# 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](https://docs.newrelic.com/docs/data-apis/ingest-apis/telemetry-sdks-report-custom-telemetry-data/) 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](networking/internet-resource.md#clusters-connect-cloud). ## Features The New Relic Metrics Sink connector provides the following features: * **Supports multiple tasks**: The connector supports running one or more tasks. More tasks might improve performance. * **Input data formats**: The connector supports Bytes, AVRO, JSON_SR (JSON Schema), JSON (Schemaless) and PROTOBUF input data 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_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](connect-api-section.md#ccloud-connect-api) section. ## Limitations Be sure to review the following information. * For connector limitations, see [New Relic Metrics Sink Connector](limits.md#cc-new-relic-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). ## 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" : ""` * `"collector.metadata.kafka.partition" : ""` Gauge : ```json { "name" : "service.response.duration", "type" : "gauge", "value" : 7.8, "timestamp" : 1655970976, "attributes" : { "host.name" : "dev.server.com", "app.name" : "foo", "collector.metadata.kafka.topic" : "", "collector.metadata.kafka.partition" : "" } } ``` Count : ```json { "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" : "", "collector.metadata.kafka.partition" : "" } } ``` Summary : ```json { "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" : "", "collector.metadata.kafka.partition" : "" } } ``` ## 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](https://www.confluent.io/confluent-cloud/) cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud. - A [New Relic API license key](https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/) for the New Relic account where the connector sinks data. - 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).
- 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 **New Relic Metrics Sink** connector card.  #### Step 4: Enter the connector details #### NOTE * Ensure you have all your [prerequisites](#cc-new-relic-metrics-sink-prereqs) completed. * An asterisk ( \* ) designates a required entry. At the **Add New Relic 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: - **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](https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/). - **New Relic Data Center host region**: Options are `EU` and `US`. Defaults to `US`. 2. Click **Continue**. ### Configuration - **Input Kafka record value 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. ### **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). - **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-` topic and continues processing. If set to `fail`, the connector stops. **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-new-relic-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. More tasks might improve performance. 2. Click **Continue**. ### Review and Launch 1. Verify the connection details. 2. 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 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-new-relic-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 ``` 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. ```none { "topics":"orders", "input.data.format": "JSON", "connector.class": "NewRelicMetricsSink", "name": "NewRelicMetricsSink_0", "kafka.auth.mode": "", "kafka.api.key": "****************", "kafka.api.secret": "*************************************************", "newrelic.ingest.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](service-account.md#s3-cloud-service-account), specify the **Resource ID** in the property `kafka.service.account.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 ``` * `"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](https://docs.newrelic.com/docs/apis/intro-apis/new-relic-api-keys/). **SMTs**: For details about adding SMTs using the Confluent CLI, see the [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms) documentation. See [Configuration Properties](cc-azure-functions-sink.md#cc-azure-functions-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 .json ``` For example: ```none confluent connect cluster create --config-file new-relic-metrics-sink-config.json ``` Example output: ```none Created connector NewRelicMetricsSink_0 lcc-ix4dl ``` #### 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 +-----------+-------------------------+---------+------+ 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](connect-api-section.md#ccloud-connect-api) section. ## 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.
* Type: string * Importance: low `topics` : Identifies the topic name or a comma-separated list of topic names.
* 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.
* 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 `${connector}` in the value to use it as a placeholder for the logical cluster ID.
* Type: string * Default: success-${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.
* 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.
* 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 * Default: JSON * 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, whenever possible.
* 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.
* 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- 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 ### 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)
* 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)
* Type: string * Importance: low `header.converter` : The converter class for the headers. This is used to serialize and deserialize the headers of the messages.
* 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.
* 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.
* 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.
* Type: boolean * Importance: low `value.converter.auto.register.schemas` : Specify if the Serializer should attempt to register the Schema.
* 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.
* Type: boolean * Importance: low `value.converter.enhanced.avro.schema.support` : Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.
* Type: boolean * Importance: low `value.converter.enhanced.protobuf.schema.support` : Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.
* Type: boolean * Importance: low `value.converter.flatten.unions` : Whether to flatten unions (oneofs). Applicable for Protobuf Converters.
* Type: boolean * Importance: low `value.converter.generate.index.for.unions` : Whether to generate an index suffix for unions. Applicable for Protobuf Converters.
* Type: boolean * Importance: low `value.converter.generate.struct.for.nulls` : Whether to generate a struct variable for null values. Applicable for Protobuf Converters.
* Type: boolean * Importance: low `value.converter.int.for.enums` : Whether to represent enums as integers. Applicable for Protobuf Converters.
* Type: boolean * Importance: low `value.converter.latest.compatibility.strict` : Verify latest subject version is backward compatible when use.latest.version is true.
* Type: boolean * Importance: low `value.converter.object.additional.properties` : Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.
* Type: boolean * Importance: low `value.converter.optional.for.nullables` : Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.
* Type: boolean * Importance: low `value.converter.optional.for.proto2` : Whether proto2 optionals are supported. Applicable for Protobuf Converters.
* 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.
* Type: boolean * Importance: low `value.converter.use.latest.version` : Use latest version of schema in subject for serialization when auto.register.schemas is false.
* Type: boolean * Importance: low `value.converter.use.optional.for.nonrequired` : Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.
* 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.
* 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.
