Navigate Apache Beam contributions from finding an issue to submitting a pull request, a proposed code change, and responding to reviews. Covers environment setup, tests, formatting, licensing requirements, and closer evaluation of changes to core components.
Guides the contribution workflow for Apache Beam, including creating PRs, issue management, code review process, release cycles, and rigorous evaluation rules for high-risk core component changes. Use when contributing code, creating PRs, or modifying core Beam components.
Review the instructions the agent will receive before you install.
./local-env-setup.sh
./start-build-env.sh
.take-issue # Assigns issue to you
.free-issue # Unassigns issue from you
.close-issue # Closes the issue
AGENT DIRECTIVE: modification to Core Components carries an inherently elevated risk of silent regression across distributed runners. Refer to
.github/autolabeler.ymlunder the"core"label for the authoritative list of core component paths (includingsdks/java/core,runners/core-*,model,sdks/python/apache_beam/transforms|coders,sdks/go/pkg/beam/core,FileIO, andGCS). If you are an AI agent modifying files matched by these paths, evaluate your changes with scrutiny.
retest this pleaseR: @username to request specific reviewercore label touch foundational Beam semantics or common code paths; review with extra scrutiny for non-obvious failure modes, edge case, and performance regressionsRun automatically on PRs. To run locally:
./gradlew javaPreCommit # Java
./gradlew :sdks:python:test # Python
./gradlew :sdks:go:test # Go
Run after merge. Trigger phrases in PR comments start specific test suites. See trigger phrase catalog.
./gradlew spotlessApply
# Uses yapf, isort, pylint
pre-commit run --all-files
./gradlew formatChanges
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Guides adding metadata, extra information attached to data elements, to Apache Beam's WindowedValue. Maps the changes needed across serialization, storage, processing, and runner interfaces so new fields remain available during pipeline execution.
Learn Apache Beam's core concepts through Java and Python examples. Understand distributed datasets, processing steps, pipeline execution, and time windows, with guidance on aggregation, late data, error handling, and sharing processing steps across languages.
Helps investigate and modify Apache Beam's automated build and test workflows in GitHub Actions. Explains workflow types, failure logs, targeted reruns, local reproduction, and the cloud credentials needed for workflows that access GCP.
Build Apache Beam connectors that read and write external data. This guide covers Java implementation, schema support for structured records, access from Python and Go, Managed API integration, and testing.
Build and test Apache Beam with Gradle, the system that manages its project tasks. Find relevant configuration files, inspect library dependencies, run formatting checks, publish locally, and investigate build failures.
Work with Apache Beam I/O connectors, components that read and write external data. Get guidance on connector usage, unit and integration tests, troubleshooting data connections, and the basic structure of new connectors.