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 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.

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 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 Log Analytics 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.

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.

Prerequisites

Authorized access to a Confluent Cloud cluster on Microsoft Azure (Azure).
The Azure Log Analytics workspace ID and shared keys.
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 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 Log Analytics 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 Log Analytics Sink Connector screen, complete the steps under the following tabs.

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 Input Kafka record value format (data coming from the Kafka topic): AVRO, BYTES, JSON, JSON_SR (JSON Schema), PROTOBUF, or STRING. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON_SR (JSON Schema) or Protobuf).
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).
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?.
- 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.
- Transforms and Predicates: See the Single Message Transforms (SMT) documentation for details.
See Configuration Properties for all property values and definitions.
Click Continue.

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.

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.

{
  "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 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, 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:
```
confluent iam service-account list
```
For example:
```
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.
"azure.loganalytics.shared.key": Enter with workspace shared key. For more information, see Workspace Shared Keys.
"tasks.max": Enter the maximum number of tasks for the connector to use. More tasks may improve performance.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI.

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-log-analytics-sink-config.json

Example output:

Created connector AzureLogAnalyticsSink_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   | 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.

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, 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.

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 Azure Log Analytics Workspace?¶

azure.loganalytics.workspace.id

Workspace Id for Azure Log Analytics.

Type: string
Importance: high

azure.loganalytics.shared.key

Shared key for Azure Log Analytics.

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.

It should contain only letters, numbers and (_)underscore character.

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.

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.

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.

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.

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.

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.

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).

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

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.