runners

Development

Guides understanding and working with Apache Beam runners (Direct, Dataflow, Flink, Spark, etc.). Use when configuring pipelines for different execution environments or debugging runner-specific issues.

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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

Runner	Location	Description
Direct	`runners/direct-java/`	Local execution for testing
Prism	`runners/prism/`	Portable local runner
Dataflow	`runners/google-cloud-dataflow-java/`	Google Cloud Dataflow
Flink	`runners/flink/`	Apache Flink
Spark	`runners/spark/`	Apache Spark
Jet	`runners/jet/`	Hazelcast Jet
Twister2	`runners/twister2/`	Twister2

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

Option	Description
`--project`	GCP project
`--region`	GCP region
`--tempLocation`	GCS temp location
`--stagingLocation`	GCS staging
`--numWorkers`	Initial workers
`--maxNumWorkers`	Max workers
`--workerMachineType`	VM type

Flink

Option	Description
`--flinkMaster`	Flink master address
`--parallelism`	Default parallelism
`--checkpointingInterval`	Checkpoint interval

Spark

Option	Description
`--sparkMaster`	Spark master URL
`--sparkConf`	Additional Spark config

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