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Contributing to RAPIDS Accelerator for Apache Spark

Contributions to RAPIDS Accelerator for Apache Spark fall into the following three categories.

  1. To report a bug, request a new feature, or report a problem with documentation, please file an issue describing in detail the problem or new feature. The project team evaluates and triages issues, and schedules them for a release. If you believe the issue needs priority attention, please comment on the issue to notify the team.
  2. To propose and implement a new Feature, please file a new feature request issue. Describe the intended feature and discuss the design and implementation with the team and community. Once the team agrees that the plan looks good, go ahead and implement it using the code contributions guide below.
  3. To implement a feature or bug-fix for an existing outstanding issue, please follow the code contributions guide below. If you need more context on a particular issue, please ask in a comment.

Branching Convention

There are two types of branches in this repository:

  • branch-[version]: are development branches which can change often. Note that we merge into the branch with the greatest version number, as that is our default branch.

  • main: is the branch with the latest released code, and the version tag (i.e. v0.1.0) is held here. main will change with new releases, but otherwise it should not change with every pull request merged, making it a more stable branch.

Git Submodules

This repository uses git submodules. The submodules may need to be updated after the repository is cloned or after moving to a new commit via git submodule update --init. See the Git documentation on submodules for more information.

Building From Source

We use Maven for most aspects of the build. We test the build with latest patch versions for Maven 3.6.x, 3.8.x and 3.9.x. Maven version 3.6.0 or more recent is enforced.

Some important parts of the build execute in the verify phase of the Maven build lifecycle. We recommend when building at least running to the verify phase, e.g.:

mvn verify

After a successful build, the RAPIDS Accelerator jar will be in the dist/target/ directory. This will build the plugin for a single version of Spark. By default, this is Apache Spark 3.1.1. To build against other versions of Spark you use the -Dbuildver=XXX command line option to Maven. For instance to build Spark 3.1.1 you would use:

mvn -Dbuildver=311 verify

You can find all available build versions in the top level pom.xml file. If you are building for Databricks then you should use the jenkins/databricks/build.sh script and modify it for the version you want.

To get an uber jar with more than 1 version you have to mvn package each version and then use one of the defined profiles in the dist module, or a comma-separated list of build versions. See the next section for more details.

You might see a warning during scala-maven-plugin compile goal invocation.

[INFO] Compiling 94 Scala sources and 1 Java source to /home/user/gits/NVIDIA/spark-rapids/tests/target/spark3XY/test-classes ...
OpenJDK 64-Bit Server VM warning: CodeCache is full. Compiler has been disabled.
OpenJDK 64-Bit Server VM warning: Try increasing the code cache size using -XX:ReservedCodeCacheSize=
CodeCache: size=245760Kb used=236139Kb max_used=243799Kb free=9620Kb
 bounds [0x00007f9681000000, 0x00007f9690000000, 0x00007f9690000000]
 total_blobs=60202 nmethods=59597 adapters=504
 compilation: disabled (not enough contiguous free space left)

It can be mitigated by increasing ReservedCodeCacheSize passed in the MAVEN_OPTS environment variable.

Building a Distribution for Multiple Versions of Spark

By default, the distribution jar only includes code for a single version of Spark, albeit the jar file layout will be such that it can be accessed only using the Shim loading logic for multiple Spark versions. See below for dist jar creation without the need for a special shim class loader.

If you want to create a jar with multiple versions we have the following options.

  1. Build for all Apache Spark versions and CDH with no SNAPSHOT versions of Spark, only released. Use -PnoSnapshots.
  2. Build for all Apache Spark versions and CDH including SNAPSHOT versions of Spark we have supported for. Use -Psnapshots.
  3. Build for all Apache Spark versions, CDH and Databricks with no SNAPSHOT versions of Spark, only released. Use -PnoSnaphsotsWithDatabricks.
  4. Build for all Apache Spark versions, CDH and Databricks including SNAPSHOT versions of Spark we have supported for. Use -PsnapshotsWithDatabricks
  5. Build for an arbitrary combination of comma-separated build versions using -Dincluded_buildvers=<CSV list of build versions>. E.g., -Dincluded_buildvers=312,330

You must first build each of the versions of Spark and then build one final time using the profile for the option you want.

