SparkContext (Spark 3.4.0 JavaDoc)

Object
- org.apache.spark.SparkContext

All Implemented Interfaces:

org.apache.spark.internal.Logging
```
public class SparkContext
extends Object
implements org.apache.spark.internal.Logging
```
Main entry point for Spark functionality. A SparkContext represents the connection to a Spark cluster, and can be used to create RDDs, accumulators and broadcast variables on that cluster.

Note:

Only one SparkContext should be active per JVM. You must stop() the active SparkContext before creating a new one. param: config a Spark Config object describing the application configuration. Any settings in this config overrides the default configs as well as system properties.

Nested Class Summary
- Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging
  org.apache.spark.internal.Logging.SparkShellLoggingFilter

Constructor Summary

Constructors
Constructor and Description
`SparkContext()` Create a SparkContext that loads settings from system properties (for instance, when launching with ./bin/spark-submit).
`SparkContext(SparkConf config)`
`SparkContext(String master, String appName, SparkConf conf)` Alternative constructor that allows setting common Spark properties directly
`SparkContext(String master, String appName, String sparkHome, scala.collection.Seq<String> jars, scala.collection.Map<String,String> environment)` Alternative constructor that allows setting common Spark properties directly

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`addArchive(String path)` :: Experimental :: Add an archive to be downloaded and unpacked with this Spark job on every node.
`void`	`addFile(String path)` Add a file to be downloaded with this Spark job on every node.
`void`	`addFile(String path, boolean recursive)` Add a file to be downloaded with this Spark job on every node.
`void`	`addJar(String path)` Adds a JAR dependency for all tasks to be executed on this `SparkContext` in the future.
`void`	`addSparkListener(SparkListenerInterface listener)` :: DeveloperApi :: Register a listener to receive up-calls from events that happen during execution.
`scala.Option<String>`	`applicationAttemptId()`
`String`	`applicationId()` A unique identifier for the Spark application.
`String`	`appName()`
`scala.collection.Seq<String>`	`archives()`
`RDD<scala.Tuple2<String,PortableDataStream>>`	`binaryFiles(String path, int minPartitions)` Get an RDD for a Hadoop-readable dataset as PortableDataStream for each file (useful for binary data)
`RDD<byte[]>`	`binaryRecords(String path, int recordLength, org.apache.hadoop.conf.Configuration conf)` Load data from a flat binary file, assuming the length of each record is constant.
`<T> Broadcast<T>`	`broadcast(T value, scala.reflect.ClassTag<T> evidence$9)` Broadcast a read-only variable to the cluster, returning a `Broadcast` object for reading it in distributed functions.
`void`	`cancelAllJobs()` Cancel all jobs that have been scheduled or are running.
`void`	`cancelJob(int jobId)` Cancel a given job if it's scheduled or running.
`void`	`cancelJob(int jobId, String reason)` Cancel a given job if it's scheduled or running.
`void`	`cancelJobGroup(String groupId)` Cancel active jobs for the specified group.
`void`	`cancelStage(int stageId)` Cancel a given stage and all jobs associated with it.
`void`	`cancelStage(int stageId, String reason)` Cancel a given stage and all jobs associated with it.
`void`	`clearCallSite()` Clear the thread-local property for overriding the call sites of actions and RDDs.
`void`	`clearJobGroup()` Clear the current thread's job group ID and its description.
`<T> CollectionAccumulator<T>`	`collectionAccumulator()` Create and register a `CollectionAccumulator`, which starts with empty list and accumulates inputs by adding them into the list.
`<T> CollectionAccumulator<T>`	`collectionAccumulator(String name)` Create and register a `CollectionAccumulator`, which starts with empty list and accumulates inputs by adding them into the list.
`int`	`defaultMinPartitions()` 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.
`int`	`defaultParallelism()` Default level of parallelism to use when not given by user (e.g.
`String`	`deployMode()`
`DoubleAccumulator`	`doubleAccumulator()` Create and register a double accumulator, which starts with 0 and accumulates inputs by `add`.
`DoubleAccumulator`	`doubleAccumulator(String name)` Create and register a double accumulator, which starts with 0 and accumulates inputs by `add`.
`<T> RDD<T>`	`emptyRDD(scala.reflect.ClassTag<T> evidence$8)` Get an RDD that has no partitions or elements.
`scala.collection.Seq<String>`	`files()`
`scala.collection.Seq<Schedulable>`	`getAllPools()` :: DeveloperApi :: Return pools for fair scheduler
`scala.Option<String>`	`getCheckpointDir()`
`SparkConf`	`getConf()` Return a copy of this SparkContext's configuration.
`scala.collection.Map<String,scala.Tuple2<Object,Object>>`	`getExecutorMemoryStatus()` Return a map from the block manager to the max memory available for caching and the remaining memory available for caching.
`String`	`getLocalProperty(String key)` Get a local property set in this thread, or null if it is missing.
`static SparkContext`	`getOrCreate()` This function may be used to get or instantiate a SparkContext and register it as a singleton object.
`static SparkContext`	`getOrCreate(SparkConf config)` This function may be used to get or instantiate a SparkContext and register it as a singleton object.
`scala.collection.Map<Object,RDD<?>>`	`getPersistentRDDs()` Returns an immutable map of RDDs that have marked themselves as persistent via cache() call.
`scala.Option<Schedulable>`	`getPoolForName(String pool)` :: DeveloperApi :: Return the pool associated with the given name, if one exists
`RDDInfo[]`	`getRDDStorageInfo()` :: DeveloperApi :: Return information about what RDDs are cached, if they are in mem or on disk, how much space they take, etc.
`scala.Enumeration.Value`	`getSchedulingMode()` Return current scheduling mode
`org.apache.hadoop.conf.Configuration`	`hadoopConfiguration()` A default Hadoop Configuration for the Hadoop code (e.g.
`<K,V> RDD<scala.Tuple2<K,V>>`	`hadoopFile(String path, Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass, Class<K> keyClass, Class<V> valueClass, int minPartitions)` Get an RDD for a Hadoop file with an arbitrary InputFormat
`<K,V,F extends org.apache.hadoop.mapred.InputFormat<K,V>> RDD<scala.Tuple2<K,V>>`	`hadoopFile(String path, scala.reflect.ClassTag<K> km, scala.reflect.ClassTag<V> vm, scala.reflect.ClassTag<F> fm)` 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.
`<K,V,F extends org.apache.hadoop.mapred.InputFormat<K,V>> RDD<scala.Tuple2<K,V>>`	`hadoopFile(String path, int minPartitions, scala.reflect.ClassTag<K> km, scala.reflect.ClassTag<V> vm, scala.reflect.ClassTag<F> fm)` 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.
`<K,V> RDD<scala.Tuple2<K,V>>`	`hadoopRDD(org.apache.hadoop.mapred.JobConf conf, Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass, Class<K> keyClass, Class<V> valueClass, int minPartitions)` Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf given its InputFormat and other necessary info (e.g.
`boolean`	`isLocal()`
`boolean`	`isStopped()`
`static scala.Option<String>`	`jarOfClass(Class<?> cls)` Find the JAR from which a given class was loaded, to make it easy for users to pass their JARs to SparkContext.
`static scala.Option<String>`	`jarOfObject(Object obj)` Find the JAR that contains the class of a particular object, to make it easy for users to pass their JARs to SparkContext.
`scala.collection.Seq<String>`	`jars()`
`boolean`	`killExecutor(String executorId)` :: DeveloperApi :: Request that the cluster manager kill the specified executor.
`boolean`	`killExecutors(scala.collection.Seq<String> executorIds)` :: DeveloperApi :: Request that the cluster manager kill the specified executors.
`boolean`	`killTaskAttempt(long taskId, boolean interruptThread, String reason)` Kill and reschedule the given task attempt.
`scala.collection.Seq<String>`	`listArchives()` :: Experimental :: Returns a list of archive paths that are added to resources.
