Datadog Metrics Sink for Confluent Cloud

You can use the fully-managed Datadog Metrics Sink connector for Confluent Cloud to export data from Apache Kafka® to Datadog using the post time-series metrics API. The connector can be used to export Kafka records in Avro, JSON Schema (JSON-SR), Protobuf, JSON (schemaless), or Bytes format to a Datadog endpoint.

Note

This Quick Start is for the fully-managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see Datadog Metrics 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 Datadog Metrics Sink connector supports the following features:

At least once delivery: This connector guarantees that records from the Kafka topic are delivered at least once.
Automatically creates topics: The following three topics are automatically created when the connector starts:
- Success topic
- Error topic
- Dead letter queue (DLQ) topic
The suffix for each topic name is the connector’s logical ID. In the example below, there are the three connector topics and one pre-existing Kafka topic named pageviews.
Connector Topics
If the records sent to the topic are not in the correct format, or if important fields are missing in the record, the errors are recorded in the error topic, and the connector continues to run.
Supported data formats: The connector supports Avro, JSON Schema (JSON-SR), Protobuf, JSON (schemaless), and Bytes formats. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf). See Schema Registry Enabled Environments for additional information.
Supports multiple tasks: The connector supports running one or more tasks. More tasks may improve performance (that is, consumer lag is reduced with multiple tasks running).
Batches multiple Datadog metrics: The connector tries to batch metrics in a single payload for each API request (maximum payload size 3.2 MB). For more information, see the post time-series metrics API docs.
Supported metrics types: The connector supports Gauge, Rate, and Count metric types. Each metric type has a different schema. Kafka topics that contain one of these metric types must have records that adhere to the metric type schema. For additional information, see Metric types.

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 Datadog Metrics 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.

Kafka record mapping

The connector accepts a struct type as the Kafka record. Additionally, the Kafka topic requires certain fields. There must be a name field, a timestamp field, and a values field. The values field entry refers to the metrics value. The timestamp value must be in UNIX epoch format.

An optional dimensions entry provides support for metrics filtering. The metrics can be filtered using hosts (hostname), interval values, and tag key values. The connector accepts metrics defined by the Datadog custom metrics properties.

The following shows a Kafka record sample with optional fields noted:

{
  "name": string,
  "type": string,              -- optional (DEFAULT = gauge)
  "timestamp": long,
  "dimensions": {              -- optional
    "host": string,            -- optional
    "interval": int,           -- optional (DEFAULT = 0)
    <tag1-key>: <tag1-value>,  -- optional
    <tag2-key>: <tag2-value>,
    ....
  },
  "values": {
    "doubleValue": double
  }
}

The connector maps the submitted Kafka record to the metrics payload that is accepted by the Datadog post time-series metrics API. The Datadog Metrics Sink connector maps a Kafka record in this format:

{
  "name": "test.metric",
  "type": "gauge",
  "timestamp": 1615466162,
  "dimensions": {
    "host": "metric.host",
    "interval": 1,
    "tag1": "postman",
    "tag2": "linux"
  },
  "values": {
    "doubleValue": 0.966121580485208
  }
}

to this acceptable Datadog post time-series metrics API format:

{
  "series": [
    {
      "host": "metric.host",
      "metric": "test.metric",
      "points": [
        [
          "1615466162",
          "0.966121580485208"
        ]
      ],
      "tags": [
        "host:metric.host",
        "interval:1",
        "tag1:postman",
        "tag2:linux"
      ],
      "type": "gauge",
      "interval": 1
    }
  ]
}

Quick Start

Use this quick start to get up and running with the Confluent Cloud Datadog Metrics Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to a Datadog project.

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. 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.
At least one source Kafka topic must exist in your Confluent Cloud cluster before creating the sink connector.
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.
You must have an active Datadog account and API key. To create an API key for your Datadog project, see Add an API key or client token.
Tip
You can register for a Datadog account here. A running agent is not required You can skip the agent setup when setting up your account.

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 Datadog Metrics 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 Datadog Metrics Sink Connector screen, complete the following:

Select topic

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

Select the Datadog domain name. Choose either COM or EU, depending on the domain where your Datadog project is located.
In the Datadog API key field, enter the API key for your Datadog project which is required by the Datadog agent to submit metrics and events to Datadog. To create an API key, see Add an API key or client token.
Click Continue.

