Microsoft SQL Server CDC Source (Debezium) Connector for Confluent Cloud

Note

If you are installing the connector locally for Confluent Platform, see Debezium SQL Server Source Connector for Confluent Platform.

The Kafka Connect Microsoft SQL Server Change Data Capture (CDC) Source (Debezium) connector for Confluent Cloud can obtain a snapshot of the existing data in a Microsoft SQL Server database and then monitor and record all subsequent row-level changes to that data. The connector supports Avro, JSON Schema, Protobuf, or JSON (schemaless) output data formats. All of the events for each table are recorded in a separate Apache Kafka® topic. The events can then be easily consumed by applications and services. Note that deleted records are not captured.

Important

After this connector moves from Preview to General Availability (GA), Confluent Cloud Enterprise customers must have a Confluent Cloud annual commitment to use this connector. Contact your Confluent Account Executive to learn more and to update your subscription, if necessary.

Features

The Microsoft SQL Server CDC Source (Debezium) connector provides the following features:

  • Topics created automatically: The connector automatically creates Kafka topics using the naming convention: <database.server.name>.<schemaName>.<tableName>. The tables are created with the properties: topic.creation.default.partitions=1 and topic.creation.default.replication.factor=3.
  • Tables included and Tables excluded: Allows you to set whether a table is or is not monitored for changes. By default, the connector monitors every non-system table.
  • Snapshot mode: Allows you to specify the criteria for running a snapshot. The default is initial which specifies the connector can run a snapshot only when no offsets have been recorded for the logical server name.
  • Tombstones on delete: Allows to configure whether a tombstone event should be generated after a delete event. Default is true.
  • Database authentication: password authentication.
  • Data formats: The connector supports Avro, JSON Schema, Protobuf, or JSON (schemaless) output data. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).

For more information, see the Confluent Cloud connector limitations.

Caution

Preview connectors are not currently supported and are not recommended for production use.

Quick Start

Use this quick start to get up and running with the Confluent Cloud Microsoft SQL Server CDC Source (Debezium) connector. The quick start provides the basics of selecting the connector and configuring it to obtain a snapshot of the existing data in a Microsoft SQL Server database and then monitoring and recording all subsequent row-level changes.

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

  • The Confluent Cloud CLI installed and configured for the cluster. See Install and Configure the Confluent Cloud CLI.

  • Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).

  • SQL Server configured for change data capture (CDC). See Setting up SQL Server.

  • Public access must be enabled for your database, unless the environment is configured for VPC peering. The example below shows the AWS Management Console when setting up a Microsoft SQL Server database.

    AWS example showing public access for Microsoft SQL Server

    Public access enabled

  • Public inbound traffic access (0.0.0.0/0) must be allowed to the VPC where the database is located, unless the environment is configured for VPC peering. The example below shows the AWS Management Console when setting up security group rules for the VPC.

    AWS example showing security group rules

    Open inbound traffic

    Note

    See your specific cloud platform documentation for how to configure security rules for your VPC.

  • Kafka cluster credentials. You can use one of the following ways to get credentials:

    • Create a Confluent Cloud API key and secret. To create a key and secret, go to Kafka API keys in your cluster or you can autogenerate the API key and secret directly in the UI when setting up the connector.
    • Create a Confluent Cloud service account for the connector.
  • An ACL to create a topic prefix is required. Note that the prefix is the database server name (for example, if the server name is cdc in the configuration property "database.server.name": "cdc"). See ccloud kafka acl create for the CLI command reference.

    ccloud kafka acl create --allow --service-account "<service-account-id>" --operation "CREATE" --prefix --topic "<database.server.name>"
    
    ccloud kafka acl create --allow --service-account "<service-account-id>" --operation "WRITE" --prefix --topic "<database.server.name>"
    

Using the Confluent Cloud GUI

Step 1: Launch your Confluent Cloud cluster.

See the Quick Start for Apache Kafka using Confluent Cloud for installation instructions.

Step 2: Add a connector.

Click Connectors. If you already have connectors in your cluster, click Add connector.

Step 3: Select your connector.

Click the Microsoft SQL Server CDC Source connector icon.

Microsoft SQL Server CDC Source Connector Icon

Step 4: Set up the connection.

Complete the following and click Continue.

Note

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

  2. Enter your Kafka Cluster credentials. The credentials are either the API key and secret or the service account API key and secret.

  3. Add the connection details for the database.

    Important

    Do not include jdbc:xxxx:// in the Connection host field. The example below shows a sample host address.

    ../../_images/ccloud-postgresql-source-connect-to-data.png
  4. Add the Database details for your database. Review the following notes for more information about field selections.

