Class/Object

org.apache.spark

SparkContext

Related Docs: object SparkContext | package spark

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class SparkContext extends Logging with ExecutorAllocationClient

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.

Only one SparkContext may be active per JVM. You must stop() the active SparkContext before creating a new one. This limitation may eventually be removed; see SPARK-2243 for more details.

Source
SparkContext.scala
Linear Supertypes
ExecutorAllocationClient, Logging, AnyRef, Any
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  1. SparkContext
  2. ExecutorAllocationClient
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Visibility
  1. Public
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Instance Constructors

  1. new SparkContext(master: String, appName: String, sparkHome: String = null, jars: Seq[String] = Nil, environment: Map[String, String] = Map())

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    Alternative constructor that allows setting common Spark properties directly

    Alternative constructor that allows setting common Spark properties directly

    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.

  2. new SparkContext(master: String, appName: String, conf: SparkConf)

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    Alternative constructor that allows setting common Spark properties directly

    Alternative constructor that allows setting common Spark properties directly

    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 org.apache.spark.SparkConf object specifying other Spark parameters

  3. new SparkContext()

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    Create a SparkContext that loads settings from system properties (for instance, when launching with ./bin/spark-submit).

  4. new SparkContext(config: SparkConf)

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    config

    a Spark Config object describing the application configuration. Any settings in this config overrides the default configs as well as system properties.

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  4. def addFile(path: String, recursive: Boolean): Unit

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    Add a file to be downloaded with this Spark job on every node.

    Add a file to be downloaded with this Spark job on every node. The path passed 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.

    A directory can be given if the recursive option is set to true. Currently directories are only supported for Hadoop-supported filesystems.

  5. def addFile(path: String): Unit

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    Add a file to be downloaded with this Spark job on every node.

    Add a file to be downloaded with this Spark job on every node. The path passed 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.

  6. def addJar(path: String): Unit

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    Adds a JAR dependency for all tasks to be executed on this SparkContext in the future.

    Adds a JAR dependency for all tasks to be executed on this SparkContext in the future. The path passed 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.

  7. def addSparkListener(listener: SparkListenerInterface): Unit

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    :: DeveloperApi :: Register a listener to receive up-calls from events that happen during execution.

    :: DeveloperApi :: Register a listener to receive up-calls from events that happen during execution.

    Annotations
    @DeveloperApi()
  8. def appName: String

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  9. def applicationAttemptId: Option[String]

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  10. def applicationId: String

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    A unique identifier for the Spark application.

    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' )

  11. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  12. def binaryFiles(path: String, minPartitions: Int = defaultMinPartitions): RDD[(String, PortableDataStream)]

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    Get an RDD for a Hadoop-readable dataset as PortableDataStream for each file (useful for binary data)

    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)
    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.

    Note

    On some filesystems, .../path/* can be a more efficient way to read all files in a directory rather than .../path/ or .../path

    ,

    Small files are preferred; very large files may cause bad performance.

  13. def binaryRecords(path: String, recordLength: Int, conf: Configuration = hadoopConfiguration): RDD[Array[Byte]]

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    Load data from a flat binary file, assuming the length of each record is constant.

    Load data from a flat binary file, assuming the length of each record is constant.

    Note: We ensure that the byte array for each record in the resulting RDD has the provided record length.

    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

  14. def broadcast[T](value: T)(implicit arg0: ClassTag[T]): Broadcast[T]

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    Broadcast a read-only variable to the cluster, returning a org.apache.spark.broadcast.Broadcast object for reading it in distributed functions.

    Broadcast a read-only variable to the cluster, returning a org.apache.spark.broadcast.Broadcast object for reading it in distributed functions. The variable will be sent to each cluster only once.

  15. def cancelAllJobs(): Unit

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    Cancel all jobs that have been scheduled or are running.

  16. def cancelJobGroup(groupId: String): Unit

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    Cancel active jobs for the specified group.

