Google Cloud BigTable Sink Connector for Confluent Cloud

The Kafka Connect Google Cloud BigTable Sink connector for Confluent Cloud moves data from Apache Kafka® to Google Cloud BigTable. It writes data from a topic in Kafka to a table in the specified BigTable instance.

Features

  • Supports Inserts and Upserts: The connector can insert rows and update rows in Google Cloud BigTable.
  • Automatically create tables and column families: The connector can create missing tables and can create missing column families.
  • Row key can be constructed from record fields: A comma-separated list of Kafka record key field names can be concatenated to form the row key.
  • At least once delivery: The connector guarantees that records are delivered at least once.
  • Supports multiple tasks: The connector supports running one or more tasks.
  • Input data formats: Supports Avro, JSON Schema, or Protobuf input data. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).

See Configuration Properties for configuration property values and descriptions. See Cloud connector limitations for additional information.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

Quick Start

Use this quick start to get up and running with the Google Cloud BigTable Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to a BigTable instance.

Prerequisites
  • Authorized access to a BigTable instance on Google Cloud.

  • A Google Cloud service account JSON key file. You create and download a key when creating a service account. The key must be downloaded as a JSON file. The service account must have write permissions for BigTable. The minimum permissions are:

    bigtable.tables.create
    bigtable.tables.mutateRows
    bigtable.tables.get
    bigtable.tables.update
    bigtable.tables.readRows
    bigtable.tables.list
    bigtable.tables.delete
    
  • Kafka cluster credentials. The following lists the different ways you can provide credentials.
    • Enter an existing service account resource ID.
    • Create a Confluent Cloud service account for the connector. Make sure to review the ACL entries required in the service account documentation. Some connectors have specific ACL requirements.
    • Create a Confluent Cloud API key and secret. To create a key and secret, you can use confluent api-key create or you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.
  • The BigTable instance and the Kafka cluster should be in the same region.
  • The Confluent Cloud CLI installed and configured for the cluster. See Install and Configure the Confluent Cloud CLI.
  • Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster.

See the Quick Start for Apache Kafka using Confluent Cloud for installation instructions.

Step 2: Add a connector.

In the left navigation menu, click Data integration, and then click Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector.

Click the Google BigTable Sink connector icon.

Google Cloud BigTable Sink Connector Icon

Step 4: Set up the connection.

Note

  • Make sure you have all your prerequisites completed.
  • An asterisk ( * ) designates a required entry.

Complete the following and click Continue.

  1. Select one or more topics.

  2. Enter a Connector Name.

  3. Select an Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR (JSON Schema), or PROTOBUF. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).

  4. Select an Input record key format: AVRO, BYTES, JSON, JSON_SR (JSON Schema), PROTOBUF, or STRING. A valid schema must be available in Schema Registry to use a schema-based message format.

  5. Enter your Kafka Cluster credentials. The credentials are either the API key and secret or the service account API key and secret.

  6. Upload your Google Cloud credentials JSON file.

  7. Enter your BigTable Project ID and Instance ID.

  8. Select one of the following Insert modes:

    • INSERT: Use the standard INSERT row function. An error occurs if the row already exists in the table.
    • UPSERT: This mode is similar to INSERT. However, if the row already exists, the UPSERT function overwrites column values with the new values provided. UPSERT is the default insert mode.
  9. Enter Max batch size. The maximum number of records that can be batched into a single insert or upsert for the table. When Insert mode is INSERT, the max batch size should be set to 1.

  10. Enter the optional Data mapping details:

    • Table name format: Defaults to the name of the Kafka topic. To create a table name format use the syntax ${topic}. For example, to create a table named kafka-orders based on a Kafka topic named orders, you would enter kafka-${topic} in this field.

    • Row key definition: A comma-separated list of Kafka record key field names to be concatenated to form the row key. The list order specifies the order of fields used to form the row key. For example: If you enter field names username, post_id, time_stamp, and the record fields contain username:bob, post_id:213, and time_stamp:131420, the resulting row key would be bob#213#131420. Note that this example uses # as the Row key delimiter.

      Note

      If the Row key definition property is left empty and the Kafka record key is a struct, all the fields in the struct are used to construct the row key. If the record key is a byte array, the row key is set to the byte array as is. If the record key is a primitive, the row key is set to the primitive (stringified).

    • Row key delimiter: This is the delimiter used to separate the concatenated fields used in the row key. If this property is left empty, the key fields are concatenated with no delimiter.

    • Auto create tables: Designates whether to automatically create tables if they don’t already exist.

    • Auto create column families: Designates whether to automatically create column families if they don’t already exist.

  11. Enter the number of tasks in use by the connector. More tasks may improve performance.

See Configuration Properties for configuration property values and descriptions.

