Quick Start

• Interactive Analysis with the Spark Shell
  • Basics
  • More on RDD Operations
  • Caching
• Standalone Applications
• Where to Go from Here

This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark’s interactive shell (in Python or Scala), then show how to write standalone applications in Java, Scala, and Python. See the programming
guide for a more complete reference.

To follow along with this guide, first download a packaged release of Spark from the Spark website. Since we won’t be using HDFS, you can
download a package for any version of Hadoop.

Interactive Analysis with the Spark Shell

Basics

Spark’s shell provides a simple way to learn the API, as well as a powerful tool to analyze data interactively. It is available in either Scala (which runs on the Java VM and is thus a good way to use existing Java libraries) or Python. Start it by running
the following in the Spark directory:

• Scala
• Python

./bin/spark-shell

Spark’s primary abstraction is a distributed collection of items called a Resilient Distributed Dataset (RDD). RDDs can be created from Hadoop InputFormats (such as HDFS files) or by transforming other RDDs. Let’s
make a new RDD from the text of the README file in the Spark source directory:

scala> val textFile = sc.textFile("README.md")
textFile: spark.RDD[String] = spark.MappedRDD@2ee9b6e3

RDDs have actions, which return values, and transformations,
which return pointers to new RDDs. Let’s start with a few actions:

scala> textFile.count() // Number of items in this RDD
res0: Long = 126

scala> textFile.first() // First item in this RDD
res1: String = # Apache Spark

Now let’s use a transformation. We will use the filter transformation
to return a new RDD with a subset of the items in the file.

scala> val linesWithSpark = textFile.filter(line => line.contains("Spark"))
linesWithSpark: spark.RDD[String] = spark.FilteredRDD@7dd4af09

We can chain together transformations and actions:

scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"?
res3: Long = 15

More on RDD Operations

RDD actions and transformations can be used for more complex computations. Let’s say we want to find the line with the most words:

• Scala
• Python

scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a > b) a else b)
res4: Long = 15

This first maps a line to an integer value, creating a new RDD. reduce is called on
that RDD to find the largest line count. The arguments to mapand reduce are
Scala function literals (closures), and can use any language feature or Scala/Java library. For example, we can easily call functions declared elsewhere. We’ll use Math.max() function
to make this code easier to understand:

scala> import java.lang.Math
import java.lang.Math

scala> textFile.map(line => line.split(" ").size).reduce((a, b) => Math.max(a, b))
res5: Int = 15

One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can implement MapReduce flows easily:

scala> val wordCounts = textFile.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey((a, b) => a + b)
wordCounts: spark.RDD[(String, Int)] = spark.ShuffledAggregatedRDD@71f027b8

Here, we combined the flatMap, map and reduceByKey transformations
to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the collect action:

scala> wordCounts.collect()
res6: Array[(String, Int)] = Array((means,1), (under,2), (this,3), (Because,1), (Python,2), (agree,1), (cluster.,1), ...)

Caching

Spark also supports pulling data sets into a cluster-wide in-memory cache. This is very useful when data is accessed repeatedly, such as when querying a small “hot” dataset or when running an iterative algorithm like PageRank. As a simple example, let’s mark
our linesWithSpark dataset to be cached:

• Scala
• Python

scala> linesWithSpark.cache()
res7: spark.RDD[String] = spark.FilteredRDD@17e51082

scala> linesWithSpark.count()
res8: Long = 15

scala> linesWithSpark.count()
res9: Long = 15

It may seem silly to use Spark to explore and cache a 100-line text file. The interesting part is that these same functions can be used on very large data sets, even when they are striped across tens or hundreds
of nodes. You can also do this interactively by connecting bin/spark-shellto a cluster, as described in the programming
guide.

Standalone Applications

Now say we wanted to write a standalone application using the Spark API. We will walk through a simple application in both Scala (with SBT), Java (with Maven), and Python.

• Scala
• Java
• Python

We’ll create a very simple Spark application in Scala. So simple, in fact, that it’s named SimpleApp.scala:

/* SimpleApp.scala */
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.SparkConf

object SimpleApp {
  def main(args: Array[String]) {
    val logFile = "YOUR_SPARK_HOME/README.md" // Should be some file on your system
    val conf = new SparkConf().setAppName("Simple Application")
    val sc = new SparkContext(conf)
    val logData = sc.textFile(logFile, 2).cache()
    val numAs = logData.filter(line => line.contains("a")).count()
    val numBs = logData.filter(line => line.contains("b")).count()
    println("Lines with a: %s, Lines with b: %s".format(numAs, numBs))
  }
}

This program just counts the number of lines containing ‘a’ and the number containing ‘b’ in the Spark README. Note that you’ll need to replace YOUR_SPARK_HOME with the location where Spark is installed. Unlike
the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the program.

We pass the SparkContext constructor a SparkConf object
which contains information about our application.

Our application depends on the Spark API, so we’ll also include an sbt configuration file, simple.sbt which
explains that Spark is a dependency. This file also adds a repository that Spark depends on:

name := "Simple Project"

version := "1.0"

scalaVersion := "2.10.4"

libraryDependencies += "org.apache.spark" %% "spark-core" % "1.1.0-SNAPSHOT"

For sbt to work correctly, we’ll need to layout SimpleApp.scala and simple.sbt according
to the typical directory structure. Once that is in place, we can create a JAR package containing the application’s code, then use the spark-submit script
to run our program.

# Your directory layout should look like this
$ find .
.
./simple.sbt
./src
./src/main
./src/main/scala
./src/main/scala/SimpleApp.scala

# Package a jar containing your application
$ sbt package
...
[info] Packaging {..}/{..}/target/scala-2.10/simple-project_2.10-1.0.jar

# Use spark-submit to run your application
$ YOUR_SPARK_HOME/bin/spark-submit \
  --class "SimpleApp" \
  --master local[4] \
  target/scala-2.10/simple-project_2.10-1.0.jar
...
Lines with a: 46, Lines with b: 23

Where to Go from Here

Congratulations on running your first Spark application!

• For an in-depth overview of the API, start with the Spark programming guide, or see “Programming
  Guides” menu for other components.
• For running applications on a cluster, head to the deployment overview.
• Finally, Spark includes several samples in the examples directory (Scala, Java, Python).
  You can run them as follows:

# For Scala and Java, use run-example:
./bin/run-example SparkPi

# For Python examples, use spark-submit directly:
./bin/spark-submit examples/src/main/python/pi.py