Google Cloud Functions Gen 2 Sink Connector for Confluent Cloud¶

The fully-managed Google Cloud Functions Gen 2 Sink connector for Confluent Cloud moves data from an Apache Kafka® topic to a specified Google Cloud Functions. The connector supports Avro, JSON Schema, JSON (schemaless), and Protobuf data output format from Kafka topics.

Features¶

The Google Cloud Functions Gen 2 Sink connector includes the following features:

Google Cloud Functions Gen 2 and Gen 1 support: The connector supports both Gen 2 and Gen 1 functions while delivering improved performance.
Secure access and data exchange: The connector supports the following authentication mechanisms:
- Google Cloud Service Account
- None
API error reporting management: You can configure the connector to notify you when an API error occurs through email or through the Confluent Cloud user interface. You also can configure the connector to ignore when an API error occurs.
Supported data formats: The connector supports Avro, Bytes, JSON (schemaless), JSON Schema, and Protobuf data formats. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf). For additional information, see Schema Registry Enabled Environments.
Schema Registry and Schema Context support: The connector allows you to map an API to a specific schema context so that you can leverage the schema context feature in different environments.
Custom offset support: The connector allows you to configure custom offsets using the Confluent Cloud Console to prevent data loss and data duplication.
Configurable retry functionality: The connector allows you to customize retry settings based on your requirements.

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

Limitations¶

Be sure to review the following information.

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.
The connector only supports invoking only a single function.
The target Google Function should be in the same region as your Confluent Cloud cluster.
The connector is only supported in Google Cloud clusters.
Messages in the reporter topic can be out of order relative to the order that the records were provided

Quick Start¶

Use this quick start to get up and running with the Google Cloud Functions Gen 2 Sink connector on Confluent Cloud connector.

Prerequisites¶

Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud).
The Confluent CLI installed and configured for the cluster. For help, see Install the Confluent CLI.
Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). For more information, see Schema Registry Enabled Environments.
At least one source Kafka topic must exist in your Confluent Cloud cluster before creating the sink connector.

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 Cloud Functions Gen 2 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 Functions Gen 2 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.

Select the Input Kafka record value format (data coming from the Kafka topic): AVRO, or BYTES, JSON, JSON_SR, or PROTOBUF. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON Schema, or Protobuf). For more information, see Schema Registry Enabled Environments. Note that to consume STRING data, select schemaless JSON.
Function Name: Name of the function to be invoked
Region Name: Region of the given function to be invoked as in ‘https://<region-name>-<project-id>.cloudfunctions.net/’.
Project ID: Project identifier for the given function to be invoked as in ‘https://<region-name>-<project-id>.cloudfunctions.net/’.

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?.
Behavior On Errors: Select the error handling behavior setting for handling error responses from HTTP requests. Valid options are IGNORE and FAIL. This defaults to FAIL.
Max poll interval (ms): The maximum delay between subsequent consume requests to Kafka. This configuration property can 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).
Max poll records: The maximum number of records to consume from Kafka in a single request. This configuration property can be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 500 records.
Retry Backoff Policy: The backoff policy to use in terms of a retry. Must be configured to CONSTANT_VALUE OR EXPONENTIAL_WITH_JITTER.
Retry Backoff (ms): The time in milliseconds to wait following an error before the connector retries the task.
Retry HTTP Status Codes: The HTTP response status codes returned that prompt the connector to retry the request. Enter a comma- separated list of codes or range of codes. Ranges are specified with a start and optional end code. Range boundaries are inclusive. For example: 400- includes all codes greater than or equal to 400 and 400-500 includes codes from 400 to 500, including 500. Multiple ranges and single codes can be specified together to achieve fine-grained control over retry behavior. For example: 404,408,500- prompts the connector to retry on 404 NOT FOUND, 408 REQUEST TIMEOUT, and all 5xx error codes. Note that some status codes are always retried, such as unauthorized, timeouts, and too many requests.
Maximum Retries: The maximum number of times the connector retries a request when an error occurs, before the task fails.
Behavior for null valued records: Behavior of the connector when it encounters a record with a null value. Valid options are IGNORE and FAIL. This defaults to IGNORE.
Connect timeout (ms): Timeout for the connection to the Google Cloud Functions. Default is 30000 ms.
Request timeout (ms): Timeout for the request to the Google Cloud Functions. Default is 30000 ms.
Batch max size: The number of records accumulated in a batch before the Google Cloud Functions API is invoked. Default is 1.
Batch json as array: Whether or not to use an array to bundle JSON records. Setting this to true will send records as a JSON array. Default is false.

