<a id="cc-datadog-metrics-sink"></a>

# Datadog Metrics Sink for Confluent Cloud

The fully managed Datadog Metrics Sink connector for Confluent Cloud exports
data from Apache Kafka® to Datadog using the [post time-series metrics API](https://docs.datadoghq.com/api/latest/metrics/#submit-metrics). 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](https://docs.confluent.io/kafka-connectors/datadog-metrics/current/).
* 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](networking/internet-resource.md#clusters-connect-cloud).

## 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](dead-letter-queue.md#ccloud-dlq-topics)

  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.
  ![Datadog Metrics Sink Connector Topics](images/ccloud-datadog-metrics-sink-topics.png)

  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](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf).
* **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](https://docs.datadoghq.com/api/latest/metrics/#submit-metrics).
* **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](https://docs.datadoghq.com/developers/metrics/types/?tab=count#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](connect-api-section.md#ccloud-connect-api) section.

## Limitations

Be sure to review the following information.

* For connector limitations, see [Datadog Metrics Sink Connector](limits.md#datadog-metrics-sink-limits) limitations.
* If you plan to use one or more Single Message Transformations (SMTs), see [SMT Limitations](single-message-transforms.md#cc-single-message-transforms-limitations).

## 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](https://docs.datadoghq.com/developers/metrics/#custom-metrics-properties)
properties.

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

```bash
{
  "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](https://docs.datadoghq.com/api/latest/metrics/#submit-metrics). The Datadog Metrics Sink connector maps a Kafka record in this format:

```json
{
  "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:

```json
{
  "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.

<a id="cc-datadog-metrics-sink-prereqs"></a>

Prerequisites
: - Authorized access to a [Confluent Cloud](https://www.confluent.io/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](https://docs.confluent.io/confluent-cli/current/install.html).
  - [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
  - 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](overview.md#connect-internet-access-resources). To use a set of public egress IP addresses, see [Public Egress IP Addresses for Confluent Cloud Connectors](static-egress-ip.md#cc-static-egress-ips).
  - 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](https://docs.datadoghq.com/account_management/api-app-keys/#add-an-api-key-or-client-token).
  <br/>
  - Kafka cluster credentials. The following lists the different ways you can provide credentials.
    - Enter an existing [service account](service-account.md#s3-cloud-service-account) resource ID.
    - Create a Confluent Cloud [service account](service-account.md#s3-cloud-service-account) for the connector. Make sure to review the ACL entries required in the [service account documentation](service-account.md#s3-cloud-service-account). 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](https://docs.confluent.io/confluent-cli/current/command-reference/api-key/confluent_api-key_create.html) *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](../get-started/index.md#cloud-create-kafka-cluster).

#### 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.

![Datadog Metrics Sink Connector Card](images/ccloud-datadog-metrics-sink-icon.png)

<a id="cc-datadog-metrics-sink-setup-connection"></a>

#### Step 4: Enter the connector details

#### NOTE
* Ensure you have all your [prerequisites](#cc-datadog-metrics-sink-prereqs) completed.
* An asterisk ( \* ) designates a required entry.

At the **Add Datadog Metrics Sink Connector** screen, complete the following:

### Topic selection

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 access

1. 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](service-account.md#s3-cloud-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.
2. Click **Continue**.

### Authentication

1. Configure the authentication properties:
   - **Datadog API key**: 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](https://docs.datadoghq.com/account_management/api-app-keys/#add-an-api-key-or-client-token).
   - **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 configuration is not set, Datadog API is determined by `datadog.domain` configuration value.
   - **Datadog domain name**: Select the **Datadog domain name**. Choose either [COM](https://datadoghq.com/) or [EU](https://datadoghq.eu/),
     depending on the domain where your Datadog project is located.
2. Click **Continue**.

### Configuration

#### NOTE
Configuration properties that are not shown in the
Cloud Console use the default values.  See
[Configuration Properties](#cc-datadog-metrics-sink-config-properties) for all property
values and definitions.

- **Input Kafka record value format**: Select the input Kafka record value format (data coming from the
  Kafka topic). Valid entires are AVRO, JSON_SR, PROTOBUF, JSON, or BYTES.
  A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) to use a
  schema-based message format (for example, Avro, JSON Schema, or Protobuf).

### **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?](../sr/faqs-cc.md#faq-schema-contexts).
- **Max Retry Time in Milliseconds**: In case of error, while executing a post request, the connector retries 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.

**Additional Configs**

- **Value Converter Decimal Format**: Specifies the `JSON` or `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 `DECIMAL` logical type values in `JSON` or `JSON_SR` as a number representing the decimal value.
- **Value Converter Replace Null With Default**: Specifies whether to replace fields that have a default value and that are null to the default value. When set to `true`, the connector uses the default value; otherwise, it uses `null`. Applies to the `JSON` converter.
- **Schema GUID For Key Converter**: Sets the schema GUID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema GUID for deserializing message keys. This property is applicable only when `key.converter.key.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
- **Value Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for values. The deserializer reads schema IDs from message headers.
- **Schema GUID For Value Converter**: Sets the schema GUID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema GUID for deserializing message values. This property is applicable only when `value.converter.value.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
- **Value Converter Reference Subject Name Strategy**: Sets the subject reference name strategy for values. Valid entries are `DefaultReferenceSubjectNameStrategy` or `QualifiedReferenceSubjectNameStrategy`. You can use this strategy only with `PROTOBUF` format; the default strategy is `DefaultReferenceSubjectNameStrategy`.
- **Schema ID For Value Converter**: Sets the schema ID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema ID for deserializing message values. This property is applicable only when `value.converter.value.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
- **Value Converter Schemas Enable**: Includes schema within each of the serialized values. Input messages must contain `schema` and `payload` fields and must not contain additional fields. For plain `JSON` data, set this to `false`. Applies to the `JSON` converter.
- **Errors Tolerance**: Use this property to configure the connector’s error handling behavior.

  #### WARNING
  Use this property with caution for sink connectors, as it can lead to data loss. If you set this property to `all`, the connector does not fail on errant records, but logs them (and sends to DLQ for sink connectors) and continues processing. If you set this property to `none`, the connector task fails on errant records.
- **Value Converter Connect Meta Data**: Enables the Connect converter to add its metadata to the output schema. Applies to Avro converters.
- **Value Converter Value Subject Name Strategy**: Determines how to construct the subject name under which the value schema is registered with Schema Registry.
- **Key Converter Key Subject Name Strategy**: Determines how to construct the subject name for key schema registration.
- **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. Applies to the `AVRO`, `PROTOBUF`, and `JSON_SR` converters.
- **Key Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for keys. The deserializer reads schema IDs from message headers.
- **Schema ID For Key Converter**: Sets the schema ID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema ID for deserializing message keys. This property is applicable only when `key.converter.key.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.

**Auto-restart policy**

- **Enable Connector Auto-restart**: Enables 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. If disabled, you must manually restart the connector.

**Consumer configuration**

- **Max poll interval(ms)**: Sets 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**: Sets 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 Transformations**: To add a new SMT, see [Add transforms](single-message-transforms.md#cc-single-message-transforms-ui).
  For more information about unsupported SMTs, see
  [Unsupported transformations](single-message-transforms.md#cc-single-message-transforms-unsupported-transforms).

**Processing position**

- **Set offsets**: Click **Set offsets** to define a specific offset for
  this connector to begin procession data from. For more information
  on managing offsets, see [Manage offsets](offsets.md#connect-custom-offsets).

See [Configuration Properties](#cc-datadog-metrics-sink-config-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.

1. To change the number of recommended tasks, enter the number of
   [tasks](/platform/current/connect/concepts.html#tasks) for the connector to use in
   the **Tasks** field.
2. Click **Continue**.

### Review and Launch

1. Verify the connection details.
2. Click **Launch**.
   ![Launch the connector](images/ccloud-datadog-metrics-sink-launch-connector.png)

   The status for the connector should go from **Provisioning** to
   **Running**.
   ![Connector status](images/ccloud-datadog-metrics-sink-status.png)

#### 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"`).

![Datadog metric graph](images/ccloud-datadog-metrics-sink-graph.png)

For more information and examples to use with the Confluent Cloud API for Connect,
see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section.

### 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](#cc-datadog-metrics-sink-prereqs) completed.

#### Step 1: List the available connectors

Enter the following command to list available connectors:

```none
confluent connect plugin list
```

#### Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

```none
confluent connect plugin describe <connector-plugin-name>
```

The command output shows the required and optional configuration properties.

<a id="cc-datadog-cli-configuration-file"></a>

#### 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.

```json
{
  "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](service-account.md#s3-cloud-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:
  ```bash
  confluent iam service-account list
  ```

  For example:
  ```bash
  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](http://datadoghq.com/) or [EU](http://datadoghq.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](https://docs.datadoghq.com/account_management/api-app-keys/#add-an-api-key-or-client-token).
* `"tasks.max"`: Enter the maximum number of [tasks](/platform/current/connect/concepts.html#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).

**SMTs**: For details about adding SMTs using the Confluent CLI, see the [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms) documentation. For a list of SMTs that are not supported with this connector, see [Unsupported transformations](single-message-transforms.md#cc-single-message-transforms-unsupported-transforms).

See [Configuration Properties](#cc-datadog-metrics-sink-config-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:

```none
confluent connect cluster create --config-file <file-name>.json
```

For example:

```none
confluent connect cluster create --config-file datadog-metrics-sink-config.json
```

Example output:

```none
Created connector DatadogMetricsSinkConnector_0 lcc-do6vzd
```

#### Step 5: Check the connector status

Enter the following command to check the connector status:

```none
confluent connect cluster list
```

Example output:

```none
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"`).

![Datadog metric graph](images/ccloud-datadog-metrics-sink-graph.png)

For more information and examples to use with the Confluent Cloud API for Connect,
see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section.

<a id="cc-datadog-metrics-sink-config-properties"></a>

## 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](/platform/current/connect/kafka_connectors.html).

### 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.
  <br/>
  * Type: string
  * Importance: low

`topics`
: Identifies the topic name or a comma-separated list of topic names.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Default: JSON
  * Importance: high

### How should we connect to your data?

`name`
: Sets a name for your connector.
  <br/>
  * 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, whenever possible.
  <br/>
  * Type: string
  * Valid Values: SERVICE_ACCOUNT, KAFKA_API_KEY
  * Importance: high

`kafka.api.key`
: Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Importance: high

`kafka.api.secret`
: Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.
  <br/>
  * 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
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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).
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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](https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#auto-offset-reset)
  <br/>
  * 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](https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#isolation-level)
  <br/>
  * Type: string
  * Importance: low

`header.converter`
: The converter class for the headers. This is used to serialize and deserialize the headers of the messages.
  <br/>
  * Type: string
  * Importance: low

`key.converter.use.schema.guid`
: The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
  <br/>
  * Type: string
  * Importance: low

`key.converter.use.schema.id`
: The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
  <br/>
  * Type: int
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.auto.register.schemas`
: Specify if the Serializer should attempt to register the Schema.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.enhanced.avro.schema.support`
: Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.enhanced.protobuf.schema.support`
: Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.flatten.unions`
: Whether to flatten unions (oneofs). Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.generate.index.for.unions`
: Whether to generate an index suffix for unions. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.generate.struct.for.nulls`
: Whether to generate a struct variable for null values. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.int.for.enums`
: Whether to represent enums as integers. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.latest.compatibility.strict`
: Verify latest subject version is backward compatible when use.latest.version is true.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.object.additional.properties`
: Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.optional.for.nullables`
: Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.optional.for.proto2`
: Whether proto2 optionals are supported. Applicable for Protobuf Converters.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.latest.version`
: Use latest version of schema in subject for serialization when auto.register.schemas is false.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.optional.for.nonrequired`
: Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.schema.guid`
: The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
  <br/>
  * Type: string
  * Importance: low

