# Datagen Source Connector for Confluent Cloud The fully managed Datagen Source connector for Confluent Cloud generates mock data for development and testing. The connector supports Avro, JSON Schema, Protobuf, and JSON (schemaless) output formats. The mock source data is provided through GitHub from [datagen resources](https://github.com/confluentinc/kafka-connect-datagen/tree/master/src/main/resources). **This connector is not suitable for production use.** 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. #### NOTE * This Quick Start is for the fully managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see [Datagen Source Connector for Confluent Platform](https://docs.confluent.io/kafka-connectors/datagen/current/). * 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). ## Limitations Be sure to review the following information. * If you plan to use one or more Single Message Transformations (SMTs), see [SMT Limitations](single-message-transforms.md#cc-single-message-transforms-limitations). ## Quick start options There are several ways to start using the Datagen Source connector in Confluent Cloud. You can get started quickly using a tutorial or you can manually configure the connector. Note that the tutorials are available only if your user account has [OrganizationAdmin](managed-connector-rbac.md#managed-connector-rbac-role-mappings) RBAC role privileges. The following descriptions provide additional information about how to start using the Datagen Source connector. * Start the **Produce sample data** quick start tutorial from the Confluent Cloud home page after you first launch Confluent Cloud. Using this tutorial, you can create a Kafka topic and configure the Datagen Source connector to produce sample records in the topic. This tutorial is available to you after you log into your new Confluent Cloud cluster and may be launched later using the following UI tile. ![Sample data tutorial](images/ccloud-datagen-sample-data-tutorial.png) * Start the **Launch Sample Data** quick start tutorial from the Datagen Source connector tile. This quick start automatically creates a `sample_data` Kafka topic and launches the Datagen Source connector using the quick start template you select. Note that this connector quick start is unavailable if you have already started the **Produce sample data** tutorial. ![Sample data using Datagen](images/ccloud-datagen-sample-data-launch.png) * Manually configure and launch the Datagen Source connector using the [quick start](#cc-datagen-source-st-qs) instructions provided in this document. ## Quick Start Use this quick start to get up and running with the Confluent Cloud Datagen source connector. The quick start provides the basics of selecting the connector and configuring it to use for testing and development. This connector is not suitable for production use. 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). - [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, 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 **Datagen Source** connector card. Note that you may see a **Launch Sample Data** tutorial when you click this tile. For more information, see [Quick start options](#cc-datagen-source-qs-options). ![Datagen Source Connector Card](images/ccloud-datagen-source-icon.png) #### Step 4: Enter the connector details #### NOTE * Ensure you have all your [prerequisites](#cc-datagen-source-prereqs) completed. * An asterisk ( \* ) designates a required entry. At the **Add Datagen Source Connector** screen, complete the following: ### Topic selection Select the topic you want to send data to 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**. ### Configuration - **Select a template**: Select from built-in quickstart schema specifications. Cannot be used with schema.string. Refer to kafka-connect-datagen on Github for additional information. - **Schema String**: **Schema String**: Provide a custom JSON-encoded Avro schema. The length of the schema string cannot exceed 10000 characters. This option cannot be used with the **Sample data template** option. For schema string examples, see [datagen resources](https://github.com/confluentinc/kafka-connect-datagen/tree/master/src/main/resources). For supported annotations, see [annotation types](https://github.com/confluentinc/avro-random-generator#annotation-types). Note the following current annotation type limitations: - The **options** annotation type object `file` variant is not supported. The JSON `array` variant is supported. - The **regex** annotation type is not supported. **Output messages** - **Select output record value format**: Under **Output Kafka record value format**, select an output message format (data coming from the connector): AVRO, JSON_SR (JSON Schema), PROTOBUF, or JSON. [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, or PROTOBUF). for more information. ### **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). - **Schema Keyfield**: Name of a field to use as the message key. - **Max interval between messages (ms)**: Sets the maximum interval (in milliseconds) between messages. The default value is 1000. **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**: 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. - **Key Converter Schema ID Serializer**: The class name of the schema ID serializer for keys. This is used to serialize schema IDs in the message headers. - **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`. - **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. - **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. - **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. - **Value Converter Schema ID Serializer**: The class name of the schema ID serializer for values. This is used to serialize schema IDs in the message headers. **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. **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). See [Configuration Properties](#cc-datagen-source-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 tasks, use the Range Slider to select the desired number of tasks. 2. Click **Continue**. ### Review and Launch 1. Verify the connection details by previewing the running configuration. 