Stream from Pub/Sub to BigQuery

This tutorial uses the Pub/Sub Topic to BigQuery template to create and run a Dataflow template job using the Google Cloud console or Google Cloud CLI. The tutorial walks you through a streaming pipeline example that reads JSON-encoded messages from Pub/Sub, transforms message data with the Apache Beam SDK, and writes the results to a BigQuery table.

Streaming analytics and data integration pipelines use Pub/Sub to ingest and distribute data. Pub/Sub enables you to create systems of event producers and consumers, called publishers and subscribers. Publishers send events to the Pub/Sub service asynchronously, and Pub/Sub delivers the events to all services that need to react to them.

Dataflow is a fully-managed service for transforming and enriching data in stream (real-time) and batch modes. It provides a simplified pipeline development environment that uses the Apache Beam SDK to transform incoming data and then output the transformed data.

Objectives

• Create a Pub/Sub topic.
• Create a BigQuery dataset with a table and schema.
• Use a Google-provided streaming template to stream data from your Pub/Sub topic to BigQuery using Dataflow.

Costs

This tutorial uses the following billable components of Google Cloud:

• Dataflow
• Pub/Sub
• Cloud Storage
• Cloud Scheduler
• BigQuery

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

When you finish this tutorial, you can avoid continued billing by deleting the resources you created. For more information, see Clean up.

Before you begin

This section shows you how to enable APIs, create a service account, and grant an Owner role to the service account. In a production environment, don't grant the Owner role. Instead, use the appropriate Dataflow-specific permissions and roles.

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. In the Google Cloud console, on the project selector page, select or create 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.

   Go to project selector

3. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

4. Enable the Dataflow, Compute Engine, Logging, Cloud Storage, Cloud Storage JSON, BigQuery, Pub/Sub, Resource Manager, and Cloud Scheduler APIs.

   Enable the APIs

5. In the Google Cloud console, on the project selector page, select or create 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.

   Go to project selector

6. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

7. Enable the Dataflow, Compute Engine, Logging, Cloud Storage, Cloud Storage JSON, BigQuery, Pub/Sub, Resource Manager, and Cloud Scheduler APIs.

   Enable the APIs

8. If you're using the Google Cloud CLI to complete this tutorial, install and initialize the Google Cloud CLI. If you're using the Google Cloud console to complete this tutorial, skip this step.

Create the example source and sink

This section explains how to create the following:

• A streaming source of data using Pub/Sub
• A dataset to load the data into BigQuery

Create a Cloud Storage bucket

Begin by creating a Cloud Storage bucket using the Google Cloud console or Google Cloud CLI. This bucket is used as a temporary storage location by the Dataflow pipeline.

Console

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

   Go to the Cloud Storage page

2. Click Create bucket.

3. On the Create a bucket page, for Name your bucket, enter a name that meets the bucket naming requirements. Cloud Storage bucket names must be globally unique. Do not select the other options.

4. Click Create.

gcloud

Use the gsutil mb command:

gsutil mb gs://BUCKET_NAME

Replace BUCKET_NAME with a name for your Cloud Storage bucket that meets the bucket naming requirements. Cloud Storage bucket names must be globally unique.

Create a Pub/Sub topic and subscription

Create a Pub/Sub topic and then create a subscription to that topic.

Console

To create a topic, complete the following steps.

1. In the Google Cloud console, go to the Pub/Sub Topics page.

   Go to Topics

2. Click Create topic.

3. In the Topic ID field, enter an ID for your topic. For information about how to name a topic, see Guidelines to name a topic or a subscription.

4. Retain the option Add a default subscription. Do not select the other options.

5. Click Create topic.

gcloud

To create a topic, run the gcloud pubsub topics create command. For information about how to name a subscription, see Guidelines to name a topic or a subscription.

gcloud pubsub topics create TOPIC_ID

Replace TOPIC_ID with a name for your Pub/Sub topic.

To create a subscription to your topic, run the gcloud pubsub subscriptions create command:

gcloud pubsub subscriptions create --topic TOPIC_ID SUBSCRIPTION_ID

Replace SUBSCRIPTION_ID with a name for your Pub/Sub subscription.

