Downloading Spark

The first step to using Spark is to download and unpack it. Let’s start by downloading a recent precompiled released version of Spark. Visit http://spark.apache.org/downloads.html, select the package type of “Pre-built for Hadoop 2.4 and later,” and click “Direct Download.” This will download a compressed TAR file, or tarball, called spark-1.2.0-bin-hadoop2.4.tgz.

You don’t need to have Hadoop, but if you have an existing Hadoop cluster or HDFS installation, download the matching version. You can do so from http://spark.apache.org/downloads.html by selecting a different package type, but they will have slightly different filenames. Building from source is also possible; you can find the latest source code on GitHub or select the package type of “Source Code” when downloading.

Now that we have downloaded Spark, let’s unpack it and take a look at what comes with the default Spark distribution. To do that, open a terminal, change to the directory where you downloaded Spark, and untar the file. This will create a new directory with the same name but without the final .tgz suffix. Change into that directory and see what’s inside. You can use the following commands to accomplish all of that:

cd ~
tar -xf spark-1.2.0-bin-hadoop2.4.tgz
cd spark-1.2.0-bin-hadoop2.4
ls

In the line containing the tar command, the x flag tells tar we are extracting files, and the f flag specifies the name of the tarball. The ls command lists the contents of the Spark directory. Let’s briefly consider the names and purposes of some of the more important files and directories you see here that come with Spark:

README.md

Contains short instructions for getting started with Spark.

bin

Contains executable files that can be used to interact with Spark in various ways (e.g., the Spark shell, which we will cover later in this chapter).

core, streaming, python, …

Contains the source code of major components of the Spark project.

examples

Contains some helpful Spark standalone jobs that you can look at and run to learn about the Spark API.

Don’t worry about the large number of directories and files the Spark project comes with; we will cover most of these in the rest of this book. For now, let’s dive right in and try out Spark’s Python and Scala shells. We will start by running some of the examples that come with Spark. Then we will write, compile, and run a simple Spark job of our own.

All of the work we will do in this chapter will be with Spark running in local mode; that is, nondistributed mode, which uses only a single machine. Spark can run in a variety of different modes, or environments. Beyond local mode, Spark can also be run on Mesos, YARN, or the Standalone Scheduler included in the Spark distribution. We will cover the various deployment modes in detail in Chapter 7.

Introduction to Spark’s Python and Scala Shells

Spark comes with interactive shells that enable ad hoc data analysis. Spark’s shells will feel familiar if you have used other shells such as those in R, Python, and Scala, or operating system shells like Bash or the Windows command prompt.

Unlike most other shells, however, which let you manipulate data using the disk and memory on a single machine, Spark’s shells allow you to interact with data that is distributed on disk or in memory across many machines, and Spark takes care of automatically distributing this processing.

Because Spark can load data into memory on the worker nodes, many distributed computations, even ones that process terabytes of data across dozens of machines, can run in a few seconds. This makes the sort of iterative, ad hoc, and exploratory analysis commonly done in shells a good fit for Spark. Spark provides both Python and Scala shells that have been augmented to support connecting to a cluster.

The easiest way to demonstrate the power of Spark’s shells is to start using one of them for some simple data analysis. Let’s walk through the example from the Quick Start Guide in the official Spark documentation.

The first step is to open up one of Spark’s shells. To open the Python version of the Spark shell, which we also refer to as the PySpark Shell, go into your Spark directory and type:

bin/pyspark

(Or binpyspark in Windows.) To open the Scala version of the shell, type:

bin/spark-shell

The shell prompt should appear within a few seconds. When the shell starts, you will notice a lot of log messages. You may need to press Enter once to clear the log output and get to a shell prompt. Figure 2-1 shows what the PySpark shell looks like when you open it.

Figure 2-1. The PySpark shell with default logging output

You may find the logging statements that get printed in the shell distracting. You can control the verbosity of the logging. To do this, you can create a file in the conf directory called log4j.properties. The Spark developers already include a template for this file called log4j.properties.template. To make the logging less verbose, make a copy of conf/log4j.properties.template called conf/log4j.properties and find the following line:

log4j.rootCategory=INFO, console

Then lower the log level so that we show only the WARN messages, and above by changing it to the following:

log4j.rootCategory=WARN, console

When you reopen the shell, you should see less output (Figure 2-2).

