Datadog Metrics Sink for Confluent Cloud

You can use the Kafka Connect Datadog Metrics sink connector 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.

Important

If you are still on Confluent Cloud Enterprise, please contact your Confluent Account Executive for more information about using this connector.

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:

    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

    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).

  • 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.

Refer to Cloud connector limitations for additional information.

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.

Prerequsites
  • Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud Platform (GCP).

  • The Confluent Cloud CLI installed and configured for the cluster. See Install and Configure the Confluent Cloud CLI.

  • Schema Registry 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.

  • 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.

Using the Confluent Cloud GUI

Step 1: Launch your Confluent Cloud cluster.

See the Quick Start for Apache Kafka using Confluent Cloud for installation instructions.

Step 2: Add a connector.

Click Connectors. If you already have connectors in your cluster, click Add connector.

Step 3: Select your connector.

Click the Datadog Metrics Sink connector icon.

Datadog Metrics Sink Connector Icon

Step 4: Set up the connection.

Note

  • Make sure you have all your prerequisites completed.
  • An asterisk ( * ) designates a required entry.
  1. Select one or more topics.
  2. Enter a connector Name.
  3. Select an Input message format (data coming from the Kafka topic): AVRO, PROTOBUF, JSON_SR (JSON Schema), PROTOBUF, JSON (schemaless), or BYTES. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
  4. Enter your Kafka Cluster credentials. The credentials are either the cluster API key and secret or the service account API key and secret.
  5. Enter the Datadog domain name. Choose either COM or EU, depending on the domain where your Datadog project is located.
  6. Enter your 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.
  7. Enter the Max Retry Time. 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).
  8. Enter the number of tasks to use with the connector. More tasks may improve performance (that is, consumer lag is reduced with multiple tasks running).

Step 5: Launch the connector.

Verify the connection details and click Launch.

Launch the connector

Step 6: Check the connector status.

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

Connector status

Step 7: 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

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

Tip

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

Using the Confluent Cloud CLI

Complete the following steps to set up and run the connector using the Confluent Cloud CLI.

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors.

Enter the following command to list available connectors:

ccloud connector-catalog list

Step 2: Show the required connector configuration properties.

Enter the following command to show the required connector properties:

ccloud connector-catalog describe <connector-catalog-name>

For example:

ccloud connector-catalog describe DatadogMetricsSink

Example output:

Following are the required configs:
connector.class: DatadogMetricsSink
input.data.format
name
kafka.api.key
kafka.api.secret
datadog.domain
datadog.api.key
tasks.max
topics

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.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 message 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.api.key" and ""kafka.api.secret": These credentials are either the cluster API key and secret or the service account API key and secret.
  • "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).

Step 3: Load the properties file and create the connector.

Enter the following command to load the configuration and start the connector:

ccloud connector create --config <file-name>.json

For example:

ccloud connector create --config datadog-metrics-sink-config.json

Example output:

Created connector DatadogMetricsSinkConnector_0 lcc-do6vzd

Step 4: Check the connector status.

Enter the following command to check the connector status:

ccloud connector list

Example output:

ID           |             Name              | Status  | Type | Trace
+------------+-------------------------------+---------+------+-------+
lcc-do6vzd   | DatadogMetricsSinkConnector_0 | RUNNING | sink |

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

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

Tip

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

Next Steps

See also

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 Cloud CLI to manage your resources in Confluent Cloud.

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