Process a Bigtable change stream

This tutorial shows how to deploy a data pipeline to Dataflow for a real-time stream of database changes sourced from a Bigtable table's change stream. The output of the pipeline is written to a series of files on Cloud Storage.

An example dataset for a music listening application is provided. In this tutorial, you track songs that are listened to and then rank the top five over a period.

This tutorial is intended for technical users familiar with writing code and deploying data pipelines to Google Cloud.

Objectives

This tutorial shows you how to do the following:

Create a Bigtable table with a change stream enabled.
Deploy a pipeline on Dataflow that transforms and outputs the change stream.
View the results of your data pipeline.

Costs

In this document, you use the following billable components of Google Cloud:

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 the tasks that are described in this document, you can avoid continued billing by deleting the resources that you created. For more information, see Clean up.

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.

Install the Google Cloud CLI.

To initialize the gcloud CLI, run the following command:

gcloud init

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.

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

Enable the Dataflow, Cloud Bigtable API, Cloud Bigtable Admin API, and Cloud Storage APIs:

gcloud services enable dataflow.googleapis.com bigtable.googleapis.com bigtableadmin.googleapis.com storage.googleapis.com

Install the Google Cloud CLI.

To initialize the gcloud CLI, run the following command:

gcloud init

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.

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

Enable the Dataflow, Cloud Bigtable API, Cloud Bigtable Admin API, and Cloud Storage APIs:

gcloud services enable dataflow.googleapis.com bigtable.googleapis.com bigtableadmin.googleapis.com storage.googleapis.com

Update and install the cbt CLI .

gcloud components update
gcloud components install cbt

Prepare the environment

Get the code

Clone the repository that contains the sample code. If you already previously downloaded this repository, pull to get the latest version.

git clone https://github.com/GoogleCloudPlatform/java-docs-samples.git
cd java-docs-samples/bigtable/beam/change-streams

Create a bucket

Create a Cloud Storage bucket:

gcloud storage buckets create gs://BUCKET_NAME

Replace BUCKET_NAME with a bucket name that meets the bucket naming requirements.

Create a Bigtable instance

You can use an existing instance for this tutorial or create an instance with the default configurations in a region near you.

Create a table

The sample application tracks the songs that users listen to and stores the listen events in Bigtable. Create a table with a change stream enabled that has one column family (cf) and one column (song) and uses user IDs for row keys.

Create the table.

gcloud bigtable instances tables create song-rank \
--column-families=cf --change-stream-retention-period=7d \
--instance=BIGTABLE_INSTANCE_ID --project=PROJECT_ID

Replace the following:

PROJECT_ID: the ID of the project that you are using
BIGTABLE_INSTANCE_ID: the ID of the instance to contain the new table

Start the pipeline

This pipeline transforms the change stream by doing the following:

Reads the change stream
Gets the song name
Groups the song listen events into N-second windows
Counts the top five songs
Outputs the results

Run the pipeline.

mvn compile exec:java -Dexec.mainClass=SongRank \
"-Dexec.args=--project=PROJECT_ID --bigtableProjectId=PROJECT_ID \
--bigtableInstanceId=BIGTABLE_INSTANCE_ID --bigtableTableId=song-rank \
--outputLocation=gs://BUCKET_NAME/ \
--runner=dataflow --region=BIGTABLE_REGION --experiments=use_runner_v2"

Replace BIGTABLE_REGION with the ID of the region that your Bigtable instance is in, such as us-east5.

Understand the pipeline

The following snippets of code from the pipeline can help you understand the code you are running.

Reading the change stream

The code in this sample configures the source stream with the parameters for the specific Bigtable instance and table.

p.apply(
        "Stream from Bigtable",
        BigtableIO.readChangeStream()
            .withProjectId(options.getBigtableProjectId())
            .withInstanceId(options.getBigtableInstanceId())
            .withTableId(options.getBigtableTableId())
            .withAppProfileId(options.getBigtableAppProfile())

    )

Getting the song name

When a song is listened to, the song name is written to the column family cf and column qualifier song, so the code extracts the value from the change stream mutation and outputs it to the next step of the pipeline.

private static class ExtractSongName extends DoFn<KV<ByteString, ChangeStreamMutation>, String> {

  @DoFn.ProcessElement
  public void processElement(ProcessContext c) {

    for (Entry e : Objects.requireNonNull(Objects.requireNonNull(c.element()).getValue())
        .getEntries()) {
      if (e instanceof SetCell) {
        SetCell setCell = (SetCell) e;
        if ("cf".equals(setCell.getFamilyName())
            && "song".equals(setCell.getQualifier().toStringUtf8())) {
          c.output(setCell.getValue().toStringUtf8());
        }
      }
    }
  }
}

Counting the top five songs

You can use the built-in Beam functions Count and Top.of to get the top five songs in the current window.

