
setFeaturesCol(String) - Method in class org.apache.spark.ml.clustering.KMeans
 
setFeaturesCol(String) - Method in class org.apache.spark.ml.clustering.LDA
The features for LDA should be a Vector representing the word counts in a document.
setFeaturesCol(String) - Method in class org.apache.spark.ml.clustering.LDAModel
The features for LDA should be a Vector representing the word counts in a document.
setFeaturesCol(String) - Method in class org.apache.spark.ml.feature.ChiSqSelector
 
setFeaturesCol(String) - Method in class org.apache.spark.ml.feature.ChiSqSelectorModel
 
setFeaturesCol(String) - Method in class org.apache.spark.ml.feature.RFormula
 
setFeaturesCol(String) - Method in class org.apache.spark.ml.PredictionModel
 
setFeaturesCol(String) - Method in class org.apache.spark.ml.Predictor
 
setFeaturesCol(String) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegression
 
setFeaturesCol(String) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
setFeaturesCol(String) - Method in class org.apache.spark.ml.regression.IsotonicRegression
 
setFeaturesCol(String) - Method in class org.apache.spark.ml.regression.IsotonicRegressionModel
 
setFeatureSubsetStrategy(String) - Method in class org.apache.spark.ml.classification.RandomForestClassifier
 
setFeatureSubsetStrategy(String) - Method in class org.apache.spark.ml.regression.RandomForestRegressor
 
setFinalRDDStorageLevel(StorageLevel) - Method in class org.apache.spark.mllib.recommendation.ALS
 
setFitIntercept(boolean) - Method in class org.apache.spark.ml.classification.LogisticRegression
Whether to fit an intercept term.
setFitIntercept(boolean) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegression
Set if we should fit the intercept Default is true.
setFitIntercept(boolean) - Method in class org.apache.spark.ml.regression.LinearRegression
Set if we should fit the intercept Default is true.
setFormula(String) - Method in class org.apache.spark.ml.feature.RFormula
Sets the formula to use for this transformer.
setGaps(boolean) - Method in class org.apache.spark.ml.feature.RegexTokenizer
 
setGradient(Gradient) - Method in class org.apache.spark.mllib.optimization.GradientDescent
Set the gradient function (of the loss function of one single data example) to be used for SGD.
setGradient(Gradient) - Method in class org.apache.spark.mllib.optimization.LBFGS
Set the gradient function (of the loss function of one single data example) to be used for L-BFGS.
setHalfLife(double, String) - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
Set the half life and time unit ("batches" or "points") for forgetful algorithms.
setHandleInvalid(String) - Method in class org.apache.spark.ml.feature.StringIndexer
 
setHandleInvalid(String) - Method in class org.apache.spark.ml.feature.StringIndexerModel
 
setIfMissing(String, String) - Method in class org.apache.spark.SparkConf
Set a parameter if it isn't already configured
setImplicitPrefs(boolean) - Method in class org.apache.spark.ml.recommendation.ALS
 
setImplicitPrefs(boolean) - Method in class org.apache.spark.mllib.recommendation.ALS
 
setImpurity(String) - Method in class org.apache.spark.ml.classification.DecisionTreeClassifier
 
setImpurity(String) - Method in class org.apache.spark.ml.classification.GBTClassifier
The impurity setting is ignored for GBT models.
setImpurity(String) - Method in class org.apache.spark.ml.classification.RandomForestClassifier
 
setImpurity(String) - Method in class org.apache.spark.ml.regression.DecisionTreeRegressor
 
setImpurity(String) - Method in class org.apache.spark.ml.regression.GBTRegressor
The impurity setting is ignored for GBT models.
setImpurity(String) - Method in class org.apache.spark.ml.regression.RandomForestRegressor
 
setImpurity(Impurity) - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
setIndices(int[]) - Method in class org.apache.spark.ml.feature.VectorSlicer
 
setInitialCenters(Vector[], double[]) - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
Specify initial centers directly.
setInitializationMode(String) - Method in class org.apache.spark.mllib.clustering.KMeans
Set the initialization algorithm.
setInitializationMode(String) - Method in class org.apache.spark.mllib.clustering.PowerIterationClustering
Set the initialization mode.
setInitializationSteps(int) - Method in class org.apache.spark.mllib.clustering.KMeans
Set the number of steps for the k-means|| initialization mode.
setInitialModel(GaussianMixtureModel) - Method in class org.apache.spark.mllib.clustering.GaussianMixture
Set the initial GMM starting point, bypassing the random initialization.
setInitialModel(KMeansModel) - Method in class org.apache.spark.mllib.clustering.KMeans
Set the initial starting point, bypassing the random initialization or k-means|| The condition model.k == this.k must be met, failure results in an IllegalArgumentException.
setInitialWeights(Vector) - Method in class org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD
Set the initial weights.
setInitialWeights(Vector) - Method in class org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
Set the initial weights.
setInitMode(String) - Method in class org.apache.spark.ml.clustering.KMeans
 
setInitSteps(int) - Method in class org.apache.spark.ml.clustering.KMeans
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.Binarizer
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.Bucketizer
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.CountVectorizer
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.CountVectorizerModel
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.HashingTF
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.IDF
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.IDFModel
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.IndexToString
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.MinMaxScaler
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.MinMaxScalerModel
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.OneHotEncoder
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.PCA
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.PCAModel
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.QuantileDiscretizer
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.StandardScaler
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.StandardScalerModel
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.StopWordsRemover
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.StringIndexer
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.StringIndexerModel
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.VectorIndexer
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.VectorIndexerModel
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.VectorSlicer
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.Word2Vec
 
setInputCol(String) - Method in class org.apache.spark.ml.feature.Word2VecModel
 
setInputCol(String) - Method in class org.apache.spark.ml.UnaryTransformer
 
setInputCols(String[]) - Method in class org.apache.spark.ml.feature.Interaction
 
setInputCols(String[]) - Method in class org.apache.spark.ml.feature.VectorAssembler
 
setIntercept(boolean) - Method in class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
Set if the algorithm should add an intercept.
setIntermediateRDDStorageLevel(StorageLevel) - Method in class org.apache.spark.mllib.recommendation.ALS
 
setInverse(boolean) - Method in class org.apache.spark.ml.feature.DCT
 
setIsotonic(boolean) - Method in class org.apache.spark.ml.regression.IsotonicRegression
 
setIsotonic(boolean) - Method in class org.apache.spark.mllib.regression.IsotonicRegression
 
setItemCol(String) - Method in class org.apache.spark.ml.recommendation.ALS
 
setItemCol(String) - Method in class org.apache.spark.ml.recommendation.ALSModel
 
setIterations(int) - Method in class org.apache.spark.mllib.recommendation.ALS
 
setJars(Seq<String>) - Method in class org.apache.spark.SparkConf
Set JAR files to distribute to the cluster.
setJars(String[]) - Method in class org.apache.spark.SparkConf
Set JAR files to distribute to the cluster.
setJavaHome(String) - Method in class org.apache.spark.launcher.SparkLauncher
Set a custom JAVA_HOME for launching the Spark application.
setJobDescription(String) - Method in class org.apache.spark.SparkContext
Set a human readable description of the current job.
setJobGroup(String, String, boolean) - Method in class org.apache.spark.api.java.JavaSparkContext
Assigns a group ID to all the jobs started by this thread until the group ID is set to a different value or cleared.
setJobGroup(String, String) - Method in class org.apache.spark.api.java.JavaSparkContext
Assigns a group ID to all the jobs started by this thread until the group ID is set to a different value or cleared.
setJobGroup(String, String, boolean) - Method in class org.apache.spark.SparkContext
Assigns a group ID to all the jobs started by this thread until the group ID is set to a different value or cleared.
setK(int) - Method in class org.apache.spark.ml.clustering.KMeans
 
setK(int) - Method in class org.apache.spark.ml.clustering.LDA
 
setK(int) - Method in class org.apache.spark.ml.feature.PCA
 
setK(int) - Method in class org.apache.spark.mllib.clustering.BisectingKMeans
Sets the desired number of leaf clusters (default: 4).
setK(int) - Method in class org.apache.spark.mllib.clustering.GaussianMixture
Set the number of Gaussians in the mixture model.
setK(int) - Method in class org.apache.spark.mllib.clustering.KMeans
Set the number of clusters to create (k).
setK(int) - Method in class org.apache.spark.mllib.clustering.LDA
Number of topics to infer.
setK(int) - Method in class org.apache.spark.mllib.clustering.PowerIterationClustering
 
setK(int) - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
Set the number of clusters.
setKappa(double) - Method in class org.apache.spark.mllib.clustering.OnlineLDAOptimizer
Learning rate: exponential decay rate---should be between (0.5, 1.0] to guarantee asymptotic convergence.
setKeyOrdering(Ordering<K>) - Method in class org.apache.spark.rdd.ShuffledRDD
Set key ordering for RDD's shuffle.
setLabelCol(String) - Method in class org.apache.spark.ml.classification.OneVsRest
 
setLabelCol(String) - Method in class org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
 
setLabelCol(String) - Method in class org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
 
setLabelCol(String) - Method in class org.apache.spark.ml.evaluation.RegressionEvaluator
 
setLabelCol(String) - Method in class org.apache.spark.ml.feature.ChiSqSelector
 
setLabelCol(String) - Method in class org.apache.spark.ml.feature.ChiSqSelectorModel
 
setLabelCol(String) - Method in class org.apache.spark.ml.feature.RFormula
 
setLabelCol(String) - Method in class org.apache.spark.ml.Predictor
 
setLabelCol(String) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegression
 
setLabelCol(String) - Method in class org.apache.spark.ml.regression.IsotonicRegression
 
setLabels(String[]) - Method in class org.apache.spark.ml.feature.IndexToString
 
setLambda(double) - Method in class org.apache.spark.mllib.classification.NaiveBayes
 
setLambda(double) - Method in class org.apache.spark.mllib.recommendation.ALS
 
setLayers(int[]) - Method in class org.apache.spark.ml.classification.MultilayerPerceptronClassifier
 
setLearningDecay(double) - Method in class org.apache.spark.ml.clustering.LDA
 
setLearningOffset(double) - Method in class org.apache.spark.ml.clustering.LDA
 
setLearningRate(double) - Method in class org.apache.spark.mllib.feature.Word2Vec
 
setLearningRate(double) - Method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
setLocalProperty(String, String) - Method in class org.apache.spark.api.java.JavaSparkContext
Set a local property that affects jobs submitted from this thread, such as the Spark fair scheduler pool.
setLocalProperty(String, String) - Method in class org.apache.spark.SparkContext
Set a local property that affects jobs submitted from this thread, such as the Spark fair scheduler pool.
setLogLevel(String) - Method in class org.apache.spark.api.java.JavaSparkContext
Control our logLevel.
setLogLevel(String) - Method in class org.apache.spark.SparkContext
Control our logLevel.
setLoss(Loss) - Method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
setLossType(String) - Method in class org.apache.spark.ml.classification.GBTClassifier
 
setLossType(String) - Method in class org.apache.spark.ml.regression.GBTRegressor
 
setMainClass(String) - Method in class org.apache.spark.launcher.SparkLauncher
Sets the application class name for Java/Scala applications.
setMapSideCombine(boolean) - Method in class org.apache.spark.rdd.ShuffledRDD
Set mapSideCombine flag for RDD's shuffle.
setMaster(String) - Method in class org.apache.spark.launcher.SparkLauncher
Set the Spark master for the application.
setMaster(String) - Method in class org.apache.spark.SparkConf
The master URL to connect to, such as "local" to run locally with one thread, "local[4]" to run locally with 4 cores, or "spark://master:7077" to run on a Spark standalone cluster.
setMax(double) - Method in class org.apache.spark.ml.feature.MinMaxScaler
 
setMax(double) - Method in class org.apache.spark.ml.feature.MinMaxScalerModel
 
setMaxBins(int) - Method in class org.apache.spark.ml.classification.DecisionTreeClassifier
 
setMaxBins(int) - Method in class org.apache.spark.ml.classification.GBTClassifier
 
setMaxBins(int) - Method in class org.apache.spark.ml.classification.RandomForestClassifier
 
setMaxBins(int) - Method in class org.apache.spark.ml.regression.DecisionTreeRegressor
 
setMaxBins(int) - Method in class org.apache.spark.ml.regression.GBTRegressor
 
setMaxBins(int) - Method in class org.apache.spark.ml.regression.RandomForestRegressor
 
setMaxBins(int) - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
setMaxCategories(int) - Method in class org.apache.spark.ml.feature.VectorIndexer
 
setMaxDepth(int) - Method in class org.apache.spark.ml.classification.DecisionTreeClassifier
 
setMaxDepth(int) - Method in class org.apache.spark.ml.classification.GBTClassifier
 
setMaxDepth(int) - Method in class org.apache.spark.ml.classification.RandomForestClassifier
 
setMaxDepth(int) - Method in class org.apache.spark.ml.regression.DecisionTreeRegressor
 
setMaxDepth(int) - Method in class org.apache.spark.ml.regression.GBTRegressor
 
setMaxDepth(int) - Method in class org.apache.spark.ml.regression.RandomForestRegressor
 
setMaxDepth(int) - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
setMaxIter(int) - Method in class org.apache.spark.ml.classification.GBTClassifier
 
setMaxIter(int) - Method in class org.apache.spark.ml.classification.LogisticRegression
Set the maximum number of iterations.
setMaxIter(int) - Method in class org.apache.spark.ml.classification.MultilayerPerceptronClassifier
Set the maximum number of iterations.
setMaxIter(int) - Method in class org.apache.spark.ml.clustering.KMeans
 
setMaxIter(int) - Method in class org.apache.spark.ml.clustering.LDA
 
setMaxIter(int) - Method in class org.apache.spark.ml.feature.Word2Vec
 
setMaxIter(int) - Method in class org.apache.spark.ml.recommendation.ALS
 
setMaxIter(int) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegression
Set the maximum number of iterations.
setMaxIter(int) - Method in class org.apache.spark.ml.regression.GBTRegressor
 
setMaxIter(int) - Method in class org.apache.spark.ml.regression.LinearRegression
Set the maximum number of iterations.
setMaxIterations(int) - Method in class org.apache.spark.mllib.clustering.BisectingKMeans
Sets the max number of k-means iterations to split clusters (default: 20).
setMaxIterations(int) - Method in class org.apache.spark.mllib.clustering.GaussianMixture
Set the maximum number of iterations to run.
setMaxIterations(int) - Method in class org.apache.spark.mllib.clustering.KMeans
Set maximum number of iterations to run.
setMaxIterations(int) - Method in class org.apache.spark.mllib.clustering.LDA
Maximum number of iterations for learning.
setMaxIterations(int) - Method in class org.apache.spark.mllib.clustering.PowerIterationClustering
 
setMaxLocalProjDBSize(long) - Method in class org.apache.spark.mllib.fpm.PrefixSpan
Sets the maximum number of items (including delimiters used in the internal storage format) allowed in a projected database before local processing (default: 32000000L).
setMaxMemoryInMB(int) - Method in class org.apache.spark.ml.classification.DecisionTreeClassifier
 
setMaxMemoryInMB(int) - Method in class org.apache.spark.ml.classification.GBTClassifier
 
setMaxMemoryInMB(int) - Method in class org.apache.spark.ml.classification.RandomForestClassifier
 
setMaxMemoryInMB(int) - Method in class org.apache.spark.ml.regression.DecisionTreeRegressor
 
setMaxMemoryInMB(int) - Method in class org.apache.spark.ml.regression.GBTRegressor
 
setMaxMemoryInMB(int) - Method in class org.apache.spark.ml.regression.RandomForestRegressor
 
setMaxMemoryInMB(int) - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
setMaxNumIterations(int) - Method in class org.apache.spark.mllib.optimization.LBFGS
Deprecated.
setMaxPatternLength(int) - Method in class org.apache.spark.mllib.fpm.PrefixSpan
Sets maximal pattern length (default: 10).
setMetricName(String) - Method in class org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
 
setMetricName(String) - Method in class org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
 
setMetricName(String) - Method in class org.apache.spark.ml.evaluation.RegressionEvaluator
 
setMin(double) - Method in class org.apache.spark.ml.feature.MinMaxScaler
 
setMin(double) - Method in class org.apache.spark.ml.feature.MinMaxScalerModel
 
setMinConfidence(double) - Method in class org.apache.spark.mllib.fpm.AssociationRules
Sets the minimal confidence (default: 0.8).
setMinCount(int) - Method in class org.apache.spark.ml.feature.Word2Vec
 
setMinCount(int) - Method in class org.apache.spark.mllib.feature.Word2Vec
 
setMinDF(double) - Method in class org.apache.spark.ml.feature.CountVectorizer
 
setMinDivisibleClusterSize(double) - Method in class org.apache.spark.mllib.clustering.BisectingKMeans
Sets the minimum number of points (if >= 1.0) or the minimum proportion of points (if < 1.0) of a divisible cluster (default: 1).
setMinDocFreq(int) - Method in class org.apache.spark.ml.feature.IDF
 
setMiniBatchFraction(double) - Method in class org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD
Set the fraction of each batch to use for updates.
setMiniBatchFraction(double) - Method in class org.apache.spark.mllib.clustering.OnlineLDAOptimizer
Mini-batch fraction in (0, 1], which sets the fraction of document sampled and used in each iteration.
setMiniBatchFraction(double) - Method in class org.apache.spark.mllib.optimization.GradientDescent
:: Experimental :: Set fraction of data to be used for each SGD iteration.
setMiniBatchFraction(double) - Method in class org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
Set the fraction of each batch to use for updates.
setMinInfoGain(double) - Method in class org.apache.spark.ml.classification.DecisionTreeClassifier
 
setMinInfoGain(double) - Method in class org.apache.spark.ml.classification.GBTClassifier
 
setMinInfoGain(double) - Method in class org.apache.spark.ml.classification.RandomForestClassifier
 
setMinInfoGain(double) - Method in class org.apache.spark.ml.regression.DecisionTreeRegressor
 
setMinInfoGain(double) - Method in class org.apache.spark.ml.regression.GBTRegressor
 
setMinInfoGain(double) - Method in class org.apache.spark.ml.regression.RandomForestRegressor
 
setMinInfoGain(double) - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
setMinInstancesPerNode(int) - Method in class org.apache.spark.ml.classification.DecisionTreeClassifier
 
setMinInstancesPerNode(int) - Method in class org.apache.spark.ml.classification.GBTClassifier
 
setMinInstancesPerNode(int) - Method in class org.apache.spark.ml.classification.RandomForestClassifier
 
setMinInstancesPerNode(int) - Method in class org.apache.spark.ml.regression.DecisionTreeRegressor
 
setMinInstancesPerNode(int) - Method in class org.apache.spark.ml.regression.GBTRegressor
 
setMinInstancesPerNode(int) - Method in class org.apache.spark.ml.regression.RandomForestRegressor
 
setMinInstancesPerNode(int) - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
setMinSupport(double) - Method in class org.apache.spark.mllib.fpm.FPGrowth
Sets the minimal support level (default: 0.3).
setMinSupport(double) - Method in class org.apache.spark.mllib.fpm.PrefixSpan
Sets the minimal support level (default: 0.1).
setMinTF(double) - Method in class org.apache.spark.ml.feature.CountVectorizer
 
setMinTF(double) - Method in class org.apache.spark.ml.feature.CountVectorizerModel
 
setMinTokenLength(int) - Method in class org.apache.spark.ml.feature.RegexTokenizer
 
setModelType(String) - Method in class org.apache.spark.ml.classification.NaiveBayes
Set the model type using a string (case-sensitive).
setModelType(String) - Method in class org.apache.spark.mllib.classification.NaiveBayes
 
setN(int) - Method in class org.apache.spark.ml.feature.NGram
 
setName(String) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Assign a name to this RDD
setName(String) - Method in class org.apache.spark.api.java.JavaPairRDD
Assign a name to this RDD
setName(String) - Method in class org.apache.spark.api.java.JavaRDD
Assign a name to this RDD
setName(String) - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
 
setName(String) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
setName(String) - Method in class org.apache.spark.rdd.RDD
Assign a name to this RDD
setNames(String[]) - Method in class org.apache.spark.ml.feature.VectorSlicer
 
setNonnegative(boolean) - Method in class org.apache.spark.ml.recommendation.ALS
 
setNonnegative(boolean) - Method in class org.apache.spark.mllib.recommendation.ALS
 
setNumBlocks(int) - Method in class org.apache.spark.ml.recommendation.ALS
Sets both numUserBlocks and numItemBlocks to the specific value.
setNumBuckets(int) - Method in class org.apache.spark.ml.feature.QuantileDiscretizer
 
setNumClasses(int) - Method in class org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
Set the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression.
setNumClasses(int) - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
setNumCorrections(int) - Method in class org.apache.spark.mllib.optimization.LBFGS
Set the number of corrections used in the LBFGS update.
setNumFeatures(int) - Method in class org.apache.spark.ml.feature.HashingTF
 
setNumFolds(int) - Method in class org.apache.spark.ml.tuning.CrossValidator
 
setNumItemBlocks(int) - Method in class org.apache.spark.ml.recommendation.ALS
 
setNumIterations(int) - Method in class org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD
Set the number of iterations of gradient descent to run per update.
setNumIterations(int) - Method in class org.apache.spark.mllib.feature.Word2Vec
 
setNumIterations(int) - Method in class org.apache.spark.mllib.optimization.GradientDescent
Set the number of iterations for SGD.
setNumIterations(int) - Method in class org.apache.spark.mllib.optimization.LBFGS
Set the maximal number of iterations for L-BFGS.
setNumIterations(int) - Method in class org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
Set the number of iterations of gradient descent to run per update.
setNumIterations(int) - Method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
setNumPartitions(int) - Method in class org.apache.spark.ml.feature.Word2Vec
 
setNumPartitions(int) - Method in class org.apache.spark.mllib.feature.Word2Vec
 
setNumPartitions(int) - Method in class org.apache.spark.mllib.fpm.FPGrowth
Sets the number of partitions used by parallel FP-growth (default: same as input data).
setNumTopFeatures(int) - Method in class org.apache.spark.ml.feature.ChiSqSelector
 
setNumTrees(int) - Method in class org.apache.spark.ml.classification.RandomForestClassifier
 
setNumTrees(int) - Method in class org.apache.spark.ml.regression.RandomForestRegressor
 
setNumUserBlocks(int) - Method in class org.apache.spark.ml.recommendation.ALS
 
setOptimizeDocConcentration(boolean) - Method in class org.apache.spark.ml.clustering.LDA
 
setOptimizeDocConcentration(boolean) - Method in class org.apache.spark.mllib.clustering.OnlineLDAOptimizer
Sets whether to optimize docConcentration parameter during training.
setOptimizer(String) - Method in class org.apache.spark.ml.clustering.LDA
 
setOptimizer(LDAOptimizer) - Method in class org.apache.spark.mllib.clustering.LDA
:: DeveloperApi ::
setOptimizer(String) - Method in class org.apache.spark.mllib.clustering.LDA
Set the LDAOptimizer used to perform the actual calculation by algorithm name.
setOrNull(long, int, int) - Method in class org.apache.spark.sql.types.Decimal
Set this Decimal to the given unscaled Long, with a given precision and scale, and return it, or return null if it cannot be set due to overflow.
setOutputCol(String) - Method in class org.apache.spark.ml.feature.Binarizer
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.Bucketizer
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.ChiSqSelector
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.ChiSqSelectorModel
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.CountVectorizer
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.CountVectorizerModel
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.HashingTF
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.IDF
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.IDFModel
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.IndexToString
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.Interaction
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.MinMaxScaler
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.MinMaxScalerModel
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.OneHotEncoder
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.PCA
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.PCAModel
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.QuantileDiscretizer
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.StandardScaler
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.StandardScalerModel
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.StopWordsRemover
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.StringIndexer
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.StringIndexerModel
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.VectorAssembler
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.VectorIndexer
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.VectorIndexerModel
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.VectorSlicer
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.Word2Vec
 
setOutputCol(String) - Method in class org.apache.spark.ml.feature.Word2VecModel
 
setOutputCol(String) - Method in class org.apache.spark.ml.UnaryTransformer
 
setP(double) - Method in class org.apache.spark.ml.feature.Normalizer
 
setParent(Estimator<M>) - Method in class org.apache.spark.ml.Model
Sets the parent of this model (Java API).
setPattern(String) - Method in class org.apache.spark.ml.feature.RegexTokenizer
 
setPeacePeriod(int) - Method in class org.apache.spark.mllib.stat.test.StreamingTest
 
setPredictionCol(String) - Method in class org.apache.spark.ml.classification.OneVsRest
 
setPredictionCol(String) - Method in class org.apache.spark.ml.clustering.KMeans
 
setPredictionCol(String) - Method in class org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
 
setPredictionCol(String) - Method in class org.apache.spark.ml.evaluation.RegressionEvaluator
 
setPredictionCol(String) - Method in class org.apache.spark.ml.PredictionModel
 
setPredictionCol(String) - Method in class org.apache.spark.ml.Predictor
 
setPredictionCol(String) - Method in class org.apache.spark.ml.recommendation.ALS
 
setPredictionCol(String) - Method in class org.apache.spark.ml.recommendation.ALSModel
 
setPredictionCol(String) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegression
 
setPredictionCol(String) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
setPredictionCol(String) - Method in class org.apache.spark.ml.regression.IsotonicRegression
 
setPredictionCol(String) - Method in class org.apache.spark.ml.regression.IsotonicRegressionModel
 
setProbabilityCol(String) - Method in class org.apache.spark.ml.classification.ProbabilisticClassificationModel
 
setProbabilityCol(String) - Method in class org.apache.spark.ml.classification.ProbabilisticClassifier
 
setProductBlocks(int) - Method in class org.apache.spark.mllib.recommendation.ALS
 
setPropertiesFile(String) - Method in class org.apache.spark.launcher.SparkLauncher
Set a custom properties file with Spark configuration for the application.
setQuantileCalculationStrategy(Enumeration.Value) - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
setQuantileProbabilities(double[]) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegression
 
setQuantileProbabilities(double[]) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
setQuantilesCol(String) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegression
 
setQuantilesCol(String) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
setRandomCenters(int, double, long) - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
Initialize random centers, requiring only the number of dimensions.
setRank(int) - Method in class org.apache.spark.ml.recommendation.ALS
 
setRank(int) - Method in class org.apache.spark.mllib.recommendation.ALS
 
setRatingCol(String) - Method in class org.apache.spark.ml.recommendation.ALS
 
setRawPredictionCol(String) - Method in class org.apache.spark.ml.classification.ClassificationModel
 
setRawPredictionCol(String) - Method in class org.apache.spark.ml.classification.Classifier
 
setRawPredictionCol(String) - Method in class org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
 
setRegParam(double) - Method in class org.apache.spark.ml.classification.LogisticRegression
Set the regularization parameter.
setRegParam(double) - Method in class org.apache.spark.ml.recommendation.ALS
 
setRegParam(double) - Method in class org.apache.spark.ml.regression.LinearRegression
Set the regularization parameter.
setRegParam(double) - Method in class org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD
Set the regularization parameter.
setRegParam(double) - Method in class org.apache.spark.mllib.optimization.GradientDescent
Set the regularization parameter.
setRegParam(double) - Method in class org.apache.spark.mllib.optimization.LBFGS
Set the regularization parameter.
setRest(long, int, VD, ED) - Method in class org.apache.spark.graphx.impl.AggregatingEdgeContext
 
setRuns(int) - Method in class org.apache.spark.mllib.clustering.KMeans
:: Experimental :: Set the number of runs of the algorithm to execute in parallel.
setSample(RDD<Object>) - Method in class org.apache.spark.mllib.stat.KernelDensity
Sets the sample to use for density estimation.
setSample(JavaRDD<Double>) - Method in class org.apache.spark.mllib.stat.KernelDensity
Sets the sample to use for density estimation (for Java users).
setScalingVec(Vector) - Method in class org.apache.spark.ml.feature.ElementwiseProduct
 
setScoreCol(String) - Method in class org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
Deprecated.
use setRawPredictionCol() instead
setSeed(long) - Method in class org.apache.spark.ml.classification.DecisionTreeClassifier
 
setSeed(long) - Method in class org.apache.spark.ml.classification.GBTClassifier
 
setSeed(long) - Method in class org.apache.spark.ml.classification.MultilayerPerceptronClassifier
Set the seed for weights initialization.
setSeed(long) - Method in class org.apache.spark.ml.classification.RandomForestClassifier
 
setSeed(long) - Method in class org.apache.spark.ml.clustering.KMeans
 
setSeed(long) - Method in class org.apache.spark.ml.clustering.LDA
 
setSeed(long) - Method in class org.apache.spark.ml.clustering.LDAModel
 
setSeed(long) - Method in class org.apache.spark.ml.feature.Word2Vec
 
setSeed(long) - Method in class org.apache.spark.ml.recommendation.ALS
 
setSeed(long) - Method in class org.apache.spark.ml.regression.DecisionTreeRegressor
 