Create and run Cloud Scheduler jobs

Create and run two Cloud Scheduler jobs, one that publishes "positive ratings" and a second that publishes "negative ratings" to your Pub/Sub topic.

Console

Create a Cloud Scheduler job for positive ratings.

1. Visit the Cloud Scheduler page in the console.

   Go to the Cloud Scheduler page

2. Click the Create a job button.

3. Enter the name positive-ratings-publisher.

4. Select a region for your job to run in. Select a Dataflow region close to where you run the commands in this tutorial. The value of the REGION variable must be a valid region name. For more information about regions and locations, see Dataflow locations.

5. Specify the frequency for your job, using the unix-cron format: * * * * *

   See Configuring Cron Job Schedules for more information.

6. Select your timezone.

7. Click Continue

8. Select Pub/Sub from the Target dropdown.

9. Select your Topic name from the dropdown.

10. Add the following Message string to be sent to your target:
    {"url": "https://beam.apache.org/", "review": "positive"}

11. Click Create.

You now have a cron job that sends a message with a positive rating to your Pub/Sub topic every minute. Your Cloud Function is subscribed to that topic.

Create a Cloud Scheduler job for negative ratings.

1. On the Cloud Scheduler page in the console, click the Create a job button.

2. Enter the name negative-ratings-publisher.

3. Select a region for your job to run in.

4. Specify the frequency for your job, using the unix-cron format: */2 * * * *

   See Configuring Cron Job Schedules for more information.

5. Select your timezone.

6. Click Continue

7. Select Pub/Sub from the Target dropdown.

8. Select your Topic name from the dropdown.

9. Add the following Message string to be sent to your target:
   {"url": "https://beam.apache.org/", "review": "negative"}

10. Click Create.

You now have a cron job that sends a message with a negative rating to your Pub/Sub topic every two minutes. Your Cloud Function is subscribed to that topic.

gcloud

1. To create a Cloud Scheduler job for this tutorial, use the gcloud scheduler jobs create command. This step creates a publisher for "positive ratings" that publishes one message per minute.

   gcloud scheduler jobs create pubsub positive-ratings-publisher \
     --schedule="* * * * *" \
     --location=DATAFLOW_REGION \
     --topic="TOPIC_ID" \
     --message-body='{"url": "https://beam.apache.org/", "review": "positive"}'
   

   Replace DATAFLOW_REGION with the regional endpoint for deploying your Dataflow job. Select a Dataflow region close to where you run the commands in this tutorial. The value of the REGION variable must be a valid region name. For more information about regions and locations, see Dataflow locations.

2. To start the Cloud Scheduler job, use the gcloud scheduler jobs run command.

   gcloud scheduler jobs run --location=DATAFLOW_REGION positive-ratings-publisher
   

3. Create and run another similar publisher for "negative ratings" that publishes one message every two minutes. This step creates a publisher for "negative ratings" that publishes one message every two minutes.

   gcloud scheduler jobs create pubsub negative-ratings-publisher \
     --schedule="*/2 * * * *" \
     --location=DATAFLOW_REGION  \
     --topic="TOPIC_ID" \
     --message-body='{"url": "https://beam.apache.org/", "review": "negative"}'
   

4. Start the second Cloud Scheduler job.

   gcloud scheduler jobs run --location=DATAFLOW_REGION negative-ratings-publisher
   

Create a BigQuery dataset

Create a BigQuery dataset and table with the appropriate schema for your Pub/Sub topic.

Console

Create a BigQuery dataset.

1. Open the BigQuery page in the Google Cloud console.

   Go to the BigQuery page

2. In the Explorer panel, select the project where you want to create the dataset.

3. Expand the Actions option and click Create dataset.

4. On the Create dataset page:

   • For Dataset ID, enter tutorial_dataset.

   • For Data location, choose a geographic location for the dataset. After a dataset is created, the location can't be changed.