    • Tables included: Enter a comma-separated list of fully-qualified table identifiers for the connector to monitor. By default, the connector monitors all non-system tables. A fully-qualified table name is in the form schemaName.tableName.
    • Tables excluded: Enter a comma-separated list of fully-qualified table identifiers for the connector to ignore. A fully-qualified table name is in the form schemaName.tableName. This property cannot be used with the property Tables included.
    • Snapshot mode: Specifies the criteria for performing a database snapshot when the connector starts.
      • The default setting is initial, which specifies the connector can perform a snapshot only if no offsets have been recorded for the logical server name.
      • never specifies that the connector should never perform snapshots, and that when starting for the first time with a logical server name, the connector should read from where it last left off (i.e., the last LSN position) or start from the beginning, from the point of view of the logical replication slot.
      • exported specifies that the database snapshot is based on the point in time when the replication slot was created. Note that this is a good way to perform a lock-free snapshot (see Snapshot isolation).
    • Tombstones on delete: Configure whether a tombstone event should be generated after a delete event. The default is true.
  5. Enter values for the following properties:

    • Poll interval (ms): The time in milliseconds that the connector waits before polling for new CDC events. Defaults to 1000 ms (1 second).
    • Max batch size: Integer that defines the maximum batch size to process each iteration. Defaults to 1000 events.

  6. Select an Output message format (data coming from the connector): AVRO, JSON (schemaless), 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 (JSON Schema), or Protobuf).

  7. Enter the number of tasks in use by the connector. Refer to Confluent Cloud connector limitations for additional information.

Step 5: Launch the connector.

Verify the connection details and click Launch.

Step 6: Check the connector status.

The status for the connector should go from Provisioning to Running. It may take a few minutes.

Step 7: Check the Kafka topic.

After the connector is running, verify that messages are populating your Kafka topic.

Note

A topic named dbhistory.<databas.server.name>.<connect-id> is automatically created. This topic is created based on the database.history.kafka.topic property (that may be configured). This topic has one partition.

For additional information about this connector, see Debezium SQL Server Source Connector for Confluent Platform. Note that not all Confluent Platform connector features are provided in the Confluent Cloud connector.

See also

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL example. This example also shows how to use Confluent Cloud CLI to manage your resources in Confluent Cloud.

../../_images/topology.png

Using the Confluent Cloud CLI

Complete the following steps to set up and run the connector using the Confluent Cloud CLI.

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors.

Enter the following command to list available connectors:

ccloud connector-catalog list

Step 2: Show the required connector configuration properties.

Enter the following command to show the required connector properties:

ccloud connector-catalog describe <connector-catalog-name>

For example:

ccloud connector-catalog describe MicrosoftSqlServerSource

Example output:

Following are the required configs:
connector.class: SqlServerCdcSource
name
kafka.api.key
kafka.api.secret
database.hostname
database.port
database.user
database.password
database.dbname
database.server.name
output.data.format
tasks.max

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": "SqlServerCdcSource",
  "name": "SqlServerCdcSourceConnector_0",
  "kafka.api.key": "****************",
  "kafka.api.secret": "****************************************************************",
  "database.hostname": "connect-sqlserver-cdc.<host-id>.us-west-2.rds.amazonaws.com",
  "database.port": "1433",
  "database.user": "admin",
  "database.password": "************",
  "database.dbname": "database-name",
  "database.server.name": "sql",
  "table.whitelist":"public.passengers",
  "snapshot.mode": "initial",
  "output.data.format": "JSON",
  "tasks.max": "1"
}

Note the following property definitions:

  • "connector.class": Identifies the connector plugin name.
  • "name": Sets a name for your new connector.
  • "table.whitelist": (Optional) Enter a comma-separated list of fully-qualified table identifiers for the connector to monitor. By default, the connector monitors all non-system tables. A fully-qualified table name is in the form schemaName.tableName.
  • "snapshot.mode": Specifies the criteria for performing a database snapshot when the connector starts.
    • The default setting is initial, which specifies the connector can perform a snapshot only if no offsets have been recorded for the logical server name.
    • never specifies that the connector should never perform snapshots, and that when starting for the first time with a logical server name, the connector should read from where it last left off (i.e., the last LSN position) or start from the beginning, from the point of view of the logical replication slot.
    • exported specifies that the database snapshot is based on the point in time when the replication slot was created. Note that this is a good way to perform a lock-free snapshot (see Snapshot isolation).
  • "output.data.format": Sets the output message format (data coming from the connector). Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
  • "tasks.max": Enter the number of tasks in use by the connector. Refer to Confluent Cloud connector limitations for additional information.

Note

Configuration properties that are not listed use the default values. For default values and property definitions, see SQL Server Source Connector (Debezium) Configuration Properties.

Step 4: Load the properties file and create the connector.

Enter the following command to load the configuration and start the connector:

ccloud connector create --config <file-name>.json

For example:

ccloud connector create --config microsoft-sql-cdc-source.json

Example output:

Created connector SqlServerCdcSourceConnector_0 lcc-ix4dl

Step 5: Check the connector status.

Enter the following command to check the connector status:

ccloud connector list

Example output:

ID          |            Name                | Status  |  Type
+-----------+--------------------------------+---------+-------+
lcc-ix4dl   | SqlServerCdcSourceConnector_0  | RUNNING | source

Step 6: Check the Kafka topic.

After the connector is running, verify that messages are populating your Kafka topic.

Note

A topic named dbhistory.<databas.server.name>.<connect-id> is automatically created. This topic is created based on the database.history.kafka.topic property (that may be configured). This topic has one partition.

For additional information about this connector, see Debezium SQL Server Source Connector for Confluent Platform. Note that not all Confluent Platform connector features are provided in the Confluent Cloud connector.

Next Steps

See also

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL example. This example also shows how to use Confluent Cloud CLI to manage your resources in Confluent Cloud.

../../_images/topology.png