# Azure Cognitive Search Sink Connector for Confluent Cloud The fully managed Azure Cognitive Search Sink connector for Confluent Cloud moves data from Apache Kafka® topics to Azure Cognitive Search. The connector writes each event from a Kafka topic (as a document) to an index in Azure Cognitive Search. The connector uses the [Azure Cognitive Search REST API](https://docs.microsoft.com/en-us/rest/api/searchservice/) to send records as documents. Confluent Cloud is available through [Azure Marketplace](https://azuremarketplace.microsoft.com/en/marketplace/apps/confluentinc.confluent-cloud-azure-prod?tab=Overview) or [directly from Confluent](https://www.confluent.io/get-started/). #### NOTE * This Quick Start is for the fully managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see [Azure Cognitive Search Sink connector for Confluent Platform](https://docs.confluent.io/kafka-connectors/azure-search/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). ## Features The Azure Cognitive Search Sink connector supports the following features: * **At least once delivery**: This connector guarantees that records from the Kafka topic are delivered at least once. * **Supports multiple tasks**: The connector supports running one or more tasks. More tasks may improve performance. * **Ordered writes**: The connector writes records in exactly the same order that it receives them. And for uniqueness, the Kafka coordinates (topic, partition, and offset) can be used as the document key. Otherwise, the connector uses the record key as the document key. * **Automatically creates topics**: The following three topics are automatically created when the connector starts: - Success topic - Error topic - [Dead letter queue (DLQ) topic](dead-letter-queue.md#ccloud-dlq-topics) The suffix for each topic name is the connector’s logical ID. In the example below, there are the three connector topics and one pre-existing Kafka topic named pageviews. ![Automatic Sink Connector Topics](images/ccloud-datadog-metrics-sink-topics.png) If the records sent to the topic are not in the correct format, or if important fields are missing in the record, the errors are recorded in the error topic, and the connector continues to run. * **Automatic retries**: The connector will retry all requests (that can be retried) when the Azure Cognitive Search service is unavailable. The maximum amount of time that the connector spends retrying can be specified by the `max.retry.ms` configuration property. * **Supported data formats**: The connector supports Avro, JSON Schema (JSON-SR), and Protobuf input formats. [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use these Schema Registry-based formats. 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 [Azure Cognitive Search Sink Connector](limits.md#azure-cognitive-search-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). ## Azure service principal You need an Azure RBAC service principal to run the connector. When you create the service principal, the output from the Azure CLI command provides the necessary authentication and authorization details that you add to the connector configuration. #### NOTE If you want to assign roles to an existing service principal using the Azure portal instead of the CLI, see [Assign Azure roles using the Azure portal](https://docs.microsoft.com/en-us/azure/role-based-access-control/role-assignments-portal?tabs=current). Complete the following steps to create the service principal using the Azure CLI. 1. Log in to the Azure CLI. ```text az login ``` 2. Enter the following command to create the service principal: ```text az ad sp create-for-rbac --name --scopes \ /subscriptions//resourceGroups/ ``` For example: ```text az ad sp create-for-rbac --name azure_search --scopes /subscriptions/ d92eeba4-...omitted...-37c2bd9259d0/resourceGroups/connect-azure Creating 'Contributor' role assignment under scope '/subscriptions/ d92eeba4-...omitted...-37c2bd9259d0/resourceGroups/connect-azure' The output includes credentials that you must protect. Be sure that you do not include these credentials in your code or check the credentials into your source control. { "appId": "8ec186f9-...omitted...-e575b928b00a", "displayName": "azure_search", "name": "8ec186f9-...omitted...-e575b928b00a", "password": "jdGzGTwCKQ...omitted...QwE3hx", "tenant": "0893715b-...omitted...-2789e1ead045" } ``` Save the following details to use in the connector configuration: * Use the `"appId"` output for the connector UI field named **Azure Client ID** (CLI property `azure.search.client.id`). * Use the `"password"` output for the connector UI field named **Azure Client Secret** (CLI property `azure.search.client.secret`). * Use the `"tenant"` output for the connector UI field named **Azure Tenant ID** (CLI property `azure.search.tenant.id`). ## Quick Start Use this quick start to get up and running with the Confluent Cloud Azure Cognitive Search Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events. Prerequisites : - Authorized access to a [Confluent Cloud](https://www.confluent.io/confluent-cloud/) cluster on Microsoft Azure (Azure). - An Azure [service principal](#cc-azure-cognitive-search-sink-service-principal), an Azure [Cognitive Search API key](https://docs.microsoft.com/en-us/azure/search/search-security-api-keys), and subscription details for the connector configuration. - 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). - At