Debezium connector for SQL Server

Debezium connector for SQL Server

Overview

The Debezium SQL Server connector is based on the change data capture feature that is available in SQL Server 2016 Service Pack 1 (SP1) and later Standard edition or Enterprise edition. The SQL Server capture process monitors designated databases and tables, and stores the changes into specifically created change tables that have stored procedure facades.

To enable the Debezium SQL Server connector to capture change event records for database operations, you must first enable change data capture on the SQL Server database. CDC must be enabled on both the database and on each table that you want to capture. After you set up CDC on the source database, the connector can capture row-level INSERT, UPDATE, and DELETE operations that occur in the database. The connector writes event records for each source table to a Kafka topic especially dedicated to that table. One topic exists for each captured table. Client applications read the Kafka topics for the database tables that they follow, and can respond to the row-level events they they consume from those topics.

The first time that the connector connects to a SQL Server database or cluster, it takes a consistent snapshot of the schemas for all tables for which it is configured to capture changes, and streams this state to Kafka. After the snapshot is complete, the connector continuously captures subsequent row-level changes that occur. By first establishing a consistent view of all of the data, the connector can continue reading without having lost any of the changes that were made while the snapshot was taking place.

The Debezium SQL Server connector is tolerant of failures. As the connector reads changes and produces events, it periodically records the position of events in the database log (LSN / Log Sequence Number). If the connector stops for any reason (including communication failures, network problems, or crashes), after a restart the connector resumes reading the SQL Server CDC tables from the last point that it read.

Offsets are committed periodically. They are not committed at the time that a change event occurs. As a result, following an outage, duplicate events might be generated.

Fault tolerance also applies to snapshots. That is, if the connector stops during a snapshot, the connector begins a new snapshot when it restarts.

How the SQL Server connector works

To optimally configure and run a Debezium SQL Server connector, it is helpful to understand how the connector performs snapshots, streams change events, determines Kafka topic names, and uses metadata.

Snapshots

SQL Server CDC is not designed to store a complete history of database changes. For the Debezium SQL Server connector to establish a baseline for the current state of the database, it uses a process called snapshotting.

You can configure how the connector creates snapshots. By default, the connector’s snapshot mode is set to initial. Based on this initial snapshot mode, the first time that the connector starts, it performs an initial consistent snapshot of the database. This initial snapshot captures the structure and data for any tables that match the criteria defined by the include and exclude properties that are configured for the connector (for example, table.include.list, column.include.list, table.exclude.list, and so forth).

When the connector creates a snapshot, it completes the following tasks:

Determines the tables to be captured.
Obtains a lock on the SQL Server tables for which CDC is enabled to prevent structural changes from occurring during creation of the snapshot. The level of the lock is determined by snapshot.isolation.mode configuration option.
Reads the maximum log sequence number (LSN) position in the server’s transaction log.
Captures the structure of all relevant tables.
Releases the locks obtained in Step 2, if necessary. In most cases, locks are held for only a short period of time.
Scans the SQL Server source tables and schemas to be captured based on the LSN position that was read in Step 3, generates a READ event for each row in the table, and writes the events to the Kafka topic for the table.
Records the successful completion of the snapshot in the connector offsets.

The resulting initial snapshot captures the current state of each row in the tables that are enabled for CDC. From this baseline state, the connector captures subsequent changes as they occur.

Reading the change data tables

When the connector first starts, it takes a structural snapshot of the structure of the captured tables and persists this information to its internal database history topic. The connector then identifies a change table for each source table, and completes the following steps.

For each change table, the connector read all of the changes that were created between the last stored maximum LSN and the current maximum LSN.
The connector sorts the changes that it reads in ascending order, based on the values of their commit LSN and change LSN. This sorting order ensures that the changes are replayed by Debezium in the same order in which they occurred in the database.
The connector passes the commit and change LSNs as offsets to Kafka Connect.
The connector stores the maximum LSN and restarts the process from Step 1.

After a restart, the connector resumes processing from the last offset (commit and change LSNs) that it read.

The connector is able to detect whether CDC is enabled or disabled for included source tables and adjust its behavior.

Topic names

The SQL Server connector writes events for all INSERT, UPDATE, and DELETE operations for a specific table to a single Kafka topic. By default, the Kafka topic name takes the form serverName.schemaName.tableName. The following list provides definitions for the components of the default name:

serverName

The logical name of the connector, as specified by the database.server.name configuration property.

schemaName

The name of the database schema in which the change event occurred.

tableName

The name of the database table in which the change event occurred.

For example, suppose that fulfillment is the logical server name in the configuration for a connector that is capturing changes in a SQL Server installation. The server has an inventory database with the schema name dbo, and the database contains tables with the names products, products_on_hand, customers, and orders. The connector would stream records to the following Kafka topics:

fulfillment.dbo.products
fulfillment.dbo.products_on_hand
fulfillment.dbo.customers
fulfillment.dbo.orders

If the default topic name do not meet your requirements, you can configure custom topic names. To configure custom topic names, you specify regular expressions in the logical topic routing SMT. For more information about using the logical topic routing SMT to customize topic naming, see Topic routing.

Schema change topic

For each table for which CDC is enabled, the Debezium SQL Server connector stores a history of schema changes in a database history topic. This topic reflects an internal connector state and you should not use it directly. Application that require notifications about schema changes, should obtain the information from the public schema change topic. The connector writes all of these events to a Kafka topic named <serverName>, where serverName is the name of the connector that is specified in the database.server.name configuration property.

The format of the messages that a connector emits to its schema change topic is in an incubating state and can change without notice.

Debezium emits a message to the schema change topic when the following events occur:

You enable CDC for a table.
You disable CDC for a table.
You alter the structure of a table for which CDC is enabled by following the schema evolution procedure.

A message to the schema change topic contains a logical representation of the table schema, for example:

{
  "schema": {
  ...
  },
  "payload": {
    "source": {
      "version": "1.4.2.Final",
      "connector": "sqlserver",
      "name": "server1",
      "ts_ms": 1588252618953,
      "snapshot": "true",
      "db": "testDB",
      "schema": "dbo",
      "table": "customers",
      "change_lsn": null,
      "commit_lsn": "00000025:00000d98:00a2",
      "event_serial_no": null
    },
    "databaseName": "testDB", (1)
    "schemaName": "dbo",
    "ddl": null, (2)
    "tableChanges": [ (3)
      {
        "type": "CREATE", (4)
        "id": "\"testDB\".\"dbo\".\"customers\"", (5)
        "table": { (6)
          "defaultCharsetName": null,
          "primaryKeyColumnNames": [ (7)
            "id"
          ],
          "columns": [ (8)
            {
              "name": "id",
              "jdbcType": 4,
              "nativeType": null,
              "typeName": "int identity",
              "typeExpression": "int identity",
              "charsetName": null,
              "length": 10,
              "scale": 0,
              "position": 1,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            },
            {
              "name": "first_name",
              "jdbcType": 12,
              "nativeType": null,
              "typeName": "varchar",
              "typeExpression": "varchar",
              "charsetName": null,
              "length": 255,
              "scale": null,
              "position": 2,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            },
            {
              "name": "last_name",
              "jdbcType": 12,
              "nativeType": null,
              "typeName": "varchar",
              "typeExpression": "varchar",
              "charsetName": null,
              "length": 255,
              "scale": null,
              "position": 3,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            },
            {
              "name": "email",
              "jdbcType": 12,
              "nativeType": null,
              "typeName": "varchar",
              "typeExpression": "varchar",
              "charsetName": null,
              "length": 255,
              "scale": null,
              "position": 4,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            }
          ]
        }
      }
    ]
  }
}

