Apache Beam Go SDK quickstart

This quickstart shows you how to run an example pipeline written with the Apache Beam Go SDK, using the Direct Runner. The Direct Runner executes pipelines locally on your machine.

If you’re interested in contributing to the Apache Beam Go codebase, see the Contribution Guide.

On this page:

Set up your development environment

Make sure you have a Go development environment ready. If not, follow the instructions in the Download and install page.

Clone the GitHub repository

Clone or download the apache/beam-starter-go GitHub repository and change into the beam-starter-go directory.

git clone https://github.com/apache/beam-starter-go.git
cd beam-starter-go

Run the quickstart

Run the following command:

go run main.go --inputText="Greetings"

The output is similar to the following:


The lines might appear in a different order.

Explore the code

The main code file for this quickstart is main.go (GitHub). The code performs the following steps:

  1. Create a Beam pipeline.
  2. Create an initial PCollection.
  3. Apply transforms.
  4. Run the pipeline, using the Direct Runner.

Create a pipeline

Before creating a pipeline, call the Init function:


Then create the pipeline:

pipeline, scope := beam.NewPipelineWithRoot()

The NewPipelineWithRoot function returns a new Pipeline object, along with the pipeline’s root scope. A scope is a hierarchical grouping for composite transforms.

Create an initial PCollection

The PCollection abstraction represents a potentially distributed, multi-element data set. A Beam pipeline needs a source of data to populate an initial PCollection. The source can be bounded (with a known, fixed size) or unbounded (with unlimited size).

This example uses the Create function to create a PCollection from an in-memory array of strings. The resulting PCollection contains the strings “hello”, “world!", and a user-provided input string.

elements := beam.Create(scope, "hello", "world!", input_text)

Apply transforms to the PCollection

Transforms can change, filter, group, analyze, or otherwise process the elements in a PCollection.

This example adds a ParDo transform to convert the input strings to title case:

elements = beam.ParDo(scope, strings.Title, elements)

The ParDo function takes the parent scope, a transform function that will be applied to the data, and the input PCollection. It returns the output PCollection.

The previous example uses the built-in strings.Title function for the transform. You can also provide an application-defined function to a ParDo. For example:

func logAndEmit(ctx context.Context, element string, emit func(string)) {
    beamLog.Infoln(ctx, element)

This function logs the input element and returns the same element unmodified. Create a ParDo for this function as follows:

beam.ParDo(scope, logAndEmit, elements)

At runtime, the ParDo will call the logAndEmit function on each element in the input collection.

Run the pipeline

The code shown in the previous sections defines a pipeline, but does not process any data yet. To process data, you run the pipeline:

beamx.Run(ctx, pipeline)

A Beam runner runs a Beam pipeline on a specific platform. This example uses the Direct Runner, which is the default runner if you don’t specify one. The Direct Runner runs the pipeline locally on your machine. It is meant for testing and development, rather than being optimized for efficiency. For more information, see Using the Direct Runner.

For production workloads, you typically use a distributed runner that runs the pipeline on a big data processing system such as Apache Flink, Apache Spark, or Google Cloud Dataflow. These systems support massively parallel processing.

Next Steps

Please don’t hesitate to reach out if you encounter any issues!