    Cancel active jobs for the specified group. See org.apache.spark.SparkContext.setJobGroup for more information.

  17. def checkpointFile[T](path: String)(implicit arg0: ClassTag[T]): RDD[T]

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    Attributes
    protected[org.apache.spark]
  18. def clearCallSite(): Unit

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    Clear the thread-local property for overriding the call sites of actions and RDDs.

  19. def clearJobGroup(): Unit

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    Clear the current thread's job group ID and its description.

  20. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  21. def collectionAccumulator[T](name: String): CollectionAccumulator[T]

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    Create and register a CollectionAccumulator, which starts with empty list and accumulates inputs by adding them into the list.

  22. def collectionAccumulator[T]: CollectionAccumulator[T]

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    Create and register a CollectionAccumulator, which starts with empty list and accumulates inputs by adding them into the list.

  23. def defaultMinPartitions: Int

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    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.

    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

  24. def defaultParallelism: Int

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    Default level of parallelism to use when not given by user (e.g.

    Default level of parallelism to use when not given by user (e.g. parallelize and makeRDD).

  25. def deployMode: String

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  26. def doubleAccumulator(name: String): DoubleAccumulator

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    Create and register a double accumulator, which starts with 0 and accumulates inputs by add.

  27. def doubleAccumulator: DoubleAccumulator

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    Create and register a double accumulator, which starts with 0 and accumulates inputs by add.

  28. def emptyRDD[T](implicit arg0: ClassTag[T]): RDD[T]

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    Get an RDD that has no partitions or elements.

  29. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
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  30. def equals(arg0: Any): Boolean

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  31. def files: Seq[String]

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  32. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  33. def getAllPools: Seq[Schedulable]

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    :: DeveloperApi :: Return pools for fair scheduler

    :: DeveloperApi :: Return pools for fair scheduler

    Annotations
    @DeveloperApi()
  34. def getCheckpointDir: Option[String]

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  35. final def getClass(): Class[_]

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    Definition Classes
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  36. def getConf: SparkConf

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    Return a copy of this SparkContext's configuration.

    Return a copy of this SparkContext's configuration. The configuration cannot be changed at runtime.

  37. def getExecutorMemoryStatus: Map[String, (Long, Long)]

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    Return a map from the slave to the max memory available for caching and the remaining memory available for caching.

  38. def getExecutorStorageStatus: Array[StorageStatus]

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    :: DeveloperApi :: Return information about blocks stored in all of the slaves

    :: DeveloperApi :: Return information about blocks stored in all of the slaves

    Annotations
    @DeveloperApi()
  39. def getLocalProperty(key: String): String

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    Get a local property set in this thread, or null if it is missing.

    Get a local property set in this thread, or null if it is missing. See org.apache.spark.SparkContext.setLocalProperty.

  40. def getPersistentRDDs: Map[Int, RDD[_]]

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    Returns an immutable map of RDDs that have marked themselves as persistent via cache() call.

    Returns an immutable map of RDDs that have marked themselves as persistent via cache() call. Note that this does not necessarily mean the caching or computation was successful.

  41. def getPoolForName(pool: String): Option[Schedulable]

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    :: DeveloperApi :: Return the pool associated with the given name, if one exists

    :: DeveloperApi :: Return the pool associated with the given name, if one exists

    Annotations
    @DeveloperApi()
  42. def getRDDStorageInfo: Array[RDDInfo]

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    :: DeveloperApi :: Return information about what RDDs are cached, if they are in mem or on disk, how much space they take, etc.

    :: DeveloperApi :: Return information about what RDDs are cached, if they are in mem or on disk, how much space they take, etc.

    Annotations
    @DeveloperApi()
  43. def getSchedulingMode: SchedulingMode

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    Return current scheduling mode

  44. def hadoopConfiguration: Configuration

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    A default Hadoop Configuration for the Hadoop code (e.g.

    A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse.

    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.

