Apache Kafka to BigQuery template

The Apache Kafka to BigQuery template is a streaming pipeline that ingests text data from Apache Kafka for BigQuery clusters, and then outputs the resulting records to BigQuery tables. Any errors that occur while inserting data into the output table are inserted into a separate errors table in BigQuery.

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

Pipeline requirements

  • The Apache Kafka broker server must be running and be reachable from the Dataflow worker machines.
  • The Apache Kafka topics must exist.
  • You must enable the Dataflow, BigQuery, and Cloud Storage APIs. If authentication is required, you must also enable the Secret Manager API.
  • Create a BigQuery dataset and table with the appropriate schema for your Kafka input topic. If you're using multiple schemas in the same topic and want to write to multiple tables, you don't need to create the table before configuring the pipeline.
  • When the dead-letter (unprocessed messages) queue for the template is enabled, create an empty table that doesn't have a schema for the dead-letter queue.

Kafka message format

The Apache Kafka to BigQuery template supports reading messages from Kafka in the following formats: CONFLUENT_AVRO_WIRE_FORMAT, AVRO_BINARY_FORMAT, and JSON.

Authentication

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

Template parameters

Required parameters

  • readBootstrapServerAndTopic : Kafka Topic to read the input from.
  • kafkaReadAuthenticationMode : The mode of authentication to use with the Kafka cluster. Use NONE for no authentication or SASL_PLAIN for SASL/PLAIN username and password. Apache Kafka for BigQuery only supports the SASL_PLAIN authentication mode. Defaults to: SASL_PLAIN.
  • writeMode : Write Mode: write records to one table or multiple tables (based on schema). The DYNAMIC_TABLE_NAMES mode is supported only for AVRO_CONFLUENT_WIRE_FORMAT Source Message Format and SCHEMA_REGISTRY Schema Source. The target table name will be auto-generated based on the Avro schema name of each message, it could either be a single schema (creating a single table) or multiple schemas (creating multiple tables). The SINGLE_TABLE_NAME mode writes to a single table (single schema) specified by the user. Defaults to SINGLE_TABLE_NAME.
  • useBigQueryDLQ : If true, failed messages will be written to BigQuery with extra error information. The deadletter table should be created with no schema. Defaults to: false.
  • 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.

Optional parameters

  • outputTableSpec : BigQuery table location to write the output to. The name should be in the format <project>:<dataset>.<table_name>. The table's schema must match input objects.
  • persistKafkaKey : If true, the pipeline will persist the Kafka message key in the BigQuery table, in a _key field of type BYTES. Default is false (Key is ignored).
  • outputProject : BigQuery output project in wehich the dataset resides. Tables will be created dynamically in the dataset. Defaults to empty.
  • outputDataset : BigQuery output dataset to write the output to. Tables will be created dynamically in the dataset. If the tables are created beforehand, the table names should follow the specified naming convention. The name should be bqTableNamePrefix + Avro Schema FullName , each word will be separated by a hyphen '-'. Defaults to empty.
  • bqTableNamePrefix : Naming prefix to be used while creating BigQuery output tables. Only applicable when using schema registry. Defaults to empty.
  • createDisposition : BigQuery CreateDisposition. For example, CREATE_IF_NEEDED, CREATE_NEVER. Defaults to: CREATE_IF_NEEDED.
  • writeDisposition : BigQuery WriteDisposition. For example, WRITE_APPEND, WRITE_EMPTY or WRITE_TRUNCATE. Defaults to: WRITE_APPEND.
  • useAutoSharding : If true, the pipeline uses auto-sharding when writng to BigQueryThe default value is true.
  • numStorageWriteApiStreams : Specifies the number of write streams, this parameter must be set. Default is 0.
  • storageWriteApiTriggeringFrequencySec : Specifies the triggering frequency in seconds, this parameter must be set. Default is 5 seconds.
  • useStorageWriteApiAtLeastOnce : This parameter takes effect only if "Use BigQuery Storage Write API" is enabled. If enabled the at-least-once semantics will be used for Storage Write API, otherwise exactly-once semantics will be used. Defaults to: false.
  • outputDeadletterTable : BigQuery table for failed messages. Messages failed to reach the output table for different reasons (e.g., mismatched schema, malformed json) are written to this table. (Example: your-project-id:your-dataset.your-table-name).
  • 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. (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. (Example: projects/<PROJECT_ID>/secrets/<SECRET_ID>/versions/<SECRET_VERSION>). Defaults to empty.
  • schemaFormat : The Kafka schema format. Can be provided as SINGLE_SCHEMA_FILE or SCHEMA_REGISTRY. If SINGLE_SCHEMA_FILE is specified, all messages should have the schema mentioned in the avro schema file. 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.

Run the template

Console

  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 BigQuery 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.

gcloud

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_BigQuery \
    --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 BigQuery 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 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.

API

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_BigQuery",
   }
}

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 BigQuery 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 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 BigQuery with Dataflow.

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