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.

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 Connect section.

Limitations

Be sure to review the following information.

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 key(s).
  • 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.

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

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

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 Connect 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-catalog-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 create --config <file-name>.json

For example:

confluent connect create --config 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 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 Connect 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 this connector.

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

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
  • 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
  • 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

Number of tasks for this connector

tasks.max
  • 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.

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