`scala.collection.Seq<String>`	`listFiles()` Returns a list of file paths that are added to resources.
`scala.collection.Seq<String>`	`listJars()` Returns a list of jar files that are added to resources.
`LongAccumulator`	`longAccumulator()` Create and register a long accumulator, which starts with 0 and accumulates inputs by `add`.
`LongAccumulator`	`longAccumulator(String name)` Create and register a long accumulator, which starts with 0 and accumulates inputs by `add`.
`<T> RDD<T>`	`makeRDD(scala.collection.Seq<T> seq, int numSlices, scala.reflect.ClassTag<T> evidence$2)` Distribute a local Scala collection to form an RDD.
`<T> RDD<T>`	`makeRDD(scala.collection.Seq<scala.Tuple2<T,scala.collection.Seq<String>>> seq, scala.reflect.ClassTag<T> evidence$3)` Distribute a local Scala collection to form an RDD, with one or more location preferences (hostnames of Spark nodes) for each object.
`String`	`master()`
`<K,V,F extends org.apache.hadoop.mapreduce.InputFormat<K,V>> RDD<scala.Tuple2<K,V>>`	`newAPIHadoopFile(String path, Class<F> fClass, Class<K> kClass, Class<V> vClass, org.apache.hadoop.conf.Configuration conf)` Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.
`<K,V,F extends org.apache.hadoop.mapreduce.InputFormat<K,V>> RDD<scala.Tuple2<K,V>>`	`newAPIHadoopFile(String path, scala.reflect.ClassTag<K> km, scala.reflect.ClassTag<V> vm, scala.reflect.ClassTag<F> fm)` Smarter version of `newApiHadoopFile` that uses class tags to figure out the classes of keys, values and the `org.apache.hadoop.mapreduce.InputFormat` (new MapReduce API) so that user don't need to pass them directly.
`<K,V,F extends org.apache.hadoop.mapreduce.InputFormat<K,V>> RDD<scala.Tuple2<K,V>>`	`newAPIHadoopRDD(org.apache.hadoop.conf.Configuration conf, Class<F> fClass, Class<K> kClass, Class<V> vClass)` Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.
`<T> RDD<T>`	`objectFile(String path, int minPartitions, scala.reflect.ClassTag<T> evidence$4)` Load an RDD saved as a SequenceFile containing serialized objects, with NullWritable keys and BytesWritable values that contain a serialized partition.
`static void`	`org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)`
`static org.slf4j.Logger`	`org$apache$spark$internal$Logging$$log_()`
`<T> RDD<T>`	`parallelize(scala.collection.Seq<T> seq, int numSlices, scala.reflect.ClassTag<T> evidence$1)` Distribute a local Scala collection to form an RDD.
`RDD<Object>`	`range(long start, long end, long step, int numSlices)` Creates a new RDD[Long] containing elements from `start` to `end`(exclusive), increased by `step` every element.
`void`	`register(AccumulatorV2<?,?> acc)` Register the given accumulator.
`void`	`register(AccumulatorV2<?,?> acc, String name)` Register the given accumulator with given name.
`void`	`removeSparkListener(SparkListenerInterface listener)` :: DeveloperApi :: Deregister the listener from Spark's listener bus.
`boolean`	`requestExecutors(int numAdditionalExecutors)` :: DeveloperApi :: Request an additional number of executors from the cluster manager.
`boolean`	`requestTotalExecutors(int numExecutors, int localityAwareTasks, scala.collection.immutable.Map<String,Object> hostToLocalTaskCount)` Update the cluster manager on our scheduling needs.
`scala.collection.Map<String,ResourceInformation>`	`resources()`
`<T,U,R> PartialResult<R>`	`runApproximateJob(RDD<T> rdd, scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func, ApproximateEvaluator<U,R> evaluator, long timeout)` :: DeveloperApi :: Run a job that can return approximate results.
`<T,U> Object`	`runJob(RDD<T> rdd, scala.Function1<scala.collection.Iterator<T>,U> func, scala.reflect.ClassTag<U> evidence$15)` Run a job on all partitions in an RDD and return the results in an array.
`<T,U> void`	`runJob(RDD<T> rdd, scala.Function1<scala.collection.Iterator<T>,U> processPartition, scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler, scala.reflect.ClassTag<U> evidence$17)` Run a job on all partitions in an RDD and pass the results to a handler function.
`<T,U> Object`	`runJob(RDD<T> rdd, scala.Function1<scala.collection.Iterator<T>,U> func, scala.collection.Seq<Object> partitions, scala.reflect.ClassTag<U> evidence$13)` Run a function on a given set of partitions in an RDD and return the results as an array.
`<T,U> Object`	`runJob(RDD<T> rdd, scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func, scala.reflect.ClassTag<U> evidence$14)` Run a job on all partitions in an RDD and return the results in an array.
`<T,U> void`	`runJob(RDD<T> rdd, scala.Function2<TaskContext,scala.collection.Iterator<T>,U> processPartition, scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler, scala.reflect.ClassTag<U> evidence$16)` Run a job on all partitions in an RDD and pass the results to a handler function.
`<T,U> Object`	`runJob(RDD<T> rdd, scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func, scala.collection.Seq<Object> partitions, scala.reflect.ClassTag<U> evidence$12)` Run a function on a given set of partitions in an RDD and return the results as an array.
`<T,U> void`	`runJob(RDD<T> rdd, scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func, scala.collection.Seq<Object> partitions, scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler, scala.reflect.ClassTag<U> evidence$11)` Run a function on a given set of partitions in an RDD and pass the results to the given handler function.
`<K,V> RDD<scala.Tuple2<K,V>>`	`sequenceFile(String path, Class<K> keyClass, Class<V> valueClass)` Get an RDD for a Hadoop SequenceFile with given key and value types.
`<K,V> RDD<scala.Tuple2<K,V>>`	`sequenceFile(String path, Class<K> keyClass, Class<V> valueClass, int minPartitions)` Get an RDD for a Hadoop SequenceFile with given key and value types.
`<K,V> RDD<scala.Tuple2<K,V>>`	`sequenceFile(String path, int minPartitions, scala.reflect.ClassTag<K> km, scala.reflect.ClassTag<V> vm, scala.Function0<org.apache.spark.WritableConverter<K>> kcf, scala.Function0<org.apache.spark.WritableConverter<V>> vcf)` Version of sequenceFile() for types implicitly convertible to Writables through a WritableConverter.
`void`	`setCallSite(String shortCallSite)` Set the thread-local property for overriding the call sites of actions and RDDs.
`void`	`setCheckpointDir(String directory)` Set the directory under which RDDs are going to be checkpointed.
`void`	`setJobDescription(String value)` Set a human readable description of the current job.
`void`	`setJobGroup(String groupId, String description, boolean interruptOnCancel)` Assigns a group ID to all the jobs started by this thread until the group ID is set to a different value or cleared.
`void`	`setLocalProperty(String key, String value)` Set a local property that affects jobs submitted from this thread, such as the Spark fair scheduler pool.
`void`	`setLogLevel(String logLevel)` Control our logLevel.
`String`	`sparkUser()`
`long`	`startTime()`
`SparkStatusTracker`	`statusTracker()`
`void`	`stop()` Shut down the SparkContext.
`void`	`stop(int exitCode)` Shut down the SparkContext with exit code that will passed to scheduler backend.
`<T,U,R> SimpleFutureAction<R>`	`submitJob(RDD<T> rdd, scala.Function1<scala.collection.Iterator<T>,U> processPartition, scala.collection.Seq<Object> partitions, scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler, scala.Function0<R> resultFunc)` Submit a job for execution and return a FutureJob holding the result.
`RDD<String>`	`textFile(String path, int minPartitions)` 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.
`scala.Option<String>`	`uiWebUrl()`
`<T> RDD<T>`	`union(RDD<T> first, scala.collection.Seq<RDD<T>> rest, scala.reflect.ClassTag<T> evidence$7)` Build the union of a list of RDDs passed as variable-length arguments.
`<T> RDD<T>`	`union(scala.collection.Seq<RDD<T>> rdds, scala.reflect.ClassTag<T> evidence$6)` Build the union of a list of RDDs.
`String`	`version()` The version of Spark on which this application is running.
`RDD<scala.Tuple2<String,String>>`	`wholeTextFiles(String path, int minPartitions)` Read a directory of text files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI.