  45. def hadoopFile[K, V, F <: InputFormat[K, V]](path: String)(implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)]

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    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.

    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)

    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.

  46. def hadoopFile[K, V, F <: InputFormat[K, V]](path: String, minPartitions: Int)(implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)]

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    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.

    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)

    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.

  47. def hadoopFile[K, V](path: String, inputFormatClass: Class[_ <: InputFormat[K, V]], keyClass: Class[K], valueClass: Class[V], minPartitions: Int = defaultMinPartitions): RDD[(K, V)]

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    Get an RDD for a Hadoop file with an arbitrary InputFormat

    Get an RDD for a Hadoop file with an arbitrary InputFormat

    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.

  48. def hadoopRDD[K, V](conf: JobConf, inputFormatClass: Class[_ <: InputFormat[K, V]], keyClass: Class[K], valueClass: Class[V], minPartitions: Int = defaultMinPartitions): RDD[(K, V)]

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    Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf given its InputFormat and other necessary info (e.g.

    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).

    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

    Class of the InputFormat

    keyClass

    Class of the keys

    valueClass

    Class of the values

    minPartitions

    Minimum number of Hadoop Splits to generate. 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.

  49. def hashCode(): Int

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  50. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
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    Logging
  51. final def isInstanceOf[T0]: Boolean

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  52. def isLocal: Boolean

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  53. def isStopped: Boolean

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    returns

    true if context is stopped or in the midst of stopping.

  54. def isTraceEnabled(): Boolean

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    protected
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    Logging
  55. def jars: Seq[String]

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  56. def killExecutor(executorId: String): Boolean

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    :: DeveloperApi :: Request that the cluster manager kill the specified executor.

    :: DeveloperApi :: Request that the cluster manager kill the specified executor.

    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}}.

    returns

    whether the request is received.

    Definition Classes
    SparkContext → ExecutorAllocationClient
    Annotations
    @DeveloperApi()
  57. def killExecutors(executorIds: Seq[String]): Boolean

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    :: DeveloperApi :: Request that the cluster manager kill the specified executors.

    :: DeveloperApi :: Request that the cluster manager kill the specified executors.

    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}}.

    returns

    whether the request is received.

    Definition Classes
    SparkContext → ExecutorAllocationClient
    Annotations
    @DeveloperApi()
  58. def listFiles(): Seq[String]

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    Returns a list of file paths that are added to resources.

  59. def listJars(): Seq[String]

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    Returns a list of jar files that are added to resources.

  60. val localProperties: InheritableThreadLocal[Properties]

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    Attributes
    protected[org.apache.spark]
  61. def log: Logger

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    protected
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    Logging
  62. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
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    Logging
  63. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
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    Logging
  64. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  65. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  66. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  67. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  68. def logName: String

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    Attributes
    protected
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    Logging
  69. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  70. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  71. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  72. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  73. def longAccumulator(name: String): LongAccumulator

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    Create and register a long accumulator, which starts with 0 and accumulates inputs by add.

  74. def longAccumulator: LongAccumulator

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    Create and register a long accumulator, which starts with 0 and accumulates inputs by add.

  75. def makeRDD[T](seq: Seq[(T, Seq[String])])(implicit arg0: ClassTag[T]): RDD[T]

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    Distribute a local Scala collection to form an RDD, with one or more location preferences (hostnames of Spark nodes) for each object.

    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.

  76. def makeRDD[T](seq: Seq[T], numSlices: Int = defaultParallelism)(implicit arg0: ClassTag[T]): RDD[T]

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    Distribute a local Scala collection to form an RDD.

    Distribute a local Scala collection to form an RDD.

    This method is identical to parallelize.

  77. def master: String

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  78. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
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  79. def newAPIHadoopFile[K, V, F <: InputFormat[K, V]](path: String, fClass: Class[F], kClass: Class[K], vClass: Class[V], conf: Configuration = hadoopConfiguration): RDD[(K, V)]

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    Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.

    Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.

    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.

  80. def newAPIHadoopFile[K, V, F <: InputFormat[K, V]](path: String)(implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)]

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    Get an RDD for a Hadoop file with an arbitrary new API InputFormat.

  81. def newAPIHadoopRDD[K, V, F <: InputFormat[K, V]](conf: Configuration = hadoopConfiguration, fClass: Class[F], kClass: Class[K], vClass: Class[V]): RDD[(K, V)]

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    Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.

    Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.

    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

    Class of the InputFormat

    kClass

    Class of the keys

    vClass

    Class of the values 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.

  82. final def notify(): Unit

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    Definition Classes
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  83. final def notifyAll(): Unit

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    Definition Classes
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  84. def objectFile[T](path: String, minPartitions: Int = defaultMinPartitions)(implicit arg0: ClassTag[T]): RDD[T]

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    Load an RDD saved as a SequenceFile containing serialized objects, with NullWritable keys and BytesWritable values that contain a serialized partition.

    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.

  85. def parallelize[T](seq: Seq[T], numSlices: Int = defaultParallelism)(implicit arg0: ClassTag[T]): RDD[T]

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    Distribute a local Scala collection to form an RDD.

    Distribute a local Scala collection to form an RDD.

    Note

    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.

    ,

    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.

  86. def range(start: Long, end: Long, step: Long = 1, numSlices: Int = defaultParallelism): RDD[Long]

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    Creates a new RDD[Long] containing elements from start to end(exclusive), increased by step every element.

    Creates a new RDD[Long] containing elements from start to end(exclusive), increased by step every element.

    start

    the start value.

    end

    the end value.

    step

    the incremental step

    numSlices

    the partition number of the new RDD.

    Note

    if we need to cache this RDD, we should make sure each partition does not exceed limit.

  87. def register(acc: AccumulatorV2[_, _], name: String): Unit

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    Register the given accumulator with given name.

    Register the given accumulator with given name. Note that accumulators must be registered before use, or it will throw exception.

  88. def register(acc: AccumulatorV2[_, _]): Unit

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    Register the given accumulator.

    Register the given accumulator. Note that accumulators must be registered before use, or it will throw exception.

  89. def requestExecutors(numAdditionalExecutors: Int): Boolean

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    :: DeveloperApi :: Request an additional number of executors from the cluster manager.

    :: DeveloperApi :: Request an additional number of executors from the cluster manager.

    returns

    whether the request is received.

    Definition Classes
    SparkContext → ExecutorAllocationClient
    Annotations
    @DeveloperApi()
  90. def requestTotalExecutors(numExecutors: Int, localityAwareTasks: Int, hostToLocalTaskCount: Map[String, Int]): Boolean

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    Update the cluster manager on our scheduling needs.

    Update the cluster manager on our scheduling needs. Three bits of information are included to help it make decisions.

    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.

    Definition Classes
    SparkContext → ExecutorAllocationClient
    Annotations
    @DeveloperApi()
  91. def runApproximateJob[T, U, R](rdd: RDD[T], func: (TaskContext, Iterator[T]) ⇒ U, evaluator: ApproximateEvaluator[U, R], timeout: Long): PartialResult[R]

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    :: DeveloperApi :: Run a job that can return approximate results.

    :: DeveloperApi :: Run a job that can return approximate results.

    Annotations
    @DeveloperApi()
  92. def runJob[T, U](rdd: RDD[T], processPartition: (Iterator[T]) ⇒ U, resultHandler: (Int, U) ⇒ Unit)(implicit arg0: ClassTag[U]): Unit

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    Run a job on all partitions in an RDD and pass the results to a handler function.

  93. def runJob[T, U](rdd: RDD[T], processPartition: (TaskContext, Iterator[T]) ⇒ U, resultHandler: (Int, U) ⇒ Unit)(implicit arg0: ClassTag[U]): Unit

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    Run a job on all partitions in an RDD and pass the results to a handler function.

