New Relic Metrics Sink Connector for Confluent Cloud¶

The fully-managed 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.

Note

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 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 Usage Examples section.

Limitations¶

Be sure to review the following information.

For connector limitations, see New Relic 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.

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

Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud.
A New Relic API license key for the New Relic account where the connector sinks data.
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.

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.

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 topics you want to connect from the Topics list.

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

Enter New Relic 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.
Select the New Relic Data Center host region Options are EU and US. Defaults to US.
Click Continue.

Select the Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR, PROTOBUF, JSON, or BYTES. A valid schema must be available in Schema Registry to use a schema-based message format.
Show advanced configurations
- Schema context: Select a schema context to use for this connector, if using a schema-based data format. This property defaults to the Default context, which configures the connector to use the default schema set up for Schema Registry in your Confluent Cloud environment. A schema context allows you to use separate schemas (like schema sub-registries) tied to topics in different Kafka clusters that share the same Schema Registry environment. For example, if you select a non-default context, a Source connector uses only that schema context to register a schema and a Sink connector uses only that schema context to read from. For more information about setting up a schema context, see What are schema contexts and when should you use them?.
- New Relic Client Timeout: The time in milliseconds (ms) to wait for a response from the New Relic API. Defaults to 2000 ms.
- New Relic Max Retry Time: The maximum time in ms that the connector continues to retry sending a batch of metrics. Defaults to 5000 ms.
- Behavior on Error: How the connector behaves when an error occurs while extracting metrics from a Kafka record value. Valid options are log and fail. log (the default) logs the error message in the error-<connector-id> topic and continues processing. If set to fail, the connector stops.
- Transforms and Predicates: See the Single Message Transforms (SMT) documentation for details.
See Configuration Properties for all property values and definitions.
Click Continue.

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 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.

{
  "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 cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file 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 cluster 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 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

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

Type: list
Importance: high

Schema Config¶

schema.context.name

Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.

Type: string
Default: default
Importance: medium

Input messages¶

input.data.format

Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON or BYTES. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.

Type: string
Importance: high

How should we connect to your data?¶

name

Sets a name for your connector.

Type: string
Valid Values: A string at most 64 characters long
Importance: high

Kafka Cluster credentials¶

kafka.auth.mode

Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode.

Type: string
Default: KAFKA_API_KEY
Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
Importance: high

kafka.api.key

Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.

Type: password
Importance: high

kafka.service.account.id

The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.

Type: string
Importance: high

kafka.api.secret

Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.

Type: password
Importance: high

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

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

Next Steps¶

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.