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 types

Definition Classes: spark

package ui

Definition Classes: spark

package unsafe

Definition Classes: spark

package util

Spark utilities.

Definition Classes: spark

ErrorClassesJsonReader

InterruptibleIterator

PartitionEvaluatorFactory

SparkFirehoseListener

SparkStatusTracker

class SparkStatusTracker extends AnyRef

Low-level status reporting APIs for monitoring job and stage progress.

These APIs intentionally provide very weak consistency semantics; consumers of these APIs should be prepared to handle empty / missing information. For example, a job's stage ids may be known but the status API may not have any information about the details of those stages, so getStageInfo could potentially return None for a valid stage id.

To limit memory usage, these APIs only provide information on recent jobs / stages. These APIs will provide information for the last spark.ui.retainedStages stages and spark.ui.retainedJobs jobs.

NOTE: this class's constructor should be considered private and may be subject to change.

Source: SparkStatusTracker.scala

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def getActiveJobIds(): Array[Int]
Returns an array containing the ids of all active jobs.
Returns an array containing the ids of all active jobs.
This method does not guarantee the order of the elements in its result.
def getActiveStageIds(): Array[Int]
Returns an array containing the ids of all active stages.
Returns an array containing the ids of all active stages.
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final def getClass(): Class[_ <: AnyRef]
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def getExecutorInfos: Array[SparkExecutorInfo]
Returns information of all known executors, including host, port, cacheSize, numRunningTasks and memory metrics.
Returns information of all known executors, including host, port, cacheSize, numRunningTasks and memory metrics. Note this include information for both the driver and executors.
def getJobIdsForGroup(jobGroup: String): Array[Int]
Return a list of all known jobs in a particular job group.
Return a list of all known jobs in a particular job group. If jobGroup is null, then returns all known jobs that are not associated with a job group.
The returned list may contain running, failed, and completed jobs, and may vary across invocations of this method. This method does not guarantee the order of the elements in its result.
def getJobIdsForTag(jobTag: String): Array[Int]
Return a list of all known jobs with a particular tag.
Return a list of all known jobs with a particular tag.
The returned list may contain running, failed, and completed jobs, and may vary across invocations of this method. This method does not guarantee the order of the elements in its result.
def getJobInfo(jobId: Int): Option[SparkJobInfo]
Returns job information, or None if the job info could not be found or was garbage collected.
def getStageInfo(stageId: Int): Option[SparkStageInfo]
Returns stage information, or None if the stage info could not be found or was garbage collected.
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SparkStatusTracker

class SparkStatusTracker extends AnyRef

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SparkStatusTracker

class SparkStatusTracker extends AnyRef

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SparkStatusTracker