# InfluxDB 3 Sink Connector for Confluent Cloud The fully managed InfluxDB 3 Sink connector for Confluent Cloud writes data from an Apache Kafka® topic to an InfluxDB table. #### 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 InfluxDB 3 Sink connector supports the following features: * **At least once delivery**: Guarantees that records from the Kafka topic are delivered at least once. * **Supports multiple tasks**: Supports running one or more tasks. More tasks may improve performance. * **Supports gzip compression**: Allows enabling gzip compression for more efficient data transfer. * **Supports SSL/TLS secure connection**: Supports secure communication using SSL/TLS and custom CA certificates. * **Flexible measurement (table) naming**: Supports setting the measurement name per record or configuring it dynamically using `measurement.name.format`. Also supports `${topic}` substitution to automatically include the Kafka topic name in the measurement. * **Supports Dead Letter Queue (DLQ)**: Routes invalid records (bad measurement, invalid timestamp, unsupported types) to DLQ when configured. * **Supports tag extraction**: Automatically extracts tags from record fields based on user configuration. * **Field type support**: Supports ingestion of numeric, string, boolean, and timestamp field types. 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 [InfluxDB 3 Sink Connector](limits.md#cc-influxdb3-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). ## Record structure Each record is in JSON format. It can contain a number of InfluxDB fields, a tag section (`"tags"`), and a measurement section (`"measurement"`). The following example shows the record structure required for the connector. ```none { "measurement":"measurement-name", "tags": { "tag1":"value1", "tag2":"value2" }, "time-field": , "field1": , "field2": , ... } ``` Note the following: * The `"tags"` section is optional. This section provides the list of tags associated with the set of fields. Each tag must be a key-value pair of type string. * The `"measurement"` field takes the name of the InfluxDB measurement. This field is optional. However, if you do not provide the measurement name here then you must specify the measurement name in the `measurement.name.format` [configuration property](#cc-influxdb3-sink-config-properties). Also, specifying this field will override whatever is specified in the Kafka record. * You can use multiple fields in a record. Fields can be of type int, float, boolean or string. * You can designate one of the fields to have the record timestamp information using the `event.time.fieldname` [configuration property](#cc-influxdb3-sink-config-properties). If left unspecified, the timestamp used is the Kafka record timestamp. * For AVRO, PROTOBUF, and JSON_SR the structure remains the same. Note that the corresponding schema must be in Schema Registry. ## Quick Start Use this quick start to get up and running with the Confluent Cloud InfluxDB 3 Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to an InfluxDB bucket. 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. - The Confluent CLI installed and configured for the cluster. See [Install the Confluent CLI](https://docs.confluent.io/confluent-cli/current/install.html). - Authorized access to write data to InfluxDB. For more information, see [writing data to InfluxDB](https://docs.influxdata.com/influxdb/v2.1/api/#operation/PostWrite). - [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). - At least one source Kafka topic must exist in your Confluent Cloud cluster before creating the sink 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 **InfluxDB 3 Sink** connector card.  #### Step 4: Enter the connector details #### NOTE * Make sure you have all your [prerequisites](#cc-influxdb3-sink-prereqs) completed. * An asterisk ( \* ) designates a required entry. At the **Add InfluxDB 3 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: **InfluxDB** - **API URL**: Fully-qualified InfluxDB API URL used for establishing a connection. For example, `http://influxdb-test.com:8086`. - **Token**: Token to authenticate with the InfluxDB host. For more details, see [Create a token in InfluxDB](https://docs.influxdata.com/influxdb3/cloud-serverless/admin/tokens/create-token/). - **Database**: Database name for InfluxDB. - **SSL Enabled**: Controls whether to enable SSL/TLS for InfluxDB connection. Default value is `false`. When set to `true`, SSL/TLS is enforced for secure connections. Define the following configurations when SSL is enabled. - **Disable Server Certificate Validation**: Controls whether to disable server certificate validation. - **SSL Roots File**: Upload `.PEM` files for server side certificates. - **SSL Protocol**: The SSL/TLS protocol to use for the connection. 2. Click **Continue**. ### Configuration **Data encryption** - Enable **Client-Side Field Level Encryption** for data encryption. Specify a **Service Account** to access the Schema Registry and associated encryption rules or keys with that schema. For more information on CSFLE or CSPE setup, see [Manage encryption for connectors](csfle.md#connect-csfle). **Input messages** - **Input Kafka record value format**: Select an Input Kafka record value format (data coming from the Kafka topic). Valid values are AVRO, PROTOBUF, JSON_SR (JSON Schema), or JSON (schemaless). A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) to use a schema-based message format (for example, Avro, JSON_SR, or 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). **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. - **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`. - **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. - **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. - **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. **Write