setSeed(long) - Method in class org.apache.spark.ml.regression.GBTRegressor
 
setSeed(long) - Method in class org.apache.spark.ml.regression.RandomForestRegressor
 
setSeed(long) - Method in class org.apache.spark.mllib.clustering.BisectingKMeans
Sets the random seed (default: hash value of the class name).
setSeed(long) - Method in class org.apache.spark.mllib.clustering.GaussianMixture
Set the random seed
setSeed(long) - Method in class org.apache.spark.mllib.clustering.KMeans
Set the random seed for cluster initialization.
setSeed(long) - Method in class org.apache.spark.mllib.clustering.LDA
Random seed
setSeed(long) - Method in class org.apache.spark.mllib.feature.Word2Vec
 
setSeed(long) - Method in class org.apache.spark.mllib.random.ExponentialGenerator
 
setSeed(long) - Method in class org.apache.spark.mllib.random.GammaGenerator
 
setSeed(long) - Method in class org.apache.spark.mllib.random.LogNormalGenerator
 
setSeed(long) - Method in class org.apache.spark.mllib.random.PoissonGenerator
 
setSeed(long) - Method in class org.apache.spark.mllib.random.StandardNormalGenerator
 
setSeed(long) - Method in class org.apache.spark.mllib.random.UniformGenerator
 
setSeed(long) - Method in class org.apache.spark.mllib.random.WeibullGenerator
 
setSeed(long) - Method in class org.apache.spark.mllib.recommendation.ALS
 
setSeed(long) - Method in class org.apache.spark.util.random.BernoulliCellSampler
 
setSeed(long) - Method in class org.apache.spark.util.random.BernoulliSampler
 
setSeed(long) - Method in class org.apache.spark.util.random.PoissonSampler
 
setSeed(long) - Method in interface org.apache.spark.util.random.Pseudorandom
Set random seed.
setSerializer(Serializer) - Method in class org.apache.spark.rdd.CoGroupedRDD
Set a serializer for this RDD's shuffle, or null to use the default (spark.serializer)
setSerializer(Serializer) - Method in class org.apache.spark.rdd.ShuffledRDD
Set a serializer for this RDD's shuffle, or null to use the default (spark.serializer)
setSmoothing(double) - Method in class org.apache.spark.ml.classification.NaiveBayes
Set the smoothing parameter.
setSolver(String) - Method in class org.apache.spark.ml.regression.LinearRegression
Set the solver algorithm used for optimization.
setSparkHome(String) - Method in class org.apache.spark.launcher.SparkLauncher
Set a custom Spark installation location for the application.
setSparkHome(String) - Method in class org.apache.spark.SparkConf
Set the location where Spark is installed on worker nodes.
setSplits(double[]) - Method in class org.apache.spark.ml.feature.Bucketizer
 
setSrcOnly(long, int, VD) - Method in class org.apache.spark.graphx.impl.AggregatingEdgeContext
 
setStages(PipelineStage[]) - Method in class org.apache.spark.ml.Pipeline
 
setStandardization(boolean) - Method in class org.apache.spark.ml.classification.LogisticRegression
Whether to standardize the training features before fitting the model.
setStandardization(boolean) - Method in class org.apache.spark.ml.regression.LinearRegression
Whether to standardize the training features before fitting the model.
setStatement(String) - Method in class org.apache.spark.ml.feature.SQLTransformer
 
setStepSize(double) - Method in class org.apache.spark.ml.classification.GBTClassifier
 
setStepSize(double) - Method in class org.apache.spark.ml.feature.Word2Vec
 
setStepSize(double) - Method in class org.apache.spark.ml.regression.GBTRegressor
 
setStepSize(double) - Method in class org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD
Set the step size for gradient descent.
setStepSize(double) - Method in class org.apache.spark.mllib.optimization.GradientDescent
Set the initial step size of SGD for the first step.
setStepSize(double) - Method in class org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
Set the step size for gradient descent.
setStopWords(String[]) - Method in class org.apache.spark.ml.feature.StopWordsRemover
 
setSubsamplingRate(double) - Method in class org.apache.spark.ml.classification.GBTClassifier
 
setSubsamplingRate(double) - Method in class org.apache.spark.ml.classification.RandomForestClassifier
 
setSubsamplingRate(double) - Method in class org.apache.spark.ml.clustering.LDA
 
setSubsamplingRate(double) - Method in class org.apache.spark.ml.regression.GBTRegressor
 
setSubsamplingRate(double) - Method in class org.apache.spark.ml.regression.RandomForestRegressor
 
setSubsamplingRate(double) - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
setTaskContext(TaskContext) - Static method in class org.apache.spark.TaskContext
Set the thread local TaskContext.
setTau0(double) - Method in class org.apache.spark.mllib.clustering.OnlineLDAOptimizer
A (positive) learning parameter that downweights early iterations.
setTestMethod(String) - Method in class org.apache.spark.mllib.stat.test.StreamingTest
 
setThreshold(double) - Method in class org.apache.spark.ml.classification.LogisticRegression
 
setThreshold(double) - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
setThreshold(double) - Method in class org.apache.spark.ml.feature.Binarizer
 
setThreshold(double) - Method in class org.apache.spark.mllib.classification.LogisticRegressionModel
Sets the threshold that separates positive predictions from negative predictions in Binary Logistic Regression.
setThreshold(double) - Method in class org.apache.spark.mllib.classification.SVMModel
Sets the threshold that separates positive predictions from negative predictions.
setThresholds(double[]) - Method in class org.apache.spark.ml.classification.LogisticRegression
 
setThresholds(double[]) - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
setThresholds(double[]) - Method in class org.apache.spark.ml.classification.ProbabilisticClassificationModel
 
setThresholds(double[]) - Method in class org.apache.spark.ml.classification.ProbabilisticClassifier
 
setTol(double) - Method in class org.apache.spark.ml.classification.LogisticRegression
Set the convergence tolerance of iterations.
setTol(double) - Method in class org.apache.spark.ml.classification.MultilayerPerceptronClassifier
Set the convergence tolerance of iterations.
setTol(double) - Method in class org.apache.spark.ml.clustering.KMeans
 
setTol(double) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegression
Set the convergence tolerance of iterations.
setTol(double) - Method in class org.apache.spark.ml.regression.LinearRegression
Set the convergence tolerance of iterations.
setToLowercase(boolean) - Method in class org.apache.spark.ml.feature.RegexTokenizer
 
setTopicConcentration(double) - Method in class org.apache.spark.ml.clustering.LDA
 
setTopicConcentration(double) - Method in class org.apache.spark.mllib.clustering.LDA
Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms.
setTopicDistributionCol(String) - Method in class org.apache.spark.ml.clustering.LDA
 
setTrainRatio(double) - Method in class org.apache.spark.ml.tuning.TrainValidationSplit
 
setTreeStrategy(Strategy) - Method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
setUpdater(Updater) - Method in class org.apache.spark.mllib.optimization.GradientDescent
Set the updater function to actually perform a gradient step in a given direction.
setUpdater(Updater) - Method in class org.apache.spark.mllib.optimization.LBFGS
Set the updater function to actually perform a gradient step in a given direction.
setupGroups(int) - Method in class org.apache.spark.rdd.PartitionCoalescer
Initializes targetLen partition groups and assigns a preferredLocation This uses coupon collector to estimate how many preferredLocations it must rotate through until it has seen most of the preferred locations (2 * n log(n))
setUseNodeIdCache(boolean) - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
setUserBlocks(int) - Method in class org.apache.spark.mllib.recommendation.ALS
 
setUserCol(String) - Method in class org.apache.spark.ml.recommendation.ALS
 
setUserCol(String) - Method in class org.apache.spark.ml.recommendation.ALSModel
 
setValidateData(boolean) - Method in class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
Set if the algorithm should validate data before training.
setValidationTol(double) - Method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
setValue(R) - Method in class org.apache.spark.Accumulable
Set the accumulator's value; only allowed on master
setVectorSize(int) - Method in class org.apache.spark.ml.feature.Word2Vec
 
setVectorSize(int) - Method in class org.apache.spark.mllib.feature.Word2Vec
 
setVerbose(boolean) - Method in class org.apache.spark.launcher.SparkLauncher
Enables verbose reporting for SparkSubmit.
setVocabSize(int) - Method in class org.apache.spark.ml.feature.CountVectorizer
 
setWeightCol(String) - Method in class org.apache.spark.ml.classification.LogisticRegression
Whether to over-/under-sample training instances according to the given weights in weightCol.
setWeightCol(String) - Method in class org.apache.spark.ml.regression.IsotonicRegression
 
setWeightCol(String) - Method in class org.apache.spark.ml.regression.LinearRegression
Whether to over-/under-sample training instances according to the given weights in weightCol.
setWindowSize(int) - Method in class org.apache.spark.ml.feature.Word2Vec
 
setWindowSize(int) - Method in class org.apache.spark.mllib.feature.Word2Vec
 
setWindowSize(int) - Method in class org.apache.spark.mllib.stat.test.StreamingTest
 
setWithMean(boolean) - Method in class org.apache.spark.ml.feature.StandardScaler
 
setWithMean(boolean) - Method in class org.apache.spark.mllib.feature.StandardScalerModel
 
setWithStd(boolean) - Method in class org.apache.spark.ml.feature.StandardScaler
 
setWithStd(boolean) - Method in class org.apache.spark.mllib.feature.StandardScalerModel
 
sha1(Column) - Static method in class org.apache.spark.sql.functions
Calculates the SHA-1 digest of a binary column and returns the value as a 40 character hex string.
sha2(Column, int) - Static method in class org.apache.spark.sql.functions
Calculates the SHA-2 family of hash functions of a binary column and returns the value as a hex string.
shape() - Method in class org.apache.spark.mllib.random.GammaGenerator
 
shiftLeft(Column, int) - Static method in class org.apache.spark.sql.functions
Shift the the given value numBits left.
shiftRight(Column, int) - Static method in class org.apache.spark.sql.functions
Shift the the given value numBits right.
shiftRightUnsigned(Column, int) - Static method in class org.apache.spark.sql.functions
Unsigned shift the the given value numBits right.
SHORT() - Static method in class org.apache.spark.sql.Encoders
An encoder for nullable short type.
ShortDecimal() - Static method in class org.apache.spark.sql.types.DecimalType
 
ShortestPaths - Class in org.apache.spark.graphx.lib
Computes shortest paths to the given set of landmark vertices, returning a graph where each vertex attribute is a map containing the shortest-path distance to each reachable landmark.
ShortestPaths() - Constructor for class org.apache.spark.graphx.lib.ShortestPaths
 
shortName() - Method in class org.apache.spark.ml.source.libsvm.DefaultSource
 
shortName() - Method in interface org.apache.spark.sql.sources.DataSourceRegister
The string that represents the format that this data source provider uses.
ShortType - Static variable in class org.apache.spark.sql.types.DataTypes
Gets the ShortType object.
ShortType - Class in org.apache.spark.sql.types
:: DeveloperApi :: The data type representing Short values.
shouldDistributeGaussians(int, int) - Static method in class org.apache.spark.mllib.clustering.GaussianMixture
Heuristic to distribute the computation of the MultivariateGaussians, approximately when d > 25 except for when k is very small.
shouldGoLeft(Vector) - Method in interface org.apache.spark.ml.tree.Split
Return true (split to left) or false (split to right).
shouldGoLeft(int, Split[]) - Method in interface org.apache.spark.ml.tree.Split
Return true (split to left) or false (split to right).
shouldOverwrite() - Method in class org.apache.spark.ml.util.MLWriter
 
shouldOwn(Param<?>) - Method in interface org.apache.spark.ml.param.Params
Validates that the input param belongs to this instance.
show(int) - Method in class org.apache.spark.sql.DataFrame
Displays the DataFrame in a tabular form.
show() - Method in class org.apache.spark.sql.DataFrame
Displays the top 20 rows of DataFrame in a tabular form.
show(boolean) - Method in class org.apache.spark.sql.DataFrame
Displays the top 20 rows of DataFrame in a tabular form.
show(int, boolean) - Method in class org.apache.spark.sql.DataFrame
Displays the DataFrame in a tabular form.
show(int) - Method in class org.apache.spark.sql.Dataset
Displays the content of this Dataset in a tabular form.
show() - Method in class org.apache.spark.sql.Dataset
Displays the top 20 rows of Dataset in a tabular form.
show(boolean) - Method in class org.apache.spark.sql.Dataset
Displays the top 20 rows of Dataset in a tabular form.
show(int, boolean) - Method in class org.apache.spark.sql.Dataset
Displays the Dataset in a tabular form.
showBytesDistribution(String, Function2<TaskInfo, TaskMetrics, Option<Object>>, Seq<Tuple2<TaskInfo, TaskMetrics>>) - Static method in class org.apache.spark.scheduler.StatsReportListener
 
showBytesDistribution(String, Option<org.apache.spark.util.Distribution>) - Static method in class org.apache.spark.scheduler.StatsReportListener
 
showBytesDistribution(String, org.apache.spark.util.Distribution) - Static method in class org.apache.spark.scheduler.StatsReportListener
 
showDistribution(String, org.apache.spark.util.Distribution, Function1<Object, String>) - Static method in class org.apache.spark.scheduler.StatsReportListener
 
showDistribution(String, Option<org.apache.spark.util.Distribution>, Function1<Object, String>) - Static method in class org.apache.spark.scheduler.StatsReportListener
 
showDistribution(String, Option<org.apache.spark.util.Distribution>, String) - Static method in class org.apache.spark.scheduler.StatsReportListener
 
showDistribution(String, String, Function2<TaskInfo, TaskMetrics, Option<Object>>, Seq<Tuple2<TaskInfo, TaskMetrics>>) - Static method in class org.apache.spark.scheduler.StatsReportListener
 
showMillisDistribution(String, Option<org.apache.spark.util.Distribution>) - Static method in class org.apache.spark.scheduler.StatsReportListener
 
showMillisDistribution(String, Function2<TaskInfo, TaskMetrics, Option<Object>>, Seq<Tuple2<TaskInfo, TaskMetrics>>) - Static method in class org.apache.spark.scheduler.StatsReportListener
 
showMillisDistribution(String, Function1<BatchInfo, Option<Object>>) - Method in class org.apache.spark.streaming.scheduler.StatsReportListener
 
SHUFFLE() - Static method in class org.apache.spark.storage.BlockId
 
SHUFFLE_DATA() - Static method in class org.apache.spark.storage.BlockId
 
SHUFFLE_INDEX() - Static method in class org.apache.spark.storage.BlockId
 
ShuffleBlockId - Class in org.apache.spark.storage
 
ShuffleBlockId(int, int, int) - Constructor for class org.apache.spark.storage.ShuffleBlockId
 
ShuffleDataBlockId - Class in org.apache.spark.storage
 
ShuffleDataBlockId(int, int, int) - Constructor for class org.apache.spark.storage.ShuffleDataBlockId
 
ShuffleDependency<K,V,C> - Class in org.apache.spark
:: DeveloperApi :: Represents a dependency on the output of a shuffle stage.
ShuffleDependency(RDD<? extends Product2<K, V>>, Partitioner, Option<Serializer>, Option<Ordering<K>>, Option<Aggregator<K, V, C>>, boolean, ClassTag<K>, ClassTag<V>, ClassTag<C>) - Constructor for class org.apache.spark.ShuffleDependency
 
ShuffledRDD<K,V,C> - Class in org.apache.spark.rdd
:: DeveloperApi :: The resulting RDD from a shuffle (e.g.
ShuffledRDD(RDD<? extends Product2<K, V>>, Partitioner, ClassTag<K>, ClassTag<V>, ClassTag<C>) - Constructor for class org.apache.spark.rdd.ShuffledRDD
 
shuffleHandle() - Method in class org.apache.spark.ShuffleDependency
 
shuffleId() - Method in class org.apache.spark.CleanShuffle
 
shuffleId() - Method in class org.apache.spark.FetchFailed
 
shuffleId() - Method in class org.apache.spark.ShuffleDependency
 
shuffleId() - Method in class org.apache.spark.storage.ShuffleBlockId
 
shuffleId() - Method in class org.apache.spark.storage.ShuffleDataBlockId
 
shuffleId() - Method in class org.apache.spark.storage.ShuffleIndexBlockId
 
ShuffleIndexBlockId - Class in org.apache.spark.storage
 
ShuffleIndexBlockId(int, int, int) - Constructor for class org.apache.spark.storage.ShuffleIndexBlockId
 
shuffleManager() - Method in class org.apache.spark.SparkEnv
 
shuffleRead() - Method in class org.apache.spark.status.api.v1.ExecutorStageSummary
 
shuffleReadBytes() - Method in class org.apache.spark.status.api.v1.StageData
 
ShuffleReadMetricDistributions - Class in org.apache.spark.status.api.v1
 
ShuffleReadMetrics - Class in org.apache.spark.status.api.v1
 
shuffleReadMetrics() - Method in class org.apache.spark.status.api.v1.TaskMetricDistributions
 
shuffleReadMetrics() - Method in class org.apache.spark.status.api.v1.TaskMetrics
 
shuffleReadRecords() - Method in class org.apache.spark.status.api.v1.StageData
 
shuffleWrite() - Method in class org.apache.spark.status.api.v1.ExecutorStageSummary
 
shuffleWriteBytes() - Method in class org.apache.spark.status.api.v1.StageData
 
ShuffleWriteMetricDistributions - Class in org.apache.spark.status.api.v1
 
ShuffleWriteMetrics - Class in org.apache.spark.status.api.v1
 
shuffleWriteMetrics() - Method in class org.apache.spark.status.api.v1.TaskMetricDistributions
 
shuffleWriteMetrics() - Method in class org.apache.spark.status.api.v1.TaskMetrics
 
shuffleWriteRecords() - Method in class org.apache.spark.status.api.v1.StageData
 
sigma() - Method in class org.apache.spark.mllib.stat.distribution.MultivariateGaussian
 
sigmas() - Method in class org.apache.spark.mllib.clustering.ExpectationSum
 
SignalLoggerHandler - Class in org.apache.spark.util
 
SignalLoggerHandler(String, Logger) - Constructor for class org.apache.spark.util.SignalLoggerHandler
 
signum(Column) - Static method in class org.apache.spark.sql.functions
Computes the signum of the given value.
signum(String) - Static method in class org.apache.spark.sql.functions
Computes the signum of the given column.
SimpleFutureAction<T> - Class in org.apache.spark
A FutureAction holding the result of an action that triggers a single job.
simpleString() - Method in class org.apache.spark.sql.hive.HiveContext.QueryExecution
 
simpleString() - Method in class org.apache.spark.sql.types.ArrayType
 
simpleString() - Method in class org.apache.spark.sql.types.ByteType
 
simpleString() - Method in class org.apache.spark.sql.types.DataType
Readable string representation for the type.
simpleString() - Method in class org.apache.spark.sql.types.DecimalType
 
simpleString() - Method in class org.apache.spark.sql.types.IntegerType
 
simpleString() - Method in class org.apache.spark.sql.types.LongType
 
simpleString() - Method in class org.apache.spark.sql.types.MapType
 
simpleString() - Method in class org.apache.spark.sql.types.ShortType
 
simpleString() - Method in class org.apache.spark.sql.types.StructType
 
SimpleUpdater - Class in org.apache.spark.mllib.optimization
:: DeveloperApi :: A simple updater for gradient descent *without* any regularization.
SimpleUpdater() - Constructor for class org.apache.spark.mllib.optimization.SimpleUpdater
 
SIMR_REGEX() - Static method in class org.apache.spark.SparkMasterRegex
 
sin(Column) - Static method in class org.apache.spark.sql.functions
Computes the sine of the given value.
sin(String) - Static method in class org.apache.spark.sql.functions
Computes the sine of the given column.
SingularValueDecomposition<UType,VType> - Class in org.apache.spark.mllib.linalg
Represents singular value decomposition (SVD) factors.
SingularValueDecomposition(UType, Vector, VType) - Constructor for class org.apache.spark.mllib.linalg.SingularValueDecomposition
 
sinh(Column) - Static method in class org.apache.spark.sql.functions
Computes the hyperbolic sine of the given value.
sinh(String) - Static method in class org.apache.spark.sql.functions
Computes the hyperbolic sine of the given column.
size() - Method in class org.apache.spark.ml.attribute.AttributeGroup
Size of the attribute group.
size() - Method in class org.apache.spark.ml.param.ParamMap
Number of param pairs in this map.
size() - Method in class org.apache.spark.mllib.linalg.DenseVector
 
size() - Method in class org.apache.spark.mllib.linalg.SparseVector
 
size() - Method in interface org.apache.spark.mllib.linalg.Vector
Size of the vector.
size() - Method in class org.apache.spark.rdd.PartitionGroup
 
size(Column) - Static method in class org.apache.spark.sql.functions
Returns length of array or map.
size() - Method in interface org.apache.spark.sql.Row
Number of elements in the Row.
size() - Method in class org.apache.spark.storage.MemoryEntry
 
SizeEstimator - Class in org.apache.spark.util
:: DeveloperApi :: Estimates the sizes of Java objects (number of bytes of memory they occupy), for use in memory-aware caches.
SizeEstimator() - Constructor for class org.apache.spark.util.SizeEstimator
 
sizeInBytes() - Method in class org.apache.spark.sql.sources.BaseRelation
Returns an estimated size of this relation in bytes.
sizeInBytes() - Method in class org.apache.spark.sql.sources.HadoopFsRelation
 
sketch(RDD<K>, int, ClassTag<K>) - Static method in class org.apache.spark.RangePartitioner
Sketches the input RDD via reservoir sampling on each partition.
skewness(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the skewness of the values in a group.
skewness(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the skewness of the values in a group.
skip(long) - Method in class org.apache.spark.storage.BufferReleasingInputStream
 
skippedStages() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
slack() - Method in class org.apache.spark.rdd.PartitionCoalescer
 
slice(Time, Time) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return all the RDDs between 'fromDuration' to 'toDuration' (both included)
slice(org.apache.spark.streaming.Interval) - Method in class org.apache.spark.streaming.dstream.DStream
Return all the RDDs defined by the Interval object (both end times included)
slice(Time, Time) - Method in class org.apache.spark.streaming.dstream.DStream
Return all the RDDs between 'fromTime' to 'toTime' (both included)
slideDuration() - Method in class org.apache.spark.streaming.dstream.DStream
Time interval after which the DStream generates a RDD
slideDuration() - Method in class org.apache.spark.streaming.dstream.InputDStream
 
sliding(int, int) - Method in class org.apache.spark.mllib.rdd.RDDFunctions
Returns a RDD from grouping items of its parent RDD in fixed size blocks by passing a sliding window over them.
sliding(int) - Method in class org.apache.spark.mllib.rdd.RDDFunctions
sliding(Int, Int)* with step = 1.
SnappyCompressionCodec - Class in org.apache.spark.io
:: DeveloperApi :: Snappy implementation of CompressionCodec.
SnappyCompressionCodec(SparkConf) - Constructor for class org.apache.spark.io.SnappyCompressionCodec
 
SnappyOutputStreamWrapper - Class in org.apache.spark.io
Wrapper over SnappyOutputStream which guards against write-after-close and double-close issues.
SnappyOutputStreamWrapper(SnappyOutputStream) - Constructor for class org.apache.spark.io.SnappyOutputStreamWrapper
 
socketStream(String, int, Function<InputStream, Iterable<T>>, StorageLevel) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream from network source hostname:port.
socketStream(String, int, Function1<InputStream, Iterator<T>>, StorageLevel, ClassTag<T>) - Method in class org.apache.spark.streaming.StreamingContext
Create a input stream from TCP source hostname:port.
socketTextStream(String, int, StorageLevel) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream from network source hostname:port.
socketTextStream(String, int) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream from network source hostname:port.
socketTextStream(String, int, StorageLevel) - Method in class org.apache.spark.streaming.StreamingContext
Create a input stream from TCP source hostname:port.
Sort() - Static method in class org.apache.spark.mllib.tree.configuration.QuantileStrategy
 
sort(String, String...) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame sorted by the specified column, all in ascending order.
sort(Column...) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame sorted by the given expressions.
sort(String, Seq<String>) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame sorted by the specified column, all in ascending order.
sort(Seq<Column>) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame sorted by the given expressions.
sort_array(Column) - Static method in class org.apache.spark.sql.functions
Sorts the input array for the given column in ascending order, according to the natural ordering of the array elements.
sort_array(Column, boolean) - Static method in class org.apache.spark.sql.functions
Sorts the input array for the given column in ascending / descending order, according to the natural ordering of the array elements.
sortBy(Function<T, S>, boolean, int) - Method in class org.apache.spark.api.java.JavaRDD
Return this RDD sorted by the given key function.
sortBy(Function1<T, K>, boolean, int, Ordering<K>, ClassTag<K>) - Method in class org.apache.spark.rdd.RDD
Return this RDD sorted by the given key function.
sortByKey() - Method in class org.apache.spark.api.java.JavaPairRDD
Sort the RDD by key, so that each partition contains a sorted range of the elements in ascending order.
sortByKey(boolean) - Method in class org.apache.spark.api.java.JavaPairRDD
Sort the RDD by key, so that each partition contains a sorted range of the elements.
sortByKey(boolean, int) - Method in class org.apache.spark.api.java.JavaPairRDD
Sort the RDD by key, so that each partition contains a sorted range of the elements.
sortByKey(Comparator<K>) - Method in class org.apache.spark.api.java.JavaPairRDD
Sort the RDD by key, so that each partition contains a sorted range of the elements.
sortByKey(Comparator<K>, boolean) - Method in class org.apache.spark.api.java.JavaPairRDD
Sort the RDD by key, so that each partition contains a sorted range of the elements.
sortByKey(Comparator<K>, boolean, int) - Method in class org.apache.spark.api.java.JavaPairRDD
Sort the RDD by key, so that each partition contains a sorted range of the elements.
sortByKey(boolean, int) - Method in class org.apache.spark.rdd.OrderedRDDFunctions
Sort the RDD by key, so that each partition contains a sorted range of the elements.
sortWithinPartitions(String, String...) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame with each partition sorted by the given expressions.
sortWithinPartitions(Column...) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame with each partition sorted by the given expressions.
sortWithinPartitions(String, Seq<String>) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame with each partition sorted by the given expressions.
sortWithinPartitions(Seq<Column>) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame with each partition sorted by the given expressions.
soundex(Column) - Static method in class org.apache.spark.sql.functions
* Return the soundex code for the specified expression.
SPARK_JOB_DESCRIPTION() - Static method in class org.apache.spark.SparkContext
 
SPARK_JOB_GROUP_ID() - Static method in class org.apache.spark.SparkContext
 
SPARK_JOB_INTERRUPT_ON_CANCEL() - Static method in class org.apache.spark.SparkContext
 
SPARK_MASTER - Static variable in class org.apache.spark.launcher.SparkLauncher
The Spark master.
spark_partition_id() - Static method in class org.apache.spark.sql.functions
Partition ID of the Spark task.
SPARK_REGEX() - Static method in class org.apache.spark.SparkMasterRegex
 
SparkAppHandle - Interface in org.apache.spark.launcher
A handle to a running Spark application.
SparkAppHandle.Listener - Interface in org.apache.spark.launcher
Listener for updates to a handle's state.
SparkAppHandle.State - Enum in org.apache.spark.launcher
Represents the application's state.
SparkConf - Class in org.apache.spark
Configuration for a Spark application.
SparkConf(boolean) - Constructor for class org.apache.spark.SparkConf
 
SparkConf() - Constructor for class org.apache.spark.SparkConf
Create a SparkConf that loads defaults from system properties and the classpath
sparkContext() - Method in class org.apache.spark.rdd.RDD
The SparkContext that created this RDD.
SparkContext - Class in org.apache.spark
Main entry point for Spark functionality.
SparkContext(SparkConf) - Constructor for class org.apache.spark.SparkContext
 
SparkContext() - Constructor for class org.apache.spark.SparkContext
Create a SparkContext that loads settings from system properties (for instance, when launching with ./bin/spark-submit).
SparkContext(SparkConf, Map<String, Set<SplitInfo>>) - Constructor for class org.apache.spark.SparkContext
:: DeveloperApi :: Alternative constructor for setting preferred locations where Spark will create executors.
SparkContext(String, String, SparkConf) - Constructor for class org.apache.spark.SparkContext
Alternative constructor that allows setting common Spark properties directly
SparkContext(String, String, String, Seq<String>, Map<String, String>, Map<String, Set<SplitInfo>>) - Constructor for class org.apache.spark.SparkContext
Alternative constructor that allows setting common Spark properties directly
sparkContext() - Method in class org.apache.spark.sql.SQLContext
 
sparkContext() - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
The underlying SparkContext
sparkContext() - Method in class org.apache.spark.streaming.StreamingContext
Return the associated Spark context
SparkContext.DoubleAccumulatorParam$ - Class in org.apache.spark
 
