Apache Kafka to Cloud Storage template

The Apache Kafka to Cloud Storage template is a streaming pipeline that ingests text data from Google Cloud Managed Service for Apache Kafka and outputs the records to Cloud Storage.

You can also use the Apache Kafka to BigQuery template with self-managed or external Kafka.

Pipeline requirements

  • The output Cloud Storage bucket must exist.
  • The Apache Kafka broker server must be running and be reachable from the Dataflow worker machines.
  • The Apache Kafka topics must exist.

Kafka message format

The Apache Kafka to Cloud Storage template supports reading messages from Kafka in the following formats: CONFLUENT_AVRO_WIRE_FORMAT and JSON.

Output file format

The output file format is the same format as the input Kafka message. For example, if you select JSON for the Kafka message format, JSON files are written to the output Cloud Storage bucket.

Authentication

The Apache Kafka to Cloud Storage template supports SASL/PLAIN authentication to Kafka brokers.

Template parameters

Required parameters

  • readBootstrapServerAndTopic: Kafka Topic to read the input from.
  • outputDirectory: The path and filename prefix for writing output files. Must end with a slash. For example, gs://your-bucket/your-path/.
  • kafkaReadAuthenticationMode: The mode of authentication to use with the Kafka cluster. Use KafkaAuthenticationMethod.NONE for no authentication, KafkaAuthenticationMethod.SASL_PLAIN for SASL/PLAIN username and password, and KafkaAuthenticationMethod.TLS for certificate-based authentication. KafkaAuthenticationMethod.APPLICATION_DEFAULT_CREDENTIALS should be used only for Google Cloud Apache Kafka for BigQuery cluster, it allows to authenticate using application default credentials.
  • messageFormat: The format of the Kafka messages to read. The supported values are AVRO_CONFLUENT_WIRE_FORMAT (Confluent Schema Registry encoded Avro), AVRO_BINARY_ENCODING (Plain binary Avro), and JSON. Defaults to: AVRO_CONFLUENT_WIRE_FORMAT.
  • useBigQueryDLQ: If true, failed messages will be written to BigQuery with extra error information. Defaults to: false.