least one index must exist in Azure Cognitive Search. - All record schema fields must be present as Azure search service index fields. - 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 **Azure Cognitive Search Sink** connector card. ![Azure Cognitive Search Sink Connector Card](images/ccloud-azure-cognitive-search-sink-icon.png) #### Step 4: Enter the connector details #### NOTE * Ensure you have all your [prerequisites](#cc-azure-cognitive-search-sink-prereqs) completed. * An asterisk ( \* ) designates a required entry. At the **Add Azure Cognitive Search 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: - **Azure Search Service Name**: The name of the Azure Search service. - **Azure Search Api Key**: The API key for the Azure Search service - **Azure Client ID**: Client ID of service principal of your subscription. - **Azure Client Secret**: Client secret of service principal of your subscription. - **Azure Tenant ID**: Tenant ID of service principal of your subscription. - **Azure Subscription ID**: Azure subscription ID for your Azure account. - **ResourceGroup Name**: `ResourceGroup` in which Azure Search service exists. 2. Click **Continue**. ### Configuration #### NOTE Configuration properties that are not shown in the Cloud Console use the default values. See [Configuration Properties](#cc-azure-cognitive-search-sink-config-properties) for all property values and descriptions. - **Input Kafka record value format**: Select an input Kafka record value format (data coming from the Kafka topic). Valid entries are AVRO, JSON_SR (JSON Schema), or PROTOBUF. 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. - **Index Name Pattern**: Enter the **Index Pattern Name**, which is the name of the index to write records as documents to. Use `${topic}` within the pattern to specify the topic of the record. ### **Show advanced configurations** - **Write Method**: The method used to write Kafka records to an index. Available methods are `Upload` which functions like `upsert` and `MergeOrUpload`, which updates an existing document with the specified fields. If the document doesn’t exist, it behaves like `Upload`. - **Delete Enabled**: Whether documents will be deleted if the record value is null. - **Key Mode**: Determines what will be used for the document key id. The available modes are: - `KEY`: The Kafka record key is used as the document key. - `COORDINATES`: The Kafka coordinates (topic, partition, and offset) are concatenated to form the document key. This allows for unique document keys. - **Max Batch Size**: The maximum number of Kafka records that will be sent per request. To disable batching of records, set this value to 1. - **Maximum Retry Time (ms)**: The maximum amount of time in milliseconds that the connector will attempt its request before aborting it. **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. **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`. - **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`. **Schema Config** - **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). **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). - 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**. ![Launch the connector](images/ccloud-azure-cognitive-search-sink-launch-connector.png) The status for the connector should go from **Provisioning** to **Running**. ![Connector status](images/ccloud-azure-cognitive-search-sink-status.png) #### Step 5: Check for documents. Verify that documents are populating the search index. 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-azure-cognitive-search-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": "AzureCognitiveSearchSink", "input.data.format": "AVRO", "name": "AzureCognitiveSearchSink_0", "kafka.api.key": "****************", "kafka.api.secret": "************************************************", "azure.search.service.name": "", "azure.search.api.key": "", "azure.search.client.id": "", "azure.search.client.secret": "", "azure.search.tenant.id": "", "azure.search.subscription.id": "", "azure.search.resourcegroup.name": "", "index.name": "", "tasks.max": "1", "topics": "" } ``` Note the following property definitions: * `"connector.class"`: Identifies the connector plugin name. * `"input.data.format"`: Sets the input Kafka record value format (data coming from the Kafka topic). Valid entries are **AVRO**, **JSON_SR**, and **PROTOBUF**. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). * `"name"`: Sets a name for your new connector. * `"kafka.api.key"` and `""kafka.api.secret"`: These credentials are either the cluster API key and secret *or* the [service account](service-account.md#s3-cloud-service-account) API key and secret. * `azure.search.<...