Table 1. Descriptions of fields in messages emitted to the schema change topic
Item	Field name	Description
1	`databaseName` `schemaName`	Identifies the database and the schema that contain the change.
2	`ddl`	Always `null` for the SQL Server connector. For other connectors, this field contains the DDL responsible for the schema change. This DDL is not available to SQL Server connectors.
3	`tableChanges`	An array of one or more items that contain the schema changes generated by a DDL command.
4	`type`	Describes the kind of change. The value is one of the following: `CREATE` - table created `ALTER` - table modified `DROP` - table deleted
5	`id`	Full identifier of the table that was created, altered, or dropped.
6	`table`	Represents table metadata after the applied change.
7	`primaryKeyColumnNames`	List of columns that compose the table’s primary key.
8	`columns`	Metadata for each column in the changed table.

In messages that the connector sends to the schema change topic, the key is the name of the database that contains the schema change. In the following example, the payload field contains the key:

{
  "schema": {
    "type": "struct",
    "fields": [
      {
        "type": "string",
        "optional": false,
        "field": "databaseName"
      }
    ],
    "optional": false,
    "name": "io.debezium.connector.sqlserver.SchemaChangeKey"
  },
  "payload": {
    "databaseName": "testDB"
  }
}

Data change events

The Debezium SQL Server connector generates a data change event for each row-level INSERT, UPDATE, and DELETE operation. Each event contains a key and a value. The structure of the key and the value depends on the table that was changed.

Debezium and Kafka Connect are designed around continuous streams of event messages. However, the structure of these events may change over time, which can be difficult for consumers to handle. To address this, each event contains the schema for its content or, if you are using a schema registry, a schema ID that a consumer can use to obtain the schema from the registry. This makes each event self-contained.

The following skeleton JSON shows the basic four parts of a change event. However, how you configure the Kafka Connect converter that you choose to use in your application determines the representation of these four parts in change events. A schema field is in a change event only when you configure the converter to produce it. Likewise, the event key and event payload are in a change event only if you configure a converter to produce it. If you use the JSON converter and you configure it to produce all four basic change event parts, change events have this structure:

{
 "schema": { (1)
   ...
  },
 "payload": { (2)
   ...
 },
 "schema": { (3)
   ...
 },
 "payload": { (4)
   ...
 },
}

Table 2. Overview of change event basic content
Item	Field name	Description
1	`schema`	The first `schema` field is part of the event key. It specifies a Kafka Connect schema that describes what is in the event key’s `payload` portion. In other words, the first `schema` field describes the structure of the primary key, or the unique key if the table does not have a primary key, for the table that was changed. It is possible to override the table’s primary key by setting the message.key.columns connector configuration property. In this case, the first schema field describes the structure of the key identified by that property.
2	`payload`	The first `payload` field is part of the event key. It has the structure described by the previous `schema` field and it contains the key for the row that was changed.
3	`schema`	The second `schema` field is part of the event value. It specifies the Kafka Connect schema that describes what is in the event value’s `payload` portion. In other words, the second `schema` describes the structure of the row that was changed. Typically, this schema contains nested schemas.
4	`payload`	The second `payload` field is part of the event value. It has the structure described by the previous `schema` field and it contains the actual data for the row that was changed.

By default, the connector streams change event records to topics with names that are the same as the event’s originating table. See topic names.

The SQL Server connector ensures that all Kafka Connect schema names adhere to the Avro schema name format. This means that the logical server name must start with a Latin letter or an underscore, that is, a-z, A-Z, or . Each remaining character in the logical server name and each character in the database and table names must be a Latin letter, a digit, or an underscore, that is, a-z, A-Z, 0-9, or \. If there is an invalid character it is replaced with an underscore character.

This can lead to unexpected conflicts if the logical server name, a database name, or a table name contains invalid characters, and the only characters that distinguish names from one another are invalid and thus replaced with underscores.

Change event keys

A change event’s key contains the schema for the changed table’s key and the changed row’s actual key. Both the schema and its corresponding payload contain a field for each column in the changed table’s primary key (or unique key constraint) at the time the connector created the event.

Consider the following customers table, which is followed by an example of a change event key for this table.

Example table

CREATE TABLE customers (
  id INTEGER IDENTITY(1001,1) NOT NULL PRIMARY KEY,
  first_name VARCHAR(255) NOT NULL,
  last_name VARCHAR(255) NOT NULL,
  email VARCHAR(255) NOT NULL UNIQUE
);

Example change event key

Every change event that captures a change to the customers table has the same event key schema. For as long as the customers table has the previous definition, every change event that captures a change to the customers table has the following key structure, which in JSON, looks like this:

{
    "schema": { (1)
        "type": "struct",
        "fields": [ (2)
            {
                "type": "int32",
                "optional": false,
                "field": "id"
            }
        ],
        "optional": false, (3)
        "name": "server1.dbo.customers.Key" (4)
    },
    "payload": { (5)
        "id": 1004
    }
}

Table 3. Description of change event key
Item	Field name	Description
1	`schema`	The schema portion of the key specifies a Kafka Connect schema that describes what is in the key’s `payload` portion.
2	`fields`	Specifies each field that is expected in the `payload`, including each field’s name, type, and whether it is required. In this example, there is one required field named `id` of type `int32`.
3	`optional`	Indicates whether the event key must contain a value in its `payload` field. In this example, a value in the key’s payload is required. A value in the key’s payload field is optional when a table does not have a primary key.
4	`server1.dbo.customers.Key`	Name of the schema that defines the structure of the key’s payload. This schema describes the structure of the primary key for the table that was changed. Key schema names have the format connector-name.database-schema-name.table-name.`Key`. In this example: `server1` is the name of the connector that generated this event. `dbo` is the database schema for the table that was changed. `customers` is the table that was updated.
5	`payload`	Contains the key for the row for which this change event was generated. In this example, the key, contains a single `id` field whose value is `1004`.

Although the column.exclude.list and column.include.list connector configuration properties allow you to capture only a subset of table columns, all columns in a primary or unique key are always included in the event’s key.

If the table does not have a primary or unique key, then the change event’s key is null. This makes sense since the rows in a table without a primary or unique key constraint cannot be uniquely identified.

Change event values

The value in a change event is a bit more complicated than the key. Like the key, the value has a schema section and a payload section. The schema section contains the schema that describes the Envelope structure of the payload section, including its nested fields. Change events for operations that create, update or delete data all have a value payload with an envelope structure.

