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title: Building Spark
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Building Spark using Maven requires Maven 3.0.4 or newer and Java 6+.
# Setting up Maven's Memory Usage
You'll need to configure Maven to use more memory than usual by setting `MAVEN_OPTS`. We recommend the following settings:
{% highlight bash %}
export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"
{% endhighlight %}
If you don't run this, you may see errors like the following:
[INFO] Compiling 203 Scala sources and 9 Java sources to /Users/me/Development/spark/core/target/scala-{{site.SCALA_BINARY_VERSION}}/classes...
[ERROR] PermGen space -> [Help 1]
[INFO] Compiling 203 Scala sources and 9 Java sources to /Users/me/Development/spark/core/target/scala-{{site.SCALA_BINARY_VERSION}}/classes...
[ERROR] Java heap space -> [Help 1]
You can fix this by setting the `MAVEN_OPTS` variable as discussed before.
**Note:** *For Java 8 and above this step is not required.*
# Specifying the Hadoop Version
Because HDFS is not protocol-compatible across versions, if you want to read from HDFS, you'll need to build Spark against the specific HDFS version in your environment. You can do this through the "hadoop.version" property. If unset, Spark will build against Hadoop 1.0.4 by default. Note that certain build profiles are required for particular Hadoop versions:
<table class="table">
<thead>
<tr><th>Hadoop version</th><th>Profile required</th></tr>
</thead>
<tbody>
<tr><td>0.23.x</td><td>hadoop-0.23</td></tr>
<tr><td>1.x to 2.1.x</td><td>(none)</td></tr>
<tr><td>2.2.x</td><td>hadoop-2.2</td></tr>
<tr><td>2.3.x</td><td>hadoop-2.3</td></tr>
<tr><td>2.4.x</td><td>hadoop-2.4</td></tr>
</tbody>
</table>
For Apache Hadoop versions 1.x, Cloudera CDH "mr1" distributions, and other Hadoop versions without YARN, use:
{% highlight bash %}
# Apache Hadoop 1.2.1
mvn -Dhadoop.version=1.2.1 -DskipTests clean package
# Cloudera CDH 4.2.0 with MapReduce v1
mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -DskipTests clean package
# Apache Hadoop 0.23.x
mvn -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package
{% endhighlight %}
For Apache Hadoop 2.x, 0.23.x, Cloudera CDH, and other Hadoop versions with YARN, you can enable the "yarn" profile and optionally set the "yarn.version" property if it is different from "hadoop.version". As of Spark 1.3, Spark only supports YARN versions 2.2.0 and later.
Examples:
{% highlight bash %}
# Apache Hadoop 2.2.X
mvn -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -DskipTests clean package
# Apache Hadoop 2.3.X
mvn -Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0 -DskipTests clean package
# Apache Hadoop 2.4.X or 2.5.X
mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=VERSION -DskipTests clean package
Versions of Hadoop after 2.5.X may or may not work with the -Phadoop-2.4 profile (they were
released after this version of Spark).
# Different versions of HDFS and YARN.
mvn -Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0 -Dyarn.version=2.2.0 -DskipTests clean package
{% endhighlight %}
# Building With Hive and JDBC Support
To enable Hive integration for Spark SQL along with its JDBC server and CLI,
add the `-Phive` and `Phive-thriftserver` profiles to your existing build options.
By default Spark will build with Hive 0.13.1 bindings. You can also build for
Hive 0.12.0 using the `-Phive-0.12.0` profile.
{% highlight bash %}
# Apache Hadoop 2.4.X with Hive 13 support
mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -Phive -Phive-thriftserver -DskipTests clean package
# Apache Hadoop 2.4.X with Hive 12 support
mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -Phive -Phive-0.12.0 -Phive-thriftserver -DskipTests clean package
{% endhighlight %}
# Building for Scala 2.11
To produce a Spark package compiled with Scala 2.11, use the `-Dscala-2.11` property:
dev/change-version-to-2.11.sh
mvn -Pyarn -Phadoop-2.4 -Dscala-2.11 -DskipTests clean package
Scala 2.11 support in Spark is experimental and does not support a few features.
