<a id="cc-azure-log-analytics-sink"></a>

# Azure Log Analytics Sink Connector for Confluent Cloud

The Azure Log Analytics Sink connector extracts records from Apache Kafka® topics and
sends the records as JSON to an [Azure Log Analytics](https://learn.microsoft.com/en-us/azure/azure-monitor/logs/log-analytics-tutorial) workspace.

#### NOTE
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 Log Analytics 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 topics-to-tables**: The connector can process data from
  multiple topics and send the data to the respective tables in the Azure Log
  Analytics workspace.
* **Supports multiple tasks**: The connector supports running one or more
  tasks. More tasks may improve performance.
* **Supported input data formats**: The connector supports Avro, JSON Schema
  (JSON-SR), Protobuf, JSON, STRING, and BYTES 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 Log Analytics Sink Connector](limits.md#azure-log-analytics-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).

## Quick Start

Use this quick start to get up and running with the fully managed Azure Log
Analytics Sink connector. The quick start provides the basics of selecting the
connector and configuring it to stream events.

<a id="cc-azure-log-analytics-sink-prereqs"></a>

Prerequisites
: - Authorized access to a [Confluent Cloud](https://www.confluent.io/confluent-cloud/) cluster on Microsoft Azure (Azure).
  - The Azure Log Analytics workspace ID and [shared keys](https://learn.microsoft.com/en-us/rest/api/loganalytics/workspace-shared-keys/get-shared-keys?tabs=HTTP).
  - 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 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 Log Analytics Sink** connector card.

![Azure Log Analytics Sink Connector Card](images/ccloud-azure-log-analytics-sink-icon.png)

<a id="cc-azure-log-analytics-sink-setup-connection"></a>

#### Step 4: Enter the connector details

#### NOTE
* Ensure you have all your [prerequisites](#cc-azure-log-analytics-sink-prereqs) completed.
* An asterisk ( \* ) designates a required entry.

At the **Add Azure Log Analytics Sink Connector** screen, complete the steps under the following tabs.

### 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 Log Analytics Workspace Id**: Enter the **Azure Log Analytics Workspace ID**. For more information,
     see [Workspaces](https://learn.microsoft.com/en-us/rest/api/loganalytics/workspaces).
   - **Azure Log Analytics Shared Key**: Enter the **Azure Log Analytics Shared Key**. For more information,
     see [Workspace Shared Keys](https://learn.microsoft.com/en-us/rest/api/loganalytics/workspace-shared-keys).
2. Click **Continue**.

### Configuration

- **Input Kafka record value format**: Select an input Kafka record value format (data coming from the
  Kafka topic). Valid entries are AVRO, BYTES, JSON, JSON_SR (JSON Schema), PROTOBUF, or
  STRING. 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 (JSON
  Schema) or Protobuf).
- **Azure Log Analytics Topic2Table Map**: Enter the **Azure Log Analytics Topic2Table Map**. This is an optional
  map for topics to tables. Use comma-separated tuples. For example,
  `<topic-1>:<table-1>,<topic-2>:<table-2>,...`. If the topic2table
  map doesn’t contain the topic for a record, the connector creates a
  table using the topic name. A valid table name must start with
  letters. A table name must not exceed 100 characters and can contain
  only letters, numbers, and the underscore character (_). Note that if
  this optional property is used, the topic name must not be modified
  using a Single Message Transform (SMT).
- **Timestamp field**: Enter a **Timestamp field**. The name of a field in the data that
  contains the timestamp of the data item. If you specify a field, its
  contents are used for `TimeGenerated`. If you don’t specify a
  timestamp field, the default value used for `TimeGenerated` is the
  time that the message is ingested. The contents of the message field
  must follow the ISO 8601 format: `YYYY-MM-DDThh:mm:ssZ`. Note that
  if the `TimeGenerated` value is older than two days before the
  received time, the row is dropped.

