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contributing

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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.

설치 방법 보기

이럴 때 유용합니다

  • Prepare a first Apache Beam contribution
  • Organize a pull request and its related issue
  • Check tests and formatting before submission
  • Review risks in core component changes

포함된 파일(1)

  • SKILL.md5.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Contributing to Apache Beam

Getting Started

Prerequisites

  • GitHub account
  • Java JDK 11 (preferred, or 8, 17, 21)
  • Latest Go 1.x
  • Docker
  • Python (any supported version for manual testing, all versions for running test suites)
  • For large contributions: signed ICLA to Apache Software Foundation

Environment Setup Options

Local Setup (automated)

./local-env-setup.sh

Docker-based Setup

./start-build-env.sh

Contribution Workflow

1. Find or Create an Issue

2. Claim the Issue

.take-issue    # Assigns issue to you
.free-issue    # Unassigns issue from you
.close-issue   # Closes the issue

3. For Large Changes

4. Make Your Changes

  • Every source file needs Apache license header
  • New dependencies must have Apache-compatible open source licenses
  • Add unit tests for your changes
  • Document public-facing APIs in a standard way (Javadoc, docstrings/pydoc).
    • Documentation should be user-friendly, informative.
    • Implementation details belong in inline code comments.
  • Use descriptive commit messages

Working on Core Components (High Risk — Heightened Scrutiny)

AGENT DIRECTIVE: modification to Core Components carries an inherently elevated risk of silent regression across distributed runners. Refer to .github/autolabeler.yml under the "core" label for the authoritative list of core component paths (including sdks/java/core, runners/core-*, model, sdks/python/apache_beam/transforms|coders, sdks/go/pkg/beam/core, FileIO, and GCS). If you are an AI agent modifying files matched by these paths, evaluate your changes with scrutiny.

5. Create Pull Request

  • Link to the issue in PR description
  • Pre-commit tests run automatically
  • If tests fail unrelated to your change, comment: retest this please

6. Code Review

  • Reviewers are auto-assigned within a few hours
  • Use R: @username to request specific reviewer
  • No response in 3 days? Email dev@beam.apache.org

Code Review Best Practices

For Authors

  • Provide context in issue and PR description
  • Avoid huge mega-changes
  • Add follow-up changes as "fixup" commits (don't squash until approved)
  • Squash fixup commits after approval

For Reviewers

  • PRs can only be merged by Beam committers
  • PRs with the core label touch foundational Beam semantics or common code paths; review with extra scrutiny for non-obvious failure modes, edge case, and performance regressions

Testing Workflows

Pre-commit Tests

Run automatically on PRs. To run locally:

./gradlew javaPreCommit      # Java
./gradlew :sdks:python:test  # Python
./gradlew :sdks:go:test      # Go

Post-commit Tests

Run after merge. Trigger phrases in PR comments start specific test suites. See trigger phrase catalog.

Formatting

Java

./gradlew spotlessApply

Python

# Uses yapf, isort, pylint
pre-commit run --all-files

CHANGES.md

./gradlew formatChanges

Release Cycle

  • Minor releases every 6 weeks
  • Check release calendar
  • Changes must be in master before release branch is cut

Stale PRs

  • PRs become stale after 60 days of author inactivity
  • Community will close stale PRs
  • Authors can reopen closed PRs

Key Resources

Communication

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같은 저장소의 스킬

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Adding trace context or drain flags to WindowedValue
  • Checking metadata preservation through Windmill storage
  • Reviewing propagation through grouping and timers
apache/beam8.7천2026년 10월 19일 업데이트

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Learn how Pipeline, PCollection, and PTransform fit together
  • Understand grouping, aggregation, merging, and splitting
  • Study time windows, output timing, and late data
apache/beam8.7천2026년 10월 19일 업데이트

ci-cd

무료

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Investigating failed checks on a pull request
  • Rerunning a specific test workflow
  • Reproducing CI test commands locally
apache/beam8.7천2026년 10월 19일 업데이트

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Add a connector for an external data system
  • Make a Java connector available to Python and Go
  • Integrate a connector with Beam YAML and the Managed API
apache/beam8.7천2026년 10월 19일 업데이트

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Build or test a specific Apache Beam module
  • Inspect library dependencies and version conflicts
  • Run formatting and pre-commit checks
apache/beam8.7천2026년 10월 19일 업데이트

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Add BigQuery or Kafka reads and writes to a pipeline
  • Run unit and integration tests for connectors
  • Investigate data source or destination issues
apache/beam8.7천2026년 10월 19일 업데이트

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