Figure 2-2. The PySpark shell with less logging output

IPYTHON=1 ./bin/pyspark

IPYTHON_OPTS="notebook" ./bin/pyspark

set IPYTHON=1
binpyspark

In Spark, we express our computation through operations on distributed collections that are automatically parallelized across the cluster. These collections are called resilient distributed datasets, or RDDs. RDDs are Spark’s fundamental abstraction for distributed data and computation.

Before we say more about RDDs, let’s create one in the shell from a local text file and do some very simple ad hoc analysis by following Example 2-1 for Python or Example 2-2 for Scala.

>>> lines = sc.textFile("README.md") # Create an RDD called lines

>>> lines.count() # Count the number of items in this RDD
127
>>> lines.first() # First item in this RDD, i.e. first line of README.md
u'# Apache Spark'

scala> val lines = sc.textFile("README.md") // Create an RDD called lines
lines: spark.RDD[String] = MappedRDD[...]

scala> lines.count() // Count the number of items in this RDD
res0: Long = 127

scala> lines.first() // First item in this RDD, i.e. first line of README.md
res1: String = # Apache Spark

To exit either shell, press Ctrl-D.

In Examples 2-1 and 2-2, the variable called lines is an RDD, created here from a text file on our local machine. We can run various parallel operations on the RDD, such as counting the number of elements in the dataset (here, lines of text in the file) or printing the first one. We will discuss RDDs in great depth in later chapters, but before we go any further, let’s take a moment now to introduce basic Spark concepts.

Introduction to Core Spark Concepts

Now that you have run your first Spark code using the shell, it’s time to learn about programming in it in more detail.

At a high level, every Spark application consists of a driver program that launches various parallel operations on a cluster. The driver program contains your application’s main function and defines distributed datasets on the cluster, then applies operations to them. In the preceding examples, the driver program was the Spark shell itself, and you could just type in the operations you wanted to run.

Driver programs access Spark through a SparkContext object, which represents a connection to a computing cluster. In the shell, a SparkContext is automatically created for you as the variable called sc. Try printing out sc to see its type, as shown in Example 2-3.

>>> sc
<pyspark.context.SparkContext object at 0x1025b8f90>

Once you have a SparkContext, you can use it to build RDDs. In Examples 2-1 and 2-2, we called sc.textFile() to create an RDD representing the lines of text in a file. We can then run various operations on these lines, such as count().

To run these operations, driver programs typically manage a number of nodes called executors. For example, if we were running the count() operation on a cluster, different machines might count lines in different ranges of the file. Because we just ran the Spark shell locally, it executed all its work on a single machine—but you can connect the same shell to a cluster to analyze data in parallel. Figure 2-3 shows how Spark executes on a cluster.

Figure 2-3. Components for distributed execution in Spark

Finally, a lot of Spark’s API revolves around passing functions to its operators to run them on the cluster. For example, we could extend our README example by filtering the lines in the file that contain a word, such as Python, as shown in Example 2-4 (for Python) and Example 2-5 (for Scala).

>>> lines = sc.textFile("README.md")

>>> pythonLines = lines.filter(lambda line: "Python" in line)

>>> pythonLines.first()
u'## Interactive Python Shell'

scala> val lines = sc.textFile("README.md") // Create an RDD called lines
lines: spark.RDD[String] = MappedRDD[...]

scala> val pythonLines = lines.filter(line => line.contains("Python"))
pythonLines: spark.RDD[String] = FilteredRDD[...]

scala> pythonLines.first()
res0: String = ## Interactive Python Shell

While we will cover the Spark API in more detail later, a lot of its magic is that function-based operations like filter also parallelize across the cluster. That is, Spark automatically takes your function (e.g., line.contains("Python")) and ships it to executor nodes. Thus, you can write code in a single driver program and automatically have parts of it run on multiple nodes. Chapter 3 covers the RDD API in detail.

Standalone Applications

The final piece missing in this quick tour of Spark is how to use it in standalone programs. Apart from running interactively, Spark can be linked into standalone applications in either Java, Scala, or Python. The main difference from using it in the shell is that you need to initialize your own SparkContext. After that, the API is the same.

The process of linking to Spark varies by language. In Java and Scala, you give your application a Maven dependency on the spark-core artifact. As of the time of writing, the latest Spark version is 1.2.0, and the Maven coordinates for that are:

groupId = org.apache.spark
artifactId = spark-core_2.10
version = 1.2.0

Maven is a popular package management tool for Java-based languages that lets you link to libraries in public repositories. You can use Maven itself to build your project, or use other tools that can talk to the Maven repositories, including Scala’s sbt tool or Gradle. Popular integrated development environments like Eclipse also allow you to directly add a Maven dependency to a project.