Step 5: Launch the connector.

Verify the connection details and click Launch.

Step 6: Check the connector status.

The status for the connector should go from Provisioning to Running.

Step 7: Check the results in BigTable.

Check your BigTable instance to verify that the table is being populated.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Dead Letter Queue for details.

See also

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.

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Using the Confluent Cloud CLI

Complete the following steps to set up and run the connector using the Confluent Cloud CLI.

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors.

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: Show the required connector configuration properties.

Enter the following command to show the required connector properties:

confluent connect plugin describe <connector-catalog-name>

For example:

ccloud connector-catalog describe BigTableSink

Example output:

Following are the required configs:
connector.class: BigTableSink
input.data.format
name
kafka.api.key
kafka.api.secret
gcp.bigtable.credentials.json
gcp.bigtable.project.id
gcp.bigtable.instance.id
tasks.max
topics

Step 3: Create the connector configuration file.

Create a JSON file that contains the connector configuration properties. The following example shows the required connector properties.

{
   "name": "BigTableSinkConnector_0",
   "config": {
      "topics": "pageviews",
      "input.data.format": "AVRO",
      "input.key.format": "STRING",
      "connector.class": "BigTableSink",
      "name": "BigTableSinkConnector_0",
      "kafka.api.key": "****************",
      "kafka.api.secret": "*************************************************",
      "gcp.bigtable.credentials.json": "*",
      "gcp.bigtable.project.id": "connect-123456789",
      "gcp.bigtable.instance.id": "confluent",
      "insert.mode": "INSERT",
      "auto.create.tables": "true",
      "auto.create.column.families": "true",
      "tasks.max": "1"
   }
}

Note the following property definitions:

  • "name": Sets a name for your new connector.
  • "connector.class": Identifies the connector plugin name.
  • "topics": Identifies the topic name or a comma-separated list of topic names.
  • "input.data.format": Sets the input Kafka record value format (data coming from the Kafka topic). Valid entries are AVRO, JSON_SR, or PROTOBUF. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
  • "input.key.format": Sets the input record key format (data coming from the Kafka topic). Valid entries are AVRO, BYTES, JSON, JSON_SR (JSON Schema), PROTOBUF, or STRING. You must have Confluent Cloud Schema Registry configured if using a schema-based message format.
  • "gcp.bigtable.credentials.json": This property contains the contents of the downloaded JSON file. See Formatting keyfile credentials for details about how to format and use the contents of the downloaded credentials file.
  • "insert.mode": Enter an insert mode. The default mode is UPSERT.
    • "INSERT": This option provides the standard insert row function. An error occurs if the row already exists in the table.
    • "UPSERT": This mode is similar to INSERT. However, if the row already exists, the UPSERT function overwrites column values with the new values provided.
  • max.batch.size: (Optional) The maximum number of records that can be batched into a single insert or upsert for the table. When insert.mode is INSERT, the max batch size should be set to 1. The default value is 1000.
  • "auto.create.tables": Designates to automatically create tables if they don’t already exist. The default is false.
  • "auto.create.column.families": Designates whether to automatically create column families if they don’t already exist. The default is false.

See Configuration Properties for configuration property values and descriptions.

Step 4: Load the configuration file and create the connector.

Enter the following command to load the configuration and start the connector:

ccloud connector create --config <file-name>.json

For example:

ccloud connector create --config bigtable-sink-config.json

Example output:

Created connector BigTableSinkConnector_0 lcc-ix4dl

Step 5: Check the connector status.

Enter the following command to check the connector status:

ccloud connector list

Example output:

ID          |       Name              | Status  | Type
+-----------+-------------------------+---------+------+
lcc-ix4dl   | BigTableSinkConnector_0 | RUNNING | sink

Step 6: Check the results in BigTable.

Check your BigTable instance to verify that the table is being populated.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Dead Letter Queue for details.

See also

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.

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Formatting keyfile credentials

The contents of the downloaded credentials file must be converted to string format before it can be used in the connector configuration.

  1. Convert the JSON file contents into string format. You can use an online converter tool to do this. For example: JSON to String Online Converter.

  2. Add the escape character \ before all \n entries in the Private Key section so that each section begins with \\n (see the highlighted lines below). The example below has been formatted so that the \\n entries are easier to see. Most of the credentials key has been omitted.

    Tip

    A script is available that converts the credentials to a string and also adds the additional escape characters where needed. See Stringify GCP Credentials.

