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.

Important

If you are still on Confluent Cloud Enterprise, please contact your Confluent Account Executive for more information about using this connector.

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.
  • Tasks per connector: Organizations can run multiple connectors with a limit of one task per connector (that is, "tasks.max": "1").
  • Snapshot mode: Allows you to specify the criteria for running a snapshot.
  • 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).

See the Microsoft SQL Server CDC Source connector configuration properties for values and definitions. See the Confluent Cloud connector limitations for additional information.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

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 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).

  • Public access may be required for your database. See Internet Access to Resources for details. 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

  • For networking considerations, see Internet access to resources. To use static egress IPs, see Static Egress IP Addresses. The following example 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, you can use the Confluent Cloud CLI or you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.
    • Create a Confluent Cloud service account for the connector. Make sure to review the ACL entries required in the service account documentation. Some connectors have specific ACL requirements.

Using the Confluent Cloud Console

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.

In the left navigation menu, click Data integration, and then 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. When selected, the connector takes a snapshot of the structure and data from captured tables. This is useful if you want the topics populated with a complete representation of captured table data when the connector starts.
      • schema_only takes a snapshot of the structure of the captured tables only. Useful if you want to only capture changes to data that happen from when the connector is started.
    • Tombstones on delete: Configure whether a tombstone event should be generated after a delete event. The default is true.
    • Columns Excluded: An optional, comma-separated list of regular expressions that match the fully-qualified names of columns to exclude from change event record values. Fully-qualified names for columns are in the form databaseName.tableName.columnName.
  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 the values for the following properties:

    • 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).

    • After-state only: (Optional) Defaults to true, which results in the Kafka record having only the record state from change events applied. Select false to maintain the prior record states after applying the change events.

    • JSON output decimal format: (Optional) Defaults to BASE64.

      JSON output decimal format property
  7. Enter the number of tasks in use by the connector. Organizations can run multiple connectors with a limit of one task per connector (that is, "tasks.max": "1").

  8. Transforms and Predicates: See the Single Message Transforms (SMT) documentation for details.

See the Microsoft SQL Server CDC Source connector configuration properties for values and definitions.

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.<database.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 more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

See also

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 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.include.list":"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.include.list": (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. When selected, the connector takes a snapshot of the structure and data from captured tables. This is useful if you want the topics populated with a complete representation of captured table data when the connector starts.
    • Setting this to schema.only takes a snapshot of the structure of the captured tables only. Useful if you want to only capture changes to data that happen from when the connector is started.
  • "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).

  • "after.state.only": (Optional) Defaults to true, which results in the Kafka record having only the record state from change events applied. Enter false to maintain the prior record states after applying the change events.

  • "json.output.decimal.format": (Optional) Defaults to BASE64. Specify the JSON/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.
    • NUMERIC to serialize Connect DECIMAL logical type values in JSON or JSON_SR as a number representing the decimal value.
  • "column.exclude.list": (Optional) A comma-separated list of regular expressions that match the fully-qualified names of columns to exclude from change event record values. Fully-qualified names for columns are in the form databaseName.tableName.columnName.

  • "tasks.max": Enter the number of tasks in use by the connector. Organizations can run multiple connectors with a limit of one task per connector (that is, "tasks.max": "1").

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

See the Microsoft SQL Server CDC Source connector configuration properties for values and definitions.

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.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

Note

A topic named dbhistory.<database.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.

Configuration Properties

The following connector configuration properties can be used with the Microsoft SQL Server CDC Source connector for Confluent Cloud.

database.hostname

IP address or hostname of the Microsoft SQL Server.

  • Type: String
  • Importance: High
database.port

Integer port number of the Microsoft Microsoft SQL Server.

  • Type: Integer
  • Importance: Low
  • Default: 1433
database.user

Username to use when connecting to the Microsoft SQL Server.

  • Type: String
  • Importance: High
database.password

Password to use when connecting to the Microsoft SQL Server.

  • Type: Password
  • Importance: High
database.dbname

The name of the Microsoft SQL database from which to stream changes.

  • Type: String
  • Importance: High
database.server.name

The logical name of the Microsoft SQL Server cluster. This logical name forms the namespace and is used in all Kafka topic names and Connect schema names. If Avro data format is used, the logical name is also used for the namespaces of the corresponding Avro schema. Kafka topics are created with the prefix database.server.name. Only alphanumeric characters, underscores, hyphens, and periods (dots) are allowed.

  • Type: String
  • Importance: High
table.include.list

An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be monitored. Any table not included is excluded from monitoring. Each identifier is in the form schemaName.tableName. By default the connector monitors every non-system table in each monitored schema. May not be used with table.exclude.list.

  • Type: List of strings
  • Importance: Low
table.exclude.list

An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be excluded from monitoring. An excluded table is not monitored. Each identifier is in the form schemaName.tableName. May not be used with table.include.list.

  • Type: List of Strings
  • Importance: Low
snapshot.mode

The criteria for running a snapshot when the connector starts up. The default setting is initial. When selected, the connector takes a snapshot of the structure and data from captured tables. This is useful if you want the topics populated with a complete representation of captured table data when the connector starts. If you use the option schema_only, the connector completes a snapshot of the schemas and not the data. This option is useful when you do not need the topics to contain a consistent snapshot of the data, but need them to have only the changes since the connector was started.

  • Type: String
  • Importance: Medium
  • Default: initial
  • Valid values: [initial, schema_only]
tombstones.on.delete

Controls whether a tombstone event should be generated after a delete event. When set to true (default), the delete operations are represented by a delete event and a subsequent tombstone event. When set to false, only a delete event is sent. Emitting a tombstone event allows Kafka to completely delete all events pertaining to the given key when the source record is deleted.

  • Type: String
  • Importance: High
  • Default: true
column.exclude.list

An optional comma-separated list of regular expressions that match the fully-qualified names of columns that should be excluded from change event message values. Fully-qualified names for columns use the format schemaName.tableName.columnName.

  • Type: List of strings
  • Importance: Low
poll.interval.ms

Positive integer value that specifies the number of milliseconds (ms) the connector should wait before polling for new change events. Defaults to 1000 ms.

  • Type: Integer
  • Importance: Low
  • Default: 1000
max.batch.size

Positive integer value that specifies the maximum size of each batch of change events that may be processed.

  • Type: Integer
  • Importance: Low
  • Default: 1000
event.processing.failure.handling.mode

Specifies how the connector should react to exceptions during deserialization of binlog events.

  • Type: String
  • Importance: Low
  • Default: fail
  • Valid values: [fail, skip, warn]
heartbeat.interval.ms

Controls how frequently the connector sends heartbeat messages to a Kafka topic. The default value 0 specifies that the connector does not send heartbeat messages.

  • Type: Int
  • Importance: Low
  • Default: 0
after.state.only

Defaults to true, which results in the Kafka record having only the record state from change events applied. Enter false to maintain the prior record states after applying the change events.

  • Type: String
  • Importance: Low
  • Default: true
  • Valid values: [true, false]
json.output.decimal.format

Specify the JSON or JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals. The BASE64 option designates that the connector serialize DECIMAL logical types as base64 encoded binary data and. The NUMERIC option designates that the connector serialize Connect DECIMAL logical type values in JSON or JSON_SR as a number that represents the decimal value.

  • Type: String
  • Importance: Low
  • Default: BASE64
  • Valid values: [BASE64, NUMERIC]

Next Steps

See also

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 Cloud CLI to manage your resources in Confluent Cloud.

../_images/topology.png