AWS Lambda Sink Connector for Confluent Cloud¶

The fully-managed AWS Lambda Sink connector for Confluent Cloud pulls records from one or more Apache Kafka® topics, converts them to JSON, and executes an AWS Lambda function. The AWS Lambda function can be invoked by this connector either synchronously or asynchronously.

In synchronous mode, records within a topic and partition are processed sequentially. Records within different topic partitions can be processed in parallel. The response from AWS Lambda is written to success-<connector-id> topic. If an error occurs during Lambda execution, the connector is configured to write the error to the error-<connector-id> topic and proceed. For additional details about Lambda invocation, see Synchronous invocation.
In asynchronous mode, the connector operates in a fire-and-forget mode. Records are processed on a best-effort, sequential basis. The connector does not attempt any retries. AWS Lambda automatically retries up to two times, after which AWS Lambda can move the request to a dead letter queue. For additional details about Lambda invocation, see Asynchronous invocation.

Note

This Quick Start is for the fully-managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see AWS Lambda Sink Connector for Confluent Platform.
If you require private networking for fully-managed connectors, make sure to set up the proper networking beforehand. For more information, see Manage Networking for Confluent Cloud Connectors.

Features¶

The AWS Lambda Sink connector provides the following features:

Supports multiple Lambda functions: The connector supports a single AWS Lambda function or multiple Lambda functions.
Provider integration support: The connector supports IAM role-based authorization using Confluent Provider Integration. For more information about provider integration setup, see the IAM roles authentication.
Synchronous and Asynchronous Lambda function invocation: The AWS Lambda function can be invoked by this connector either synchronously or asynchronously.
At-least-once delivery: The connector guarantees at-least-once processing semantics. Under certain circumstances, a record may be processed more than once. You should design your AWS Lambda function to be idempotent. If you have configured the connector to log the response from the Lambda function to a Kafka topic, the topic can contain duplicate records. You can enable Kafka log compaction on the topic to remove duplicate records. Alternatively, you can write a ksqlDB query to detect duplicate records in a time window.
Supports multiple tasks: The connector supports running one or more tasks. More tasks may improve performance.
Results topics: In synchronous mode, AWS Lambda results are stored in the success-<connector-id> and error-<connector-id> topics.
Input Data Format with or without a Schema: The connector supports input data from Kafka topics in Avro, JSON Schema (JSON_SR), Protobuf, JSON (schemaless), or Bytes format. Schema Registry must be enabled to use a Schema Registry-based format.

Note

If no schema is defined, values are encoded as plain strings. For example, "name": "Kimberley Human" is encoded as name=Kimberley Human.
Backward compatibility: The API for this connector is compatible with earlier versions.

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 AWS Lambda 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 Confluent Cloud AWS Lambda Sink connector. The quick start provides the basics of selecting the connector and configuring it to send records to AWS Lambda.

Prerequisites

Authorized access to a Confluent Cloud cluster on AWS.

Confluent Cloud is available through the AWS Marketplace or directly from Confluent.
The Confluent CLI installed and configured for the cluster. 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). See Schema Registry Enabled Environments for additional information.

Note

If no schema is defined, values are encoded as plain strings. For example, "name": "Kimberley Human" is encoded as name=Kimberley Human.
For networking considerations, see Networking and DNS. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.
Your AWS Lambda project should be in the same region as your Confluent Cloud cluster where you are running the connector.
An AWS account configured with Access Keys.

You need to configure a Lambda IAM policy for the account to allow lambda:InvokeFunction and lambda:GetFunction. The following shows a JSON example for setting this policy:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "VisualEditor0",
            "Effect": "Allow",
            "Action": [
                "lambda:InvokeFunction",
                "lambda:GetFunction"
            ],
            "Resource": "*"
        }
    ]
}

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.

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 AWS Lambda 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 AWS Lambda 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.

