Create a Dataflow pipeline using Go

Create a Dataflow pipeline using Go

This page shows you how to use the Apache Beam SDK for Go to build a program that defines a pipeline. Then, you run the pipeline locally and on the Dataflow service. For an introduction to the WordCount pipeline, see the How to use WordCount in Apache Beam video.

Before you begin

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
2. Install the Google Cloud CLI.

3. To initialize the gcloud CLI, run the following command:

   gcloud init

4. Create or select a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   • Create a Google Cloud project:

     gcloud projects create PROJECT_ID

     Replace PROJECT_ID with a name for the Google Cloud project you are creating.

   • Select the Google Cloud project that you created:

     gcloud config set project PROJECT_ID

     Replace PROJECT_ID with your Google Cloud project name.

5. Make sure that billing is enabled for your Google Cloud project.

6. Enable the Dataflow, Compute Engine, Cloud Logging, Cloud Storage, Google Cloud Storage JSON, and Cloud Resource Manager APIs:

   gcloud services enable dataflow compute_component logging storage_component storage_api cloudresourcemanager.googleapis.com

7. Create local authentication credentials for your Google Account:

   gcloud auth application-default login

8. Grant roles to your Google Account. Run the following command once for each of the following IAM roles: roles/iam.serviceAccountUser

   gcloud projects add-iam-policy-binding PROJECT_ID --member="user:EMAIL_ADDRESS" --role=ROLE

   • Replace PROJECT_ID with your project ID.
   • Replace EMAIL_ADDRESS with your email address.
   • Replace ROLE with each individual role.
9. Install the Google Cloud CLI.

10. To initialize the gcloud CLI, run the following command:

    gcloud init

11. Create or select a Google Cloud project.

    Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

    • Create a Google Cloud project:

      gcloud projects create PROJECT_ID

      Replace PROJECT_ID with a name for the Google Cloud project you are creating.

    • Select the Google Cloud project that you created:

      gcloud config set project PROJECT_ID

      Replace PROJECT_ID with your Google Cloud project name.

12. Make sure that billing is enabled for your Google Cloud project.

13. Enable the Dataflow, Compute Engine, Cloud Logging, Cloud Storage, Google Cloud Storage JSON, and Cloud Resource Manager APIs:

    gcloud services enable dataflow compute_component logging storage_component storage_api cloudresourcemanager.googleapis.com

14. Create local authentication credentials for your Google Account:

    gcloud auth application-default login

15. Grant roles to your Google Account. Run the following command once for each of the following IAM roles: roles/iam.serviceAccountUser

    gcloud projects add-iam-policy-binding PROJECT_ID --member="user:EMAIL_ADDRESS" --role=ROLE

    • Replace PROJECT_ID with your project ID.
    • Replace EMAIL_ADDRESS with your email address.
    • Replace ROLE with each individual role.

16. Grant roles to your Compute Engine default service account. Run the following command once for each of the following IAM roles:

    • roles/dataflow.admin
    • roles/dataflow.worker
    • roles/storage.objectAdmin

    gcloud projects add-iam-policy-binding PROJECT_ID --member="serviceAccount:[e-mail]" --role=SERVICE_ACCOUNT_ROLE

    • Replace PROJECT_ID with your project ID.
    • Replace PROJECT_NUMBER with your project number. To find your project number, see Identify projects or use the gcloud projects describe command.
    • Replace SERVICE_ACCOUNT_ROLE with each individual role.
17. Create a Cloud Storage bucket and configure it as follows:
    • Set the storage class to S (Standard).
    • Set the storage location to the following: US (United States).
    • Replace BUCKET_NAME with a unique bucket name. Don't include sensitive information in the bucket name because the bucket namespace is global and publicly visible.

    gcloud storage buckets create gs://BUCKET_NAME --default-storage-class STANDARD --location US

18. Copy the Google Cloud project ID and the Cloud Storage bucket name. You need these values later in this quickstart.

Set up your development environment

The Apache Beam SDK is an open source programming model for data pipelines. You define a pipeline with an Apache Beam program and then choose a runner, such as Dataflow, to run your pipeline.

We recommend that you use the latest version of Go when working with the Apache Beam SDK for Go. If you don't have the latest version of Go installed, use Go's Download and install guide to download and install Go for your specific operating system.

To verify the version of Go that you have installed, run the following command in your local terminal:

go version

Run the Beam wordcount example

The Apache Beam SDK for Go includes a wordcount pipeline example. The wordcount example does the following:

1. Reads a text file as input. By default, it reads a text file located in a Cloud Storage bucket with the resource name gs://dataflow-samples/shakespeare/kinglear.txt.
2. Parses each line into words.
3. Performs a frequency count on the tokenized words.

To run the latest version of the Beam wordcount example on your local machine, use the following command. The input flag specifies the file to read, and the output flag specifies the filename for the frequency count output.

go run github.com/apache/beam/sdks/v2/go/examples/wordcount@latest \
  --input gs://dataflow-samples/shakespeare/kinglear.txt \
  --output outputs

After the pipeline completes, view the output results:

more outputs*

To exit, press q.

Modify the pipeline code

The Beam wordcount pipeline distinguishes between uppercase and lowercase words. The following steps show how to create your own Go module, modify the wordcount pipeline so that the pipeline is not case-sensitive, and run it on Dataflow.

Create a Go module

To make changes to the pipeline code, follow these steps.

1. Create a directory for your Go module in a location of your choice:

   mkdir wordcount
   cd wordcount
   

2. Create a Go module. For this example, use example/dataflow as the module path.

   go mod init example/dataflow
   

3. Download the latest copy of the wordcount code from the Apache Beam GitHub repository. Put this file into the wordcount directory you created.