SparkContext.DoubleAccumulatorParam$() - Constructor for class org.apache.spark.SparkContext.DoubleAccumulatorParam$
 
SparkContext.FloatAccumulatorParam$ - Class in org.apache.spark
 
SparkContext.FloatAccumulatorParam$() - Constructor for class org.apache.spark.SparkContext.FloatAccumulatorParam$
 
SparkContext.IntAccumulatorParam$ - Class in org.apache.spark
 
SparkContext.IntAccumulatorParam$() - Constructor for class org.apache.spark.SparkContext.IntAccumulatorParam$
 
SparkContext.LongAccumulatorParam$ - Class in org.apache.spark
 
SparkContext.LongAccumulatorParam$() - Constructor for class org.apache.spark.SparkContext.LongAccumulatorParam$
 
SparkEnv - Class in org.apache.spark
:: DeveloperApi :: Holds all the runtime environment objects for a running Spark instance (either master or worker), including the serializer, Akka actor system, block manager, map output tracker, etc.
SparkEnv(String, org.apache.spark.rpc.RpcEnv, ActorSystem, Serializer, Serializer, CacheManager, MapOutputTracker, ShuffleManager, org.apache.spark.broadcast.BroadcastManager, BlockTransferService, org.apache.spark.storage.BlockManager, SecurityManager, String, org.apache.spark.metrics.MetricsSystem, MemoryManager, org.apache.spark.scheduler.OutputCommitCoordinator, SparkConf) - Constructor for class org.apache.spark.SparkEnv
 
SparkException - Exception in org.apache.spark
 
SparkException(String, Throwable) - Constructor for exception org.apache.spark.SparkException
 
SparkException(String) - Constructor for exception org.apache.spark.SparkException
 
SparkFiles - Class in org.apache.spark
Resolves paths to files added through SparkContext.addFile().
SparkFiles() - Constructor for class org.apache.spark.SparkFiles
 
sparkFilesDir() - Method in class org.apache.spark.SparkEnv
 
SparkFirehoseListener - Class in org.apache.spark
Class that allows users to receive all SparkListener events.
SparkFirehoseListener() - Constructor for class org.apache.spark.SparkFirehoseListener
 
SparkFlumeEvent - Class in org.apache.spark.streaming.flume
A wrapper class for AvroFlumeEvent's with a custom serialization format.
SparkFlumeEvent() - Constructor for class org.apache.spark.streaming.flume.SparkFlumeEvent
 
SparkJobInfo - Interface in org.apache.spark
Exposes information about Spark Jobs.
SparkJobInfoImpl - Class in org.apache.spark
 
SparkJobInfoImpl(int, int[], JobExecutionStatus) - Constructor for class org.apache.spark.SparkJobInfoImpl
 
SparkLauncher - Class in org.apache.spark.launcher
Launcher for Spark applications.
SparkLauncher() - Constructor for class org.apache.spark.launcher.SparkLauncher
 
SparkLauncher(Map<String, String>) - Constructor for class org.apache.spark.launcher.SparkLauncher
Creates a launcher that will set the given environment variables in the child.
SparkListener - Interface in org.apache.spark.scheduler
:: DeveloperApi :: Interface for listening to events from the Spark scheduler.
SparkListenerApplicationEnd - Class in org.apache.spark.scheduler
 
SparkListenerApplicationEnd(long) - Constructor for class org.apache.spark.scheduler.SparkListenerApplicationEnd
 
SparkListenerApplicationStart - Class in org.apache.spark.scheduler
 
SparkListenerApplicationStart(String, Option<String>, long, String, Option<String>, Option<Map<String, String>>) - Constructor for class org.apache.spark.scheduler.SparkListenerApplicationStart
 
SparkListenerBlockManagerAdded - Class in org.apache.spark.scheduler
 
SparkListenerBlockManagerAdded(long, BlockManagerId, long) - Constructor for class org.apache.spark.scheduler.SparkListenerBlockManagerAdded
 
SparkListenerBlockManagerRemoved - Class in org.apache.spark.scheduler
 
SparkListenerBlockManagerRemoved(long, BlockManagerId) - Constructor for class org.apache.spark.scheduler.SparkListenerBlockManagerRemoved
 
SparkListenerBlockUpdated - Class in org.apache.spark.scheduler
 
SparkListenerBlockUpdated(BlockUpdatedInfo) - Constructor for class org.apache.spark.scheduler.SparkListenerBlockUpdated
 
SparkListenerEnvironmentUpdate - Class in org.apache.spark.scheduler
 
SparkListenerEnvironmentUpdate(Map<String, Seq<Tuple2<String, String>>>) - Constructor for class org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
 
SparkListenerEvent - Interface in org.apache.spark.scheduler
 
SparkListenerExecutorAdded - Class in org.apache.spark.scheduler
 
SparkListenerExecutorAdded(long, String, ExecutorInfo) - Constructor for class org.apache.spark.scheduler.SparkListenerExecutorAdded
 
SparkListenerExecutorMetricsUpdate - Class in org.apache.spark.scheduler
Periodic updates from executors.
SparkListenerExecutorMetricsUpdate(String, Seq<Tuple4<Object, Object, Object, TaskMetrics>>) - Constructor for class org.apache.spark.scheduler.SparkListenerExecutorMetricsUpdate
 
SparkListenerExecutorRemoved - Class in org.apache.spark.scheduler
 
SparkListenerExecutorRemoved(long, String, String) - Constructor for class org.apache.spark.scheduler.SparkListenerExecutorRemoved
 
SparkListenerJobEnd - Class in org.apache.spark.scheduler
 
SparkListenerJobEnd(int, long, JobResult) - Constructor for class org.apache.spark.scheduler.SparkListenerJobEnd
 
SparkListenerJobStart - Class in org.apache.spark.scheduler
 
SparkListenerJobStart(int, long, Seq<StageInfo>, Properties) - Constructor for class org.apache.spark.scheduler.SparkListenerJobStart
 
SparkListenerStageCompleted - Class in org.apache.spark.scheduler
 
SparkListenerStageCompleted(StageInfo) - Constructor for class org.apache.spark.scheduler.SparkListenerStageCompleted
 
SparkListenerStageSubmitted - Class in org.apache.spark.scheduler
 
SparkListenerStageSubmitted(StageInfo, Properties) - Constructor for class org.apache.spark.scheduler.SparkListenerStageSubmitted
 
SparkListenerTaskEnd - Class in org.apache.spark.scheduler
 
SparkListenerTaskEnd(int, int, String, TaskEndReason, TaskInfo, TaskMetrics) - Constructor for class org.apache.spark.scheduler.SparkListenerTaskEnd
 
SparkListenerTaskGettingResult - Class in org.apache.spark.scheduler
 
SparkListenerTaskGettingResult(TaskInfo) - Constructor for class org.apache.spark.scheduler.SparkListenerTaskGettingResult
 
SparkListenerTaskStart - Class in org.apache.spark.scheduler
 
SparkListenerTaskStart(int, int, TaskInfo) - Constructor for class org.apache.spark.scheduler.SparkListenerTaskStart
 
SparkListenerUnpersistRDD - Class in org.apache.spark.scheduler
 
SparkListenerUnpersistRDD(int) - Constructor for class org.apache.spark.scheduler.SparkListenerUnpersistRDD
 
SparkMasterRegex - Class in org.apache.spark
A collection of regexes for extracting information from the master string.
SparkMasterRegex() - Constructor for class org.apache.spark.SparkMasterRegex
 
sparkPartitionId() - Static method in class org.apache.spark.sql.functions
Deprecated.
As of 1.6.0, replaced by spark_partition_id. This will be removed in Spark 2.0.
sparkProperties() - Method in class org.apache.spark.ui.env.EnvironmentListener
 
SparkShutdownHook - Class in org.apache.spark.util
 
SparkShutdownHook(int, Function0<BoxedUnit>) - Constructor for class org.apache.spark.util.SparkShutdownHook
 
SparkStageInfo - Interface in org.apache.spark
Exposes information about Spark Stages.
SparkStageInfoImpl - Class in org.apache.spark
 
SparkStageInfoImpl(int, int, long, String, int, int, int, int) - Constructor for class org.apache.spark.SparkStageInfoImpl
 
SparkStatusTracker - Class in org.apache.spark
Low-level status reporting APIs for monitoring job and stage progress.
sparkUser() - Method in class org.apache.spark.api.java.JavaSparkContext
 
sparkUser() - Method in class org.apache.spark.scheduler.SparkListenerApplicationStart
 
sparkUser() - Method in class org.apache.spark.SparkContext
 
sparkUser() - Method in class org.apache.spark.status.api.v1.ApplicationAttemptInfo
 
sparse(int, int, int[], int[], double[]) - Static method in class org.apache.spark.mllib.linalg.Matrices
Creates a column-major sparse matrix in Compressed Sparse Column (CSC) format.
sparse(int, int[], double[]) - Static method in class org.apache.spark.mllib.linalg.Vectors
Creates a sparse vector providing its index array and value array.
sparse(int, Seq<Tuple2<Object, Object>>) - Static method in class org.apache.spark.mllib.linalg.Vectors
Creates a sparse vector using unordered (index, value) pairs.
sparse(int, Iterable<Tuple2<Integer, Double>>) - Static method in class org.apache.spark.mllib.linalg.Vectors
Creates a sparse vector using unordered (index, value) pairs in a Java friendly way.
SparseMatrix - Class in org.apache.spark.mllib.linalg
Column-major sparse matrix.
SparseMatrix(int, int, int[], int[], double[], boolean) - Constructor for class org.apache.spark.mllib.linalg.SparseMatrix
 
SparseMatrix(int, int, int[], int[], double[]) - Constructor for class org.apache.spark.mllib.linalg.SparseMatrix
Column-major sparse matrix.
SparseVector - Class in org.apache.spark.mllib.linalg
A sparse vector represented by an index array and an value array.
SparseVector(int, int[], double[]) - Constructor for class org.apache.spark.mllib.linalg.SparseVector
 
sparsity() - Method in class org.apache.spark.ml.attribute.NumericAttribute
 
spdiag(Vector) - Static method in class org.apache.spark.mllib.linalg.SparseMatrix
Generate a diagonal matrix in SparseMatrix format from the supplied values.
SpecialLengths - Class in org.apache.spark.api.r
 
SpecialLengths() - Constructor for class org.apache.spark.api.r.SpecialLengths
 
speculative() - Method in class org.apache.spark.scheduler.TaskInfo
 
speculative() - Method in class org.apache.spark.status.api.v1.TaskData
 
speye(int) - Static method in class org.apache.spark.mllib.linalg.Matrices
Generate a sparse Identity Matrix in Matrix format.
speye(int) - Static method in class org.apache.spark.mllib.linalg.SparseMatrix
Generate an Identity Matrix in SparseMatrix format.
SpillListener - Class in org.apache.spark
A SparkListener that detects whether spills have occurred in Spark jobs.
SpillListener() - Constructor for class org.apache.spark.SpillListener
 
split() - Method in class org.apache.spark.ml.tree.InternalNode
 
Split - Interface in org.apache.spark.ml.tree
:: DeveloperApi :: Interface for a "Split," which specifies a test made at a decision tree node to choose the left or right path.
split() - Method in class org.apache.spark.mllib.tree.model.Node
 
Split - Class in org.apache.spark.mllib.tree.model
:: DeveloperApi :: Split applied to a feature param: feature feature index param: threshold Threshold for continuous feature.
Split(int, double, Enumeration.Value, List<Object>) - Constructor for class org.apache.spark.mllib.tree.model.Split
 
split(Column, String) - Static method in class org.apache.spark.sql.functions
Splits str around pattern (pattern is a regular expression).
SPLIT_INFO_REFLECTIONS() - Static method in class org.apache.spark.rdd.HadoopRDD
 
splitIndex() - Method in class org.apache.spark.storage.RDDBlockId
 
SplitInfo - Class in org.apache.spark.scheduler
 
SplitInfo(Class<?>, String, String, long, Object) - Constructor for class org.apache.spark.scheduler.SplitInfo
 
splits() - Method in interface org.apache.spark.api.java.JavaRDDLike
 
splits() - Method in class org.apache.spark.ml.feature.Bucketizer
Parameter for mapping continuous features into buckets.
sprand(int, int, double, Random) - Static method in class org.apache.spark.mllib.linalg.Matrices
Generate a SparseMatrix consisting of i.i.d. gaussian random numbers.
sprand(int, int, double, Random) - Static method in class org.apache.spark.mllib.linalg.SparseMatrix
Generate a SparseMatrix consisting of i.i.d.
sprandn(int, int, double, Random) - Static method in class org.apache.spark.mllib.linalg.Matrices
Generate a SparseMatrix consisting of i.i.d. gaussian random numbers.
sprandn(int, int, double, Random) - Static method in class org.apache.spark.mllib.linalg.SparseMatrix
Generate a SparseMatrix consisting of i.i.d.
sqdist(Vector, Vector) - Static method in class org.apache.spark.mllib.linalg.Vectors
Returns the squared distance between two Vectors.
sql(String) - Method in class org.apache.spark.sql.SQLContext
 
sqlContext() - Method in class org.apache.spark.ml.clustering.LDAModel
 
sqlContext() - Method in class org.apache.spark.sql.DataFrame
 
sqlContext() - Method in class org.apache.spark.sql.Dataset
 
sqlContext() - Method in class org.apache.spark.sql.sources.BaseRelation
 
SQLContext - Class in org.apache.spark.sql
The entry point for working with structured data (rows and columns) in Spark.
SQLContext(SparkContext) - Constructor for class org.apache.spark.sql.SQLContext
 
SQLContext(JavaSparkContext) - Constructor for class org.apache.spark.sql.SQLContext
 
SQLContext.implicits$ - Class in org.apache.spark.sql
:: Experimental :: (Scala-specific) Implicit methods available in Scala for converting common Scala objects into DataFrames.
SQLContext.implicits$() - Constructor for class org.apache.spark.sql.SQLContext.implicits$
 
SQLContext.implicits$.StringToColumn - Class in org.apache.spark.sql
Converts $"col name" into an Column.
SQLContext.implicits$.StringToColumn(StringContext) - Constructor for class org.apache.spark.sql.SQLContext.implicits$.StringToColumn
 
SQLContext.QueryExecution - Class in org.apache.spark.sql
 
SQLContext.QueryExecution(LogicalPlan) - Constructor for class org.apache.spark.sql.SQLContext.QueryExecution
 
SQLContext.SparkPlanner - Class in org.apache.spark.sql
 
SQLContext.SparkPlanner() - Constructor for class org.apache.spark.sql.SQLContext.SparkPlanner
 
SQLImplicits - Class in org.apache.spark.sql
A collection of implicit methods for converting common Scala objects into DataFrames.
SQLImplicits() - Constructor for class org.apache.spark.sql.SQLImplicits
 
sqlParser() - Method in class org.apache.spark.sql.SQLContext
 
SQLTransformer - Class in org.apache.spark.ml.feature
:: Experimental :: Implements the transformations which are defined by SQL statement.
SQLTransformer(String) - Constructor for class org.apache.spark.ml.feature.SQLTransformer
 
SQLTransformer() - Constructor for class org.apache.spark.ml.feature.SQLTransformer
 
sqlType() - Method in class org.apache.spark.mllib.linalg.VectorUDT
 
sqlType() - Method in class org.apache.spark.sql.types.UserDefinedType
Underlying storage type for this UDT
SQLUserDefinedType - Annotation Type in org.apache.spark.sql.types
::DeveloperApi:: A user-defined type which can be automatically recognized by a SQLContext and registered.
sqrt(Column) - Static method in class org.apache.spark.sql.functions
Computes the square root of the specified float value.
sqrt(String) - Static method in class org.apache.spark.sql.functions
Computes the square root of the specified float value.
squaredDist(Vector) - Method in class org.apache.spark.util.Vector
 
SquaredError - Class in org.apache.spark.mllib.tree.loss
:: DeveloperApi :: Class for squared error loss calculation.
SquaredError() - Constructor for class org.apache.spark.mllib.tree.loss.SquaredError
 
SquaredL2Updater - Class in org.apache.spark.mllib.optimization
:: DeveloperApi :: Updater for L2 regularized problems.
SquaredL2Updater() - Constructor for class org.apache.spark.mllib.optimization.SquaredL2Updater
 
Src - Static variable in class org.apache.spark.graphx.TripletFields
Expose the source and edge fields but not the destination field.
srcAttr() - Method in class org.apache.spark.graphx.EdgeContext
The vertex attribute of the edge's source vertex.
srcAttr() - Method in class org.apache.spark.graphx.EdgeTriplet
The source vertex attribute
srcAttr() - Method in class org.apache.spark.graphx.impl.AggregatingEdgeContext
 
srcId() - Method in class org.apache.spark.graphx.Edge
 
srcId() - Method in class org.apache.spark.graphx.EdgeContext
The vertex id of the edge's source vertex.
srcId() - Method in class org.apache.spark.graphx.impl.AggregatingEdgeContext
 
srdd() - Method in class org.apache.spark.api.java.JavaDoubleRDD
 
ssc() - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
 
ssc() - Method in class org.apache.spark.streaming.dstream.DStream
 
stackTrace() - Method in class org.apache.spark.ExceptionFailure
 
stage() - Method in class org.apache.spark.scheduler.AskPermissionToCommitOutput
 
stageAttemptId() - Method in class org.apache.spark.scheduler.SparkListenerTaskEnd
 
stageAttemptId() - Method in class org.apache.spark.scheduler.SparkListenerTaskStart
 
StageData - Class in org.apache.spark.status.api.v1
 
stageFailed(String) - Method in class org.apache.spark.scheduler.StageInfo
 
stageId() - Method in class org.apache.spark.scheduler.SparkListenerTaskEnd
 
stageId() - Method in class org.apache.spark.scheduler.SparkListenerTaskStart
 
stageId() - Method in class org.apache.spark.scheduler.StageInfo
 
stageId() - Method in interface org.apache.spark.SparkStageInfo
 
stageId() - Method in class org.apache.spark.SparkStageInfoImpl
 
stageId() - Method in class org.apache.spark.status.api.v1.StageData
 
stageId() - Method in class org.apache.spark.TaskContext
The ID of the stage that this task belong to.
stageIds() - Method in class org.apache.spark.scheduler.SparkListenerJobStart
 
stageIds() - Method in interface org.apache.spark.SparkJobInfo
 
stageIds() - Method in class org.apache.spark.SparkJobInfoImpl
 
stageIds() - Method in class org.apache.spark.status.api.v1.JobData
 
stageIdToActiveJobIds() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
stageIdToData() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
stageIdToInfo() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
stageInfo() - Method in class org.apache.spark.scheduler.SparkListenerStageCompleted
 
stageInfo() - Method in class org.apache.spark.scheduler.SparkListenerStageSubmitted
 
StageInfo - Class in org.apache.spark.scheduler
:: DeveloperApi :: Stores information about a stage to pass from the scheduler to SparkListeners.
StageInfo(int, int, String, int, Seq<RDDInfo>, Seq<Object>, String, Seq<Seq<TaskLocation>>) - Constructor for class org.apache.spark.scheduler.StageInfo
 
stageInfos() - Method in class org.apache.spark.scheduler.SparkListenerJobStart
 
stageLogInfo(int, String, boolean) - Method in class org.apache.spark.scheduler.JobLogger
Write info into log file
stages() - Method in class org.apache.spark.ml.Pipeline
param for pipeline stages
stages() - Method in class org.apache.spark.ml.PipelineModel
 
StageStatus - Enum in org.apache.spark.status.api.v1
 
StandardNormalGenerator - Class in org.apache.spark.mllib.random
:: DeveloperApi :: Generates i.i.d.
StandardNormalGenerator() - Constructor for class org.apache.spark.mllib.random.StandardNormalGenerator
 
StandardScaler - Class in org.apache.spark.ml.feature
:: Experimental :: Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set.
StandardScaler(String) - Constructor for class org.apache.spark.ml.feature.StandardScaler
 
StandardScaler() - Constructor for class org.apache.spark.ml.feature.StandardScaler
 
StandardScaler - Class in org.apache.spark.mllib.feature
Standardizes features by removing the mean and scaling to unit std using column summary statistics on the samples in the training set.
StandardScaler(boolean, boolean) - Constructor for class org.apache.spark.mllib.feature.StandardScaler
 
StandardScaler() - Constructor for class org.apache.spark.mllib.feature.StandardScaler
 
StandardScalerModel - Class in org.apache.spark.ml.feature
 
StandardScalerModel - Class in org.apache.spark.mllib.feature
Represents a StandardScaler model that can transform vectors.
StandardScalerModel(Vector, Vector, boolean, boolean) - Constructor for class org.apache.spark.mllib.feature.StandardScalerModel
 
StandardScalerModel(Vector, Vector) - Constructor for class org.apache.spark.mllib.feature.StandardScalerModel
 
StandardScalerModel(Vector) - Constructor for class org.apache.spark.mllib.feature.StandardScalerModel
 
starGraph(SparkContext, int) - Static method in class org.apache.spark.graphx.util.GraphGenerators
Create a star graph with vertex 0 being the center.
start() - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Start the execution of the streams.
start() - Method in class org.apache.spark.streaming.dstream.ConstantInputDStream
 
start() - Method in class org.apache.spark.streaming.dstream.InputDStream
Method called to start receiving data.
start() - Method in class org.apache.spark.streaming.dstream.ReceiverInputDStream
 
start() - Method in class org.apache.spark.streaming.StreamingContext
Start the execution of the streams.
startApplication(SparkAppHandle.Listener...) - Method in class org.apache.spark.launcher.SparkLauncher
Starts a Spark application.
startIndexInLevel(int) - Static method in class org.apache.spark.mllib.tree.model.Node
Return the index of the first node in the given level.
startPosition() - Method in exception org.apache.spark.sql.AnalysisException
 
startsWith(Column) - Method in class org.apache.spark.sql.Column
String starts with.
startsWith(String) - Method in class org.apache.spark.sql.Column
String starts with another string literal.
startTime() - Method in class org.apache.spark.api.java.JavaSparkContext
 
startTime() - Method in class org.apache.spark.SparkContext
 
startTime() - Method in class org.apache.spark.status.api.v1.ApplicationAttemptInfo
 
startTime() - Method in class org.apache.spark.streaming.scheduler.OutputOperationInfo
 
startTime() - Method in class org.apache.spark.ui.jobs.JobProgressListener
 
stat() - Method in class org.apache.spark.sql.DataFrame
Returns a DataFrameStatFunctions for working statistic functions support.
StatCounter - Class in org.apache.spark.util
A class for tracking the statistics of a set of numbers (count, mean and variance) in a numerically robust way.
StatCounter(TraversableOnce<Object>) - Constructor for class org.apache.spark.util.StatCounter
 
StatCounter() - Constructor for class org.apache.spark.util.StatCounter
Initialize the StatCounter with no values.
state() - Method in class org.apache.spark.scheduler.local.StatusUpdate
 
State<S> - Class in org.apache.spark.streaming
:: Experimental :: Abstract class for getting and updating the state in mapping function used in the mapWithState operation of a pair DStream (Scala) or a JavaPairDStream (Java).
State() - Constructor for class org.apache.spark.streaming.State
 
stateChanged(SparkAppHandle) - Method in interface org.apache.spark.launcher.SparkAppHandle.Listener
Callback for changes in the handle's state.
statement() - Method in class org.apache.spark.ml.feature.SQLTransformer
SQL statement parameter.
stateSnapshots() - Method in class org.apache.spark.streaming.api.java.JavaMapWithStateDStream
 
stateSnapshots() - Method in class org.apache.spark.streaming.dstream.MapWithStateDStream
Return a pair DStream where each RDD is the snapshot of the state of all the keys.
StateSpec<KeyType,ValueType,StateType,MappedType> - Class in org.apache.spark.streaming
:: Experimental :: Abstract class representing all the specifications of the DStream transformation mapWithState operation of a pair DStream (Scala) or a JavaPairDStream (Java).
StateSpec() - Constructor for class org.apache.spark.streaming.StateSpec
 
staticPageRank(int, double) - Method in class org.apache.spark.graphx.GraphOps
Run PageRank for a fixed number of iterations returning a graph with vertex attributes containing the PageRank and edge attributes the normalized edge weight.
staticPersonalizedPageRank(long, int, double) - Method in class org.apache.spark.graphx.GraphOps
Run Personalized PageRank for a fixed number of iterations with with all iterations originating at the source node returning a graph with vertex attributes containing the PageRank and edge attributes the normalized edge weight.
statistic() - Method in class org.apache.spark.mllib.stat.test.ChiSqTestResult
 
statistic() - Method in class org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult
 
statistic() - Method in interface org.apache.spark.mllib.stat.test.TestResult
Test statistic.
Statistics - Class in org.apache.spark.mllib.stat
 
Statistics() - Constructor for class org.apache.spark.mllib.stat.Statistics
 
Statistics - Class in org.apache.spark.streaming.receiver
:: DeveloperApi :: Statistics for querying the supervisor about state of workers.
Statistics(int, int, int, String) - Constructor for class org.apache.spark.streaming.receiver.Statistics
 
stats() - Method in class org.apache.spark.api.java.JavaDoubleRDD
Return a StatCounter object that captures the mean, variance and count of the RDD's elements in one operation.
stats() - Method in class org.apache.spark.mllib.tree.model.Node
 
stats() - Method in class org.apache.spark.rdd.DoubleRDDFunctions
Return a StatCounter object that captures the mean, variance and count of the RDD's elements in one operation.
StatsReportListener - Class in org.apache.spark.scheduler
:: DeveloperApi :: Simple SparkListener that logs a few summary statistics when each stage completes
StatsReportListener() - Constructor for class org.apache.spark.scheduler.StatsReportListener
 
StatsReportListener - Class in org.apache.spark.streaming.scheduler
:: DeveloperApi :: A simple StreamingListener that logs summary statistics across Spark Streaming batches param: numBatchInfos Number of last batches to consider for generating statistics (default: 10)
StatsReportListener(int) - Constructor for class org.apache.spark.streaming.scheduler.StatsReportListener
 
status() - Method in class org.apache.spark.scheduler.TaskInfo
 
status() - Method in interface org.apache.spark.SparkJobInfo
 
status() - Method in class org.apache.spark.SparkJobInfoImpl
 
status() - Method in class org.apache.spark.status.api.v1.JobData
 
status() - Method in class org.apache.spark.status.api.v1.StageData
 
statusTracker() - Method in class org.apache.spark.api.java.JavaSparkContext
 
statusTracker() - Method in class org.apache.spark.SparkContext
 
StatusUpdate - Class in org.apache.spark.scheduler.local
 
StatusUpdate(long, Enumeration.Value, ByteBuffer) - Constructor for class org.apache.spark.scheduler.local.StatusUpdate
 
std() - Method in class org.apache.spark.ml.attribute.NumericAttribute
 
std() - Method in class org.apache.spark.ml.feature.StandardScalerModel
 
std() - Method in class org.apache.spark.mllib.feature.StandardScalerModel
 
std() - Method in class org.apache.spark.mllib.random.LogNormalGenerator
 
stddev(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: alias for stddev_samp.
stddev(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: alias for stddev_samp.
stddev_pop(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the population standard deviation of the expression in a group.
stddev_pop(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the population standard deviation of the expression in a group.
stddev_samp(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the sample standard deviation of the expression in a group.
stddev_samp(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the sample standard deviation of the expression in a group.
stdev() - Method in class org.apache.spark.api.java.JavaDoubleRDD
Compute the standard deviation of this RDD's elements.
stdev() - Method in class org.apache.spark.rdd.DoubleRDDFunctions
Compute the standard deviation of this RDD's elements.
stdev() - Method in class org.apache.spark.util.StatCounter
Return the standard deviation of the values.
stop() - Method in class org.apache.spark.api.java.JavaSparkContext
Shut down the SparkContext.
stop() - Method in interface org.apache.spark.broadcast.BroadcastFactory
 
stop() - Method in class org.apache.spark.broadcast.HttpBroadcastFactory
 
stop() - Method in class org.apache.spark.broadcast.TorrentBroadcastFactory
 
stop() - Method in interface org.apache.spark.launcher.SparkAppHandle
Asks the application to stop.
stop() - Method in class org.apache.spark.SparkContext
 
stop() - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Stop the execution of the streams.
stop(boolean) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Stop the execution of the streams.
stop(boolean, boolean) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Stop the execution of the streams.
stop() - Method in class org.apache.spark.streaming.dstream.ConstantInputDStream
 
stop() - Method in class org.apache.spark.streaming.dstream.InputDStream
Method called to stop receiving data.
stop() - Method in class org.apache.spark.streaming.dstream.ReceiverInputDStream
 
stop(String) - Method in class org.apache.spark.streaming.receiver.Receiver
Stop the receiver completely.
stop(String, Throwable) - Method in class org.apache.spark.streaming.receiver.Receiver
Stop the receiver completely due to an exception
stop(boolean) - Method in class org.apache.spark.streaming.StreamingContext
Stop the execution of the streams immediately (does not wait for all received data to be processed).
stop(boolean, boolean) - Method in class org.apache.spark.streaming.StreamingContext
Stop the execution of the streams, with option of ensuring all received data has been processed.
StopCoordinator - Class in org.apache.spark.scheduler
 