Optional parameters

  • windowDuration: The window duration/size in which data will be written to Cloud Storage. Allowed formats are: Ns (for seconds, example: 5s), Nm (for minutes, example: 12m), Nh (for hours, example: 2h). For example, 5m. Defaults to: 5m.
  • outputFilenamePrefix: The prefix to place on each windowed file. For example, output-. Defaults to: output.
  • numShards: The maximum number of output shards produced when writing. A higher number of shards means higher throughput for writing to Cloud Storage, but potentially higher data aggregation cost across shards when processing output Cloud Storage files. Default value is decided by Dataflow.
  • enableCommitOffsets: Commit offsets of processed messages to Kafka. If enabled, this will minimize the gaps or duplicate processing of messages when restarting the pipeline. Requires specifying the Consumer Group ID. Defaults to: false.
  • consumerGroupId: The unique identifier for the consumer group that this pipeline belongs to. Required if Commit Offsets to Kafka is enabled. Defaults to empty.
  • kafkaReadOffset: The starting point for reading messages when no committed offsets exist. The earliest starts from the beginning, the latest from the newest message. Defaults to: latest.
  • kafkaReadUsernameSecretId: The Google Cloud Secret Manager secret ID that contains the Kafka username to use with SASL_PLAIN authentication. For example, projects/<PROJECT_ID>/secrets/<SECRET_ID>/versions/<SECRET_VERSION>. Defaults to empty.
  • kafkaReadPasswordSecretId: The Google Cloud Secret Manager secret ID that contains the Kafka password to use with SASL_PLAIN authentication. For example, projects/<PROJECT_ID>/secrets/<SECRET_ID>/versions/<SECRET_VERSION>. Defaults to empty.
  • kafkaReadKeystoreLocation: The Google Cloud Storage path to the Java KeyStore (JKS) file that contains the TLS certificate and private key to use when authenticating with the Kafka cluster. For example, gs://your-bucket/keystore.jks.
  • kafkaReadTruststoreLocation: The Google Cloud Storage path to the Java TrustStore (JKS) file that contains the trusted certificates to use to verify the identity of the Kafka broker.
  • kafkaReadTruststorePasswordSecretId: The Google Cloud Secret Manager secret ID that contains the password to use to access the Java TrustStore (JKS) file for Kafka TLS authentication For example, projects/<PROJECT_ID>/secrets/<SECRET_ID>/versions/<SECRET_VERSION>.
  • kafkaReadKeystorePasswordSecretId: The Google Cloud Secret Manager secret ID that contains the password to use to access the Java KeyStore (JKS) file for Kafka TLS authentication. For example, projects/<PROJECT_ID>/secrets/<SECRET_ID>/versions/<SECRET_VERSION>.
  • kafkaReadKeyPasswordSecretId: The Google Cloud Secret Manager secret ID that contains the password to use to access the private key within the Java KeyStore (JKS) file for Kafka TLS authentication. For example, projects/<PROJECT_ID>/secrets/<SECRET_ID>/versions/<SECRET_VERSION>.
  • schemaFormat: The Kafka schema format. Can be provided as SINGLE_SCHEMA_FILE or SCHEMA_REGISTRY. If SINGLE_SCHEMA_FILE is specified, use the schema mentioned in the avro schema file for all messages. If SCHEMA_REGISTRY is specified, the messages can have either a single schema or multiple schemas. Defaults to: SINGLE_SCHEMA_FILE.
  • confluentAvroSchemaPath: The Google Cloud Storage path to the single Avro schema file used to decode all of the messages in a topic. Defaults to empty.
  • schemaRegistryConnectionUrl: The URL for the Confluent Schema Registry instance used to manage Avro schemas for message decoding. Defaults to empty.
  • binaryAvroSchemaPath: The Google Cloud Storage path to the Avro schema file used to decode binary-encoded Avro messages. Defaults to empty.
  • schemaRegistryAuthenticationMode: Schema Registry authentication mode. Can be NONE, TLS or OAUTH. Defaults to: NONE.
  • schemaRegistryTruststoreLocation: Location of the SSL certificate where the trust store for authentication to Schema Registry are stored. For example, /your-bucket/truststore.jks.
  • schemaRegistryTruststorePasswordSecretId: SecretId in secret manager where the password to access secret in truststore is stored. For example, projects/your-project-number/secrets/your-secret-name/versions/your-secret-version.
  • schemaRegistryKeystoreLocation: Keystore location that contains the SSL certificate and private key. For example, /your-bucket/keystore.jks.
  • schemaRegistryKeystorePasswordSecretId: SecretId in secret manager where the password to access the keystore file For example, projects/your-project-number/secrets/your-secret-name/versions/your-secret-version.
  • schemaRegistryKeyPasswordSecretId: SecretId of password required to access the client's private key stored within the keystore For example, projects/your-project-number/secrets/your-secret-name/versions/your-secret-version.
  • schemaRegistryOauthClientId: Client ID used to authenticate the Schema Registry client in OAUTH mode. Required for AVRO_CONFLUENT_WIRE_FORMAT message format.
  • schemaRegistryOauthClientSecretId: The Google Cloud Secret Manager secret ID that contains the Client Secret to use to authenticate the Schema Registry client in OAUTH mode. Required for AVRO_CONFLUENT_WIRE_FORMAT message format. For example, projects/<PROJECT_ID>/secrets/<SECRET_ID>/versions/<SECRET_VERSION>.
  • schemaRegistryOauthScope: The access token scope used to authenticate the Schema Registry client in OAUTH mode. This field is optional, as the request can be made without a scope parameter passed. For example, openid.
  • schemaRegistryOauthTokenEndpointUrl: The HTTP(S)-based URL for the OAuth/OIDC identity provider used to authenticate the Schema Registry client in OAUTH mode. Required for AVRO_CONFLUENT_WIRE_FORMAT message format.
  • outputDeadletterTable: Fully Qualified BigQuery table name for failed messages. Messages failed to reach the output table for different reasons (e.g., mismatched schema, malformed json) are written to this table.The table will be created by the template. For example, your-project-id:your-dataset.your-table-name.

