Google Cloud BigTable Sink Connector for Confluent Cloud

Note

If you are installing the connector locally for Confluent Platform, see Google Cloud BigTable Sink Connector for Confluent Platform.

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).

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

Limitations

Be sure to review the following information.

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 CLI installed and configured for the cluster. See Install and Configure the Confluent 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 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 card.

Google Cloud BigTable Sink Connector Card

Step 4: Enter the connector details.

Note

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

At the Add Google Cloud BigTable Sink Connector screen, complete the following:

If you’ve already populated your Kafka topics, select the topic(s) you want to connect from the Topics list.

To create a new topic, click +Add new topic.

Step 5: 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 CLI

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

../_images/topology.png

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

Use the following configuration properties with this connector.

Which topics do you want to get data from?

topics

Identifies the topic name or a comma-separated list of topic names.

  • Type: list
  • Importance: high

Input messages

input.data.format

Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON or BYTES. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.

  • Type: string
  • Importance: high
input.key.format

Sets the input Kafka record key format. Valid entries are AVRO, JSON_SR, PROTOBUF, STRING or JSON. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF

  • Type: string
  • Default: JSON
  • Valid Values: AVRO, BYTES, JSON, JSON_SR, PROTOBUF, STRING
  • Importance: high

How should we connect to your data?

name

Sets a name for your connector.

  • Type: string
  • Valid Values: A string at most 64 characters long
  • Importance: high

Kafka Cluster credentials

kafka.auth.mode

Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode.

  • Type: string
  • Default: KAFKA_API_KEY
  • Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
  • Importance: high
kafka.api.key
  • Type: password
  • Importance: high
kafka.service.account.id

The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.

  • Type: string
  • Importance: high
kafka.api.secret
  • Type: password
  • Importance: high

GCP credentials

gcp.bigtable.credentials.json

GCP service account JSON file with write permissions for Cloud Bigtable.

  • Type: password
  • Importance: high

How should we connect to your Cloud BigTable instance?

gcp.bigtable.project.id

The ID of the Cloud Bigtable project to connect to.

  • Type: string
  • Importance: high
gcp.bigtable.instance.id

The ID of the Cloud Bigtable instance to connect to.

  • Type: string
  • Importance: high

Database details

insert.mode

The insertion mode to use.

  • Type: string
  • Default: UPSERT
  • Valid Values: INSERT, UPSERT
  • Importance: high

Connection details

max.batch.size

The maximum number of records that can be batched into a batch of upserts. Note that since only a batch size of 1 for inserts is supported, max.batch.size must be exactly 1 when insert.mode is set to INSERT.

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

Data mapping

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}
  • Importance: medium
bigtable.row.key.definition

A comma separated list of Kafka Record key field names that specifies the order of Kafka key fields to be concatenated to form the row key.

For example the list: ‘username, post_id, time_stamp’ when applied to a Kafka key: {‘username’: ‘bob’,’post_id’: ‘213’, ‘time_stamp’: ‘123123’} and with delimiter # gives the row key ‘bob#213#123123’. You can also access terms nested in the key by using . as a delimiter. If this configuration is empty or unspecified and the Kafka Message Key is a: STRUCT: all the fields in the struct are used to construct the row key. BYTE ARRAY: the row key is set to the byte array as is. PRIMITIVE: the row key is set to the primitive stringified.

If prefixes, more complicated delimiters, and string constants are required in your Row Key, consider configuring an SMT to add relevant fields to the Kafka Record key.

  • Type: list
  • Default: “”
  • Importance: medium
bigtable.row.key.delimiter

The delimiter used in concatenating Kafka key fields in the row key. If this configuration is empty or unspecified, the key fields will be concatenated together directly.

  • 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

Number of tasks for this connector

tasks.max
  • Type: int
  • Valid Values: [1,…]
  • Importance: high

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