Consider the same sample table that was used to show an example of a change event key:

CREATE TABLE customers (
  id INTEGER IDENTITY(1001,1) NOT NULL PRIMARY KEY,
  first_name VARCHAR(255) NOT NULL,
  last_name VARCHAR(255) NOT NULL,
  email VARCHAR(255) NOT NULL UNIQUE
);

The value portion of a change event for a change to this table is described for each event type.

create events

The following example shows the value portion of a change event that the connector generates for an operation that creates data in the customers table:

{
  "schema": { (1)
    "type": "struct",
    "fields": [
      {
        "type": "struct",
        "fields": [
          {
            "type": "int32",
            "optional": false,
            "field": "id"
          },
          {
            "type": "string",
            "optional": false,
            "field": "first_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "last_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "email"
          }
        ],
        "optional": true,
        "name": "server1.dbo.customers.Value", (2)
        "field": "before"
      },
      {
        "type": "struct",
        "fields": [
          {
            "type": "int32",
            "optional": false,
            "field": "id"
          },
          {
            "type": "string",
            "optional": false,
            "field": "first_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "last_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "email"
          }
        ],
        "optional": true,
        "name": "server1.dbo.customers.Value",
        "field": "after"
      },
      {
        "type": "struct",
        "fields": [
          {
            "type": "string",
            "optional": false,
            "field": "version"
          },
          {
            "type": "string",
            "optional": false,
            "field": "connector"
          },
          {
            "type": "string",
            "optional": false,
            "field": "name"
          },
          {
            "type": "int64",
            "optional": false,
            "field": "ts_ms"
          },
          {
            "type": "boolean",
            "optional": true,
            "default": false,
            "field": "snapshot"
          },
          {
            "type": "string",
            "optional": false,
            "field": "db"
          },
          {
            "type": "string",
            "optional": false,
            "field": "schema"
          },
          {
            "type": "string",
            "optional": false,
            "field": "table"
          },
          {
            "type": "string",
            "optional": true,
            "field": "change_lsn"
          },
          {
            "type": "string",
            "optional": true,
            "field": "commit_lsn"
          },
          {
            "type": "int64",
            "optional": true,
            "field": "event_serial_no"
          }
        ],
        "optional": false,
        "name": "io.debezium.connector.sqlserver.Source", (3)
        "field": "source"
      },
      {
        "type": "string",
        "optional": false,
        "field": "op"
      },
      {
        "type": "int64",
        "optional": true,
        "field": "ts_ms"
      }
    ],
    "optional": false,
    "name": "server1.dbo.customers.Envelope" (4)
  },
  "payload": { (5)
    "before": null, (6)
    "after": { (7)
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "john.doe@example.org"
    },
    "source": { (8)
      "version": "1.4.2.Final",
      "connector": "sqlserver",
      "name": "server1",
      "ts_ms": 1559729468470,
      "snapshot": false,
      "db": "testDB",
      "schema": "dbo",
      "table": "customers",
      "change_lsn": "00000027:00000758:0003",
      "commit_lsn": "00000027:00000758:0005",
      "event_serial_no": "1"
    },
    "op": "c", (9)
    "ts_ms": 1559729471739 (10)
  }
}

Table 4. Descriptions of *create* event value fields
Item	Field name	Description
1	`schema`	The value’s schema, which describes the structure of the value’s payload. A change event’s value schema is the same in every change event that the connector generates for a particular table.
2	`name`	In the `schema` section, each `name` field specifies the schema for a field in the value’s payload. `server1.dbo.customers.Value` is the schema for the payload’s `before` and `after` fields. This schema is specific to the `customers` table. Names of schemas for `before` and `after` fields are of the form `logicalName.database-schemaName.tableName.Value`, which ensures that the schema name is unique in the database. This means that when using the Avro converter, the resulting Avro schema for each table in each logical source has its own evolution and history.
3	`name`	`io.debezium.connector.sqlserver.Source` is the schema for the payload’s `source` field. This schema is specific to the SQL Server connector. The connector uses it for all events that it generates.
4	`name`	`server1.dbo.customers.Envelope` is the schema for the overall structure of the payload, where `server1` is the connector name, `dbo` is the database schema name, and `customers` is the table.
5	`payload`	The value’s actual data. This is the information that the change event is providing. It may appear that the JSON representations of the events are much larger than the rows they describe. This is because the JSON representation must include the schema and the payload portions of the message. However, by using the Avro converter, you can significantly decrease the size of the messages that the connector streams to Kafka topics.
6	`before`	An optional field that specifies the state of the row before the event occurred. When the `op` field is `c` for create, as it is in this example, the `before` field is `null` since this change event is for new content.
7	`after`	An optional field that specifies the state of the row after the event occurred. In this example, the `after` field contains the values of the new row’s `id`, `first_name`, `last_name`, and `email` columns.
8	`source`	Mandatory field that describes the source metadata for the event. This field contains information that you can use to compare this event with other events, with regard to the origin of the events, the order in which the events occurred, and whether events were part of the same transaction. The source metadata includes: Debezium version Connector type and name Database and schema names Timestamp for when the change was made in the database If the event was part of a snapshot Name of the table that contains the new row Server log offsets
9	`op`	Mandatory string that describes the type of operation that caused the connector to generate the event. In this example, `c` indicates that the operation created a row. Valid values are: `c` = create `u` = update `d` = delete `r` = read (applies to only snapshots)
10	`ts_ms`	Optional field that displays the time at which the connector processed the event. In the event message envelope, the time is based on the system clock in the JVM running the Kafka Connect task. In the `source` object, `ts_ms` indicates the time when a change was committed in the database. By comparing the value for `payload.source.ts_ms` with the value for `payload.ts_ms`, you can determine the lag between the source database update and Debezium.

update events

The value of a change event for an update in the sample customers table has the same schema as a create event for that table. Likewise, the event value’s payload has the same structure. However, the event value payload contains different values in an update event. Here is an example of a change event value in an event that the connector generates for an update in the customers table:

{
  "schema": { ... },
  "payload": {
    "before": { (1)
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "john.doe@example.org"
    },
    "after": { (2)
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "noreply@example.org"
    },
    "source": { (3)
      "version": "1.4.2.Final",
      "connector": "sqlserver",
      "name": "server1",
      "ts_ms": 1559729995937,
      "snapshot": false,
      "db": "testDB",
      "schema": "dbo",
      "table": "customers",
      "change_lsn": "00000027:00000ac0:0002",
      "commit_lsn": "00000027:00000ac0:0007",
      "event_serial_no": "2"
    },
    "op": "u", (4)
    "ts_ms": 1559729998706  (5)
  }
}