StopCoordinator() - Constructor for class org.apache.spark.scheduler.StopCoordinator
 
StopExecutor - Class in org.apache.spark.scheduler.local
 
StopExecutor() - Constructor for class org.apache.spark.scheduler.local.StopExecutor
 
stopped() - Method in class org.apache.spark.SparkContext
 
stopWords() - Method in class org.apache.spark.ml.feature.StopWordsRemover
the stop words set to be filtered out Default: StopWords.English
StopWordsRemover - Class in org.apache.spark.ml.feature
:: Experimental :: A feature transformer that filters out stop words from input.
StopWordsRemover(String) - Constructor for class org.apache.spark.ml.feature.StopWordsRemover
 
StopWordsRemover() - Constructor for class org.apache.spark.ml.feature.StopWordsRemover
 
storageLevel() - Method in class org.apache.spark.status.api.v1.RDDPartitionInfo
 
storageLevel() - Method in class org.apache.spark.status.api.v1.RDDStorageInfo
 
storageLevel() - Method in class org.apache.spark.storage.BlockStatus
 
storageLevel() - Method in class org.apache.spark.storage.BlockUpdatedInfo
 
storageLevel() - Method in class org.apache.spark.storage.RDDInfo
 
StorageLevel - Class in org.apache.spark.storage
:: DeveloperApi :: Flags for controlling the storage of an RDD.
StorageLevel() - Constructor for class org.apache.spark.storage.StorageLevel
 
storageLevel() - Method in class org.apache.spark.streaming.dstream.DStream
 
storageLevel() - Method in class org.apache.spark.streaming.receiver.Receiver
 
storageLevelCache() - Static method in class org.apache.spark.storage.StorageLevel
:: DeveloperApi :: Read StorageLevel object from ObjectInput stream.
StorageLevels - Class in org.apache.spark.api.java
Expose some commonly useful storage level constants.
StorageLevels() - Constructor for class org.apache.spark.api.java.StorageLevels
 
StorageListener - Class in org.apache.spark.ui.storage
:: DeveloperApi :: A SparkListener that prepares information to be displayed on the BlockManagerUI.
StorageListener(StorageStatusListener) - Constructor for class org.apache.spark.ui.storage.StorageListener
 
StorageStatus - Class in org.apache.spark.storage
:: DeveloperApi :: Storage information for each BlockManager.
StorageStatus(BlockManagerId, long) - Constructor for class org.apache.spark.storage.StorageStatus
 
StorageStatus(BlockManagerId, long, Map<BlockId, BlockStatus>) - Constructor for class org.apache.spark.storage.StorageStatus
Create a storage status with an initial set of blocks, leaving the source unmodified.
storageStatusList() - Method in class org.apache.spark.storage.StorageStatusListener
 
storageStatusList() - Method in class org.apache.spark.ui.exec.ExecutorsListener
 
storageStatusList() - Method in class org.apache.spark.ui.storage.StorageListener
 
StorageStatusListener - Class in org.apache.spark.storage
:: DeveloperApi :: A SparkListener that maintains executor storage status.
StorageStatusListener() - Constructor for class org.apache.spark.storage.StorageStatusListener
 
store(Iterator<T>) - Method in interface org.apache.spark.streaming.receiver.ActorHelper
Store an iterator of received data as a data block into Spark's memory.
store(ByteBuffer) - Method in interface org.apache.spark.streaming.receiver.ActorHelper
Store the bytes of received data as a data block into Spark's memory.
store(T) - Method in interface org.apache.spark.streaming.receiver.ActorHelper
Store a single item of received data to Spark's memory.
store(T) - Method in class org.apache.spark.streaming.receiver.Receiver
Store a single item of received data to Spark's memory.
store(ArrayBuffer<T>) - Method in class org.apache.spark.streaming.receiver.Receiver
Store an ArrayBuffer of received data as a data block into Spark's memory.
store(ArrayBuffer<T>, Object) - Method in class org.apache.spark.streaming.receiver.Receiver
Store an ArrayBuffer of received data as a data block into Spark's memory.
store(Iterator<T>) - Method in class org.apache.spark.streaming.receiver.Receiver
Store an iterator of received data as a data block into Spark's memory.
store(Iterator<T>, Object) - Method in class org.apache.spark.streaming.receiver.Receiver
Store an iterator of received data as a data block into Spark's memory.
store(Iterator<T>) - Method in class org.apache.spark.streaming.receiver.Receiver
Store an iterator of received data as a data block into Spark's memory.
store(Iterator<T>, Object) - Method in class org.apache.spark.streaming.receiver.Receiver
Store an iterator of received data as a data block into Spark's memory.
store(ByteBuffer) - Method in class org.apache.spark.streaming.receiver.Receiver
Store the bytes of received data as a data block into Spark's memory.
store(ByteBuffer, Object) - Method in class org.apache.spark.streaming.receiver.Receiver
Store the bytes of received data as a data block into Spark's memory.
Strategy - Class in org.apache.spark.mllib.tree.configuration
Stores all the configuration options for tree construction param: algo Learning goal.
Strategy(Enumeration.Value, Impurity, int, int, int, Enumeration.Value, Map<Object, Object>, int, double, int, double, boolean, int) - Constructor for class org.apache.spark.mllib.tree.configuration.Strategy
 
Strategy(Enumeration.Value, Impurity, int, int, int, Map<Integer, Integer>) - Constructor for class org.apache.spark.mllib.tree.configuration.Strategy
Java-friendly constructor for Strategy
STREAM() - Static method in class org.apache.spark.storage.BlockId
 
StreamBlockId - Class in org.apache.spark.storage
 
StreamBlockId(int, long) - Constructor for class org.apache.spark.storage.StreamBlockId
 
streamId() - Method in class org.apache.spark.storage.StreamBlockId
 
streamId() - Method in class org.apache.spark.streaming.receiver.Receiver
Get the unique identifier the receiver input stream that this receiver is associated with.
streamId() - Method in class org.apache.spark.streaming.scheduler.ReceiverInfo
 
streamIdToInputInfo() - Method in class org.apache.spark.streaming.scheduler.BatchInfo
 
streamIdToNumRecords() - Method in class org.apache.spark.streaming.scheduler.BatchInfo
 
StreamingContext - Class in org.apache.spark.streaming
Main entry point for Spark Streaming functionality.
StreamingContext(SparkContext, Duration) - Constructor for class org.apache.spark.streaming.StreamingContext
Create a StreamingContext using an existing SparkContext.
StreamingContext(SparkConf, Duration) - Constructor for class org.apache.spark.streaming.StreamingContext
Create a StreamingContext by providing the configuration necessary for a new SparkContext.
StreamingContext(String, String, Duration, String, Seq<String>, Map<String, String>) - Constructor for class org.apache.spark.streaming.StreamingContext
Create a StreamingContext by providing the details necessary for creating a new SparkContext.
StreamingContext(String, Configuration) - Constructor for class org.apache.spark.streaming.StreamingContext
Recreate a StreamingContext from a checkpoint file.
StreamingContext(String) - Constructor for class org.apache.spark.streaming.StreamingContext
Recreate a StreamingContext from a checkpoint file.
StreamingContext(String, SparkContext) - Constructor for class org.apache.spark.streaming.StreamingContext
Recreate a StreamingContext from a checkpoint file using an existing SparkContext.
StreamingContextState - Enum in org.apache.spark.streaming
:: DeveloperApi :: Represents the state of a StreamingContext.
StreamingKMeans - Class in org.apache.spark.mllib.clustering
StreamingKMeans provides methods for configuring a streaming k-means analysis, training the model on streaming, and using the model to make predictions on streaming data.
StreamingKMeans(int, double, String) - Constructor for class org.apache.spark.mllib.clustering.StreamingKMeans
 
StreamingKMeans() - Constructor for class org.apache.spark.mllib.clustering.StreamingKMeans
 
StreamingKMeansModel - Class in org.apache.spark.mllib.clustering
StreamingKMeansModel extends MLlib's KMeansModel for streaming algorithms, so it can keep track of a continuously updated weight associated with each cluster, and also update the model by doing a single iteration of the standard k-means algorithm.
StreamingKMeansModel(Vector[], double[]) - Constructor for class org.apache.spark.mllib.clustering.StreamingKMeansModel
 
StreamingLinearAlgorithm<M extends GeneralizedLinearModel,A extends GeneralizedLinearAlgorithm<M>> - Class in org.apache.spark.mllib.regression
:: DeveloperApi :: StreamingLinearAlgorithm implements methods for continuously training a generalized linear model model on streaming data, and using it for prediction on (possibly different) streaming data.
StreamingLinearAlgorithm() - Constructor for class org.apache.spark.mllib.regression.StreamingLinearAlgorithm
 
StreamingLinearRegressionWithSGD - Class in org.apache.spark.mllib.regression
Train or predict a linear regression model on streaming data.
StreamingLinearRegressionWithSGD() - Constructor for class org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
Construct a StreamingLinearRegression object with default parameters: {stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0}.
StreamingListener - Interface in org.apache.spark.streaming.scheduler
:: DeveloperApi :: A listener interface for receiving information about an ongoing streaming computation.
StreamingListenerBatchCompleted - Class in org.apache.spark.streaming.scheduler
 
StreamingListenerBatchCompleted(BatchInfo) - Constructor for class org.apache.spark.streaming.scheduler.StreamingListenerBatchCompleted
 
StreamingListenerBatchStarted - Class in org.apache.spark.streaming.scheduler
 
StreamingListenerBatchStarted(BatchInfo) - Constructor for class org.apache.spark.streaming.scheduler.StreamingListenerBatchStarted
 
StreamingListenerBatchSubmitted - Class in org.apache.spark.streaming.scheduler
 
StreamingListenerBatchSubmitted(BatchInfo) - Constructor for class org.apache.spark.streaming.scheduler.StreamingListenerBatchSubmitted
 
StreamingListenerEvent - Interface in org.apache.spark.streaming.scheduler
:: DeveloperApi :: Base trait for events related to StreamingListener
StreamingListenerOutputOperationCompleted - Class in org.apache.spark.streaming.scheduler
 
StreamingListenerOutputOperationCompleted(OutputOperationInfo) - Constructor for class org.apache.spark.streaming.scheduler.StreamingListenerOutputOperationCompleted
 
StreamingListenerOutputOperationStarted - Class in org.apache.spark.streaming.scheduler
 
StreamingListenerOutputOperationStarted(OutputOperationInfo) - Constructor for class org.apache.spark.streaming.scheduler.StreamingListenerOutputOperationStarted
 
StreamingListenerReceiverError - Class in org.apache.spark.streaming.scheduler
 
StreamingListenerReceiverError(ReceiverInfo) - Constructor for class org.apache.spark.streaming.scheduler.StreamingListenerReceiverError
 
StreamingListenerReceiverStarted - Class in org.apache.spark.streaming.scheduler
 
StreamingListenerReceiverStarted(ReceiverInfo) - Constructor for class org.apache.spark.streaming.scheduler.StreamingListenerReceiverStarted
 
StreamingListenerReceiverStopped - Class in org.apache.spark.streaming.scheduler
 
StreamingListenerReceiverStopped(ReceiverInfo) - Constructor for class org.apache.spark.streaming.scheduler.StreamingListenerReceiverStopped
 
StreamingLogisticRegressionWithSGD - Class in org.apache.spark.mllib.classification
Train or predict a logistic regression model on streaming data.
StreamingLogisticRegressionWithSGD() - Constructor for class org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD
Construct a StreamingLogisticRegression object with default parameters: {stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0, regParam: 0.0}.
StreamingTest - Class in org.apache.spark.mllib.stat.test
 
StreamingTest() - Constructor for class org.apache.spark.mllib.stat.test.StreamingTest
 
StreamInputInfo - Class in org.apache.spark.streaming.scheduler
:: DeveloperApi :: Track the information of input stream at specified batch time.
StreamInputInfo(int, long, Map<String, Object>) - Constructor for class org.apache.spark.streaming.scheduler.StreamInputInfo
 
string() - Method in class org.apache.spark.sql.ColumnName
Creates a new StructField of type string.
STRING() - Static method in class org.apache.spark.sql.Encoders
An encoder for nullable string type.
StringArrayParam - Class in org.apache.spark.ml.param
:: DeveloperApi :: Specialized version of Param[Array[String} for Java.
StringArrayParam(Params, String, String, Function1<String[], Object>) - Constructor for class org.apache.spark.ml.param.StringArrayParam
 
StringArrayParam(Params, String, String) - Constructor for class org.apache.spark.ml.param.StringArrayParam
 
StringContains - Class in org.apache.spark.sql.sources
A filter that evaluates to true iff the attribute evaluates to a string that contains the string value.
StringContains(String, String) - Constructor for class org.apache.spark.sql.sources.StringContains
 
StringEndsWith - Class in org.apache.spark.sql.sources
A filter that evaluates to true iff the attribute evaluates to a string that starts with value.
StringEndsWith(String, String) - Constructor for class org.apache.spark.sql.sources.StringEndsWith
 
StringIndexer - Class in org.apache.spark.ml.feature
:: Experimental :: A label indexer that maps a string column of labels to an ML column of label indices.
StringIndexer(String) - Constructor for class org.apache.spark.ml.feature.StringIndexer
 
StringIndexer() - Constructor for class org.apache.spark.ml.feature.StringIndexer
 
StringIndexerModel - Class in org.apache.spark.ml.feature
:: Experimental :: Model fitted by StringIndexer.
StringIndexerModel(String, String[]) - Constructor for class org.apache.spark.ml.feature.StringIndexerModel
 
StringIndexerModel(String[]) - Constructor for class org.apache.spark.ml.feature.StringIndexerModel
 
stringRddToDataFrameHolder(RDD<String>) - Method in class org.apache.spark.sql.SQLImplicits
Creates a single column DataFrame from an RDD[String].
stringResult() - Method in class org.apache.spark.sql.hive.HiveContext.QueryExecution
Returns the result as a hive compatible sequence of strings.
StringRRDD<T> - Class in org.apache.spark.api.r
An RDD that stores R objects as Array[String].
StringRRDD(RDD<T>, byte[], String, byte[], Object[], ClassTag<T>) - Constructor for class org.apache.spark.api.r.StringRRDD
 
StringStartsWith - Class in org.apache.spark.sql.sources
A filter that evaluates to true iff the attribute evaluates to a string that starts with value.
StringStartsWith(String, String) - Constructor for class org.apache.spark.sql.sources.StringStartsWith
 
stringToText(String) - Static method in class org.apache.spark.SparkContext
 
StringType - Static variable in class org.apache.spark.sql.types.DataTypes
Gets the StringType object.
StringType - Class in org.apache.spark.sql.types
:: DeveloperApi :: The data type representing String values.
stringWritableConverter() - Static method in class org.apache.spark.SparkContext
 
stronglyConnectedComponents(int) - Method in class org.apache.spark.graphx.GraphOps
Compute the strongly connected component (SCC) of each vertex and return a graph with the vertex value containing the lowest vertex id in the SCC containing that vertex.
StronglyConnectedComponents - Class in org.apache.spark.graphx.lib
Strongly connected components algorithm implementation.
StronglyConnectedComponents() - Constructor for class org.apache.spark.graphx.lib.StronglyConnectedComponents
 
struct(Seq<StructField>) - Method in class org.apache.spark.sql.ColumnName
Creates a new StructField of type struct.
struct(StructType) - Method in class org.apache.spark.sql.ColumnName
Creates a new StructField of type struct.
struct(Column...) - Static method in class org.apache.spark.sql.functions
Creates a new struct column.
struct(String, String...) - Static method in class org.apache.spark.sql.functions
Creates a new struct column that composes multiple input columns.
struct(Seq<Column>) - Static method in class org.apache.spark.sql.functions
Creates a new struct column.
struct(String, Seq<String>) - Static method in class org.apache.spark.sql.functions
Creates a new struct column that composes multiple input columns.
StructField - Class in org.apache.spark.sql.types
A field inside a StructType.
StructField(String, DataType, boolean, Metadata) - Constructor for class org.apache.spark.sql.types.StructField
 
StructField() - Constructor for class org.apache.spark.sql.types.StructField
No-arg constructor for kryo.
StructType - Class in org.apache.spark.sql.types
:: DeveloperApi :: A StructType object can be constructed by
StructType(StructField[]) - Constructor for class org.apache.spark.sql.types.StructType
 
StructType() - Constructor for class org.apache.spark.sql.types.StructType
No-arg constructor for kryo.
subgraph(Function1<EdgeTriplet<VD, ED>, Object>, Function2<Object, VD, Object>) - Method in class org.apache.spark.graphx.Graph
Restricts the graph to only the vertices and edges satisfying the predicates.
subgraph(Function1<EdgeTriplet<VD, ED>, Object>, Function2<Object, VD, Object>) - Method in class org.apache.spark.graphx.impl.GraphImpl
 
submissionTime() - Method in class org.apache.spark.scheduler.StageInfo
When this stage was submitted from the DAGScheduler to a TaskScheduler.
submissionTime() - Method in interface org.apache.spark.SparkStageInfo
 
submissionTime() - Method in class org.apache.spark.SparkStageInfoImpl
 
submissionTime() - Method in class org.apache.spark.status.api.v1.JobData
 
submissionTime() - Method in class org.apache.spark.streaming.scheduler.BatchInfo
 
submitJob(RDD<T>, Function1<Iterator<T>, U>, Seq<Object>, Function2<Object, U, BoxedUnit>, Function0<R>) - Method in class org.apache.spark.SparkContext
Submit a job for execution and return a FutureJob holding the result.
subsamplingRate() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
subsetAccuracy() - Method in class org.apache.spark.mllib.evaluation.MultilabelMetrics
Returns subset accuracy (for equal sets of labels)
substitutor() - Method in class org.apache.spark.sql.hive.HiveContext
 
substr(Column, Column) - Method in class org.apache.spark.sql.Column
An expression that returns a substring.
substr(int, int) - Method in class org.apache.spark.sql.Column
An expression that returns a substring.
substring(Column, int, int) - Static method in class org.apache.spark.sql.functions
Substring starts at pos and is of length len when str is String type or returns the slice of byte array that starts at pos in byte and is of length len when str is Binary type
substring_index(Column, String, int) - Static method in class org.apache.spark.sql.functions
Returns the substring from string str before count occurrences of the delimiter delim.
subtract(JavaDoubleRDD) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Return an RDD with the elements from this that are not in other.
subtract(JavaDoubleRDD, int) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Return an RDD with the elements from this that are not in other.
subtract(JavaDoubleRDD, Partitioner) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Return an RDD with the elements from this that are not in other.
subtract(JavaPairRDD<K, V>) - Method in class org.apache.spark.api.java.JavaPairRDD
Return an RDD with the elements from this that are not in other.
subtract(JavaPairRDD<K, V>, int) - Method in class org.apache.spark.api.java.JavaPairRDD
Return an RDD with the elements from this that are not in other.
subtract(JavaPairRDD<K, V>, Partitioner) - Method in class org.apache.spark.api.java.JavaPairRDD
Return an RDD with the elements from this that are not in other.
subtract(JavaRDD<T>) - Method in class org.apache.spark.api.java.JavaRDD
Return an RDD with the elements from this that are not in other.
subtract(JavaRDD<T>, int) - Method in class org.apache.spark.api.java.JavaRDD
Return an RDD with the elements from this that are not in other.
subtract(JavaRDD<T>, Partitioner) - Method in class org.apache.spark.api.java.JavaRDD
Return an RDD with the elements from this that are not in other.
subtract(RDD<T>) - Method in class org.apache.spark.rdd.RDD
Return an RDD with the elements from this that are not in other.
subtract(RDD<T>, int) - Method in class org.apache.spark.rdd.RDD
Return an RDD with the elements from this that are not in other.
subtract(RDD<T>, Partitioner, Ordering<T>) - Method in class org.apache.spark.rdd.RDD
Return an RDD with the elements from this that are not in other.
subtract(Dataset<T>) - Method in class org.apache.spark.sql.Dataset
Returns a new Dataset where any elements present in other have been removed.
subtract(Vector) - Method in class org.apache.spark.util.Vector
 
subtractByKey(JavaPairRDD<K, W>) - Method in class org.apache.spark.api.java.JavaPairRDD
Return an RDD with the pairs from this whose keys are not in other.
subtractByKey(JavaPairRDD<K, W>, int) - Method in class org.apache.spark.api.java.JavaPairRDD
Return an RDD with the pairs from `this` whose keys are not in `other`.
subtractByKey(JavaPairRDD<K, W>, Partitioner) - Method in class org.apache.spark.api.java.JavaPairRDD
Return an RDD with the pairs from `this` whose keys are not in `other`.
subtractByKey(RDD<Tuple2<K, W>>, ClassTag<W>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Return an RDD with the pairs from this whose keys are not in other.
subtractByKey(RDD<Tuple2<K, W>>, int, ClassTag<W>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Return an RDD with the pairs from `this` whose keys are not in `other`.
subtractByKey(RDD<Tuple2<K, W>>, Partitioner, ClassTag<W>) - Method in class org.apache.spark.rdd.PairRDDFunctions
Return an RDD with the pairs from `this` whose keys are not in `other`.
succeededTasks() - Method in class org.apache.spark.status.api.v1.ExecutorStageSummary
 
Success - Class in org.apache.spark
:: DeveloperApi :: Task succeeded.
Success() - Constructor for class org.apache.spark.Success
 
successful() - Method in class org.apache.spark.scheduler.TaskInfo
 
sum() - Method in class org.apache.spark.api.java.JavaDoubleRDD
Add up the elements in this RDD.
sum() - Method in class org.apache.spark.rdd.DoubleRDDFunctions
Add up the elements in this RDD.
sum(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the sum of all values in the expression.
sum(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the sum of all values in the given column.
sum(String...) - Method in class org.apache.spark.sql.GroupedData
Compute the sum for each numeric columns for each group.
sum(Seq<String>) - Method in class org.apache.spark.sql.GroupedData
Compute the sum for each numeric columns for each group.
sum() - Method in class org.apache.spark.util.StatCounter
 
sum() - Method in class org.apache.spark.util.Vector
 
sumApprox(long, Double) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Approximate operation to return the sum within a timeout.
sumApprox(long) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Approximate operation to return the sum within a timeout.
sumApprox(long, double) - Method in class org.apache.spark.rdd.DoubleRDDFunctions
Approximate operation to return the sum within a timeout.
sumDistinct(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the sum of distinct values in the expression.
sumDistinct(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the sum of distinct values in the expression.
summary() - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
Gets summary of model on training set.
summary() - Method in class org.apache.spark.ml.regression.LinearRegressionModel
Gets summary (e.g.
supportedFeatureSubsetStrategies() - Static method in class org.apache.spark.ml.classification.RandomForestClassifier
Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2
supportedFeatureSubsetStrategies() - Static method in class org.apache.spark.ml.regression.RandomForestRegressor
Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2
supportedFeatureSubsetStrategies() - Static method in class org.apache.spark.mllib.tree.RandomForest
List of supported feature subset sampling strategies.
supportedImpurities() - Static method in class org.apache.spark.ml.classification.DecisionTreeClassifier
Accessor for supported impurities: entropy, gini
supportedImpurities() - Static method in class org.apache.spark.ml.classification.RandomForestClassifier
Accessor for supported impurity settings: entropy, gini
supportedImpurities() - Static method in class org.apache.spark.ml.regression.DecisionTreeRegressor
Accessor for supported impurities: variance
supportedImpurities() - Static method in class org.apache.spark.ml.regression.RandomForestRegressor
Accessor for supported impurity settings: variance
supportedLossTypes() - Static method in class org.apache.spark.ml.classification.GBTClassifier
Accessor for supported loss settings: logistic
supportedLossTypes() - Static method in class org.apache.spark.ml.regression.GBTRegressor
Accessor for supported loss settings: squared (L2), absolute (L1)
supportedModelTypes() - Static method in class org.apache.spark.mllib.classification.NaiveBayes
 
supportsRelocationOfSerializedObjects() - Method in class org.apache.spark.serializer.KryoSerializer
 
SVDPlusPlus - Class in org.apache.spark.graphx.lib
Implementation of SVD++ algorithm.
SVDPlusPlus() - Constructor for class org.apache.spark.graphx.lib.SVDPlusPlus
 
SVDPlusPlus.Conf - Class in org.apache.spark.graphx.lib
Configuration parameters for SVDPlusPlus.
SVDPlusPlus.Conf(int, int, double, double, double, double, double, double) - Constructor for class org.apache.spark.graphx.lib.SVDPlusPlus.Conf
 
SVMDataGenerator - Class in org.apache.spark.mllib.util
:: DeveloperApi :: Generate sample data used for SVM.
SVMDataGenerator() - Constructor for class org.apache.spark.mllib.util.SVMDataGenerator
 
SVMModel - Class in org.apache.spark.mllib.classification
Model for Support Vector Machines (SVMs).
SVMModel(Vector, double) - Constructor for class org.apache.spark.mllib.classification.SVMModel
 
SVMWithSGD - Class in org.apache.spark.mllib.classification
Train a Support Vector Machine (SVM) using Stochastic Gradient Descent.
SVMWithSGD() - Constructor for class org.apache.spark.mllib.classification.SVMWithSGD
Construct a SVM object with default parameters: {stepSize: 1.0, numIterations: 100, regParm: 0.01, miniBatchFraction: 1.0}.
symbolToColumn(Symbol) - Method in class org.apache.spark.sql.SQLImplicits
An implicit conversion that turns a Scala Symbol into a Column.
SYSTEM_DEFAULT() - Static method in class org.apache.spark.sql.types.DecimalType
 
systemProperties() - Method in class org.apache.spark.ui.env.EnvironmentListener
 

T

t() - Method in class org.apache.spark.SerializableWritable
 
table(String) - Method in class org.apache.spark.sql.DataFrameReader
Returns the specified table as a DataFrame.
table(String) - Method in class org.apache.spark.sql.SQLContext
 
tableNames() - Method in class org.apache.spark.sql.SQLContext
 
tableNames(String) - Method in class org.apache.spark.sql.SQLContext
 
tables() - Method in class org.apache.spark.sql.SQLContext
 
tables(String) - Method in class org.apache.spark.sql.SQLContext
 
TableScan - Interface in org.apache.spark.sql.sources
::DeveloperApi:: A BaseRelation that can produce all of its tuples as an RDD of Row objects.
tachyonFolderName() - Method in class org.apache.spark.SparkContext
 
tag() - Method in class org.apache.spark.sql.types.BinaryType
 
tag() - Method in class org.apache.spark.sql.types.BooleanType
 
tag() - Method in class org.apache.spark.sql.types.ByteType
 
tag() - Method in class org.apache.spark.sql.types.DateType
 
tag() - Method in class org.apache.spark.sql.types.DecimalType
 
tag() - Method in class org.apache.spark.sql.types.DoubleType
 
tag() - Method in class org.apache.spark.sql.types.FloatType
 
tag() - Method in class org.apache.spark.sql.types.IntegerType
 
tag() - Method in class org.apache.spark.sql.types.LongType
 
tag() - Method in class org.apache.spark.sql.types.ShortType
 
tag() - Method in class org.apache.spark.sql.types.StringType
 
tag() - Method in class org.apache.spark.sql.types.TimestampType
 
take(int) - Method in interface org.apache.spark.api.java.JavaRDDLike
Take the first num elements of the RDD.
take(int) - Method in class org.apache.spark.rdd.RDD
Take the first num elements of the RDD.
take(int) - Method in class org.apache.spark.sql.DataFrame
Returns the first n rows in the DataFrame.
take(int) - Method in class org.apache.spark.sql.Dataset
Returns the first num elements of this Dataset as an array.
takeAsList(int) - Method in class org.apache.spark.sql.DataFrame
Returns the first n rows in the DataFrame as a list.
takeAsList(int) - Method in class org.apache.spark.sql.Dataset
Returns the first num elements of this Dataset as an array.
takeAsync(int) - Method in interface org.apache.spark.api.java.JavaRDDLike
The asynchronous version of the take action, which returns a future for retrieving the first num elements of this RDD.
takeAsync(int) - Method in class org.apache.spark.rdd.AsyncRDDActions
Returns a future for retrieving the first num elements of the RDD.
takeOrdered(int, Comparator<T>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Returns the first k (smallest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.
takeOrdered(int) - Method in interface org.apache.spark.api.java.JavaRDDLike
Returns the first k (smallest) elements from this RDD using the natural ordering for T while maintain the order.
takeOrdered(int, Ordering<T>) - Method in class org.apache.spark.rdd.RDD
Returns the first k (smallest) elements from this RDD as defined by the specified implicit Ordering[T] and maintains the ordering.
takeSample(boolean, int) - Method in interface org.apache.spark.api.java.JavaRDDLike
 
takeSample(boolean, int, long) - Method in interface org.apache.spark.api.java.JavaRDDLike
 
takeSample(boolean, int, long) - Method in class org.apache.spark.rdd.RDD
Return a fixed-size sampled subset of this RDD in an array
tallSkinnyQR(boolean) - Method in class org.apache.spark.mllib.linalg.distributed.RowMatrix
Compute QR decomposition for RowMatrix.
tan(Column) - Static method in class org.apache.spark.sql.functions
Computes the tangent of the given value.
tan(String) - Static method in class org.apache.spark.sql.functions
Computes the tangent of the given column.
tanh(Column) - Static method in class org.apache.spark.sql.functions
Computes the hyperbolic tangent of the given value.
tanh(String) - Static method in class org.apache.spark.sql.functions
Computes the hyperbolic tangent of the given column.
targetStorageLevel() - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
 
targetStorageLevel() - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
task() - Method in class org.apache.spark.CleanupTaskWeakReference
 
taskAttemptId() - Method in class org.apache.spark.TaskContext
An ID that is unique to this task attempt (within the same SparkContext, no two task attempts will share the same attempt ID).
TaskCommitDenied - Class in org.apache.spark
:: DeveloperApi :: Task requested the driver to commit, but was denied.
TaskCommitDenied(int, int, int) - Constructor for class org.apache.spark.TaskCommitDenied
 