.apply(Count.perElement())
.apply("Top songs", Top.of(5, new SongComparator()).withoutDefaults())

Outputting the results

This pipeline writes the results to standard out as well as files. For the files, it windows the writes into groups of 10 elements or one-minute segments.

.apply("Print", ParDo.of(new PrintFn()))
.apply(
    "Collect at least 10 elements or 1 minute of elements",
    Window.<String>into(new GlobalWindows())
        .triggering(
            Repeatedly.forever(
                AfterFirst.of(
                    AfterPane.elementCountAtLeast(10),
                    AfterProcessingTime
                        .pastFirstElementInPane()
                        .plusDelayOf(Duration.standardMinutes(1)
                        )
                )
            ))
        .discardingFiredPanes())
.apply(
    "Output top songs",
    TextIO.write()
        .to(options.getOutputLocation() + "song-charts/")
        .withSuffix(".txt")
        .withNumShards(1)
        .withWindowedWrites()
);

View the pipeline

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

Go to Dataflow
Click the job with a name that begins with song-rank.
At the bottom of the screen click Show to open the logs panel.
Click Worker logs to monitor the output logs of the change stream.

Note: The log might take a few minutes to appear while the job initializes.

Stream writes

Use the cbt CLI to write a number of song listens for various users to the song-rank table. This is designed to write over a few minutes to simulate song listens streaming in over time.

cbt -instance=BIGTABLE_INSTANCE_ID -project=PROJECT_ID import \
song-rank song-rank-data.csv  column-family=cf batch-size=1

View the output

Read the output on Cloud Storage to see the most popular songs.

gcloud storage cat gs://BUCKET_NAME/song-charts/GlobalWindow-pane-0-00000-of-00001.txt

Example output:

2023-07-06T19:53:38.232Z [KV{The Wheels on the Bus, 199}, KV{Twinkle, Twinkle, Little Star, 199}, KV{Ode to Joy , 192}, KV{Row, Row, Row Your Boat, 186}, KV{Take Me Out to the Ball Game, 182}]
2023-07-06T19:53:49.536Z [KV{Old MacDonald Had a Farm, 20}, KV{Take Me Out to the Ball Game, 18}, KV{Für Elise, 17}, KV{Ode to Joy , 15}, KV{Mary Had a Little Lamb, 12}]
2023-07-06T19:53:50.425Z [KV{Twinkle, Twinkle, Little Star, 20}, KV{The Wheels on the Bus, 17}, KV{Row, Row, Row Your Boat, 13}, KV{Happy Birthday to You, 12}, KV{Over the Rainbow, 9}]

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

Delete a Google Cloud project:

gcloud projects delete PROJECT_ID

Delete individual resources

Delete the bucket and files.

gcloud storage rm --recursive gs://BUCKET_NAME/

Disable the change stream on the table.

gcloud bigtable instances tables update song-rank --instance=BIGTABLE_INSTANCE_ID \
--clear-change-stream-retention-period

Delete the table song-rank.

cbt -instance=BIGTABLE_INSTANCE_ID -project=PROJECT_ID deletetable song-rank

Stop the change stream pipeline.
1. List the jobs to get the job ID.
```
gcloud dataflow jobs list --region=BIGTABLE_REGION
```
2. Cancel the job.
```
gcloud dataflow jobs cancel JOB_ID --region=BIGTABLE_REGION
```
  Replace JOB_ID with the job ID displayed after the prior command.

Process a Bigtable change stream

Objectives

Costs

Before you begin

Prepare the environment

Get the code

Create a bucket

Create a Bigtable instance

Create a table

Start the pipeline

Understand the pipeline

Reading the change stream

Getting the song name

Counting the top five songs

Outputting the results

View the pipeline

Stream writes

View the output

Clean up

Delete the project

Delete individual resources

What's next