`value.converter.use.schema.id`
: The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
  <br/>
  * Type: int
  * Importance: low

`value.converter.wrapper.for.nullables`
: Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Default: all
  * Importance: low

`key.converter.key.schema.id.deserializer`
: The class name of the schema ID deserializer for keys. This is used to deserialize schema IDs from the message headers.
  <br/>
  * Type: string
  * Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer
  * Importance: low

`key.converter.key.subject.name.strategy`
: How to construct the subject name for key schema registration.
  <br/>
  * 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:
  <br/>
  BASE64 to serialize DECIMAL logical types as base64 encoded binary data and
  <br/>
  NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.
  <br/>
  * Type: string
  * Default: BASE64
  * Importance: low

`value.converter.flatten.singleton.unions`
: Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Default: false
  * Importance: low

`value.converter.value.schema.id.deserializer`
: The class name of the schema ID deserializer for values. This is used to deserialize schema IDs from the message headers.
  <br/>
  * Type: string
  * Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer
  * 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.
  <br/>
  * Type: string
  * Default: TopicNameStrategy
  * Importance: low

### Auto-restart policy

`auto.restart.on.user.error`
: Enable connector to automatically restart on user-actionable errors.
  <br/>
  * Type: boolean
  * Default: true
  * Importance: medium

<a id="cc-datadog-metrics-sink-faq"></a>

## Frequently asked questions

The frequently asked questions (FAQs) address common questions and issues
encountered while using the fully managed Datadog Metrics Sink connector for Confluent Cloud.

### Connectivity and authentication

<a id="cc-datadog-metrics-sink-faq-failed-to-respond"></a>

#### How do I troubleshoot the error “Datadog failed to respond on a put request. The connector will retry sending metrics again.”?

This error indicates that the connector’s HTTP client did not receive a response
from the Datadog API endpoint. It is typically caused by network-level
interruptions, Datadog-side issues, or configuration errors. To troubleshoot:

1. **Verify the Datadog endpoint is reachable.** Use connectivity test tools such
   as `telnet` or `netcat` to confirm the endpoint host and port are accessible.
2. **Validate your API key.** Use the [Datadog Validate Key API](https://docs.datadoghq.com/api/latest/authentication/#validate-api-key)
   to confirm your key is valid. For example:
   ```bash
   curl -X GET "https://api.datadoghq.com/api/v1/validate" \
     -H "Accept: application/json" \
     -H "DD-API-KEY: <YOUR_API_KEY>" \
     -v
   ```
3. **Verify the** `datadog.domain` **setting.** The connector only supports `COM`
   and `EU`. The value `COM` maps to `https://api.datadoghq.com` and `EU`
   maps to `https://api.datadoghq.eu`. Using an incorrect domain causes
   connectivity failures.
4. **Check for Datadog outages.** Review the [Datadog Status Page](https://status.datadoghq.com/) for any ongoing incidents in your region.
5. **Adjust the retry time.** Increase the `max.retry.time.ms` configuration
   property (default: `5000` ms) to allow more time for transient network issues
   to resolve before the connector reports an error.

<a id="cc-datadog-metrics-sink-faq-domain"></a>

#### What values are supported for the `datadog.domain` configuration?

Only `COM` and `EU` are supported. Use `COM` if your Datadog project is on
[datadoghq.com](https://datadoghq.com/) and `EU` if it is on
[datadoghq.eu](https://datadoghq.eu/). These values determine which Datadog
API endpoint the connector uses to submit metrics.

### Data format and schema

<a id="cc-datadog-metrics-sink-faq-record-format"></a>

#### What record format does the Datadog Metrics Sink connector expect?

The connector expects a struct-type Kafka record with the following required
fields:

- `name` (string): The metric name.
- `timestamp` (long): The metric timestamp in UNIX epoch format.
- `values` (struct): Contains a `doubleValue` (double) field for the metric
  value.