Run the template

ConsolegcloudAPI
  1. Go to the Dataflow Create job from template page.
    2. Go to Create job from template
  2. In the Job name field, enter a unique job name.
  3. Optional: For Regional endpoint, select a value from the drop-down menu. The default region is us-central1.

    For a list of regions where you can run a Dataflow job, see Dataflow locations.

  4. From the Dataflow template drop-down menu, select the Kafka to Cloud Storage template.
  5. In the provided parameter fields, enter your parameter values.
  6. Optional: To switch from exactly-once processing to at-least-once streaming mode, select At Least Once.
  7. Click Run job.

In your shell or terminal, run the template:

gcloud dataflow flex-template run JOB_NAME \
    --project=PROJECT_ID \
    --region=REGION_NAME \
    --template-file-gcs-location=gs://dataflow-templates-REGION_NAME/VERSION/flex/Kafka_to_Gcs_Flex \
    --parameters \
outputTableSpec=BIGQUERY_TABLE,\
inputTopics=KAFKA_TOPICS,\
javascriptTextTransformGcsPath=PATH_TO_JAVASCRIPT_UDF_FILE,\
javascriptTextTransformFunctionName=JAVASCRIPT_FUNCTION,\
bootstrapServers=KAFKA_SERVER_ADDRESSES

Replace the following:

  • PROJECT_ID: the Google Cloud project ID where you want to run the Dataflow job
  • JOB_NAME: a unique job name of your choice
  • REGION_NAME: the region where you want to deploy your Dataflow job—for example, us-central1
  • VERSION: the version of the template that you want to use

    You can use the following values:

  • BIGQUERY_TABLE: your Cloud Storage table name
  • KAFKA_TOPICS: the Apache Kakfa topic list. If multiple topics are provided, you need to escape commas. See gcloud topic escaping.
  • PATH_TO_JAVASCRIPT_UDF_FILE: the Cloud Storage URI of the .js file that defines the JavaScript user-defined function (UDF) you want to use—for example, gs://my-bucket/my-udfs/my_file.js
  • JAVASCRIPT_FUNCTION: the name of the JavaScript user-defined function (UDF) that you want to use

    For example, if your JavaScript function code is myTransform(inJson) { /*...do stuff...*/ }, then the function name is myTransform. For sample JavaScript UDFs, see UDF Examples.

  • KAFKA_SERVER_ADDRESSES: the Apache Kafka broker server IP address list. Each IP address needs to have the port number that the server is accessible from. For example: 35.70.252.199:9092. If multiple addresses are provided, you need to escape commas. See gcloud topic escaping.

To run the template using the REST API, send an HTTP POST request. For more information on the API and its authorization scopes, see projects.templates.launch.

POST https://dataflow.googleapis.com/v1b3/projects/PROJECT_ID/locations/LOCATION/flexTemplates:launch
{
   "launch_parameter": {
      "jobName": "JOB_NAME",
      "parameters": {
          "outputTableSpec": "BIGQUERY_TABLE",
          "inputTopics": "KAFKA_TOPICS",
          "javascriptTextTransformGcsPath": "PATH_TO_JAVASCRIPT_UDF_FILE",
          "javascriptTextTransformFunctionName": "JAVASCRIPT_FUNCTION",
          "bootstrapServers": "KAFKA_SERVER_ADDRESSES"
      },
      "containerSpecGcsPath": "gs://dataflow-templates-LOCATION/VERSION/flex/Kafka_to_Gcs_Flex",
   }
}

Replace the following:

  • PROJECT_ID: the Google Cloud project ID where you want to run the Dataflow job
  • JOB_NAME: a unique job name of your choice
  • LOCATION: the region where you want to deploy your Dataflow job—for example, us-central1
  • VERSION: the version of the template that you want to use

    You can use the following values:

  • BIGQUERY_TABLE: your Cloud Storage table name
  • KAFKA_TOPICS: the Apache Kakfa topic list. If multiple topics are provided, you need to escape commas. See gcloud topic escaping.
  • PATH_TO_JAVASCRIPT_UDF_FILE: the Cloud Storage URI of the .js file that defines the JavaScript user-defined function (UDF) you want to use—for example, gs://my-bucket/my-udfs/my_file.js
  • JAVASCRIPT_FUNCTION: the name of the JavaScript user-defined function (UDF) that you want to use

    For example, if your JavaScript function code is myTransform(inJson) { /*...do stuff...*/ }, then the function name is myTransform. For sample JavaScript UDFs, see UDF Examples.