Table 5. Descriptions of *update* event value fields
Item	Field name	Description
1	`before`	An optional field that specifies the state of the row before the event occurred. In an update event value, the `before` field contains a field for each table column and the value that was in that column before the database commit. In this example, the `email` value is `john.doe@example.org.`
2	`after`	An optional field that specifies the state of the row after the event occurred. You can compare the `before` and `after` structures to determine what the update to this row was. In the example, the `email` value is now `noreply@example.org`.
3	`source`	Mandatory field that describes the source metadata for the event. The `source` field structure has the same fields as in a create event, but some values are different, for example, the sample update event has a different offset. The source metadata includes: Debezium version Connector type and name Database and schema names Timestamp for when the change was made in the database If the event was part of a snapshot Name of the table that contains the new row Server log offsets The `event_serial_no` field differentiates events that have the same commit and change LSN. Typical situations for when this field has a value other than `1`: update events have the value set to `2` because the update generates two events in the CDC change table of SQL Server (see the source documentation for details). The first event contains the old values and the second contains contains new values. The connector uses values in the first event to create the second event. The connector drops the first event. When a primary key is updated SQL Server emits two evemts. A delete event for the removal of the record with the old primary key value and a create event for the addition of the record with the new primary key. Both operations share the same commit and change LSN and their event numbers are `1` and `2`, respectively.
4	`op`	Mandatory string that describes the type of operation. In an update event value, the `op` field value is `u`, signifying that this row changed because of an update.
5	`ts_ms`	Optional field that displays the time at which the connector processed the event. In the event message envelope, the time is based on the system clock in the JVM running the Kafka Connect task. In the `source` object, `ts_ms` indicates the time when the change was committed to the database. By comparing the value for `payload.source.ts_ms` with the value for `payload.ts_ms`, you can determine the lag between the source database update and Debezium.

Updating the columns for a row’s primary/unique key changes the value of the row’s key. When a key changes, Debezium outputs three events: a delete event and a tombstone event with the old key for the row, followed by a create event with the new key for the row.

delete events

The value in a delete change event has the same schema portion as create and update events for the same table. The payload portion in a delete event for the sample customers table looks like this:

{
  "schema": { ... },
  },
  "payload": {
    "before": { <>
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "noreply@example.org"
    },
    "after": null, (2)
    "source": { (3)
      "version": "1.4.2.Final",
      "connector": "sqlserver",
      "name": "server1",
      "ts_ms": 1559730445243,
      "snapshot": false,
      "db": "testDB",
      "schema": "dbo",
      "table": "customers",
      "change_lsn": "00000027:00000db0:0005",
      "commit_lsn": "00000027:00000db0:0007",
      "event_serial_no": "1"
    },
    "op": "d", (4)
    "ts_ms": 1559730450205 (5)
  }
}

Table 6. Descriptions of *delete* event value fields
Item	Field name	Description
1	`before`	Optional field that specifies the state of the row before the event occurred. In a delete event value, the `before` field contains the values that were in the row before it was deleted with the database commit.
2	`after`	Optional field that specifies the state of the row after the event occurred. In a delete event value, the `after` field is `null`, signifying that the row no longer exists.
3	`source`	Mandatory field that describes the source metadata for the event. In a delete event value, the `source` field structure is the same as for create and update events for the same table. Many `source` field values are also the same. In a delete event value, the `ts_ms` and `pos` field values, as well as other values, might have changed. But the `source` field in a delete event value provides the same metadata: Debezium version Connector type and name Database and schema names Timestamp for when the change was made in the database If the event was part of a snapshot Name of the table that contains the new row Server log offsets
4	`op`	Mandatory string that describes the type of operation. The `op` field value is `d`, signifying that this row was deleted.
5	`ts_ms`	Optional field that displays the time at which the connector processed the event. In the event message envelope, the time is based on the system clock in the JVM running the Kafka Connect task. In the `source` object, `ts_ms` indicates the time that the change was made in the database. By comparing the value for `payload.source.ts_ms` with the value for `payload.ts_ms`, you can determine the lag between the source database update and Debezium.

SQL Server connector events are designed to work with Kafka log compaction. Log compaction enables removal of some older messages as long as at least the most recent message for every key is kept. This lets Kafka reclaim storage space while ensuring that the topic contains a complete data set and can be used for reloading key-based state.

Tombstone events

When a row is deleted, the delete event value still works with log compaction, because Kafka can remove all earlier messages that have that same key. However, for Kafka to remove all messages that have that same key, the message value must be null. To make this possible, after Debezium’s SQL Server connector emits a delete event, the connector emits a special tombstone event that has the same key but a null value.

Transaction metadata

Debezium can generate events that represent transaction boundaries and that enrich data change event messages.

Database transactions are represented by a statement block that is enclosed between the BEGIN and END keywords. Debezium generates transaction boundary events for the BEGIN and END delimiters in every transaction. Transaction boundary events contain the following fields:

status

BEGIN or END

id

String representation of unique transaction identifier.

event_count (for END events)

Total number of events emitted by the transaction.

data_collections (for END events)

An array of pairs of data_collection and event_count that provides the number of events emitted by changes originating from given data collection.

The following example shows a typical transaction boundary message:

Example: SQL Server connector transaction boundary event

{
  "status": "BEGIN",
  "id": "00000025:00000d08:0025",
  "event_count": null,
  "data_collections": null
}
{
  "status": "END",
  "id": "00000025:00000d08:0025",
  "event_count": 2,
  "data_collections": [
    {
      "data_collection": "testDB.dbo.tablea",
      "event_count": 1
    },
    {
      "data_collection": "testDB.dbo.tableb",
      "event_count": 1
    }
  ]
}

The transaction events are written to the topic named <database.server.name>.transaction.

Change data event enrichment

When transaction metadata is enabled, the data message Envelope is enriched with a new transaction field. This field provides information about every event in the form of a composite of fields:

id

String representation of unique transaction identifier

total_order

The absolute position of the event among all events generated by the transaction

data_collection_order

The per-data collection position of the event among all events that were emitted by the transaction

The following example shows what a typical message looks like:

{
  "before": null,
  "after": {
    "pk": "2",
    "aa": "1"
  },
  "source": {
...
  },
  "op": "c",
  "ts_ms": "1580390884335",
  "transaction": {
    "id": "00000025:00000d08:0025",
    "total_order": "1",
    "data_collection_order": "1"
  }
}

Data type mappings

The Debezium SQL Server connector represents changes to table row data by producing events that are structured like the table in which the row exists. Each event contains fields to represent the column values for the row. The way in which an event represents the column values for an operation depends on the SQL data type of the column. In the event, the connector maps the fields for each SQL Server data type to both a literal type and a semantic type.

The connector can map SQL Server data types to both literal and semantic types.

Literal type

Describes how the value is literally represented by using Kafka Connect schema types, namely INT8, INT16, INT32, INT64, FLOAT32, FLOAT64, BOOLEAN, STRING, BYTES, ARRAY, MAP, and STRUCT.

Semantic type

Describes how the Kafka Connect schema captures the meaning of the field using the name of the Kafka Connect schema for the field.

Basic types

The following table shows how the connector maps basic SQL Server data types.