TaskCompletionListener - Interface in org.apache.spark.util
:: DeveloperApi ::
TaskContext - Class in org.apache.spark
Contextual information about a task which can be read or mutated during execution.
TaskContext() - Constructor for class org.apache.spark.TaskContext
 
TaskData - Class in org.apache.spark.status.api.v1
 
TaskEndReason - Interface in org.apache.spark
:: DeveloperApi :: Various possible reasons why a task ended.
TaskFailedReason - Interface in org.apache.spark
:: DeveloperApi :: Various possible reasons why a task failed.
taskId() - Method in class org.apache.spark.scheduler.local.KillTask
 
taskId() - Method in class org.apache.spark.scheduler.local.StatusUpdate
 
taskId() - Method in class org.apache.spark.scheduler.TaskInfo
 
taskId() - Method in class org.apache.spark.status.api.v1.TaskData
 
taskId() - Method in class org.apache.spark.storage.TaskResultBlockId
 
taskInfo() - Method in class org.apache.spark.scheduler.SparkListenerTaskEnd
 
taskInfo() - Method in class org.apache.spark.scheduler.SparkListenerTaskGettingResult
 
taskInfo() - Method in class org.apache.spark.scheduler.SparkListenerTaskStart
 
TaskInfo - Class in org.apache.spark.scheduler
:: DeveloperApi :: Information about a running task attempt inside a TaskSet.
TaskInfo(long, int, int, long, String, String, Enumeration.Value, boolean) - Constructor for class org.apache.spark.scheduler.TaskInfo
 
TaskKilled - Class in org.apache.spark
:: DeveloperApi :: Task was killed intentionally and needs to be rescheduled.
TaskKilled() - Constructor for class org.apache.spark.TaskKilled
 
TaskKilledException - Exception in org.apache.spark
:: DeveloperApi :: Exception thrown when a task is explicitly killed (i.e., task failure is expected).
TaskKilledException() - Constructor for exception org.apache.spark.TaskKilledException
 
taskLocality() - Method in class org.apache.spark.scheduler.TaskInfo
 
TaskLocality - Class in org.apache.spark.scheduler
 
TaskLocality() - Constructor for class org.apache.spark.scheduler.TaskLocality
 
taskLocality() - Method in class org.apache.spark.status.api.v1.TaskData
 
taskLocalityPreferences() - Method in class org.apache.spark.scheduler.StageInfo
 
TaskMetricDistributions - Class in org.apache.spark.status.api.v1
 
taskMetrics() - Method in class org.apache.spark.scheduler.SparkListenerExecutorMetricsUpdate
 
taskMetrics() - Method in class org.apache.spark.scheduler.SparkListenerTaskEnd
 
taskMetrics() - Method in class org.apache.spark.status.api.v1.TaskData
 
TaskMetrics - Class in org.apache.spark.status.api.v1
 
taskMetrics() - Method in class org.apache.spark.TaskContext
::DeveloperApi::
TASKRESULT() - Static method in class org.apache.spark.storage.BlockId
 
TaskResultBlockId - Class in org.apache.spark.storage
 
TaskResultBlockId(long) - Constructor for class org.apache.spark.storage.TaskResultBlockId
 
TaskResultLost - Class in org.apache.spark
:: DeveloperApi :: The task finished successfully, but the result was lost from the executor's block manager before it was fetched.
TaskResultLost() - Constructor for class org.apache.spark.TaskResultLost
 
tasks() - Method in class org.apache.spark.status.api.v1.StageData
 
TaskSorting - Enum in org.apache.spark.status.api.v1
 
taskTime() - Method in class org.apache.spark.status.api.v1.ExecutorStageSummary
 
taskType() - Method in class org.apache.spark.scheduler.SparkListenerTaskEnd
 
TEST() - Static method in class org.apache.spark.storage.BlockId
 
TestResult<DF> - Interface in org.apache.spark.mllib.stat.test
Trait for hypothesis test results.
text(String...) - Method in class org.apache.spark.sql.DataFrameReader
Loads a text file and returns a DataFrame with a single string column named "value".
text(Seq<String>) - Method in class org.apache.spark.sql.DataFrameReader
Loads a text file and returns a DataFrame with a single string column named "value".
text(String) - Method in class org.apache.spark.sql.DataFrameWriter
Saves the content of the DataFrame in a text file at the specified path.
textFile(String) - Method in class org.apache.spark.api.java.JavaSparkContext
Read a text file from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI, and return it as an RDD of Strings.
textFile(String, int) - Method in class org.apache.spark.api.java.JavaSparkContext
Read a text file from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI, and return it as an RDD of Strings.
textFile(String, int) - Method in class org.apache.spark.SparkContext
Read a text file from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI, and return it as an RDD of Strings.
textFileStream(String) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them as text files (using key as LongWritable, value as Text and input format as TextInputFormat).
textFileStream(String) - Method in class org.apache.spark.streaming.StreamingContext
Create a input stream that monitors a Hadoop-compatible filesystem for new files and reads them as text files (using key as LongWritable, value as Text and input format as TextInputFormat).
theta() - Method in class org.apache.spark.ml.classification.NaiveBayesModel
 
theta() - Method in class org.apache.spark.mllib.classification.NaiveBayesModel
 
threshold() - Method in class org.apache.spark.ml.feature.Binarizer
Param for threshold used to binarize continuous features.
threshold() - Method in class org.apache.spark.ml.tree.ContinuousSplit
 
threshold() - Method in class org.apache.spark.mllib.tree.model.Split
 
thresholds() - Method in class org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
Returns thresholds in descending order.
throwBalls() - Method in class org.apache.spark.rdd.PartitionCoalescer
 
time() - Method in class org.apache.spark.scheduler.SparkListenerApplicationEnd
 
time() - Method in class org.apache.spark.scheduler.SparkListenerApplicationStart
 
time() - Method in class org.apache.spark.scheduler.SparkListenerBlockManagerAdded
 
time() - Method in class org.apache.spark.scheduler.SparkListenerBlockManagerRemoved
 
time() - Method in class org.apache.spark.scheduler.SparkListenerExecutorAdded
 
time() - Method in class org.apache.spark.scheduler.SparkListenerExecutorRemoved
 
time() - Method in class org.apache.spark.scheduler.SparkListenerJobEnd
 
time() - Method in class org.apache.spark.scheduler.SparkListenerJobStart
 
Time - Class in org.apache.spark.streaming
This is a simple class that represents an absolute instant of time.
Time(long) - Constructor for class org.apache.spark.streaming.Time
 
timeout(Duration) - Method in class org.apache.spark.streaming.StateSpec
Set the duration after which the state of an idle key will be removed.
times(int) - Method in class org.apache.spark.streaming.Duration
 
timestamp() - Method in class org.apache.spark.sql.ColumnName
Creates a new StructField of type timestamp.
TIMESTAMP() - Static method in class org.apache.spark.sql.Encoders
An encoder for nullable timestamp type.
TimestampType - Static variable in class org.apache.spark.sql.types.DataTypes
Gets the TimestampType object.
TimestampType - Class in org.apache.spark.sql.types
:: DeveloperApi :: The data type representing java.sql.Timestamp values.
TimeTrackingOutputStream - Class in org.apache.spark.storage
Intercepts write calls and tracks total time spent writing in order to update shuffle write metrics.
TimeTrackingOutputStream(ShuffleWriteMetrics, OutputStream) - Constructor for class org.apache.spark.storage.TimeTrackingOutputStream
 
timeUnit() - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
 
TIMING_DATA() - Static method in class org.apache.spark.api.r.SpecialLengths
 
to(Time, Duration) - Method in class org.apache.spark.streaming.Time
 
to_date(Column) - Static method in class org.apache.spark.sql.functions
Converts the column into DateType.
to_utc_timestamp(Column, String) - Static method in class org.apache.spark.sql.functions
Assumes given timestamp is in given timezone and converts to UTC.
toArray() - Method in interface org.apache.spark.api.java.JavaRDDLike
Deprecated.
As of Spark 1.0.0, toArray() is deprecated, use JavaRDDLike.collect() instead
toArray() - Method in class org.apache.spark.input.PortableDataStream
Read the file as a byte array
toArray() - Method in class org.apache.spark.mllib.linalg.DenseVector
 
toArray() - Method in interface org.apache.spark.mllib.linalg.Matrix
Converts to a dense array in column major.
toArray() - Method in class org.apache.spark.mllib.linalg.SparseVector
 
toArray() - Method in interface org.apache.spark.mllib.linalg.Vector
Converts the instance to a double array.
toArray() - Method in class org.apache.spark.rdd.RDD
Return an array that contains all of the elements in this RDD.
toAttributes() - Method in class org.apache.spark.sql.types.StructType
 
toBigDecimal() - Method in class org.apache.spark.sql.types.Decimal
 
toBlockMatrix() - Method in class org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
 
toBlockMatrix(int, int) - Method in class org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
 
toBlockMatrix() - Method in class org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
 
toBlockMatrix(int, int) - Method in class org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
 
toBreeze() - Method in interface org.apache.spark.mllib.linalg.distributed.DistributedMatrix
Collects data and assembles a local dense breeze matrix (for test only).
toBreeze() - Method in interface org.apache.spark.mllib.linalg.Matrix
Converts to a breeze matrix.
toBreeze() - Method in interface org.apache.spark.mllib.linalg.Vector
Converts the instance to a breeze vector.
toByte() - Method in class org.apache.spark.sql.types.Decimal
 
toColumn(Encoder<B>, Encoder<O>) - Method in class org.apache.spark.sql.expressions.Aggregator
Returns this Aggregator as a TypedColumn that can be used in Dataset or DataFrame operations.
toCoordinateMatrix() - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
Converts to CoordinateMatrix.
toCoordinateMatrix() - Method in class org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
 
toDebugString() - Method in interface org.apache.spark.api.java.JavaRDDLike
A description of this RDD and its recursive dependencies for debugging.
toDebugString() - Method in class org.apache.spark.mllib.tree.model.DecisionTreeModel
Print the full model to a string.
toDebugString() - Method in class org.apache.spark.rdd.RDD
A description of this RDD and its recursive dependencies for debugging.
toDebugString() - Method in class org.apache.spark.SparkConf
Return a string listing all keys and values, one per line.
toDebugString() - Method in class org.apache.spark.sql.types.Decimal
 
toDegrees(Column) - Static method in class org.apache.spark.sql.functions
Converts an angle measured in radians to an approximately equivalent angle measured in degrees.
toDegrees(String) - Static method in class org.apache.spark.sql.functions
Converts an angle measured in radians to an approximately equivalent angle measured in degrees.
toDense() - Method in class org.apache.spark.mllib.linalg.SparseMatrix
Generate a DenseMatrix from the given SparseMatrix.
toDense() - Method in interface org.apache.spark.mllib.linalg.Vector
Converts this vector to a dense vector.
toDF(String...) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame with columns renamed.
toDF() - Method in class org.apache.spark.sql.DataFrame
Returns the object itself.
toDF(Seq<String>) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame with columns renamed.
toDF() - Method in class org.apache.spark.sql.DataFrameHolder
 
toDF(Seq<String>) - Method in class org.apache.spark.sql.DataFrameHolder
 
toDF() - Method in class org.apache.spark.sql.Dataset
Converts this strongly typed collection of data to generic Dataframe.
toDouble() - Method in class org.apache.spark.sql.types.Decimal
 
toDS() - Method in class org.apache.spark.sql.Dataset
Returns this Dataset.
toDS() - Method in class org.apache.spark.sql.DatasetHolder
 
toEdgeTriplet() - Method in class org.apache.spark.graphx.EdgeContext
Converts the edge and vertex properties into an EdgeTriplet for convenience.
toErrorString() - Method in class org.apache.spark.ExceptionFailure
 
toErrorString() - Method in class org.apache.spark.ExecutorLostFailure
 
toErrorString() - Method in class org.apache.spark.FetchFailed
 
toErrorString() - Static method in class org.apache.spark.Resubmitted
 
toErrorString() - Method in class org.apache.spark.TaskCommitDenied
 
toErrorString() - Method in interface org.apache.spark.TaskFailedReason
Error message displayed in the web UI.
toErrorString() - Static method in class org.apache.spark.TaskKilled
 
toErrorString() - Static method in class org.apache.spark.TaskResultLost
 
toErrorString() - Static method in class org.apache.spark.UnknownReason
 
toFloat() - Method in class org.apache.spark.sql.types.Decimal
 
toFormattedString() - Method in class org.apache.spark.streaming.Duration
 
toHiveString(Tuple2<Object, DataType>) - Static method in class org.apache.spark.sql.hive.HiveContext
 
toHiveStructString(Tuple2<Object, DataType>) - Static method in class org.apache.spark.sql.hive.HiveContext
Hive outputs fields of structs slightly differently than top level attributes.
toIndexedRowMatrix() - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
Converts to IndexedRowMatrix.
toIndexedRowMatrix() - Method in class org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
 
toInt() - Method in class org.apache.spark.sql.types.Decimal
 
toInt() - Method in class org.apache.spark.storage.StorageLevel
 
toJavaBigDecimal() - Method in class org.apache.spark.sql.types.Decimal
 
toJavaDStream() - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Convert to a JavaDStream
toJavaRDD() - Method in class org.apache.spark.rdd.RDD
 
toJavaRDD() - Method in class org.apache.spark.sql.DataFrame
Returns the content of the DataFrame as a JavaRDD of Rows.
toJson() - Method in class org.apache.spark.mllib.linalg.DenseVector
 
toJson() - Method in class org.apache.spark.mllib.linalg.SparseVector
 
toJson() - Method in interface org.apache.spark.mllib.linalg.Vector
Converts the vector to a JSON string.
toJSON() - Method in class org.apache.spark.sql.DataFrame
Returns the content of the DataFrame as a RDD of JSON strings.
Tokenizer - Class in org.apache.spark.ml.feature
:: Experimental :: A tokenizer that converts the input string to lowercase and then splits it by white spaces.
Tokenizer(String) - Constructor for class org.apache.spark.ml.feature.Tokenizer
 
Tokenizer() - Constructor for class org.apache.spark.ml.feature.Tokenizer
 
toLocal() - Method in class org.apache.spark.ml.clustering.DistributedLDAModel
Convert this distributed model to a local representation.
toLocal() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
Convert model to a local model.
toLocalIterator() - Method in interface org.apache.spark.api.java.JavaRDDLike
Return an iterator that contains all of the elements in this RDD.
toLocalIterator() - Method in class org.apache.spark.rdd.RDD
Return an iterator that contains all of the elements in this RDD.
toLocalMatrix() - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
Collect the distributed matrix on the driver as a `DenseMatrix`.
toLong() - Method in class org.apache.spark.sql.types.Decimal
 
toLowercase() - Method in class org.apache.spark.ml.feature.RegexTokenizer
Indicates whether to convert all characters to lowercase before tokenizing.
toMetadata(Metadata) - Method in class org.apache.spark.ml.attribute.Attribute
Converts to ML metadata with some existing metadata.
toMetadata() - Method in class org.apache.spark.ml.attribute.Attribute
Converts to ML metadata
toMetadata(Metadata) - Method in class org.apache.spark.ml.attribute.AttributeGroup
Converts to ML metadata with some existing metadata.
toMetadata() - Method in class org.apache.spark.ml.attribute.AttributeGroup
Converts to ML metadata
toOld() - Method in interface org.apache.spark.ml.tree.Split
Convert to old Split format
top(int, Comparator<T>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Returns the top k (largest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.
top(int) - Method in interface org.apache.spark.api.java.JavaRDDLike
Returns the top k (largest) elements from this RDD using the natural ordering for T and maintains the order.
top(int, Ordering<T>) - Method in class org.apache.spark.rdd.RDD
 
toPairDStreamFunctions(DStream<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>, Ordering<K>) - Static method in class org.apache.spark.streaming.dstream.DStream
 
toPairDStreamFunctions(DStream<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>, Ordering<K>) - Static method in class org.apache.spark.streaming.StreamingContext
Deprecated.
As of 1.3.0, replaced by implicit functions in the DStream companion object. This is kept here only for backward compatibility.
topByKey(int, Ordering<V>) - Method in class org.apache.spark.mllib.rdd.MLPairRDDFunctions
Returns the top k (largest) elements for each key from this RDD as defined by the specified implicit Ordering[T].
topDocumentsPerTopic(int) - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
Return the top documents for each topic
topic() - Method in class org.apache.spark.streaming.kafka.OffsetRange
 
topicAndPartition() - Method in class org.apache.spark.streaming.kafka.OffsetRange
Kafka TopicAndPartition object, for convenience
topicAssignments() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
Return the top topic for each (doc, term) pair.
topicConcentration() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
 
topicConcentration() - Method in class org.apache.spark.mllib.clustering.EMLDAOptimizer
 
topicConcentration() - Method in class org.apache.spark.mllib.clustering.LDAModel
Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms.
topicConcentration() - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
 
topicDistributions() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
For each document in the training set, return the distribution over topics for that document ("theta_doc").
topicDistributions(RDD<Tuple2<Object, Vector>>) - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
Predicts the topic mixture distribution for each document (often called "theta" in the literature).
topicDistributions(JavaPairRDD<Long, Vector>) - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
Java-friendly version of topicDistributions
topics() - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
 
topicsMatrix() - Method in class org.apache.spark.ml.clustering.LDAModel
Inferred topics, where each topic is represented by a distribution over terms.
topicsMatrix() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
Inferred topics, where each topic is represented by a distribution over terms.
topicsMatrix() - Method in class org.apache.spark.mllib.clustering.LDAModel
Inferred topics, where each topic is represented by a distribution over terms.
topicsMatrix() - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
 
toPMML(StreamResult) - Method in interface org.apache.spark.mllib.pmml.PMMLExportable
Export the model to the stream result in PMML format
toPMML(String) - Method in interface org.apache.spark.mllib.pmml.PMMLExportable
:: Experimental :: Export the model to a local file in PMML format
toPMML(SparkContext, String) - Method in interface org.apache.spark.mllib.pmml.PMMLExportable
:: Experimental :: Export the model to a directory on a distributed file system in PMML format
toPMML(OutputStream) - Method in interface org.apache.spark.mllib.pmml.PMMLExportable
:: Experimental :: Export the model to the OutputStream in PMML format
toPMML() - Method in interface org.apache.spark.mllib.pmml.PMMLExportable
:: Experimental :: Export the model to a String in PMML format
topNode() - Method in class org.apache.spark.mllib.tree.model.DecisionTreeModel
 
topTopicsPerDocument(int) - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
For each document, return the top k weighted topics for that document and their weights.
toRadians(Column) - Static method in class org.apache.spark.sql.functions
Converts an angle measured in degrees to an approximately equivalent angle measured in radians.
toRadians(String) - Static method in class org.apache.spark.sql.functions
Converts an angle measured in degrees to an approximately equivalent angle measured in radians.
toRDD(JavaDoubleRDD) - Static method in class org.apache.spark.api.java.JavaDoubleRDD
 
toRDD(JavaPairRDD<K, V>) - Static method in class org.apache.spark.api.java.JavaPairRDD
 
toRDD(JavaRDD<T>) - Static method in class org.apache.spark.api.java.JavaRDD
 
toRowMatrix() - Method in class org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
 
toRowMatrix() - Method in class org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
 
TorrentBroadcastFactory - Class in org.apache.spark.broadcast
A Broadcast implementation that uses a BitTorrent-like protocol to do a distributed transfer of the broadcasted data to the executors.
TorrentBroadcastFactory() - Constructor for class org.apache.spark.broadcast.TorrentBroadcastFactory
 
toSchemaRDD() - Method in class org.apache.spark.sql.DataFrame
Deprecated.
As of 1.3.0, replaced by toDF(). This will be removed in Spark 2.0.
toSeq() - Method in class org.apache.spark.ml.param.ParamMap
Converts this param map to a sequence of param pairs.
toSeq() - Method in interface org.apache.spark.sql.Row
Return a Scala Seq representing the row.
toShort() - Method in class org.apache.spark.sql.types.Decimal
 
toSparkContext(JavaSparkContext) - Static method in class org.apache.spark.api.java.JavaSparkContext
 
toSparse() - Method in class org.apache.spark.mllib.linalg.DenseMatrix
Generate a SparseMatrix from the given DenseMatrix.
toSparse() - Method in class org.apache.spark.mllib.linalg.DenseVector
 
toSparse() - Method in class org.apache.spark.mllib.linalg.SparseVector
 
toSparse() - Method in interface org.apache.spark.mllib.linalg.Vector
Converts this vector to a sparse vector with all explicit zeros removed.
toSplitInfo(Class<?>, String, InputSplit) - Static method in class org.apache.spark.scheduler.SplitInfo
 
toSplitInfo(Class<?>, String, InputSplit) - Static method in class org.apache.spark.scheduler.SplitInfo
 
toString() - Method in class org.apache.spark.Accumulable
 
toString() - Method in class org.apache.spark.api.java.JavaRDD
 
toString() - Method in class org.apache.spark.broadcast.Broadcast
 
toString() - Method in class org.apache.spark.graphx.EdgeDirection
 
toString() - Method in class org.apache.spark.graphx.EdgeTriplet
 
toString() - Method in class org.apache.spark.ml.attribute.Attribute
 
toString() - Method in class org.apache.spark.ml.attribute.AttributeGroup
 
toString() - Method in class org.apache.spark.ml.classification.DecisionTreeClassificationModel
 
toString() - Method in class org.apache.spark.ml.classification.GBTClassificationModel
 
toString() - Method in class org.apache.spark.ml.classification.NaiveBayesModel
 
toString() - Method in class org.apache.spark.ml.classification.RandomForestClassificationModel
 
toString() - Method in class org.apache.spark.ml.feature.RFormula
 
toString() - Method in class org.apache.spark.ml.feature.RFormulaModel
 
toString() - Method in class org.apache.spark.ml.param.Param
 
toString() - Method in class org.apache.spark.ml.param.ParamMap
 
toString() - Method in class org.apache.spark.ml.regression.DecisionTreeRegressionModel
 
toString() - Method in class org.apache.spark.ml.regression.GBTRegressionModel
 
toString() - Method in class org.apache.spark.ml.regression.RandomForestRegressionModel
 
toString() - Method in class org.apache.spark.ml.tree.InternalNode
 
toString() - Method in class org.apache.spark.ml.tree.LeafNode
 
toString() - Method in interface org.apache.spark.ml.util.Identifiable
 
toString() - Method in class org.apache.spark.mllib.classification.LogisticRegressionModel
 
toString() - Method in class org.apache.spark.mllib.classification.SVMModel
 
toString() - Method in class org.apache.spark.mllib.fpm.AssociationRules.Rule
 
toString() - Method in class org.apache.spark.mllib.linalg.DenseVector
 
toString() - Method in interface org.apache.spark.mllib.linalg.Matrix
A human readable representation of the matrix
toString(int, int) - Method in interface org.apache.spark.mllib.linalg.Matrix
A human readable representation of the matrix with maximum lines and width
toString() - Method in class org.apache.spark.mllib.linalg.SparseVector
 
toString() - Method in class org.apache.spark.mllib.regression.GeneralizedLinearModel
Print a summary of the model.
toString() - Method in class org.apache.spark.mllib.regression.LabeledPoint
 
toString() - Method in class org.apache.spark.mllib.stat.test.BinarySample
 
toString() - Method in class org.apache.spark.mllib.stat.test.ChiSqTestResult
 
toString() - Method in class org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult
 
toString() - Method in interface org.apache.spark.mllib.stat.test.TestResult
String explaining the hypothesis test result.
toString() - Method in class org.apache.spark.mllib.tree.model.DecisionTreeModel
Print a summary of the model.
toString() - Method in class org.apache.spark.mllib.tree.model.InformationGainStats
 
toString() - Method in class org.apache.spark.mllib.tree.model.Node
 
toString() - Method in class org.apache.spark.mllib.tree.model.Predict
 
toString() - Method in class org.apache.spark.mllib.tree.model.Split
 
toString() - Method in class org.apache.spark.partial.BoundedDouble
 
toString() - Method in class org.apache.spark.partial.PartialResult
 
toString() - Method in class org.apache.spark.rdd.RDD
 
toString() - Method in class org.apache.spark.scheduler.InputFormatInfo
 
toString() - Method in class org.apache.spark.scheduler.SplitInfo
 
toString() - Method in class org.apache.spark.SerializableWritable
 
toString() - Method in class org.apache.spark.sql.Column
 
toString() - Method in interface org.apache.spark.sql.Row
 
toString() - Method in class org.apache.spark.sql.sources.HadoopFsRelation
 
toString() - Method in class org.apache.spark.sql.types.Decimal
 
toString() - Method in class org.apache.spark.sql.types.DecimalType
 
toString() - Method in class org.apache.spark.sql.types.Metadata
 
toString() - Method in class org.apache.spark.sql.types.StructField
 
toString() - Method in class org.apache.spark.storage.BlockId
 
toString() - Method in class org.apache.spark.storage.BlockManagerId
 
toString() - Method in class org.apache.spark.storage.RDDInfo
 
toString() - Method in class org.apache.spark.storage.StorageLevel
 
toString() - Method in class org.apache.spark.streaming.Duration
 
toString() - Method in class org.apache.spark.streaming.kafka.Broker
 
toString() - Method in class org.apache.spark.streaming.kafka.OffsetRange
 
toString() - Method in class org.apache.spark.streaming.State
 
toString() - Method in class org.apache.spark.streaming.Time
 
toString() - Method in class org.apache.spark.util.MutablePair
 
toString() - Method in class org.apache.spark.util.StatCounter
 
toString() - Method in class org.apache.spark.util.Vector
 
toStructField(Metadata) - Method in class org.apache.spark.ml.attribute.Attribute
Converts to a StructField with some existing metadata.
toStructField() - Method in class org.apache.spark.ml.attribute.Attribute
Converts to a StructField.
toStructField(Metadata) - Method in class org.apache.spark.ml.attribute.AttributeGroup
Converts to a StructField with some existing metadata.
toStructField() - Method in class org.apache.spark.ml.attribute.AttributeGroup
Converts to a StructField.
totalBlocksFetched() - Method in class org.apache.spark.status.api.v1.ShuffleReadMetricDistributions
 
totalBlocksFetched() - Method in class org.apache.spark.status.api.v1.ShuffleReadMetrics
 
totalCores() - Method in class org.apache.spark.scheduler.cluster.ExecutorInfo
 
totalDelay() - Method in class org.apache.spark.streaming.scheduler.BatchInfo
Time taken for all the jobs of this batch to finish processing from the time they were submitted.
totalDuration() - Method in class org.apache.spark.status.api.v1.ExecutorSummary
 
totalInputBytes() - Method in class org.apache.spark.status.api.v1.ExecutorSummary
 
