Azure Cosmos DB Source Connector for Confluent Cloud

The fully-managed Azure Cosmos Source connector for Confluent Cloud reads records from an Azure Cosmos database and writes data to Apache Kafka® topics in Confluent Cloud.

Features

The Azure Cosmos DB Source connector supports the following features:

  • Topic to Container mapping: The connector can map a container (table) to an individual Kafka topic (that is, topic1#con1,topic2#con2).
  • 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. Note that one container (table) can be handled by one task.
  • Offset management capabilities: Supports offset management. For more information, see Manage custom offsets.

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.

Manage custom offsets

Custom offsets for managed connectors is Early Access

Confluent uses Early Access releases to gather feedback. This service should be used only for evaluation and non-production testing purposes, or to provide feedback to Confluent, particularly as it becomes more widely available in follow-on preview editions.

Early Access is intended for evaluation use in development and testing environments only and not for production use. The warranty, SLA, and Support Services provisions of your agreement with Confluent do not apply to Early Access. Confluent considers Early Access to be a Proof of Concept as defined in the Confluent Cloud Terms of Service. Confluent may discontinue providing preview releases of the Early Access releases at any time at the sole discretion of Confluent.

You can manage the offsets for this connector. Offsets provide information on the point in the source system from which the connector accesses data. For more information, see Manage Offsets for Fully-Managed Connectors.

To manage offsets:

Note

The Azure Cosmos DB Source connector allows reading from multiple containers using a single connector. In the following examples, the connector is reading from two different containers and writing to two different topics. Therefore, the offset is an array with two elements, each of which specifies a container and database name.

To get the current offset, make a GET request that specifies the environment, Kafka cluster, and connector name.

GET /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets
Host: https://api.confluent.cloud

Response:

Successful calls return HTTP 200 with a JSON payload that describes the offset.

{
    "id": "lcc-example123",
    "name": "{connector_name}",
    "offsets": [
        {
            "partition": {
              "Container": "container2",
              "DatabaseName": "my-cosmos-db"
            },
            "offset": {
              "recordContinuationToken": "\"24764\""
            }
        },
        {
            "partition": {
              "Container": "container1",
              "DatabaseName": "my-cosmos-db"
            },
            "offset": {
              "recordContinuationToken": "\"18460\""
            }
        }
    ],
    "metadata": {
        "observed_at": "2024-03-28T17:57:48.139635200Z"
    }
}

Responses include the following information:

  • The position of latest offset.
  • The observed time of the offset in the metadata portion of the payload. The observed_at time indicates a snapshot in time for when the API retrieved the offset. A running connector is always updating its offsets. Use observed_at to get a sense for the gap between real time and the time at which the request was made. By default, offsets are observed every minute. Calling get repeatedly will fetch more recently observed offsets.
  • Information about the connector.

JSON payload

The table below offers a description of the unique fields in the JSON payload for managing offsets of the CosmosDB Source connector.

Field Definition Required/Optional
Container The value from connect.cosmos.containers.topicmap in the connector configuration, which is the format topic#container. For example, topic2#container2 Required
DatabaseName The value from connect.cosmos.databasename in the connector configuration. Required
recordContinuationToken The last processed changeFeed or the point in the changeFeed to begin processing. Required

Quick Start

Use this quick start to get up and running with the Confluent Cloud Azure Cosmos DB Source connector. The quick start provides the basics of selecting the connector and configuring it to stream events from a database to Kafka.

Prerequisites
  • Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud.

  • 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).

  • Authorized access to read data Azure Cosmos. For more information, see Secure access to data in Azure Cosmos DB.

  • The Azure Cosmos DB is configured to use the Core (SQL) API.

    Use Core SQL API

    Core (SQL) API selection

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 Cosmos DB Source connector card.

Azure Cosmos DB Source Connector Card

Step 4: Enter the connector details

Note

  • Make sure you have all your prerequisites completed.
  • An asterisk ( * ) designates a required entry.