2. Once you’ve validated that the properties are configured to your satisfaction, click **Launch**. ![Launch the connector](images/ccloud-datagen-launch-connector.png) The status for the connector should go from **Provisioning** to **Running**. ![Check the connector status](images/ccloud-datagen-status.png) #### Step 5: Check the Kafka topic After the connector is running, verify that messages are populating your Kafka topic. 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-datagen-source-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 { "name" : "", "connector.class": "DatagenSource", "kafka.auth.mode": "KAFKA_API_KEY", "kafka.api.key": "", "kafka.api.secret" : "", "kafka.topic" : "topic1, topic2", "output.data.format" : "JSON", "quickstart" : "PAGEVIEWS", "tasks.max" : "1" } ``` Note the following property definitions: * `"name"`: Sets a name for your new connector. * `"connector.class"`: Identifies the connector plugin class. * `"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 ``` * `"kafka.topic"`: Enter one topic or multiple comma-separated topics. * `"output.data.format"`: Sets the output Kafka record value format (data coming from the connector). Valid entries are **AVRO**, **JSON_SR**, **PROTOBUF**, or **JSON** (schemaless). You must have Confluent Cloud Schema Registry configured if using a schema-based format (for example, Avro). The following two configuration properties are available to create sample source data: * `"schema.string"`: Provide a custom JSON-encoded Avro schema. The length of the schema string cannot exceed 10000 characters. This option cannot be used with the `quickstart` property. For schema string examples, see [datagen resources](https://github.com/confluentinc/kafka-connect-datagen/tree/master/src/main/resources). For supported annotations, see [annotation types](https://github.com/confluentinc/avro-random-generator#annotation-types). Note the following current annotation type limitations: - The **options** annotation type object `file` variant is not supported. The JSON `array` variant is supported. - The **regex** annotation type is not supported. * `"quickstart"`: Enter one of the listed Quick Start schema names. This property cannot be used with the `schema.string` property. To view the sample data and schema specifications, see [datagen resources](https://github.com/confluentinc/kafka-connect-datagen/tree/master/src/main/resources). > ### **Show quickstart schema list** > - CLICKSTREAM_CODES > - CLICKSTREAM > - CLICKSTREAM_USERS > - ORDERS > - RATINGS > - USERS > - USERS_ARRAY > - PAGEVIEWS > - STOCK_TRADES > - INVENTORY > - PRODUCT > - PURCHASES > - TRANSACTIONS > - STORES > - CREDIT_CARDS > - CAMPAIGN_FINANCE > - FLEET_MGMT_DESCRIPTION > - FLEET_MGMT_LOCATION > - FLEET_MGMT_SENSORS > - PIZZA_ORDERS > - PIZZA_ORDERS_COMPLETED > - PIZZA_ORDERS_CANCELLED > - INSURANCE_OFFERS > - INSURANCE_CUSTOMERS > - INSURANCE_CUSTOMER_ACTIVITY > - GAMING_GAMES > - GAMING_PLAYERS > - GAMING_PLAYER_ACTIVITY > - PAYROLL_EMPLOYEE > - PAYROLL_EMPLOYEE_LOCATION > - PAYROLL_BONUS > - SYSLOG_LOGS > - DEVICE_INFORMATION > - SIEM_LOGS > - SHOES > - SHOE_CUSTOMERS > - SHOE_ORDERS > - SHOE_CLICKSTREAM **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-datagen-source-config-properties) for all property values and definitions. #### Step 4: Load the configuration 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 datagen-source-config.json ``` Example output: ```none Created connector confluent-datagen-source 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 | confluent-datagen-source | RUNNING | source ``` #### Step 6: Check the Kafka topic. After the connector is running, verify that messages are populating your Kafka topic. 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. ## Example Follow the steps in the [Quick Start for Confluent Cloud](../get-started/index.md#cloud-quickstart) to stream sample data to Kafka using the Datagen Source connector for Confluent Cloud. ## 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). ### 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 ### Which topic do you want to send data to? `kafka.topic` : Identifies the topic name to write the data to.
* Type: string * 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 ### Output messages `output.data.format` : Sets the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or 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 * Default: JSON * Importance: high ### Datagen Details `quickstart` : Select from built-in quickstart schema specifications. Cannot be used with schema.string. Refer to kafka-connect-datagen on Github for additional information.
* Type: string * Default: “” * Importance: high `schema.string` : The literal JSON-encoded Avro schema to use. Cannot be set with quickstart.
* Type: string * Default: “” * Valid Values: A string at most 10000 characters long * Importance: medium `schema.keyfield` : Name of the field to use as message key. It’s optional when using quickstart.
* Type: string * Default: “” * Importance: medium `max.interval` : Set the maximum interval (in milliseconds) between each message.
* Type: int * Default: 1000 * Valid Values: [10,…] for non-dedicated clusters and [5,…] for dedicated clusters * Importance: high ### Number of tasks for this connector `tasks.max` : Maximum number of tasks for the connector.
* Type: int * Valid Values: [1,…] * Importance: high ### Additional Configs `header.converter` : The converter class for the headers. This is used to serialize and deserialize the headers of the messages.
* Type: string * Importance: low `producer.override.compression.type` : The compression type for all data generated by the producer. Valid values are none, gzip, snappy, lz4, and zstd.
* Type: string * Importance: low `producer.override.linger.ms` : The producer groups together any records that arrive in between request transmissions into a single batched request. More details can be found in the documentation: [https://docs.confluent.io/platform/current/installation/configuration/producer-configs.html#linger-ms](https://docs.confluent.io/platform/current/installation/configuration/producer-configs.html#linger-ms).
