Google Cloud BigTable Sink Connector for Confluent Cloud¶

The fully-managed 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.

Note

This is a Quick Start for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see Google Cloud BigTable Sink Connector for Confluent Platform.

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 Usage Examples section.

Limitations¶

Be sure to review the following information.

For connector limitations, see Google BigTable Sink Connector limitations.
If you plan to use one or more Single Message Transforms (SMTs), see SMT Limitations.
If you plan to use Confluent Cloud Schema Registry, see Schema Registry Enabled Environments.

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 Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector¶

Click the Google 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 topics you want to connect from the Topics list.

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

Note

Configuration properties that are not shown in the Cloud Console use the default values. See Configuration Properties for all property values and definitions.

Select an Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR (JSON Schema), PROTOBUF, JSON, BYTES. 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).
Select an insert mode–the insertion mode to use:
- 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.
Show advanced configurations
- Schema context: Select a schema context to use for this connector, if using a schema-based data format. This property defaults to the Default context, which configures the connector to use the default schema set up for Schema Registry in your Confluent Cloud environment. A schema context allows you to use separate schemas (like schema sub-registries) tied to topics in different Kafka clusters that share the same Schema Registry environment. For example, if you select a non-default context, a Source connector uses only that schema context to register a schema and a Sink connector uses only that schema context to read from. For more information about setting up a schema context, see What are schema contexts and when should you use them?.
- Input record key format: AVRO, JSON_SR (JSON Schema), PROTOBUF, JSON, STRING, or BYTES. A valid schema must be available in Schema Registry to use a schema-based message format.
- 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.
- Table name format: A format string for the destination table name, which may contain ${topic} as a placeholder for the originating topic name. For example, to create a table named kafka-orders based on a Kafka topic named orders, you would enter kafka-${topic} in this field.
- Roy 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.
  
  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: 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.
- Auto create tables: Whether to automatically create the destination table if it is found to be missing.
- Auto create column families: Designates whether to automatically create column families if they don’t already exist.
See Configuration Properties for all property values and definitions.
Click Continue.

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 Usage Examples section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See View Connector Dead Letter Queue Errors in Confluent Cloud for details.

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: List the connector configuration properties¶

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

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:

confluent connect cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file 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:

confluent connect cluster 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 Usage Examples section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See View Connector Dead Letter Queue Errors in Confluent Cloud for details.

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.

Convert the JSON file contents into string format.

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 Google Cloud 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"
  }

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 the fully-managed connector. For self-managed connector property definitions and other details, see the connector docs in Self-managed connectors for Confluent Platform.

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

Schema Config¶

schema.context.name

Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.

Type: string
Default: default
Importance: medium

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
Default: JSON
Importance: high

input.key.format

Sets the input Kafka record key format. Valid entries are AVRO, BYTES, JSON, JSON_SR, PROTOBUF, or STRING. 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

Kafka API Key. Required when kafka.auth.mode==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

Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.

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

Consumer configuration¶

max.poll.interval.ms

The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).

Type: long
Default: 300000 (5 minutes)
Valid Values: [60000,…,1800000] for non-dedicated clusters and [60000,…] for dedicated clusters
Importance: low

max.poll.records

The maximum number of records to consume from Kafka in a single request. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 500 records.

Type: long
Default: 500
Valid Values: [1,…,500] for non-dedicated clusters and [1,…] for dedicated clusters
Importance: low

Number of tasks for this connector¶

tasks.max

Maximum number of tasks for the connector.

Type: int
Valid Values: [1,…]
Importance: high

Next Steps¶

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.