Runners

Apache Beam Runners

Overview

Runners execute Beam pipelines on distributed processing backends. Each runner translates the portable Beam model to its native execution engine.

Available Runners

Direct Runner

For local development and testing.

Java

PipelineOptions options = PipelineOptionsFactory.create();
options.setRunner(DirectRunner.class);
Pipeline p = Pipeline.create(options);

Python

options = PipelineOptions()
options.view_as(StandardOptions).runner = 'DirectRunner'
p = beam.Pipeline(options=options)

Command Line

--runner=DirectRunner

Dataflow Runner

Prerequisites

• GCP project with Dataflow API enabled
• Service account with Dataflow Admin role
• GCS bucket for staging

Java Usage

DataflowPipelineOptions options = PipelineOptionsFactory.as(DataflowPipelineOptions.class);
options.setRunner(DataflowRunner.class);
options.setProject("my-project");
options.setRegion("us-central1");
options.setTempLocation("gs://my-bucket/temp");

Python Usage

options = PipelineOptions([
    '--runner=DataflowRunner',
    '--project=my-project',
    '--region=us-central1',
    '--temp_location=gs://my-bucket/temp'
])

Runner v2

--experiments=use_runner_v2

Custom SDK Container

--sdkContainerImage=gcr.io/project/beam_java11_sdk:custom

Flink Runner

Embedded Mode

FlinkPipelineOptions options = PipelineOptionsFactory.as(FlinkPipelineOptions.class);
options.setRunner(FlinkRunner.class);
options.setFlinkMaster("[local]");

Cluster Mode

options.setFlinkMaster("host:port");

Portable Mode (Python)

options = PipelineOptions([
    '--runner=FlinkRunner',
    '--flink_master=host:port',
    '--environment_type=LOOPBACK'  # or DOCKER, EXTERNAL
])

Spark Runner

Java

SparkPipelineOptions options = PipelineOptionsFactory.as(SparkPipelineOptions.class);
options.setRunner(SparkRunner.class);
options.setSparkMaster("local[*]");  # or spark://host:port

Python (Portable)

options = PipelineOptions([
    '--runner=SparkRunner',
    '--spark_master_url=local[*]'
])

Testing with Runners

ValidatesRunner Tests

Tests that validate runner correctness:

# Direct Runner
./gradlew :runners:direct-java:validatesRunner

# Flink Runner
./gradlew :runners:flink:1.18:validatesRunner

# Spark Runner
./gradlew :runners:spark:3:validatesRunner

# Dataflow Runner
./gradlew :runners:google-cloud-dataflow-java:validatesRunner

TestPipeline with Runners

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

// Set runner via system property
-DbeamTestPipelineOptions='["--runner=TestDataflowRunner"]'

Portable Runners

Concept

• SDK-independent execution via Fn API
• SDK runs in container, communicates via gRPC

Environment Types

• DOCKER - SDK in Docker container
• LOOPBACK - SDK in same process (testing)
• EXTERNAL - SDK at specified address
• PROCESS - SDK in subprocess

Job Server

Start Flink job server:

./gradlew :runners:flink:1.18:job-server:runShadow

Start Spark job server:

./gradlew :runners:spark:3:job-server:runShadow

Runner-Specific Options

Dataflow

Flink

Spark

Building Runner Artifacts

Dataflow Worker Jar

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

Flink Job Server

./gradlew :runners:flink:1.18:job-server:shadowJar

Spark Job Server

./gradlew :runners:spark:3:job-server:shadowJar

Debugging

Direct Runner

• Enable logging: -Dorg.slf4j.simpleLogger.defaultLogLevel=debug
• Use --targetParallelism=1 for deterministic execution

Dataflow

• Check Dataflow UI: console.cloud.google.com/dataflow
• Use --experiments=upload_graph for graph debugging
• Worker logs in Cloud Logging

Portable Runners

• Enable debug logging on job server
• Check SDK harness logs in worker containers

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