At the Add Azure Cosmos DB Source Connector screen, complete the following:

  1. Select the way you want to provide Kafka Cluster credentials. You can choose one of the following options:
    • Global Access: Allows your connector to access everything you have access to. With global access, connector access will be linked to your account. This option is not recommended for production.
    • Granular access: Limits the access for your connector. You will be able to manage connector access through a service account. This option is recommended for production.
    • Use an existing API key: Allows you to enter an API key and secret part you have stored. You can enter an API key and secret (or generate these in the Cloud Console).
  2. Click Continue.

Step 5: Check for files.

Verify that data is being produced in Kafka.

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.

Using the Confluent CLI

To set up and run the connector using the Confluent CLI, complete the following steps.

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": "CosmosDbSourceConnector_0",
  "config": {
    "connector.class": "CosmosDbSource",
    "name": "CosmosDbSourceConnector_0",
    "connect.cosmos.connection.endpoint": "https://confluent-azure-cosmosdb.documents.azure.com:443/",
    "connect.cosmos.master.key": "****************************************",
    "connect.cosmos.databasename": "ToDoList",
    "connect.cosmos.containers.topicmap": "Kafka-Items#Items",
    "output.data.format": "AVRO",
    "connect.cosmos.messagekey.enabled": "true",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "****************",
    "kafka.api.secret": "**********************************",
    "tasks.max": "1"
  }
}

Note the following property definitions:

  • "connector.class": Identifies the connector plugin name.
  • "name": Sets a name for your new connector.
  • "connect.cosmos.containers.topicmap": Enter a comma-delimited list of Kafka topics mapped to Cosmos containers. For example: topic1#con1,topic2#con2. The field accepts regex pattern *[\\w.-]+ *#[^,]+(, *[\\w.-]+ *#[^,]+)*.
  • "output.data.format" (data going to the Kafka topic): Supports AVRO, JSON_SR (JSON Schema), PROTOBUF, or JSON (schemaless). 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.
  • "connect.cosmos.messagekey.enabled": Whether or not to set a Kafka message key. Defaults to id. To set a different field for the message key, add the configuration property connect.cosmos.messagekey.field.
  • "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
    
  • "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 3: 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-cosmos-source-config.json

Example output:

Created connector CosmosDbSourceConnector_0 lcc-do6vzd

Step 4: 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   |CosmosDbSourceConnector_0   | RUNNING | Source |       |

Step 5: Check for files.

Verify that data is being produced in Kafka.

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.

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.

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

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

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 Cosmos DB database?

connect.cosmos.connection.endpoint

Cosmos endpoint URL.

  • Type: string
  • Importance: high
connect.cosmos.master.key

Cosmos connection master (primary) key.

  • Type: password
  • Importance: high
connect.cosmos.databasename

Name of the database to read from.

  • Type: string
  • Importance: high

Database details

connect.cosmos.containers.topicmap

A comma delimited list of Kafka topics mapped to Cosmos containers. For example: topic1#con1,topic2#con2.

  • Type: string
  • Valid Values: Must match the regex \s*[\w.-]+ *#[^,]+(, *[\w.-]+ *#[^,]+)*
  • Importance: high

Connection details

connect.cosmos.task.timeout

The maximum number of milliseconds the source task will use to read documents before sending them to Kafka.

  • Type: int
  • Default: 5000
  • Importance: low
connect.cosmos.task.buffer.size

The max size the container of documents (in bytes) the source task will buffer before sending them to Kafka.

  • Type: int
  • Default: 10000
  • Valid Values: [1,…,1000000]
  • Importance: low
connect.cosmos.task.batch.size

The max number of documents the source task will buffer before sending them to Kafka.

  • Type: int
  • Default: 100
  • Valid Values: [1,…]
  • Importance: low
connect.cosmos.task.poll.interval

The polling interval in milliseconds that a source task polls for changes.

  • Type: int
  • Default: 1000
  • Importance: low

Output messages

output.data.format

Sets the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. 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
connect.cosmos.messagekey.enabled

Whether to set the Kafka message key.

  • Type: boolean
  • Default: true
  • Importance: high
connect.cosmos.messagekey.field

The document field to use as the message key.

  • Type: string
  • Default: id
  • Importance: high

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.

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