Create a Dataflow pipeline using Python

Create a Dataflow pipeline using Python

In this quickstart, you learn how to use the Apache Beam SDK for Python to build a program that defines a pipeline. Then, you run the pipeline by using a direct local runner or a cloud-based runner such as Dataflow. For an introduction to the WordCount pipeline, see the How to use WordCount in Apache Beam video.

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To follow step-by-step guidance for this task directly in the Google Cloud console, click Guide me:

Guide me

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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, BigQuery, Cloud Pub/Sub, Cloud Datastore, and Cloud Resource Manager APIs:

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

7. Create local authentication credentials for your user account:

   gcloud auth application-default login

8. Grant roles to your user 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:USER_IDENTIFIER" --role=ROLE

   • Replace PROJECT_ID with your project ID.

   • Replace USER_IDENTIFIER with the identifier for your user account. For example, user:[e-mail].

   • 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, BigQuery, Cloud Pub/Sub, Cloud Datastore, and Cloud Resource Manager APIs:

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

14. Create local authentication credentials for your user account:

    gcloud auth application-default login

15. Grant roles to your user 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:USER_IDENTIFIER" --role=ROLE

    • Replace PROJECT_ID with your project ID.

    • Replace USER_IDENTIFIER with the identifier for your user account. For example, user:[e-mail].

    • 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 document.

Set up your environment

Dataflow no longer supports pipelines using Python 2. For more information, see Python 2 support on Google Cloud page. For details about supported versions, see Apache Beam runtime support.

In this section, use the command prompt to set up an isolated Python virtual environment to run your pipeline project by using venv. This process lets you isolate the dependencies of one project from the dependencies of other projects.

If you don't have a command prompt readily available, you can use Cloud Shell. Cloud Shell already has the package manager for Python 3 installed, so you can skip to creating a virtual environment.

To install Python and then create a virtual environment, follow these steps:

1. Check that you have Python 3 and pip running in your system:

   python --version
   python -m pip --version

2. If required, install Python 3 and then set up a Python virtual environment: follow the instructions provided in the Installing Python and Setting up venv sections of the Setting up a Python development environment page. If you're using Python 3.10 or later, you must also enable Dataflow Runner v2. To use Runner v1, use Python 3.9 or earlier.

Note: Cython is not required, but if it is installed, the version must be 0.28.1 or later. To check your Cython version, run pip show cython.

After you complete the quickstart, you can deactivate the virtual environment by running deactivate.

Get the Apache Beam SDK

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.

To download and install the Apache Beam SDK, follow these steps:

1. Verify that you are in the Python virtual environment that you created in the preceding section. Ensure that the prompt starts with <env_name>, where env_name is the name of the virtual environment.
2. Install the Python wheel packaging standard:

   pip install wheel

3. Install the latest version of the Apache Beam SDK for Python:

pip install 'apache-beam[gcp]'

On Microsoft Windows, use the following command:

pip install apache-beam[gcp]

Depending on the connection, your installation might take a while.

Run the pipeline locally

To see how a pipeline runs locally, use a ready-made Python module for the wordcount example that is included with the apache_beam package.

The wordcount pipeline example does the following:

1. Takes a text file as input.

   This text file is 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 stage the wordcount pipeline locally, follow these steps:

1. From your local terminal, run the wordcount example:

   python -m apache_beam.examples.wordcount \
     --output outputs

2. View the output of the pipeline:

   more outputs*

3. To exit, press q.

Running the pipeline locally lets you test and debug your Apache Beam program. You can view the wordcount.py source code on Apache Beam GitHub.

Run the pipeline on the Dataflow service

In this section, run the wordcount example pipeline from the apache_beam package on the Dataflow service. This example specifies DataflowRunner as the parameter for --runner.

• Run the pipeline:

  python -m apache_beam.examples.wordcount \
      --region DATAFLOW_REGION \
      --input gs://dataflow-samples/shakespeare/kinglear.txt \
      --output gs://BUCKET_NAME/results/outputs \
      --runner DataflowRunner \
      --project PROJECT_ID \
      --temp_location gs://BUCKET_NAME/tmp/

  Replace the following:

  • DATAFLOW_REGION: the region where you want to deploy the Dataflow job—for example, europe-west1

    The --region flag overrides the default region that is set in the metadata server, your local client, or environment variables.

  • BUCKET_NAME: the Cloud Storage bucket name that you copied earlier
  • PROJECT_ID: the Google Cloud project ID that you copied earlier

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

When you run a pipeline using Dataflow, your results are stored in a Cloud Storage bucket. In this section, verify that the pipeline is running by using either the Google Cloud console or the local terminal.

Google Cloud console

To view your results in Google Cloud console, follow these steps:

1. 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.

2. Go to the Cloud Storage Buckets page.

   Go to Buckets

3. From the list of buckets in your project, click the storage bucket that you created earlier.

   In the wordcount directory, the output files that your job created are displayed.

Local 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 Cloud Storage bucket used in the pipeline program.

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

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

Modify the pipeline code

The wordcount pipeline in the previous examples distinguishes between uppercase and lowercase words. The following steps show how to modify the pipeline so that the wordcount pipeline is not case-sensitive.

1. On your local machine, download the latest copy of the wordcount code from the Apache Beam GitHub repository.
2. From the local terminal, run the pipeline:

   python wordcount.py --output outputs

3. View the results:

   more outputs*

4. To exit, press q.
5. In an editor of your choice, open the wordcount.py file.
6. Inside the run function, examine the pipeline steps:

   counts = (
           lines
           | 'Split' >> (beam.ParDo(WordExtractingDoFn()).with_output_types(str))
           | 'PairWithOne' >> beam.Map(lambda x: (x, 1))
           | 'GroupAndSum' >> beam.CombinePerKey(sum))

   After split, the lines are split into words as strings.

7. To lowercase the strings, modify the line after split:

   counts = (
           lines
           | 'Split' >> (beam.ParDo(WordExtractingDoFn()).with_output_types(str))
           | 'lowercase' >> beam.Map(str.lower)
           | 'PairWithOne' >> beam.Map(lambda x: (x, 1))
           | 'GroupAndSum' >> beam.CombinePerKey(sum)) 

   This modification maps the str.lower function onto every word. This line is equivalent to beam.Map(lambda word: str.lower(word)).
8. Save the file and run the modified wordcount job:

   python wordcount.py --output outputs

9. View the results of the modified pipeline:

   more outputs*

10. To exit, press q.
11. Run the modified pipeline on the Dataflow service:

    python wordcount.py \
        --region DATAFLOW_REGION \
        --input gs://dataflow-samples/shakespeare/kinglear.txt \
        --output gs://BUCKET_NAME/results/outputs \
        --runner DataflowRunner \
        --project PROJECT_ID \
        --temp_location gs://BUCKET_NAME/tmp/

    Replace the following:

    • DATAFLOW_REGION: the region where you want to deploy the Dataflow job
    • BUCKET_NAME: your Cloud Storage bucket name
    • PROJECT_ID: you Google Cloud project ID

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

• Read about the Apache Beam programming model.
• Interactively develop a pipeline using an Apache Beam notebook.
• Learn how to design and create your own pipeline.
• Work through the WordCount and Mobile Gaming examples.