You can also install some manually and build a combined jar. For instance to build non-snapshot versions:

mvn clean
mvn -Dbuildver=311 install -Drat.skip=true -DskipTests
mvn -Dbuildver=312 install -Drat.skip=true -DskipTests
mvn -Dbuildver=313 install -Drat.skip=true -DskipTests
mvn -Dbuildver=320 install -Drat.skip=true -DskipTests
mvn -Dbuildver=321 install -Drat.skip=true -DskipTests
mvn -Dbuildver=321cdh install -Drat.skip=true -DskipTests
mvn -pl dist -PnoSnapshots package -DskipTests

Verify that shim-specific classes are hidden from a conventional classloader.

$ javap -cp dist/target/rapids-4-spark_2.12-23.10.0-SNAPSHOT-cuda11.jar com.nvidia.spark.rapids.shims.SparkShimImpl
Error: class not found: com.nvidia.spark.rapids.shims.SparkShimImpl

However, its bytecode can be loaded if prefixed with spark3XY not contained in the package name

$ javap -cp dist/target/rapids-4-spark_2.12-23.10.0-SNAPSHOT-cuda11.jar spark320.com.nvidia.spark.rapids.shims.SparkShimImpl | head -2
Warning: File dist/target/rapids-4-spark_2.12-23.10.0-SNAPSHOT-cuda11.jar(/spark320/com/nvidia/spark/rapids/shims/SparkShimImpl.class) does not contain class spark320.com.nvidia.spark.rapids.shims.SparkShimImpl
Compiled from "SparkShims.scala"
public final class com.nvidia.spark.rapids.shims.SparkShimImpl {

Building with buildall script

There is a build script build/buildall that automates the local build process. Use ./build/buildall --help for up-to-date use information.

By default, it builds everything that is needed to create a distribution jar for all released (noSnapshots) Spark versions except for Databricks. Other profiles that you can pass using --profile=<distribution profile> include

  • snapshots that includes all released (noSnapshots) and snapshots Spark versions except for Databricks
  • minimumFeatureVersionMix that currently includes 321cdh, 312, 320, 330 is recommended for catching incompatibilities already in the local development cycle

For initial quick iterations we can use --profile=<buildver> to build a single-shim version. e.g., --profile=311 for Spark 3.1.1.

The option --module=<module> allows to limit the number of build steps. When iterating, we often don't have the need for the entire build. We may be interested in building everything necessary just to run integration tests (--module=integration_tests), or we may want to just rebuild the distribution jar (--module=dist)

By default, buildall builds up to 4 shims in parallel using xargs -P <n>. This can be adjusted by specifying the environment variable BUILD_PARALLEL=<n>.

Building against different CUDA Toolkit versions

You can build against different versions of the CUDA Toolkit by modifying the variable cuda.version:

  • -Dcuda.version=cuda11 (CUDA 11.x, default)
  • -Dcuda.version=cuda12 (CUDA 12.x)

Building a Distribution for a Single Spark Release

In many situations the user knows that the Plugin jar will be deployed for a single specific Spark release. It is most commonly the case when a container image for a cloud or local deployment includes Spark binaries as well. In such a case it is advantageous to create a jar with a conventional class directory structure avoiding complications such as #3704. To this end add -DallowConventionalDistJar=true when invoking Maven.

mvn package -pl dist -am -Dbuildver=340 -DallowConventionalDistJar=true

Verify com.nvidia.spark.rapids.shims.SparkShimImpl is conventionally loadable:

$ javap -cp dist/target/rapids-4-spark_2.12-23.10.0-SNAPSHOT-cuda11.jar com.nvidia.spark.rapids.shims.SparkShimImpl | head -2
Compiled from "SparkShims.scala"
public final class com.nvidia.spark.rapids.shims.SparkShimImpl {

Building and Testing with JDK9+

We support JDK8 as our main JDK version, and test JDK8, JDK11 and JDK17. It is possible to build and run with more modern JDK versions, however these are untested. The first step is to set JAVA_HOME in the environment to your JDK root directory. NOTE: for JDK17, we only support build against spark 3.3.0+

Also make sure to use scala-maven-plugin version scala.plugin.version 4.6.0 or later to correctly process maven.compiler.release flag if cross-compilation is required.

mvn clean verify -Dbuildver=330 -P<jdk11|jdk17>

Building and Testing with ARM

To build our project on ARM platform, please add -Parm64 to your Maven commands. NOTE: Build process does not require an ARM machine, so if you want to build the artifacts only on X86 machine, please also add -DskipTests in commands.

mvn clean verify -Dbuildver=311 -Parm64

Iterative development during local testing

When iterating on changes impacting the dist module artifact directly or via dependencies you might find the jar creation step unacceptably slow. Due to the current size of the artifact rapids-4-spark_2.12 Maven Jar Plugin spends the bulk of the time compressing the artifact content. Since the JAR file specification focuses on the file entry layout in a ZIP archive without requiring file entries to be compressed it is possible to skip compression, and increase the speed of creating rapids-4-spark_2.12 jar ~3x for a single Spark version Shim alone.

To this end in a pre-production build you can set the Boolean property dist.jar.compress to false, its default value is true.

The time saved is more significant if you are merely changing the aggregator module, or the dist module, or just incorporating changes from spark-rapids-jni

For example, to quickly repackage rapids-4-spark after the initial ./build/buildall you can iterate by invoking

mvn package -pl dist -PnoSnapshots -Ddist.jar.compress=false

or similarly

 ./build/buildall --rebuild-dist-only --option="-Ddist.jar.compress=false"

Code contributions

Source code layout

Conventional code locations in Maven modules are found under src/main/<language>. In addition to that and in order to support multiple versions of Apache Spark with the minimum amount of source code we maintain Spark-version-specific locations within non-shim modules if necessary. This allows us to switch between incompatible parent classes inside without copying the shared code to dedicated shim modules.

Thus, the conventional source code root directories src/main/<language> contain the files that are source-compatible with all supported Spark releases, both upstream and vendor-specific.

The following acronyms may appear in directory names:

Acronym Definition Example Example Explanation
db Databricks 332db Databricks Spark based on Spark 3.3.2
cdh Cloudera CDH 321cdh Cloudera CDH Spark based on Apache Spark 3.2.1

The version-specific directory names have one of the following forms / use cases:

  • src/main/spark${buildver}, example: src/main/spark332db
  • src/test/spark${buildver}, example: src/test/spark340

with a special shim descriptor as a Scala/Java comment. See shimplify.md

Setting up an Integrated Development Environment

Our project currently uses build-helper-maven-plugin for shimming against conflicting definitions of superclasses in upstream versions that cannot be resolved without significant code duplication otherwise. To this end different source directories with differently implemented same-named classes are added for compilation depending on the targeted Spark version.

This may require some modifications to IDEs' standard Maven import functionality.

IntelliJ IDEA

Last tested with IntelliJ IDEA 2023.1.2 (Community Edition)

Manual Maven Install for a target Spark build

Before proceeding with importing spark-rapids into IDEA or switching to a different Spark release profile, execute the install phase with the corresponding buildver, e.g. for Spark 3.4.0:

 mvn clean install -Dbuildver=340 -Dskip -DskipTests
Importing the project

Our build relies on symlink generation for Spark release-specific sources. It is recommended to install the IDEA Resolve Symlinks plugin via Marketplace tab.