  94. def runJob[T, U](rdd: RDD[T], func: (Iterator[T]) ⇒ U)(implicit arg0: ClassTag[U]): Array[U]

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    Run a job on all partitions in an RDD and return the results in an array.

  95. def runJob[T, U](rdd: RDD[T], func: (TaskContext, Iterator[T]) ⇒ U)(implicit arg0: ClassTag[U]): Array[U]

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    Run a job on all partitions in an RDD and return the results in an array.

  96. def runJob[T, U](rdd: RDD[T], func: (Iterator[T]) ⇒ U, partitions: Seq[Int])(implicit arg0: ClassTag[U]): Array[U]

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    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.

  97. def runJob[T, U](rdd: RDD[T], func: (TaskContext, Iterator[T]) ⇒ U, partitions: Seq[Int])(implicit arg0: ClassTag[U]): Array[U]

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    Run a function on a given set of partitions in an RDD and return the results as an array.

  98. def runJob[T, U](rdd: RDD[T], func: (TaskContext, Iterator[T]) ⇒ U, partitions: Seq[Int], resultHandler: (Int, U) ⇒ Unit)(implicit arg0: ClassTag[U]): Unit

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    Run a function on a given set of partitions in an RDD and pass the results to the given handler function.

    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.

  99. def sequenceFile[K, V](path: String, minPartitions: Int = defaultMinPartitions)(implicit km: ClassTag[K], vm: ClassTag[V], kcf: () ⇒ WritableConverter[K], vcf: () ⇒ WritableConverter[V]): RDD[(K, V)]

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    Version of sequenceFile() for types implicitly convertible to Writables through a WritableConverter.

    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.

    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.

  100. def sequenceFile[K, V](path: String, keyClass: Class[K], valueClass: Class[V]): RDD[(K, V)]

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    Get an RDD for a Hadoop SequenceFile with given key and value types.

    Get an RDD for a Hadoop SequenceFile with given key and value types.

    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.

  101. def sequenceFile[K, V](path: String, keyClass: Class[K], valueClass: Class[V], minPartitions: Int): RDD[(K, V)]

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    Get an RDD for a Hadoop SequenceFile with given key and value types.

    Get an RDD for a Hadoop SequenceFile with given key and value types.

    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.

  102. def setCallSite(shortCallSite: String): Unit

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    Set the thread-local property for overriding the call sites of actions and RDDs.

  103. def setCheckpointDir(directory: String): Unit

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    Set the directory under which RDDs are going to be checkpointed.

    Set the directory under which RDDs are going to be checkpointed. The directory must be a HDFS path if running on a cluster.

  104. def setJobDescription(value: String): Unit

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    Set a human readable description of the current job.

  105. def setJobGroup(groupId: String, description: String, interruptOnCancel: Boolean = false): Unit

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    Assigns a group ID to all the jobs started by this thread until the group ID is set to a different value or cleared.

    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")

    If interruptOnCancel is set to true for the job group, 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.

  106. def setLocalProperty(key: String, value: String): Unit

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    Set a local property that affects jobs submitted from this thread, such as the Spark fair scheduler pool.

    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 org.apache.spark.TaskContext#getLocalProperty.

    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.

  107. def setLogLevel(logLevel: String): Unit

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    Control our logLevel.

    Control our logLevel. This overrides any user-defined log settings.

    logLevel

    The desired log level as a string. Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN

  108. val sparkUser: String

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  109. val startTime: Long

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  110. def statusTracker: SparkStatusTracker

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  111. def stop(): Unit

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  112. def submitJob[T, U, R](rdd: RDD[T], processPartition: (Iterator[T]) ⇒ U, partitions: Seq[Int], resultHandler: (Int, U) ⇒ Unit, resultFunc: ⇒ R): SimpleFutureAction[R]

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    Submit a job for execution and return a FutureJob holding the result.