Enter the following details:
1. AWS Lambda function configuration mode: The mode in which to run the connector. Options are multiple to invoke multiple AWS Lambda functions or single (the default) to invoke a single function. One connector instance can support a maximum of 10 functions.
2. AWS Lambda function to topic map: A map of Kafka topics to AWS Lambda functions when you select multiple as the configuration mode. Enter the map as comma-separated tuples. For example: <topic-1>;<function-1>,<topic-2>;<function-2>,.... You can map a maximum of three functions to a single topic.
3. AWS Lambda function name: The AWS Lambda function to invoke when you select single as the configuration mode.
4. AWS Lambda Region: The AWS Lambda Region is hard-coded to the region where the Confluent Cloud cluster is running.
5. Under Amazon credentials, select how you want to authenticate with AWS:
  - If you select Access Keys, enter your AWS credentials in the Amazon Access Key ID and Amazon Secret Access Key fields. For information about how to set these up, see Access Keys.
  - If you select IAM Roles, choose an existing integration name under Provider integration name dropdown that has access to your resource. For more information, see Quick Start for Confluent Cloud Provider Integration.
Click Continue.

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 the Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR, PROTOBUF, JSON, or BYTES. A valid schema must be available in Schema Registry to use a schema-based message format.
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?.
- AWS Lambda invocation type: The mode in which the AWS Lambda function is invoked. Two modes are supported: sync and async. For more details about Lambda invocation, see Synchronous invocation or Asynchronous invocation.
- Batch size: The maximum number of Kafka records to combine in a single AWS Lambda function invocation. You should set this as high as possible, without exceeding AWS Lambda invocation payload limits. To disable batching of records, set this value to 1.
- Record Converter Class: Record converter class to convert Kafka records to AWS Lambda payload.
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.
Transforms
- Single Message Transforms: To add a new SMT, see Add transforms. For more information about unsupported SMTs, see Unsupported transformations.
See Configuration Properties for all property values and definitions.
Click Continue.

Step 5: Check the Lambda function metrics¶

Go to the AWS Lambda console, open the Lambda function, and verify that records are processing. You can check for processing on the Monitoring tab on the Lambda function page. In synchronous mode, AWS Lambda results are stored in the following topics:

success-<connector-id>
error-<connector-id>

To quickly identify results that are specific to the AWS Lambda function, a message header is included that provides the AWS Lambda function name. Search for the function name in either topic to identify results for the function.

For configuration validation failure or system lag issues, see Troubleshooting.

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

Note

When you launch a connector, a Dead Letter Queue (DLQ) topic is automatically created. See View Connector Dead Letter Queue Errors in Confluent Cloud for details. If an InvalidRequestContentException occurs, the entire batch containing the failed record is sent to the DLQ.

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 an example configuration. For two additional examples, see Configuration JSON Examples.

{
  "connector.class": "LambdaSink",
  "name": "LambdaSinkConnector_0",
  "topics": "topic_aws_lambda_1",
  "input.data.format": "JSON",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "****************",
  "kafka.api.secret": "*************************************************",
  "aws.access.key.id": "****************",
  "aws.secret.access.key": "********************************************",
  "aws.lambda.configuration.mode": "single",
  "aws.lambda.function.name": "lambda-test",
  "aws.lambda.invocation.type": "sync",
  "behavior.on.error": "fail",
  "tasks.max": "1"
}

Note the following required property definitions:

"connector.class": Identifies the connector plugin name.
"name": Sets a name for your new connector.
"topics": Identifies the topic name or a comma-separated list of topic names.

