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.

Confluent Cloud is available through Google Cloud Marketplace or directly from Confluent.

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.
Client-side field level encryption (CSFLE) support: The connector supports CSFLE for sensitive data. For more information about CSFLE setup, see the connector configuration.

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.

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
If you plan to migrate from Gen 1 to Gen 2 connector, enable cross-cloud support in the Gen 2 connector if the function and Kafka cluster are in different regions. For more information, contact Confluent account team or Confluent Support.
The connector does not support private networking.

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

To create and launch a Kafka cluster in Confluent Cloud, see Create a kafka cluster in Confluent Cloud.

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:

Select or create new topics

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.

Kafka credentials

Select the way you want to provide Kafka Cluster credentials. You can choose one of the following options:
- My account: This setting allows your connector to globally access everything that you have access to. With a user account, the connector uses an API key and secret to access the Kafka cluster. This option is not recommended for production.
- Service account: This setting limits the access for your connector by using a service account. This option is recommended for production.
- Use an existing API key: This setting allows you to specify an API key and a secret pair. You can use an existing pair or create a new one. This method is not recommended for production environments.
Note

Freight clusters support only service accounts for Kafka authentication.

Click Continue.

Authentication

In the Authentication Type field, select the authentication type for the given function. Currently the connector supports Google Cloud Service Account authentication only.
Click Continue.

Configuration

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/’.

Data encryption

(Optional) Enable Client-Side Field Level Encryption for data decryption. Specify a Service Account to access the Schema Registry and associated encryption rules or keys with that schema. Select the connector behavior (ERROR or NONE) on data decryption failure. If set to ERROR, the connector fails and writes the encrypted data in the DLQ. If set to NONE, the connector writes the encrypted data in the target system without decryption. For more information on CSFLE setup, see Manage CSFLE for connectors.
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.
Auto-restart policy
- Enable Connector Auto-restart: Control the auto-restart behavior of the connector and its task in the event of user-actionable errors. Defaults to true, enabling the connector to automatically restart in case of user-actionable errors. Set this property to false to disable auto-restart for failed connectors. In such cases, you would need to manually restart the connector.
Consumer configuration
- Max poll interval(ms): Set the maximum delay between subsequent consume requests to Kafka. Use this property to improve connector performance in cases when the connector cannot send records to the sink system. The default is 300,000 milliseconds (5 minutes).
- Max poll records: Set the maximum number of records to consume from Kafka in a single request. Use this property to improve connector performance in cases when the connector cannot send records to the sink system. The default is 500 records.
- 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.
Transforms
- Single Message Transforms: To add a new SMT, see Add transforms. For more information about unsupported SMTs, see Unsupported transformations.
For all property values and definitions, see Configuration Properties.
Click Continue.

Sizing

Based on the number of topic partitions you select, you will be provided with a recommended number of tasks.

To change the number of recommended tasks, enter the number of tasks for the connector to use in the Tasks field.
Click Continue.

Review and Launch

Verify the connection details.
Click Continue.

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

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 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

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.

Note

(Optional) To enable CSFLE for data encryption, specify the following properties:

csfle.enabled: Flag to indicate whether the connector honors CSFLE rules.
sr.service.account.id: A Service Account to access the Schema Registry and associated encryption rules or keys with that schema.
csfle.onFailure: Configures the connector behavior (ERROR or NONE) on data decryption failure. If set to ERROR, the connector fails and writes the encrypted data in the DLQ. If set to NONE, the connector writes the encrypted data in the target system without decryption.

Warning

Security Risk: Dead Letter Queue (DLQ) with CSFLE

When using CSFLE with connectors that route failed messages to a Dead Letter Queue (DLQ), be aware that data sent to the DLQ is written in plaintext (unencrypted). This poses a significant security risk as sensitive data that should be encrypted may be exposed in the DLQ.

Do not use DLQ with CSFLE in the current version. If you need error handling for CSFLE-enabled data, use alternative approaches such as:

Setting the connector behavior to ERROR to throw exceptions instead of routing to DLQ
Implementing custom error handling in your applications
Using NONE to pass encrypted data through without decryption

For more information on CSFLE setup, see Manage CSFLE for connectors.

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 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.

Legacy to Gen 2 Connector Migration

Use the following steps to migrate to Gen 2 connector. Implement and validate any connector changes in a pre-production environment before promoting to production.