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.internal.Logging
$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

- Constructor Detail
 - SparkContext
```
public SparkContext(SparkConf config)
```
 - SparkContext
```
public SparkContext()
```
 Create a SparkContext that loads settings from system properties (for instance, when launching with ./bin/spark-submit).
 - SparkContext
```
public SparkContext(String master,
 String appName,
 SparkConf conf)
```
 Alternative constructor that allows setting common Spark properties directly
 
 Parameters:
 
 master - Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]).
 
 appName - A name for your application, to display on the cluster web UI
 
 conf - a SparkConf object specifying other Spark parameters
 - SparkContext
```
public SparkContext(String master,
 String appName,
 String sparkHome,
 scala.collection.Seq<String> jars,
 scala.collection.Map<String,String> environment)
```
 Alternative constructor that allows setting common Spark properties directly
 
 Parameters:
 
 master - Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]).
 
 appName - A name for your application, to display on the cluster web UI.
 
 sparkHome - Location where Spark is installed on cluster nodes.
 
 jars - Collection of JARs to send to the cluster. These can be paths on the local file system or HDFS, HTTP, HTTPS, or FTP URLs.
 
 environment - Environment variables to set on worker nodes.
- Method Detail
 - getOrCreate
```
public static SparkContext getOrCreate(SparkConf config)
```
 This function may be used to get or instantiate a SparkContext and register it as a singleton object. Because we can only have one active SparkContext per JVM, this is useful when applications may wish to share a SparkContext.
 
 Parameters:
 
 config - SparkConfig that will be used for initialisation of the SparkContext
 
 Returns:
 
 current SparkContext (or a new one if it wasn't created before the function call)
 - getOrCreate
```
public static SparkContext getOrCreate()
```
 This function may be used to get or instantiate a SparkContext and register it as a singleton object. Because we can only have one active SparkContext per JVM, this is useful when applications may wish to share a SparkContext.
 This method allows not passing a SparkConf (useful if just retrieving).
 
 Returns:
 
 current SparkContext (or a new one if wasn't created before the function call)
 - jarOfClass
```
public static scala.Option<String> jarOfClass(Class<?> cls)
```
 Find the JAR from which a given class was loaded, to make it easy for users to pass their JARs to SparkContext.
 
 Parameters:
 
 cls - class that should be inside of the jar
 
 Returns:
 
 jar that contains the Class, None if not found
 - jarOfObject
```
public static scala.Option<String> jarOfObject(Object obj)
```
 Find the JAR that contains the class of a particular object, to make it easy for users to pass their JARs to SparkContext. In most cases you can call jarOfObject(this) in your driver program.
 
 Parameters:
 
 obj - reference to an instance which class should be inside of the jar
 
 Returns:
 
 jar that contains the class of the instance, None if not found
 - org$apache$spark$internal$Logging$$log_
```
public static org.slf4j.Logger org$apache$spark$internal$Logging$$log_()
```
 - org$apache$spark$internal$Logging$$log__$eq
```
public static void org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)
```
 - startTime
```
public long startTime()
```
 - getConf
```
public SparkConf getConf()
```
 Return a copy of this SparkContext's configuration. The configuration ''cannot'' be changed at runtime.
 
 Returns:
 
 (undocumented)
 - resources
```
public scala.collection.Map<String,ResourceInformation> resources()
```
 - jars
```
public scala.collection.Seq<String> jars()
```
 - files
```
public scala.collection.Seq<String> files()
```
 - archives
```
public scala.collection.Seq<String> archives()
```
 - master
```
public String master()
```
 - deployMode
```
public String deployMode()
```
 - appName
```
public String appName()
```
 - isLocal
```
public boolean isLocal()
```
 - isStopped
```
public boolean isStopped()
```
 Returns:
 
 true if context is stopped or in the midst of stopping.
 - statusTracker
```
public SparkStatusTracker statusTracker()
```
 - uiWebUrl
```
public scala.Option<String> uiWebUrl()
```
 - hadoopConfiguration
```
public org.apache.hadoop.conf.Configuration hadoopConfiguration()
```
 A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse.
 
 Returns:
 
 (undocumented)
 
 Note:
 
 As it will be reused in all Hadoop RDDs, it's better not to modify it unless you plan to set some global configurations for all Hadoop RDDs.
 - sparkUser
```
public String sparkUser()
```
 - applicationId
```
public String applicationId()
```
 A unique identifier for the Spark application. Its format depends on the scheduler implementation. (i.e. in case of local spark app something like 'local-1433865536131' in case of YARN something like 'application_1433865536131_34483' in case of MESOS something like 'driver-20170926223339-0001' )
 
 Returns:
 
 (undocumented)
 - applicationAttemptId
```
public scala.Option<String> applicationAttemptId()
```
 - setLogLevel
```
public void setLogLevel(String logLevel)
```
 Control our logLevel. This overrides any user-defined log settings.
 
 Parameters:
 
 logLevel - The desired log level as a string. Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN
 - setLocalProperty
```
public void setLocalProperty(String key,
 String value)
```
 Set a local property that affects jobs submitted from this thread, such as the Spark fair scheduler pool. User-defined properties may also be set here. These properties are propagated through to worker tasks and can be accessed there via TaskContext.getLocalProperty(java.lang.String).
 These properties are inherited by child threads spawned from this thread. This may have unexpected consequences when working with thread pools. The standard java implementation of thread pools have worker threads spawn other worker threads. As a result, local properties may propagate unpredictably.
 
 Parameters:
 
 key - (undocumented)
 
 value - (undocumented)
 - getLocalProperty
```
public String getLocalProperty(String key)
```
 Get a local property set in this thread, or null if it is missing. See org.apache.spark.SparkContext.setLocalProperty.
 