  113. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  114. def textFile(path: String, minPartitions: Int = defaultMinPartitions): RDD[String]

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    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.

  115. def toString(): String

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    Definition Classes
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  116. def uiWebUrl: Option[String]

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  117. def union[T](first: RDD[T], rest: RDD[T]*)(implicit arg0: ClassTag[T]): RDD[T]

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    Build the union of a list of RDDs passed as variable-length arguments.

  118. def union[T](rdds: Seq[RDD[T]])(implicit arg0: ClassTag[T]): RDD[T]

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    Build the union of a list of RDDs.

  119. def version: String

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    The version of Spark on which this application is running.

  120. final def wait(): Unit

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    Definition Classes
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    Annotations
    @throws( ... )
  121. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
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    Annotations
    @throws( ... )
  122. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  123. def wholeTextFiles(path: String, minPartitions: Int = defaultMinPartitions): RDD[(String, String)]

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    Read a directory of text files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI.

    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.

    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)
    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.

    Note

    On some filesystems, .../path/* can be a more efficient way to read all files in a directory rather than .../path/ or .../path

    ,

    Small files are preferred, large file is also allowable, but may cause bad performance.

Deprecated Value Members

  1. def accumulable[R, T](initialValue: R, name: String)(implicit param: AccumulableParam[R, T]): Accumulable[R, T]

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    Create an org.apache.spark.Accumulable shared variable, with a name for display in the Spark UI.

    Create an org.apache.spark.Accumulable shared variable, with a name for display in the Spark UI. Tasks can add values to the accumulable using the += operator. Only the driver can access the accumulable's value.

    R

    accumulator result type

    T

    type that can be added to the accumulator

    Annotations
    @deprecated
    Deprecated

    (Since version 2.0.0) use AccumulatorV2

  2. def accumulable[R, T](initialValue: R)(implicit param: AccumulableParam[R, T]): Accumulable[R, T]

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    Create an org.apache.spark.Accumulable shared variable, to which tasks can add values with +=.

    Create an org.apache.spark.Accumulable shared variable, to which tasks can add values with +=. Only the driver can access the accumulable's value.

    R

    accumulator result type

    T

    type that can be added to the accumulator

    Annotations
    @deprecated
    Deprecated

    (Since version 2.0.0) use AccumulatorV2

  3. def accumulableCollection[R, T](initialValue: R)(implicit arg0: (R) ⇒ Growable[T] with TraversableOnce[T] with Serializable, arg1: ClassTag[R]): Accumulable[R, T]

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    Create an accumulator from a "mutable collection" type.

    Create an accumulator from a "mutable collection" type.

    Growable and TraversableOnce are the standard APIs that guarantee += and ++=, implemented by standard mutable collections. So you can use this with mutable Map, Set, etc.

    Annotations
    @deprecated
    Deprecated

    (Since version 2.0.0) use AccumulatorV2

  4. def accumulator[T](initialValue: T, name: String)(implicit param: AccumulatorParam[T]): Accumulator[T]

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    Create an org.apache.spark.Accumulator variable of a given type, with a name for display in the Spark UI.

    Create an org.apache.spark.Accumulator variable of a given type, with a name for display in the Spark UI. Tasks can "add" values to the accumulator using the += method. Only the driver can access the accumulator's value.

    Annotations
    @deprecated
    Deprecated

    (Since version 2.0.0) use AccumulatorV2

  5. def accumulator[T](initialValue: T)(implicit param: AccumulatorParam[T]): Accumulator[T]

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    Create an org.apache.spark.Accumulator variable of a given type, which tasks can "add" values to using the += method.

    Create an org.apache.spark.Accumulator variable of a given type, which tasks can "add" values to using the += method. Only the driver can access the accumulator's value.

    Annotations
    @deprecated
    Deprecated

    (Since version 2.0.0) use AccumulatorV2

Inherited from ExecutorAllocationClient

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Ungrouped