Packages

package root

Definition Classes: root

package org

Definition Classes: root

package apache

Definition Classes: org

package spark

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 internal

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

:: Experimental ::

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 rdd

Provides several RDD implementations.

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

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 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 util

Spark utilities.

Definition Classes: spark

Aggregator

BarrierTaskContext

BarrierTaskInfo

ComplexFutureAction

Dependency

ExceptionFailure

ExecutorLostFailure

ExecutorPlugin

FetchFailed

FutureAction

HashPartitioner

InterruptibleIterator

JobExecutionStatus

JobSubmitter

NarrowDependency

OneToOneDependency

Partition

Partitioner

RangeDependency

RangePartitioner

Resubmitted

SerializableWritable

ShuffleDependency

SimpleFutureAction

SparkConf

SparkContext

SparkEnv

SparkException

SparkExecutorInfo

SparkFiles

SparkFirehoseListener

SparkJobInfo

SparkStageInfo

SparkStatusTracker

Success

TaskCommitDenied

TaskContext

TaskEndReason

TaskFailedReason

TaskKilled

TaskKilledException

TaskResultLost

UnknownReason

WritableConverter

WritableFactory

org.apache.spark

FetchFailed

case class FetchFailed(bmAddress: BlockManagerId, shuffleId: Int, mapId: Int, reduceId: Int, message: String) extends TaskFailedReason with Product with Serializable

:: DeveloperApi :: Task failed to fetch shuffle data from a remote node. Probably means we have lost the remote executors the task is trying to fetch from, and thus need to rerun the previous stage.

Annotations: @DeveloperApi()

Linear Supertypes

Serializable, Serializable, Product, Equals, TaskFailedReason, TaskEndReason, AnyRef, Any

Ordering

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By Inheritance

Inherited

FetchFailed
Serializable
Serializable
Product
Equals
TaskFailedReason
TaskEndReason
AnyRef
Any

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Visibility

Public
All

Instance Constructors

new FetchFailed(bmAddress: BlockManagerId, shuffleId: Int, mapId: Int, reduceId: Int, message: String)

Value Members

final def !=(arg0: Any): Boolean

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

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

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
val bmAddress: BlockManagerId
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@native() @throws( ... )
def countTowardsTaskFailures: Boolean
Fetch failures lead to a different failure handling path: (1) we don't abort the stage after 4 task failures, instead we immediately go back to the stage which generated the map output, and regenerate the missing data.
Fetch failures lead to a different failure handling path: (1) we don't abort the stage after 4 task failures, instead we immediately go back to the stage which generated the map output, and regenerate the missing data. (2) we don't count fetch failures for blacklisting, since presumably its not the fault of the executor where the task ran, but the executor which stored the data. This is especially important because we might rack up a bunch of fetch-failures in rapid succession, on all nodes of the cluster, due to one bad node.

Definition Classes
FetchFailed → TaskFailedReason
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native()
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val mapId: Int
val message: String
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
val reduceId: Int
val shuffleId: Int
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toErrorString: String
Error message displayed in the web UI.
Error message displayed in the web UI.

Definition Classes
FetchFailed → TaskFailedReason
final def wait(): Unit

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

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

Definition Classes
AnyRef
Annotations
@native() @throws( ... )

Packages

FetchFailed

case class FetchFailed(bmAddress: BlockManagerId, shuffleId: Int, mapId: Int, reduceId: Int, message: String) extends TaskFailedReason with Product with Serializable

Instance Constructors

Value Members

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from TaskFailedReason

Inherited from TaskEndReason

Inherited from AnyRef

Inherited from Any

Ungrouped

Packages

FetchFailed 

case class FetchFailed(bmAddress: BlockManagerId, shuffleId: Int, mapId: Int, reduceId: Int, message: String) extends TaskFailedReason with Product with Serializable

Instance Constructors

Value Members

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from TaskFailedReason

Inherited from TaskEndReason

Inherited from AnyRef

Inherited from Any

Ungrouped

FetchFailed