 Parameters:
 
 key - (undocumented)
 
 Returns:
 
 (undocumented)
 - setJobDescription
```
public void setJobDescription(String value)
```
 Set a human readable description of the current job.
 - setJobGroup
```
public void setJobGroup(String groupId,
 String description,
 boolean interruptOnCancel)
```
 Assigns a group ID to all the jobs started by this thread until the group ID is set to a different value or cleared.
 Often, a unit of execution in an application consists of multiple Spark actions or jobs. Application programmers can use this method to group all those jobs together and give a group description. Once set, the Spark web UI will associate such jobs with this group.
 The application can also use org.apache.spark.SparkContext.cancelJobGroup to cancel all running jobs in this group. For example,
```
 // In the main thread:
 sc.setJobGroup("some_job_to_cancel", "some job description")
 sc.parallelize(1 to 10000, 2).map { i => Thread.sleep(10); i }.count()

 // In a separate thread:
 sc.cancelJobGroup("some_job_to_cancel")
 
```
 Parameters:
 
 interruptOnCancel - If true, then job cancellation will result in Thread.interrupt() being called on the job's executor threads. This is useful to help ensure that the tasks are actually stopped in a timely manner, but is off by default due to HDFS-1208, where HDFS may respond to Thread.interrupt() by marking nodes as dead.
 
 groupId - (undocumented)
 
 description - (undocumented)
 - clearJobGroup
```
public void clearJobGroup()
```
 Clear the current thread's job group ID and its description.
 - parallelize
```
public <T> RDD<T> parallelize(scala.collection.Seq<T> seq,
 int numSlices,
 scala.reflect.ClassTag<T> evidence$1)
```
 Distribute a local Scala collection to form an RDD.
 
 Parameters:
 
 seq - Scala collection to distribute
 
 numSlices - number of partitions to divide the collection into
 
 evidence$1 - (undocumented)
 
 Returns:
 
 RDD representing distributed collection
 
 Note:
 
 Parallelize acts lazily. If seq is a mutable collection and is altered after the call to parallelize and before the first action on the RDD, the resultant RDD will reflect the modified collection. Pass a copy of the argument to avoid this., avoid using parallelize(Seq()) to create an empty RDD. Consider emptyRDD for an RDD with no partitions, or parallelize(Seq[T]()) for an RDD of T with empty partitions.
 - range
```
public RDD<Object> range(long start,
 long end,
 long step,
 int numSlices)
```
 Creates a new RDD[Long] containing elements from start to end(exclusive), increased by step every element.
 
 Parameters:
 
 start - the start value.
 
 end - the end value.
 
 step - the incremental step
 
 numSlices - number of partitions to divide the collection into
 
 Returns:
 
 RDD representing distributed range
 
 Note:
 
 if we need to cache this RDD, we should make sure each partition does not exceed limit.
 - makeRDD
```
public <T> RDD<T> makeRDD(scala.collection.Seq<T> seq,
 int numSlices,
 scala.reflect.ClassTag<T> evidence$2)
```
 Distribute a local Scala collection to form an RDD.
 This method is identical to parallelize.
 
 Parameters:
 
 seq - Scala collection to distribute
 
 numSlices - number of partitions to divide the collection into
 
 evidence$2 - (undocumented)
 
 Returns:
 
 RDD representing distributed collection
 - makeRDD
```
public <T> RDD<T> makeRDD(scala.collection.Seq<scala.Tuple2<T,scala.collection.Seq<String>>> seq,
 scala.reflect.ClassTag<T> evidence$3)
```
 Distribute a local Scala collection to form an RDD, with one or more location preferences (hostnames of Spark nodes) for each object. Create a new partition for each collection item.
 
 Parameters:
 
 seq - list of tuples of data and location preferences (hostnames of Spark nodes)
 
 evidence$3 - (undocumented)
 
 Returns:
 
 RDD representing data partitioned according to location preferences
 - textFile
```
public RDD<String> textFile(String path,
 int minPartitions)
```
 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. The text files must be encoded as UTF-8.
 
 Parameters:
 
 path - path to the text file on a supported file system
 
 minPartitions - suggested minimum number of partitions for the resulting RDD
 
 Returns:
 
 RDD of lines of the text file
 - wholeTextFiles
```
public RDD<scala.Tuple2<String,String>> wholeTextFiles(String path,
 int minPartitions)
```
 Read a directory of text files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. Each file is read as a single record and returned in a key-value pair, where the key is the path of each file, the value is the content of each file. The text files must be encoded as UTF-8.
 
 For example, if you have the following files:
```
 hdfs://a-hdfs-path/part-00000
 hdfs://a-hdfs-path/part-00001
 ...
 hdfs://a-hdfs-path/part-nnnnn
 
```
 Do val rdd = sparkContext.wholeTextFile("hdfs://a-hdfs-path"),
 
 then rdd contains
```
 (a-hdfs-path/part-00000, its content)
 (a-hdfs-path/part-00001, its content)
 ...
 (a-hdfs-path/part-nnnnn, its content)
 
```
 Parameters:
 
 path - Directory to the input data files, the path can be comma separated paths as the list of inputs.
 
 minPartitions - A suggestion value of the minimal splitting number for input data.
 
 Returns:
 
 RDD representing tuples of file path and the corresponding file content
 
 Note:
 
 Small files are preferred, large file is also allowable, but may cause bad performance., On some filesystems, .../path/* can be a more efficient way to read all files in a directory rather than .../path/ or .../path, Partitioning is determined by data locality. This may result in too few partitions by default.
 - binaryFiles
```
public RDD<scala.Tuple2<String,PortableDataStream>> binaryFiles(String path,
 int minPartitions)
```
 Get an RDD for a Hadoop-readable dataset as PortableDataStream for each file (useful for binary data)
 For example, if you have the following files:
```
 hdfs://a-hdfs-path/part-00000
 hdfs://a-hdfs-path/part-00001
 ...
 hdfs://a-hdfs-path/part-nnnnn
 
```
 Do val rdd = sparkContext.binaryFiles("hdfs://a-hdfs-path"),
 then rdd contains
```
 (a-hdfs-path/part-00000, its content)
 (a-hdfs-path/part-00001, its content)
 ...
 (a-hdfs-path/part-nnnnn, its content)
 
```
 Parameters:
 
 path - Directory to the input data files, the path can be comma separated paths as the list of inputs.
 
 minPartitions - A suggestion value of the minimal splitting number for input data.
 
 Returns:
 
 RDD representing tuples of file path and corresponding file content
 
 Note:
 
 Small files are preferred; very large files may cause bad performance., On some filesystems, .../path/* can be a more efficient way to read all files in a directory rather than .../path/ or .../path, Partitioning is determined by data locality. This may result in too few partitions by default.
 - binaryRecords
```
public RDD<byte[]> binaryRecords(String path,
 int recordLength,
 org.apache.hadoop.conf.Configuration conf)
```
 Load data from a flat binary file, assuming the length of each record is constant.
 
 Parameters:
 
 path - Directory to the input data files, the path can be comma separated paths as the list of inputs.
 
 recordLength - The length at which to split the records
 
 conf - Configuration for setting up the dataset.
 
 Returns:
 
 An RDD of data with values, represented as byte arrays
 
 Note:
 
 We ensure that the byte array for each record in the resulting RDD has the provided record length.
 - hadoopRDD
```
public <K,V> RDD<scala.Tuple2<K,V>> hadoopRDD(org.apache.hadoop.mapred.JobConf conf,
 Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass,
 Class<K> keyClass,
 Class<V> valueClass,
 int minPartitions)
```
 Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf given its InputFormat and other necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable), using the older MapReduce API (org.apache.hadoop.mapred).
 