### **Show advanced configurations**

- **Schema context**: Select a schema context to use for this connector, if using
  a schema-based data format. This property defaults to the **Default** context,
  which configures the connector to use the default schema set up for Schema Registry in your
  Confluent Cloud environment. A schema context allows you to use separate schemas (like
  schema sub-registries) tied to topics in different Kafka clusters that share the
  same Schema Registry environment. For example, if you select a non-default context, a
  **Source** connector uses only that schema context to register a schema and a
  **Sink** connector uses only that schema context to read from. For more
  information about setting up a schema context, see [What are schema contexts and when should you use them?](../sr/faqs-cc.md#faq-schema-contexts).
- **Behavior on Error**: The connector’s behavior if an error
  occurs when extracting data from the Kafka topic. Valid options
  are `log` (the default) and `fail`. The `log` option logs
  the error message in the `error-<connector-id>` topic and
  continues processing. `fail` stops the connector.
- **Maximum batch size**: The maximum number of Kafka records to
  combine when sending a batch of records to the Azure Log Analytics
  workspace. Defaults to `500`. The minimum value allowed is `1`.
- **Maximum Pending Requests**: The maximum number of concurrent
  pending requests the connector can make to Azure Log Analytics.
  Defaults to `1`, which is the minimum value allowed. Maximum
  allowed is `128`.
- **Request Timeout (ms)**: The maximum time, in milliseconds, that
  the connector attempts to request Azure Log Analytics before timing
  out (socket timeout). Defaults to `10000` ms (10 seconds).
- **Retry Timeout (ms)**: The amount of time the connector retries
  a request if it receives a retriable response (for example,
  response codes 429, 500, or 503). Defaults to `10000` ms (10
  seconds) Entering a value of `-1` results in indefinite retries.

**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`.
- **Errors Tolerance**: Use this property to configure the connector’s error handling behavior.

  #### WARNING
  Use this property with caution for sink connectors, as it can lead to data loss. If you set this property to `all`, the connector does not fail on errant records, but logs them (and sends to DLQ for sink connectors) and continues processing. If you set this property to `none`, the connector task fails on errant records.
- **Value Converter Connect Meta Data**: Enables the Connect converter to add its metadata to the output schema. Applies to Avro converters.
- **Value Converter Value Subject Name Strategy**: Determines how to construct the subject name under which the value schema is registered with Schema Registry.
- **Key Converter Key Subject Name Strategy**: Determines how to construct the subject name for key schema registration.
- **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`.

**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.

**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).

See [Configuration Properties](#cc-azure-log-analytics-sink-config-properties) for all
property values and definitions.

- Click **Continue**.

### Sizing

Based on the number of topic partitions you select, you will be provided
with a recommended number of tasks.

1. To change the number of 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**.

   The status for the connector should go from **Provisioning** to
   **Running**.

#### Step 5: Check for records

Verify that data is exported from Kafka to the Azure Log Analytics workspace.
There may be a slight delay due to data ingestion latency. For details, see
[Checking ingestion time](https://learn.microsoft.com/en-us/azure/azure-monitor/logs/data-ingestion-time#checking-ingestion-time).

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-log-analytics-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 <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.

```json
{
  "name": "AzureLogAnalyticsSink_0",
  "config": {
    "topics": "orders",
    "input.data.format": "AVRO",
    "connector.class": "AzureLogAnalyticsSink",
    "name": "AzureLogAnalyticsSink_0",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "<my-kafka-api-key>",
    "kafka.api.secret": "<my-kafka-api-secret>",
    "azure.loganalytics.workspace.id": "<log-analytics-workspace-ID>",
    "azure.loganalytics.shared.key": "<log-analyticsshared-key>",
    "tasks.max": "1"
  }
}
```

Note the following property definitions:

* `"name"`: Sets a name for your new connector.
* `"topics"`: Enter the topic name or a comma-separated list of topic names.
* `"input.data.format"`:  Sets the input Kafka record value format (data
  coming from the Kafka topic). Valid entries are AVRO, BYTES, JSON, JSON_SR
  (JSON Schema), PROTOBUF, or STRING. You must have [Schema Registry](../get-started/schema-registry.md#cloud-sr-config)
  configured if using a schema-based message format (for example, Avro, JSON_SR
  (JSON Schema), or Protobuf).
* `"connector.class"`: Identifies the connector plugin name.

* `"kafka.auth.mode"`: Identifies the connector authentication mode you want to use. There are two options: `SERVICE_ACCOUNT` or `KAFKA_API_KEY` (the default). To use an API key and secret, specify the configuration properties `kafka.api.key` and `kafka.api.secret`, as shown in the example configuration (above).  To use a [service account](service-account.md#s3-cloud-service-account), specify the **Resource ID** in the property `kafka.service.account.id=<service-account-resource-ID>`. To list the available service account resource IDs, use the following command:
  ```bash
  confluent iam service-account list
  ```

  For example:
  ```bash
  confluent iam service-account list

     Id     | Resource ID |       Name        |    Description
  +---------+-------------+-------------------+-------------------
     123456 | sa-l1r23m   | sa-1              | Service account 1
     789101 | sa-l4d56p   | sa-2              | Service account 2
  ```

* `"azure.loganalytics.workspace.id"`: Enter the workspace ID. For more
  information, see [Workspaces](https://learn.microsoft.com/en-us/rest/api/loganalytics/workspaces).
* `"azure.loganalytics.shared.key"`: Enter with workspace shared key. For
  more information, see [Workspace Shared Keys](https://learn.microsoft.com/en-us/rest/api/loganalytics/workspace-shared-keys).
* `"tasks.max"`: Enter the maximum number of [tasks](/platform/current/connect/concepts.html#tasks)
  for the connector to use. More tasks may improve performance.

