Project: Apache Spark (App Definition and Development / Streaming & Messaging)
File: building-spark.md

layout: global title: Building Spark redirect_from: "building-with-maven.html" license: | Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. This will become a table of contents (this text will be scraped). {:toc} The Maven-based build is the build of reference for Apache Spark. Building Spark using Maven requires Maven 3.9.6 and Java 17/21. Spark requires Scala 2.13; support for Scala 2.12 was removed in Spark 4.0.0. You'll need to configure Maven to use more memory than usual by setting `MAVEN_OPTS`: ```sh 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-{{site.SCALABINARYVERSION}}/classes... [ERROR] Java heap space -> [Help 1] You can fix these problems by setting the `MAVEN_OPTS` variable as discussed before. Note: If using `build/mvn` with no `MAVENOPTS` set, the script will automatically add the above options to the `MAVENOPTS` environment variable. The `test` phase of the Spark build will automatically add these options to `MAVEN_OPTS`, even when not using `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 (, ) 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. To create a Spark distribution like those distributed by the 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` 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 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.

-Pyarn -Phive -Phive-thriftserver -DskipTests clean package 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. ./build/mvn -Pkubernetes -DskipTests clean package 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{{site.SCALABINARY_VERSION}} clean install where `spark-streaming{{site.SCALABINARY_VERSION}}` is the `artifactId` as defined in `streaming/pom.xml` file. ./build/mvn -Pconnect -DskipTests clean package ./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{{site.SCALABINARY_VERSION}}`. 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: it only scans the paths `src/main` and `src/test` (see ), so it will only work from within certain submodules that have that structure. you'll typically need to run `mvn install` from the project root for compilation within specific submodules to work; this is because submodules that depend on other submodules do so via the `spark-parent` module). 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 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. 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 . 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 . 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`: ```xml <arg>-Xmax-classfile-name</arg> <arg>128</arg> ``` and in `project/SparkBuild.scala` add: ```scala scalacOptions in Compile ++= Seq("-Xmax-classfile-name", "128"), ``` to the `sharedSettings` val. See also if you are unsure of where to add these lines. For help in setting up IntelliJ IDEA or Eclipse for Spark development, and troubleshooting, refer to the . Tests are run by default via the . Note that tests should not be run as root or an admin

The following is an example of a command to run the tests: ./build/mvn test The following is an example of a command to run the tests: ./build/sbt test For information about how to run individual tests, refer to the . 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. 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 To run the SparkR tests you will need to install the , , , and 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 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 . 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{{site.SCALABINARY_VERSION}} or ./build/sbt docker-integration-tests/test <! When other versions of Scala like 2.13 are supported, it will be possible to build for that version. Change the major Scala version using (e.g. 2.13): ./dev/change-scala-version.sh 2.13 Enable the profile (e.g. 2.13): ./build/mvn -Pscala-2.13 compile ./build/sbt -Pscala-2.13 compile --> While the Spark project does not maintain its own Jenkins infrastructure, do. To run tests with Jenkins: ./dev/run-tests-jenkins If you use an individual repository or a repository on GitHub Enterprise, export the environment variables below before running the above command. <table> <thead><tr><th>Variable Name</th><th>Default</th><th>Meaning</th></tr></thead> <tr> <td><code>SPARKPROJECTURL</code></td> <td>https://github.com/apache/spark</td> <td> The Spark project URL of GitHub Enterprise. </td> </tr> <tr> <td><code>GITHUBAPIBASE</code></td> <td>https://api.github.com/repos/apache/spark</td> <td> The Spark project API server URL of GitHub Enterprise. </td> </tr> </table> 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. ```sh export SPARKLOCALHOSTNAME="your-IPv6-address" # e.g. '[2600:1700:232e:3de0:...]' export DEFAULTARTIFACTREPOSITORY=https://ipv6.repo1.maven.org/maven2/ export MAVEN_OPTS="-Djava.net.preferIPv6Addresses=true" export SBT_OPTS="-Djava.net.preferIPv6Addresses=true" export SERIALSBTTESTS=1 ``` 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: ```bash export SPARKPROTOCEXEC_PATH=/path-to-protoc-exe ./build/mvn -Puser-defined-protoc -DskipDefaultProtoc clean package ``` or ```bash export SPARKPROTOCEXEC_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 .