      {
          "name" : "BigTableSinkConnector_0",
          "connector.class" : "BigTableSink",
          "kafka.api.key" : "<my-kafka-api-keyk>",
          "kafka.api.secret" : "<my-kafka-api-secret>",
          "input.data.format": "AVRO",
          "topics" : "pageviews",
          "gcp.bigtable.credentials.json" : "{\"type\":\"service_account\",\"project_id\":\"connect-
          1234567\",\"private_key_id\":\"omitted\",
          \"private_key\":\"-----BEGIN PRIVATE KEY-----
          \\nMIIEvAIBADANBgkqhkiG9w0BA
          \\n6MhBA9TIXB4dPiYYNOYwbfy0Lki8zGn7T6wovGS5\opzsIh
          \\nOAQ8oRolFp\rdwc2cC5wyZ2+E+bhwn
          \\nPdCTW+oZoodY\\nOGB18cCKn5mJRzpiYsb5eGv2fN\/J
          \\n...rest of key omitted...
          \\n-----END PRIVATE KEY-----\\n\",
          \"client_email\":\"pub-sub@connect-123456789.iam.gserviceaccount.com\",
          \"client_id\":\"123456789\",\"auth_uri\":\"https:\/\/accounts.google.com\/o\/oauth2\/
          auth\",\"token_uri\":\"https:\/\/oauth2.googleapis.com\/
          token\",\"auth_provider_x509_cert_url\":\"https:\/\/
          www.googleapis.com\/oauth2\/v1\/
          certs\",\"client_x509_cert_url\":\"https:\/\/www.googleapis.com\/
          robot\/v1\/metadata\/x509\/pub-sub%40connect-
          123456789.iam.gserviceaccount.com\"}",
          "gcp.bigtable.project.id": "<project-id>",
          "gcp.bigtable.instance.id": "<instance-id",
          "insert.mode": "UPSERT",
          "auto.create.tables": "true",
          "auto.create.column.families": "true",
          "tasks.max": "1"
      }
    
  3. Add all the converted string content to the "gcp.bigtable.credentials.json" credentials section of your configuration file as shown in the example above.

Configuration Properties

input.data.format

Sets the input message. Valid entries are AVRO, JSON_SR, or PROTOBUF. You must have Confluent Cloud Schema Registry configured to use these formats.

  • Type: string
  • Valid Values: [AVRO, JSON_SR, PROTOBUF, STRING, or JSON]
  • Importance: high
input.key.format

Sets the input record key format. Valid entries are AVRO, BYTES, JSON_SR, PROTOBUF, STRING or JSON. You must have Confluent Cloud Schema Registry configured if using a schema-based message format.

  • Type: string
  • Default: JSON
  • Valid Values: [AVRO, BYTES, JSON_SR, PROTOBUF, STRING, or JSON]
  • Importance: high
insert.mode

Defines the insertion mode to use. Supported modes are:

  • INSERT - Insert new record only. An error occurs if the row already exists in the table.
  • UPSERT - If the row already exists, this function overwrites column values with the new values provided.
  • Type: string
  • Default: UPSERT
  • Valid Values: [UPSERT, INSERT]
  • Importance: high
max.batch.size

The maximum number of records that can be batched into a single insert or upsert to Google Cloud BigTable. When insert.mode is INSERT, the max batch size should be set to 1.

  • Type: int
  • Default: 1000
  • Valid Values: [1,…5000]
  • Importance: medium
table.name.format

A format string for the destination table name, which may contain ${topic} as a placeholder for the originating topic name. For example, kafka_${topic} for the topic ‘orders’ will map to the table name kafka_orders.

  • Type: string
  • Default: ${topic}
  • Valid Values: The table name must be valid after replacing``${topic}``.
  • Importance: medium
bigtable.row.key.definition

A comma-separated list of Kafka record key field names to be concatenated to form the row key. The list order specifies the order of fields used to form the row key. For example: If you use field names username, post_id, time_stamp, and the record fields contain username:bob, post_id:213, and time_stamp:131420, the resulting row key would be bob#213#131420. Note that this example uses # as the bigtable.row.key.delimiter.

  • Type: list
  • Default: “”
  • Importance: medium

Note

If the bigtable.row.key.definition property is left empty and the Kafka record key is a struct, all the fields in the struct are used to construct the row key. If the record key is a byte array, the row key is set to the byte array as is. If the record key is a primitive, the row key is set to the primitive (stringified).

bigtable.row.key.delimiter

The delimiter used to separate the concatenated fields used in the row key. If this property is left empty, the key fields are concatenated with no delimiter.

  • Type: string
  • Default: “”
  • Importance: low
auto.create.tables

Whether to automatically create the destination table if it is found to be missing.

  • Type: boolean
  • Default: false
  • Importance: medium
auto.create.column.families

Whether to automatically create missing columns families in the table relative to the record schema.

  • Type: boolean
  • Default: false
  • Importance: medium

Next Steps

See also

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.

../_images/topology.png