* Type: int * Importance: low `value.converter.wrapper.for.nullables` : Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.
* 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.
* 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.
* 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.
* 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.
* 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:
BASE64 to serialize DECIMAL logical types as base64 encoded binary data and
NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.
* Type: string * Default: BASE64 * Importance: low `value.converter.flatten.singleton.unions` : Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.
* 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.
* 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.
* 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.
* 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.
* 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.
* 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.
* Type: string * Default: TopicNameStrategy * Importance: low ### Auto-restart policy `auto.restart.on.user.error` : Enable connector to automatically restart on user-actionable errors.
* Type: boolean * Default: true * Importance: medium ## FAQs Find answers to frequently asked questions about the fully managed New Relic Metrics Sink connector for Confluent Cloud. ### Why can’t I see my metrics in New Relic even though the connector is running? If the connector status shows `RUNNING` but you do not see metrics in New Relic, check the following: * **Check the Dead Letter Queue (DLQ)**: Look for messages in the DLQ topic. Records that fail processing are routed to the DLQ when `errors.tolerance` is set to `all`. * **Check the error topic**: Inspect the `error-` topic for errors. By default, `behavior.on.error` is set to `log` for this connector. This means the connector continues running even when errors occur, so you might not see a `FAILED` status even though records are failing. * **Verify the data format**: Ensure that the data in the Kafka topic matches one of the [supported metric formats](#cc-new-relic-metrics-sink) (Gauge, Count, or Summary). The connector only processes metrics-type data. * **Query metrics in New Relic**: Use the [New Relic Query Language (NRQL)](https://docs.newrelic.com/docs/nrql/get-started/introduction-nrql-new-relics-query-language/) to query your posted metrics from the New Relic observability platform or [NerdGraph](https://docs.newrelic.com/docs/apis/nerdgraph/get-started/introduction-new-relic-nerdgraph/). For details on querying metric data, see the [New Relic metric data type](https://docs.newrelic.com/docs/data-apis/understand-data/metric-data/metric-data-type/) documentation. ### Why is the connector failing with a “DataException: field cannot be null or empty string” error? This error occurs when the connector cannot find a required field in the Kafka record payload. Each metric record must contain the following required fields: * `name` (string): The metric name. * `value` (numeric or object): The metric value. * `timestamp` (long): The metric timestamp. For Count and Summary metrics, the `interval.ms` field is also required. Ensure that the messages produced to the input Kafka topic comply with the [supported metric formats](#cc-new-relic-metrics-sink) and that no required fields are null or empty. ### Why is the connector failing with “Encountered a data type casting exception”? This error indicates a schema mismatch between the data in the Kafka topic and what the connector expects. Common causes include: * **The \`\`attributes\`\` field is defined as a string instead of a map**: If you are using a schema-based format (Avro, JSON_SR, or Protobuf), the `attributes` field must be defined as a map of string key-value pairs, not a serialized JSON string. For example, in Avro, use `{"type": "map", "values": "string"}` for the `attributes` field. * **Mismatched data types**: Ensure that `value` is a numeric type (double or float for Gauge and Count metrics) and `timestamp` is a long type. If you are using a schema-based format, verify that your schema definition aligns with the [supported metric formats](#cc-new-relic-metrics-sink). ### Can I use this connector to send non-metrics data to New Relic? No. The New Relic Metrics Sink connector is designed exclusively for ingesting metrics data (Gauge, Count, and Summary types) from Kafka topics and posting them to the New Relic Metric API. Using the connector with non-metrics data, such as CDC records from a Debezium source connector or log data, is not supported and will result in data conversion errors. For sending non-metrics data to New Relic, consider using the [HTTP Sink V2 connector](cc-http-sink-v2.md#cc-http-sink-v2) to target the appropriate New Relic ingest API endpoint. ### Why are messages ending up in the Dead Letter Queue (DLQ)? Messages are routed to the DLQ when the connector encounters errors during record processing. Common causes include: * **Incorrect data format**: The message payload does not match the expected metric format. Each record must include the required `name`, `value`, and `timestamp` fields. * **Missing required fields**: One or more required fields (such as `name`, `type`, or `interval.ms` for Count and Summary metrics) are missing or null. * **Schema mismatch**: The schema of the data in the topic does not match what the connector expects. For example, the `attributes` field is encoded as a string rather than a map. To troubleshoot, inspect the DLQ messages and check the header metadata (such as `__connect.errors.exception.class.name`) for details about the specific error. ### Why does consumer lag appear to persist after messages are routed to the DLQ? The New Relic Metrics Sink connector uses a pre-commit offset strategy. This means the connector does not commit the offset of messages routed to the DLQ until a subsequent valid message is successfully processed and sent to New Relic. If only invalid messages are being produced to the topic, the consumer lag does not decrease until a valid message is processed. This is expected behavior and does not indicate a problem with the connector. ### What metric type is used if I don’t specify the `type` field in the record? If no `type` field is specified in the metric record, the connector defaults to the **Gauge** metric type. If you need to use Count or Summary metrics, you must explicitly set the `type` field and include the required `interval.ms` field. ### What is the maximum payload size the connector can send to New Relic? The connector batches records to ensure the payload does not exceed the New Relic Metric API limit of **1 MB** per request. If your records are large, the connector automatically adjusts the batch size to stay within this limit. No additional configuration is needed. ## 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. [](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html)