For information about transforms and predicates, see the Single Message Transforms (SMT) documentation for details. For a list of SMTs that are not supported with this connector, see Unsupported transformations.

For all property values and definitions, see Configuration Properties.

Click Continue.

Step 5: Check for records¶

Verify that records are being produced at the endpoint.

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

Tip

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

Using the Confluent CLI¶

To set up and run the connector using the Confluent CLI, complete the following steps, but ensure you have met all prerequisites.

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.

{
  "topics": "topic_0",
  "schema.context.name": "default",
  "input.data.format": "JSON",
  "connector.class": "GoogleCloudFunctionsGen2Sink",
  "name": "GoogleCloudFunctionsGen2SinkConnector_0",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "****************",
  "kafka.api.secret": "****************************************************************",
  "max.poll.interval.ms": "300000",
  "max.poll.records": "500",
  "tasks.max": "1",
  "gcf.auth.type": "Google Cloud Service Account",
  "gcp.credentials.json": "*\n*************************\n",
  "behavior.on.error": "FAIL",
  "max.retries": "5",
  "retry.backoff.policy": "EXPONENTIAL_WITH_JITTER",
  "retry.backoff.ms": "3000",
  "retry.on.status.codes": "401,429,500-",
  "gcf.connect.timeout.ms": "30000",
  "gcf.request.timeout.ms": "30000",
  "behavior.on.null.values": "IGNORE",
  "gcf.name": "function-1",
  "gcf.region.name": "us-central1",
  "gcf.project.id": "connect-2024",
  "max.batch.size": "1",
  "batch.json.as.array": "false"
}

Note the following property definitions:

"connector.class": Identifies the connector plugin name.
"input.data.format": Sets the input Kafka record value format (data coming from the Kafka topic). Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON Schema or Protobuf).

"kafka.auth.mode": Identifies the connector authentication mode you want to use. There are two options: SERVICE_ACCOUNT or KAFKA_API_KEY (the default). To use an API key and secret, specify the configuration properties kafka.api.key and kafka.api.secret, as shown in the example configuration (above). To use a service account, specify the Resource ID in the property kafka.service.account.id=<service-account-resource-ID>. To list the available service account resource IDs, use the following command:
```
confluent iam service-account list
```
For example:
```
confluent iam service-account list

   Id     | Resource ID |       Name        |    Description
+---------+-------------+-------------------+-------------------
   123456 | sa-l1r23m   | sa-1              | Service account 1
   789101 | sa-l4d56p   | sa-2              | Service account 2
```
"name": Sets a name for your new connector.
"topics": Identifies the topic name or a comma-separated list of topic names.
"tasks.max": Enter the maximum number of tasks for the connector to use. More tasks might improve performance.
"gcf.name": Name of the function to be invoked.
"gcf.region.name": Region of the given function to be invoked as in ‘https://<region-name>-<project-id>.cloudfunctions.net/’.
"gcf.project.id": Project ID for the given function to be invoked as in ‘https://<region-name>-<project-id>.cloudfunctions.net/’.
"gcf.auth.type": Authentication type for the given function. Currently the connector supports Google Cloud Service Account authentication and unauthorized invocation.

Single Message Transforms: For details about adding SMTs using the CLI, see the Single Message Transforms (SMT) documentation. For all property values and descriptions, see Configuration Properties.

Step 4: Load the properties file and create the connector¶

To load the configuration and start the connector, run the following Confluent CLI command:

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

For example:

confluent connect cluster create --config-file google-cloud-functions-gen2-sink-config.json

Example output:

Created connector GoogleCloudFunctionsGen2SinkConnector_0 lcc-do6vzd

Step 5: Check the connector status.¶

To check the connector status, run the following Confluent CLI command:

confluent connect cluster list

Example output:

ID           |             Name                           | Status  | Type | Trace |
+------------+--------------------------------------------+---------+------+-------+
lcc-do6vzd   | GoogleCloudFunctionsGen2SinkConnector_0    | RUNNING | sink |       |

Step 6: Check for records¶

Verify that records are populating the endpoint.

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

Tip

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

Configuration Properties¶

Use the following configuration properties with the fully-managed Google Cloud Functions Gen 2 Sink connector.

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.