 Parameters:
 
 conf - JobConf for setting up the dataset. Note: This will be put into a Broadcast. Therefore if you plan to reuse this conf to create multiple RDDs, you need to make sure you won't modify the conf. A safe approach is always creating a new conf for a new RDD.
 
 inputFormatClass - storage format of the data to be read
 
 keyClass - Class of the key associated with the inputFormatClass parameter
 
 valueClass - Class of the value associated with the inputFormatClass parameter
 
 minPartitions - Minimum number of Hadoop Splits to generate.
 
 Returns:
 
 RDD of tuples of key and corresponding value
 
 Note:
 
 Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.
 - hadoopFile
```
public <K,V> RDD<scala.Tuple2<K,V>> hadoopFile(String path,
 Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass,
 Class<K> keyClass,
 Class<V> valueClass,
 int minPartitions)
```
 Get an RDD for a Hadoop file with an arbitrary InputFormat
 
 Parameters:
 
 path - directory to the input data files, the path can be comma separated paths as a list of inputs
 
 inputFormatClass - storage format of the data to be read
 
 keyClass - Class of the key associated with the inputFormatClass parameter
 
 valueClass - Class of the value associated with the inputFormatClass parameter
 
 minPartitions - suggested minimum number of partitions for the resulting RDD
 
 Returns:
 
 RDD of tuples of key and corresponding value
 
 Note:
 
 Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.
 - hadoopFile
```
public <K,V,F extends org.apache.hadoop.mapred.InputFormat<K,V>> RDD<scala.Tuple2<K,V>> hadoopFile(String path,
 int minPartitions,
 scala.reflect.ClassTag<K> km,
 scala.reflect.ClassTag<V> vm,
 scala.reflect.ClassTag<F> fm)
```
 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. Instead, callers can just write, for example,
```
 val file = sparkContext.hadoopFile[LongWritable, Text, TextInputFormat](path, minPartitions)
 
```
 Parameters:
 
 path - directory to the input data files, the path can be comma separated paths as a list of inputs
 
 minPartitions - suggested minimum number of partitions for the resulting RDD
 
 km - (undocumented)
 
 vm - (undocumented)
 
 fm - (undocumented)
 
 Returns:
 
 RDD of tuples of key and corresponding value
 
 Note:
 
 Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.
 - hadoopFile
```
public <K,V,F extends org.apache.hadoop.mapred.InputFormat<K,V>> RDD<scala.Tuple2<K,V>> hadoopFile(String path,
 scala.reflect.ClassTag<K> km,
 scala.reflect.ClassTag<V> vm,
 scala.reflect.ClassTag<F> fm)
```
 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. Instead, callers can just write, for example,
```
 val file = sparkContext.hadoopFile[LongWritable, Text, TextInputFormat](path)
 
```
 Parameters:
 
 path - directory to the input data files, the path can be comma separated paths as a list of inputs
 
 km - (undocumented)
 
 vm - (undocumented)
 
 fm - (undocumented)
 
 Returns:
 
 RDD of tuples of key and corresponding value
 
 Note:
 
 Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.
 - newAPIHadoopFile
```
public <K,V,F extends org.apache.hadoop.mapreduce.InputFormat<K,V>> RDD<scala.Tuple2<K,V>> newAPIHadoopFile(String path,
 scala.reflect.ClassTag<K> km,
 scala.reflect.ClassTag<V> vm,
 scala.reflect.ClassTag<F> fm)
```
 Smarter version of newApiHadoopFile that uses class tags to figure out the classes of keys, values and the org.apache.hadoop.mapreduce.InputFormat (new MapReduce API) so that user don't need to pass them directly. Instead, callers can just write, for example: val file = sparkContext.hadoopFile[LongWritable, Text, TextInputFormat](path)
 
 Parameters:
 
 path - directory to the input data files, the path can be comma separated paths as a list of inputs
 
 km - (undocumented)
 
 vm - (undocumented)
 
 fm - (undocumented)
 
 Returns:
 
 RDD of tuples of key and corresponding value
 
 Note:
 
 Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.
 - newAPIHadoopFile
```
public <K,V,F extends org.apache.hadoop.mapreduce.InputFormat<K,V>> RDD<scala.Tuple2<K,V>> newAPIHadoopFile(String path,
 Class<F> fClass,
 Class<K> kClass,
 Class<V> vClass,
 org.apache.hadoop.conf.Configuration conf)
```
 Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.
 
 Parameters:
 
 path - directory to the input data files, the path can be comma separated paths as a list of inputs
 
 fClass - storage format of the data to be read
 
 kClass - Class of the key associated with the fClass parameter
 
 vClass - Class of the value associated with the fClass parameter
 
 conf - Hadoop configuration
 
 Returns:
 
 RDD of tuples of key and corresponding value
 
 Note:
 
 Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.
 - newAPIHadoopRDD
```
public <K,V,F extends org.apache.hadoop.mapreduce.InputFormat<K,V>> RDD<scala.Tuple2<K,V>> newAPIHadoopRDD(org.apache.hadoop.conf.Configuration conf,
 Class<F> fClass,
 Class<K> kClass,
 Class<V> vClass)
```
 Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.
 
 Parameters:
 
 conf - Configuration for setting up the dataset. Note: This will be put into a Broadcast. Therefore if you plan to reuse this conf to create multiple RDDs, you need to make sure you won't modify the conf. A safe approach is always creating a new conf for a new RDD.
 
 fClass - storage format of the data to be read
 
 kClass - Class of the key associated with the fClass parameter
 
 vClass - Class of the value associated with the fClass parameter
 
 Returns:
 
 (undocumented)
 
 Note:
 
 Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.
 - sequenceFile
```
public <K,V> RDD<scala.Tuple2<K,V>> sequenceFile(String path,
 Class<K> keyClass,
 Class<V> valueClass,
 int minPartitions)
```
 Get an RDD for a Hadoop SequenceFile with given key and value types.
 
 Parameters:
 
 path - directory to the input data files, the path can be comma separated paths as a list of inputs
 
 keyClass - Class of the key associated with SequenceFileInputFormat
 
 valueClass - Class of the value associated with SequenceFileInputFormat
 
 minPartitions - suggested minimum number of partitions for the resulting RDD
 
 Returns:
 
 RDD of tuples of key and corresponding value
 
 Note:
 
 Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.
 - sequenceFile
```
public <K,V> RDD<scala.Tuple2<K,V>> sequenceFile(String path,
 Class<K> keyClass,
 Class<V> valueClass)
```
 Get an RDD for a Hadoop SequenceFile with given key and value types.
 
 Parameters:
 
 path - directory to the input data files, the path can be comma separated paths as a list of inputs
 
 keyClass - Class of the key associated with SequenceFileInputFormat
 
 valueClass - Class of the value associated with SequenceFileInputFormat
 
 Returns:
 
 RDD of tuples of key and corresponding value
 
 Note:
 
 Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.
 - sequenceFile
```
public <K,V> RDD<scala.Tuple2<K,V>> sequenceFile(String path,
 int minPartitions,
 scala.reflect.ClassTag<K> km,
 scala.reflect.ClassTag<V> vm,
 scala.Function0<org.apache.spark.WritableConverter<K>> kcf,
 scala.Function0<org.apache.spark.WritableConverter<V>> vcf)
```
 Version of sequenceFile() for types implicitly convertible to Writables through a WritableConverter. For example, to access a SequenceFile where the keys are Text and the values are IntWritable, you could simply write
```
 sparkContext.sequenceFile[String, Int](path, ...)
 
