Stream messages from Pub/Sub by using Dataflow

Dataflow is a fully-managed service for transforming and enriching data in stream (real-time) and batch modes with equal reliability and expressiveness. It provides a simplified pipeline development environment using the Apache Beam SDK, which has a rich set of windowing and session analysis primitives as well as an ecosystem of source and sink connectors. This quickstart shows you how to use Dataflow to:

  • Read messages published to a Pub/Sub topic
  • Window (or group) the messages by timestamp
  • Write the messages to Cloud Storage

This quickstart introduces you to using Dataflow in Java and Python. SQL is also supported. This quickstart is also offered as a Google Cloud Skills Boost tutorial which offers temporary credentials to get you started.

You can also start by using UI-based Dataflow templates if you do not intend to do custom data processing.

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.

    • 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 API, Pub/Sub, Resource Manager, and Cloud Scheduler APIs: 

    gcloud services enable dataflow.googleapis.com  compute.googleapis.com  logging.googleapis.com  storage-component.googleapis.com  storage-api.googleapis.com  pubsub.googleapis.com  cloudresourcemanager.googleapis.com  cloudscheduler.googleapis.com

  7. Set up authentication:

    1. Create the service account: 

      gcloud iam service-accounts create SERVICE_ACCOUNT_NAME

      Replace SERVICE_ACCOUNT_NAME with a name for the service account.

    2. Grant roles to the service account. Run the following command once for each of the following IAM roles: roles/dataflow.worker, roles/storage.objectAdmin, roles/pubsub.admin: 

      gcloud projects add-iam-policy-binding PROJECT_ID --member="serviceAccount:SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com" --role=ROLE

      Replace the following:

      • SERVICE_ACCOUNT_NAME: the name of the service account
      • PROJECT_ID: the project ID where you created the service account
      • ROLE: the role to grant

    3. Grant the required role to the principal that will attach the service account to other resources. 

      gcloud iam service-accounts add-iam-policy-binding SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com --member="user:USER_EMAIL" --role=roles/iam.serviceAccountUser

      Replace the following:

      • SERVICE_ACCOUNT_NAME: the name of the service account
      • PROJECT_ID: the project ID where you created the service account
      • USER_EMAIL: the email address for a Google Account
    8.
  8. Install the Google Cloud CLI.

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

    gcloud init

  10. Create or select a Google Cloud 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.

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

  12. Enable the Dataflow, Compute Engine, Cloud Logging, Cloud Storage, Google Cloud Storage JSON API, Pub/Sub, Resource Manager, and Cloud Scheduler APIs: 

    gcloud services enable dataflow.googleapis.com  compute.googleapis.com  logging.googleapis.com  storage-component.googleapis.com  storage-api.googleapis.com  pubsub.googleapis.com  cloudresourcemanager.googleapis.com  cloudscheduler.googleapis.com

  13. Set up authentication:

    1. Create the service account: 

      gcloud iam service-accounts create SERVICE_ACCOUNT_NAME

      Replace SERVICE_ACCOUNT_NAME with a name for the service account.

    2. Grant roles to the service account. Run the following command once for each of the following IAM roles: roles/dataflow.worker, roles/storage.objectAdmin, roles/pubsub.admin: 

      gcloud projects add-iam-policy-binding PROJECT_ID --member="serviceAccount:SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com" --role=ROLE

      Replace the following:

      • SERVICE_ACCOUNT_NAME: the name of the service account
      • PROJECT_ID: the project ID where you created the service account
      • ROLE: the role to grant

    3. Grant the required role to the principal that will attach the service account to other resources. 

      gcloud iam service-accounts add-iam-policy-binding SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com --member="user:USER_EMAIL" --role=roles/iam.serviceAccountUser

      Replace the following:

      • SERVICE_ACCOUNT_NAME: the name of the service account
      • PROJECT_ID: the project ID where you created the service account
      • USER_EMAIL: the email address for a Google Account
    14.

