Azure Cognitive Search Sink Connector for Confluent Cloud

The fully-managed Azure Cognitive Search Sink connector for Confluent Cloud can move data from Apache Kafka® 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 to send records as documents.

Note

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:

    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

    Connector Topics

    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 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 section.

Limitations

Be sure to review the following information.

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.

Complete the following steps to create the service principal using the Azure CLI.

  1. Log in to the Azure CLI.

    az login
    
  2. Enter the following command to create the service principal:

    az ad sp create-for-rbac --name <Name of service principal> --scopes \
    /subscriptions/<SubscriptionID>/resourceGroups/<Resource_Group>
    

    For example:

    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).

      Tip

      You can create a more granular service principal if needed. For example, the following command creates the contributor role assignment specifically to access Azure search services.

      az ad sp create-for-rbac --name <Name of service principal> --scopes
       /subscriptions/<SubscriptionID>/resourceGroups/<Resource Group>
       /providers/Microsoft.Search/searchServices/<Search Service Name>
       --role Reader
      

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 cluster on Microsoft Azure (Azure).
  • An Azure service principal, an Azure Cognitive Search API key, and subscription details for the connector configuration.
  • The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.
  • Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
  • 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.

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

Step 4: Enter the connector details

Note

  • Ensure you have all your prerequisites completed.
  • An asterisk ( * ) designates a required entry.

At the Add Azure Cognitive Search Sink Connector screen, complete the following:

If you’ve already populated your Kafka topics, select the topics you want to connect from the Topics list.

To create a new topic, click +Add new topic.

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 section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See View Connector Dead Letter Queue Errors in Confluent Cloud for details.

Using the Confluent CLI

Complete the following steps to set up and run the connector using the Confluent CLI.

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

Step 3: Create the connector configuration file

Create a JSON file that contains the connector configuration properties. The following example shows the required connector properties.

{
  "connector.class": "AzureCognitiveSearchSink",
  "input.data.format": "AVRO",
  "name": "AzureCognitiveSearchSink_0",
  "kafka.api.key": "****************",
  "kafka.api.secret": "************************************************",
  "azure.search.service.name": "<service_name>",
  "azure.search.api.key": "<api_key>",
  "azure.search.client.id": "<client_id>",
  "azure.search.client.secret": "<client_secret>",
  "azure.search.tenant.id": "<tenant_id>",
  "azure.search.subscription.id": "<subscription_id>",
  "azure.search.resourcegroup.name": "<resource_group>",
  "index.name": "<index_name>",
  "tasks.max": "1",
  "topics": "<topic_name>"
}

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 API key and secret.
  • azure.search.<...> Required Azure and Azure search connection details. See Azure service principal and Azure Cognitive Search API key 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 for the connector to use. More tasks may improve performance.
  • "topics": Enter the topic name or a comma-separated list of topic names.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI. See Unsupported transformations for a list of SMTs that are not supported with this connector.

See Configuration 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:

confluent connect cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file azure-search-sink-config.json

Example output:

Created connector AzureCognitiveSearchSink_0 lcc-do6vzd

Step 5: Check the connector status

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID           |             Name             | Status  | Type | 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 section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See View Connector Dead Letter Queue Errors in Confluent Cloud for details.

Configuration Properties

Use the following configuration properties with the fully-managed connector. For self-managed connector property definitions and other details, see the connector docs in Self-managed connectors for Confluent Platform.

Which topics do you want to get data from?

topics

Identifies the topic name or a comma-separated list of topic names.

  • Type: list
  • Importance: high

Schema Config

schema.context.name

Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.

  • Type: string
  • Default: default
  • Importance: medium

Input messages

input.data.format

Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR 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.

  • Type: string
  • Default: KAFKA_API_KEY
  • Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
  • Importance: high
kafka.api.key

Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.

  • Type: password
  • Importance: high
kafka.service.account.id

The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.

  • Type: string
  • Importance: high
kafka.api.secret

Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.

  • Type: password
  • Importance: high

How should we connect to 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 or the “latest” offset (the default). 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

  • 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

  • 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
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.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.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.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

Next Steps

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.

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