totalIterations() - Method in interface org.apache.spark.ml.classification.LogisticRegressionTrainingSummary
Number of training iterations until termination
totalIterations() - Method in class org.apache.spark.ml.regression.LinearRegressionTrainingSummary
 
totalShuffleRead() - Method in class org.apache.spark.status.api.v1.ExecutorSummary
 
totalShuffleWrite() - Method in class org.apache.spark.status.api.v1.ExecutorSummary
 
totalTasks() - Method in class org.apache.spark.status.api.v1.ExecutorSummary
 
toTuple() - Method in class org.apache.spark.graphx.EdgeTriplet
 
toUnscaledLong() - Method in class org.apache.spark.sql.types.Decimal
 
train(DataFrame) - Method in class org.apache.spark.ml.classification.DecisionTreeClassifier
 
train(DataFrame) - Method in class org.apache.spark.ml.classification.GBTClassifier
 
train(DataFrame) - Method in class org.apache.spark.ml.classification.LogisticRegression
 
train(DataFrame) - Method in class org.apache.spark.ml.classification.MultilayerPerceptronClassifier
Train a model using the given dataset and parameters.
train(DataFrame) - Method in class org.apache.spark.ml.classification.NaiveBayes
 
train(DataFrame) - Method in class org.apache.spark.ml.classification.RandomForestClassifier
 
train(DataFrame) - Method in class org.apache.spark.ml.Predictor
Train a model using the given dataset and parameters.
train(RDD<ALS.Rating<ID>>, int, int, int, int, double, boolean, double, boolean, StorageLevel, StorageLevel, int, long, ClassTag<ID>, Ordering<ID>) - Static method in class org.apache.spark.ml.recommendation.ALS
 
train(DataFrame) - Method in class org.apache.spark.ml.regression.DecisionTreeRegressor
 
train(DataFrame) - Method in class org.apache.spark.ml.regression.GBTRegressor
 
train(DataFrame) - Method in class org.apache.spark.ml.regression.LinearRegression
 
train(DataFrame) - Method in class org.apache.spark.ml.regression.RandomForestRegressor
 
train(RDD<LabeledPoint>, int, double, double, Vector) - Static method in class org.apache.spark.mllib.classification.LogisticRegressionWithSGD
Train a logistic regression model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int, double, double) - Static method in class org.apache.spark.mllib.classification.LogisticRegressionWithSGD
Train a logistic regression model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int, double) - Static method in class org.apache.spark.mllib.classification.LogisticRegressionWithSGD
Train a logistic regression model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int) - Static method in class org.apache.spark.mllib.classification.LogisticRegressionWithSGD
Train a logistic regression model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>) - Static method in class org.apache.spark.mllib.classification.NaiveBayes
 
train(RDD<LabeledPoint>, double) - Static method in class org.apache.spark.mllib.classification.NaiveBayes
 
train(RDD<LabeledPoint>, double, String) - Static method in class org.apache.spark.mllib.classification.NaiveBayes
 
train(RDD<LabeledPoint>, int, double, double, double, Vector) - Static method in class org.apache.spark.mllib.classification.SVMWithSGD
Train a SVM model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int, double, double, double) - Static method in class org.apache.spark.mllib.classification.SVMWithSGD
Train a SVM model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int, double, double) - Static method in class org.apache.spark.mllib.classification.SVMWithSGD
Train a SVM model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int) - Static method in class org.apache.spark.mllib.classification.SVMWithSGD
Train a SVM model given an RDD of (label, features) pairs.
train(RDD<Vector>, int, int, int, String, long) - Static method in class org.apache.spark.mllib.clustering.KMeans
Trains a k-means model using the given set of parameters.
train(RDD<Vector>, int, int, int, String) - Static method in class org.apache.spark.mllib.clustering.KMeans
Trains a k-means model using the given set of parameters.
train(RDD<Vector>, int, int) - Static method in class org.apache.spark.mllib.clustering.KMeans
Trains a k-means model using specified parameters and the default values for unspecified.
train(RDD<Vector>, int, int, int) - Static method in class org.apache.spark.mllib.clustering.KMeans
Trains a k-means model using specified parameters and the default values for unspecified.
train(RDD<Rating>, int, int, double, int, long) - Static method in class org.apache.spark.mllib.recommendation.ALS
 
train(RDD<Rating>, int, int, double, int) - Static method in class org.apache.spark.mllib.recommendation.ALS
 
train(RDD<Rating>, int, int, double) - Static method in class org.apache.spark.mllib.recommendation.ALS
 
train(RDD<Rating>, int, int) - Static method in class org.apache.spark.mllib.recommendation.ALS
 
train(RDD<LabeledPoint>, int, double, double, double, Vector) - Static method in class org.apache.spark.mllib.regression.LassoWithSGD
Train a Lasso model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int, double, double, double) - Static method in class org.apache.spark.mllib.regression.LassoWithSGD
Train a Lasso model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int, double, double) - Static method in class org.apache.spark.mllib.regression.LassoWithSGD
Train a Lasso model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int) - Static method in class org.apache.spark.mllib.regression.LassoWithSGD
Train a Lasso model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int, double, double, Vector) - Static method in class org.apache.spark.mllib.regression.LinearRegressionWithSGD
Train a Linear Regression model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int, double, double) - Static method in class org.apache.spark.mllib.regression.LinearRegressionWithSGD
Train a LinearRegression model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int, double) - Static method in class org.apache.spark.mllib.regression.LinearRegressionWithSGD
Train a LinearRegression model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int) - Static method in class org.apache.spark.mllib.regression.LinearRegressionWithSGD
Train a LinearRegression model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int, double, double, double, Vector) - Static method in class org.apache.spark.mllib.regression.RidgeRegressionWithSGD
Train a RidgeRegression model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int, double, double, double) - Static method in class org.apache.spark.mllib.regression.RidgeRegressionWithSGD
Train a RidgeRegression model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int, double, double) - Static method in class org.apache.spark.mllib.regression.RidgeRegressionWithSGD
Train a RidgeRegression model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, int) - Static method in class org.apache.spark.mllib.regression.RidgeRegressionWithSGD
Train a RidgeRegression model given an RDD of (label, features) pairs.
train(RDD<LabeledPoint>, Strategy) - Static method in class org.apache.spark.mllib.tree.DecisionTree
Method to train a decision tree model.
train(RDD<LabeledPoint>, Enumeration.Value, Impurity, int) - Static method in class org.apache.spark.mllib.tree.DecisionTree
Method to train a decision tree model.
train(RDD<LabeledPoint>, Enumeration.Value, Impurity, int, int) - Static method in class org.apache.spark.mllib.tree.DecisionTree
Method to train a decision tree model.
train(RDD<LabeledPoint>, Enumeration.Value, Impurity, int, int, int, Enumeration.Value, Map<Object, Object>) - Static method in class org.apache.spark.mllib.tree.DecisionTree
Method to train a decision tree model.
train(RDD<LabeledPoint>, BoostingStrategy) - Static method in class org.apache.spark.mllib.tree.GradientBoostedTrees
Method to train a gradient boosting model.
train(JavaRDD<LabeledPoint>, BoostingStrategy) - Static method in class org.apache.spark.mllib.tree.GradientBoostedTrees
Java-friendly API for GradientBoostedTrees$.train(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, org.apache.spark.mllib.tree.configuration.BoostingStrategy)
trainClassifier(RDD<LabeledPoint>, int, Map<Object, Object>, String, int, int) - Static method in class org.apache.spark.mllib.tree.DecisionTree
Method to train a decision tree model for binary or multiclass classification.
trainClassifier(JavaRDD<LabeledPoint>, int, Map<Integer, Integer>, String, int, int) - Static method in class org.apache.spark.mllib.tree.DecisionTree
Java-friendly API for DecisionTree$.trainClassifier(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, int, scala.collection.immutable.Map<java.lang.Object, java.lang.Object>, java.lang.String, int, int)
trainClassifier(RDD<LabeledPoint>, Strategy, int, String, int) - Static method in class org.apache.spark.mllib.tree.RandomForest
Method to train a decision tree model for binary or multiclass classification.
trainClassifier(RDD<LabeledPoint>, int, Map<Object, Object>, int, String, String, int, int, int) - Static method in class org.apache.spark.mllib.tree.RandomForest
Method to train a decision tree model for binary or multiclass classification.
trainClassifier(JavaRDD<LabeledPoint>, int, Map<Integer, Integer>, int, String, String, int, int, int) - Static method in class org.apache.spark.mllib.tree.RandomForest
Java-friendly API for RandomForest$.trainClassifier(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, org.apache.spark.mllib.tree.configuration.Strategy, int, java.lang.String, int)
trainImplicit(RDD<Rating>, int, int, double, int, double, long) - Static method in class org.apache.spark.mllib.recommendation.ALS
 
trainImplicit(RDD<Rating>, int, int, double, int, double) - Static method in class org.apache.spark.mllib.recommendation.ALS
 
trainImplicit(RDD<Rating>, int, int, double, double) - Static method in class org.apache.spark.mllib.recommendation.ALS
 
trainImplicit(RDD<Rating>, int, int) - Static method in class org.apache.spark.mllib.recommendation.ALS
 
trainingLogLikelihood() - Method in class org.apache.spark.ml.clustering.DistributedLDAModel
Log likelihood of the observed tokens in the training set, given the current parameter estimates: log P(docs | topics, topic distributions for docs, Dirichlet hyperparameters)
trainOn(DStream<Vector>) - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
Update the clustering model by training on batches of data from a DStream.
trainOn(JavaDStream<Vector>) - Method in class org.apache.spark.mllib.clustering.StreamingKMeans
Java-friendly version of trainOn.
trainOn(DStream<LabeledPoint>) - Method in class org.apache.spark.mllib.regression.StreamingLinearAlgorithm
Update the model by training on batches of data from a DStream.
trainOn(JavaDStream<LabeledPoint>) - Method in class org.apache.spark.mllib.regression.StreamingLinearAlgorithm
Java-friendly version of trainOn.
trainRegressor(RDD<LabeledPoint>, Map<Object, Object>, String, int, int) - Static method in class org.apache.spark.mllib.tree.DecisionTree
Method to train a decision tree model for regression.
trainRegressor(JavaRDD<LabeledPoint>, Map<Integer, Integer>, String, int, int) - Static method in class org.apache.spark.mllib.tree.DecisionTree
Java-friendly API for DecisionTree$.trainRegressor(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, scala.collection.immutable.Map<java.lang.Object, java.lang.Object>, java.lang.String, int, int)
trainRegressor(RDD<LabeledPoint>, Strategy, int, String, int) - Static method in class org.apache.spark.mllib.tree.RandomForest
Method to train a decision tree model for regression.
trainRegressor(RDD<LabeledPoint>, Map<Object, Object>, int, String, String, int, int, int) - Static method in class org.apache.spark.mllib.tree.RandomForest
Method to train a decision tree model for regression.
trainRegressor(JavaRDD<LabeledPoint>, Map<Integer, Integer>, int, String, String, int, int, int) - Static method in class org.apache.spark.mllib.tree.RandomForest
Java-friendly API for RandomForest$.trainRegressor(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, org.apache.spark.mllib.tree.configuration.Strategy, int, java.lang.String, int)
TrainValidationSplit - Class in org.apache.spark.ml.tuning
:: Experimental :: Validation for hyper-parameter tuning.
TrainValidationSplit(String) - Constructor for class org.apache.spark.ml.tuning.TrainValidationSplit
 
TrainValidationSplit() - Constructor for class org.apache.spark.ml.tuning.TrainValidationSplit
 
TrainValidationSplitModel - Class in org.apache.spark.ml.tuning
:: Experimental :: Model from train validation split.
transform(DataFrame) - Method in class org.apache.spark.ml.classification.ClassificationModel
Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters: - predicted labels as predictionCol of type Double - raw predictions (confidences) as rawPredictionCol of type Vector.
transform(DataFrame) - Method in class org.apache.spark.ml.classification.OneVsRestModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.classification.ProbabilisticClassificationModel
Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters: - predicted labels as predictionCol of type Double - raw predictions (confidences) as rawPredictionCol of type Vector - probability of each class as probabilityCol of type Vector.
transform(DataFrame) - Method in class org.apache.spark.ml.clustering.KMeansModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.clustering.LDAModel
Transforms the input dataset.
transform(DataFrame) - Method in class org.apache.spark.ml.feature.Binarizer
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.Bucketizer
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.ChiSqSelectorModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.ColumnPruner
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.CountVectorizerModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.HashingTF
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.IDFModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.IndexToString
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.Interaction
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.MinMaxScalerModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.OneHotEncoder
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.PCAModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.RFormulaModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.SQLTransformer
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.StandardScalerModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.StopWordsRemover
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.StringIndexerModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.VectorAssembler
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.VectorAttributeRewriter
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.VectorIndexerModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.VectorSlicer
 
transform(DataFrame) - Method in class org.apache.spark.ml.feature.Word2VecModel
Transform a sentence column to a vector column to represent the whole sentence.
transform(DataFrame) - Method in class org.apache.spark.ml.PipelineModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.PredictionModel
Transforms dataset by reading from featuresCol, calling predict(), and storing the predictions as a new column predictionCol.
transform(DataFrame) - Method in class org.apache.spark.ml.recommendation.ALSModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.regression.IsotonicRegressionModel
 
transform(DataFrame, ParamPair<?>, ParamPair<?>...) - Method in class org.apache.spark.ml.Transformer
Transforms the dataset with optional parameters
transform(DataFrame, ParamPair<?>, Seq<ParamPair<?>>) - Method in class org.apache.spark.ml.Transformer
Transforms the dataset with optional parameters
transform(DataFrame, ParamMap) - Method in class org.apache.spark.ml.Transformer
Transforms the dataset with provided parameter map as additional parameters.
transform(DataFrame) - Method in class org.apache.spark.ml.Transformer
Transforms the input dataset.
transform(DataFrame) - Method in class org.apache.spark.ml.tuning.CrossValidatorModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.tuning.TrainValidationSplitModel
 
transform(DataFrame) - Method in class org.apache.spark.ml.UnaryTransformer
 
transform(Vector) - Method in class org.apache.spark.mllib.feature.ChiSqSelectorModel
Applies transformation on a vector.
transform(Vector) - Method in class org.apache.spark.mllib.feature.ElementwiseProduct
Does the hadamard product transformation.
transform(Iterable<Object>) - Method in class org.apache.spark.mllib.feature.HashingTF
Transforms the input document into a sparse term frequency vector.
transform(Iterable<?>) - Method in class org.apache.spark.mllib.feature.HashingTF
Transforms the input document into a sparse term frequency vector (Java version).
transform(RDD<D>) - Method in class org.apache.spark.mllib.feature.HashingTF
Transforms the input document to term frequency vectors.
transform(JavaRDD<D>) - Method in class org.apache.spark.mllib.feature.HashingTF
Transforms the input document to term frequency vectors (Java version).
transform(RDD<Vector>) - Method in class org.apache.spark.mllib.feature.IDFModel
Transforms term frequency (TF) vectors to TF-IDF vectors.
transform(Vector) - Method in class org.apache.spark.mllib.feature.IDFModel
Transforms a term frequency (TF) vector to a TF-IDF vector
transform(JavaRDD<Vector>) - Method in class org.apache.spark.mllib.feature.IDFModel
Transforms term frequency (TF) vectors to TF-IDF vectors (Java version).
transform(Vector) - Method in class org.apache.spark.mllib.feature.Normalizer
Applies unit length normalization on a vector.
transform(Vector) - Method in class org.apache.spark.mllib.feature.PCAModel
Transform a vector by computed Principal Components.
transform(Vector) - Method in class org.apache.spark.mllib.feature.StandardScalerModel
Applies standardization transformation on a vector.
transform(Vector) - Method in interface org.apache.spark.mllib.feature.VectorTransformer
Applies transformation on a vector.
transform(RDD<Vector>) - Method in interface org.apache.spark.mllib.feature.VectorTransformer
Applies transformation on an RDD[Vector].
transform(JavaRDD<Vector>) - Method in interface org.apache.spark.mllib.feature.VectorTransformer
Applies transformation on an JavaRDD[Vector].
transform(String) - Method in class org.apache.spark.mllib.feature.Word2VecModel
 
transform(Function1<DataFrame, DataFrame>) - Method in class org.apache.spark.sql.DataFrame
Concise syntax for chaining custom transformations.
transform(Function1<Dataset<T>, Dataset<U>>) - Method in class org.apache.spark.sql.Dataset
Concise syntax for chaining custom transformations.
transform(Function<R, JavaRDD<U>>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.
transform(Function2<R, Time, JavaRDD<U>>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.
transform(List<JavaDStream<?>>, Function2<List<JavaRDD<?>>, Time, JavaRDD<T>>) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create a new DStream in which each RDD is generated by applying a function on RDDs of the DStreams.
transform(Function1<RDD<T>, RDD<U>>, ClassTag<U>) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.
transform(Function2<RDD<T>, Time, RDD<U>>, ClassTag<U>) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.
transform(Seq<DStream<?>>, Function2<Seq<RDD<?>>, Time, RDD<T>>, ClassTag<T>) - Method in class org.apache.spark.streaming.StreamingContext
Create a new DStream in which each RDD is generated by applying a function on RDDs of the DStreams.
Transformer - Class in org.apache.spark.ml
:: DeveloperApi :: Abstract class for transformers that transform one dataset into another.
Transformer() - Constructor for class org.apache.spark.ml.Transformer
 
transformImpl(DataFrame) - Method in class org.apache.spark.ml.classification.GBTClassificationModel
 
transformImpl(DataFrame) - Method in class org.apache.spark.ml.classification.RandomForestClassificationModel
 
transformImpl(DataFrame) - Method in class org.apache.spark.ml.PredictionModel
 
transformImpl(DataFrame) - Method in class org.apache.spark.ml.regression.GBTRegressionModel
 
transformImpl(DataFrame) - Method in class org.apache.spark.ml.regression.RandomForestRegressionModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.classification.OneVsRest
 
transformSchema(StructType) - Method in class org.apache.spark.ml.classification.OneVsRestModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.clustering.KMeans
 
transformSchema(StructType) - Method in class org.apache.spark.ml.clustering.KMeansModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.clustering.LDA
 
transformSchema(StructType) - Method in class org.apache.spark.ml.clustering.LDAModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.Binarizer
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.Bucketizer
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.ChiSqSelector
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.ChiSqSelectorModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.ColumnPruner
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.CountVectorizer
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.CountVectorizerModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.HashingTF
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.IDF
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.IDFModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.IndexToString
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.Interaction
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.MinMaxScaler
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.MinMaxScalerModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.OneHotEncoder
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.PCA
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.PCAModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.QuantileDiscretizer
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.RFormula
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.RFormulaModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.SQLTransformer
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.StandardScaler
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.StandardScalerModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.StopWordsRemover
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.StringIndexer
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.StringIndexerModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.VectorAssembler
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.VectorAttributeRewriter
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.VectorIndexer
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.VectorIndexerModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.VectorSlicer
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.Word2Vec
 
transformSchema(StructType) - Method in class org.apache.spark.ml.feature.Word2VecModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.Pipeline
 
transformSchema(StructType) - Method in class org.apache.spark.ml.PipelineModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.PipelineStage
:: DeveloperApi ::
transformSchema(StructType, boolean) - Method in class org.apache.spark.ml.PipelineStage
:: DeveloperApi ::
transformSchema(StructType) - Method in class org.apache.spark.ml.PredictionModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.Predictor
 
transformSchema(StructType) - Method in class org.apache.spark.ml.recommendation.ALS
 
transformSchema(StructType) - Method in class org.apache.spark.ml.recommendation.ALSModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegression
 
transformSchema(StructType) - Method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.regression.IsotonicRegression
 
transformSchema(StructType) - Method in class org.apache.spark.ml.regression.IsotonicRegressionModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.tuning.CrossValidator
 
transformSchema(StructType) - Method in class org.apache.spark.ml.tuning.CrossValidatorModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.tuning.TrainValidationSplit
 
transformSchema(StructType) - Method in class org.apache.spark.ml.tuning.TrainValidationSplitModel
 
transformSchema(StructType) - Method in class org.apache.spark.ml.UnaryTransformer
 
transformToPair(Function<R, JavaPairRDD<K2, V2>>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.
transformToPair(Function2<R, Time, JavaPairRDD<K2, V2>>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream.
transformToPair(List<JavaDStream<?>>, Function2<List<JavaRDD<?>>, Time, JavaPairRDD<K, V>>) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create a new DStream in which each RDD is generated by applying a function on RDDs of the DStreams.
transformWith(JavaDStream<U>, Function3<R, JavaRDD<U>, Time, JavaRDD<W>>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.
transformWith(JavaPairDStream<K2, V2>, Function3<R, JavaPairRDD<K2, V2>, Time, JavaRDD<W>>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.
transformWith(DStream<U>, Function2<RDD<T>, RDD<U>, RDD<V>>, ClassTag<U>, ClassTag<V>) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.
transformWith(DStream<U>, Function3<RDD<T>, RDD<U>, Time, RDD<V>>, ClassTag<U>, ClassTag<V>) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.
transformWithToPair(JavaDStream<U>, Function3<R, JavaRDD<U>, Time, JavaPairRDD<K2, V2>>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.
transformWithToPair(JavaPairDStream<K2, V2>, Function3<R, JavaPairRDD<K2, V2>, Time, JavaPairRDD<K3, V3>>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
Return a new DStream in which each RDD is generated by applying a function on each RDD of 'this' DStream and 'other' DStream.
translate(Column, String, String) - Static method in class org.apache.spark.sql.functions
Translate any character in the src by a character in replaceString.
transpose() - Method in class org.apache.spark.mllib.linalg.DenseMatrix
 
transpose() - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
Transpose this BlockMatrix.
transpose() - Method in class org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
 
transpose() - Method in interface org.apache.spark.mllib.linalg.Matrix
Transpose the Matrix.
transpose() - Method in class org.apache.spark.mllib.linalg.SparseMatrix
 
treeAggregate(U, Function2<U, T, U>, Function2<U, U, U>, int) - Method in interface org.apache.spark.api.java.JavaRDDLike
Aggregates the elements of this RDD in a multi-level tree pattern.
treeAggregate(U, Function2<U, T, U>, Function2<U, U, U>) - Method in interface org.apache.spark.api.java.JavaRDDLike
treeAggregate(U, Function2<U, T, U>, Function2<U, U, U>, int, ClassTag<U>) - Method in class org.apache.spark.mllib.rdd.RDDFunctions
treeAggregate(U, Function2<U, T, U>, Function2<U, U, U>, int, ClassTag<U>) - Method in class org.apache.spark.rdd.RDD
Aggregates the elements of this RDD in a multi-level tree pattern.
treeReduce(Function2<T, T, T>, int) - Method in interface org.apache.spark.api.java.JavaRDDLike
Reduces the elements of this RDD in a multi-level tree pattern.
treeReduce(Function2<T, T, T>) - Method in interface org.apache.spark.api.java.JavaRDDLike
treeReduce(Function2<T, T, T>, int) - Method in class org.apache.spark.mllib.rdd.RDDFunctions
treeReduce(Function2<T, T, T>, int) - Method in class org.apache.spark.rdd.RDD
Reduces the elements of this RDD in a multi-level tree pattern.
trees() - Method in class org.apache.spark.ml.classification.GBTClassificationModel
 
trees() - Method in class org.apache.spark.ml.classification.RandomForestClassificationModel
 
trees() - Method in class org.apache.spark.ml.regression.GBTRegressionModel
 
trees() - Method in class org.apache.spark.ml.regression.RandomForestRegressionModel
 
trees() - Method in class org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
 
trees() - Method in class org.apache.spark.mllib.tree.model.RandomForestModel
 
treeStrategy() - Method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
treeString() - Method in class org.apache.spark.sql.types.StructType
 
treeWeights() - Method in class org.apache.spark.ml.classification.GBTClassificationModel
 
treeWeights() - Method in class org.apache.spark.ml.classification.RandomForestClassificationModel
 
treeWeights() - Method in class org.apache.spark.ml.regression.GBTRegressionModel
 
treeWeights() - Method in class org.apache.spark.ml.regression.RandomForestRegressionModel
 
treeWeights() - Method in class org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
 
triangleCount() - Method in class org.apache.spark.graphx.GraphOps
Compute the number of triangles passing through each vertex.
TriangleCount - Class in org.apache.spark.graphx.lib
Compute the number of triangles passing through each vertex.
TriangleCount() - Constructor for class org.apache.spark.graphx.lib.TriangleCount
 
trim(Column) - Static method in class org.apache.spark.sql.functions
Trim the spaces from both ends for the specified string column.
TripletFields - Class in org.apache.spark.graphx
Represents a subset of the fields of an [[EdgeTriplet]] or [[EdgeContext]].
TripletFields() - Constructor for class org.apache.spark.graphx.TripletFields
Constructs a default TripletFields in which all fields are included.
TripletFields(boolean, boolean, boolean) - Constructor for class org.apache.spark.graphx.TripletFields
 
triplets() - Method in class org.apache.spark.graphx.Graph
An RDD containing the edge triplets, which are edges along with the vertex data associated with the adjacent vertices.
triplets() - Method in class org.apache.spark.graphx.impl.GraphImpl
Return a RDD that brings edges together with their source and destination vertices.
truePositiveRate(double) - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns true positive rate for a given label (category)
trunc(Column, String) - Static method in class org.apache.spark.sql.functions
Returns date truncated to the unit specified by the format.
tuple(Encoder<T1>, Encoder<T2>) - Static method in class org.apache.spark.sql.Encoders
An encoder for 2-ary tuples.
tuple(Encoder<T1>, Encoder<T2>, Encoder<T3>) - Static method in class org.apache.spark.sql.Encoders
An encoder for 3-ary tuples.
tuple(Encoder<T1>, Encoder<T2>, Encoder<T3>, Encoder<T4>) - Static method in class org.apache.spark.sql.Encoders
An encoder for 4-ary tuples.
tuple(Encoder<T1>, Encoder<T2>, Encoder<T3>, Encoder<T4>, Encoder<T5>) - Static method in class org.apache.spark.sql.Encoders
An encoder for 5-ary tuples.
tValues() - Method in class org.apache.spark.ml.regression.LinearRegressionSummary
T-statistic of estimated coefficients and intercept.
TwitterUtils - Class in org.apache.spark.streaming.twitter
 
TwitterUtils() - Constructor for class org.apache.spark.streaming.twitter.TwitterUtils
 
TypedColumn<T,U> - Class in org.apache.spark.sql
A Column where an Encoder has been given for the expected input and return type.
TypedColumn(Expression, ExpressionEncoder<U>) - Constructor for class org.apache.spark.sql.TypedColumn
 
typeName() - Method in class org.apache.spark.mllib.linalg.VectorUDT
 
typeName() - Method in class org.apache.spark.sql.types.DataType
Name of the type used in JSON serialization.
typeName() - Method in class org.apache.spark.sql.types.DecimalType
 