To start working with the project in IDEA is as easy as opening the top level (parent) pom.xml.

In IDEA 2022.3.1 unselect "Import using the new IntelliJ Workspace Model API (experimental)". After 2023.1.2 the default "Enable fast import" can be used.

In order to make sure that IDEA handles profile-specific source code roots within a single Maven module correctly, unselect "Keep source and test folders on reimport".

If you develop a feature that has to interact with the Shim layer or simply need to test the Plugin with a different Spark version, open Maven tool window and select one of the release3xx profiles (e.g, release320) for Apache Spark 3.2.0. Make sure Manual Maven Install for that profile has been executed.

Go to File | Settings | Build, Execution, Deployment | Build Tools | Maven | Importing and make sure that Generated sources folders is set to Detect automatically and Phase to be used for folders update is changed to process-test-resources.

In the Maven tool window hit

  1. Reload all projects
  2. Generate Sources and Update Folders For all Projects.

Known Issues:

  • With the old "slow" Maven importer it might be necessary to bump up maximum Java Heap size -Xmx via File | Settings | Build, Execution, Deployment | Build Tools | Maven | Importing | VM options for importer

  • When IDEA is upgraded, it might be necessary to remove .idea from the local git repository root.

  • There is a known issue that the test sources added via the build-helper-maven-plugin are not handled properly. The workaround is to mark the affected folders such as tests/src/test/spark3* manually as Test Sources Root

  • There is a known issue where, even after selecting a different Maven profile in the Maven submenu, the source folders from a previously selected profile may remain active. As a workaround, when switching to a different profile, go to File | Project Structure ... | Modules, select the rapids-4-spark-sql_2.12 module, click Sources, and delete all the shim source roots from the Source Folders list. Make sure the right test source folders are in rapids-4-spark-sql_2.12 and rapids-4-spark-tests_2.12. Re-execute the steps above: reload, and Generate Sources ....

If you see Scala symbols unresolved (highlighted red) in IDEA please try the following steps to resolve it:

  • Make sure there are no relevant poms in File | Settings | Build, Execution, Deployment | Build Tools | Maven | Ignored Files

  • Restart IDEA and click Reload All Maven Projects again

Bloop Build Server

Bloop is a build server and a set of tools around Build Server Protocol (BSP) for Scala providing an integration path with IDEs that support it. In fact, you can generate a Bloop project from Maven just for the Maven modules and profiles you are interested in. For example, to generate the Bloop projects for the Spark 3.2.0 dependency just for the production code run:

mvn install ch.epfl.scala:bloop-maven-plugin:bloopInstall -pl aggregator -am \
  -DdownloadSources=true \
  -Dbuildver=320 \
  -DskipTests \
  -Dskip \
  -Dmaven.scalastyle.skip=true \
  -Dmaven.updateconfig.skip=true

With --generate-bloop we integrated Bloop project generation into buildall. It makes it easier to generate projects for multiple Spark dependencies using the same profiles as our regular build. It makes sure that the project files belonging to different Spark dependencies are not clobbered by repeated bloopInstall Maven plugin invocations, and it uses jq to post-process JSON-formatted project files such that they compile project classes into non-overlapping set of output directories.

To activate the Spark dependency version 3XY you currently are working with update the symlink .bloop to point to the corresponding directory .bloop-spark3XY

Example usage:

./build/buildall --generate-bloop --profile=311,330
rm -vf .bloop
ln -s .bloop-spark330 .bloop

You can now open the spark-rapids as a BSP project in IDEA

Read on for VS Code Scala Metals instructions.

Bloop, Scala Metals, and Visual Studio Code

Last tested with 1.63.0-insider (Universal) Commit: bedf867b5b02c1c800fbaf4d6ce09cefba

Another, and arguably more popular, use of Bloop arises in connection with Scala Metals and VS @Code. Scala Metals implements the Language Server Protocol (LSP) for Scala, and enables features such as context-aware autocomplete, and code browsing between Scala symbol definitions, references and vice versa. LSP is supported by many editors including Vim and Emacs.