* Type: long * Valid Values: [100,…,1000] * 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.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: none * Importance: low `key.converter.key.schema.id.serializer` : The class name of the schema ID serializer for keys. This is used to serialize schema IDs in the message headers.
* Type: string * Default: io.confluent.kafka.serializers.schema.id.PrefixSchemaIdSerializer * 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.serializer` : The class name of the schema ID serializer for values. This is used to serialize schema IDs in the message headers.
* Type: string * Default: io.confluent.kafka.serializers.schema.id.PrefixSchemaIdSerializer * 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 ## Frequently Asked Questions The frequently asked questions (FAQs) address common questions and issues encountered while using the fully managed Datagen Source connector for Confluent Cloud. ### Schema configuration #### Can I use both `quickstart` and `schema.string` in the same connector? No. The `quickstart` and `schema.string` properties are mutually exclusive. Use `quickstart` to select a built-in schema template, or use `schema.string` to provide a custom JSON-encoded Avro schema. If both are specified, the connector returns a validation error. #### How do I configure a custom Avro schema with `schema.string`? Set `schema.string` to a JSON-encoded Avro schema and remove any `quickstart` value. For example: ```none { "name" : "my-datagen-connector", "connector.class": "DatagenSource", "kafka.auth.mode": "KAFKA_API_KEY", "kafka.api.key": "", "kafka.api.secret" : "", "kafka.topic" : "my-topic", "output.data.format" : "AVRO", "schema.string" : "{\"namespace\": \"example\", \"name\": \"my_record\", \"type\": \"record\", \"fields\": [{\"name\": \"id\", \"type\": {\"type\": \"long\", \"arg.properties\": {\"iteration\": {\"start\": 0}}}}, {\"name\": \"name\", \"type\": {\"type\": \"string\", \"arg.properties\": {\"options\": [\"Alice\", \"Bob\", \"Carol\"]}}}]}", "tasks.max" : "1" } ``` Note the following limitations for `schema.string`: - The schema string cannot exceed 10,000 characters. - The `regex` annotation type is not supported. - The `options` annotation type `file` variant is not supported. Use the JSON `array` variant instead. - Complex nested object types in the `options` annotation are not fully supported and may cause parse errors. For schema examples and supported annotations, see [datagen resources](https://github.com/confluentinc/kafka-connect-datagen/tree/master/src/main/resources) and [annotation types](https://github.com/confluentinc/avro-random-generator#annotation-types). #### Why does the connector return “Unable to parse the provided schema” for `schema.string`? This error occurs when the value of `schema.string` is not valid JSON-encoded Avro. Common causes include: - **Malformed JSON**: Ensure all quotes are properly escaped and the JSON structure is valid. - **Unsupported annotation types**: The `regex` annotation and the `file` variant of `options` are not supported. - **Unsupported complex types**: Nested object types in the `options` section may cause parse failures. - **Exceeding character limit**: The `schema.string` value cannot exceed 10,000 characters. Validate your schema JSON independently before providing it to the connector configuration. ### Output format and Schema Registry #### What is the difference between `JSON` and `JSON_SR` output formats? - **JSON**: Produces schemaless JSON records. Confluent Cloud Schema Registry is not required. - **JSON_SR**: Produces JSON records with a schema registered in Confluent Cloud Schema Registry. You must have Confluent Cloud Schema Registry enabled in your environment to use this format. Similarly, the **AVRO** and **PROTOBUF** output formats require Confluent Cloud Schema Registry to be enabled. If you see serialization errors, verify that Confluent Cloud Schema Registry is configured and accessible. #### Does the connector register a new schema in Schema Registry? Yes. When using a schema-based output format (AVRO, JSON_SR, or PROTOBUF), the connector registers the schema it uses with Confluent Cloud Schema Registry. If the topic already has a registered schema that differs from the connector’s schema, schema compatibility checks apply. If the schemas are incompatible, the connector fails with a compatibility error. To avoid conflicts, ensure the schema provided through `quickstart` or `schema.string` is compatible with any existing schema registered for the target topic. ### Connector behavior #### Can I use the Datagen Source connector in a production environment? No. The Datagen Source connector is designed for development and testing purposes only. It generates mock data and is not suitable for production workloads. #### The data preview is stuck in “generating preview” state. How do I resolve this? If a data preview request is stuck, you cannot cancel it directly. To work around this issue: - Delete the output topic associated with the preview. This expires the preview request. - If you cannot delete the topic, the preview automatically expires and is removed after 7 days. #### How do I produce a separate key field from the Datagen connector? If your schema includes a field you want to use as the message key, set the `schema.keyfield` configuration property to the name of that field. The connector extracts the specified field and produces it as the record key. ## 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. [![image](images/topology.png)](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html) ## Suggested Reading Blog post: [Creating a Serverless Environment for Testing Your Apache Kafka Applications](https://www.confluent.io/blog/testing-kafka-applications/)