- Building with
- Building a Runnable Distribution
- Setting up Maven’s Memory Usage
- Specifying the Hadoop Version
- Building With Hive and JDBC Support
- Building for Scala 2.11
- Spark Tests in Maven
- Continuous Compilation
- Building Spark with IntelliJ IDEA or Eclipse
- Running Java 8 Test Suites
- Building for PySpark on YARN
- Packaging without Hadoop Dependencies for YARN
- Building with SBT
- Testing with SBT
- Speeding up Compilation with Zinc
Building Spark using Maven requires Maven 3.0.4 or newer and Java 6+.
Note: Building Spark with Java 7 or later can create JAR files that may not be readable with early versions of Java 6, due to the large number of files in the JAR archive. Build with Java 6 if this is an issue for your deployment.
Spark now comes packaged with a self-contained Maven installation to ease building and deployment of Spark from source located under the
build/ directory. This script will automatically download and setup all necessary build requirements (Maven, Scala, and Zinc) locally within the
build/ directory itself. It honors any
mvn binary if present already, however, will pull down its own copy of Scala and Zinc regardless to ensure proper version requirements are met.
build/mvn execution acts as a pass through to the
mvn call allowing easy transition from previous build methods. As an example, one can build a version of Spark as follows:
build/mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package
Other build examples can be found below.
Note: When building on an encrypted filesystem (if your home directory is encrypted, for example), then the Spark build might fail with a “Filename too long” error. As a workaround, add the following in the configuration args of the
scala-maven-plugin in the project
scalacOptions in Compile ++= Seq("-Xmax-classfile-name", "128"),
sharedSettings val. See also this PR if you are unsure of where to add these lines.
Building a Runnable Distribution
To create a Spark distribution like those distributed by the
Spark Downloads page, and that is laid out so as
to be runnable, use
make-distribution.sh in the project root directory. It can be configured
with Maven profile settings and so on like the direct Maven build. Example:
./make-distribution.sh --name custom-spark --tgz -Phadoop-2.4 -Pyarn
For more information on usage, run
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:
export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"
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-2.10/classes... [ERROR] PermGen space -> [Help 1] [INFO] Compiling 203 Scala sources and 9 Java sources to /Users/me/Development/spark/core/target/scala-2.10/classes... [ERROR] Java heap space -> [Help 1]
You can fix this by setting the
MAVEN_OPTS variable as discussed before.
* For Java 8 and above this step is not required.
* If using
MAVEN_OPTS were not already set, the script will automate this for you.
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 2.2.0 by default. Note that certain build profiles are required for particular Hadoop versions:
|Hadoop version||Profile required|
|1.x to 2.1.x||hadoop-1|
|2.6.x and later 2.x||hadoop-2.6|
For Apache Hadoop versions 1.x, Cloudera CDH “mr1” distributions, and other Hadoop versions without YARN, use:
# Apache Hadoop 1.2.1 mvn -Dhadoop.version=1.2.1 -Phadoop-1 -DskipTests clean package # Cloudera CDH 4.2.0 with MapReduce v1 mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -Phadoop-1 -DskipTests clean package
You can enable the “yarn” profile and optionally set the “yarn.version” property if it is different from “hadoop.version”. Spark only supports YARN versions 2.2.0 and later.
# Apache Hadoop 2.2.X mvn -Pyarn -Phadoop-2.2 -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
Building With Hive and JDBC Support
To enable Hive integration for Spark SQL along with its JDBC server and CLI,
Phive-thriftserver profiles to your existing build options.
By default Spark will build with Hive 0.13.1 bindings.
# 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
Building for Scala 2.11
To produce a Spark package compiled with Scala 2.11, use the
dev/change-version-to-2.11.sh mvn -Pyarn -Phadoop-2.4 -Dscala-2.11 -DskipTests clean package
Spark does not yet support its JDBC component for Scala 2.11.
Spark Tests in Maven
Tests are run by default via 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
We use the scala-maven-plugin which supports incremental and continuous compilation. E.g.
should run continuous compilation (i.e. wait for changes). However, this has not been tested extensively. A couple of gotchas to note:
it only scans the paths
src/test(see docs), so it will only work from within certain submodules that have that structure.
you’ll typically need to run
mvn installfrom the project root for compilation within specific submodules to work; this is because submodules that depend on other submodules do so via the
Thus, the full flow for running continuous-compilation of the
core submodule may look more like:
$ mvn install
$ cd core
$ mvn scala:cc
Building Spark with IntelliJ IDEA or Eclipse
For help in setting up IntelliJ IDEA or Eclipse for Spark development, and troubleshooting, refer to the wiki page for IDE setup.
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
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). 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:
build/sbt -Pyarn -Phadoop-2.3 assembly
Testing with SBT
Some of the tests require Spark to be packaged first, so always run
build/sbt assembly the first time. The following is an example of a correct (build, test) sequence:
build/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver assembly build/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver test
To run only a specific test suite as follows:
build/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:
build/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver core/test
Speeding up Compilation with Zinc
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.
If using the
zinc will automatically be downloaded and leveraged for all
builds. This process will auto-start after the first time
build/mvn is called and bind to port
3030 unless the
ZINC_PORT environment variable is set. The
zinc process can subsequently be
shut down at any time by running
build/zinc-<version>/bin/zinc -shutdown and will automatically
build/mvn is called.