```
 WritableConverters are provided in a somewhat strange way (by an implicit function) to support both subclasses of Writable and types for which we define a converter (e.g. Int to IntWritable). The most natural thing would've been to have implicit objects for the converters, but then we couldn't have an object for every subclass of Writable (you can't have a parameterized singleton object). We use functions instead to create a new converter for the appropriate type. In addition, we pass the converter a ClassTag of its type to allow it to figure out the Writable class to use in the subclass case.
 Parameters:
 
 path - directory to the input data files, the path can be comma separated paths as a list of inputs
 
 minPartitions - suggested minimum number of partitions for the resulting RDD
 
 km - (undocumented)
 
 vm - (undocumented)
 
 kcf - (undocumented)
 
 vcf - (undocumented)
 
 Returns:
 
 RDD of tuples of key and corresponding value
 
 Note:
 
 Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.
 - objectFile
```
public <T> RDD<T> objectFile(String path,
 int minPartitions,
 scala.reflect.ClassTag<T> evidence$4)
```
 Load an RDD saved as a SequenceFile containing serialized objects, with NullWritable keys and BytesWritable values that contain a serialized partition. This is still an experimental storage format and may not be supported exactly as is in future Spark releases. It will also be pretty slow if you use the default serializer (Java serialization), though the nice thing about it is that there's very little effort required to save arbitrary objects.
 
 Parameters:
 
 path - directory to the input data files, the path can be comma separated paths as a list of inputs
 
 minPartitions - suggested minimum number of partitions for the resulting RDD
 
 evidence$4 - (undocumented)
 
 Returns:
 
 RDD representing deserialized data from the file(s)
 - union
```
public <T> RDD<T> union(scala.collection.Seq<RDD<T>> rdds,
 scala.reflect.ClassTag<T> evidence$6)
```
 Build the union of a list of RDDs.
 - union
```
public <T> RDD<T> union(RDD<T> first,
 scala.collection.Seq<RDD<T>> rest,
 scala.reflect.ClassTag<T> evidence$7)
```
 Build the union of a list of RDDs passed as variable-length arguments.
 - emptyRDD
```
public <T> RDD<T> emptyRDD(scala.reflect.ClassTag<T> evidence$8)
```
 Get an RDD that has no partitions or elements.
 - register
```
public void register(AccumulatorV2<?,?> acc)
```
 Register the given accumulator.
 
 Parameters:
 
 acc - (undocumented)
 
 Note:
 
 Accumulators must be registered before use, or it will throw exception.
 - register
```
public void register(AccumulatorV2<?,?> acc,
 String name)
```
 Register the given accumulator with given name.
 
 Parameters:
 
 acc - (undocumented)
 
 name - (undocumented)
 
 Note:
 
 Accumulators must be registered before use, or it will throw exception.
 - longAccumulator
```
public LongAccumulator longAccumulator()
```
 Create and register a long accumulator, which starts with 0 and accumulates inputs by add.
 
 Returns:
 
 (undocumented)
 - longAccumulator
```
public LongAccumulator longAccumulator(String name)
```
 Create and register a long accumulator, which starts with 0 and accumulates inputs by add.
 
 Parameters:
 
 name - (undocumented)
 
 Returns:
 
 (undocumented)
 - doubleAccumulator
```
public DoubleAccumulator doubleAccumulator()
```
 Create and register a double accumulator, which starts with 0 and accumulates inputs by add.
 
 Returns:
 
 (undocumented)
 - doubleAccumulator
```
public DoubleAccumulator doubleAccumulator(String name)
```
 Create and register a double accumulator, which starts with 0 and accumulates inputs by add.
 
 Parameters:
 
 name - (undocumented)
 
 Returns:
 
 (undocumented)
 - collectionAccumulator
```
public <T> CollectionAccumulator<T> collectionAccumulator()
```
 Create and register a CollectionAccumulator, which starts with empty list and accumulates inputs by adding them into the list.
 
 Returns:
 
 (undocumented)
 - collectionAccumulator
```
public <T> CollectionAccumulator<T> collectionAccumulator(String name)
```
 Create and register a CollectionAccumulator, which starts with empty list and accumulates inputs by adding them into the list.
 
 Parameters:
 
 name - (undocumented)
 
 Returns:
 
 (undocumented)
 - broadcast
```
public <T> Broadcast<T> broadcast(T value,
 scala.reflect.ClassTag<T> evidence$9)
```
 Broadcast a read-only variable to the cluster, returning a Broadcast object for reading it in distributed functions. The variable will be sent to each executor only once.
 
 Parameters:
 
 value - value to broadcast to the Spark nodes
 
 evidence$9 - (undocumented)
 
 Returns:
 
 Broadcast object, a read-only variable cached on each machine
 - addFile
```
public void addFile(String path)
```
 Add a file to be downloaded with this Spark job on every node.
 If a file is added during execution, it will not be available until the next TaskSet starts.
 
 Parameters:
 
 path - can be either a local file, a file in HDFS (or other Hadoop-supported filesystems), or an HTTP, HTTPS or FTP URI. To access the file in Spark jobs, use SparkFiles.get(fileName) to find its download location.
 
 Note:
 
 A path can be added only once. Subsequent additions of the same path are ignored.
 - listFiles
```
public scala.collection.Seq<String> listFiles()
```
 Returns a list of file paths that are added to resources.
 
 Returns:
 
 (undocumented)
 - addArchive
```
public void addArchive(String path)
```
 :: Experimental :: Add an archive to be downloaded and unpacked with this Spark job on every node.
 If an archive is added during execution, it will not be available until the next TaskSet starts.
 
 Parameters:
 
 path - can be either a local file, a file in HDFS (or other Hadoop-supported filesystems), or an HTTP, HTTPS or FTP URI. To access the file in Spark jobs, use SparkFiles.get(paths-to-files) to find its download/unpacked location. The given path should be one of .zip, .tar, .tar.gz, .tgz and .jar.
 
 Since:
 
 3.1.0
 
 Note:
 
 A path can be added only once. Subsequent additions of the same path are ignored.
 - listArchives
```
public scala.collection.Seq<String> listArchives()
```
 :: Experimental :: Returns a list of archive paths that are added to resources.
 
 Returns:
 
 (undocumented)
 
 Since:
 
 3.1.0
 - addFile
```
public void addFile(String path,
 boolean recursive)
```
 Add a file to be downloaded with this Spark job on every node.
 If a file is added during execution, it will not be available until the next TaskSet starts.
 
 Parameters:
 
 path - can be either a local file, a file in HDFS (or other Hadoop-supported filesystems), or an HTTP, HTTPS or FTP URI. To access the file in Spark jobs, use SparkFiles.get(fileName) to find its download location.
 
 recursive - if true, a directory can be given in path. Currently directories are only supported for Hadoop-supported filesystems.
 
 Note:
 
 A path can be added only once. Subsequent additions of the same path are ignored.
 - addSparkListener
```
public void addSparkListener(SparkListenerInterface listener)
```
 :: DeveloperApi :: Register a listener to receive up-calls from events that happen during execution.
 
 Parameters:
 
 listener - (undocumented)
 - removeSparkListener
```
public void removeSparkListener(SparkListenerInterface listener)
```
 :: DeveloperApi :: Deregister the listener from Spark's listener bus.
 