>` Required Azure and Azure search connection details. See Azure [service principal](#cc-azure-cognitive-search-sink-service-principal) and Azure [Cognitive Search API key](https://docs.microsoft.com/en-us/azure/search/search-security-api-keys) for property details. * `"index.name"`: The name of the search index to write records to (as documents). * `"tasks.max"`: Enter the maximum number of [tasks](/platform/current/connect/concepts.html#tasks) for the connector to use. More tasks may improve performance. * `"topics"`: Enter the topic name or a comma-separated list of topic names. **SMTs**: For details about adding SMTs using the Confluent CLI, see the [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms) documentation. For a list of SMTs that are not supported with this connector, see [Unsupported transformations](single-message-transforms.md#cc-single-message-transforms-unsupported-transforms). See [Configuration Properties](#cc-azure-cognitive-search-sink-config-properties) for all property values and descriptions. #### 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 azure-search-sink-config.json ``` Example output: ```none Created connector AzureCognitiveSearchSink_0 lcc-do6vzd ``` #### 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 | Trace +------------+------------------------------+---------+------+-------+ lcc-do6vzd | AzureCognitiveSearchSink_0 | RUNNING | sink | | ``` #### Step 6: Check for documents. Verify that the Azure search index is being populated. 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 ### 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 and PROTOBUF. Note that you need to have Confluent Cloud Schema Registry configured
* 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 ### How should we connect to your Azure Search Service `azure.search.service.name` : The name of the Azure Search service
* Type: string * Importance: high `azure.search.api.key` : The api key for the Azure Search service
* Type: password * Importance: high `azure.search.client.id` : Client ID of service principal of your subscription
* Type: password * Importance: high `azure.search.client.secret` : Client Secret of service principal of your subscription
* Type: password * Importance: high `azure.search.tenant.id` : Tenant ID of service principal of your subscription
* Type: password * Importance: high `azure.search.subscription.id` : Azure Subscription ID for your Azure Account
* Type: password * Importance: high `azure.search.resourcegroup.name` : ResourceGroup in which Azure Search Service exists
* Type: string * Importance: high ### Search Service Write Details `index.name` : The name of the index to write records as documents to. Use `${topic}` within the pattern to specify the topic of the record
* Type: string * Importance: high `write.method` : The method used to write Kafka records to an index. Available methods are `Upload` - Functions like upsert. A document is inserted if it does not existed and updated/replaced if it does `MergeOrUpload` - Updates an existing document with the specified fields. If the document doesn’t exist, behaves like `Upload`
* Type: string * Default: Upload * Importance: high `delete.enabled` : Whether documents will be deleted if the record value is null
* Type: boolean * Default: false * Importance: high `key.mode` : Determines what will be used for the document key id. The available modes are:`KEY` - the Kafka record key is used as the document key `COORDINATES` - the Kafka coordinates (topic, partition, and offset) are concatenated to form the document key. This allows for unique document keys
* Type: string * Default: KEY * Importance: medium `max.batch.size` : The maximum number of Kafka records that will be sent per request. To disable batching of records, set this value to 1
* Type: int * Default: 1 * Valid Values: [1,…,1000] * Importance: high `max.retry.ms` : The maximum amount of time in ms that the connector will attempt its request before aborting it
* Type: int * Default: 300000 (5 minutes) * Valid Values: [0,…] * 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 ### Auto-restart policy `auto.restart.on.user.error` : Enable connector to automatically restart on user-actionable errors.
* Type: boolean * Default: true * Importance: medium ### 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 `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 ## Frequently asked questions Find answers to frequently asked questions about the Azure Cognitive Search Sink connector. ### Why does the connector fail with a duplicate properties error from Azure Search? You see an error similar to: ```text The connector failed because duplicate properties were detected and Azure Search does not support that. Please make sure that your data does not contain properties with the same name. ``` This error occurs when: - The Azure index defines a key field (for example, `HotelId`). - The connector is configured with `key.mode` (`KEY` or `COORDINATES`), which automatically populates the key field from the record key. - The same field is also present in the record value, causing Azure Search to receive the key property twice. To avoid duplicate properties, use one of the following methods: - Use SMTs to move the key field out of the value. For example, to move `HotelId` from the value to the key only, add these properties to your connector configuration: ```json { "transforms": "valToKey,extract,removeField", "transforms.valToKey.type": "org.apache.kafka.connect.transforms.ValueToKey", "transforms.valToKey.fields": "HotelId", "transforms.extract.type": "org.apache.kafka.connect.transforms.ExtractField$Key", "transforms.extract.field": "HotelId", "transforms.removeField.type": "org.apache.kafka.connect.transforms.ReplaceField$Value", "transforms.removeField.exclude": "HotelId" } ``` This transform chain does the following: 1. Moves `HotelId` from the record value to the key structure. 2. Extracts `HotelId` as the key value. 