Using the Direct Runner

The Direct Runner executes pipelines on your machine and is designed to validate that pipelines adhere to the Apache Beam model as closely as possible. Instead of focusing on efficient pipeline execution, the Direct Runner performs additional checks to ensure that users do not rely on semantics that are not guaranteed by the model. Some of these checks include:

Using the Direct Runner for testing and development helps ensure that pipelines are robust across different Beam runners. In addition, debugging failed runs can be a non-trivial task when a pipeline executes on a remote cluster. Instead, it is often faster and simpler to perform local unit testing on your pipeline code. Unit testing your pipeline locally also allows you to use your preferred local debugging tools.

Here are some resources with information about how to test your pipelines.

The Direct Runner is not designed for production pipelines, because it’s optimized for correctness rather than performance. The Direct Runner must fit all user data in memory, whereas the Flink and Spark runners can spill data to disk if it doesn’t fit in memory. Consequently, Flink and Spark runners are able to run larger pipelines and are better suited to production workloads.

Direct Runner prerequisites and setup

Specify your dependency

When using Java, you must specify your dependency on the Direct Runner in your pom.xml.


This section is not applicable to the Beam SDK for Python.

Pipeline options for the Direct Runner

For general instructions on how to set pipeline options, see the programming guide.

When executing your pipeline from the command-line, set runner to direct or DirectRunner. The default values for the other pipeline options are generally sufficient.

See the reference documentation for the DirectOptions DirectOptions interface for defaults and additional pipeline configuration options.

Additional information and caveats

Memory considerations

Local execution is limited by the memory available in your local environment. It is highly recommended that you run your pipeline with data sets small enough to fit in local memory. You can create a small in-memory data set using a CreateCreate transform, or you can use a ReadRead transform to work with small local or remote files.

Streaming execution

Streaming support for Python DirectRunner is limited. For known issues, see:

If your pipeline uses an unbounded data source or sink, you must set the streaming option to true.

Parallel execution

Python FnApiRunner supports multi-threading and multi-processing mode.

Setting parallelism

The number of worker threads is defined by the targetParallelism pipeline option. By default, targetParallelism is the greater of the number of available processors and 3.

Number of threads or subprocesses is defined by setting the direct_num_workers pipeline option. From 2.22.0, direct_num_workers = 0 is supported. When direct_num_workers is set to 0, it will set the number of threads/subprocess to the number of cores of the machine where the pipeline is running.

Setting running mode

In Beam 2.19.0 and newer, you can use the direct_running_mode pipeline option to set the running mode. direct_running_mode can be one of ['in_memory', 'multi_threading', 'multi_processing'].

in_memory: Runner and workers’ communication happens in memory (not through gRPC). This is a default mode.

multi_threading: Runner and workers communicate through gRPC and each worker runs in a thread.

multi_processing: Runner and workers communicate through gRPC and each worker runs in a subprocess.

Before deploying pipeline to remote runner

While testing on the direct runner is convenient, it can still behave differently from remote runners beyond Beam model semantics, especially for runtime environment related issues. In general, it is recommended to test your pipeline on targeted remote runner in small scale before fully deploying into production.