"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
```
"input.data.format": Sets the input Kafka record value format (data coming from the Kafka topic). Valid entries are AVRO, JSON_SR (JSON Schema), PROTOBUF, JSON (Schemaless), or BYTES. You must have Confluent Cloud Schema Registry configured if using a schema-based message format.

Note

If no schema is defined, values are encoded as plain strings. For example, "name": "Kimberley Human" is encoded as name=Kimberley Human.
"aws.access.key.id" and "aws.secret.access.key": Enter the AWS Access Key ID and Secret. For information about how to set these up, see Access Keys.
"aws.lambda.configuration.mode": The mode in which to run the connector. Options are multiple to invoke multiple AWS Lambda functions or single (the default) to invoke a single function. One connector instance can support a maximum of 10 functions.
"aws.lambda.function.name": The AWS Lambda function to invoke for single configuration mode.
"aws.lambda.topic2function.map": A map of Kafka topics to AWS Lambda functions for multiple configuration mode. Enter the map as comma- separated tuples. For example: <topic-1>;<function-1>,<topic-2>;<function-2>,.... You can map a maximum of three functions to a single topic.
"aws.lambda.invocation.type":
- "sync": Records within a topic and partition are processed sequentially. Records within different topic partitions can be processed in parallel. If configured, the response from AWS Lambda can be written to a Kafka topic. If an error occurs during Lambda execution, the connector can be configured to either ignore the error and proceed, log the error, or stop the connector completely. For additional details about Lambda invocation, see Synchronous invocation.
- "async": The connector operates in a fire-and-forget mode. Records are processed on a best-effort, sequential basis. The connector does not attempt any retries. AWS Lambda automatically retries up to two times, after which AWS Lambda can move the request to a dead letter queue (DLQ). For additional details about Lambda invocation, see Asynchronous invocation.
"behavior.on.error": Select the error handling behavior for the AWS Lambda function invocations:
- "fail": Stops the connector in case of an error.
- "ignore": Continues to process the next set of records.
- "log": Logs the error message in error-<connector-id> topic and continues processing.
Note

The setting is only applicable when aws.lambda.invocation.type is set to sync mode.
"tasks.max": Enter the number of tasks in use by the connector. Refer to Confluent Cloud connector limitations for additional information.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI.

See Configuration Properties for all property values and definitions.

Step 4: Load the properties 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 lambda-sink-config.json

Example output:

Created connector LambdaSinkConnector_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   | LambdaSinkConnector_0 | RUNNING | sink

Step 6: Check the Lambda function metrics.¶

Go to the AWS Lambda console, open the Lambda function, and verify that records are processing. You can check for processing on the Monitoring tab on the Lambda function page. In synchronous mode, AWS Lambda results are stored in the following topics:

success-<connector-id>
error-<connector-id>

To quickly identify results that are specific to the AWS Lambda function, a message header is included that provides the AWS Lambda function name. Search for the function name in either topic to identify results for the function.

For configuration validation failure or system lag issues, see Troubleshooting.

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

Note

When you launch a connector, a Dead Letter Queue (DLQ) topic is automatically created. See View Connector Dead Letter Queue Errors in Confluent Cloud for details. If an InvalidRequestContentException occurs, the entire batch containing the failed record is sent to the DLQ.

Configuration JSON Examples¶

Select the following tabs for additional configuration examples.

{
  "connector.class": "LambdaSink",
  "name": "LambdaSinkConnector_0",
  "topics": "topic_aws_lambda_1,topic_aws_lambda_2",
  "input.data.format": "JSON",
  "connector.class": "LambdaSink",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "****************",
  "kafka.api.secret": "********************************************",
  "aws.access.key.id": "****************",
  "aws.secret.access.key": "****************************************",
  "aws.lambda.function.name": "lambda-test",
  "aws.lambda.configuration.mode": "single",
  "aws.lambda.invocation.type": "sync",
  "behavior.on.error": "fail",
  "tasks.max": "1"
}

{
  "connector.class": "LambdaSink",
  "name": "LambdaSinkConnector_0",
  "topics": "topic_aws_lambda_1,topic_aws_lambda_2",
  "input.data.format": "JSON",
  "connector.class": "LambdaSink",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "****************",
  "kafka.api.secret": "********************************************",
  "aws.access.key.id": "****************",
  "aws.secret.access.key": "****************************************",
  "aws.lambda.topic2function.map": "topic_aws_lambda_1;arn:aws:lambda:<region>:<account_id>:function:lambda1:lambdatest2,topic_aws_lambda_2;arn:aws:lambda:<region>:<account_id>:function:lambda1",
  "aws.lambda.configuration.mode": "multiple",
  "aws.lambda.invocation.type": "sync",
  "behavior.on.error": "fail",
  "tasks.max": "1"
}

Troubleshooting¶