Configuration

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 (for example, Avro, JSON Schema, or Protobuf). See Schema Registry Enabled Environments for additional information.
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?.
- Max Retry Time. In case of error, while executing a post request, the connector will retry until this time (in ms) elapses. You should set this value to be at least 1000 milliseconds (ms). The default retry time is 5000 ms (5 seconds).
- Behavior on Error. Error handling behavior setting when an error occurs while extracting metrics from a Kafka record value. Valid options are log and fail. log logs the error message in the error-<connector-id> topic and continues processing, and fail stops the connector in case of an error.
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.

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

Step 5: Check for records

Verify that metrics are being produced. Go to the Metrics Explorer in your Datadog project and search for the graph with the name you used for the Kafka topic metric property (for example, "metric": "test.metric").

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.

{
  "connector.class": "DatadogMetricsSink",
  "input.data.format": "JSON",
  "name": "DatadogMetricsSinkConnector_0",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "****************",
  "kafka.api.secret": "****************************************************************",
  "datadog.domain": "COM",
  "datadog.api.key": "**************************************************",
  "tasks.max": "1",
  "topics": "<topic-1>, <topic-2>",
  "max.retry.time.ms": "5000"
}

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, JSON, or BYTES. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
"name": Sets a name for your new connector.

"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
```

"datadog.domain": Use either COM or EU, depending on the domain where your Datadog project is located.
"datadog.api.key": This is the API key for your Datadog project. To create an API key, see Add an API key or client token.
"tasks.max": Enter the maximum number of tasks for the connector to use. More tasks may improve performance (that is, consumer lag is reduced with multiple tasks running).
"topics": Enter the topic name or a comma-separated list of topic names.
"max.retry.time.ms": When a post request error occurs, the connector will retry until the amount of time entered elapses. You should set this value to be at least 1000 milliseconds (ms). The default retry time is 5000 ms (5 seconds).

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI. See Unsupported transformations for a list of SMTs that are not supported with this connector.

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 datadog-metrics-sink-config.json

Example output:

Created connector DatadogMetricsSinkConnector_0 lcc-do6vzd

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 | Trace
+------------+-------------------------------+---------+------+-------+
lcc-do6vzd   | DatadogMetricsSinkConnector_0 | RUNNING | sink |

Step 6: Check for records.

Verify that metrics are being produced. Go to the Metrics Explorer in your Datadog project and search for the graph with the name you used for the Kafka topic metric property (for example, "metric": "test.metric").

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.

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.regex

A regular expression that matches the names of the topics to consume from. This is useful when you want to consume from multiple topics that match a certain pattern without having to list them all individually.

Type: string
Importance: low

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

How should we connect to Datadog?

datadog.api.key

Datadog API key is required by the Datadog agent to submit metrics and events to datadog

Type: password
Importance: high

datadog.site

Datadog site to which the datadog account belongs to. There are five possible values: US1, US3, US5, EU1 or US1-FED. This setting is used to determine the Datadog API, connector will use to post metrics to. In case this config is not configured, Datadog API is determined by datadog.domain config value.

Type: string
Default: “”
Importance: high

datadog.domain

Datadog domain to which the datadog account belongs to. The two possible values are EU or COM. If datadog.site is not configured, then this setting will determine the Datadog API which connector will use to post metrics to. The value EU will map to https://api.datadoghq.eu and COM will map to https://api.datadoghq.com.

Type: string
Default: COM
Importance: low

Datadog Details

max.retry.time.ms

In case of error, while executing a post request, the connector will retry until this time (in ms) elapses. The default value is 5000 (5 seconds). It’s recommended to set this value to be at least 1 second.

Type: int
Default: 5000 (5 seconds)
Valid Values: [1000,…]
Importance: low

How should we handle errors?

behavior.on.error

Error handling behavior setting when an error occurs while extracting metric from Kafka record value. Valid options are ‘log’ and ‘fail’. ‘log’ logs the error message in error-<connector-id> topic and continues processing, ‘fail’ stops the connector in case of an error.

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

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

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.