  • KAFKA_SERVER_ADDRESSES: the Apache Kafka broker server IP address list. Each IP address needs to have the port number that the server is accessible from. For example: 35.70.252.199:9092. If multiple addresses are provided, you need to escape commas. See gcloud topic escaping.

For more information, see Write data from Kafka to Cloud Storage with Dataflow.

Java
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 * Copyright (C) 2024 Google LLC
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 * Licensed under the Apache License, Version 2.0 (the "License"); you may not
 * use this file except in compliance with the License. You may obtain a copy of
 * the License at
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 */
package com.google.cloud.teleport.v2.templates;

import com.google.cloud.teleport.metadata.Template;
import com.google.cloud.teleport.metadata.TemplateCategory;
import com.google.cloud.teleport.metadata.TemplateParameter;
import com.google.cloud.teleport.v2.kafka.dlq.BigQueryDeadLetterQueue;
import com.google.cloud.teleport.v2.kafka.dlq.BigQueryDeadLetterQueueOptions;
import com.google.cloud.teleport.v2.kafka.options.KafkaReadOptions;
import com.google.cloud.teleport.v2.kafka.options.SchemaRegistryOptions;
import com.google.cloud.teleport.v2.kafka.transforms.KafkaTransform;
import com.google.cloud.teleport.v2.kafka.utils.KafkaConfig;
import com.google.cloud.teleport.v2.kafka.utils.KafkaTopicUtils;
import com.google.cloud.teleport.v2.transforms.WriteTransform;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import org.apache.beam.runners.dataflow.options.DataflowPipelineOptions;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.PipelineResult;
import org.apache.beam.sdk.io.kafka.KafkaIO;
import org.apache.beam.sdk.io.kafka.KafkaRecord;
import org.apache.beam.sdk.options.Default;
import org.apache.beam.sdk.options.PipelineOptions;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.transforms.errorhandling.BadRecord;
import org.apache.beam.sdk.transforms.errorhandling.BadRecordRouter;
import org.apache.beam.sdk.transforms.errorhandling.ErrorHandler;
import org.apache.beam.sdk.values.PCollection;

@Template(
    name = "Kafka_to_Gcs_Flex",
    category = TemplateCategory.STREAMING,
    displayName = "Kafka to Cloud Storage",
    description =
        "A streaming pipeline which ingests data from Kafka and writes to a pre-existing Cloud"
            + " Storage bucket with a variety of file types.",
    optionsClass = KafkaToGcsFlex.KafkaToGcsOptions.class,
    flexContainerName = "kafka-to-gcs-flex",
    contactInformation = "https://cloud.google.com/support",
    requirements = {"The output Google Cloud Storage directory must exist."})
public class KafkaToGcsFlex {

  public interface KafkaToGcsOptions
      extends PipelineOptions,
          DataflowPipelineOptions,
          KafkaReadOptions,
          SchemaRegistryOptions,
          BigQueryDeadLetterQueueOptions {

    // This is a duplicate option that already exist in KafkaReadOptions but keeping it here
    // so the KafkaTopic appears above the authentication enum on the Templates UI.
    @TemplateParameter.KafkaReadTopic(
        order = 1,
        name = "readBootstrapServerAndTopic",
        groupName = "Source",
        description = "Source Kafka Topic",
        helpText = "Kafka Topic to read the input from.")
    String getReadBootstrapServerAndTopic();

    void setReadBootstrapServerAndTopic(String value);

    @TemplateParameter.Duration(
        order = 20,
        optional = true,
        groupName = "Destination",
        description = "Window duration",
        helpText =
            "The window duration/size in which data will be written to Cloud Storage. Allowed formats are: Ns (for "
                + "seconds, example: 5s), Nm (for minutes, example: 12m), Nh (for hours, example: 2h).",
        example = "5m")
    @Default.String("5m")
    String getWindowDuration();

    void setWindowDuration(String windowDuration);