Configuration** - **Write Precision**: The write precision of Influx DB timestamp. Valid values are `microseconds`, `milliseconds`, `nanoseconds`, and `seconds`. The default value is `milliseconds`. - **Event Time field name**: The name of the field in the Kafka record that contains the event time that the connector uses when it writes to an InfluxDB data point. If nothing is entered, the default value is the Kafka record timestamp that identifies when the Kafka record was created, which corresponds to the time that the event was processed. - **Measurement (Table) Name Format**: A format string for the destination measurement (table) name that may contain ‘${topic}’ as a placeholder for the originating topic name. For example, `kafka_${topic}` for the topic ‘orders’ will map to the measurement (table) name ‘kafka_orders’. If the measurement (table) name format is not provided, the connector uses the ‘measurement’ field value present in the Kafka message. If such a field is not present in the message, the message is sent to the DLQ. - **Enable compression**: Specifies whether gzip compression is enabled. Defaults to `false`. **Retries** - **Backoff Time**: Backoff time duration in milliseconds that the connector waits before retrying. Defaults to `1000` ms. - **Max retries**: The maximum number of times to retry a task when errors occur and before the task fails. Defaults to `10`. **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). For all property values and definitions, see [Configuration Properties](#cc-influxdb3-sink-config-properties). - 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. 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 files Verify that data is being produced at the InfluxDB host. 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 To set up and run the connector using the Confluent CLI, complete the following steps. #### NOTE Make sure you have all your [prerequisites](#cc-influxdb3-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. ```json { "connector.class": "InfluxDB3Sink", "topics": "orders", "input.data.format": "JSON", "name": "InfluxDB3Sink_0", "kafka.api.key": "****************", "kafka.api.secret": "*********************************", "influxdb.url": "http://influxdb-test.com:8086", "influxdb.token": "***************************", "influxdb.database": "", "tasks.max": "1", } ``` Note the following property definitions: * `"connector.class"`: Identifies the connector plugin name. * `"topics"`: Enter the topic name or a comma-separated list of topic names. * `"input.data.format"` (data coming from the Kafka topic): Supports AVRO, PROTOBUF, JSON_SR (JSON Schema), or JSON (schemaless). A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) to use a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). * `"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 ``` * `"influxdb.url"`: Fully-qualified InfluxDB API URL used for establishing a connection. For example, `http://influxdb-test.com:8086` * `"influxdb.token"`: Token to authenticate with the InfluxDB host. * `"influxdb.org.id"`: The InfluxDB organization ID. * `"influxdb.database"`: Database name for InfluxDB 3. * `"tasks.max"`: Enter the maximum number of [tasks](/platform/current/connect/concepts.html#tasks) for the connector to use. More tasks may improve performance. **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-influxdb3-sink-config-properties) for all property values and descriptions. #### Step 3: 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 influxdb3-sink-config.json ``` Example output: ```none Created connector InfluxDB3Sink_0 lcc-do6vzd ``` #### Step 4: 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 | Trace +------------+---------------------------+---------+------+-------+ lcc-do6vzd | InfluxDB3Sink_0 | RUNNING | sink | | ``` #### Step 5: Check for files Verify that data is being produced at the InfluxDB 3 host. 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 ### 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, or plain JSON. 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, 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 ### InfluxDB `influxdb.url` : Fully qualified InfluxDB v3 API URL used for establishing connection.
* Type: string * Importance: high `influxdb.token` : Token to authenticate with InfluxDB v3.
* Type: password * Importance: high `influxdb.database` : Database name for InfluxDB v3.
* Type: string * Importance: high `influxdb.ssl.enabled` : Controls whether to enable SSL/TLS for InfluxDB connections. If true, SSL/TLS will be enforced for secure connections.
* Type: boolean * Default: false * Importance: medium `influxdb.ssl.disable.server.certificate.validation` : Disable server SSL certificate validation. Use with caution in production environments.
* Type: boolean * Default: false * Importance: medium `influxdb.ssl.roots.file` : Upload the certificates file in PEM format for custom CA certificates.
* Type: password * Default: [hidden] * Importance: medium `influxdb.ssl.protocol` : The SSL/TLS protocol to use for connections.
* Type: string * Default: TLSv1.2 * Importance: medium ### Write Configuration `write.precision` : Write precision of InfluxDB timestamp. Valid values are Seconds, Milliseconds, Microseconds, and Nanoseconds. Note that if the kafka record timestamp is used, instead of specifying a timestamp field, using ‘event.time.fieldname’, then the kafka timestamp(in Milliseconds) will be converted the precision defined here. Otherwise you must provide the correct time unit of the ‘event.time.fieldname’ here.