4. If you are using a non-Linux operating system, you must get the Go unix package. This package is required to run pipelines on the Dataflow service.

   go get -u golang.org/x/sys/unix
   

5. Ensure that the go.mod file matches the module's source code:

   go mod tidy
   

Run the unmodified pipeline

Verify the unmodified wordcount pipeline runs locally.

1. From the terminal, build and run the pipeline locally:

    go run wordcount.go --input gs://dataflow-samples/shakespeare/kinglear.txt \
        --output outputs
   

2. View the output results:

    more outputs*
   

3. To exit, press q.

Change the pipeline code

To change the pipeline so that it is not case-sensitive, modify the code to apply the strings.ToLower function to all words.

1. In an editor of your choice, open the wordcount.go file.

2. Examine the init block (comments have been removed for clarity):

    func init() {
      register.DoFn3x0[context.Context, string, func(string)](&extractFn{})
      register.Function2x1(formatFn)
      register.Emitter1[string]()
    }
   

3. Add a new line to register the strings.ToLower function:

    func init() {
      register.DoFn3x0[context.Context, string, func(string)](&extractFn{})
      register.Function2x1(formatFn)
      register.Emitter1[string]()
      register.Function1x1(strings.ToLower)
    }
   

4. Examine the CountWords function:

    func CountWords(s beam.Scope, lines beam.PCollection) beam.PCollection {
      s = s.Scope("CountWords")
   
      // Convert lines of text into individual words.
      col := beam.ParDo(s, &extractFn{SmallWordLength: *smallWordLength}, lines)
   
      // Count the number of times each word occurs.
      return stats.Count(s, col)
    }
   

5. To lowercase the words, add a ParDo that applies strings.ToLower to every word:

    func CountWords(s beam.Scope, lines beam.PCollection) beam.PCollection {
      s = s.Scope("CountWords")
   
      // Convert lines of text into individual words.
      col := beam.ParDo(s, &extractFn{SmallWordLength: *smallWordLength}, lines)
   
      // Map all letters to lowercase.
      lowercaseWords := beam.ParDo(s, strings.ToLower, col)
   
      // Count the number of times each word occurs.
      return stats.Count(s, lowercaseWords)
    }
   

6. Save the file.

Run the updated pipeline locally

Run your updated wordcount pipeline locally and verify the output has changed.

1. Build and run the modified wordcount pipeline:

    go run wordcount.go --input gs://dataflow-samples/shakespeare/kinglear.txt \
        --output outputs
   

2. View the output results of the modified pipeline. All words should be lowercase.

    more outputs*
   

3. To exit, press q.

Run the pipeline on the Dataflow service

To run the updated wordcount example on the Dataflow service, use the following command:

go run wordcount.go --input gs://dataflow-samples/shakespeare/kinglear.txt \
    --output gs://BUCKET_NAME/results/outputs \
    --runner dataflow \
    --project PROJECT_ID \
    --region DATAFLOW_REGION \
    --staging_location gs://BUCKET_NAME/binaries/

Replace the following:

• BUCKET_NAME: the Cloud Storage bucket name.

• PROJECT_ID: the Google Cloud project ID.

• DATAFLOW_REGION: the region where you want to deploy the Dataflow job. For example, europe-west1. For a list of available locations, see Dataflow locations. The --region flag overrides the default region that is set in the metadata server, your local client, or environment variables.

Note: To specify a user-managed worker service account, include the --service_account_email pipeline option. User-managed worker service accounts are recommended for production workloads. If you don't specify a worker service account when you create a job, Dataflow uses the Compute Engine default service account.

View your results

You can see a list of your Dataflow jobs in Google Cloud console. In the Google Cloud console, go to the Dataflow Jobs page.

Go to Jobs

The Jobs page displays details of your wordcount job, including a status of Running at first, and then Succeeded.

When you run a pipeline using Dataflow, your results are stored in a Cloud Storage bucket. View the output results by using either the Google Cloud console or the local terminal.

Console

To view your results in the Google Cloud console, go to the Cloud Storage Buckets page.

Go to Buckets

From the list of buckets in your project, click the storage bucket that you created earlier. The output files that your job created are displayed in the results directory.

Terminal

View the results from your terminal or by using Cloud Shell.

1. To list the output files, use the gcloud storage ls command:

   gcloud storage ls gs://BUCKET_NAME/results/outputs* --long
   

   Replace BUCKET_NAME with the name of the specified output Cloud Storage bucket.

2. To view the results in the output files, use the gcloud storage cat command:

   gcloud storage cat gs://BUCKET_NAME/results/outputs*
   

Clean up

To avoid incurring charges to your Google Cloud account for the resources used on this page, delete the Google Cloud project with the resources.

Note: If you followed this quickstart in a new project, then you can delete the project.

1. In the Google Cloud console, go to the Cloud Storage Buckets page.

   Go to Buckets

2. Click the checkbox for the bucket that you want to delete.
3. To delete the bucket, click Delete, and then follow the instructions.

4. If you keep your project, revoke the roles that you granted to the Compute Engine default service account. Run the following command once for each of the following IAM roles:

   • roles/dataflow.admin
   • roles/dataflow.worker
   • roles/storage.objectAdmin

   gcloud projects remove-iam-policy-binding PROJECT_ID \
       --member=serviceAccount:[e-mail] \
       --role=SERVICE_ACCOUNT_ROLE

5. Optional: Revoke the authentication credentials that you created, and delete the local credential file.

   gcloud auth application-default revoke

6. Optional: Revoke credentials from the gcloud CLI.

   gcloud auth revoke

What's next

• Programming model for Apache Beam
• Setting pipeline options
• Pipeline options reference
• Deploying a pipeline