Building Spark

Building Apache Spark

Apache Maven

The Maven-based build is the build of reference for Apache Spark. Building Spark using Maven requires Maven 3.9.9 and Java 17/21. Spark requires Scala 2.13; support for Scala 2.12 was removed in Spark 4.0.0.

Setting up Maven’s Memory Usage

You’ll need to configure Maven to use more memory than usual by setting MAVEN_OPTS:

export MAVEN_OPTS="-Xss64m -Xmx2g -XX:ReservedCodeCacheSize=1g"

(The 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-2.13/classes...
[ERROR] Java heap space -> [Help 1]

You can fix these problems by setting the MAVEN_OPTS variable as discussed before.

Note:

build/mvn

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) locally within the build/ directory itself. It honors any mvn binary if present already, however, will pull down its own copy of Scala 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 -Phive -Phive-thriftserver -Pyarn -Pkubernetes

This will build Spark distribution along with Python pip and R packages. For more information on usage, run ./dev/make-distribution.sh --help

Specifying the Hadoop Version and Enabling YARN

You can specify the exact version of Hadoop to compile against through the hadoop.version property.

You can enable the yarn profile and optionally set the yarn.version property if it is different from hadoop.version.

Example:

./build/mvn -Pyarn -Dhadoop.version=3.4.0 -DskipTests clean package

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 2.3.10.

# With Hive 2.3.10 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 yarn.application.classpath. The hadoop-provided profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself.

Building with Kubernetes support

./build/mvn -Pkubernetes -DskipTests clean package

Building submodules individually

It’s possible to build Spark submodules using the mvn -pl option.

For instance, you can build the Spark Streaming module using:

./build/mvn -pl :spark-streaming_2.13 clean install

where spark-streaming_2.13 is the artifactId as defined in streaming/pom.xml file.

Building with JVM Profile support

./build/mvn -Pjvm-profiler -DskipTests clean package

Note: The jvm-profiler profile builds the assembly without including the dependency ap-loader, you can download it manually from maven central repo and use it together with spark-profiler_2.13.

Continuous Compilation

We use the scala-maven-plugin which supports incremental and continuous compilation. E.g.

./build/mvn scala:cc

should run continuous compilation (i.e. wait for changes). However, this has not been tested extensively. A couple of gotchas to note:

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:

./build/sbt package

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 prompt.

Setting up SBT’s Memory Usage

Configure the JVM options for SBT in .jvmopts at the project root, for example:

-Xmx2g
-XX:ReservedCodeCacheSize=1g

For the meanings of these two options, please carefully read the Setting up Maven’s Memory Usage section.

Speeding up Compilation

Developers who compile Spark frequently may want to speed up compilation; e.g., 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.

Encrypted Filesystems

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 pom.xml:

<arg>-Xmax-classfile-name</arg>
<arg>128</arg>

and in project/SparkBuild.scala add:

scalacOptions in Compile ++= Seq("-Xmax-classfile-name", "128"),

to the 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.

Running Tests

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:

./build/mvn test

Testing with SBT

The following is an example of a command to run the tests:

./build/sbt test

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 packaging/classic/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.sh with the --pip option.

PySpark Tests with Maven or SBT

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

If you are building PySpark with SBT and wish to run the PySpark tests, you will need to build Spark with Hive support and also build the test components:

./build/sbt -Phive clean package
./build/sbt test:compile
./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

Running R Tests (deprecated)

To run the SparkR tests you will need to install the knitr, rmarkdown, testthat, e1071 and survival packages first:

Rscript -e "install.packages(c('knitr', 'rmarkdown', 'devtools', 'testthat', 'e1071', 'survival'), repos='https://cloud.r-project.org/')"

You can run just the SparkR tests using the command:

./R/run-tests.sh

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_2.13

or

./build/sbt docker-integration-tests/test

Building and testing on an IPv6-only environment

Use Apache Spark GitBox URL because GitHub doesn’t support IPv6 yet.

https://gitbox.apache.org/repos/asf/spark.git

To build and run tests on IPv6-only environment, the following configurations are required.

export SPARK_LOCAL_HOSTNAME="your-IPv6-address" # e.g. '[2600:1700:232e:3de0:...]'
export DEFAULT_ARTIFACT_REPOSITORY=https://ipv6.repo1.maven.org/maven2/
export MAVEN_OPTS="-Djava.net.preferIPv6Addresses=true"
export SBT_OPTS="-Djava.net.preferIPv6Addresses=true"
export SERIAL_SBT_TESTS=1

Building with a user-defined protoc

When the user cannot use the official protoc binary files to build the core module in the compilation environment, for example, compiling core module on CentOS 6 or CentOS 7 which the default glibc version is less than 2.14, we can try to compile and test by specifying the user-defined protoc binary files as follows:

export SPARK_PROTOC_EXEC_PATH=/path-to-protoc-exe
./build/mvn -Puser-defined-protoc -DskipDefaultProtoc clean package

or

export SPARK_PROTOC_EXEC_PATH=/path-to-protoc-exe
./build/sbt -Puser-defined-protoc clean package

The user-defined protoc binary files can be produced in the user’s compilation environment by source code compilation, for compilation steps, please refer to protobuf.