Optional fields include:

- `type` (string): The metric type (`gauge`, `rate`, or `count`). Defaults
  to `gauge` if not specified.
- `dimensions` (struct): Supports `host` (string), `interval` (int), and
  custom tag key-value pairs for metrics filtering.

Records that are missing the required fields or use an incorrect format are
recorded in the error topic, and the connector continues to run.

<a id="cc-datadog-metrics-sink-faq-metric-types"></a>

#### What metric types are supported?

The connector supports **Gauge**, **Rate**, and **Count** metric types. Each
Kafka topic that contains a specific metric type must have records that adhere to
the corresponding schema. For details on each type, see [Datadog Metric Types](https://docs.datadoghq.com/developers/metrics/types/?tab=count#metric-types).

### Consumer lag and dead letter queue (DLQ)

<a id="cc-datadog-metrics-sink-faq-consumer-lag-dlq"></a>

#### Why does consumer lag remain high even though records are being routed to the dead letter queue?

The Datadog Metrics Sink connector does not commit the offset of messages routed
to the DLQ until a subsequent valid message is successfully processed and sent to
Datadog. This means:

- If all remaining messages in a partition are invalid and routed to the DLQ,
  consumer lag does not decrease until a valid message is produced to the topic.
- This is expected behavior for sink connectors that use `preCommit` for offset
  management, which includes the Datadog Metrics Sink connector.

To investigate:

- Check the DLQ topic (`dlq-<connector-ID>`) for records and inspect the
  [DLQ message headers](dead-letter-queue.md#ccloud-dlq-topics) to understand why records are
  failing.
- Ensure that the records in your source topic match the expected schema for the
  configured metric type.

<a id="cc-datadog-metrics-sink-faq-dlq-records"></a>

#### How do I view and process records in the dead letter queue?

Navigate to the DLQ topic (`dlq-<connector-ID>`) in the Cloud Console
and inspect the message headers for error details. The headers contain the
exception class, message, and stack trace that caused the record to fail. For
more details, see [Dead Letter Queue](dead-letter-queue.md#ccloud-dlq-topics).

### Monitoring

<a id="cc-datadog-metrics-sink-faq-monitor-connector"></a>

#### How do I monitor the Datadog Metrics Sink connector itself using Datadog?

You can use the [Datadog Confluent Cloud integration](https://docs.datadoghq.com/integrations/confluent_cloud/) to scrape connector
metrics directly into Datadog. To set this up:

1. Install the [Confluent Cloud integration tile](https://app.datadoghq.com/integrations/confluent-cloud) in your Datadog
   account.
2. Add your Confluent Cloud [Cloud API key](https://docs.confluent.io/cloud/current/monitoring/metrics-api.html#datadog).
3. Add your Confluent Cloud cluster ID (`lkc-xxxxx`).
4. Add the connector ID (`lcc-xxxxx`) for each connector you want to monitor.

This provides visibility into metrics such as connector record throughput and
consumer lag. For details, see the [Datadog integration data collected](https://docs.datadoghq.com/integrations/confluent_cloud/#data-collected)
documentation and the [Confluent Cloud Metrics](../monitoring/metrics-api.md#metrics-api).

### Performance

<a id="cc-datadog-metrics-sink-faq-batch-size"></a>

#### How does the connector batch metrics for Datadog API requests?

The connector batches multiple metrics into a single payload for each API request,
up to the Datadog maximum payload size of 3.2 MB. This batching improves
throughput and reduces the number of API calls. To further improve performance,
increase the number of tasks using the `tasks.max` configuration property to
reduce consumer lag.

## 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](/platform/current/tutorials/examples/cloud-etl/docs/index.html).
This example also shows how to use Confluent CLI to manage your resources in
Confluent Cloud.

[![image](images/topology.png)](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html)