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Google Cloud Dataflow Template Pipelines

These Dataflow templates are an effort to solve simple, but large, in-Cloud data tasks, including data import/export/backup/restore and bulk API operations, without a development environment. The technology under the hood which makes these operations possible is the Google Cloud Dataflow service combined with a set of Apache Beam SDK templated pipelines.

Google is providing this collection of pre-implemented Dataflow templates as a reference and to provide easy customization for developers wanting to extend their functionality.

Open in Cloud Shell

Note on Default Branch

As of November 18, 2021, our default branch is now named "main". This does not affect forks. If you would like your fork and its local clone to reflect these changes you can follow GitHub's branch renaming guide.

Building

Maven commands should be run on the parent POM. An example would be:

mvn clean package -pl v2/pubsub-binary-to-bigquery -am

Template Pipelines

For documentation on each template's usage and parameters, please see the official docs.

Getting Started

Requirements

  • Java 11
  • Maven 3

Building the Project

Build the entire project using the maven compile command.

mvn clean compile

Building/Testing from IntelliJ

IntelliJ, by default, will often skip necessary Maven goals, leading to build failures. You can fix these in the Maven view by going to Module_Name > Plugins > Plugin_Name where Module_Name and Plugin_Name are the names of the respective module and plugin with the rule. From there, right-click the rule and select "Execute Before Build".

The list of known rules that require this are:

  • common > Plugins > protobuf > protobuf:compile
  • common > Plugins > protobuf > protobuf:test-compile

Formatting Code

From either the root directory or v2/ directory, run:

mvn spotless:apply

This will format the code and add a license header. To verify that the code is formatted correctly, run:

mvn spotless:check

Executing a Template File

Once the template is staged on Google Cloud Storage, it can then be executed using the gcloud CLI tool. Please check Running classic templates or Using Flex Templates for more information.

Developing/Contributing Templates

Templates Plugin

Templates plugin was created to make the workflow of creating, testing and releasing Templates easier.

Before using the plugin, please make sure that the gcloud CLI is installed and up-to-date, and that the client is properly authenticated using:

gcloud init
gcloud auth application-default login

After authenticated, install the plugin into your local repository:

mvn clean install -pl plugins/templates-maven-plugin -am

Staging (Deploying) Templates

To stage a Template, it is necessary to upload the images to Artifact Registry (for Flex templates) and copy the template to Cloud Storage.

Although there are different steps that depend on the kind of template being developed. The plugin allows a template to be staged using the following single command:

mvn clean package -PtemplatesStage  \
  -DskipTests \
  -DprojectId="{projectId}" \
  -DbucketName="{bucketName}" \
  -DstagePrefix="images/$(date +%Y_%m_%d)_01" \
  -DtemplateName="Cloud_PubSub_to_GCS_Text_Flex" \
  -pl v2/googlecloud-to-googlecloud -am

Notes:

  • Change -pl v2/googlecloud-to-googlecloud and -DtemplateName to point to the specific Maven module where your template is located. Even though -pl is not required, it allows the command to run considerably faster.
  • In case -DtemplateName is not specified, all templates for the module will be staged.

Running a Template

A template can also be executed on Dataflow, directly from the command line. The command-line is similar to staging a template, but it is required to specify -Dparameters with the parameters that will be used when launching the template. For example:

mvn clean package -PtemplatesRun \
  -DskipTests \
  -DprojectId="{projectId}" \
  -DbucketName="{bucketName}" \
  -Dregion="us-central1" \
  -DtemplateName="Cloud_PubSub_to_GCS_Text_Flex" \
  -Dparameters="inputTopic=projects/{projectId}/topics/{topicName},windowDuration=15s,outputDirectory=gs://{outputDirectory}/out,outputFilenamePrefix=output-,outputFilenameSuffix=.txt" \
  -pl v2/googlecloud-to-googlecloud -am

Notes:

  • When running a template, -DtemplateName is mandatory, as -Dparameters= are different across templates.
  • -PtemplatesRun is self-contained, i.e., it is not required to run ** Deploying/Staging Templates** before. In case you want to run a previously staged template, the existing path can be provided as -DspecPath=gs://.../path
  • -DjobName="{name}" may be informed if a specific name is desirable ( optional).
  • If you encounter the error Template run failed: File too large, try adding -DskipShade to the mvn args.

Running Integration Tests

To run integration tests, the developer plugin can be also used to stage template on-demand (in case the parameter -DspecPath= is not specified).

