Packages

Definition Classes: root

Definition Classes: root

Definition Classes: org

Core Spark functionality.

Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

Definition Classes: apache

package api

Definition Classes: spark

package broadcast

Spark's broadcast variables, used to broadcast immutable datasets to all nodes.

Definition Classes: spark

package graphx

ALPHA COMPONENT GraphX is a graph processing framework built on top of Spark.

Definition Classes: spark

package input

Definition Classes: spark

package io

IO codecs used for compression.

IO codecs used for compression. See org.apache.spark.io.CompressionCodec.

Definition Classes: spark

package launcher

Definition Classes: spark

package mapred

Definition Classes: spark

package metrics

Definition Classes: spark

package ml

DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

Definition Classes: spark

package mllib

RDD-based machine learning APIs (in maintenance mode).

The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,

no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
bug fixes in the RDD-based APIs will still be accepted.

The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.

Definition Classes: spark
See also: SPARK-4591 to track the progress of feature parity

package partial

Support for approximate results.

Support for approximate results. This provides convenient api and also implementation for approximate calculation.

Definition Classes: spark
See also: org.apache.spark.rdd.RDD.countApprox

package paths

Definition Classes: spark

package rdd

Provides several RDD implementations.

Provides several RDD implementations. See org.apache.spark.rdd.RDD.

Definition Classes: spark

package resource

Definition Classes: spark

package scheduler

Spark's scheduling components.

Spark's scheduling components. This includes the org.apache.spark.scheduler.DAGScheduler and lower level org.apache.spark.scheduler.TaskScheduler.

Definition Classes: spark

package security

Definition Classes: spark

package serializer

Pluggable serializers for RDD and shuffle data.

Definition Classes: spark
See also: org.apache.spark.serializer.Serializer

package shuffle

Definition Classes: spark

package sql

Allows the execution of relational queries, including those expressed in SQL using Spark.

Definition Classes: spark

package status

Definition Classes: spark

package storage

Definition Classes: spark

package streaming

Spark Streaming functionality.

Spark Streaming functionality. org.apache.spark.streaming.StreamingContext serves as the main entry point to Spark Streaming, while org.apache.spark.streaming.dstream.DStream is the data type representing a continuous sequence of RDDs, representing a continuous stream of data.

In addition, org.apache.spark.streaming.dstream.PairDStreamFunctions contains operations available only on DStreams of key-value pairs, such as groupByKey and reduceByKey. These operations are automatically available on any DStream of the right type (e.g. DStream[(Int, Int)] through implicit conversions.

For the Java API of Spark Streaming, take a look at the org.apache.spark.streaming.api.java.JavaStreamingContext which serves as the entry point, and the org.apache.spark.streaming.api.java.JavaDStream and the org.apache.spark.streaming.api.java.JavaPairDStream which have the DStream functionality.

Definition Classes: spark

package ui

Definition Classes: spark

package unsafe

Definition Classes: spark

package util

Spark utilities.

Definition Classes: spark

ErrorClassesJsonReader

InterruptibleIterator

PartitionEvaluatorFactory

SparkFirehoseListener

BreakingChangeInfo

case class BreakingChangeInfo(migrationMessage: Seq[String], mitigationConfig: Option[MitigationConfig] = None, needsAudit: Boolean = true) extends Product with Serializable

Additional information if the error was caused by a breaking change.

migrationMessage: A message explaining how the user can migrate their job to work with the breaking change.
mitigationConfig: A spark config flag that can be used to mitigate the breaking change.
needsAudit: If true, the breaking change should be inspected manually. If false, the spark job should be retried by setting the mitigationConfig.

Source: ErrorClassesJSONReader.scala

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new BreakingChangeInfo(migrationMessage: Seq[String], mitigationConfig: Option[MitigationConfig] = None, needsAudit: Boolean = true)
migrationMessage
A message explaining how the user can migrate their job to work with the breaking change.
mitigationConfig
A spark config flag that can be used to mitigate the breaking change.
needsAudit
If true, the breaking change should be inspected manually. If false, the spark job should be retried by setting the mitigationConfig.

Value Members

final def !=(arg0: Any): Boolean
Definition Classes
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final def ##: Int
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final def ==(arg0: Any): Boolean
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final def asInstanceOf[T0]: T0
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final def getClass(): Class[_ <: AnyRef]
Definition Classes
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@IntrinsicCandidate() @native()
final def isInstanceOf[T0]: Boolean
Definition Classes
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val migrationMessage: Seq[String]
val mitigationConfig: Option[MitigationConfig]
final def ne(arg0: AnyRef): Boolean
Definition Classes
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val needsAudit: Boolean
final def notify(): Unit
Definition Classes
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@IntrinsicCandidate() @native()
final def notifyAll(): Unit
Definition Classes
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@IntrinsicCandidate() @native()
def productElementNames: Iterator[String]
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final def synchronized[T0](arg0: => T0): T0
Definition Classes
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final def wait(arg0: Long, arg1: Int): Unit
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@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long): Unit
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@throws(classOf[java.lang.InterruptedException]) @native()
final def wait(): Unit
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Deprecated Value Members

def finalize(): Unit
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.Throwable]) @Deprecated
Deprecated
(Since version 9)

Packages

BreakingChangeInfo

case class BreakingChangeInfo(migrationMessage: Seq[String], mitigationConfig: Option[MitigationConfig] = None, needsAudit: Boolean = true) extends Product with Serializable

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BreakingChangeInfo

case class BreakingChangeInfo(migrationMessage: Seq[String], mitigationConfig: Option[MitigationConfig] = None, needsAudit: Boolean = true) extends Product with Serializable

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Inherited from Product

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BreakingChangeInfo