Here we document the integration with VS code. It makes development on a remote node almost as easy as local development, which comes very handy when working in Cloud environments.

Run ./build/buildall --generate-bloop --profile=<profile> to generate Bloop projects for required Spark dependencies, e.g. --profile=320 for Spark 3.2.0. When developing remotely this is done on the remote node.

Install Scala Metals extension in VS Code, either locally or into a Remote-SSH extension destination depending on your target environment. When your project folder is open in VS Code, it may prompt you to import Maven project. IMPORTANT: always decline with "Don't ask again", otherwise it will overwrite the Bloop projects generated with the default 311 profile. If you need to use a different profile, always rerun the command above manually. When regenerating projects it's recommended to proceed to Metals "Build commands" View, and click:

  1. "Restart build server"
  2. "Clean compile workspace" to avoid stale class files.

Now you should be able to see Scala class members in the Explorer's Outline view and in the Breadcrumbs view at the top of the Editor with a Scala file open.

Check Metals logs, "Run Doctor" etc. if something is not working as expected. You can also verify that the Bloop build server and the Metals language server are running by executing jps in the Terminal window:

jps -l
72960 sun.tools.jps.Jps
72356 bloop.Server
72349 scala.meta.metals.Main
Known Issues
java.lang.RuntimeException: boom

Metals background compilation process status appears to be resetting to 0% after reaching 99% and you see a peculiar error message java.lang.RuntimeException: boom. You can work around it by making sure Metals Server (Bloop client) and Bloop Server are both running on Java 11+.

  1. To this end make sure that Bloop projects are generated using Java 11+

    JAVA_HOME=/usr/lib/jvm/java-11-openjdk-amd64 \
  mvn install ch.epfl.scala:bloop-maven-plugin:bloopInstall \
  -DdownloadSources=true \
  -Dbuildver=331 \
  -Dskip -DskipTests

  2. Add metals.javaHome to VSCode preferences to point to Java 11+.

Other IDEs

We welcome pull requests with tips on how to setup your favorite IDE!

Your first issue

  1. Read the Developer Overview to understand how the RAPIDS Accelerator plugin works.
  2. Find an issue to work on. The best way is to look for the good first issue or help wanted labels.
  3. Comment on the issue stating that you are going to work on it.
  4. Code! Make sure to update unit tests and integration tests if needed! refer to test section
  5. When done, create your pull request.
  6. Verify that CI passes all status checks. Fix if needed.
  7. Wait for other developers to review your code and update code as needed.
  8. Once reviewed and approved, a project committer will merge your pull request.

Remember, if you are unsure about anything, don't hesitate to comment on issues and ask for clarifications!

Code Formatting

RAPIDS Accelerator for Apache Spark follows the same coding style guidelines as the Apache Spark project. For IntelliJ IDEA users, an example code style settings file is available in the docs/dev/ directory.

Scala

This project follows the official Scala style guide and the Databricks Scala guide, preferring the latter.

Java

This project follows the Oracle Java code conventions and the Scala conventions detailed above, preferring the latter.

Sign your work

We require that all contributors sign-off on their commits. This certifies that the contribution is your original work, or you have the rights to submit it under the same license, or a compatible license.

Any contribution which contains commits that are not signed off will not be accepted.

To sign off on a commit use the --signoff (or -s) option when committing your changes:

git commit -s -m "Add cool feature."

This will append the following to your commit message:

Signed-off-by: Your Name <[email protected]>

The sign-off is a simple line at the end of the explanation for the patch. Your signature certifies that you wrote the patch or otherwise have the right to pass it on as an open-source patch. Use your real name, no pseudonyms or anonymous contributions. If you set your user.name and user.email git configs, you can sign your commit automatically with git commit -s.

