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

Partitioner

Companion class Partitioner

object Partitioner extends Serializable

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def defaultPartitioner(rdd: RDD[_], others: RDD[_]*): Partitioner
Choose a partitioner to use for a cogroup-like operation between a number of RDDs.
Choose a partitioner to use for a cogroup-like operation between a number of RDDs.
If spark.default.parallelism is set, we'll use the value of SparkContext defaultParallelism as the default partitions number, otherwise we'll use the max number of upstream partitions.
When available, we choose the partitioner from rdds with maximum number of partitions. If this partitioner is eligible (number of partitions within an order of maximum number of partitions in rdds), or has partition number higher than default partitions number - we use this partitioner.
Otherwise, we'll use a new HashPartitioner with the default partitions number.
Unless spark.default.parallelism is set, the number of partitions will be the same as the number of partitions in the largest upstream RDD, as this should be least likely to cause out-of-memory errors.
We use two method parameters (rdd, others) to enforce callers passing at least 1 RDD.
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Packages

Partitioner

Companion class Partitioner

object Partitioner extends Serializable

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Partitioner 

Companion class Partitioner

object Partitioner extends Serializable

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Partitioner