     Note: If you choose EU or an EU-based region for the dataset location, your Core Dataflow Customer Data resides in the EU. Core Dataflow Customer Data is defined in the Service Specific Terms.

   • Do not select the other options.

   • Click Create dataset.

Create a BigQuery table with a schema.

1. In the Explorer panel, expand your project and select your tutorial_dataset dataset.

2. Expand the Actions option and click Open.

3. In the details panel, click Create table .

4. On the Create table page, in the Source section, select Empty table.

5. On the Create table page, in the Destination section:

   • Verify that Dataset name is set to tutorial_dataset.
   • In the Table name field, enter tutorial.
   • Verify that Table type is set to Native table.

6. In the Schema section, enter the schema definition. Enable Edit as text and enter the following table schema as a JSON array.

   [
     {
       "mode": "NULLABLE",
       "name": "url",
       "type": "STRING"
     },
     {
       "mode": "NULLABLE",
       "name": "review",
       "type": "STRING"
     }
   ]
   

7. For Partition and cluster settings leave the default value — No partitioning.

8. In the Advanced options section, for Encryption leave the default value: Google-managed key. By default, Dataflow encrypts customer content stored at rest.

9. Click Create table.

gcloud

Use the bq mk command to create the dataset.

bq --location=DATAFLOW_REGION mk \
PROJECT_ID:tutorial_dataset

Replace PROJECT_ID with the project ID of your project.

Use the bq mk command with the --table or -t flag to create a table in your dataset.

bq mk \
    --table \
    PROJECT_ID:tutorial_dataset.tutorial \
    url:STRING,review:STRING

Run the pipeline

Run a streaming pipeline using the Google-provided Pub/Sub Topic to BigQuery template. The pipeline gets incoming data from the Pub/Sub topic and outputs the data to your BigQuery dataset.

Console

1. In the Google Cloud console, go to the Dataflow Jobs page.

   Go to Jobs

2. Click Create job from template.

3. Enter a Job name for your Dataflow job.

4. For Dataflow template, select the Pub/Sub Topic to BigQuery template.

5. For Input Pub/Sub topic, enter the following:

   projects/PROJECT_ID/topics/TOPIC_ID
   

   Replace PROJECT_ID with the project ID of the project where you created your BigQuery dataset and TOPIC_ID with the name of your Pub/Sub topic.

6. For BigQuery output table, enter the following:

   PROJECT_ID:tutorial_dataset.tutorial
   

7. For Temporary location, enter the following:

   gs://BUCKET_NAME/temp/
   

   Replace BUCKET_NAME with the name of your Cloud Storage bucket. The temp folder stores temporary files, like the staged pipeline job.

8. Click Run job.

gcloud

Note: To use the Google Cloud CLI to run classic templates, you must have Google Cloud CLI version 138.0.0 or later.

To run the template in your shell or terminal, use the gcloud dataflow jobs run command.

gcloud dataflow jobs run JOB_NAME \
    --gcs-location gs://dataflow-templates/latest/PubSub_to_BigQuery \
    --region DATAFLOW_REGION \
    --staging-location gs://BUCKET_NAME/temp \
    --parameters \
inputTopic=projects/PROJECT_ID/topics/TOPIC_ID,\
outputTableSpec=PROJECT_ID:tutorial_dataset.tutorial,\
outputDeadletterTable=PROJECT_ID:tutorial_dataset.tutorial

Replace JOB_NAME with a unique name of your choice.

View your results

View the data written to your BigQuery table.

Console

1. In the Google Cloud console, go to the BigQuery page.
   Go to the BigQuery page

2. In the query editor, run the following query:

   SELECT * FROM `PROJECT_ID.tutorial_dataset.tutorial`
   LIMIT 1000
   

   It can take up to a minute for data to start appearing in your table.

   The query returns rows that have been added to your table in the past 24 hours. You can also run queries using standard SQL.

gcloud

Check the results in BigQuery by running the following query:

bq query --use_legacy_sql=false 'SELECT * FROM `'"PROJECT_ID.tutorial_dataset.tutorial"'`'

While this pipeline is running, you can see new rows appended into the BigQuery table every minute.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

Delete the project

The easiest way to eliminate billing is to delete the Google Cloud project that you created for the tutorial.