3. Removes `HotelId` from the record value to prevent duplication. - Change the Azure index to use a different key field and adjust `key.mode` accordingly. ### Why does validation fail with errors such as `Invalid Azure Search Service values` or `azure.search.service.name: Could not validate region`? During connector validation, Confluent Cloud verifies your Azure Search configuration. Validation fails with messages such as: ```text Invalid Azure Search Service values ``` or ```text azure.search.service.name: Could not validate region. Please check your credentials and try again ``` This error commonly occurs because: - `azure.search.service.name` does not match the actual Azure Cognitive Search service name. - The service is not deployed in a region that is compatible with, or properly mapped to, your Confluent Cloud cluster region. - The Azure credentials used by the connector (API key or service principal) lack sufficient permissions on: - The search service - Its resource group - The associated subscription To troubleshoot this error, complete the following steps: - Verify all the Azure identifiers: - `azure.search.service.name` - `azure.search.subscription.id` - `azure.search.resourcegroup.name` - `azure.search.tenant.id` (for service principal auth) - Verify that the Azure Cognitive Search service is reachable and in the expected region. - Verify that the principal or API key has appropriate roles (for example, **Contributor** on the search service and **Reader or Contributor** on the resource group or subscription, as required by your security model). - Re-run connector validation after you fix configuration or permissions. If the error persists, contact [Confluent Support](https://support.confluent.io/) with the full validation output. ### Does the Azure Cognitive Search Sink connector support client-side field level encryption (CSFLE)? Check the latest Client-Side Field Level Encryption documentation for Confluent Cloud in [Manage CSFLE for connectors](csfle.md#connect-csfle), or contact your Confluent account team to confirm current availability and limitations for the Azure Cognitive Search Sink connector. ### Why does the connector fail with schema or field mismatch errors? The connector fails when your Kafka record schema does not match the Azure Cognitive Search index definition. Common issues include: - **Missing fields in the Azure index**: All fields in your Kafka record schema must exist in the Azure Search index. The connector fails when a record contains a field not defined in the index. - **Data type mismatches**: Field types in your schema must be compatible with the corresponding Azure Search field types (for example, `Edm.String`, `Edm.Int32`). - **Required fields not populated**: When the Azure index defines required fields, ensure all records include those fields. To resolve field mismatch errors, complete the following steps: 1. Review the Azure Search index schema and compare it with your Kafka record schema. 2. Add any missing fields to the Azure index, or use SMTs to filter out fields not needed in the index. 3. Verify that field data types are compatible between your records and the Azure index. ### How do I monitor the connector performance and throughput? Monitor the Azure Cognitive Search Sink connector using Confluent Cloud metrics and observability features: - **Connector metrics in the Cloud Console**: View real-time metrics including throughput (records per second), lag, and error rates on the connector’s detail page. - **Success and error topics**: The connector automatically creates success and error topics. Monitor these topics to track successfully processed records and records that failed to write. - **Dead letter queue (DLQ)**: Check the [DLQ topic](dead-letter-queue.md#ccloud-dlq-topics) for records that could not be processed due to errors. - **Azure Search service metrics**: Use the Azure Portal to monitor search service health, indexing operations, throttling, and storage utilization. For optimal performance, increase `tasks.max` to run more tasks in parallel, especially when you process high-volume topics. ### What happens when the connector fails to write to Azure Search? The connector includes automatic retry logic for transient failures: - **Automatic retries**: When Azure Search is temporarily unavailable or returns retryable errors (for example, timeouts, HTTP 429 throttling), the connector retries the request. - **Maximum retry duration**: Configure `max.retry.ms` to control the maximum time the connector spends retrying failed requests. The default behavior retries until the request succeeds or exceeds this duration. - **Non-retryable errors**: For permanent failures (for example, HTTP 502 Bad Gateway, HTTP 504 Gateway Timeout, invalid data, schema mismatches, authentication errors), the connector behavior depends on the `behavior.on.error` setting. When set to `FAIL`, the connector throws a `ConnectException` and stops the task. Otherwise, the connector writes the failed record to the error topic and continues processing. - **Error topic and DLQ**: Failed records are sent to the connector’s error topic or [dead letter queue](dead-letter-queue.md#ccloud-dlq-topics), depending on the failure type. To handle failures effectively, complete the following steps: - Monitor the error topic and DLQ for failed records. - Review Azure Search service quotas and ensure adequate capacity to handle your indexing load. - Verify that your Azure credentials and permissions remain valid. ## 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)