    @TemplateParameter.GcsWriteFolder(
        order = 21,
        groupName = "Destination",
        description = "Output file directory in Cloud Storage",
        helpText = "The path and filename prefix for writing output files. Must end with a slash.",
        example = "gs://your-bucket/your-path/")
    String getOutputDirectory();

    void setOutputDirectory(String outputDirectory);

    @TemplateParameter.Text(
        order = 22,
        optional = true,
        groupName = "Destination",
        description = "Output filename prefix of the files to write",
        helpText = "The prefix to place on each windowed file.",
        example = "output-")
    @Default.String("output")
    String getOutputFilenamePrefix();

    void setOutputFilenamePrefix(String outputFilenamePrefix);

    @TemplateParameter.Integer(
        order = 23,
        optional = true,
        description = "Maximum output shards",
        groupName = "Destination",
        helpText =
            "The maximum number of output shards produced when writing. A higher number of "
                + "shards means higher throughput for writing to Cloud Storage, but potentially higher "
                + "data aggregation cost across shards when processing output Cloud Storage files. "
                + "Default value is decided by Dataflow.")
    @Default.Integer(0)
    Integer getNumShards();

    void setNumShards(Integer numShards);
  }

  public static PipelineResult run(KafkaToGcsOptions options) throws Exception {
    // Create the Pipeline
    Pipeline pipeline = Pipeline.create(options);
    String bootstrapServes;
    List<String> topicsList;
    if (options.getReadBootstrapServerAndTopic() != null) {
      List<String> bootstrapServerAndTopicList =
          KafkaTopicUtils.getBootstrapServerAndTopic(
              options.getReadBootstrapServerAndTopic(), options.getProject());
      topicsList = List.of(bootstrapServerAndTopicList.get(1));
      bootstrapServes = bootstrapServerAndTopicList.get(0);
    } else {
      throw new IllegalArgumentException(
          "Please provide a valid bootstrap server which matches `[,:a-zA-Z0-9._-]+` and a topic which matches `[,a-zA-Z0-9._-]+`");
    }

    options.setStreaming(true);

    Map<String, Object> kafkaConfig = new HashMap<>(KafkaConfig.fromReadOptions(options));

    // Configure dead letter queue params
    ErrorHandler<BadRecord, ?> errorHandler = new ErrorHandler.DefaultErrorHandler<>();
    // Throwing Router throws the error instead of sending it to the DLQ. This will be the case
    // when no DLQ is configured and the pipeline will retry the failed error.
    BadRecordRouter badRecordRouter = BadRecordRouter.THROWING_ROUTER;

    if (options.getUseBigQueryDLQ()) {
      if (options.getOutputDeadletterTable() == null
          || options.getOutputDeadletterTable().isBlank()) {
        throw new IllegalArgumentException(
            "Please provide a Fully Qualified BigQuery table name when BigQuery Dead Letter"
                + "Queue is enabled");
      }
      badRecordRouter = BadRecordRouter.RECORDING_ROUTER;
      errorHandler =
          pipeline.registerBadRecordErrorHandler(
              BigQueryDeadLetterQueue.newBuilder()
                  .setTableName(options.getOutputDeadletterTable())
                  .build());
    }

    PCollection<KafkaRecord<byte[], byte[]>> kafkaRecord;
    // Step 1: Read from Kafka as bytes.
    KafkaIO.Read<byte[], byte[]> kafkaTransform =
        KafkaTransform.readBytesFromKafka(
            bootstrapServes, topicsList, kafkaConfig, options.getEnableCommitOffsets());
    kafkaRecord = pipeline.apply(kafkaTransform);

    kafkaRecord.apply(
        WriteTransform.newBuilder()
            .setOptions(options)
            .setBadRecordErrorHandler(errorHandler)
            .setBadRecordRouter(badRecordRouter)
            .build());
    if (options.getUseBigQueryDLQ()) {
      errorHandler.close();
    }
    return pipeline.run();
  }

  public static void main(String[] args) throws Exception {
    KafkaToGcsOptions options =
        PipelineOptionsFactory.fromArgs(args).withValidation().as(KafkaToGcsOptions.class);

    run(options);
  }
}

What's next