Pause the Gen 1 connector.
Get the offset for the Gen 1 connector.
Create the Gen 2 connector using the offset from the previous step.
```
confluent connect cluster create [flags]
```
For example:

Create a configuration file with connector configs and offsets.
```
{
  "name": "(connector-name)",
  "config": {
      ... // connector specific configuration
  },
  "offsets": [
      {
          "partition": {
      ... // connector specific configuration
          },
          "offset": {
      ... // connector specific configuration
          }
      }
  ]
}
```
Create a Gen 2 connector in the current or specified Kafka cluster context.
```
confluent connect cluster create --config-file config.json
```
Note

The configuration payload differs between Gen 1 and Gen 2 connectors. In the Gen 2 connector, the value field contains only the value of the key-value pair and does not include the key, topic, partition, and offset. Make necessary changes in the Gen 2 connector to match the configurations from the Gen 1 connector.
Verify the migration and confirm that the connector is running successfully with the Gen 1 payloads.
Enable cross-cloud support in Gen 2 connector, in case the function and Kafka cluster are in different regions. For more information, contact Confluent account team or Confluent Support.
Delete the Gen 1 connector.

For more information, see Manage Offsets for Fully-Managed Connectors in Confluent Cloud.

Configuration Properties

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

errors.deadletterqueue.topic.name

The name of the topic to be used as the dead letter queue (DLQ) for messages that result in an error when processed by this sink connector, or its transformations or converters. Defaults to ‘dlq-${connector}’ if not set. The DLQ topic will be created automatically if it does not exist. You can provide ${connector} in the value to use it as a placeholder for the logical cluster ID.

Type: string
Default: dlq-${connector}
Importance: low

reporter.result.topic.name

The name of the topic to produce records to after successfully processing a sink record. Defaults to ‘success-${connector}’ if not set. You can provide ${connector} in the value to use it as a placeholder for the logical cluster ID.

Type: string
Default: success-${connector}
Importance: low

reporter.error.topic.name

The name of the topic to produce records to after each unsuccessful record sink attempt. Defaults to ‘error-${connector}’ if not set. You can provide ${connector} in the value to use it as a placeholder for the logical cluster ID.

Type: string
Default: error-${connector}
Importance: low

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

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

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

Authentication

gcf.auth.type

Authentication method of the connector. Valid values are None, Google Cloud Service Account.

Type: string
Default: Google Cloud Service Account
Importance: high

gcp.credentials.json

GCP service account JSON file.

Type: password
Importance: high

Behavior on error

behavior.on.error

Error handling behavior setting for handling error response from HTTP requests.

Type: string
Default: FAIL
Importance: low

Retry configurations

max.retries

The maximum number of times to retry on errors before failing the task.

Type: int
Default: 5
Importance: medium

retry.backoff.policy

The backoff policy to use in terms of retry - CONSTANT_VALUE or EXPONENTIAL_WITH_JITTER

Type: string
Default: EXPONENTIAL_WITH_JITTER
Importance: medium

retry.backoff.ms

The initial duration in milliseconds to wait following an error before a retry attempt is made. Subsequent backoff attempts can be a constant value or exponential with jitter (can be configured using retry.backoff.policy parameter). Jitter adds randomness to the exponential backoff algorithm to prevent synchronized retries.

Type: int
Default: 3000 (3 seconds)
Valid Values: [100,…]
Importance: medium

retry.on.status.codes

Comma-separated list of HTTP status codes or range of codes to retry on. Ranges are specified with start and optional end code. Range boundaries are inclusive. For instance, 400- includes all codes greater than or equal to 400. 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- will retry on 404 NOT FOUND, 408 REQUEST TIMEOUT, and all 5xx error codes. Note that some status codes will always be retried, such as unauthorized, timeouts and too many requests.

Type: string
Default: 401,429,500-
Importance: medium

Connection configurations

gcf.connect.timeout.ms

The time in milliseconds to wait for a connection to be established

Type: int
Default: 30000 (30 seconds)
Valid Values: [1000,…,600000]
Importance: medium

gcf.request.timeout.ms

The time in milliseconds to wait for a request response from the server

Type: int
Default: 30000 (30 seconds)
Valid Values: [1000,…,600000]
Importance: medium

Behavior on records

behavior.on.null.values

How to handle records with a non-null key and a null value (i.e. Kafka tombstone records). Valid options are IGNORE and FAIL

Type: string
Default: IGNORE
Importance: low

Google Cloud Functions configurations

gcf.name

Name of the function to be invoked

Type: string
Importance: high

gcf.region.name

Region of the given function to be invoked as in ‘https://<region-name>-<project-id>.cloudfunctions.net/’

Type: string
Importance: high

gcf.project.id

Project ID for the given function to be invoked as in ‘https://<region-name>-<project-id>.cloudfunctions.net/’

Type: string
Importance: high

Batch configurations

max.batch.size

The number of records accumulated in a batch before the Google Cloud Functions API is invoked

Type: int
Default: 1
Importance: high

batch.json.as.array

Whether or not to use an array to bundle json records. Setting this to true will send records as json array.

Type: boolean
Default: false
Importance: high

report.only.status.code.to.success.topic

Whether to report only the status code to the success topic. If the API response payload is huge, it is recommended to set this to true, for better throughput.