For example, to run all the integration tests in a specific module (in the example below, v2/googlecloud-to-googlecloud):

mvn clean verify \
  -PtemplatesIntegrationTests \
  -Dproject="{project}" \
  -DartifactBucket="{bucketName}" \
  -Dregion=us-central1 \
  -pl v2/googlecloud-to-googlecloud -am

The parameter -Dtest= can be given to test a single class (e.g., -Dtest=PubsubToTextIT) or single test case (e.g., -Dtest=PubsubToTextIT#testTopicToGcs).

The same happens when the test is executed from an IDE, just make sure to add the parameters -Dproject=, -DartifactBucket= and -Dregion= as program or VM arguments.

Metadata Annotations

A template requires more information than just a name and description. For example, in order to be used from the Dataflow UI, parameters need a longer help text to guide users, as well as proper types and validations to make sure parameters are being passed correctly.

We introduced annotations to have the source code as a single source of truth, along with a set of utilities / plugins to generate template-accompanying artifacts (such as command specs, parameter specs).

@Template Annotation

Every template must be annotated with @Template. Existing templates can be used for reference, but the structure is as follows:

@Template(
    name = "BigQuery_to_Elasticsearch",
    category = TemplateCategory.BATCH,
    displayName = "BigQuery to Elasticsearch",
    description = "A pipeline which sends BigQuery records into an Elasticsearch instance as JSON documents.",
    optionsClass = BigQueryToElasticsearchOptions.class,
    flexContainerName = "bigquery-to-elasticsearch")
public class BigQueryToElasticsearch {

@TemplateParameter Annotation

A set of @TemplateParameter.{Type} annotations were created to allow the definition of options for a template, and the proper rendering in the UI, and validations by the template launch service. Examples can be found in the repository, but the general structure is as follows:

@TemplateParameter.Text(
    order = 2,
    optional = false,
    regexes = {"[,a-zA-Z0-9._-]+"},
    description = "Kafka topic(s) to read the input from",
    helpText = "Kafka topic(s) to read the input from.",
    example = "topic1,topic2")
@Validation.Required
String getInputTopics();
@TemplateParameter.GcsReadFile(
    order = 1,
    description = "Cloud Storage Input File(s)",
    helpText = "Path of the file pattern glob to read from.",
    example = "gs://your-bucket/path/*.csv")
String getInputFilePattern();
@TemplateParameter.Boolean(
    order = 11,
    optional = true,
    description = "Whether to use column alias to map the rows.",
    helpText = "If enabled (set to true) the pipeline will consider column alias (\"AS\") instead of the column name to map the rows to BigQuery.")
@Default.Boolean(false)
Boolean getUseColumnAlias();
@TemplateParameter.Enum(
    order = 21,
    enumOptions = {"INDEX", "CREATE"},
    optional = true,
    description = "Build insert method",
    helpText = "Whether to use INDEX (index, allows upsert) or CREATE (create, errors on duplicate _id) with Elasticsearch bulk requests.")
@Default.Enum("CREATE")
BulkInsertMethodOptions getBulkInsertMethod();

Note: order is relevant for templates that can be used from the UI, and specify the relative order of parameters.

@TemplateIntegrationTest Annotation

This annotation should be used by classes that are used for integration tests of other templates. This is used to wire a specific IT class with a template, and allows environment preparation / proper template staging before tests are executed on Dataflow.

Template tests have to follow this general format (please note the @TemplateIntegrationTest annotation and the TemplateTestBase super-class):

@TemplateIntegrationTest(PubsubToText.class)
@RunWith(JUnit4.class)
public final class PubsubToTextIT extends TemplateTestBase {

Please refer to Templates Plugin to use and validate such annotations.

Using UDFs

User-defined functions (UDFs) allow you to customize a template's functionality by providing a short JavaScript function without having to maintain the entire codebase. This is useful in situations which you'd like to rename fields, filter values, or even transform data formats before output to the destination. All UDFs are executed by providing the payload of the element as a string to the JavaScript function. You can then use JavaScript's in-built JSON parser or other system functions to transform the data prior to the pipeline's output. The return statement of a UDF specifies the payload to pass forward in the pipeline. This should always return a string value. If no value is returned or the function returns undefined, the incoming record will be filtered from the output.