U

U() - Method in class org.apache.spark.mllib.linalg.SingularValueDecomposition
 
udf(Function0<RT>, TypeTags.TypeTag<RT>) - Static method in class org.apache.spark.sql.functions
Defines a user-defined function of 0 arguments as user-defined function (UDF).
udf(Function1<A1, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>) - Static method in class org.apache.spark.sql.functions
Defines a user-defined function of 1 arguments as user-defined function (UDF).
udf(Function2<A1, A2, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>) - Static method in class org.apache.spark.sql.functions
Defines a user-defined function of 2 arguments as user-defined function (UDF).
udf(Function3<A1, A2, A3, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>) - Static method in class org.apache.spark.sql.functions
Defines a user-defined function of 3 arguments as user-defined function (UDF).
udf(Function4<A1, A2, A3, A4, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>) - Static method in class org.apache.spark.sql.functions
Defines a user-defined function of 4 arguments as user-defined function (UDF).
udf(Function5<A1, A2, A3, A4, A5, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>) - Static method in class org.apache.spark.sql.functions
Defines a user-defined function of 5 arguments as user-defined function (UDF).
udf(Function6<A1, A2, A3, A4, A5, A6, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>) - Static method in class org.apache.spark.sql.functions
Defines a user-defined function of 6 arguments as user-defined function (UDF).
udf(Function7<A1, A2, A3, A4, A5, A6, A7, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>) - Static method in class org.apache.spark.sql.functions
Defines a user-defined function of 7 arguments as user-defined function (UDF).
udf(Function8<A1, A2, A3, A4, A5, A6, A7, A8, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>) - Static method in class org.apache.spark.sql.functions
Defines a user-defined function of 8 arguments as user-defined function (UDF).
udf(Function9<A1, A2, A3, A4, A5, A6, A7, A8, A9, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>) - Static method in class org.apache.spark.sql.functions
Defines a user-defined function of 9 arguments as user-defined function (UDF).
udf(Function10<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>) - Static method in class org.apache.spark.sql.functions
Defines a user-defined function of 10 arguments as user-defined function (UDF).
udf() - Method in class org.apache.spark.sql.SQLContext
A collection of methods for registering user-defined functions (UDF).
UDF1<T1,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 1 arguments.
UDF10<T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 10 arguments.
UDF11<T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 11 arguments.
UDF12<T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 12 arguments.
UDF13<T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,T13,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 13 arguments.
UDF14<T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,T13,T14,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 14 arguments.
UDF15<T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,T13,T14,T15,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 15 arguments.
UDF16<T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,T13,T14,T15,T16,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 16 arguments.
UDF17<T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,T13,T14,T15,T16,T17,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 17 arguments.
UDF18<T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,T13,T14,T15,T16,T17,T18,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 18 arguments.
UDF19<T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,T13,T14,T15,T16,T17,T18,T19,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 19 arguments.
UDF2<T1,T2,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 2 arguments.
UDF20<T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,T13,T14,T15,T16,T17,T18,T19,T20,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 20 arguments.
UDF21<T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,T13,T14,T15,T16,T17,T18,T19,T20,T21,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 21 arguments.
UDF22<T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,T13,T14,T15,T16,T17,T18,T19,T20,T21,T22,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 22 arguments.
UDF3<T1,T2,T3,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 3 arguments.
UDF4<T1,T2,T3,T4,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 4 arguments.
UDF5<T1,T2,T3,T4,T5,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 5 arguments.
UDF6<T1,T2,T3,T4,T5,T6,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 6 arguments.
UDF7<T1,T2,T3,T4,T5,T6,T7,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 7 arguments.
UDF8<T1,T2,T3,T4,T5,T6,T7,T8,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 8 arguments.
UDF9<T1,T2,T3,T4,T5,T6,T7,T8,T9,R> - Interface in org.apache.spark.sql.api.java
A Spark SQL UDF that has 9 arguments.
UDFRegistration - Class in org.apache.spark.sql
Functions for registering user-defined functions.
uid() - Method in class org.apache.spark.ml.classification.DecisionTreeClassificationModel
 
uid() - Method in class org.apache.spark.ml.classification.DecisionTreeClassifier
 
uid() - Method in class org.apache.spark.ml.classification.GBTClassificationModel
 
uid() - Method in class org.apache.spark.ml.classification.GBTClassifier
 
uid() - Method in class org.apache.spark.ml.classification.LogisticRegression
 
uid() - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
uid() - Method in class org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel
 
uid() - Method in class org.apache.spark.ml.classification.MultilayerPerceptronClassifier
 
uid() - Method in class org.apache.spark.ml.classification.NaiveBayes
 
uid() - Method in class org.apache.spark.ml.classification.NaiveBayesModel
 
uid() - Method in class org.apache.spark.ml.classification.OneVsRest
 
uid() - Method in class org.apache.spark.ml.classification.OneVsRestModel
 
uid() - Method in class org.apache.spark.ml.classification.RandomForestClassificationModel
 
uid() - Method in class org.apache.spark.ml.classification.RandomForestClassifier
 
uid() - Method in class org.apache.spark.ml.clustering.KMeans
 
uid() - Method in class org.apache.spark.ml.clustering.KMeansModel
 
uid() - Method in class org.apache.spark.ml.clustering.LDA
 
uid() - Method in class org.apache.spark.ml.clustering.LDAModel
 
uid() - Method in class org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
 
uid() - Method in class org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
 
uid() - Method in class org.apache.spark.ml.evaluation.RegressionEvaluator
 
uid() - Method in class org.apache.spark.ml.feature.Binarizer
 
uid() - Method in class org.apache.spark.ml.feature.Bucketizer
 
uid() - Method in class org.apache.spark.ml.feature.ChiSqSelector
 
uid() - Method in class org.apache.spark.ml.feature.ChiSqSelectorModel
 
uid() - Method in class org.apache.spark.ml.feature.ColumnPruner
 
uid() - Method in class org.apache.spark.ml.feature.CountVectorizer
 
uid() - Method in class org.apache.spark.ml.feature.CountVectorizerModel
 
uid() - Method in class org.apache.spark.ml.feature.DCT
 
uid() - Method in class org.apache.spark.ml.feature.ElementwiseProduct
 
uid() - Method in class org.apache.spark.ml.feature.HashingTF
 
uid() - Method in class org.apache.spark.ml.feature.IDF
 
uid() - Method in class org.apache.spark.ml.feature.IDFModel
 
uid() - Method in class org.apache.spark.ml.feature.IndexToString
 
uid() - Method in class org.apache.spark.ml.feature.Interaction
 
uid() - Method in class org.apache.spark.ml.feature.MinMaxScaler
 
uid() - Method in class org.apache.spark.ml.feature.MinMaxScalerModel
 
uid() - Method in class org.apache.spark.ml.feature.NGram
 
uid() - Method in class org.apache.spark.ml.feature.Normalizer
 
uid() - Method in class org.apache.spark.ml.feature.OneHotEncoder
 
uid() - Method in class org.apache.spark.ml.feature.PCA
 
uid() - Method in class org.apache.spark.ml.feature.PCAModel
 
uid() - Method in class org.apache.spark.ml.feature.PolynomialExpansion
 
uid() - Method in class org.apache.spark.ml.feature.QuantileDiscretizer
 
uid() - Method in class org.apache.spark.ml.feature.RegexTokenizer
 
uid() - Method in class org.apache.spark.ml.feature.RFormula
 
uid() - Method in class org.apache.spark.ml.feature.RFormulaModel
 
uid() - Method in class org.apache.spark.ml.feature.SQLTransformer
 
uid() - Method in class org.apache.spark.ml.feature.StandardScaler
 
uid() - Method in class org.apache.spark.ml.feature.StandardScalerModel
 
uid() - Method in class org.apache.spark.ml.feature.StopWordsRemover
 
uid() - Method in class org.apache.spark.ml.feature.StringIndexer
 
uid() - Method in class org.apache.spark.ml.feature.StringIndexerModel
 
uid() - Method in class org.apache.spark.ml.feature.Tokenizer
 
uid() - Method in class org.apache.spark.ml.feature.VectorAssembler
 
uid() - Method in class org.apache.spark.ml.feature.VectorAttributeRewriter
 
uid() - Method in class org.apache.spark.ml.feature.VectorIndexer
 
uid() - Method in class org.apache.spark.ml.feature.VectorIndexerModel
 
uid() - Method in class org.apache.spark.ml.feature.VectorSlicer
 
uid() - Method in class org.apache.spark.ml.feature.Word2Vec
 
uid() - Method in class org.apache.spark.ml.feature.Word2VecModel
 
uid() - Method in class org.apache.spark.ml.Pipeline
 
uid() - Method in class org.apache.spark.ml.PipelineModel
 
uid() - Method in class org.apache.spark.ml.recommendation.ALS
 
uid() - Method in class org.apache.spark.ml.recommendation.ALSModel
 
uid() - Method in class org.apache.spark.ml.regression.AFTSurvivalRegression
 
uid() - Method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
uid() - Method in class org.apache.spark.ml.regression.DecisionTreeRegressionModel
 
uid() - Method in class org.apache.spark.ml.regression.DecisionTreeRegressor
 
uid() - Method in class org.apache.spark.ml.regression.GBTRegressionModel
 
uid() - Method in class org.apache.spark.ml.regression.GBTRegressor
 
uid() - Method in class org.apache.spark.ml.regression.IsotonicRegression
 
uid() - Method in class org.apache.spark.ml.regression.IsotonicRegressionModel
 
uid() - Method in class org.apache.spark.ml.regression.LinearRegression
 
uid() - Method in class org.apache.spark.ml.regression.LinearRegressionModel
 
uid() - Method in class org.apache.spark.ml.regression.RandomForestRegressionModel
 
uid() - Method in class org.apache.spark.ml.regression.RandomForestRegressor
 
uid() - Method in class org.apache.spark.ml.tuning.CrossValidator
 
uid() - Method in class org.apache.spark.ml.tuning.CrossValidatorModel
 
uid() - Method in class org.apache.spark.ml.tuning.TrainValidationSplit
 
uid() - Method in class org.apache.spark.ml.tuning.TrainValidationSplitModel
 
uid() - Method in interface org.apache.spark.ml.util.Identifiable
An immutable unique ID for the object and its derivatives.
uiTab() - Method in class org.apache.spark.streaming.StreamingContext
 
unapply(EdgeContext<VD, ED, A>) - Static method in class org.apache.spark.graphx.EdgeContext
Extractor mainly used for Graph#aggregateMessages*.
unapply(DenseVector) - Static method in class org.apache.spark.mllib.linalg.DenseVector
Extracts the value array from a dense vector.
unapply(SparseVector) - Static method in class org.apache.spark.mllib.linalg.SparseVector
 
unapply(Column) - Static method in class org.apache.spark.sql.Column
 
unapply(DataType) - Static method in class org.apache.spark.sql.types.DecimalType
 
unapply(Expression) - Static method in class org.apache.spark.sql.types.DecimalType
 
unapply(Expression) - Static method in class org.apache.spark.sql.types.NumericType
Enables matching against NumericType for expressions:
unapply(Broker) - Static method in class org.apache.spark.streaming.kafka.Broker
 
UnaryTransformer<IN,OUT,T extends UnaryTransformer<IN,OUT,T>> - Class in org.apache.spark.ml
:: DeveloperApi :: Abstract class for transformers that take one input column, apply transformation, and output the result as a new column.
UnaryTransformer() - Constructor for class org.apache.spark.ml.UnaryTransformer
 
unbase64(Column) - Static method in class org.apache.spark.sql.functions
Decodes a BASE64 encoded string column and returns it as a binary column.
unbroadcast(long, boolean, boolean) - Method in interface org.apache.spark.broadcast.BroadcastFactory
 
unbroadcast(long, boolean, boolean) - Method in class org.apache.spark.broadcast.HttpBroadcastFactory
Remove all persisted state associated with the HTTP broadcast with the given ID.
unbroadcast(long, boolean, boolean) - Method in class org.apache.spark.broadcast.TorrentBroadcastFactory
Remove all persisted state associated with the torrent broadcast with the given ID.
uncacheTable(String) - Method in class org.apache.spark.sql.SQLContext
Removes the specified table from the in-memory cache.
underlyingSplit() - Method in class org.apache.spark.scheduler.SplitInfo
 
unhandledFilters(Filter[]) - Method in class org.apache.spark.sql.sources.BaseRelation
Returns the list of Filters that this datasource may not be able to handle.
unhex(Column) - Static method in class org.apache.spark.sql.functions
Inverse of hex.
UniformGenerator - Class in org.apache.spark.mllib.random
:: DeveloperApi :: Generates i.i.d.
UniformGenerator() - Constructor for class org.apache.spark.mllib.random.UniformGenerator
 
uniformJavaRDD(JavaSparkContext, long, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
uniformJavaRDD(JavaSparkContext, long, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
uniformJavaRDD(JavaSparkContext, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
RandomRDDs.uniformJavaRDD(org.apache.spark.api.java.JavaSparkContext, long, int, long) with the default number of partitions and the default seed.
uniformJavaVectorRDD(JavaSparkContext, long, int, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
uniformJavaVectorRDD(JavaSparkContext, long, int, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
uniformJavaVectorRDD(JavaSparkContext, long, int) - Static method in class org.apache.spark.mllib.random.RandomRDDs
uniformRDD(SparkContext, long, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
Generates an RDD comprised of i.i.d. samples from the uniform distribution U(0.0, 1.0).
uniformVectorRDD(SparkContext, long, int, int, long) - Static method in class org.apache.spark.mllib.random.RandomRDDs
Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the uniform distribution on U(0.0, 1.0).
union(JavaDoubleRDD) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Return the union of this RDD and another one.
union(JavaPairRDD<K, V>) - Method in class org.apache.spark.api.java.JavaPairRDD
Return the union of this RDD and another one.
union(JavaRDD<T>) - Method in class org.apache.spark.api.java.JavaRDD
Return the union of this RDD and another one.
union(JavaRDD<T>, List<JavaRDD<T>>) - Method in class org.apache.spark.api.java.JavaSparkContext
Build the union of two or more RDDs.
union(JavaPairRDD<K, V>, List<JavaPairRDD<K, V>>) - Method in class org.apache.spark.api.java.JavaSparkContext
Build the union of two or more RDDs.
union(JavaDoubleRDD, List<JavaDoubleRDD>) - Method in class org.apache.spark.api.java.JavaSparkContext
Build the union of two or more RDDs.
union(RDD<T>) - Method in class org.apache.spark.rdd.RDD
Return the union of this RDD and another one.
union(Seq<RDD<T>>, ClassTag<T>) - Method in class org.apache.spark.SparkContext
Build the union of a list of RDDs.
union(RDD<T>, Seq<RDD<T>>, ClassTag<T>) - Method in class org.apache.spark.SparkContext
Build the union of a list of RDDs passed as variable-length arguments.
union(Dataset<T>) - Method in class org.apache.spark.sql.Dataset
Returns a new Dataset that contains the elements of both this and the other Dataset combined.
union(JavaDStream<T>) - Method in class org.apache.spark.streaming.api.java.JavaDStream
Return a new DStream by unifying data of another DStream with this DStream.
union(JavaPairDStream<K, V>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream by unifying data of another DStream with this DStream.
union(JavaDStream<T>, List<JavaDStream<T>>) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create a unified DStream from multiple DStreams of the same type and same slide duration.
union(JavaPairDStream<K, V>, List<JavaPairDStream<K, V>>) - Method in class org.apache.spark.streaming.api.java.JavaStreamingContext
Create a unified DStream from multiple DStreams of the same type and same slide duration.
union(DStream<T>) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream by unifying data of another DStream with this DStream.
union(Seq<DStream<T>>, ClassTag<T>) - Method in class org.apache.spark.streaming.StreamingContext
Create a unified DStream from multiple DStreams of the same type and same slide duration.
unionAll(DataFrame) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame containing union of rows in this frame and another frame.
UnionRDD<T> - Class in org.apache.spark.rdd
 
UnionRDD(SparkContext, Seq<RDD<T>>, ClassTag<T>) - Constructor for class org.apache.spark.rdd.UnionRDD
 
uniqueId() - Method in class org.apache.spark.storage.StreamBlockId
 
unix_timestamp() - Static method in class org.apache.spark.sql.functions
Gets current Unix timestamp in seconds.
unix_timestamp(Column) - Static method in class org.apache.spark.sql.functions
Converts time string in format yyyy-MM-dd HH:mm:ss to Unix timestamp (in seconds), using the default timezone and the default locale, return null if fail.
unix_timestamp(Column, String) - Static method in class org.apache.spark.sql.functions
Convert time string with given pattern (see [http://docs.oracle.com/javase/tutorial/i18n/format/simpleDateFormat.html]) to Unix time stamp (in seconds), return null if fail.
UnknownReason - Class in org.apache.spark
:: DeveloperApi :: We don't know why the task ended -- for example, because of a ClassNotFound exception when deserializing the task result.
UnknownReason() - Constructor for class org.apache.spark.UnknownReason
 
Unlimited() - Static method in class org.apache.spark.sql.types.DecimalType
 
unpersist() - Method in class org.apache.spark.api.java.JavaDoubleRDD
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
unpersist(boolean) - Method in class org.apache.spark.api.java.JavaDoubleRDD
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
unpersist() - Method in class org.apache.spark.api.java.JavaPairRDD
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
unpersist(boolean) - Method in class org.apache.spark.api.java.JavaPairRDD
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
unpersist() - Method in class org.apache.spark.api.java.JavaRDD
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
unpersist(boolean) - Method in class org.apache.spark.api.java.JavaRDD
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
unpersist() - Method in class org.apache.spark.broadcast.Broadcast
Asynchronously delete cached copies of this broadcast on the executors.
unpersist(boolean) - Method in class org.apache.spark.broadcast.Broadcast
Delete cached copies of this broadcast on the executors.
unpersist(boolean) - Method in class org.apache.spark.graphx.Graph
Uncaches both vertices and edges of this graph.
unpersist(boolean) - Method in class org.apache.spark.graphx.impl.EdgeRDDImpl
 
unpersist(boolean) - Method in class org.apache.spark.graphx.impl.GraphImpl
 
unpersist(boolean) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
unpersist() - Method in class org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
Unpersist intermediate RDDs used in the computation.
unpersist(boolean) - Method in class org.apache.spark.rdd.RDD
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
unpersist(boolean) - Method in class org.apache.spark.sql.DataFrame
Mark the DataFrame as non-persistent, and remove all blocks for it from memory and disk.
unpersist() - Method in class org.apache.spark.sql.DataFrame
Mark the DataFrame as non-persistent, and remove all blocks for it from memory and disk.
unpersist(boolean) - Method in class org.apache.spark.sql.Dataset
Mark the Dataset as non-persistent, and remove all blocks for it from memory and disk.
unpersist() - Method in class org.apache.spark.sql.Dataset
Mark the Dataset as non-persistent, and remove all blocks for it from memory and disk.
unpersistVertices(boolean) - Method in class org.apache.spark.graphx.Graph
Uncaches only the vertices of this graph, leaving the edges alone.
unpersistVertices(boolean) - Method in class org.apache.spark.graphx.impl.GraphImpl
 
unregister(QueryExecutionListener) - Method in class org.apache.spark.sql.util.ExecutionListenerManager
Unregisters the specified QueryExecutionListener.
unregisterDialect(JdbcDialect) - Static method in class org.apache.spark.sql.jdbc.JdbcDialects
Unregister a dialect.
Unresolved() - Static method in class org.apache.spark.ml.attribute.AttributeType
Unresolved type.
UnresolvedAttribute - Class in org.apache.spark.ml.attribute
:: DeveloperApi :: An unresolved attribute.
UnresolvedAttribute() - Constructor for class org.apache.spark.ml.attribute.UnresolvedAttribute
 
unresolvedTEncoder() - Method in class org.apache.spark.sql.Dataset
An unresolved version of the internal encoder for the type of this Dataset.
unset() - Static method in class org.apache.spark.TaskContext
Unset the thread local TaskContext.
until(Time, Duration) - Method in class org.apache.spark.streaming.Time
 
untilOffset() - Method in class org.apache.spark.streaming.kafka.OffsetRange
 
update(RDD<Vector>, double, String) - Method in class org.apache.spark.mllib.clustering.StreamingKMeansModel
Perform a k-means update on a batch of data.
update(int, int, double) - Method in interface org.apache.spark.mllib.linalg.Matrix
Update element at (i, j)
update(Function1<Object, Object>) - Method in interface org.apache.spark.mllib.linalg.Matrix
Update all the values of this matrix using the function f.
update() - Method in class org.apache.spark.scheduler.AccumulableInfo
 
update(int, Object) - Method in class org.apache.spark.sql.expressions.MutableAggregationBuffer
Update the ith value of this buffer.
update(MutableAggregationBuffer, Row) - Method in class org.apache.spark.sql.expressions.UserDefinedAggregateFunction
Updates the given aggregation buffer buffer with new input data from input.
update() - Method in class org.apache.spark.status.api.v1.AccumulableInfo
 
update(S) - Method in class org.apache.spark.streaming.State
Update the state with a new value.
update(T1, T2) - Method in class org.apache.spark.util.MutablePair
Updates this pair with new values and returns itself
updateAggregateMetrics(UIData.StageUIData, String, TaskMetrics, Option<TaskMetrics>) - Method in class org.apache.spark.ui.jobs.JobProgressListener
Upon receiving new metrics for a task, updates the per-stage and per-executor-per-stage aggregate metrics by calculating deltas between the currently recorded metrics and the new metrics.
updatePredictionError(RDD<LabeledPoint>, RDD<Tuple2<Object, Object>>, double, DecisionTreeModel, Loss) - Static method in class org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
:: DeveloperApi :: Update a zipped predictionError RDD (as obtained with computeInitialPredictionAndError)
Updater - Class in org.apache.spark.mllib.optimization
:: DeveloperApi :: Class used to perform steps (weight update) using Gradient Descent methods.
Updater() - Constructor for class org.apache.spark.mllib.optimization.Updater
 
updateStateByKey(Function2<List<V>, Optional<S>, Optional<S>>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of each key.
updateStateByKey(Function2<List<V>, Optional<S>, Optional<S>>, int) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of each key.
updateStateByKey(Function2<List<V>, Optional<S>, Optional<S>>, Partitioner) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of the key.
updateStateByKey(Function2<List<V>, Optional<S>, Optional<S>>, Partitioner, JavaPairRDD<K, S>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of the key.
updateStateByKey(Function2<Seq<V>, Option<S>, Option<S>>, ClassTag<S>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of each key.
updateStateByKey(Function2<Seq<V>, Option<S>, Option<S>>, int, ClassTag<S>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of each key.
updateStateByKey(Function2<Seq<V>, Option<S>, Option<S>>, Partitioner, ClassTag<S>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of the key.
updateStateByKey(Function1<Iterator<Tuple3<K, Seq<V>, Option<S>>>, Iterator<Tuple2<K, S>>>, Partitioner, boolean, ClassTag<S>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of each key.
updateStateByKey(Function2<Seq<V>, Option<S>, Option<S>>, Partitioner, RDD<Tuple2<K, S>>, ClassTag<S>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of the key.
updateStateByKey(Function1<Iterator<Tuple3<K, Seq<V>, Option<S>>>, Iterator<Tuple2<K, S>>>, Partitioner, boolean, RDD<Tuple2<K, S>>, ClassTag<S>) - Method in class org.apache.spark.streaming.dstream.PairDStreamFunctions
Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of each key.
upper(Column) - Static method in class org.apache.spark.sql.functions
Converts a string column to upper case.
useDisk() - Method in class org.apache.spark.storage.StorageLevel
 
useDst - Variable in class org.apache.spark.graphx.TripletFields
Indicates whether the destination vertex attribute is included.
useEdge - Variable in class org.apache.spark.graphx.TripletFields
Indicates whether the edge attribute is included.
useMemory() - Method in class org.apache.spark.storage.StorageLevel
 
useNodeIdCache() - Method in class org.apache.spark.mllib.tree.configuration.Strategy
 
useOffHeap() - Method in class org.apache.spark.storage.StorageLevel
 
user() - Method in class org.apache.spark.ml.recommendation.ALS.Rating
 
user() - Method in class org.apache.spark.mllib.recommendation.Rating
 
user() - Method in class org.apache.spark.scheduler.JobLogger
 
USER_DEFAULT() - Static method in class org.apache.spark.sql.types.DecimalType
 
userClass() - Method in class org.apache.spark.mllib.linalg.VectorUDT
 
userClass() - Method in class org.apache.spark.sql.types.UserDefinedType
Class object for the UserType
UserDefinedAggregateFunction - Class in org.apache.spark.sql.expressions
:: Experimental :: The base class for implementing user-defined aggregate functions (UDAF).
UserDefinedAggregateFunction() - Constructor for class org.apache.spark.sql.expressions.UserDefinedAggregateFunction
 
UserDefinedFunction - Class in org.apache.spark.sql
A user-defined function.
UserDefinedFunction(Object, DataType, Seq<DataType>) - Constructor for class org.apache.spark.sql.UserDefinedFunction
 
userDefinedPartitionColumns() - Method in class org.apache.spark.sql.sources.HadoopFsRelation
Optional user defined partition columns.
UserDefinedType<UserType> - Class in org.apache.spark.sql.types
::DeveloperApi:: The data type for User Defined Types (UDTs).
UserDefinedType() - Constructor for class org.apache.spark.sql.types.UserDefinedType
 
userFactors() - Method in class org.apache.spark.ml.recommendation.ALSModel
 
userFeatures() - Method in class org.apache.spark.mllib.recommendation.MatrixFactorizationModel
 
useSrc - Variable in class org.apache.spark.graphx.TripletFields
Indicates whether the source vertex attribute is included.

V

V() - Method in class org.apache.spark.mllib.linalg.SingularValueDecomposition
 
validate() - Method in class org.apache.spark.mllib.linalg.distributed.BlockMatrix
Validates the block matrix info against the matrix data (blocks) and throws an exception if any error is found.
validateData() - Method in class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
 
validateInputType(DataType) - Method in class org.apache.spark.ml.feature.DCT
 
validateInputType(DataType) - Method in class org.apache.spark.ml.feature.NGram
 
validateInputType(DataType) - Method in class org.apache.spark.ml.feature.RegexTokenizer
 
validateInputType(DataType) - Method in class org.apache.spark.ml.feature.Tokenizer
 
validateInputType(DataType) - Method in class org.apache.spark.ml.UnaryTransformer
Validates the input type.
validateParams() - Method in class org.apache.spark.ml.feature.Interaction
 
validateParams() - Method in class org.apache.spark.ml.feature.VectorSlicer
 
validateParams() - Method in interface org.apache.spark.ml.param.Params
 
validateParams() - Method in class org.apache.spark.ml.Pipeline
 
validateParams() - Method in class org.apache.spark.ml.PipelineModel
 
validateParams() - Method in class org.apache.spark.ml.tuning.CrossValidator
 
validateParams() - Method in class org.apache.spark.ml.tuning.CrossValidatorModel
 
validateParams() - Method in class org.apache.spark.ml.tuning.TrainValidationSplit
 
validateParams() - Method in class org.apache.spark.ml.tuning.TrainValidationSplitModel
 
validationMetrics() - Method in class org.apache.spark.ml.tuning.TrainValidationSplitModel
 
validationTol() - Method in class org.apache.spark.mllib.tree.configuration.BoostingStrategy
 
validators() - Method in class org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
 
validators() - Method in class org.apache.spark.mllib.classification.LogisticRegressionWithSGD
 
validators() - Method in class org.apache.spark.mllib.classification.SVMWithSGD
 
validators() - Method in class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
 
value() - Method in class org.apache.spark.Accumulable
Access the accumulator's current value; only allowed on master.
value() - Method in class org.apache.spark.broadcast.Broadcast
Get the broadcasted value.
value() - Method in class org.apache.spark.ComplexFutureAction
 
value() - Method in interface org.apache.spark.FutureAction
The value of this Future.
value() - Method in class org.apache.spark.ml.param.ParamPair
 
value() - Method in class org.apache.spark.mllib.linalg.distributed.MatrixEntry
 
value() - Method in class org.apache.spark.mllib.stat.test.BinarySample
 
value() - Method in class org.apache.spark.scheduler.AccumulableInfo
 
value() - Method in class org.apache.spark.SerializableWritable
 
value() - Method in class org.apache.spark.SimpleFutureAction
 
value() - Method in class org.apache.spark.sql.sources.EqualNullSafe
 
value() - Method in class org.apache.spark.sql.sources.EqualTo
 
value() - Method in class org.apache.spark.sql.sources.GreaterThan
 
value() - Method in class org.apache.spark.sql.sources.GreaterThanOrEqual
 
value() - Method in class org.apache.spark.sql.sources.LessThan
 
value() - Method in class org.apache.spark.sql.sources.LessThanOrEqual
 
value() - Method in class org.apache.spark.sql.sources.StringContains
 
value() - Method in class org.apache.spark.sql.sources.StringEndsWith
 
value() - Method in class org.apache.spark.sql.sources.StringStartsWith
 
value() - Method in class org.apache.spark.status.api.v1.AccumulableInfo
 
value() - Method in class org.apache.spark.storage.MemoryEntry
 
valueClassName() - Method in class org.apache.spark.ShuffleDependency
 
valueContainsNull() - Method in class org.apache.spark.sql.types.MapType
 
valueOf(String) - Static method in enum org.apache.spark.graphx.impl.EdgeActiveness
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.apache.spark.JobExecutionStatus
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.apache.spark.launcher.SparkAppHandle.State
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.apache.spark.sql.SaveMode
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.apache.spark.status.api.v1.ApplicationStatus
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.apache.spark.status.api.v1.StageStatus
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.apache.spark.status.api.v1.TaskSorting
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.apache.spark.streaming.StreamingContextState
Returns the enum constant of this type with the specified name.
values() - Method in class org.apache.spark.api.java.JavaPairRDD
Return an RDD with the values of each tuple.
values() - Static method in enum org.apache.spark.graphx.impl.EdgeActiveness
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.apache.spark.JobExecutionStatus
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.apache.spark.launcher.SparkAppHandle.State
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Method in class org.apache.spark.ml.attribute.BinaryAttribute
 
values() - Method in class org.apache.spark.ml.attribute.NominalAttribute
 
values() - Method in class org.apache.spark.mllib.linalg.DenseMatrix
 
values() - Method in class org.apache.spark.mllib.linalg.DenseVector
 
values() - Method in class org.apache.spark.mllib.linalg.SparseMatrix
 
values() - Method in class org.apache.spark.mllib.linalg.SparseVector
 
values() - Method in class org.apache.spark.rdd.PairRDDFunctions
Return an RDD with the values of each tuple.
values() - Static method in enum org.apache.spark.sql.SaveMode
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Method in class org.apache.spark.sql.sources.In
 
values() - Static method in enum org.apache.spark.status.api.v1.ApplicationStatus
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.apache.spark.status.api.v1.StageStatus
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.apache.spark.status.api.v1.TaskSorting
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.apache.spark.streaming.StreamingContextState
Returns an array containing the constants of this enum type, in the order they are declared.
valueType() - Method in class org.apache.spark.sql.types.MapType
 
var_pop(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the population variance of the values in a group.
var_pop(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the population variance of the values in a group.
var_samp(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the unbiased variance of the values in a group.
var_samp(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: returns the unbiased variance of the values in a group.
variance() - Method in class org.apache.spark.api.java.JavaDoubleRDD
Compute the variance of this RDD's elements.
variance() - Method in class org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
Unbiased estimate of sample variance of each dimension.
variance() - Method in interface org.apache.spark.mllib.stat.MultivariateStatisticalSummary
Sample variance vector.
Variance - Class in org.apache.spark.mllib.tree.impurity
:: Experimental :: Class for calculating variance during regression
Variance() - Constructor for class org.apache.spark.mllib.tree.impurity.Variance
 
variance() - Method in class org.apache.spark.rdd.DoubleRDDFunctions
Compute the variance of this RDD's elements.
variance(Column) - Static method in class org.apache.spark.sql.functions
Aggregate function: alias for var_samp.
variance(String) - Static method in class org.apache.spark.sql.functions
Aggregate function: alias for var_samp.
variance() - Method in class org.apache.spark.util.StatCounter
Return the variance of the values.
vClassTag() - Method in class org.apache.spark.api.java.JavaHadoopRDD
 
vClassTag() - Method in class org.apache.spark.api.java.JavaNewHadoopRDD
 
vClassTag() - Method in class org.apache.spark.api.java.JavaPairRDD
 
vClassTag() - Method in class org.apache.spark.streaming.api.java.JavaPairInputDStream
 
vClassTag() - Method in class org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream
 
vdTag() - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
vdTag() - Method in class org.apache.spark.graphx.VertexRDD
 
vector() - Method in class org.apache.spark.mllib.linalg.distributed.IndexedRow
 
Vector - Interface in org.apache.spark.mllib.linalg
Represents a numeric vector, whose index type is Int and value type is Double.
Vector - Class in org.apache.spark.util
 