Specifically, Spark's external Kafka library and JDBC component are not yet
supported in Scala 2.11 builds.
# Spark Tests in Maven
Tests are run by default via the [ScalaTest Maven plugin](http://www.scalatest.org/user_guide/using_the_scalatest_maven_plugin).
Some of the tests require Spark to be packaged first, so always run `mvn package` with `-DskipTests` the first time. The following is an example of a correct (build, test) sequence:
mvn -Pyarn -Phadoop-2.3 -DskipTests -Phive -Phive-thriftserver clean package
mvn -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver test
The ScalaTest plugin also supports running only a specific test suite as follows:
mvn -Dhadoop.version=... -DwildcardSuites=org.apache.spark.repl.ReplSuite test
# Continuous Compilation
We use the scala-maven-plugin which supports incremental and continuous compilation. E.g.
mvn scala:cc
should run continuous compilation (i.e. wait for changes). However, this has not been tested extensively.
# Using With IntelliJ IDEA
This setup works fine in IntelliJ IDEA 11.1.4. After opening the project via the pom.xml file in the project root folder, you only need to activate either the hadoop1 or hadoop2 profile in the "Maven Properties" popout. We have not tried Eclipse/Scala IDE with this.
# Building Spark Debian Packages
The Maven build includes support for building a Debian package containing the assembly 'fat-jar', PySpark, and the necessary scripts and configuration files. This can be created by specifying the following:
mvn -Pdeb -DskipTests clean package
The debian package can then be found under assembly/target. We added the short commit hash to the file name so that we can distinguish individual packages built for SNAPSHOT versions.
# Running Java 8 Test Suites
Running only Java 8 tests and nothing else.
mvn install -DskipTests -Pjava8-tests
Java 8 tests are run when `-Pjava8-tests` profile is enabled, they will run in spite of `-DskipTests`.
For these tests to run your system must have a JDK 8 installation.
If you have JDK 8 installed but it is not the system default, you can set JAVA_HOME to point to JDK 8 before running the tests.
# Building for PySpark on YARN
PySpark on YARN is only supported if the jar is built with Maven. Further, there is a known problem
with building this assembly jar on Red Hat based operating systems (see [SPARK-1753](https://issues.apache.org/jira/browse/SPARK-1753)). If you wish to
run PySpark on a YARN cluster with Red Hat installed, we recommend that you build the jar elsewhere,
then ship it over to the cluster. We are investigating the exact cause for this.
# Packaging without Hadoop Dependencies for YARN
The assembly jar produced by `mvn package` will, by default, include all of Spark's dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this causes multiple versions of these to appear on executor classpaths: the version packaged in the Spark assembly and the version on each node, included with yarn.application.classpath. The `hadoop-provided` profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself.
# Building with SBT
Maven is the official recommendation for packaging Spark, and is the "build of reference".
But SBT is supported for day-to-day development since it can provide much faster iterative
compilation. More advanced developers may wish to use SBT.
The SBT build is derived from the Maven POM files, and so the same Maven profiles and variables
can be set to control the SBT build. For example:
sbt/sbt -Pyarn -Phadoop-2.3 assembly
# Testing with SBT
Some of the tests require Spark to be packaged first, so always run `sbt/sbt assembly` the first time. The following is an example of a correct (build, test) sequence:
sbt/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver assembly
sbt/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver test
To run only a specific test suite as follows:
sbt/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver "test-only org.apache.spark.repl.ReplSuite"
To run test suites of a specific sub project as follows:
sbt/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver core/test
# Speeding up Compilation with Zinc
[Zinc](https://github.com/typesafehub/zinc) is a long-running server version of SBT's incremental
compiler. When run locally as a background process, it speeds up builds of Scala-based projects
like Spark. Developers who regularly recompile Spark with Maven will be the most interested in
Zinc. The project site gives instructions for building and running `zinc`; OS X users can
install it using `brew install zinc`.
