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.

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

    Definition Classes
    spark
  • Broadcast
  • package graphx

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

    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.

    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).

    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.

    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.

    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.

    Spark utilities.

    Definition Classes
    spark
p

org.apache.spark

broadcast

package broadcast

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

Source
package.scala
Linear Supertypes
AnyRef, Any

Type Members

  1. abstract class Broadcast[T] extends Serializable with Logging

    A broadcast variable.

    A broadcast variable. Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. They can be used, for example, to give every node a copy of a large input dataset in an efficient manner. Spark also attempts to distribute broadcast variables using efficient broadcast algorithms to reduce communication cost.

    Broadcast variables are created from a variable v by calling org.apache.spark.SparkContext#broadcast. The broadcast variable is a wrapper around v, and its value can be accessed by calling the value method. The interpreter session below shows this:

    scala> val broadcastVar = sc.broadcast(Array(1, 2, 3))
    broadcastVar: org.apache.spark.broadcast.Broadcast[Array[Int]] = Broadcast(0)
    
    scala> broadcastVar.value
    res0: Array[Int] = Array(1, 2, 3)

    After the broadcast variable is created, it should be used instead of the value v in any functions run on the cluster so that v is not shipped to the nodes more than once. In addition, the object v should not be modified after it is broadcast in order to ensure that all nodes get the same value of the broadcast variable (e.g. if the variable is shipped to a new node later).

    T

    Type of the data contained in the broadcast variable.

Inherited from AnyRef

Inherited from Any

Ungrouped