Create Your Pipeline

Your Beam program expresses a data processing pipeline, from start to finish. This section explains the mechanics of using the classes in the Beam SDKs to build a pipeline. To construct a pipeline using the classes in the Beam SDKs, your program will need to perform the following general steps:

Creating Your Pipeline Object

A Beam program often starts by creating a Pipeline object.

In the Beam SDKs, each pipeline is represented by an explicit object of type Pipeline. Each Pipeline object is an independent entity that encapsulates both the data the pipeline operates over and the transforms that get applied to that data.

To create a pipeline, declare a Pipeline object, and pass it some configuration options, which are explained in a section below. You pass the configuration options by creating an object of type PipelineOptions, which you can build by using the static method PipelineOptionsFactory.create().

// Start by defining the options for the pipeline.
PipelineOptions options = PipelineOptionsFactory.create();

// Then create the pipeline.
Pipeline p = Pipeline.create(options);

Configuring Pipeline Options

Use the pipeline options to configure different aspects of your pipeline, such as the pipeline runner that will execute your pipeline and any runner-specific configuration required by the chosen runner. Your pipeline options will potentially include information such as your project ID or a location for storing files.

When you run the pipeline on a runner of your choice, a copy of the PipelineOptions will be available to your code. For example, you can read PipelineOptions from a DoFn’s Context.

Setting PipelineOptions from Command-Line Arguments

While you can configure your pipeline by creating a PipelineOptions object and setting the fields directly, the Beam SDKs include a command-line parser that you can use to set fields in PipelineOptions using command-line arguments.

To read options from the command-line, construct your PipelineOptions object as demonstrated in the following example code:

MyOptions options = PipelineOptionsFactory.fromArgs(args).withValidation().create();

This interprets command-line arguments that follow the format:


Note: Appending the method .withValidation will check for required command-line arguments and validate argument values.

Building your PipelineOptions this way lets you specify any of the options as a command-line argument.

Note: The WordCount example pipeline demonstrates how to set pipeline options at runtime by using command-line options.

Creating Custom Options

You can add your own custom options in addition to the standard PipelineOptions. To add your own options, define an interface with getter and setter methods for each option, as in the following example:

public interface MyOptions extends PipelineOptions {
    String getMyCustomOption();
    void setMyCustomOption(String myCustomOption);

You can also specify a description, which appears when a user passes --help as a command-line argument, and a default value.

You set the description and default value using annotations, as follows:

public interface MyOptions extends PipelineOptions {
    @Description("My custom command line argument.")
    String getMyCustomOption();
    void setMyCustomOption(String myCustomOption);

It’s recommended that you register your interface with PipelineOptionsFactory and then pass the interface when creating the PipelineOptions object. When you register your interface with PipelineOptionsFactory, the --help can find your custom options interface and add it to the output of the --help command. PipelineOptionsFactory will also validate that your custom options are compatible with all other registered options.

The following example code shows how to register your custom options interface with PipelineOptionsFactory:

MyOptions options = PipelineOptionsFactory.fromArgs(args)

Now your pipeline can accept --myCustomOption=value as a command-line argument.

Reading Data Into Your Pipeline

To create your pipeline’s initial PCollection, you apply a root transform to your pipeline object. A root transform creates a PCollection from either an external data source or some local data you specify.

There are two kinds of root transforms in the Beam SDKs: Read and Create. Read transforms read data from an external source, such as a text file or a database table. Create transforms create a PCollection from an in-memory java.util.Collection.

The following example code shows how to apply a TextIO.Read root transform to read data from a text file. The transform is applied to a Pipeline object p, and returns a pipeline data set in the form of a PCollection<String>:

PCollection<String> lines = p.apply(
  apply("ReadLines", TextIO.Read.from("gs://some/inputData.txt"));

Applying Transforms to Process Pipeline Data

To use transforms in your pipeline, you apply them to the PCollection that you want to transform.

To apply a transform, you call the apply method on each PCollection that you want to process, passing the desired transform object as an argument.

The Beam SDKs contain a number of different transforms that you can apply to your pipeline’s PCollections. These include general-purpose core transforms, such as ParDo or Combine. There are also pre-written composite transforms included in the SDKs, which combine one or more of the core transforms in a useful processing pattern, such as counting or combining elements in a collection. You can also define your own more complex composite transforms to fit your pipeline’s exact use case.

In the Beam Java SDK, each transform is a subclass of the base class PTransform. When you call apply on a PCollection, you pass the PTransform you want to use as an argument.

The following code shows how to apply a transform to a PCollection of strings. The transform is a user-defined custom transform that reverses the contents of each string and outputs a new PCollection containing the reversed strings.

The input is a PCollection<String> called words; the code passes an instance of a PTransform object called ReverseWords to apply, and saves the return value as the PCollection<String> called reversedWords.

PCollection<String> words = ...;

PCollection<String> reversedWords = words.apply(new ReverseWords());

Writing or Outputting Your Final Pipeline Data

Once your pipeline has applied all of its transforms, you’ll usually need to output the results. To output your pipeline’s final PCollections, you apply a Write transform to that PCollection. Write transforms can output the elements of a PCollection to an external data sink, such as a database table. You can use Write to output a PCollection at any time in your pipeline, although you’ll typically write out data at the end of your pipeline.

The following example code shows how to apply a TextIO.Write transform to write a PCollection of String to a text file:

PCollection<String> filteredWords = ...;


Running Your Pipeline

Once you have constructed your pipeline, use the run method to execute the pipeline. Pipelines are executed asynchronously: the program you create sends a specification for your pipeline to a pipeline runner, which then constructs and runs the actual series of pipeline operations.;

The run method is asynchronous. If you’d like a blocking execution instead, run your pipeline appending the waitUntilFinish method:;

What’s next