Type: boolean
Default: false
Importance: medium

Additional Configs

consumer.override.auto.offset.reset

Defines the behavior of the consumer when there is no committed position (which occurs when the group is first initialized) or when an offset is out of range. You can choose either to reset the position to the “earliest” offset (the default) or the “latest” offset. You can also select “none” if you would rather set the initial offset yourself and you are willing to handle out of range errors manually. More details: https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#auto-offset-reset

Type: string
Importance: low

consumer.override.isolation.level

Controls how to read messages written transactionally. If set to read_committed, consumer.poll() will only return transactional messages which have been committed. If set to read_uncommitted (the default), consumer.poll() will return all messages, even transactional messages which have been aborted. Non-transactional messages will be returned unconditionally in either mode. More details: https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#isolation-level

Type: string
Importance: low

header.converter

The converter class for the headers. This is used to serialize and deserialize the headers of the messages.

Type: string
Importance: low

value.converter.allow.optional.map.keys

Allow optional string map key when converting from Connect Schema to Avro Schema. Applicable for Avro Converters.

Type: boolean
Importance: low

value.converter.auto.register.schemas

Specify if the Serializer should attempt to register the Schema.

Type: boolean
Importance: low

value.converter.connect.meta.data

Allow the Connect converter to add its metadata to the output schema. Applicable for Avro Converters.

Type: boolean
Importance: low

value.converter.enhanced.avro.schema.support

Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.

Type: boolean
Importance: low

value.converter.enhanced.protobuf.schema.support

Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.flatten.unions

Whether to flatten unions (oneofs). Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.generate.index.for.unions

Whether to generate an index suffix for unions. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.generate.struct.for.nulls

Whether to generate a struct variable for null values. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.int.for.enums

Whether to represent enums as integers. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.latest.compatibility.strict

Verify latest subject version is backward compatible when use.latest.version is true.

Type: boolean
Importance: low

value.converter.object.additional.properties

Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.

Type: boolean
Importance: low

value.converter.optional.for.nullables

Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.optional.for.proto2

Whether proto2 optionals are supported. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.scrub.invalid.names

Whether to scrub invalid names by replacing invalid characters with valid characters. Applicable for Avro and Protobuf Converters.

Type: boolean
Importance: low

value.converter.use.latest.version

Use latest version of schema in subject for serialization when auto.register.schemas is false.

Type: boolean
Importance: low

value.converter.use.optional.for.nonrequired

Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.

Type: boolean
Importance: low

value.converter.wrapper.for.nullables

Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.wrapper.for.raw.primitives

Whether a wrapper message should be interpreted as a raw primitive at root level. Applicable for Protobuf Converters.

Type: boolean
Importance: low

errors.tolerance

Use this property if you would like to configure the connector’s error handling behavior. WARNING: This property should be used with CAUTION for SOURCE CONNECTORS as it may lead to dataloss. If you set this property to ‘all’, the connector will not fail on errant records, but will instead log them (and send to DLQ for Sink Connectors) and continue processing. If you set this property to ‘none’, the connector task will fail on errant records.

Type: string
Default: all
Importance: low

key.converter.key.subject.name.strategy

How to construct the subject name for key schema registration.

Type: string
Default: TopicNameStrategy
Importance: low

value.converter.decimal.format

Specify the JSON/JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals:

BASE64 to serialize DECIMAL logical types as base64 encoded binary data and

NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.

Type: string
Default: BASE64
Importance: low

value.converter.flatten.singleton.unions

Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.

Type: boolean
Default: false
Importance: low

value.converter.ignore.default.for.nullables

When set to true, this property ensures that the corresponding record in Kafka is NULL, instead of showing the default column value. Applicable for AVRO,PROTOBUF and JSON_SR Converters.

Type: boolean
Default: false
Importance: low

value.converter.reference.subject.name.strategy

Set the subject reference name strategy for value. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. Note that the subject reference name strategy can be selected only for PROTOBUF format with the default strategy being DefaultReferenceSubjectNameStrategy.

Type: string
Default: DefaultReferenceSubjectNameStrategy
Importance: low

value.converter.replace.null.with.default

Whether to replace fields that have a default value and that are null to the default value. When set to true, the default value is used, otherwise null is used. Applicable for JSON Converter.

Type: boolean
Default: true
Importance: low

value.converter.schemas.enable

Include schemas within each of the serialized values. Input messages must contain schema and payload fields and may not contain additional fields. For plain JSON data, set this to false. Applicable for JSON Converter.

Type: boolean
Default: false
Importance: low

value.converter.value.subject.name.strategy

Determines how to construct the subject name under which the value schema is registered with Schema Registry.

Type: string
Default: TopicNameStrategy
Importance: low

Auto-restart policy

auto.restart.on.user.error

Enable connector to automatically restart on user-actionable errors.

Type: boolean
Default: true
Importance: medium

Egress allowlist

connector.egress.whitelist

Type: string
Default: “”
Importance: high

Next Steps

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