spark-instrumented-optimizer/docs/quick-start.md

layout	title
global	Quick Start

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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:

./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:

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

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

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

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

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.

{% highlight scala %} scala> val linesWithSpark = textFile.filter(line => line.contains("Spark")) linesWithSpark: 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 %}

./bin/pyspark

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:

{% highlight python %}

textFile = sc.textFile("README.md") {% endhighlight %}

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

{% highlight python %}

textFile.count() # Number of items in this RDD 126

textFile.first() # First item in this RDD u'# Apache Spark' {% endhighlight %}

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.

{% highlight python %}

linesWithSpark = textFile.filter(lambda line: "Spark" in line) {% endhighlight %}

We can chain together transformations and actions:

{% highlight python %}

textFile.filter(lambda line: "Spark" in line).count() # How many lines contain "Spark"? 15 {% endhighlight %}

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:

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

{% 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 = 15 {% endhighlight %}

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

{% highlight scala %} 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 {% endhighlight %}

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:

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

{% highlight python %}

textFile.map(lambda line: len(line.split())).reduce(lambda a, b: a if (a > b) else b) 15 {% 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 and reduce are Python anonymous functions (lambdas), but we can also pass any top-level Python function we want. For example, we'll define a max function to make this code easier to understand:

{% highlight python %}

def max(a, b): ... if a > b: ... return a ... else: ... return b ...

textFile.map(lambda line: len(line.split())).reduce(max) 15 {% endhighlight %}

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

{% highlight python %}

wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a+b) {% endhighlight %}

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:

{% highlight python %}

wordCounts.collect() [(u'and', 9), (u'A', 1), (u'webpage', 1), (u'README', 1), (u'Note', 1), (u'"local"', 1), (u'variable', 1), ...] {% endhighlight %}

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:

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

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

scala> linesWithSpark.count() res9: Long = 15 {% endhighlight %}

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-shell to a cluster, as described in the programming guide.

{% highlight python %}

linesWithSpark.cache()

linesWithSpark.count() 15

linesWithSpark.count() 15 {% endhighlight %}

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/pyspark to 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.

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

{% highlight 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)) } } {% endhighlight %}

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:

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

version := "1.0"

scalaVersion := "{{site.SCALA_VERSION}}"

libraryDependencies += "org.apache.spark" %% "spark-core" % "{{site.SPARK_VERSION}}"

resolvers += "Akka Repository" at "http://repo.akka.io/releases/" {% endhighlight %}

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.

{% highlight bash %}

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 {% endhighlight %}

This example will use Maven to compile an application jar, but any similar build system will work.

We'll create a very simple Spark application, SimpleApp.java:

{% highlight java %} /* SimpleApp.java / import org.apache.spark.api.java.; import org.apache.spark.SparkConf; import org.apache.spark.api.java.function.Function;

public class SimpleApp { public static void main(String[] args) { String logFile = "YOUR_SPARK_HOME/README.md"; // Should be some file on your system SparkConf conf = new SparkConf().setAppName("Simple Application"); JavaSparkContext sc = new JavaSparkContext(conf); JavaRDD 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("Lines with a: " + numAs + ", lines with b: " + numBs);

} } {% endhighlight %}

This program just counts the number of lines containing 'a' and the number containing 'b' in a text file. Note that you'll need to replace YOUR_SPARK_HOME with the location where Spark is installed. As with the Scala example, we initialize a SparkContext, though we use the special JavaSparkContext class to get a Java-friendly one. We also create RDDs (represented by JavaRDD) and run transformations on them. Finally, we pass functions to Spark by creating classes that extend spark.api.java.function.Function. The Spark programming guide describes these differences in more detail.

To build the program, we also write a Maven pom.xml file that lists Spark as a dependency. Note that Spark artifacts are tagged with a Scala version.

{% highlight xml %} edu.berkeley simple-project 4.0.0 Simple Project jar 1.0 Akka repository http://repo.akka.io/releases org.apache.spark spark-core_{{site.SCALA_BINARY_VERSION}} {{site.SPARK_VERSION}} {% endhighlight %}

We lay out these files according to the canonical Maven directory structure: {% highlight bash %} $ find . ./pom.xml ./src ./src/main ./src/main/java ./src/main/java/SimpleApp.java {% endhighlight %}

Now, we can package the application using Maven and execute it with ./bin/spark-submit.

{% highlight bash %}

Package a jar containing your application

$ mvn package ... [INFO] Building jar: {..}/{..}/target/simple-project-1.0.jar

Use spark-submit to run your application

$ YOUR_SPARK_HOME/bin/spark-submit
--class "SimpleApp"
--master local[4]
target/simple-project-1.0.jar ... Lines with a: 46, Lines with b: 23 {% endhighlight %}

Now we will show how to write a standalone application using the Python API (PySpark).

As an example, we'll create a simple Spark application, SimpleApp.py:

{% highlight python %} """SimpleApp.py""" from pyspark import SparkContext

logFile = "YOUR_SPARK_HOME/README.md" # Should be some file on your system sc = SparkContext("local", "Simple App") logData = sc.textFile(logFile).cache()

numAs = logData.filter(lambda s: 'a' in s).count() numBs = logData.filter(lambda s: 'b' in s).count()

print "Lines with a: %i, lines with b: %i" % (numAs, numBs) {% endhighlight %}

This program just counts the number of lines containing 'a' and the number containing 'b' in a text file. Note that you'll need to replace YOUR_SPARK_HOME with the location where Spark is installed. As with the Scala and Java examples, we use a SparkContext to create RDDs. We can pass Python functions to Spark, which are automatically serialized along with any variables that they reference. For applications that use custom classes or third-party libraries, we can also add code dependencies to spark-submit through its --py-files argument by packaging them into a .zip file (see spark-submit --help for details). SimpleApp is simple enough that we do not need to specify any code dependencies.

We can run this application using the bin/spark-submit script:

{% highlight python %}

Use spark-submit to run your application

$ YOUR_SPARK_HOME/bin/spark-submit
--master local[4]
SimpleApp.py ... Lines with a: 46, Lines with b: 23 {% endhighlight python %}

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:

{% highlight bash %}

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 {% endhighlight %}