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Guides Java SDK development in Apache Beam, from finding relevant modules to building code, running focused tests, and trying examples. Also explains how to publish modified modules locally and build SDK containers.

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Guides Java SDK development in Apache Beam, including building, testing, running examples, and understanding the project structure. Use when working with Java code in sdks/java/, runners/, or examples/java/.

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설치 방법 보기

이럴 때 유용합니다

  • Finding the Java SDK module to modify
  • Running a specific test class or method
  • Checking setup with the WordCount example
  • Using modified modules through Maven Local

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SKILL.md(원문)

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Java Development in Apache Beam

Project Structure

Key Directories

  • sdks/java/core - Core Java SDK (PCollection, PTransform, Pipeline)
  • sdks/java/harness - SDK harness (container entrypoint)
  • sdks/java/io/ - I/O connectors (51+ connectors including BigQuery, Kafka, JDBC, etc.)
  • sdks/java/extensions/ - Extensions (SQL, ML, protobuf, etc.)
  • runners/ - Runner implementations:
    • runners/direct-java - Direct Runner (local execution)
    • runners/flink/ - Flink Runner
    • runners/spark/ - Spark Runner
    • runners/google-cloud-dataflow-java/ - Dataflow Runner
  • examples/java/ - Java examples including WordCount

Build System

Apache Beam uses Gradle with a custom BeamModulePlugin. Every Java project's build.gradle starts with:

apply plugin: 'org.apache.beam.module'
applyJavaNature( ... )

Common Commands

Build Commands

# Compile a specific project
./gradlew -p sdks/java/core compileJava

# Build a project (compile + tests)
./gradlew :sdks:java:harness:build

# Run WordCount example
./gradlew :examples:java:wordCount

Running Unit Tests

# Run all tests in a project
./gradlew :sdks:java:harness:test

# Run a specific test class
./gradlew :sdks:java:harness:test --tests org.apache.beam.fn.harness.CachesTest

# Run tests matching a pattern
./gradlew :sdks:java:harness:test --tests *CachesTest

# Run a specific test method
./gradlew :sdks:java:harness:test --tests *CachesTest.testClearableCache

Running Integration Tests

Integration tests have filenames ending in IT.java and use TestPipeline.

# Run I/O integration tests on Direct Runner
./gradlew :sdks:java:io:google-cloud-platform:integrationTest

# Run with custom GCP project
./gradlew :sdks:java:io:google-cloud-platform:integrationTest \
  -PgcpProject=<project> -PgcpTempRoot=gs://<bucket>/path

# Run on Dataflow Runner
./gradlew :runners:google-cloud-dataflow-java:examplesJavaRunnerV2IntegrationTest \
  -PdisableSpotlessCheck=true -PdisableCheckStyle=true -PskipCheckerFramework \
  -PgcpProject=<project> -PgcpRegion=us-central1 -PgcsTempRoot=gs://<bucket>/tmp

Code Formatting

# Format Java code
./gradlew spotlessApply

Writing Integration Tests

@Rule public TestPipeline pipeline = TestPipeline.create();

@Test
public void testSomething() {
  pipeline.apply(...);
  pipeline.run().waitUntilFinish();
}

Set pipeline options via -DbeamTestPipelineOptions='[...]':

-DbeamTestPipelineOptions='["--runner=TestDataflowRunner","--project=myproject","--region=us-central1","--stagingLocation=gs://bucket/path"]'

Using Modified Beam Code

Publish to Maven Local

# Publish a specific module
./gradlew -Ppublishing -p sdks/java/io/kafka publishToMavenLocal

# Publish all modules
./gradlew -Ppublishing publishToMavenLocal

Building SDK Container

# Build Java SDK container (for Runner v2)
./gradlew :sdks:java:container:java11:docker

# Tag and push
docker tag apache/beam_java11_sdk:2.XX.0.dev \
  "us-docker.pkg.dev/your-project/beam/beam_java11_sdk:custom"
docker push "us-docker.pkg.dev/your-project/beam/beam_java11_sdk:custom"

Building Dataflow Worker Jar

./gradlew :runners:google-cloud-dataflow-java:worker:shadowJar

Test Naming Conventions

  • Unit tests: *Test.java
  • Integration tests: *IT.java

JUnit Report Location

After running tests, find HTML reports at: <project>/build/reports/tests/test/index.html

IDE Setup (IntelliJ)

  1. Open /beam (the repository root, NOT sdks/java)
  2. Wait for indexing to complete
  3. Find examples/java/build.gradle and click Run next to wordCount task to verify setup

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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

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.

  • Prepare a first Apache Beam contribution
  • Organize a pull request and its related issue
  • Check tests and formatting before submission
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

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