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

This is a Quick Start for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see Azure Cognitive Search Sink Connector for Confluent Platform.

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
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.

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 Managed and Custom Connectors section.

Limitations¶

Be sure to review the following information.

For connector limitations, see Azure Cognitive Search Sink Connector limitations.
If you plan to use one or more Single Message Transforms (SMTs), see SMT Limitations.
If you plan to use Confluent Cloud Schema Registry, see Schema Registry Enabled Environments.

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.

Log in to the Azure CLI.
```
az login
```

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¶

See the Quick Start for Confluent Cloud for installation instructions.

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.

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 topic(s) you want to connect from the Topics list.

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

Enter your connection details:
- 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.
Click Continue.

Note

Configuration properties that are not shown in the Cloud Console use the default values. See Configuration Properties for all property values and descriptions.

Select the Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR (JSON Schema), or PROTOBUF. A valid schema must be available in Schema Registry to use a schema-based message format (for example, AVRO, JSON_SR, or PROTOBUF.
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
- 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?.
- 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.
For information about transforms and predicates, see the Single Message Transforms (SMT) documentation for details. See Unsupported transformations for a list of SMTs that are not supported with this connector.
Click Continue.

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 Managed and Custom Connectors section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue 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 Managed and Custom Connectors section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue 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]
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]
Importance: low

Number of tasks for this connector¶

tasks.max

Maximum number of tasks for the connector.

Type: int
Valid Values: [1,…]
Importance: high

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.