**SMTs**: For details about adding SMTs using the Confluent CLI, see the [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms) documentation.

See [Configuration Properties](#cc-azure-log-analytics-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 <file-name>.json
```

For example:

```none
confluent connect cluster create --config-file azure-log-analytics-sink-config.json
```

Example output:

```none
Created connector AzureLogAnalyticsSink_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   | AzureLogAnalyticsSink_0    | RUNNING | sink |       |
```

#### Step 6: Check for records.

Verify that data is exported from Kafka to the Azure Log Analytics workspace.
There may be a slight delay due to data ingestion latency. For details, see
[Checking ingestion time](https://learn.microsoft.com/en-us/azure/azure-monitor/logs/data-ingestion-time#checking-ingestion-time).

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.

<a id="cc-azure-log-analytics-sink-config-properties"></a>

## 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.
  <br/>
  * Type: string
  * Importance: low

`topics`
: Identifies the topic name or a comma-separated list of topic names.
  <br/>
  * Type: list
  * Importance: high

`errors.deadletterqueue.topic.name`
: The name of the topic to be used as the dead letter queue (DLQ) for messages that result in an error when processed by this sink connector, or its transformations or converters. Defaults to ‘dlq-${connector}’ if not set. The DLQ topic will be created automatically if it does not exist. You can provide `${connector}` in the value to use it as a placeholder for the logical cluster ID.
  <br/>
  * Type: string
  * Default: dlq-${connector}
  * Importance: low

`reporter.result.topic.name`
: The name of the topic to produce records to after successfully processing a sink record. Defaults to ‘success-${connector}’ if not set. You can provide `${connector}` in the value to use it as a placeholder for the logical cluster ID.
  <br/>
  * Type: string
  * Default: success-${connector}
  * Importance: low

`reporter.error.topic.name`
: The name of the topic to produce records to after each unsuccessful record sink attempt. Defaults to ‘error-${connector}’ if not set. You can provide `${connector}` in the value to use it as a placeholder for the logical cluster ID.
  <br/>
  * Type: string
  * Default: error-${connector}
  * Importance: low

### 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.
  <br/>
  * Type: string
  * Default: default
  * Importance: medium

### Input messages

`input.data.format`
: Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON, STRING or BYTES. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.
  <br/>
  * Type: string
  * Importance: high

### How should we connect to your data?

`name`
: Sets a name for your connector.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Importance: high

`kafka.api.secret`
: Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.
  <br/>
  * Type: password
  * Importance: high

### How should we connect to Azure Log Analytics Workspace?

`azure.loganalytics.workspace.id`
: Workspace Id for Azure Log Analytics.
  <br/>
  * Type: string
  * Importance: high

`azure.loganalytics.shared.key`
: Shared key for Azure Log Analytics.
  <br/>
  * Type: password
  * Importance: high

### Azure Log Analytics Details

`azure.loganalytics.topic2table.map`
: Map of topics to tables (optional).  Format: comma-separated tuples, e.g. <topic-1>:<table-1>,<topic-2>:<table-2>,…  Note that topic name should not be modified using regex SMT while using this option. Lastly, if the topic2table map doesn’t contain the topic for a record, a table  with the same name as the topic name would be created. A valid table name can’t exceed 100 characters.
  <br/>
  > It should contain only letters, numbers and (_)underscore  character.
  <br/>
  > It must start with letters.
  * Type: string
  * Default: “”
  * Importance: medium

`azure.loganalytics.timestamp.field`
: The name of a field in the data that contains the timestamp of the data item. If you specify a field, its contents are used for TimeGenerated. If you don’t specify this field, the default for TimeGenerated is the time that the message is ingested. The contents of the message field should follow the ISO 8601 format YYYY-MM-DDThh:mm:ssZ. Note: the Time Generated value cannot be older than 2 days before received time or the row will be dropped.
  <br/>
  * Type: string
  * Default: “”
  * Importance: low

`max.batch.size`
: The maximum number of records sent in a single request to Azure Log Analytics Workspace. Values must at least 1.
  <br/>
  * Type: int
  * Default: 500
  * Valid Values: [1,…]
  * Importance: medium

`max.pending.requests`
: The maximum number of pending requests allowed at a time. Values must be at least 1.
  <br/>
  * Type: int
  * Default: 1
  * Valid Values: [1,…,128]
  * Importance: low

`request.timeout.ms`
: The amount of time the connector tries to request the Azure log analytics system if it cannot reach it before it stops trying (socket timeout). A timeout of 10s will be Azure Log Analytics default timeout.
  <br/>
  * Type: long
  * Default: 10000 (10 seconds)
  * Valid Values: [0,…,120000]
  * Importance: low