UDF Function Specification

Template UDF Input Type Input Description UDF Output Type Output Description
Datastore Bulk Delete String A JSON string of the entity String A JSON string of the entity to delete; filter entities by returning undefined
Datastore to Pub/Sub String A JSON string of the entity String The payload to publish to Pub/Sub
Datastore to GCS Text String A JSON string of the entity String A single-line within the output file
GCS Text to BigQuery String A single-line within the input file String A JSON string which matches the destination table's schema
Pub/Sub to BigQuery String A string representation of the incoming payload String A JSON string which matches the destination table's schema
Pub/Sub to Datastore String A string representation of the incoming payload String A JSON string of the entity to write to Datastore
Pub/Sub to Splunk String A string representation of the incoming payload String The event data to be sent to Splunk HEC events endpoint. Must be a string or a stringified JSON object

UDF Examples

For a comprehensive list of samples, please check our udf-samples folder.

Adding fields

/**
 * A transform which adds a field to the incoming data.
 * @param {string} inJson
 * @return {string} outJson
 */
function transform(inJson) {
  var obj = JSON.parse(inJson);
  obj.dataFeed = "Real-time Transactions";
  obj.dataSource = "POS";
  return JSON.stringify(obj);
}

Filtering records

/**
 * A transform function which only accepts 42 as the answer to life.
 * @param {string} inJson
 * @return {string} outJson
 */
function transform(inJson) {
  var obj = JSON.parse(inJson);
  // only output objects which have an answer to life of 42.
  if (obj.hasOwnProperty('answerToLife') && obj.answerToLife === 42) {
    return JSON.stringify(obj);
  }
}

Generated Documentation

This repository contains generated documentation, which contains a list of parameters and instructions on how to customize and/or build every template.

To generate the documentation for all templates, the following command can be used:

mvn clean prepare-package \
  -DskipTests \
  -PtemplatesSpec

Release Process

Templates are released in a weekly basis (best-effort) as part of the efforts to keep Google-provided Templates updated with latest fixes and improvements.

In case desired, you can stage and use your own changes using the Staging (Deploying) Templates steps.

To execute the release of multiple templates, we provide a single Maven command to release Templates, which is a shortcut to stage all templates while running additional validations.

mvn clean verify -PtemplatesRelease \
  -DprojectId="{projectId}" \
  -DbucketName="{bucketName}" \
  -DlibrariesBucketName="{bucketName}-libraries" \
  -DstagePrefix="$(date +%Y_%m_%d)-00_RC00"

Maven artifacts

As part of the Templates development process, we release the common artifact snapshots to Maven Central, not modules that contain finalized templates. This allows users to consume those resources and modules without forking the entire project, while keeping artifacts at a reasonable size.

In order to release artifacts, ~/.m2/settings.xml should be configured to contain Sonatype's username and password:

<servers>
  <server>
    <id>ossrh</id>
    <username>(user)</username>
    <password>(password)</password>
  </server>
</servers>

And the command to release (for example, the development plugin and Spanner together):

mvn clean deploy -am -Prelease \
  -pl plugins/templates-maven-plugin \
  -pl v2/spanner-common

If you intend to use those resources in an external project, your pom.xml should include:

<repositories>
  <repository>
    <id>ossrh</id>
    <url>https://oss.sonatype.org/content/repositories/snapshots</url>
  </repository>
</repositories>
<pluginRepositories>
  <pluginRepository>
    <id>ossrh</id>
    <url>https://oss.sonatype.org/content/repositories/snapshots</url>
  </pluginRepository>
</pluginRepositories>

More Information

  • Dataflow Templates - basic template concepts.
  • Google-provided Templates - official documentation for templates provided by Google (the source code is in this repository).
  • Dataflow Cookbook: Blog, GitHub Repository - pipeline examples and practical solutions to common data processing challenges.
  • Dataflow Metrics Collector - CLI tool to collect dataflow resource & execution metrics and export to either BigQuery or Google Cloud Storage. Useful for comparison and visualization of the metrics while benchmarking the dataflow pipelines using various data formats, resource configurations etc
  • Apache Beam
    • Overview
    • Quickstart: Java, Python, Go
    • Tour of Beam - an interactive tour with learning topics covering core Beam concepts from simple ones to more advanced ones.
    • Beam Playground - an interactive environment to try out Beam transforms and examples without having to install Apache Beam.
    • Beam College - hands-on training and practical tips, including video recordings of Apache Beam and Dataflow Templates lessons.
    • Getting Started with Apache Beam - Quest - A 5 lab series that provides a Google Cloud certified badge upon completion.