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.
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 Managed and Custom Connectors 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 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.
- Select the way you want to provide Kafka Cluster credentials. You can
choose one of the following options:
- Global Access: Allows your connector to access everything you have access to. With global access, connector access will be linked to your account. This option is not recommended for production.
- Granular access: Limits the access for your connector. You will be able to manage connector access through a service account. This option is recommended for production.
- Use an existing API key: Allows you to enter an API key and secret part you have stored. You can enter an API key and secret (or generate these in the Cloud Console).
- Click Continue.
- 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
andUS
. Defaults toUS
. - 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
andfail
.log
(the default) logs the error message in theerror-<connector-id>
topic and continues processing. If set tofail
, 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.
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. More tasks may improve performance.
- Click Continue.
Verify the connection details.
Click Launch.
The status for the connector should go from Provisioning to Running.
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 Managed and Custom Connectors section.
Tip
When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue 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
orKAFKA_API_KEY
(the default). To use an API key and secret, specify the configuration propertieskafka.api.key
andkafka.api.secret
, as shown in the example configuration (above). To use a service account, specify the Resource ID in the propertykafka.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 Managed and Custom Connectors 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 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
orEU
.- 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]
- 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]
- 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.