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 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.
- 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.
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.
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 propertyazure.search.client.id
).Use the
"password"
output for the connector UI field named Azure Client Secret (CLI propertyazure.search.client.secret
).Use the
"tenant"
output for the connector UI field named Azure Tenant ID (CLI propertyazure.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 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.
- 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. 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.
- Click Continue.
- 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 likeupsert
andMergeOrUpload
, which updates an existing document with the specified fields. If the document doesn’t exist, it behaves likeUpload
.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.
Based on the number of topic partitions you select, you will be provided with a recommended number of tasks.
- To change the number of recommended tasks, enter the number of tasks for the connector to use in the Tasks field.
- 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 doesMergeOrUpload
- Updates an existing document with the specified fields. If the document doesn’t exist, behaves likeUpload
- 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 keyCOORDINATES
- 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.