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Installing Spark

  • Feb 16, 2017
This chapter is from the book

This chapter is from the book

Apache Spark in 24 Hours, Sams Teach YourselfApache Spark in 24 Hours, Sams Teach Yourself

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This chapter is from the book

This chapter is from the book

Apache Spark in 24 Hours, Sams Teach YourselfApache Spark in 24 Hours, Sams Teach Yourself

Learn More Buy

Installing Spark in Standalone Mode

In this section I will cover deploying Spark in Standalone mode on a single machine using various platforms. Feel free to choose the platform that is most relevant to you to install Spark on.

Getting Spark

In the installation steps for Linux and Mac OS X, I will use pre-built releases of Spark. You could also download the source code for Spark and build it yourself for your target platform using the build instructions provided on the official Spark website. I will use the latest Spark binary release in my examples. In either case, your first step, regardless of the intended installation platform, is to download either the release or source from: http://spark.apache.org/downloads.html

This page will allow you to download the latest release of Spark. In this example, the latest release is 1.5.2, your release will likely be greater than this (e.g. 1.6.x or 2.x.x).

FIGURE 3.1

FIGURE 3.1 The Apache Spark downloads page.

NOTE

The Spark releases do not actually include Hadoop as the names may imply. They simply include libraries to integrate with the Hadoop clusters and distributions listed. Many of the Hadoop classes are required regardless of whether you are using Hadoop. I will use the spark-1.5.2-bin-hadoop2.6.tgz package for this installation.

Caution: Using the “Without Hadoop” Builds

You may be tempted to download the “without Hadoop” or spark-x.x.x-bin-without-hadoop.tgz options if you are installing in Standalone mode and not using Hadoop.

The nomenclature can be confusing, but this build is expecting many of the required classes that are implemented in Hadoop to be present on the system. Select this option only if you have Hadoop installed on the system already. Otherwise, as I have done in my case, use one of the spark-x.x.x-bin-hadoopx.x builds.

Try it Yourself: Install Spark on Red Hat/Centos

In this example, I’m installing Spark on a Red Hat Enterprise Linux 7.1 instance. However, the same installation steps would apply to Centos distributions as well.

  1. As shown in Figure 3.1, download the spark-1.5.2-bin-hadoop2.6.tgz package from your local mirror into your home directory using wget or curl.

  2. If Java 1.7 or higher is not installed, install the Java 1.7 runtime and development environments using the OpenJDK yum packages (alternatively, you could use the Oracle JDK instead):

    sudo yum install java-1.7.0-openjdk java-1.7.0-openjdk-devel

  3. Confirm Java was successfully installed:

    $ java -version
java version "1.7.0_91"
OpenJDK Runtime Environment (rhel-2.6.2.3.el7-x86_64 u91-b00)
OpenJDK 64-Bit Server VM (build 24.91-b01, mixed mode)

  4. Extract the Spark package and create SPARK_HOME:

    tar -xzf spark-1.5.2-bin-hadoop2.6.tgz
sudo mv spark-1.5.2-bin-hadoop2.6 /opt/spark
export SPARK_HOME=/opt/spark
export PATH=$SPARK_HOME/bin:$PATH

    The SPARK_HOME environment variable could also be set using the .bashrc file or similar user or system profile scripts. You need to do this if you wish to persist the SPARK_HOME variable beyond the current session.

  5. Open the PySpark shell by running the pyspark command from any directory (as you’ve added the Spark bin directory to the PATH). If Spark has been successfully installed, you should see the following output (with informational logging messages omitted for brevity):

    Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 1.5.2
      /_/
Using Python version 2.7.5 (default, Feb 11 2014 07:46:25)
SparkContext available as sc, HiveContext available as sqlContext.
>>>

  6. You should see a similar result by running the spark-shell command from any directory.

  7. Run the included Pi Estimator example by executing the following command:

    spark-submit --class org.apache.spark.examples.SparkPi --master local $SPARK_HOME/lib/spark-examples*.jar 10

  8. If the installation was successful, you should see something similar to the following result (omitting the informational log messages). Note, this is an estimator program, so the actual result may vary:

    Pi is roughly 3.140576

NOTE

Most of the popular Linux distributions include Python 2.x with the python binary in the system path, so you normally don’t need to explicitly install Python; in fact, the yum program itself is implemented in Python.

You may also have wondered why you did not have to install Scala as a prerequisite. The Scala binaries are included in the assembly when you build or download a pre-built release of Spark.

Try it Yourself: Install Spark on Ubuntu/Debian Linux

In this example, I’m installing Spark on an Ubuntu 14.04 LTS Linux distribution.

As with the Red Hat example, Python 2. 7 is already installed with the operating system, so we do not need to install Python.

  1. As shown in Figure 3.1, download the spark-1.5.2-bin-hadoop2.6.tgz package from your local mirror into your home directory using wget or curl.