  14. Create local authentication credentials for your user account: 

    gcloud auth application-default login

Set up your Pub/Sub project

  1. Create variables for your bucket, project, and region. Cloud Storage bucket names must be globally unique. Select a Dataflow region close to where you run the commands in this quickstart. The value of the REGION variable must be a valid region name. For more information about regions and locations, see Dataflow locations. 

    BUCKET_NAME=BUCKET_NAME
PROJECT_ID=$(gcloud config get-value project)
TOPIC_ID=TOPIC_ID
REGION=DATAFLOW_REGION
SERVICE_ACCOUNT=SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com

  2. Create a Cloud Storage bucket owned by this project: 

    gcloud storage buckets create gs://$BUCKET_NAME

  3. Create a Pub/Sub topic in this project: 

    gcloud pubsub topics create $TOPIC_ID

  4. Create a Cloud Scheduler job in this project. The job publishes a message to a Pub/Sub topic at one-minute intervals.

    If an App Engine app does not exist for the project, this step will create one. 

    gcloud scheduler jobs create pubsub publisher-job --schedule="* * * * *" \
    --topic=$TOPIC_ID --message-body="Hello!" --location=$REGION

    Start the job. 

    gcloud scheduler jobs run publisher-job --location=$REGION

  5. Use the following commands to clone the quickstart repository and navigate to the sample code directory:

    Java

    git clone https://github.com/GoogleCloudPlatform/java-docs-samples.git
cd java-docs-samples/pubsub/streaming-analytics

    Python

    git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git
cd python-docs-samples/pubsub/streaming-analytics
pip install -r requirements.txt  # Install Apache Beam dependencies

Stream messages from Pub/Sub to Cloud Storage

Code sample

This sample code uses Dataflow to:

  • Read Pub/Sub messages.
  • Window (or group) messages into fixed-size intervals by publish timestamps.

  • Write the messages in each window to files in Cloud Storage.

Java


import java.io.IOException;
import org.apache.beam.examples.common.WriteOneFilePerWindow;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.gcp.pubsub.PubsubIO;
import org.apache.beam.sdk.options.Default;
import org.apache.beam.sdk.options.Description;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.StreamingOptions;
import org.apache.beam.sdk.options.Validation.Required;
import org.apache.beam.sdk.transforms.windowing.FixedWindows;
import org.apache.beam.sdk.transforms.windowing.Window;
import org.joda.time.Duration;

public class PubSubToGcs {
  /*
   * Define your own configuration options. Add your own arguments to be processed
   * by the command-line parser, and specify default values for them.
   */
  public interface PubSubToGcsOptions extends StreamingOptions {
    @Description("The Cloud Pub/Sub topic to read from.")
    @Required
    String getInputTopic();

    void setInputTopic(String value);

    @Description("Output file's window size in number of minutes.")
    @Default.Integer(1)
    Integer getWindowSize();

    void setWindowSize(Integer value);

    @Description("Path of the output file including its filename prefix.")
    @Required
    String getOutput();

    void setOutput(String value);
  }

  public static void main(String[] args) throws IOException {
    // The maximum number of shards when writing output.
    int numShards = 1;

    PubSubToGcsOptions options =
        PipelineOptionsFactory.fromArgs(args).withValidation().as(PubSubToGcsOptions.class);

    options.setStreaming(true);

    Pipeline pipeline = Pipeline.create(options);

    pipeline
        // 1) Read string messages from a Pub/Sub topic.
        .apply("Read PubSub Messages", PubsubIO.readStrings().fromTopic(options.getInputTopic()))
        // 2) Group the messages into fixed-sized minute intervals.
        .apply(Window.into(FixedWindows.of(Duration.standardMinutes(options.getWindowSize()))))
        // 3) Write one file to GCS for every window of messages.
        .apply("Write Files to GCS", new WriteOneFilePerWindow(options.getOutput(), numShards));

