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title: Spark Quick Start
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# Introduction

This document provides a quick-and-dirty look at Spark's API. See the [programming guide](scala-programming-guide.html) for a complete reference. To follow along with this guide, you only need to have successfully built Spark on one machine. Building Spark is as simple as running

{% highlight bash %}
$ sbt/sbt package
{% endhighlight %}

from within the Spark directory.

# Interactive Data Analysis with the Spark Shell

## Shell basics

Start the Spark shell by executing `./spark-shell` in the Spark directory.

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

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

RDD's have _[actions](scala-programming-guide.html#actions)_, which return values, and _[transformations](scala-programming-guide.html#transformations)_, which return pointers to new RDD's. Let's start with a few actions:

{% highlight scala %}
scala> textFile.count() // Number of items in this RDD
res0: Long = 74

scala> textFile.first() // First item in this RDD
res1: String = # Spark
{% endhighlight %}

Now let's use a transformation. We will use the [filter](scala-programming-guide.html#transformations)() transformation to return a new RDD with a subset of the items in the file.

{% highlight scala %}
scala> val sparkLinesOnly = textFile.filter(line => line.contains("Spark"))
sparkLinesOnly: spark.RDD[String] = spark.FilteredRDD@7dd4af09
{% endhighlight %}

We can chain together transformations and actions:

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

## Data flow
RDD transformations can be used for more complex computations. Let's say we want to find the line with the most words:

{% highlight scala %}
scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a < b) {b} else {a})
res4: Long = 16
{% endhighlight %}

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 [map](scala-programming-guide.html#transformations)() and [reduce](scala-programming-guide.html#actions)() are scala closures. We can easily include functions declared elsewhere, or include existing functions in our anonymous closures. For instance, we can use `Math.max()` to make this code easier to understand. 

{% highlight scala %}
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 = 16
{% endhighlight %}

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

{% highlight scala %}
scala> val wordCountRDD = textFile.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey((c1, c2) => c1 + c2)
wordCountRDD: spark.RDD[(java.lang.String, Int)] = spark.ShuffledAggregatedRDD@71f027b8
{% endhighlight %}

Here, we combined the [flatMap](scala-programming-guide.html#transformations)(), [map](scala-programming-guide.html#transformations)() and [reduceByKey](scala-programming-guide.html#transformations)() transformations to create per-word counts in the file. To collect the word counts in our shell, we can use the [collect](scala-programming-guide.html#actions)() action:

{% highlight scala %}
scala> wordCountRDD.collect()
res6: Array[(java.lang.String, Int)] = Array((need,2), ("",43), (Extra,3), (using,1), (passed,1), (etc.,1), (its,1), (`/usr/local/lib/libmesos.so`,1), (`SCALA_HOME`,1), (option,1), (these,1), (#,1), (`PATH`,,2), (200,1), (To,3),...
{% endhighlight %}

## Caching
Spark also supports pulling data sets into a cluster-wide cache. This is very useful when data is accessed iteratively, such as in machine learning jobs, or repeatedly, such as when small "hot data" is queried repeatedly. As a simple example, let's pull part of our file into memory:


{% highlight scala %}
scala> val linesWithSparkCached = linesWithSpark.cache()
linesWithSparkCached: spark.RDD[String] = spark.FilteredRDD@17e51082

scala> linesWithSparkCached.count()
res7: Long = 15

scala> linesWithSparkCached.count()
res8: Long = 15
{% endhighlight %}

It may seem silly to use a Spark to explore and cache a 30 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.

# A Spark Job in Scala
Now say we wanted to write custom job using the Spark API. We will walk through a simple job in both Scala (with sbt) and Java (with maven). If you using other build systems, please reference the Spark assembly jar in the developer guide. The first step is to publish Spark to our local Ivy/Maven repositories. From the Spark directory:

{% highlight bash %}
$ sbt/sbt publish-local
{% endhighlight %}

Next, we'll create a very simple Spark job in Scala. So simple, in fact, that it's named `SimpleJob.scala`:

{% highlight scala %}
/*** SimpleJob.scala ***/
import spark.SparkContext
import SparkContext._

object SimpleJob extends Application {
  val logFile = "/var/log/syslog" // Should be some log file on your system
  val sc = new SparkContext("local", "Simple Job", "$YOUR_SPARK_HOME", 
    "target/scala-2.9.2/simple-project_2.9.2-1.0.jar")
  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))
}
{% endhighlight %}

This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Unlike the earlier examples with the Spark Shell, which initializes its own SparkContext, we initialize a SparkContext as part of the job. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the job, the directory where Spark is installed, and a name for the jar file containing the job's sources. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes.