  2. If Java 1.7 or higher is not installed, install the Java 1.7 runtime and development environments using Ubuntu’s APT (Advanced Packaging Tool). Alternatively, you could use the Oracle JDK instead:

    sudo apt-get update
sudo apt-get install openjdk-7-jre
sudo apt-get install openjdk-7-jdk

  3. Confirm Java was successfully installed:

    $ java -version
java version "1.7.0_91"
OpenJDK Runtime Environment (IcedTea 2.6.3) (7u91-2.6.3-0ubuntu0.14.04.1)
OpenJDK 64-Bit Server VM (build 24.91-b01, mixed mode)

  4. Extract the Spark package and create SPARK_HOME:

    tar -xzf spark-1.5.2-bin-hadoop2.6.tgz
sudo mv spark-1.5.2-bin-hadoop2.6 /opt/spark
export SPARK_HOME=/opt/spark
export PATH=$SPARK_HOME/bin:$PATH

    The SPARK_HOME environment variable could also be set using the .bashrc file or similar user or system profile scripts. You will need to do this if you wish to persist the SPARK_HOME variable beyond the current session.

  5. Open the PySpark shell by running the pyspark command from any directory. If Spark has been successfully installed, you should see the following output:

    Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 1.5.2
      /_/
Using Python version 2.7.6 (default, Mar 22 2014 22:59:56)
SparkContext available as sc, HiveContext available as sqlContext.
>>>

  6. You should see a similar result by running the spark-shell command from any directory.

  7. Run the included Pi Estimator example by executing the following command:

    spark-submit --class org.apache.spark.examples.SparkPi --master local $SPARK_HOME/lib/spark-examples*.jar 10

  8. If the installation was successful, you should see something similar to the following result (omitting the informational log messages). Note, this is an estimator program, so the actual result may vary:

    Pi is roughly 3.140576

Try it Yourself: Install Spark on Mac OS X

In this example, I install Spark on OS X Mavericks (10.9.5).

Mavericks includes installed versions of Python (2.7.5) and Java (1.8), so I don’t need to install them.

  1. As shown in Figure 3.1, download the spark-1.5.2-bin-hadoop2.6.tgz package from your local mirror into your home directory using curl.

  2. Extract the Spark package and create SPARK_HOME:

    tar -xzf spark-1.5.2-bin-hadoop2.6.tgz
sudo mv spark-1.5.2-bin-hadoop2.6 /opt/spark
export SPARK_HOME=/opt/spark
export PATH=$SPARK_HOME/bin:$PATH

  3. Open the PySpark shell by running the pyspark command in the Terminal from any directory. If Spark has been successfully installed, you should see the following output:

    Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 1.5.2
      /_/
Using Python version 2.7.5 (default, Feb 11 2014 07:46:25)
SparkContext available as sc, HiveContext available as sqlContext.
>>>

    The SPARK_HOME environment variable could also be set using the .profile file or similar user or system profile scripts.

  4. You should see a similar result by running the spark-shell command in the terminal from any directory.

  5. Run the included Pi Estimator example by executing the following command:

    spark-submit --class org.apache.spark.examples.SparkPi --master local $SPARK_HOME/lib/spark-examples*.jar 10

  6. If the installation was successful, you should see something similar to the following result (omitting the informational log messages). Note, this is an estimator program, so the actual result may vary:

    Pi is roughly 3.140576

Try it Yourself: Install Spark on Microsoft Windows

Installing Spark on Windows can be more involved than installing it on Linux or Mac OS X because many of the dependencies (such as Python and Java) need to be addressed first.

This example uses a Windows Server 2012, the server version of Windows 8.

  1. You will need a decompression utility capable of extracting .tar.gz and .gz archives because Windows does not have native support for these archives. 7-zip is a suitable program for this. You can obtain it from http://7-zip.org/download.html.

  2. As shown in Figure 3.1, download the spark-1.5.2-bin-hadoop2.6.tgz package from your local mirror and extract the contents of this archive to a new directory called C:\Spark.

  3. Install Java using the Oracle JDK Version 1.7, which you can obtain from the Oracle website. In this example, I download and install the jdk-7u79-windows-x64.exe package.

  4. Disable IPv6 for Java applications by running the following command as an administrator from the Windows command prompt:

    setx /M _JAVA_OPTIONS "-Djava.net.preferIPv4Stack=true"

  5. Python is not included with Windows, so you will need to download and install it. You can obtain a Windows installer for Python from https://www.python.org/getit/. I use Python 2.7.10 in this example. Install Python into C:\Python27.

  6. Download the Hadoop common binaries necessary to run Spark compiled for Windows x64 from hadoop-common-bin. Extract these files to a new directory called C:\Hadoop.