In Python, you simply write applications as Python scripts, but you must run them using the bin/spark-submit script included in Spark. The spark-submit script includes the Spark dependencies for us in Python. This script sets up the environment for Spark’s Python API to function. Simply run your script with the line given in Example 2-6.

bin/spark-submit my_script.py

(Note that you will have to use backslashes instead of forward slashes on Windows.)

from pyspark import SparkConf, SparkContext

conf = SparkConf().setMaster("local").setAppName("My App")
sc = SparkContext(conf = conf)

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._

val conf = new SparkConf().setMaster("local").setAppName("My App")
val sc = new SparkContext(conf)

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;

SparkConf conf = new SparkConf().setMaster("local").setAppName("My App");
JavaSparkContext sc = new JavaSparkContext(conf);

// Create a Java Spark Context
SparkConf conf = new SparkConf().setAppName("wordCount");
JavaSparkContext sc = new JavaSparkContext(conf);
// Load our input data.
JavaRDD<String> input = sc.textFile(inputFile);
// Split up into words.
JavaRDD<String> words = input.flatMap(
  new FlatMapFunction<String, String>() {
    public Iterable<String> call(String x) {
      return Arrays.asList(x.split(" "));
    }});
// Transform into pairs and count.
JavaPairRDD<String, Integer> counts = words.mapToPair(
  new PairFunction<String, String, Integer>(){
    public Tuple2<String, Integer> call(String x){
      return new Tuple2(x, 1);
    }}).reduceByKey(new Function2<Integer, Integer, Integer>(){
        public Integer call(Integer x, Integer y){ return x + y;}});
// Save the word count back out to a text file, causing evaluation.
counts.saveAsTextFile(outputFile);

// Create a Scala Spark Context.
val conf = new SparkConf().setAppName("wordCount")
val sc = new SparkContext(conf)
// Load our input data.
val input =  sc.textFile(inputFile)
// Split it up into words.
val words = input.flatMap(line => line.split(" "))
// Transform into pairs and count.
val counts = words.map(word => (word, 1)).reduceByKey{case (x, y) => x + y}
// Save the word count back out to a text file, causing evaluation.
counts.saveAsTextFile(outputFile)

name := "learning-spark-mini-example"

version := "0.0.1"

scalaVersion := "2.10.4"

// additional libraries
libraryDependencies ++= Seq(
  "org.apache.spark" %% "spark-core" % "1.2.0" % "provided"
)

<project>
  <groupId>com.oreilly.learningsparkexamples.mini</groupId>
  <artifactId>learning-spark-mini-example</artifactId>
  <modelVersion>4.0.0</modelVersion>
  <name>example</name>
  <packaging>jar</packaging>
  <version>0.0.1</version>
  <dependencies>
    <dependency> <!-- Spark dependency -->
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-core_2.10</artifactId>
      <version>1.2.0</version>
      <scope>provided</scope>
    </dependency>
  </dependencies>
  <properties>
    <java.version>1.6</java.version>
  </properties>
  <build>
    <pluginManagement>
      <plugins>
        <plugin>
	  <groupId>org.apache.maven.plugins</groupId>
          <artifactId>maven-compiler-plugin</artifactId>
          <version>3.1</version>
          <configuration>
            <source>${java.version}</source>
            <target>${java.version}</target>
          </configuration>
	</plugin>
	</plugin>
      </plugins>
    </pluginManagement>
  </build>
</project>

sbt clean package
$SPARK_HOME/bin/spark-submit 
  --class com.oreilly.learningsparkexamples.mini.scala.WordCount 
  ./target/...(as above) 
  ./README.md ./wordcounts

mvn clean && mvn compile && mvn package
$SPARK_HOME/bin/spark-submit 
  --class com.oreilly.learningsparkexamples.mini.java.WordCount 
  ./target/learning-spark-mini-example-0.0.1.jar 
  ./README.md ./wordcounts

Conclusion

In this chapter, we have covered downloading Spark, running it locally on your laptop, and using it either interactively or from a standalone application. We gave a quick overview of the core concepts involved in programming with Spark: a driver program creates a SparkContext and RDDs, and then runs parallel operations on them. In the next chapter, we will dive more deeply into how RDDs operate.