scala-programming-guide.md

layout: global
title: Spark Programming Guide

• This will become a table of contents (this text will be scraped). {:toc}

Overview

At a high level, every Spark application consists of a driver program that runs the user's main function and executes various parallel operations on a cluster. The main abstraction Spark provides is a resilient distributed dataset (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to persist an RDD in memory, allowing it to be reused efficiently across parallel operations. Finally, RDDs automatically recover from node failures.

A second abstraction in Spark is shared variables that can be used in parallel operations. By default, when Spark runs a function in parallel as a set of tasks on different nodes, it ships a copy of each variable used in the function to each task. Sometimes, a variable needs to be shared across tasks, or between tasks and the driver program. Spark supports two types of shared variables: broadcast variables, which can be used to cache a value in memory on all nodes, and accumulators, which are variables that are only "added" to, such as counters and sums.

This guide shows each of these features and walks through some samples. It assumes some familiarity with Scala, especially with the syntax for closures. Note that you can also run Spark interactively using the bin/spark-shell script. We highly recommend doing that to follow along!

Linking with Spark

Spark {{site.SPARK_VERSION}} uses Scala {{site.SCALA_VERSION}}. If you write applications in Scala, you'll need to use this same version of Scala in your program -- newer major versions may not work.

To write a Spark application, you need to add a dependency on Spark. If you use SBT or Maven, Spark is available through Maven Central at:

groupId = org.apache.spark
artifactId = spark-core_{{site.SCALA_VERSION}}
version = {{site.SPARK_VERSION}}