Quickstart: stream processing with 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.

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

Before you begin

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.
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
Make sure that billing is enabled for your Cloud project. Learn how to confirm that billing is enabled for your project.
Enable the Compute Engine, Google Cloud's operations suite, Cloud Storage, Cloud Storage JSON, Pub/Sub, Cloud Scheduler, Resource Manager, and App Engine APIs.
Enable the APIs

Create a service account:
1. In the Cloud Console, go to the Create service account page.
  Go to Create service account
2. Select a project.
3. In the Service account name field, enter a name. The Cloud Console fills in the Service account ID field based on this name.
  
  In the Service account description field, enter a description. For example, Service account for quickstart.
4. Click Create.
5. Click the Select a role field.
  
  Under Quick access, click Basic, then click Owner.
  
  Note: The Role field affects which resources your service account can access in your project. You can revoke these roles or grant additional roles later. In production environments, do not grant the Owner, Editor, or Viewer roles. For more information, see Granting, changing, and revoking access to resources.
6. Click Continue.
7. Click Done to finish creating the service account.
  
  Do not close your browser window. You will use it in the next step.
Create a service account key:
1. In the Cloud Console, click the email address for the service account that you created.
2. Click Keys.
3. Click Add key, then click Create new key.
4. Click Create. A JSON key file is downloaded to your computer.
5. Click Close.
Set the environment variable GOOGLE_APPLICATION_CREDENTIALS to the path of the JSON file that contains your service account key. This variable only applies to your current shell session, so if you open a new session, set the variable again.
Example: Linux or macOS

Replace [PATH] with the path of the JSON file that contains your service account key.
```
export GOOGLE_APPLICATION_CREDENTIALS="[PATH]"
```
For example:
```
export GOOGLE_APPLICATION_CREDENTIALS="/home/user/Downloads/service-account-file.json"
```
Example: Windows

Replace [PATH] with the path of the JSON file that contains your service account key.

With PowerShell:
```
$env:GOOGLE_APPLICATION_CREDENTIALS="[PATH]"
```
For example:
```
$env:GOOGLE_APPLICATION_CREDENTIALS="C:\Users\username\Downloads\my-key.json"
```
With command prompt:
```
set GOOGLE_APPLICATION_CREDENTIALS=[PATH]
```
Install and initialize the Cloud SDK.
Create variables for your bucket, project, and region. Cloud Storage bucket names must be globally unique. Select a Dataflow region close to where you run the commands in this quickstart.
```
BUCKET_NAME=your-bucket-name
PROJECT_ID=$(gcloud config get-value project)
TOPIC_ID=your-topic-id
REGION=dataflow-region
```
Create a Cloud Storage bucket owned by this project:
```
gsutil mb gs://$BUCKET_NAME
```
Create a Pub/Sub topic in this project:
```
gcloud pubsub topics create $TOPIC_ID
```
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!"
```
Start the job.
```
gcloud scheduler jobs run publisher-job
```

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

View on GitHub Feedback


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.PipelineOptions;
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 PipelineOptions, 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

View on GitHub Feedback

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("utf-8"))


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 \
    --runner=DataflowRunner \
    --windowSize=2"

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

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.

gsutil 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

Cleanup

Delete the Cloud Scheduler job.

gcloud scheduler jobs delete publisher-job

In the Dataflow console, stop the job. Cancel the pipeline without draining it.
Delete the topic.
```
gcloud pubsub topics delete $TOPIC_ID
```

Delete the files created by the pipeline.

gsutil -m rm -rf "gs://${BUCKET_NAME}/samples/output*"
gsutil -m rm -rf "gs://${BUCKET_NAME}/temp/*"

Remove the Cloud Storage bucket.
```
gsutil rb gs://${BUCKET_NAME}
```

What's next

If you would like to window Pub/Sub messages by a custom timestamp, you can specify the timestamp as an attribute in the Pub/Sub message, and then use the custom timestamp with PubsubIO's withTimestampAttribute.
Take a look at Google's open-source Dataflow templates designed for streaming.
Read more about how Dataflow integrates with Pub/Sub.
Check out this tutorial that reads from Pub/Sub and writes to BigQuery using Dataflow Flex templates.
For more about windowing, see the Apache Beam Mobile Gaming Pipeline example.