Table 7. Data type mappings used by the SQL Server connector
SQL Server data type	Literal type (schema type)	Semantic type (schema name) and Notes
`BIT`	`BOOLEAN`	n/a
`TINYINT`	`INT16`	n/a
`SMALLINT`	`INT16`	n/a
`INT`	`INT32`	n/a
`BIGINT`	`INT64`	n/a
`REAL`	`FLOAT32`	n/a
`FLOAT[(N)]`	`FLOAT64`	n/a
`CHAR[(N)]`	`STRING`	n/a
`VARCHAR[(N)]`	`STRING`	n/a
`TEXT`	`STRING`	n/a
`NCHAR[(N)]`	`STRING`	n/a
`NVARCHAR[(N)]`	`STRING`	n/a
`NTEXT`	`STRING`	n/a
`XML`	`STRING`	`io.debezium.data.Xml` Contains the string representation of an XML document
`DATETIMEOFFSET[(P)]`	`STRING`	`io.debezium.time.ZonedTimestamp` A string representation of a timestamp with timezone information, where the timezone is GMT

Other data type mappings are described in the following sections.

If present, a column’s default value is propagated to the corresponding field’s Kafka Connect schema. Change messages will contain the field’s default value (unless an explicit column value had been given), so there should rarely be the need to obtain the default value from the schema. Passing the default value helps though with satisfying the compatibility rules when using Avro as serialization format together with the Confluent schema registry.

Temporal values

Other than SQL Server’s DATETIMEOFFSET data type (which contain time zone information), the other temporal types depend on the value of the time.precision.mode configuration property. When the time.precision.mode configuration property is set to adaptive (the default), then the connector will determine the literal type and semantic type for the temporal types based on the column’s data type definition so that events exactly represent the values in the database:

SQL Server data type	Literal type (schema type)	Semantic type (schema name) and Notes
`DATE`	`INT32`	`io.debezium.time.Date` Represents the number of days since the epoch.
`TIME(0)`, `TIME(1)`, `TIME(2)`, `TIME(3)`	`INT32`	`io.debezium.time.Time` Represents the number of milliseconds past midnight, and does not include timezone information.
`TIME(4)`, `TIME(5)`, `TIME(6)`	`INT64`	`io.debezium.time.MicroTime` Represents the number of microseconds past midnight, and does not include timezone information.
`TIME(7)`	`INT64`	`io.debezium.time.NanoTime` Represents the number of nanoseconds past midnight, and does not include timezone information.
`DATETIME`	`INT64`	`io.debezium.time.Timestamp` Represents the number of milliseconds past the epoch, and does not include timezone information.
`SMALLDATETIME`	`INT64`	`io.debezium.time.Timestamp` Represents the number of milliseconds past the epoch, and does not include timezone information.
`DATETIME2(0)`, `DATETIME2(1)`, `DATETIME2(2)`, `DATETIME2(3)`	`INT64`	`io.debezium.time.Timestamp` Represents the number of milliseconds past the epoch, and does not include timezone information.
`DATETIME2(4)`, `DATETIME2(5)`, `DATETIME2(6)`	`INT64`	`io.debezium.time.MicroTimestamp` Represents the number of microseconds past the epoch, and does not include timezone information.
`DATETIME2(7)`	`INT64`	`io.debezium.time.NanoTimestamp` Represents the number of nanoseconds past the epoch, and does not include timezone information.

When the time.precision.mode configuration property is set to connect, then the connector will use the predefined Kafka Connect logical types. This may be useful when consumers only know about the built-in Kafka Connect logical types and are unable to handle variable-precision time values. On the other hand, since SQL Server supports tenth of microsecond precision, the events generated by a connector with the connect time precision mode will result in a loss of precision when the database column has a fractional second precision value greater than 3:

SQL Server data type	Literal type (schema type)	Semantic type (schema name) and Notes
`DATE`	`INT32`	`org.apache.kafka.connect.data.Date` Represents the number of days since the epoch.
`TIME([P])`	`INT64`	`org.apache.kafka.connect.data.Time` Represents the number of milliseconds since midnight, and does not include timezone information. SQL Server allows `P` to be in the range 0-7 to store up to tenth of a microsecond precision, though this mode results in a loss of precision when `P` > 3.
`DATETIME`	`INT64`	`org.apache.kafka.connect.data.Timestamp` Represents the number of milliseconds since the epoch, and does not include timezone information.
`SMALLDATETIME`	`INT64`	`org.apache.kafka.connect.data.Timestamp` Represents the number of milliseconds past the epoch, and does not include timezone information.
`DATETIME2`	`INT64`	`org.apache.kafka.connect.data.Timestamp` Represents the number of milliseconds since the epoch, and does not include timezone information. SQL Server allows `P` to be in the range 0-7 to store up to tenth of a microsecond precision, though this mode results in a loss of precision when `P` > 3.

Timestamp values

The DATETIME, SMALLDATETIME and DATETIME2 types represent a timestamp without time zone information. Such columns are converted into an equivalent Kafka Connect value based on UTC. So for instance the DATETIME2 value “2018-06-20 15:13:16.945104” is represented by a io.debezium.time.MicroTimestamp with the value “1529507596945104”.

Note that the timezone of the JVM running Kafka Connect and Debezium does not affect this conversion.

Decimal values

SQL Server data type	Literal type (schema type)	Semantic type (schema name)
`NUMERIC[(P[,S])]`	`BYTES`	`org.apache.kafka.connect.data.Decimal`
`DECIMAL[(P[,S])]`	`BYTES`	`org.apache.kafka.connect.data.Decimal`
`SMALLMONEY`	`BYTES`	`org.apache.kafka.connect.data.Decimal`
`MONEY`	`BYTES`	`org.apache.kafka.connect.data.Decimal`

The scale schema parameter contains an integer that represents how many digits the decimal point was shifted. The connect.decimal.precision schema parameter contains an integer that represents the precision of the given decimal value.

Setting up SQL Server

For Debezium to capture change events from SQL Server tables, a SQL Server administrator with the necessary privileges must first run a query to enable CDC on the database. The administrator must then enable CDC for each table that you want Debezium to capture.

After CDC is applied, it captures all of the INSERT, UPDATE, and DELETE operations that are committed to the tables for which CDD is enabled. The Debezium connector can then capture these events and emit them to Kafka topics.

Enabling CDC on the SQL Server database

Before you can enable CDC for a table, you must enable it for the SQL Server database. A SQL Server administrator enables CDC by running a system stored procedure. System stored procedures can be run by using SQL Server Management Studio, or by using Transact-SQL.

Prerequisites

You are a member of the sysadmin fixed server role for the SQL Server.
You are a db_owner of the database.
The SQL Server Agent is running.

The SQL Server CDC feature processes changes that occur in user-created tables only. You cannot enable CDC on the SQL Server master database.

Procedure

From the View menu in SQL Server Management Studio, click Template Explorer.
In the Template Browser, expand SQL Server Templates.
Expand Change Data Capture > Configuration and then click Enable Database for CDC.
In the template, replace the database name in the USE statement with the name of the database that you want to enable for CDC.
Run the stored procedure sys.sp_cdc_enable_db to enable the database for CDC.

After the database is enabled for CDC, a schema with the name cdc is created, along with a CDC user, metadata tables, and other system objects.