* Type: string * Default: Milliseconds * Importance: medium `event.time.fieldname` : The name of field in the Kafka record that contains the event time to be written to an InfluxDB data point. By default (if this config is unspecified), the timestamp written to InfluxDB is the Kafka record timestamp (when the Kafka record was created) which corresponds to the time that the event was processed.
* Type: string * Importance: medium `measurement.name.format` : A format string for the destination measurement (table) name, which may contain ‘${topic}’ as a placeholder for the originating topic name.
For example, `kafka_${topic}` for the topic ‘orders’ will map to the measurement (table) name ‘kafka_orders’. If the measurement (table) name format is not provided the connector will use the ‘measurement’ field value present in the kafka message. If such a field is not present in the message the message will be sent to the dlq.
* Type: string * Importance: medium `influxdb.gzip.enable` : Flag to determine if gzip should be enabled.
* Type: boolean * Default: false * Importance: low ### Retries `retry.backoff.ms` : Backoff time duration to wait before retrying
* Type: int * Default: 1000 (1 second) * Importance: medium `max.retries` : The maximum number of times to retry on errors before failing the task.
* Type: int * Default: 10 * Importance: medium ### 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.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.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.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 InfluxDB 3 Sink connector for Confluent Cloud. ### What record structure does the connector require? The connector expects each record to be a flat JSON structure with optional `"tags"` and `"measurement"` fields. The only supported nested structure is the `"tags"` field, which must contain string key-value pairs. All other fields must be top-level and of a supported type (integer, float, boolean, or string). Records that include additional nested structures (such as arrays, maps, or nested objects) beyond the `"tags"` field are not supported. These complex fields are dropped before writing to InfluxDB because InfluxDB, a time-series database, accepts only simple field types. If your source topic data contains nested structures, use SMTs such as `Flatten` or `ReplaceField` to reshape the data before it reaches the connector. You can also use Flink SQL to reformat the data in the source topic. ### What happens if the measurement name is missing from the record? The measurement name determines which InfluxDB table the record is written to. You can specify the measurement name in two ways: * Include a `"measurement"` field in the record. * Set the `measurement.name.format` configuration property, which takes precedence over the `"measurement"` field in the record. If neither the `"measurement"` field is present in the record nor the `measurement.name.format` property is configured, the connector treats the record as invalid and routes it to the [Dead Letter Queue (DLQ)](dead-letter-queue.md#ccloud-dlq-topics) if configured. If DLQ is not configured, the connector fails. You can also use the `${topic}` substitution in `measurement.name.format` to dynamically include the Kafka topic name as part of the measurement name. ### Why does the connector fail with a retention policy error? The connector might fail with the following error when the timestamp of a record falls outside the database’s retention period: ```none Connector failed while writing to database due to retention policy. ``` This occurs when the timestamp associated with the record exceeds the InfluxDB retention policy duration. The record timestamp is determined by the `event.time.fieldname` configuration property. If `event.time.fieldname` is not configured, the Kafka record timestamp is used. To resolve this issue: * **Increase the retention period** on the target InfluxDB database so that the data sent by the connector is accepted. * **Skip the offending data** by using the [offset management](offsets.md#connect-custom-offsets) feature to move the connector’s offsets past the problematic records. ### How does the connector determine the record timestamp? The connector uses the following logic to determine the timestamp for each record written to InfluxDB: * If the `event.time.fieldname` property is configured and the specified field exists in the record, that field’s value is used as the timestamp. * If `event.time.fieldname` is not configured or the specified field is missing from the record, the Kafka record timestamp is used. The `write.precision` property controls the expected precision of the timestamp value (for example, milliseconds, microseconds, or nanoseconds). Ensure the timestamp values in your records match the configured precision to avoid incorrect time values in InfluxDB. ### How can you improve connector performance? To optimize the performance of the InfluxDB 3 Sink connector: * **Co-locate the connector and database**: Deploy the connector in the same cloud region as your InfluxDB instance. Cross-region setups introduce significant network latency, which degrades performance. * **Increase the number of tasks**: Use the `tasks.max` property to run more tasks in parallel. * **Enable gzip compression**: Set `influxdb.gzip.enable` to `true` to compress data before sending it to InfluxDB, reducing network transfer time. ### What input data formats does the connector support? The connector supports the following input data formats: * **JSON** (schemaless) * **JSON_SR** (JSON Schema) * **AVRO** * **PROTOBUF** For schema-based formats (AVRO, JSON_SR, and PROTOBUF), [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled in your Confluent Cloud environment. The schema must be registered in Schema Registry before producing records to the Kafka topic. ### What tag types does InfluxDB support? Tags in InfluxDB are indexed metadata used to optimize queries. The `"tags"` field in the record must be a map of string key-value pairs. Only the `string` type is supported for both tag keys and values. Tags are optional and do not need to be present in every record. ## 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)