Caution: Deleting a project has the following effects:

• Everything in the project is deleted. If you used an existing project for this tutorial, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple tutorials and quickstarts, reusing projects can help you avoid exceeding project quota limits.

1. In the Google Cloud console, go to the Manage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then click Delete.
3. In the dialog, type the project ID, and then click Shut down to delete the project.

Delete the individual resources

Stop the Dataflow pipeline

Console

1. In the Google Cloud console, go to the Dataflow Jobs page.

   Go to Jobs

2. Click the job that you want to stop.

   To stop a job, the status of the job must be running.

3. In the job details page, click Stop.

4. Click Cancel.

5. To confirm your choice, click Stop Job.

gcloud

To cancel your Dataflow job, use the gcloud dataflow jobs command.

gcloud dataflow jobs list \
  --filter 'NAME=JOB_NAME AND STATE=Running' \
  --format 'value(JOB_ID)' \
  --region "DATAFLOW_REGION" \
  | xargs gcloud dataflow jobs cancel --region "DATAFLOW_REGION"

Clean up Google Cloud project resources

Console

1. Delete the Cloud Scheduler jobs.
   1. Go to the Cloud Scheduler page in the Google Cloud console.
      Go to the Scheduler page
   2. Select your jobs.
   3. Click the Delete button at the top of the page and confirm your delete.
2. Delete the Pub/Sub topic and subscription.
   1. Go to the Pub/Sub topics page in the Google Cloud console.
      Go to the Pub/Sub topics page
   2. Select the topic that you created.
   3. Click Delete to permanently delete the topic.
   4. Go to the Pub/Sub subscriptions page in the Google Cloud console.
   5. Select the subscription created with your topic.
   6. Click Delete to permanently delete the subscription.
3. Delete the BigQuery table and dataset.
   1. In the Google Cloud console, go to the BigQuery page.
      Go to BigQuery
   2. In the Explorer panel, expand your project.
   3. Next to the dataset you want to delete, click View actions, and then click delete.
4. Delete the Cloud Storage bucket.
   1. In the Google Cloud console, go to the Cloud Storage page.
      Go to the Cloud Storage page
   2. Select the bucket that you want to delete, click Delete, and then follow the instructions.

gcloud

1. To delete the Cloud Scheduler jobs, use the gcloud scheduler jobs delete command.

   gcloud scheduler jobs delete negative-ratings-publisher --location=DATAFLOW_REGION
   

   gcloud scheduler jobs delete positive-ratings-publisher --location=DATAFLOW_REGION
   

2. To delete the Pub/Sub subscription and topic, use the gcloud pubsub subscriptions delete and the gcloud pubsub topics delete commands.

   gcloud pubsub subscriptions delete SUBSCRIPTION_ID
   gcloud pubsub topics delete TOPIC_ID
   

3. To delete the BigQuery table, use the bq rm command.

   bq rm -f -t PROJECT_ID:tutorial_dataset.tutorial
   

4. Delete the BigQuery dataset. The dataset alone does not incur any charges.

   The following command also deletes all tables in the dataset. The tables and data cannot be recovered.

   bq rm -r -f -d PROJECT_ID:tutorial_dataset
   

5. To delete the Cloud Storage bucket, use the gsutil rm command. The bucket alone does not incur any charges.

   The following command also deletes all objects in the bucket. These objects cannot be recovered.

   gsutil rm -r gs://BUCKET_NAME
   

What's next

• Extend your Dataflow template with UDFs.
• Learn more about using Dataflow templates.
• Look through all the Google-provided templates.
• Read about using Pub/Sub to create and use topics and to create and use subscriptions.
• Learn more about using Cloud Scheduler to schedule and run cron jobs.
• Read about using BigQuery to create datasets.
• Explore reference architectures, diagrams, tutorials, and best practices about Google Cloud. Take a look at our Cloud Architecture Center.