The following example shows how to enable CDC for the database MyDB:

Example: Enabling a SQL Server database for the CDC template
```
USE MyDB
GO
EXEC sys.sp_cdc_enable_db
GO
```

Enabling CDC on a SQL Server table

A SQL Server administrator must enable change data capture on the source tables that you want to Debezium to capture. The database must already be enabled for CDC. To enable CDC on a table, a SQL Server administrator runs the stored procedure sys.sp_cdc_enable_table for the table. The stored procedures can be run by using SQL Server Management Studio, or by using Transact-SQL. SQL Server CDC must be enabled for every table that you want to capture.

Prerequisites

CDC is enabled on the SQL Server database.
The SQL Server Agent is running.
You are a member of the db_owner fixed database role for the database.

Procedure

From the View menu in SQL Server Management Studio, click Template Explorer.
In the Template Browser, expand SQL Server Templates.
Expand Change Data Capture > Configuration, and then click Enable Table Specifying Filegroup Option.
In the template, replace the table name in the USE statement with the name of the table that you want to capture.

Run the stored procedure sys.sp_cdc_enable_table.

The following example shows how to enable CDC for the table MyTable:

Example: Enabling CDC for a SQL Server table

USE MyDB
GO
EXEC sys.sp_cdc_enable_table
@source_schema = N'dbo',
@source_name   = N'MyTable', (1)
@role_name     = N'MyRole',  (2)
@filegroup_name = N'MyDB_CT',(3)
@supports_net_changes = 0
GO

1	Specifies the name of the table that you want to capture.
2	Specifies a role `MyRole` to which you can add users to whom you want to grant `SELECT` permission on the captured columns of the source table. Users in the `sysadmin` or `db_owner` role also have access to the specified change tables. Set the value of `@role_name` to `NULL`, to allow only members in the `sysadmin` or `db_owner` to have full access to captured information.
3	Specifies the `filegroup` where SQL Server places the change table for the captured table. The named `filegroup` must already exist. It is best not to locate change tables in the same `filegroup` that you use for source tables.

Verifying that the user has access to the CDC table

A SQL Server administrator can run a system stored procedure to query a database or table to retrieve its CDC configuration information. The stored procedures can be run by using SQL Server Management Studio, or by using Transact-SQL.

Prerequisites

You have SELECT permission on all of the captured columns of the capture instance. Members of the db_owner database role can view information for all of the defined capture instances.
You have membership in any gating roles that are defined for the table information that the query includes.

Procedure

From the View menu in SQL Server Management Studio, click Object Explorer.
From the Object Explorer, expand Databases, and then expand your database object, for example, MyDB.
Expand Programmability > Stored Procedures > System Stored Procedures.
Run the sys.sp_cdc_help_change_data_capture stored procedure to query the table.

Queries should not return empty results.

The following example runs the stored precedure sys.sp_cdc_help_change_data_capture on the database MyDB:

Example: Querying a table for CDC configuration information
```
USE MyDB;
GO
EXEC sys.sp_cdc_help_change_data_capture
GO
```
The query returns configuration information for each table in the database that is enabled for CDC and that contains change data that the caller is authorized to access. If the result is empty, verify that the user has privileges to access both the capture instance and the CDC tables.

SQL Server on Azure

The Debezium SQL Server connector has not been tested with SQL Server on Azure.

We welcome any feedback from users who try the connector with SQL Server databases in managed environments.

SQL Server Always On

The SQL Server connector can capture changes from an Always On read-only replica.

Prerequisites

Change data capture is configured and enabled on the primary node. SQL Server does not support CDC directly on replicas.
The configuration option database.applicationIntent is set to ReadOnly. This is required by SQL Server. When Debezium detects this configuration option, it responds by taking the following actions:
- Sets snapshot.isolation.mode to snapshot, which is the only one transaction isolation mode supported for read-only replicas.
- Commits the (read-only) transaction in every execution of the streaming query loop, which is necessary to get the latest view of CDC data.

Effect of SQL Server capture job agent configuration on server load and latency

When a database administrator enables change data capture for a source table, the capture job agent begins to run. The agent reads new change event records from the transaction log and replicates the event records to a change data table. Between the time that a change is committed in the source table, and the time that the change appears in the corresponding change table, there is always a small latency interval. This latency interval represents a gap between when changes occur in the source table and when they become available for Debezium to stream to Apache Kafka.

Ideally, for applications that must respond quickly to changes in data, you want to maintain close synchronization between the source and change tables. You might imagine that running the capture agent to continuously process change events as rapidly as possible might result in increased throughput and reduced latency — populating change tables with new event records as soon as possible after the events occur, in near real time. However, this is not necessarily the case. There is a performance penalty to pay in the pursuit of more immediate synchronization. Each time that the capture job agent queries the database for new event records, it increases the CPU load on the database host. The additional load on the server can have a negative effect on overall database performance, and potentially reduce transaction efficiency, especially during times of peak database use.

It’s important to monitor database metrics so that you know if the database reaches the point where the server can no longer support the capture agent’s level of activity. If you notice performance problems, there are SQL Server capture agent settings that you can modify to help balance the overall CPU load on the database host with a tolerable degree of latency.

SQL Server capture job agent configuration parameters

On SQL Server, parameters that control the behavior of the capture job agent are defined in the SQL Server table msdb.dbo.cdc_jobs. If you experience performance issues while running the capture job agent, adjust capture jobs settings to reduce CPU load by running the sys.sp_cdc_change_job stored procedure and supplying new values.

Specific guidance about how to configure SQL Server capture job agent parameters is beyond the scope of this documentation.

The following parameters are the most significant for modifying capture agent behavior for use with the Debezium SQL Server connector:

pollinginterval

Specifies the number of seconds that the capture agent waits between log scan cycles.
A higher value reduces the load on the database host and increases latency.
A value of 0 specifies no wait between scans.
The default value is 5.

maxtrans

Specifies the maximum number of transactions to process during each log scan cycle. After the capture job processes the specified number of transactions, it pauses for the length of time that the pollinginterval specifies before the next scan begins.
A lower value reduces the load on the database host and increases latency.
The default value is 500.

maxscans

Specifies a limit on the number of scan cycles that the capture job can attempt in capturing the full contents of the database transaction log. If the continuous parameter is set to 1, the job pauses for the length of time that the pollinginterval specifies before it resumes scanning.
A lower values reduces the load on the database host and increases latency.
The default value is 10.

Additional resources

For more information about capture agent parameters, see the SQL Server documentation.

Deployment

To deploy a Debezium SQL Server connector, you install the Debezium SQL Server connector archive, configure the connector, and start the connector by adding its configuration to Kafka Connect.

Prerequisites

Apache ZooKeeper, Apache Kafka, and Kafka Connect are installed.
SQL Server is installed, is configured for CDC, and is ready to be used with the Debezium connector].

Procedure

Download the Debezium SQL Server connector plug-in archive
Extract the files into your Kafka Connect environment.
Add the directory with the JAR files to Kafka Connect’s plugin.path.
Configure the connector and add the configuration to your Kafka Connect cluster.
Restart your Kafka Connect process to pick up the new JAR files.