The sign-off means you certify the below (from developercertificate.org):

Developer Certificate of Origin
Version 1.1

Copyright (C) 2004, 2006 The Linux Foundation and its contributors.
1 Letterman Drive
Suite D4700
San Francisco, CA, 94129

Everyone is permitted to copy and distribute verbatim copies of this
license document, but changing it is not allowed.


Developer's Certificate of Origin 1.1

By making a contribution to this project, I certify that:

(a) The contribution was created in whole or in part by me and I
    have the right to submit it under the open source license
    indicated in the file; or

(b) The contribution is based upon previous work that, to the best
    of my knowledge, is covered under an appropriate open source
    license and I have the right under that license to submit that
    work with modifications, whether created in whole or in part
    by me, under the same open source license (unless I am
    permitted to submit under a different license), as indicated
    in the file; or

(c) The contribution was provided directly to me by some other
    person who certified (a), (b) or (c) and I have not modified
    it.

(d) I understand and agree that this project and the contribution
    are public and that a record of the contribution (including all
    personal information I submit with it, including my sign-off) is
    maintained indefinitely and may be redistributed consistent with
    this project or the open source license(s) involved.

Testing Your Code

Please visit the testing doc for details about how to run tests

Pre-commit hooks

We provide a basic config .pre-commit-config.yaml for pre-commit to automate some aspects of the development process. As a convenience you can enable automatic copyright year updates by following the installation instructions on the pre-commit homepage.

To this end, first install pre-commit itself using the method most suitable for your development environment. Then you will need to run pre-commit install to enable it in your local git repository. Using --allow-missing-config will make it easy to work with older branches that do not have .pre-commit-config.yaml.

pre-commit install --allow-missing-config

and setting the environment variable:

export SPARK_RAPIDS_AUTO_COPYRIGHTER=ON

The default value of SPARK_RAPIDS_AUTO_COPYRIGHTER is OFF.

When automatic copyright updater is enabled and you modify a file with a prior year in the copyright header it will be updated on git commit to the current year automatically. However, this will abort the commit process with the following error message:

Update copyright year....................................................Failed
- hook id: auto-copyrighter
- duration: 0.01s
- files were modified by this hook

You can confirm that the update has actually happened by either inspecting its effect with git diff first or simply re-executing git commit right away. The second time no file modification should be triggered by the copyright year update hook and the commit should succeed.

There is a known issue for macOS users if they use the default version of sed. The copyright update script may fail and generate an unexpected file named source-file-E. As a workaround, please install GNU sed

brew install gnu-sed
# and add to PATH to make it as default sed for your shell
export PATH="/usr/local/opt/gnu-sed/libexec/gnubin:$PATH"

Pull request status checks

A pull request should pass all status checks before being merged.

sign-off check

Please follow the steps in the Sign your work section, and make sure at least one commit in your pull request is signed-off.

blossom-ci

The check runs on NVIDIA self-hosted runner, a project committer can manually trigger it by commenting build. It includes the following steps,

  1. Mergeable check
  2. Blackduck vulnerability scan
  3. Fetch merged code (merge the pull request HEAD into BASE branch, e.g. fea-001 into branch-x)
  4. Run mvn verify and unit tests for multiple Spark versions in parallel. Ref: spark-premerge-build.sh

If it fails, you can click the Details link of this check, and go to Upload log -> Jenkins log for pull request xxx (click here) to find the uploaded log.

Options:

  1. Skip tests run by adding [skip ci] to title, this should only be used for doc-only change
  2. Run build and tests in databricks runtimes by adding [databricks] to title, this would add around 30-40 minutes

Attribution

Portions adopted from https://github.com/rapidsai/cudf/blob/main/CONTRIBUTING.md, https://github.com/NVIDIA/nvidia-docker/blob/master/CONTRIBUTING.md, and https://github.com/NVIDIA/DALI/blob/main/CONTRIBUTING.md