- Building Apache Spark
- Apache Maven
- Building a Runnable Distribution
- Specifying the Hadoop Version and Enabling YARN
- Building With Hive and JDBC Support
- Packaging without Hadoop Dependencies for YARN
- Building with Mesos support
- Building with Kafka 0.8 support
- Building with Flume support
- Building submodules individually
- Continuous Compilation
- Building with SBT
- Speeding up Compilation
- Encrypted Filesystems
- IntelliJ IDEA or Eclipse
- Running Tests
Building Apache Spark
The Maven-based build is the build of reference for Apache Spark. Building Spark using Maven requires Maven 3.3.9 or newer and Java 8+. Note that support for Java 7 was removed as of Spark 2.2.0.
Setting up Maven’s Memory Usage
You’ll need to configure Maven to use more memory than usual by setting
export MAVEN_OPTS="-Xmx2g -XX:ReservedCodeCacheSize=512m"
ReservedCodeCacheSize setting is optional but recommended.)
If you don’t add these parameters to
MAVEN_OPTS, you may see errors and warnings like the following:
[INFO] Compiling 203 Scala sources and 9 Java sources to /Users/me/Development/spark/core/target/scala-2.11/classes... [ERROR] Java heap space -> [Help 1]
You can fix these problems by setting the
MAVEN_OPTS variable as discussed before.
- If using
MAVEN_OPTSset, the script will automatically add the above options to the
testphase of the Spark build will automatically add these options to
MAVEN_OPTS, even when not using
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 -DskipTests clean package
Other build examples can be found below.
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
./dev/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:
./dev/make-distribution.sh --name custom-spark --pip --r --tgz -Psparkr -Phadoop-2.7 -Phive -Phive-thriftserver -Pmesos -Pyarn
This will build Spark distribution along with Python pip and R packages. For more information on usage, run
Specifying the Hadoop Version and Enabling YARN
You can specify the exact version of Hadoop to compile against through the
If unset, Spark will build against Hadoop 2.6.X by default.
You can enable the
yarn profile and optionally set the
yarn.version property if it is different
# Apache Hadoop 2.6.X ./build/mvn -Pyarn -DskipTests clean package # Apache Hadoop 2.7.X and later ./build/mvn -Pyarn -Phadoop-2.7 -Dhadoop.version=2.7.3 -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 1.2.1 bindings.
# With Hive 1.2.1 support ./build/mvn -Pyarn -Phive -Phive-thriftserver -DskipTests clean package
Packaging without Hadoop Dependencies for YARN
The assembly directory 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
hadoop-provided profile builds the assembly without including Hadoop-ecosystem projects,
like ZooKeeper and Hadoop itself.
Building with Mesos support
./build/mvn -Pmesos -DskipTests clean package
Building with Kafka 0.8 support
Kafka 0.8 support must be explicitly enabled with the
Note: Kafka 0.8 support is deprecated as of Spark 2.3.0.
./build/mvn -Pkafka-0-8 -DskipTests clean package
Kafka 0.10 support is still automatically built.
Building with Flume support
Apache Flume support must be explicitly enabled with the
Note: Flume support is deprecated as of Spark 2.3.0.
./build/mvn -Pflume -DskipTests clean package
Building submodules individually
It’s possible to build Spark sub-modules using the
mvn -pl option.
For instance, you can build the Spark Streaming module using:
./build/mvn -pl :spark-streaming_2.11 clean install
spark-streaming_2.11 is the
artifactId as defined in
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:
$ ./build/mvn install $ cd core $ ../build/mvn scala:cc
Building with SBT
Maven is the official build tool recommended 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:
To avoid the overhead of launching sbt each time you need to re-compile, you can launch sbt
in interactive mode by running
build/sbt, and then run all build commands at the command
Speeding up Compilation
Developers who compile Spark frequently may want to speed up compilation; e.g., by using Zinc (for developers who build with Maven) or by avoiding re-compilation of the assembly JAR (for developers who build with SBT). For more information about how to do this, refer to the Useful Developer Tools page.
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.
IntelliJ IDEA or Eclipse
For help in setting up IntelliJ IDEA or Eclipse for Spark development, and troubleshooting, refer to the Useful Developer Tools page.
Tests are run by default via the ScalaTest Maven plugin. Note that tests should not be run as root or an admin user.
The following is an example of a command to run the tests:
Testing with SBT
The following is an example of a command to run the tests:
Running Individual Tests
For information about how to run individual tests, refer to the Useful Developer Tools page.
PySpark pip installable
If you are building Spark for use in a Python environment and you wish to pip install it, you will first need to build the Spark JARs as described above. Then you can construct an sdist package suitable for setup.py and pip installable package.
cd python; python setup.py sdist
Note: Due to packaging requirements you can not directly pip install from the Python directory, rather you must first build the sdist package as described above.
Alternatively, you can also run make-distribution with the –pip option.
PySpark Tests with Maven
If you are building PySpark and wish to run the PySpark tests you will need to build Spark with Hive support.
./build/mvn -DskipTests clean package -Phive ./python/run-tests
The run-tests script also can be limited to a specific Python version or a specific module
./python/run-tests --python-executables=python --modules=pyspark-sql
Note: You can also run Python tests with an sbt build, provided you build Spark with Hive support.
Running R Tests
R -e "install.packages(c('knitr', 'rmarkdown', 'testthat', 'e1071', 'survival'), repos='http://cran.us.r-project.org')"
You can run just the SparkR tests using the command:
Running Docker-based Integration Test Suites
In order to run Docker integration tests, you have to install the
docker engine on your box.
The instructions for installation can be found at the Docker site.
Once installed, the
docker service needs to be started, if not already running.
On Linux, this can be done by
sudo service docker start.
./build/mvn install -DskipTests ./build/mvn test -Pdocker-integration-tests -pl :spark-docker-integration-tests_2.11