 Parameters:
 
 listener - (undocumented)
 - requestTotalExecutors
```
public boolean requestTotalExecutors(int numExecutors,
 int localityAwareTasks,
 scala.collection.immutable.Map<String,Object> hostToLocalTaskCount)
```
 Update the cluster manager on our scheduling needs. Three bits of information are included to help it make decisions. This applies to the default ResourceProfile.
 
 Parameters:
 
 numExecutors - The total number of executors we'd like to have. The cluster manager shouldn't kill any running executor to reach this number, but, if all existing executors were to die, this is the number of executors we'd want to be allocated.
 
 localityAwareTasks - The number of tasks in all active stages that have a locality preferences. This includes running, pending, and completed tasks.
 
 hostToLocalTaskCount - A map of hosts to the number of tasks from all active stages that would like to like to run on that host. This includes running, pending, and completed tasks.
 
 Returns:
 
 whether the request is acknowledged by the cluster manager.
 - requestExecutors
```
public boolean requestExecutors(int numAdditionalExecutors)
```
 :: DeveloperApi :: Request an additional number of executors from the cluster manager.
 
 Parameters:
 
 numAdditionalExecutors - (undocumented)
 
 Returns:
 
 whether the request is received.
 - killExecutors
```
public boolean killExecutors(scala.collection.Seq<String> executorIds)
```
 :: DeveloperApi :: Request that the cluster manager kill the specified executors.
 This is not supported when dynamic allocation is turned on.
 
 Parameters:
 
 executorIds - (undocumented)
 
 Returns:
 
 whether the request is received.
 
 Note:
 
 This is an indication to the cluster manager that the application wishes to adjust its resource usage downwards. If the application wishes to replace the executors it kills through this method with new ones, it should follow up explicitly with a call to {{SparkContext#requestExecutors}}.
 - killExecutor
```
public boolean killExecutor(String executorId)
```
 :: DeveloperApi :: Request that the cluster manager kill the specified executor.
 
 Parameters:
 
 executorId - (undocumented)
 
 Returns:
 
 whether the request is received.
 
 Note:
 
 This is an indication to the cluster manager that the application wishes to adjust its resource usage downwards. If the application wishes to replace the executor it kills through this method with a new one, it should follow up explicitly with a call to {{SparkContext#requestExecutors}}.
 - version
```
public String version()
```
 The version of Spark on which this application is running.
 - getExecutorMemoryStatus
```
public scala.collection.Map<String,scala.Tuple2<Object,Object>> getExecutorMemoryStatus()
```
 Return a map from the block manager to the max memory available for caching and the remaining memory available for caching.
 
 Returns:
 
 (undocumented)
 - getRDDStorageInfo
```
public RDDInfo[] getRDDStorageInfo()
```
 :: DeveloperApi :: Return information about what RDDs are cached, if they are in mem or on disk, how much space they take, etc.
 
 Returns:
 
 (undocumented)
 - getPersistentRDDs
```
public scala.collection.Map<Object,RDD<?>> getPersistentRDDs()
```
 Returns an immutable map of RDDs that have marked themselves as persistent via cache() call.
 
 Returns:
 
 (undocumented)
 
 Note:
 
 This does not necessarily mean the caching or computation was successful.
 - getAllPools
```
public scala.collection.Seq<Schedulable> getAllPools()
```
 :: DeveloperApi :: Return pools for fair scheduler
 
 Returns:
 
 (undocumented)
 - getPoolForName
```
public scala.Option<Schedulable> getPoolForName(String pool)
```
 :: DeveloperApi :: Return the pool associated with the given name, if one exists
 
 Parameters:
 
 pool - (undocumented)
 
 Returns:
 
 (undocumented)
 - getSchedulingMode
```
public scala.Enumeration.Value getSchedulingMode()
```
 Return current scheduling mode
 
 Returns:
 
 (undocumented)
 - addJar
```
public void addJar(String path)
```
 Adds a JAR dependency for all tasks to be executed on this SparkContext in the future.
 If a jar is added during execution, it will not be available until the next TaskSet starts.
 
 Parameters:
 
 path - can be either a local file, a file in HDFS (or other Hadoop-supported filesystems), an HTTP, HTTPS or FTP URI, or local:/path for a file on every worker node.
 
 Note:
 
 A path can be added only once. Subsequent additions of the same path are ignored.
 - listJars
```
public scala.collection.Seq<String> listJars()
```
 Returns a list of jar files that are added to resources.
 
 Returns:
 
 (undocumented)
 - stop
```
public void stop()
```
 Shut down the SparkContext.
 - stop
```
public void stop(int exitCode)
```
 Shut down the SparkContext with exit code that will passed to scheduler backend. In client mode, client side may call SparkContext.stop() to clean up but exit with code not equal to 0. This behavior cause resource scheduler such as ApplicationMaster exit with success status but client side exited with failed status. Spark can call this method to stop SparkContext and pass client side correct exit code to scheduler backend. Then scheduler backend should send the exit code to corresponding resource scheduler to keep consistent.
 
 Parameters:
 
 exitCode - Specified exit code that will passed to scheduler backend in client mode.
 - setCallSite
```
public void setCallSite(String shortCallSite)
```
 Set the thread-local property for overriding the call sites of actions and RDDs.
 
 Parameters:
 
 shortCallSite - (undocumented)
 - clearCallSite
```
public void clearCallSite()
```
 Clear the thread-local property for overriding the call sites of actions and RDDs.
 - runJob
```
public <T,U> void runJob(RDD<T> rdd,
 scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func,
 scala.collection.Seq<Object> partitions,
 scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler,
 scala.reflect.ClassTag evidence$11)
```
 Run a function on a given set of partitions in an RDD and pass the results to the given handler function. This is the main entry point for all actions in Spark.
 
 Parameters:
 
 rdd - target RDD to run tasks on
 
 func - a function to run on each partition of the RDD
 
 partitions - set of partitions to run on; some jobs may not want to compute on all partitions of the target RDD, e.g. for operations like first()
 
 resultHandler - callback to pass each result to
 
 evidence$11 - (undocumented)
 - runJob
```
public <T,U> Object runJob(RDD<T> rdd,
 scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func,
 scala.collection.Seq<Object> partitions,
 scala.reflect.ClassTag evidence$12)
```
 Run a function on a given set of partitions in an RDD and return the results as an array. The function that is run against each partition additionally takes TaskContext argument.
 
 Parameters:
 
 rdd - target RDD to run tasks on
 
 func - a function to run on each partition of the RDD
 
 partitions - set of partitions to run on; some jobs may not want to compute on all partitions of the target RDD, e.g. for operations like first()
 
 evidence$12 - (undocumented)
 
 Returns:
 
 in-memory collection with a result of the job (each collection element will contain a result from one partition)
 - runJob
```
public <T,U> Object runJob(RDD<T> rdd,
 scala.Function1<scala.collection.Iterator<T>,U> func,
 scala.collection.Seq<Object> partitions,
 scala.reflect.ClassTag evidence$13)
```
 Run a function on a given set of partitions in an RDD and return the results as an array.
 
 Parameters:
 
 rdd - target RDD to run tasks on
 
 func - a function to run on each partition of the RDD
 
 partitions - set of partitions to run on; some jobs may not want to compute on all partitions of the target RDD, e.g. for operations like first()
 
 evidence$13 - (undocumented)
 
 Returns:
 
 in-memory collection with a result of the job (each collection element will contain a result from one partition)
 - runJob
```
public <T,U> Object runJob(RDD<T> rdd,
 scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func,
 scala.reflect.ClassTag evidence$14)
```
 Run a job on all partitions in an RDD and return the results in an array. The function that is run against each partition additionally takes TaskContext argument.
 
