New Relic Metrics Sink Connector for Confluent Cloud

The Kafka Connect New Relic Metrics Sink connector for Confluent Cloud moves records from Kafka topics to a New Relic data ingestion endpoint. Currently, the connector is limited to ingesting metrics only from Kafka topics.

The connector uses the New Relic Telemetry SDK to post metrics to New Relic. The connector batches records to ensure that the payload does not exceed 1 MB.

Features

The New Relic Metrics Sink connector provides the following features:

  • Supports multiple tasks: The connector supports running one or more tasks. More tasks may improve performance.
  • Input data formats: The connector supports Bytes, AVRO, JSON_SR (JSON Schema), JSON (Schemaless) and PROTOBUF input data formats. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).

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

Limitations

Be sure to review the following information.

Supported Metrics

The connector supports the Gauge, Count, and Summary metrics. The following show sample formats for each of these metrics.

Note

Confluent adds the following two common attributes to the metrics:

  • "collector.metadata.kafka.topic" : "<metric-topic-name>"
  • "collector.metadata.kafka.partition" : "<metric-partition-name>"
Gauge
{
  "name" : "service.response.duration",
  "type" : "gauge",
  "value" : 7.8,
  "timestamp" : 1655970976,
  "attributes" : {
    "host.name" : "dev.server.com",
    "app.name" : "foo",
    "collector.metadata.kafka.topic" : "<metric-topic-name>",
    "collector.metadata.kafka.partition" : "<metric-partition-name>"
  }
}
Count
{
  "name" : "service.response.duration",
  "type" : "count",
  "value" : 10,
  "interval.ms" : 10000,
  "timestamp" : 1655970976,
  "attributes" : {
    "host.name" : "dev.server.com",
    "app.name" : "foo",
    "collector.metadata.kafka.topic" : "<metric-topic-name>",
    "collector.metadata.kafka.partition" : "<metric-partition-name>"
  }
}
Summary
{
  "name" : "service.response.duration",
  "type" : "summary",
  "value" : {
    "summary.count" : 5,
    "summary.sum" : 0.567,
    "summary.min" : 0.1,
    "summary.max" : 0.9
  },
  "interval.ms" : 10000,
  "timestamp" : 1655970976,
  "attributes" : {
    "host.name" : "dev.server.com",
    "app.name" : "foo",
    "collector.metadata.kafka.topic" : "<metric-topic-name>",
    "collector.metadata.kafka.partition" : "<metric-partition-name>"
  }
}

Quick Start

Use this quick start to get up and running with the New Relic Metrics API sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to a New Relic ingest endpoint.

Prerequisites
  • 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.

See the Quick Start for Confluent Cloud for installation instructions.

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 New Relic Metrics Sink connector card.

New Relic 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 New Relic Metrics Sink Connector screen, complete the following:

If you’ve already populated your Kafka topics, select the topic(s) you want to connect from the Topics list.

To create a new topic, click +Add new topic.

Step 5: Check for records.

Verify that records are being produced.

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

Tip

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

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 CLI to manage your resources in Confluent Cloud.

../_images/topology.png

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.
  • The example commands use Confluent CLI version 2. For more information see, Confluent CLI v2.

Step 1: List the available connectors.

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: Show the required connector configuration properties.

Enter the following command to show the required connector properties:

confluent connect plugin describe <connector-catalog-name>

For example:

confluent connect plugin describe NewRelicMetricsSink

Example output:

The following are required configs:
connector.class : NewRelicMetricsSink
topics
input.data.format
name
kafka.api.key : ["kafka.api.key" is required when "kafka.auth.mode==KAFKA_API_KEY"]
kafka.api.secret : ["kafka.api.secret" is required when "kafka.auth.mode==KAFKA_API_KEY"]
newrelic.ingest.api.key
tasks.max

Step 3: Create the connector configuration file.

Create a JSON file that contains the connector configuration properties. The following example shows the required connector properties.

{
  "topics":"orders",
  "input.data.format": "JSON",
  "connector.class": "NewRelicMetricsSink",
  "name": "NewRelicMetricsSink_0",
  "kafka.auth.mode": "<KAFKA_API_KEY>",
  "kafka.api.key": "****************",
  "kafka.api.secret": "*************************************************",
  "newrelic.ingest.api.key": "<LICENSE_API_KEY>",
  "tasks.max": "1"
}

Note the following property definitions:

  • "topics": Identifies the topic name or a comma-separated list of topic names.
  • "input.data.format": Sets the input Kafka record value format. 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).
  • "connector.class": Identifies the connector plugin name.
  • "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
    
  • "newrelic.ingest.api.key": Enter the license key for the New Relic account where the connector sends data. For more information, see New Relic API keys.

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 create --config <file-name>.json

For example:

confluent connect create --config new-relic-metrics-sink-config.json

Example output:

Created connector NewRelicMetricsSink_0 lcc-ix4dl

Step 5: Check the connector status.

Enter the following command to check the connector status:

confluent connect list

Example output:

ID          |       Name              | Status  | Type
+-----------+-------------------------+---------+------+
lcc-ix4dl   | NewRelicMetricsSink_0   | RUNNING | sink

Step 6: Check for records.

Verify that records are being produced.

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

Tip

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

Configuration Properties

Use the following configuration properties with this connector.

Which topics do you want to get data from?

topics

Identifies the topic name or a comma-separated list of topic names.

  • Type: list
  • Importance: high

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
  • 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
  • Type: password
  • Importance: high

How should we connect to New Relic?

newrelic.ingest.api.key

Ingest API key for New Relic.

  • Type: password
  • Importance: high
newrelic.datacenter.region

New Relic data center region to which the configured account belongs to. The two possible values are US or EU.

  • Type: string
  • Default: US
  • Valid Values: EU, US
  • Importance: high

New Relic Details

newrelic.client.timeout

Time, in milliseconds, to wait for a response from the New Relic API.

  • Type: int
  • Default: 2000
  • Valid Values: [1000,…,30000]
  • Importance: low
newrelic.max.retry.time.ms

The maximum time, in milliseconds, upto which connector will try sending a batch of metrics.

  • Type: int
  • Default: 5000 (5 seconds)
  • Valid Values: [1000,…,60000]
  • 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
  • Valid Values: fail, log
  • Importance: low

Number of tasks for this connector

tasks.max
  • Type: int
  • Valid Values: [1,…]
  • Importance: high

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 CLI to manage your resources in Confluent Cloud.

../_images/topology.png