  7. Set an environment variable at the machine level for HADOOP_HOME by running the following command as an administrator from the Windows command prompt:

    setx /M HADOOP_HOME C:\Hadoop

  8. Update the system path by running the following command as an administrator from the Windows command prompt:

    setx /M path "%path%;C:\Python27;%PROGRAMFILES%\Java\jdk1.7.0_79\bin;C:\Hadoop"

  9. Make a temporary directory, C:\tmp\hive, to enable the HiveContext in Spark. Set permission to this file using the winutils.exe program included with the Hadoop common binaries by running the following commands as an administrator from the Windows command prompt:

    mkdir C:\tmp\hive
C:\Hadoop\bin\winutils.exe chmod 777 /tmp/hive

  10. Test the Spark interactive shell in Python by running the following command:

    C:\Spark\bin\pyspark

    You should see the output shown in Figure 3.2.

    FIGURE 3.2

    FIGURE 3.2 The PySpark shell in Windows.

  11. You should get a similar result by running the following command to open an interactive Scala shell:

    C:\Spark\bin\spark-shell

  12. Run the included Pi Estimator example by executing the following command:

    C:\Spark\bin\spark-submit --class org.apache.spark.examples.SparkPi --master
local C:\Spark\lib\spark-examples*.jar 10

  13. If the installation was successful, you should see something similar to the following result shown in Figure 3.3. Note, this is an estimator program, so the actual result may vary:

    FIGURE 3.3

    FIGURE 3.3 The results of the SparkPi example program in Windows.

Installing a Multi-node Spark Standalone Cluster

Using the steps outlined in this section for your preferred target platform, you will have installed a single node Spark Standalone cluster. I will discuss Spark’s cluster architecture in more detail in Hour 4, “Understanding the Spark Runtime Architecture.” However, to create a multi-node cluster from a single node system, you would need to do the following:

  • Ensure all cluster nodes can resolve hostnames of other cluster members and are routable to one another (typically, nodes are on the same private subnet).

  • Enable passwordless SSH (Secure Shell) for the Spark master to the Spark slaves (this step is only required to enable remote login for the slave daemon startup and shutdown actions).

  • Configure the spark-defaults.conf file on all nodes with the URL of the Spark master node.

  • Configure the spark-env.sh file on all nodes with the hostname or IP address of the Spark master node.

  • Run the start-master.sh script from the sbin directory on the Spark master node.

  • Run the start-slave.sh script from the sbin directory on all of the Spark slave nodes.

  • Check the Spark master UI. You should see each slave node in the Workers section.

  • Run a test Spark job.

Try it Yourself: Configuring and Testing a Multinode Spark Cluster

Take your single node Spark system and create a basic two-node Spark cluster with a master node and a worker node.

In this example, I use two Linux instances with Spark installed in the same relative paths: one with a hostname of sparkmaster, and the other with a hostname of sparkslave.

  1. Ensure that each node can resolve the other. The ping command can be used for this. For example, from sparkmaster:

    ping sparkslave

  2. Ensure the firewall rules of network ACLs will allow traffic on multiple ports between cluster instances because cluster nodes will communicate using various TCP ports (normally not a concern if all cluster nodes are on the same subnet).

  3. Create and configure the spark-defaults.conf file on all nodes. Run the following commands on the sparkmaster and sparkslave hosts:

    cd $SPARK_HOME/conf
sudo cp spark-defaults.conf.template spark-defaults.conf
sudo sed -i "\$aspark.master\tspark://sparkmaster:7077" spark-defaults.conf

  4. Create and configure the spark-env.sh file on all nodes. Complete the following tasks on the sparkmaster and sparkslave hosts:

    cd $SPARK_HOME/conf
sudo cp spark-env.sh.template spark-env.sh
sudo sed -i "\$aSPARK_MASTER_IP=sparkmaster" spark-env.sh

  5. On the sparkmaster host, run the following command:

    sudo $SPARK_HOME/sbin/start-master.sh

  6. On the sparkslave host, run the following command:

    sudo $SPARK_HOME/sbin/start-slave.sh spark://sparkmaster:7077

  7. Check the Spark master web user interface (UI) at http://sparkmaster:8080/.

  8. Check the Spark worker web UI at http://sparkslave:8081/.

  9. Run the built-in Pi Estimator example from the terminal of either node:

    spark-submit --class org.apache.spark.examples.SparkPi --master spark://sparkmaster:7077 --driver-memory 512m --executor-memory 512m --executor-cores 1 $SPARK_HOME/lib/spark-examples*.jar 10

  10. If the application completes successfully, you should see something like the following (omitting informational log messages). Note, this is an estimator program, so the actual result may vary:

    Pi is roughly 3.140576

    This is a simple example. If it was a production cluster, I would set up passwordless SSH to enable the start-all.sh and stop-all.sh shell scripts. I would also consider modifying additional configuration parameters for optimization.

Caution: Spark Master Is a Single Point of Failure in Standalone Mode

Without implementing High Availability (HA), the Spark Master node is a single point of failure (SPOF) for the Spark cluster. This means that if the Spark Master node goes down, the Spark cluster would stop functioning, all currently submitted or running applications would fail, and no new applications could be submitted.

High Availability can be configured using Apache Zookeeper, a highly reliable distributed coordination service. You can also configure HA using the filesystem instead of Zookeeper; however, this is not recommended for production systems.

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