If you are working with immutable containers, see Debezium’s container images for Åpache ZooKeeper, Apache Kafka, and Kafka Connect. You can pull the official container images for Microsoft SQL Server on Linux from Docker Hub.

You can also run Debezium on Kubernetes and OpenShift.

SQL Server connector configuration example

Following is an example of the configuration for a connector instance that captures data from a SQL Server server at port 1433 on 192.168.99.100, which we logically name fullfillment. Typically, you configure the Debezium SQL Server connector in a JSON file by setting the configuration properties that are available for the connector.

You can choose to produce events for a subset of the schemas and tables in a database. Optionally, you can ignore, mask, or truncate columns that contain sensitive data, that are larger than a specified size, or that you do not need.

{
    "name": "inventory-connector", (1)
    "config": {
        "connector.class": "io.debezium.connector.sqlserver.SqlServerConnector", (2)
        "database.hostname": "192.168.99.100", (3)
        "database.port": "1433", (4)
        "database.user": "sa", (5)
        "database.password": "Password!", (6)
        "database.dbname": "testDB", (7)
        "database.server.name": "fullfillment", (8)
        "table.include.list": "dbo.customers", (9)
        "database.history.kafka.bootstrap.servers": "kafka:9092", (10)
        "database.history.kafka.topic": "dbhistory.fullfillment" (11)
    }
}

1	The name of our connector when we register it with a Kafka Connect service.
2	The name of this SQL Server connector class.
3	The address of the SQL Server instance.
4	The port number of the SQL Server instance.
5	The name of the SQL Server user
6	The password for the SQL Server user
7	The name of the database to capture changes from.
8	The logical name of the SQL Server instance/cluster, which forms a namespace and is used in all the names of the Kafka topics to which the connector writes, the Kafka Connect schema names, and the namespaces of the corresponding Avro schema when the Avro converter is used.
9	A list of all tables whose changes Debezium should capture.
10	The list of Kafka brokers that this connector will use to write and recover DDL statements to the database history topic.
11	The name of the database history topic where the connector will write and recover DDL statements. This topic is for internal use only and should not be used by consumers.

For the complete list of the configuration properties that you can set for the Debezium SQL Server connector, see SQL Server connector properties.

You can send this configuration with a POST command to a running Kafka Connect service. The service records the configuration and start up the one connector task that performs the following tasks:

Connects to the SQL Server database.
Reads the transaction log.
Records change events to Kafka topics.

Adding connector configuration

To start running a Debezium SQL Server connector, create a connector configuration, and add the configuration to your Kafka Connect cluster.

Prerequisites

CDC is enabled on SQL Server.
The Debezium SQL Server connector is installed.

Procedure

Create a configuration for the SQL Server connector.
Use the Kafka Connect REST API to add that connector configuration to your Kafka Connect cluster.

Results

When the connector starts, it performs a consistent snapshot of the SQL Server databases that the connector is configured for. The connector then starts generating data change events for row-level operations and streaming the change event records to Kafka topics.

Connector properties

The Debezium SQL Server connector has numerous configuration properties that you can use to achieve the right connector behavior for your application. Many properties have default values.

The following configuration properties are required unless a default value is available.

Property	Default	Description
name		Unique name for the connector. Attempting to register again with the same name will fail. (This property is required by all Kafka Connect connectors.)
connector.class		The name of the Java class for the connector. Always use a value of `io.debezium.connector.sqlserver.SqlServerConnector` for the SQL Server connector.
tasks.max	`1`	The maximum number of tasks that should be created for this connector. The SQL Server connector always uses a single task and therefore does not use this value, so the default is always acceptable.
database.hostname		IP address or hostname of the SQL Server database server.
database.port	`1433`	Integer port number of the SQL Server database server.
database.user		Username to use when connecting to the SQL Server database server.
database.password		Password to use when connecting to the SQL Server database server.
database.dbname		The name of the SQL Server database from which to stream the changes
database.server.name		Logical name that identifies and provides a namespace for the SQL Server database server that you want Debezium to capture. The logical name should be unique across all other connectors, since it is used as a prefix for all Kafka topic names emanating from this connector. Only alphanumeric characters and underscores should be used.
database.history.kafka.topic		The full name of the Kafka topic where the connector will store the database schema history.
database.history.kafka.bootstrap.servers		A list of host and port pairs that the connector will use for establishing an initial connection to the Kafka cluster. This connection is used for retrieving database schema history previously stored by the connector, and for writing each DDL statement read from the source database. This should point to the same Kafka cluster used by the Kafka Connect process.
table.include.list		An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables that you want Debezium to capture; any table that is not included in `table.include.list` is excluded from capture. Each identifier is of the form schemaName.tableName. By default, the connector captures all non-system tables for the designated schemas. Must not be used with `table.exclude.list`.
table.exclude.list		An optional comma-separated list of regular expressions that match fully-qualified table identifiers for the tables that you want to exclude from being captured; Debezium captures all tables that are not included in `table.exclude.list`. Each identifier is of the form schemaName.tableName. Must not be used with `table.include.list`.
column.include.list	empty string	An optional comma-separated list of regular expressions that match the fully-qualified names of columns that should be included in the change event message values. Fully-qualified names for columns are of the form schemaName.tableName.columnName. Note that primary key columns are always included in the event’s key, even if not included in the value. Do not also set the `column.exclude.list` property.
column.exclude.list	empty string	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 are of the form schemaName.tableName.columnName. Note that primary key columns are always included in the event’s key, also if excluded from the value. Do not also set the `column.include.list` property.
column.mask.hash.hashAlgorithm.with.salt.salt	n/a	An optional comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be pseudonyms in the change event message values with a field value consisting of the hashed value using the algorithm `hashAlgorithm` and salt `salt`. Based on the used hash function referential integrity is kept while data is pseudonymized. Supported hash functions are described in the MessageDigest section of the Java Cryptography Architecture Standard Algorithm Name Documentation. The hash is automatically shortened to the length of the column. Multiple properties with different lengths can be used in a single configuration, although in each the length must be a positive integer or zero. Fully-qualified names for columns are of the form schemaName.tableName.columnName. Example: `column.mask.hash.SHA-256.with.salt.CzQMA0cB5K = dbo.orders.customerName, dbo.shipment.customerName` where `CzQMA0cB5K` is a randomly selected salt. Note: Depending on the `hashAlgorithm` used, the `salt` selected and the actual data set, the resulting masked data set may not be completely anonymized.
time.precision.mode	`adaptive`	Time, date, and timestamps can be represented with different kinds of precision, including: `adaptive` (the default) captures the time and timestamp values exactly as in the database using either millisecond, microsecond, or nanosecond precision values based on the database column’s type; or `connect` always represents time and timestamp values using Kafka Connect’s built-in representations for Time, Date, and Timestamp, which uses millisecond precision regardless of the database columns’ precision. See temporal values.
include.schema.changes	`true`	Boolean value that specifies whether the connector should publish changes in the database schema to a Kafka topic with the same name as the database server ID. Each schema change is recorded with a key that contains the database name and a value that is a JSON structure that describes the schema update. This is independent of how the connector internally records database history. The default is `true`.
tombstones.on.delete	`true`	Controls whether a tombstone event should be generated after a delete event. When `true` the delete operations are represented by a delete event and a subsequent tombstone event. When `false` only a delete event is sent. Emitting the tombstone event (the default behavior) allows Kafka to completely delete all events pertaining to the given key once the source record got deleted.
column.truncate.to.length.chars	n/a	An optional comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be truncated in the change event message values if the field values are longer than the specified number of characters. Multiple properties with different lengths can be used in a single configuration, although in each the length must be a positive integer. Fully-qualified names for columns are of the form schemaName.tableName.columnName.
column.mask.with.length.chars	n/a	An optional comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be replaced in the change event message values with a field value consisting of the specified number of asterisk (``) characters. Multiple properties with different lengths can be used in a single configuration, although in each the length must be a positive integer or zero. Fully-qualified names for columns are of the form schemaName*.tableName.columnName.
column.propagate.source.type	n/a	An optional comma-separated list of regular expressions that match the fully-qualified names of columns whose original type and length should be added as a parameter to the corresponding field schemas in the emitted change messages. The schema parameters `debezium.source.column.type`, `debezium.source.column.length` and `debezium.source.column.scale` is used to propagate the original type name and length (for variable-width types), respectively. Useful to properly size corresponding columns in sink databases. Fully-qualified names for columns are of the form schemaName.tableName.columnName.
datatype.propagate.source.type+	n/a	An optional comma-separated list of regular expressions that match the database-specific data type name of columns whose original type and length should be added as a parameter to the corresponding field schemas in the emitted change messages. The schema parameters `debezium.source.column.type`, `debezium.source.column.length` and `debezium.source.column.scale` will be used to propagate the original type name and length (for variable-width types), respectively. Useful to properly size corresponding columns in sink databases. Fully-qualified data type names are of the form schemaName.tableName.typeName. See SQL Server data types for the list of SQL Server-specific data type names.
message.key.columns	n/a	A semi-colon list of regular expressions that match fully-qualified tables and columns to map a primary key. Each item (regular expression) must match the fully-qualified `<fully-qualified table>:<a comma-separated list of columns>` representing the custom key. Fully-qualified tables could be defined as schemaName.tableName.
binary.handling.mode	bytes	Specifies how binary (`binary`, `varbinary`) columns should be represented in change events, including: `bytes` represents binary data as byte array (default), `base64` represents binary data as base64-encoded String, `hex` represents binary data as hex-encoded (base16) String