This file depends on the Spark API, so we'll also include an sbt configuration file, `simple.sbt` which explains that Spark is a dependency:

{% highlight scala %}
name := "Simple Project"

version := "1.0"

scalaVersion := "2.9.2"

libraryDependencies += "org.spark-project" %% "spark-core" % "0.6.0-SNAPSHOT"
{% endhighlight %}

Of course, for sbt to work correctly, we'll need to layout `SimpleJob.scala` and `simple.sbt` according to the typical directory structure. Once that is in place, we can create a jar package containing the job's code, then use `sbt run` to execute our example job. 

{% highlight bash %}
$ find . 
.
./simple.sbt
./src
./src/main
./src/main/scala
./src/main/scala/SimpleJob.scala

$ sbt clean package
$ sbt run
...
Lines with a: 8422, Lines with b: 1836
{% endhighlight %}

This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation and consider using a distributed input source, such as HDFS.

# A Spark Job In Java
Now say we wanted to write custom job using the Spark API. We will walk through a simple job in both Scala (with sbt) and Java (with maven). If you using other build systems, please reference the Spark assembly jar in the developer guide. The first step is to publish Spark to our local Ivy/Maven repositories. From the Spark directory:

{% highlight bash %}
$ sbt/sbt publish-local
{% endhighlight %}
Next, we'll create a very simple Spark job in Scala. So simple, in fact, that it's named `SimpleJob.java`:

{% highlight java %}
/*** SimpleJob.java ***/
import spark.api.java.*;
import spark.api.java.function.Function;

public class SimpleJob {
  public static void main(String[] args) {
    String logFile = "/var/log/syslog"; // Should be some log file on your system
    JavaSparkContext sc = new JavaSparkContext("local", "Simple Job", 
      "$YOUR_SPARK_HOME", "target/simple-project-1.0.jar");
    JavaRDD<String> logData = sc.textFile(logFile).cache();

    long numAs = logData.filter(new Function<String, Boolean>() {
      public Boolean call(String s) { return s.contains("a"); }
    }).count();

    long numBs = logData.filter(new Function<String, Boolean>() {
      public Boolean call(String s) { return s.contains("b"); }
    }).count();

    System.out.println(String.format(
        "Lines with a: %s, Lines with b: %s", numAs, numBs));
  }
}
{% endhighlight %}

This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Unlike the earlier examples with the Spark Shell, which initializes its own SparkContext, we initialize a SparkContext as part of the job. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the job, the directory where Spark is installed, and a name for the jar file containing the job's sources. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes.

Our Maven `pom.xml` file will list Spark as a dependency. Note that Spark artifacts are tagged with a Scala version.

{% highlight xml %}
<project>
  <groupId>edu.berkeley</groupId>
  <artifactId>simple-project</artifactId>
  <modelVersion>4.0.0</modelVersion>
  <name>Simple Project</name>
  <packaging>jar</packaging>
  <version>1.0</version>
  <dependencies>
    <dependency> <!-- Spark dependency -->
      <groupId>org.spark-project</groupId>
      <artifactId>spark-core_2.9.2</artifactId>
      <version>0.6.0-SNAPSHOT</version>
    </dependency>
  </dependencies>
</project>
{% endhighlight %}

To make Maven happy, we lay out these files according to the canonical directory structure:
{% highlight bash %}
$ find .
./pom.xml
./src
./src/main
./src/main/java
./src/main/java/SimpleJob.java
{% endhighlight %}

Now, we can execute the job using Maven:

{% highlight bash %}
$ mvn clean package
$ mvn exec:java -Dexec.mainClass="SimpleJob"
...
Lines with a: 8422, Lines with b: 1836
{% endhighlight %}

This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation and consider using a distributed input source, such as HDFS.