Vector(double[]) - Constructor for class org.apache.spark.util.Vector
 
Vector.Multiplier - Class in org.apache.spark.util
 
Vector.Multiplier(double) - Constructor for class org.apache.spark.util.Vector.Multiplier
 
Vector.VectorAccumParam$ - Class in org.apache.spark.util
 
Vector.VectorAccumParam$() - Constructor for class org.apache.spark.util.Vector.VectorAccumParam$
 
VectorAssembler - Class in org.apache.spark.ml.feature
:: Experimental :: A feature transformer that merges multiple columns into a vector column.
VectorAssembler(String) - Constructor for class org.apache.spark.ml.feature.VectorAssembler
 
VectorAssembler() - Constructor for class org.apache.spark.ml.feature.VectorAssembler
 
VectorAttributeRewriter - Class in org.apache.spark.ml.feature
Utility transformer that rewrites Vector attribute names via prefix replacement.
VectorAttributeRewriter(String, Map<String, String>) - Constructor for class org.apache.spark.ml.feature.VectorAttributeRewriter
 
VectorIndexer - Class in org.apache.spark.ml.feature
:: Experimental :: Class for indexing categorical feature columns in a dataset of Vector.
VectorIndexer(String) - Constructor for class org.apache.spark.ml.feature.VectorIndexer
 
VectorIndexer() - Constructor for class org.apache.spark.ml.feature.VectorIndexer
 
VectorIndexerModel - Class in org.apache.spark.ml.feature
:: Experimental :: Transform categorical features to use 0-based indices instead of their original values.
Vectors - Class in org.apache.spark.mllib.linalg
 
Vectors() - Constructor for class org.apache.spark.mllib.linalg.Vectors
 
VectorSlicer - Class in org.apache.spark.ml.feature
:: Experimental :: This class takes a feature vector and outputs a new feature vector with a subarray of the original features.
VectorSlicer(String) - Constructor for class org.apache.spark.ml.feature.VectorSlicer
 
VectorSlicer() - Constructor for class org.apache.spark.ml.feature.VectorSlicer
 
VectorTransformer - Interface in org.apache.spark.mllib.feature
:: DeveloperApi :: Trait for transformation of a vector
VectorUDT - Class in org.apache.spark.mllib.linalg
:: AlphaComponent ::
VectorUDT() - Constructor for class org.apache.spark.mllib.linalg.VectorUDT
 
version() - Method in class org.apache.spark.api.java.JavaSparkContext
The version of Spark on which this application is running.
version() - Method in class org.apache.spark.SparkContext
The version of Spark on which this application is running.
vertcat(Matrix[]) - Static method in class org.apache.spark.mllib.linalg.Matrices
Vertically concatenate a sequence of matrices.
vertexAttr(long) - Method in class org.apache.spark.graphx.EdgeTriplet
Get the vertex object for the given vertex in the edge.
VertexRDD<VD> - Class in org.apache.spark.graphx
Extends RDD[(VertexId, VD)] by ensuring that there is only one entry for each vertex and by pre-indexing the entries for fast, efficient joins.
VertexRDD(SparkContext, Seq<Dependency<?>>) - Constructor for class org.apache.spark.graphx.VertexRDD
 
VertexRDDImpl<VD> - Class in org.apache.spark.graphx.impl
 
vertices() - Method in class org.apache.spark.graphx.Graph
An RDD containing the vertices and their associated attributes.
vertices() - Method in class org.apache.spark.graphx.impl.GraphImpl
 
visit(int, int, String, String, String, String[]) - Method in class org.apache.spark.util.InnerClosureFinder
 
visitMethod(int, String, String, String, String[]) - Method in class org.apache.spark.util.InnerClosureFinder
 
visitMethod(int, String, String, String, String[]) - Method in class org.apache.spark.util.ReturnStatementFinder
 
vManifest() - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
 
vocabSize() - Method in class org.apache.spark.ml.clustering.LDAModel
 
vocabSize() - Method in class org.apache.spark.mllib.clustering.DistributedLDAModel
 
vocabSize() - Method in class org.apache.spark.mllib.clustering.EMLDAOptimizer
 
vocabSize() - Method in class org.apache.spark.mllib.clustering.LDAModel
Vocabulary size (number of terms or terms in the vocabulary)
vocabSize() - Method in class org.apache.spark.mllib.clustering.LocalLDAModel
 
vocabulary() - Method in class org.apache.spark.ml.feature.CountVectorizerModel
 
VocabWord - Class in org.apache.spark.mllib.feature
Entry in vocabulary
VocabWord(String, int, int[], int[], int) - Constructor for class org.apache.spark.mllib.feature.VocabWord
 
VoidFunction<T> - Interface in org.apache.spark.api.java.function
A function with no return value.
VoidFunction2<T1,T2> - Interface in org.apache.spark.api.java.function
A two-argument function that takes arguments of type T1 and T2 with no return value.

W

w(boolean) - Method in class org.apache.spark.ml.param.BooleanParam
Creates a param pair with the given value (for Java).
w(List<Double>) - Method in class org.apache.spark.ml.param.DoubleArrayParam
Creates a param pair with a List of values (for Java and Python).
w(double) - Method in class org.apache.spark.ml.param.DoubleParam
Creates a param pair with the given value (for Java).
w(float) - Method in class org.apache.spark.ml.param.FloatParam
Creates a param pair with the given value (for Java).
w(List<Integer>) - Method in class org.apache.spark.ml.param.IntArrayParam
Creates a param pair with a List of values (for Java and Python).
w(int) - Method in class org.apache.spark.ml.param.IntParam
Creates a param pair with the given value (for Java).
w(long) - Method in class org.apache.spark.ml.param.LongParam
Creates a param pair with the given value (for Java).
w(T) - Method in class org.apache.spark.ml.param.Param
Creates a param pair with the given value (for Java).
w(List<String>) - Method in class org.apache.spark.ml.param.StringArrayParam
Creates a param pair with a List of values (for Java and Python).
waiter() - Method in class org.apache.spark.streaming.StreamingContext
 
weekofyear(Column) - Static method in class org.apache.spark.sql.functions
Extracts the week number as an integer from a given date/timestamp/string.
WeibullGenerator - Class in org.apache.spark.mllib.random
:: DeveloperApi :: Generates i.i.d.
WeibullGenerator(double, double) - Constructor for class org.apache.spark.mllib.random.WeibullGenerator
 
weightedFalsePositiveRate() - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns weighted false positive rate
weightedFMeasure(double) - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns weighted averaged f-measure
weightedFMeasure() - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns weighted averaged f1-measure
weightedPrecision() - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns weighted averaged precision
weightedRecall() - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns weighted averaged recall (equals to precision, recall and f-measure)
weightedTruePositiveRate() - Method in class org.apache.spark.mllib.evaluation.MulticlassMetrics
Returns weighted true positive rate (equals to precision, recall and f-measure)
weights() - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
 
weights() - Method in class org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel
 
weights() - Method in class org.apache.spark.ml.regression.LinearRegressionModel
 
weights() - Method in class org.apache.spark.mllib.classification.LogisticRegressionModel
 
weights() - Method in class org.apache.spark.mllib.classification.SVMModel
 
weights() - Method in class org.apache.spark.mllib.clustering.ExpectationSum
 
weights() - Method in class org.apache.spark.mllib.clustering.GaussianMixtureModel
 
weights() - Method in class org.apache.spark.mllib.regression.GeneralizedLinearModel
 
weights() - Method in class org.apache.spark.mllib.regression.LassoModel
 
weights() - Method in class org.apache.spark.mllib.regression.LinearRegressionModel
 
weights() - Method in class org.apache.spark.mllib.regression.RidgeRegressionModel
 
when(Column, Object) - Method in class org.apache.spark.sql.Column
Evaluates a list of conditions and returns one of multiple possible result expressions.
when(Column, Object) - Static method in class org.apache.spark.sql.functions
Evaluates a list of conditions and returns one of multiple possible result expressions.
where(Column) - Method in class org.apache.spark.sql.DataFrame
Filters rows using the given condition.
where(String) - Method in class org.apache.spark.sql.DataFrame
Filters rows using the given SQL expression.
wholeTextFiles(String, int) - Method in class org.apache.spark.api.java.JavaSparkContext
Read a directory of text files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI.
wholeTextFiles(String) - Method in class org.apache.spark.api.java.JavaSparkContext
Read a directory of text files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI.
wholeTextFiles(String, int) - Method in class org.apache.spark.SparkContext
Read a directory of text files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI.
Window - Class in org.apache.spark.sql.expressions
:: Experimental :: Utility functions for defining window in DataFrames.
window(Duration) - Method in class org.apache.spark.streaming.api.java.JavaDStream
Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream.
window(Duration, Duration) - Method in class org.apache.spark.streaming.api.java.JavaDStream
Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream.
window(Duration) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream which is computed based on windowed batches of this DStream.
window(Duration, Duration) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
Return a new DStream which is computed based on windowed batches of this DStream.
window(Duration) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream.
window(Duration, Duration) - Method in class org.apache.spark.streaming.dstream.DStream
Return a new DStream in which each RDD contains all the elements in seen in a sliding window of time over this DStream.
WindowSpec - Class in org.apache.spark.sql.expressions
:: Experimental :: A window specification that defines the partitioning, ordering, and frame boundaries.
withColumn(String, Column) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame by adding a column or replacing the existing column that has the same name.
withColumnRenamed(String, String) - Method in class org.apache.spark.sql.DataFrame
Returns a new DataFrame with a column renamed.
withEdges(EdgeRDD<?>) - Method in class org.apache.spark.graphx.impl.VertexRDDImpl
 
withEdges(EdgeRDD<?>) - Method in class org.apache.spark.graphx.VertexRDD
Prepares this VertexRDD for efficient joins with the given EdgeRDD.
withIndex(int) - Method in class org.apache.spark.ml.attribute.Attribute
Copy with a new index.
withIndex(int) - Method in class org.apache.spark.ml.attribute.BinaryAttribute
 
withIndex(int) - Method in class org.apache.spark.ml.attribute.NominalAttribute
 
withIndex(int) - Method in class org.apache.spark.ml.attribute.NumericAttribute
 
withIndex(int) - Static method in class org.apache.spark.ml.attribute.UnresolvedAttribute
 
withMax(double) - Method in class org.apache.spark.ml.attribute.NumericAttribute
Copy with a new max value.
withMean() - Method in class org.apache.spark.mllib.feature.StandardScalerModel
 
withMetadata(Metadata) - Method in class org.apache.spark.sql.types.MetadataBuilder
Include the content of an existing Metadata instance.
withMin(double) - Method in class org.apache.spark.ml.attribute.NumericAttribute
Copy with a new min value.
withName(String) - Method in class org.apache.spark.ml.attribute.Attribute
Copy with a new name.
withName(String) - Method in class org.apache.spark.ml.attribute.BinaryAttribute
 
withName(String) - Method in class org.apache.spark.ml.attribute.NominalAttribute
 
withName(String) - Method in class org.apache.spark.ml.attribute.NumericAttribute
 
withName(String) - Static method in class org.apache.spark.ml.attribute.UnresolvedAttribute
 
withNumValues(int) - Method in class org.apache.spark.ml.attribute.NominalAttribute
Copy with a new `numValues` and empty `values`.
withoutIndex() - Method in class org.apache.spark.ml.attribute.Attribute
Copy without the index.
withoutIndex() - Method in class org.apache.spark.ml.attribute.BinaryAttribute
 
withoutIndex() - Method in class org.apache.spark.ml.attribute.NominalAttribute
 
withoutIndex() - Method in class org.apache.spark.ml.attribute.NumericAttribute
 
withoutIndex() - Static method in class org.apache.spark.ml.attribute.UnresolvedAttribute
 
withoutMax() - Method in class org.apache.spark.ml.attribute.NumericAttribute
Copy without the max value.
withoutMin() - Method in class org.apache.spark.ml.attribute.NumericAttribute
Copy without the min value.
withoutName() - Method in class org.apache.spark.ml.attribute.Attribute
Copy without the name.
withoutName() - Method in class org.apache.spark.ml.attribute.BinaryAttribute
 
withoutName() - Method in class org.apache.spark.ml.attribute.NominalAttribute
 
withoutName() - Method in class org.apache.spark.ml.attribute.NumericAttribute
 
withoutName() - Static method in class org.apache.spark.ml.attribute.UnresolvedAttribute
 
withoutNumValues() - Method in class org.apache.spark.ml.attribute.NominalAttribute
Copy without the `numValues`.
withoutSparsity() - Method in class org.apache.spark.ml.attribute.NumericAttribute
Copy without the sparsity.
withoutStd() - Method in class org.apache.spark.ml.attribute.NumericAttribute
Copy without the standard deviation.
withoutSummary() - Method in class org.apache.spark.ml.attribute.NumericAttribute
Copy without summary statistics.
withoutValues() - Method in class org.apache.spark.ml.attribute.BinaryAttribute
Copy without the values.
withoutValues() - Method in class org.apache.spark.ml.attribute.NominalAttribute
Copy without the values.
withPosition(Option<Object>, Option<Object>) - Method in exception org.apache.spark.sql.AnalysisException
 
withSparsity(double) - Method in class org.apache.spark.ml.attribute.NumericAttribute
Copy with a new sparsity.
withStd(double) - Method in class org.apache.spark.ml.attribute.NumericAttribute
Copy with a new standard deviation.
withStd() - Method in class org.apache.spark.mllib.feature.StandardScalerModel
 
withValues(String, String) - Method in class org.apache.spark.ml.attribute.BinaryAttribute
Copy with new values.
withValues(String, String...) - Method in class org.apache.spark.ml.attribute.NominalAttribute
Copy with new values and empty `numValues`.
withValues(String[]) - Method in class org.apache.spark.ml.attribute.NominalAttribute
Copy with new values and empty `numValues`.
withValues(String, Seq<String>) - Method in class org.apache.spark.ml.attribute.NominalAttribute
Copy with new values and empty `numValues`.
word() - Method in class org.apache.spark.mllib.feature.VocabWord
 
Word2Vec - Class in org.apache.spark.ml.feature
:: Experimental :: Word2Vec trains a model of Map(String, Vector), i.e.
Word2Vec(String) - Constructor for class org.apache.spark.ml.feature.Word2Vec
 
Word2Vec() - Constructor for class org.apache.spark.ml.feature.Word2Vec
 
Word2Vec - Class in org.apache.spark.mllib.feature
 
Word2Vec() - Constructor for class org.apache.spark.mllib.feature.Word2Vec
 
Word2VecModel - Class in org.apache.spark.ml.feature
:: Experimental :: Model fitted by Word2Vec.
Word2VecModel - Class in org.apache.spark.mllib.feature
 
Word2VecModel(Map<String, float[]>) - Constructor for class org.apache.spark.mllib.feature.Word2VecModel
 
wordIndex() - Method in class org.apache.spark.mllib.feature.Word2VecModel
 
wordVectors() - Method in class org.apache.spark.mllib.feature.Word2VecModel
 
wrapperClass() - Static method in class org.apache.spark.serializer.JavaIterableWrapperSerializer
 
wrapRDD(RDD<Double>) - Method in class org.apache.spark.api.java.JavaDoubleRDD
 
wrapRDD(RDD<Tuple2<K, V>>) - Method in class org.apache.spark.api.java.JavaPairRDD
 
wrapRDD(RDD<T>) - Method in class org.apache.spark.api.java.JavaRDD
 
wrapRDD(RDD<T>) - Method in interface org.apache.spark.api.java.JavaRDDLike
 
wrapRDD(RDD<T>) - Method in class org.apache.spark.streaming.api.java.JavaDStream
 
wrapRDD(RDD<T>) - Method in interface org.apache.spark.streaming.api.java.JavaDStreamLike
 
wrapRDD(RDD<Tuple2<K, V>>) - Method in class org.apache.spark.streaming.api.java.JavaPairDStream
 
writableWritableConverter() - Static method in class org.apache.spark.SparkContext
 
write(int) - Method in class org.apache.spark.io.SnappyOutputStreamWrapper
 
write(byte[]) - Method in class org.apache.spark.io.SnappyOutputStreamWrapper
 
write(byte[], int, int) - Method in class org.apache.spark.io.SnappyOutputStreamWrapper
 
write() - Method in class org.apache.spark.ml.classification.LogisticRegressionModel
Returns a MLWriter instance for this ML instance.
write() - Method in class org.apache.spark.ml.classification.NaiveBayesModel
 
write() - Method in class org.apache.spark.ml.clustering.DistributedLDAModel
 
write() - Method in class org.apache.spark.ml.clustering.KMeansModel
 
write() - Method in class org.apache.spark.ml.clustering.LocalLDAModel
 
write() - Method in class org.apache.spark.ml.feature.ChiSqSelectorModel
 
write() - Method in class org.apache.spark.ml.feature.CountVectorizerModel
 
write() - Method in class org.apache.spark.ml.feature.IDFModel
 
write() - Method in class org.apache.spark.ml.feature.MinMaxScalerModel
 
write() - Method in class org.apache.spark.ml.feature.PCAModel
 
write() - Method in class org.apache.spark.ml.feature.StandardScalerModel
 
write() - Method in class org.apache.spark.ml.feature.StringIndexerModel
 
write() - Method in class org.apache.spark.ml.feature.VectorIndexerModel
 
write() - Method in class org.apache.spark.ml.feature.Word2VecModel
 
write() - Method in class org.apache.spark.ml.Pipeline
 
write() - Method in class org.apache.spark.ml.PipelineModel
 
write() - Method in class org.apache.spark.ml.recommendation.ALSModel
 
write() - Method in class org.apache.spark.ml.regression.AFTSurvivalRegressionModel
 
write() - Method in class org.apache.spark.ml.regression.IsotonicRegressionModel
 
write() - Method in class org.apache.spark.ml.regression.LinearRegressionModel
Returns a MLWriter instance for this ML instance.
write() - Method in class org.apache.spark.ml.tuning.CrossValidator
 
write() - Method in class org.apache.spark.ml.tuning.CrossValidatorModel
 
write() - Method in interface org.apache.spark.ml.util.MLWritable
Returns an MLWriter instance for this ML instance.
write(Kryo, Output, Iterable<?>) - Method in class org.apache.spark.serializer.JavaIterableWrapperSerializer
 
write() - Method in class org.apache.spark.sql.DataFrame
:: Experimental :: Interface for saving the content of the DataFrame out into external storage.
write(Row) - Method in class org.apache.spark.sql.sources.OutputWriter
Persists a single row.
write(int) - Method in class org.apache.spark.storage.TimeTrackingOutputStream
 
write(byte[]) - Method in class org.apache.spark.storage.TimeTrackingOutputStream
 
write(byte[], int, int) - Method in class org.apache.spark.storage.TimeTrackingOutputStream
 
write(ByteBuffer, long) - Method in class org.apache.spark.streaming.util.WriteAheadLog
Write the record to the log and return a record handle, which contains all the information necessary to read back the written record.
WriteAheadLog - Class in org.apache.spark.streaming.util
:: DeveloperApi :: This abstract class represents a write ahead log (aka journal) that is used by Spark Streaming to save the received data (by receivers) and associated metadata to a reliable storage, so that they can be recovered after driver failures.
WriteAheadLog() - Constructor for class org.apache.spark.streaming.util.WriteAheadLog
 
WriteAheadLogRecordHandle - Class in org.apache.spark.streaming.util
:: DeveloperApi :: This abstract class represents a handle that refers to a record written in a WriteAheadLog.
WriteAheadLogRecordHandle() - Constructor for class org.apache.spark.streaming.util.WriteAheadLogRecordHandle
 
writeAll(Iterator<T>, ClassTag<T>) - Method in class org.apache.spark.serializer.SerializationStream
 
writeBytes() - Method in class org.apache.spark.status.api.v1.ShuffleWriteMetricDistributions
 
writeExternal(ObjectOutput) - Method in class org.apache.spark.serializer.JavaSerializer
 
writeExternal(ObjectOutput) - Method in class org.apache.spark.storage.BlockManagerId
 
writeExternal(ObjectOutput) - Method in class org.apache.spark.storage.StorageLevel
 
writeExternal(ObjectOutput) - Method in class org.apache.spark.streaming.flume.SparkFlumeEvent
 
writeInternal(InternalRow) - Method in class org.apache.spark.sql.sources.OutputWriter
 
writeKey(T, ClassTag<T>) - Method in class org.apache.spark.serializer.SerializationStream
Writes the object representing the key of a key-value pair.
writeObject(T, ClassTag<T>) - Method in class org.apache.spark.serializer.SerializationStream
The most general-purpose method to write an object.
writeRecords() - Method in class org.apache.spark.status.api.v1.ShuffleWriteMetricDistributions
 
writeTime() - Method in class org.apache.spark.status.api.v1.ShuffleWriteMetricDistributions
 
writeTime() - Method in class org.apache.spark.status.api.v1.ShuffleWriteMetrics
 
writeValue(T, ClassTag<T>) - Method in class org.apache.spark.serializer.SerializationStream
Writes the object representing the value of a key-value pair.

Y

year(Column) - Static method in class org.apache.spark.sql.functions
Extracts the year as an integer from a given date/timestamp/string.

Z

zero() - Method in class org.apache.spark.Accumulable
 
zero(R) - Method in interface org.apache.spark.AccumulableParam
Return the "zero" (identity) value for an accumulator type, given its initial value.
zero(double) - Method in class org.apache.spark.AccumulatorParam.DoubleAccumulatorParam$
 
zero(float) - Method in class org.apache.spark.AccumulatorParam.FloatAccumulatorParam$
 
zero(int) - Method in class org.apache.spark.AccumulatorParam.IntAccumulatorParam$
 
zero(long) - Method in class org.apache.spark.AccumulatorParam.LongAccumulatorParam$
 
zero(int, int) - Static method in class org.apache.spark.mllib.clustering.ExpectationSum
 
zero(double) - Method in class org.apache.spark.SparkContext.DoubleAccumulatorParam$
 
zero(float) - Method in class org.apache.spark.SparkContext.FloatAccumulatorParam$
 
zero(int) - Method in class org.apache.spark.SparkContext.IntAccumulatorParam$
 
zero(long) - Method in class org.apache.spark.SparkContext.LongAccumulatorParam$
 
zero() - Method in class org.apache.spark.sql.expressions.Aggregator
A zero value for this aggregation.
ZERO() - Static method in class org.apache.spark.sql.types.Decimal
 
zero(Vector) - Method in class org.apache.spark.util.Vector.VectorAccumParam$
 
ZeroMQUtils - Class in org.apache.spark.streaming.zeromq
 
ZeroMQUtils() - Constructor for class org.apache.spark.streaming.zeromq.ZeroMQUtils
 
zeros(int, int) - Static method in class org.apache.spark.mllib.linalg.DenseMatrix
Generate a DenseMatrix consisting of zeros.
zeros(int, int) - Static method in class org.apache.spark.mllib.linalg.Matrices
Generate a Matrix consisting of zeros.
zeros(int) - Static method in class org.apache.spark.mllib.linalg.Vectors
Creates a vector of all zeros.
zeros(int) - Static method in class org.apache.spark.util.Vector
 
zeroTime() - Method in class org.apache.spark.streaming.dstream.DStream
 
zip(JavaRDDLike<U, ?>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Zips this RDD with another one, returning key-value pairs with the first element in each RDD, second element in each RDD, etc.
zip(RDD<U>, ClassTag<U>) - Method in class org.apache.spark.rdd.RDD
Zips this RDD with another one, returning key-value pairs with the first element in each RDD, second element in each RDD, etc.
zipPartitions(JavaRDDLike<U, ?>, FlatMapFunction2<Iterator<T>, Iterator<U>, V>) - Method in interface org.apache.spark.api.java.JavaRDDLike
Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by applying a function to the zipped partitions.
zipPartitions(RDD<B>, boolean, Function2<Iterator<T>, Iterator<B>, Iterator<V>>, ClassTag<B>, ClassTag<V>) - Method in class org.apache.spark.rdd.RDD
Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by applying a function to the zipped partitions.
zipPartitions(RDD<B>, Function2<Iterator<T>, Iterator<B>, Iterator<V>>, ClassTag<B>, ClassTag<V>) - Method in class org.apache.spark.rdd.RDD
 
zipPartitions(RDD<B>, RDD<C>, boolean, Function3<Iterator<T>, Iterator<B>, Iterator<C>, Iterator<V>>, ClassTag<B>, ClassTag<C>, ClassTag<V>) - Method in class org.apache.spark.rdd.RDD
 
zipPartitions(RDD<B>, RDD<C>, Function3<Iterator<T>, Iterator<B>, Iterator<C>, Iterator<V>>, ClassTag<B>, ClassTag<C>, ClassTag<V>) - Method in class org.apache.spark.rdd.RDD
 
zipPartitions(RDD<B>, RDD<C>, RDD<D>, boolean, Function4<Iterator<T>, Iterator<B>, Iterator<C>, Iterator<D>, Iterator<V>>, ClassTag<B>, ClassTag<C>, ClassTag<D>, ClassTag<V>) - Method in class org.apache.spark.rdd.RDD
 
zipPartitions(RDD<B>, RDD<C>, RDD<D>, Function4<Iterator<T>, Iterator<B>, Iterator<C>, Iterator<D>, Iterator<V>>, ClassTag<B>, ClassTag<C>, ClassTag<D>, ClassTag<V>) - Method in class org.apache.spark.rdd.RDD
 
zipWithIndex() - Method in interface org.apache.spark.api.java.JavaRDDLike
Zips this RDD with its element indices.
zipWithIndex() - Method in class org.apache.spark.rdd.RDD
Zips this RDD with its element indices.
zipWithUniqueId() - Method in interface org.apache.spark.api.java.JavaRDDLike
Zips this RDD with generated unique Long ids.
zipWithUniqueId() - Method in class org.apache.spark.rdd.RDD
Zips this RDD with generated unique Long ids.

_

_1() - Method in class org.apache.spark.util.MutablePair
 
_2() - Method in class org.apache.spark.util.MutablePair
 
_rddInfoMap() - Method in class org.apache.spark.ui.storage.StorageListener
 
_sqlContext() - Method in class org.apache.spark.sql.SQLContext.implicits$
 
_sqlContext() - Method in class org.apache.spark.sql.SQLImplicits
 
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