`retry.timeout.ms`
: The amount of time the connector tries to retry the request if receives a retriable response i.e 429, 500, 503. A timeout of -1 is considered as indefinite.
  <br/>
  * Type: long
  * Default: 10000 (10 seconds)
  * Valid Values: [-1,…,120000]
  * Importance: low

### How should we handle errors?

`behavior.on.error`
: Error handling behavior setting when an error occurs while extracting metric from Kafka record value. Valid options are ‘log’ and ‘fail’. ‘log’ logs the error message in error-<connector-id> topic and continues processing, ‘fail’ stops the connector in case of an error.
  <br/>
  * Type: string
  * Default: log
  * Valid Values: fail, log
  * 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).
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: int
  * Valid Values: [1,…]
  * Importance: high

### 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)
  <br/>
  * 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)
  <br/>
  * Type: string
  * Importance: low

`header.converter`
: The converter class for the headers. This is used to serialize and deserialize the headers of the messages.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.auto.register.schemas`
: Specify if the Serializer should attempt to register the Schema.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.enhanced.avro.schema.support`
: Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.enhanced.protobuf.schema.support`
: Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.flatten.unions`
: Whether to flatten unions (oneofs). Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.generate.index.for.unions`
: Whether to generate an index suffix for unions. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.generate.struct.for.nulls`
: Whether to generate a struct variable for null values. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.int.for.enums`
: Whether to represent enums as integers. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.latest.compatibility.strict`
: Verify latest subject version is backward compatible when use.latest.version is true.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.object.additional.properties`
: Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.optional.for.nullables`
: Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.optional.for.proto2`
: Whether proto2 optionals are supported. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.latest.version`
: Use latest version of schema in subject for serialization when auto.register.schemas is false.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.optional.for.nonrequired`
: Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: int
  * Importance: low

`value.converter.wrapper.for.nullables`
: Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`errors.tolerance`
: Use this property if you would like to configure the connector’s error handling behavior. WARNING: This property should be used with CAUTION for SOURCE CONNECTORS as it may lead to dataloss. If you set this property to ‘all’, the connector will not fail on errant records, but will instead log them (and send to DLQ for Sink Connectors) and continue processing. If you set this property to ‘none’, the connector task will fail on errant records.
  <br/>
  * Type: string
  * Default: all
  * 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.
  <br/>
  * 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.
  <br/>
  * 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:
  <br/>
  BASE64 to serialize DECIMAL logical types as base64 encoded binary data and
  <br/>
  NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.
  <br/>
  * Type: string
  * Default: BASE64
  * Importance: low

`value.converter.flatten.singleton.unions`
: Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Default: TopicNameStrategy
  * Importance: low

### Auto-restart policy

`auto.restart.on.user.error`
: Enable connector to automatically restart on user-actionable errors.
  <br/>
  * Type: boolean
  * Default: true
  * Importance: medium

<a id="cc-azure-log-analytics-sink-faq"></a>

## Frequently asked questions

Find answers to common questions about the Azure Log Analytics Sink connector
for Confluent Cloud.

### Why is there a delay before data appears in my Azure Log Analytics workspace?

Azure Log Analytics has built-in data ingestion latency. After the connector
successfully sends records, it can take several minutes for the data to appear
in your workspace. This delay is on the Azure side, not the connector.

To check ingestion latency, see Microsoft Azure’s documentation on
[Checking ingestion time](https://learn.microsoft.com/en-us/azure/azure-monitor/logs/data-ingestion-time#checking-ingestion-time).

If you notice a growing consumer lag or multi-day delays, this is typically a
connector throughput issue rather than Azure ingestion latency. See
[How do you improve connector throughput?](#cc-azure-log-analytics-sink-faq-throughput)
for tuning recommendations.

### How do you map Kafka topics to custom Azure Log Analytics table names?

Use the `azure.loganalytics.topic2table.map` property to map specific Kafka
topics to custom table names. Specify mappings as comma-separated
`<topic>:<table>` pairs.

For example:

```json
{
  "azure.loganalytics.topic2table.map": "orders:Orders,events:AppEvents"
}
```

If a topic is not listed in the mapping, the connector uses the topic name
as the table name. Table names must start with a letter, contain only letters,
numbers, and underscores, and must not exceed 100 characters.

Azure automatically appends the `_CL` (Custom Log) suffix to custom table
names. For example, if you specify `Orders` as the table name, the table in
Azure Log Analytics is named `Orders_CL`.

If you use this property, do not modify the topic name using a Single Message
Transformation (SMT).

### How can you use SMTs to filter audit log events for a specific cluster?

A common use case is streaming `confluent-audit-log-events` to Azure Log
Analytics and filtering for events from a specific Kafka cluster. Use the
`Filter$Value` SMT to include or exclude events based on field values.

For example, to include only events from a specific cluster:

```json
{
  "transforms": "clusterFilter",
  "transforms.clusterFilter.type": "io.confluent.connect.transforms.Filter$Value",
  "transforms.clusterFilter.filter.type": "include",
  "transforms.clusterFilter.filter.condition": "$[?(@.source == 'crn://confluent.cloud/kafka=lkc-abc123')]",
  "transforms.clusterFilter.missing.or.null.behavior": "exclude"
}
```

For example, to exclude high-volume event types such as produce, fetch, and authentication:

```json
{
  "transforms": "excludeNoisy",
  "transforms.excludeNoisy.type": "io.confluent.connect.transforms.Filter$Value",
  "transforms.excludeNoisy.filter.type": "exclude",
  "transforms.excludeNoisy.filter.condition": "$[?(@.data.methodName == 'kafka.Produce' || @.data.methodName == 'kafka.Fetch' || @.data.methodName == 'kafka.Authentication')]"
}
```

For more information about SMTs, see [Configure Single Message Transformations for Kafka Connectors in Confluent Cloud](single-message-transforms.md#cc-single-message-transforms).

<a id="cc-azure-log-analytics-sink-faq-throughput"></a>

### How do you improve connector throughput to reduce consumer lag?

If your connector is running but records are arriving in Azure Log Analytics
with significant delay, tune the following
properties:

* `max.batch.size`: Controls the number of records sent per
  batch to Azure Log Analytics. The default is `500`. Increasing this value
  allows more records per request, improving throughput.
* `max.pending.requests`: Controls the number of concurrent
  in-flight requests to Azure Log Analytics. The default is `1`. Increasing
  this allows the connector to send multiple batches concurrently.
* `tasks.max`: Distributes the workload across multiple tasks.
  Set this value up to the number of topic partitions for optimal
  parallelism.
* `consumer.override.max.poll.records`: Controls how many records
  the connector polls from the Kafka topic per cycle.

### Where do you find the Azure Log Analytics workspace ID and shared key?

To find the workspace ID and shared key required for connector authentication:

1. Sign in to the [Azure portal](https://portal.azure.com).
2. Navigate to **Log Analytics workspaces** and select your workspace.
3. In the left menu, select **Agents** under **Settings**.
4. The **Workspace ID** and **Primary key** (shared key) are displayed on the
   **Log Analytics agent instructions** page.

For more information, see Microsoft’s documentation on
[Workspace Shared Keys](https://learn.microsoft.com/en-us/rest/api/loganalytics/workspace-shared-keys/get-shared-keys).

### What happens if a record’s timestamp is older than two days?

If you configure the `azure.loganalytics.timestamp.field` property to specify
a timestamp field, and the value for a record is older than two days before
the ingestion time, Azure Log Analytics drops the record. This is an
Azure platform limitation.

To avoid this, ensure your pipeline processes records within two days of the event timestamp.

If you don’t require custom timestamps, leave the timestamp field unconfigured.
The connector then uses the ingestion time as the `TimeGenerated` value.

### Does the connector require egress access for private networking clusters?

Yes. If your Confluent Cloud dedicated cluster uses private networking, such as
Azure Private Link, you must configure egress access for the connector to
reach the Azure Log Analytics endpoint. For details on configuring
egress, see [Manage Networking for Confluent Cloud Connectors](networking/internet-resource.md#clusters-connect-cloud).

### What does the connector do when it encounters an error?

The connector’s behavior on errors is controlled by the `behavior.on.error`
property:

* `log` (default): The connector logs the error to the `error-<connector-id>`
  topic and continues processing the remaining records.
* `fail`: The connector stops immediately when an error is encountered.

For production deployments, the default `log` setting is recommended so that
individual bad records don’t halt the entire pipeline. You can monitor the
error topic to identify and address problematic records.

## 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)