    // Execute the pipeline and wait until it finishes running.
    pipeline.run().waitUntilFinish();
  }
}

Python

import argparse
from datetime import datetime
import logging
import random

from apache_beam import (
    DoFn,
    GroupByKey,
    io,
    ParDo,
    Pipeline,
    PTransform,
    WindowInto,
    WithKeys,
)
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.transforms.window import FixedWindows


class GroupMessagesByFixedWindows(PTransform):
    """A composite transform that groups Pub/Sub messages based on publish time
    and outputs a list of tuples, each containing a message and its publish time.
    """

    def __init__(self, window_size, num_shards=5):
        # Set window size to 60 seconds.
        self.window_size = int(window_size * 60)
        self.num_shards = num_shards

    def expand(self, pcoll):
        return (
            pcoll
            # Bind window info to each element using element timestamp (or publish time).
            | "Window into fixed intervals"
            >> WindowInto(FixedWindows(self.window_size))
            | "Add timestamp to windowed elements" >> ParDo(AddTimestamp())
            # Assign a random key to each windowed element based on the number of shards.
            | "Add key" >> WithKeys(lambda _: random.randint(0, self.num_shards - 1))
            # Group windowed elements by key. All the elements in the same window must fit
            # memory for this. If not, you need to use `beam.util.BatchElements`.
            | "Group by key" >> GroupByKey()
        )


class AddTimestamp(DoFn):
    def process(self, element, publish_time=DoFn.TimestampParam):
        """Processes each windowed element by extracting the message body and its
        publish time into a tuple.
        """
        yield (
            element.decode("utf-8"),
            datetime.utcfromtimestamp(float(publish_time)).strftime(
                "%Y-%m-%d %H:%M:%S.%f"
            ),
        )


class WriteToGCS(DoFn):
    def __init__(self, output_path):
        self.output_path = output_path

    def process(self, key_value, window=DoFn.WindowParam):
        """Write messages in a batch to Google Cloud Storage."""

        ts_format = "%H:%M"
        window_start = window.start.to_utc_datetime().strftime(ts_format)
        window_end = window.end.to_utc_datetime().strftime(ts_format)
        shard_id, batch = key_value
        filename = "-".join([self.output_path, window_start, window_end, str(shard_id)])

        with io.gcsio.GcsIO().open(filename=filename, mode="w") as f:
            for message_body, publish_time in batch:
                f.write(f"{message_body},{publish_time}\n".encode())


def run(input_topic, output_path, window_size=1.0, num_shards=5, pipeline_args=None):
    # Set `save_main_session` to True so DoFns can access globally imported modules.
    pipeline_options = PipelineOptions(
        pipeline_args, streaming=True, save_main_session=True
    )

    with Pipeline(options=pipeline_options) as pipeline:
        (
            pipeline
            # Because `timestamp_attribute` is unspecified in `ReadFromPubSub`, Beam
            # binds the publish time returned by the Pub/Sub server for each message
            # to the element's timestamp parameter, accessible via `DoFn.TimestampParam`.
            # https://beam.apache.org/releases/pydoc/current/apache_beam.io.gcp.pubsub.html#apache_beam.io.gcp.pubsub.ReadFromPubSub
            | "Read from Pub/Sub" >> io.ReadFromPubSub(topic=input_topic)
            | "Window into" >> GroupMessagesByFixedWindows(window_size, num_shards)
            | "Write to GCS" >> ParDo(WriteToGCS(output_path))
        )


if __name__ == "__main__":
    logging.getLogger().setLevel(logging.INFO)