Review the following if the connector fails validation or seems to be lagging.

Validation failure¶

When you launch a connector using a configuration JSON file, the configuration is validated for the following conditions:

The configuration properties aws.lambda.function.name and aws.lambda.topic2function.map properties cannot be used in the same connector configuration.
If the configuration property aws.lambda.configuration.mode is set to single, then only aws.lambda.function.name can be used.
If the configuration property aws.lambda.configuration.mode is set to multiple, then only aws.lambda.topic2function.map can be used.
A maximum of 10 functions can be used with a single connector.
A maximum of three functions can be mapped to one Kafka topic.

System lag¶

To reduce system lag, try the following:

Increase the number of connector tasks. More tasks may improve performance.
If the data is not balanced or partitioned properly, you can decrease the number of topics or topic-to-function mapping and create another connector.

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
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

AWS credentials¶

authentication.method

Select how you want to authenticate with AWS.

Type: string
Default: Access Keys
Importance: high

aws.access.key.id

The Amazon Access Key used to connect to AWS Lambda.

Type: password
Importance: high

provider.integration.id

Select an existing integration that has access to your resource. In case you need to integrate a new IAM role, use provider integration

Type: string
Importance: high

aws.secret.access.key

The Amazon Secret Key used to connect to AWS Lambda.

Type: password
Importance: high

How should we connect to AWS Lambda?¶

aws.lambda.configuration.mode

The mode in connector is configured to invoke a single AWS Lambda function or multiple Lambda functions

Type: string
Default: single
Importance: high

aws.lambda.function.name

The AWS Lambda function to invoke.

Type: string
Importance: high

aws.lambda.topic2function.map

Map of Kafka topics to Lambda functions. Format: comma-separated tuples, e.g. <topic-1>;<function-1>,<topic-2>;<function-2>,…

Type: list
Importance: high

aws.lambda.region

The AWS region where the lambda is defined. If no value for this property is provided, the value specified for the ‘kafka.region’ property is used.

Type: string
Importance: low

Lambda details¶

aws.lambda.invocation.type

The mode in which the AWS Lambda function is invoked. Two modes are supported; sync: In this mode, records are processed sequentially. The result or error from AWS Lambda function can optionally be saved to Kafka topics, success-<connector-id> or error-<connector-id>. async: In this mode, records may not be processed sequentially. The connector does not save the result or error from the AWS Lambda function execution. You can configure a dead letter queue in AWS to record function errors.

Type: string
Default: sync
Importance: high

aws.lambda.batch.size

The maximum number of Kafka records to combine in a single AWS Lambda function invocation. You should set this as high as possible, without exceeding AWS Lambda invocation payload limits. To disable batching of records, set this value to 1.

Type: int
Default: 20
Valid Values: [1,…]
Importance: low

record.converter.class

Record converter class to convert Kafka records to AWS Lambda payload. Valid entries are ‘JsonKeyValueConverter’ and ‘EscapedJsonKeyValueConverter’. JsonKeyValueConverter converts ‘key’ and ‘value’ to either JSON primitives or objects according to their schema and in case no schema is defined, they are encoded as plain strings. EscapedJsonKeyValueConverter is similar to JsonKeyValueConverter, the only difference is in case no schema is defined, ‘key’ and ‘value’ are encoded as escaped json strings. For more information, please take a look at connector documentation.

Type: string
Default: JsonKeyValueConverter
Importance: low

aws.lambda.socket.timeout

The socket timeout in milliseconds for the AWS Lambda client. This is the amount of time the client will wait for a response from the AWS Lambda service. The default value is 50000 milliseconds. Minimum value is 1000 milliseconds. Maximum value is 600000 milliseconds.

Type: int
Default: 50000
Valid Values: [1000,…,600000]
Importance: low

How should we handle errors?¶

behavior.on.error

The connector’s behavior if the execution of lambda function returns an error. This setting is applicable only when ‘AWS Lambda invocation type’ is set to ‘sync’ mode. Valid options are ‘log’, ‘fail’ and ‘ignore’. ‘log’ logs the error message in error-<connector-id> topic and continues processing, ‘fail’ stops the connector in case of an error and ‘ignore’ continues to process next set of records.

Type: string
Default: log
Importance: low

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

Auto-restart policy¶

auto.restart.on.user.error

Enable connector to automatically restart on user-actionable errors.

Type: boolean
Default: true
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 or the “latest” offset (the default). 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.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

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.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.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

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.
Try Confluent Cloud on AWS Marketplace with $1000 of free usage for 30 days, and pay as you go. No credit card is required.