 Parameters:
 
 rdd - target RDD to run tasks on
 
 func - a function to run on each partition of the RDD
 
 evidence$14 - (undocumented)
 
 Returns:
 
 in-memory collection with a result of the job (each collection element will contain a result from one partition)
 - runJob
```
public <T,U> Object runJob(RDD<T> rdd,
 scala.Function1<scala.collection.Iterator<T>,U> func,
 scala.reflect.ClassTag evidence$15)
```
 Run a job on all partitions in an RDD and return the results in an array.
 
 Parameters:
 
 rdd - target RDD to run tasks on
 
 func - a function to run on each partition of the RDD
 
 evidence$15 - (undocumented)
 
 Returns:
 
 in-memory collection with a result of the job (each collection element will contain a result from one partition)
 - runJob
```
public <T,U> void runJob(RDD<T> rdd,
 scala.Function2<TaskContext,scala.collection.Iterator<T>,U> processPartition,
 scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler,
 scala.reflect.ClassTag evidence$16)
```
 Run a job on all partitions in an RDD and pass the results to a handler function. The function that is run against each partition additionally takes TaskContext argument.
 
 Parameters:
 
 rdd - target RDD to run tasks on
 
 processPartition - a function to run on each partition of the RDD
 
 resultHandler - callback to pass each result to
 
 evidence$16 - (undocumented)
 - runJob
```
public <T,U> void runJob(RDD<T> rdd,
 scala.Function1<scala.collection.Iterator<T>,U> processPartition,
 scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler,
 scala.reflect.ClassTag evidence$17)
```
 Run a job on all partitions in an RDD and pass the results to a handler function.
 
 Parameters:
 
 rdd - target RDD to run tasks on
 
 processPartition - a function to run on each partition of the RDD
 
 resultHandler - callback to pass each result to
 
 evidence$17 - (undocumented)
 - runApproximateJob
```
public <T,U,R> PartialResult<R> runApproximateJob(RDD<T> rdd,
 scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func,
 ApproximateEvaluator<U,R> evaluator,
 long timeout)
```
 :: DeveloperApi :: Run a job that can return approximate results.
 
 Parameters:
 
 rdd - target RDD to run tasks on
 
 func - a function to run on each partition of the RDD
 
 evaluator - ApproximateEvaluator to receive the partial results
 
 timeout - maximum time to wait for the job, in milliseconds
 
 Returns:
 
 partial result (how partial depends on whether the job was finished before or after timeout)
 - submitJob
```
public <T,U,R> SimpleFutureAction<R> submitJob(RDD<T> rdd,
 scala.Function1<scala.collection.Iterator<T>,U> processPartition,
 scala.collection.Seq<Object> partitions,
 scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler,
 scala.Function0<R> resultFunc)
```
 Submit a job for execution and return a FutureJob holding the result.
 
 Parameters:
 
 rdd - target RDD to run tasks on
 
 processPartition - a function to run on each partition of the RDD
 
 partitions - set of partitions to run on; some jobs may not want to compute on all partitions of the target RDD, e.g. for operations like first()
 
 resultHandler - callback to pass each result to
 
 resultFunc - function to be executed when the result is ready
 
 Returns:
 
 (undocumented)
 - cancelJobGroup
```
public void cancelJobGroup(String groupId)
```
 Cancel active jobs for the specified group. See org.apache.spark.SparkContext.setJobGroup for more information.
 
 Parameters:
 
 groupId - (undocumented)
 - cancelAllJobs
```
public void cancelAllJobs()
```
 Cancel all jobs that have been scheduled or are running.
 - cancelJob
```
public void cancelJob(int jobId,
 String reason)
```
 Cancel a given job if it's scheduled or running.
 
 Parameters:
 
 jobId - the job ID to cancel
 
 reason - optional reason for cancellation
 
 Note:
 
 Throws InterruptedException if the cancel message cannot be sent
 - cancelJob
```
public void cancelJob(int jobId)
```
 Cancel a given job if it's scheduled or running.
 
 Parameters:
 
 jobId - the job ID to cancel
 
 Note:
 
 Throws InterruptedException if the cancel message cannot be sent
 - cancelStage
```
public void cancelStage(int stageId,
 String reason)
```
 Cancel a given stage and all jobs associated with it.
 
 Parameters:
 
 stageId - the stage ID to cancel
 
 reason - reason for cancellation
 
 Note:
 
 Throws InterruptedException if the cancel message cannot be sent
 - cancelStage
```
public void cancelStage(int stageId)
```
 Cancel a given stage and all jobs associated with it.
 
 Parameters:
 
 stageId - the stage ID to cancel
 
 Note:
 
 Throws InterruptedException if the cancel message cannot be sent
 - killTaskAttempt
```
public boolean killTaskAttempt(long taskId,
 boolean interruptThread,
 String reason)
```
 Kill and reschedule the given task attempt. Task ids can be obtained from the Spark UI or through SparkListener.onTaskStart.
 
 Parameters:
 
 taskId - the task ID to kill. This id uniquely identifies the task attempt.
 
 interruptThread - whether to interrupt the thread running the task.
 
 reason - the reason for killing the task, which should be a short string. If a task is killed multiple times with different reasons, only one reason will be reported.
 
 Returns:
 
 Whether the task was successfully killed.
 - setCheckpointDir
```
public void setCheckpointDir(String directory)
```
 Set the directory under which RDDs are going to be checkpointed.
 
 Parameters:
 
 directory - path to the directory where checkpoint files will be stored (must be HDFS path if running in cluster)
 - getCheckpointDir
```
public scala.Option<String> getCheckpointDir()
```
 - defaultParallelism
```
public int defaultParallelism()
```
 Default level of parallelism to use when not given by user (e.g. parallelize and makeRDD).
 - defaultMinPartitions
```
public int defaultMinPartitions()
```
 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. The reasons for this are discussed in https://github.com/mesos/spark/pull/718
 
 Returns:
 
 (undocumented)

Class SparkContext

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

Constructor Summary

Method Summary

Methods inherited from class Object

Methods inherited from interface org.apache.spark.internal.Logging

Constructor Detail

SparkContext

SparkContext

SparkContext

SparkContext

Method Detail

getOrCreate

getOrCreate

jarOfClass

jarOfObject

org$apache$spark$internal$Logging$$log_

org$apache$spark$internal$Logging$$log__$eq

startTime

getConf

resources

jars

files

archives

master

deployMode

appName

isLocal

isStopped

statusTracker

uiWebUrl

hadoopConfiguration

sparkUser

applicationId

applicationAttemptId

setLogLevel

setLocalProperty

getLocalProperty

setJobDescription

setJobGroup

clearJobGroup

parallelize

range

makeRDD

makeRDD

textFile

wholeTextFiles

binaryFiles

binaryRecords

hadoopRDD

hadoopFile

hadoopFile

hadoopFile

newAPIHadoopFile

newAPIHadoopFile

newAPIHadoopRDD

sequenceFile

sequenceFile

sequenceFile

objectFile

union

union

emptyRDD

register

register

longAccumulator

longAccumulator

doubleAccumulator

doubleAccumulator

collectionAccumulator

collectionAccumulator

broadcast

addFile

listFiles

addArchive

listArchives

addFile

addSparkListener

removeSparkListener