    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--input_topic",
        help="The Cloud Pub/Sub topic to read from."
        '"projects/<PROJECT_ID>/topics/<TOPIC_ID>".',
    )
    parser.add_argument(
        "--window_size",
        type=float,
        default=1.0,
        help="Output file's window size in minutes.",
    )
    parser.add_argument(
        "--output_path",
        help="Path of the output GCS file including the prefix.",
    )
    parser.add_argument(
        "--num_shards",
        type=int,
        default=5,
        help="Number of shards to use when writing windowed elements to GCS.",
    )
    known_args, pipeline_args = parser.parse_known_args()

    run(
        known_args.input_topic,
        known_args.output_path,
        known_args.window_size,
        known_args.num_shards,
        pipeline_args,
    )

Start the pipeline

To start the pipeline, run the following command:

Java

mvn compile exec:java \
  -Dexec.mainClass=com.examples.pubsub.streaming.PubSubToGcs \
  -Dexec.cleanupDaemonThreads=false \
  -Dexec.args=" \
    --project=$PROJECT_ID \
    --region=$REGION \
    --inputTopic=projects/$PROJECT_ID/topics/$TOPIC_ID \
    --output=gs://$BUCKET_NAME/samples/output \
    --gcpTempLocation=gs://$BUCKET_NAME/temp \
    --runner=DataflowRunner \
    --windowSize=2 \
    --serviceAccount=$SERVICE_ACCOUNT"

Python

python PubSubToGCS.py \
  --project=$PROJECT_ID \
  --region=$REGION \
  --input_topic=projects/$PROJECT_ID/topics/$TOPIC_ID \
  --output_path=gs://$BUCKET_NAME/samples/output \
  --runner=DataflowRunner \
  --window_size=2 \
  --num_shards=2 \
  --temp_location=gs://$BUCKET_NAME/temp \
  --service_account_email=$SERVICE_ACCOUNT

The preceding command runs locally and launches a Dataflow job that runs in the cloud. When the command returns JOB_MESSAGE_DETAILED: Workers have started successfully, exit the local program using Ctrl+C.

Observe job and pipeline progress

You can observe the job's progress in the Dataflow console.

Go to the Dataflow console

Observe the job's progress

Open the job details view to see:

  • Job structure
  • Job logs
  • Stage metrics

Observe the job's progress

You may have to wait a few minutes to see the output files in Cloud Storage.

Observe the job's progress

Alternatively, use the command line below to check which files have been written out.

gcloud storage ls gs://${BUCKET_NAME}/samples/

The output should look like the following:

Java

gs://{$BUCKET_NAME}/samples/output-22:30-22:32-0-of-1
gs://{$BUCKET_NAME}/samples/output-22:32-22:34-0-of-1
gs://{$BUCKET_NAME}/samples/output-22:34-22:36-0-of-1
gs://{$BUCKET_NAME}/samples/output-22:36-22:38-0-of-1

Python

gs://{$BUCKET_NAME}/samples/output-22:30-22:32-0
gs://{$BUCKET_NAME}/samples/output-22:30-22:32-1
gs://{$BUCKET_NAME}/samples/output-22:32-22:34-0
gs://{$BUCKET_NAME}/samples/output-22:32-22:34-1

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.

  1. Delete the Cloud Scheduler job.

    gcloud scheduler jobs delete publisher-job --location=$REGION

  2. In the Dataflow console, stop the job. Cancel the pipeline without draining it.

  3. Delete the topic.

    gcloud pubsub topics delete $TOPIC_ID

  4. Delete the files created by the pipeline.

    gcloud storage rm "gs://${BUCKET_NAME}/samples/output*" --recursive --continue-on-error
gcloud storage rm "gs://${BUCKET_NAME}/temp/*" --recursive --continue-on-error

  5. Remove the Cloud Storage bucket.

    gcloud storage rm gs://${BUCKET_NAME} --recursive

  6. Delete the service account: 
    gcloud iam service-accounts delete SERVICE_ACCOUNT_EMAIL

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

    gcloud auth application-default revoke

  8. Optional: Revoke credentials from the gcloud CLI. 

    gcloud auth revoke

What's next