The following advanced configuration properties have good defaults that will work in most situations and therefore rarely need to be specified in the connector’s configuration.

Property	Default	Description
snapshot.mode	initial	A mode for taking an initial snapshot of the structure and optionally data of captured tables. Once the snapshot is complete, the connector will continue reading change events from the database’s redo logs. The following values are supported: `initial`: Takes a snapshot of structure and data of captured tables; useful if topics should be populated with a complete representation of the data from the captured tables. `initial_only`: Takes a snapshot of structure and data like `initial` but instead does not transition into streaming changes once the snapshot has completed. `schema_only`: Takes a snapshot of the structure of captured tables only; useful if only changes happening from now onwards should be propagated to topics.
snapshot.include.collection.list	All tables specified in `table.include.list`	An optional, comma-separated list of regular expressions that match names of schemas specified in `table.include.list` for which you want to take the snapshot.
snapshot.isolation.mode	repeatable_read	Mode to control which transaction isolation level is used and how long the connector locks tables that are designated for capture. The following values are supported: `read_uncommitted` `read_committed` `repeatable_read` `snapshot` `exclusive` (`exclusive` mode uses repeatable read isolation level, however, it takes the exclusive lock on all tables to be read). The `snapshot`, `read_committed` and `read_uncommitted` modes do not prevent other transactions from updating table rows during initial snapshot. The `exclusive` and `repeatable_read` modes do prevent concurrent updates. Mode choice also affects data consistency. Only `exclusive` and `snapshot` modes guarantee full consistency, that is, initial snapshot and streaming logs constitute a linear history. In case of `repeatable_read` and `read_committed` modes, it might happen that, for instance, a record added appears twice - once in initial snapshot and once in streaming phase. Nonetheless, that consistency level should do for data mirroring. For `read_uncommitted` there are no data consistency guarantees at all (some data might be lost or corrupted).
source.timestamp.mode	commit	String that represents the criteria of the attached timestamp within the source record (ts_ms). `commit` (default) sets the source timestamp to the time when the record was committed to the database. `processing` sets the source timestamp to the time when Debezium accesses the record in the change table. Use the `processing` option if you want {prodname] to set the top level `ts_ms` value, or if you want to avoid the additional cost of Debezium querying the database to extract the LSN timestamps.
event.processing.failure.handling.mode	`fail`	Specifies how the connector should react to exceptions during processing of events. `fail` will propagate the exception (indicating the offset of the problematic event), causing the connector to stop. `warn` will cause the problematic event to be skipped and the offset of the problematic event to be logged. `skip` will cause the problematic event to be skipped.
poll.interval.ms	`1000`	Positive integer value that specifies the number of milliseconds the connector should wait during each iteration for new change events to appear. Defaults to 1000 milliseconds, or 1 second.
max.queue.size	`8192`	Positive integer value that specifies the maximum size of the blocking queue into which change events read from the database log are placed before they are written to Kafka. This queue can provide backpressure to the CDC table reader when, for example, writes to Kafka are slower or if Kafka is not available. Events that appear in the queue are not included in the offsets periodically recorded by this connector. Defaults to 8192, and should always be larger than the maximum batch size specified in the `max.batch.size` property.
max.batch.size	`2048`	Positive integer value that specifies the maximum size of each batch of events that should be processed during each iteration of this connector. Defaults to 2048.
heartbeat.interval.ms	`0`	Controls how frequently heartbeat messages are sent. This property contains an interval in milliseconds that defines how frequently the connector sends messages to a heartbeat topic. The property can be used to confirm whether the connector is still receiving change events from the database. You also should leverage heartbeat messages in cases where only records in non-captured tables are changed for a longer period of time. In such situation the connector would proceed to read the log from the database but never emit any change messages into Kafka, which in turn means that no offset updates are committed to Kafka. This may result in more change events to be re-sent after a connector restart. Set this parameter to `0` to not send heartbeat messages at all. Disabled by default.
heartbeat.topics.prefix	`__debezium-heartbeat`	Controls the naming of the topic to which heartbeat messages are sent. The topic is named according to the pattern `<heartbeat.topics.prefix>.<server.name>`.
snapshot.delay.ms		An interval in milli-seconds that the connector should wait before taking a snapshot after starting up; Can be used to avoid snapshot interruptions when starting multiple connectors in a cluster, which may cause re-balancing of connectors.
snapshot.fetch.size	`2000`	Specifies the maximum number of rows that should be read in one go from each table while taking a snapshot. The connector will read the table contents in multiple batches of this size. Defaults to 2000.
query.fetch.size		Specifies the number of rows that will be fetched for each database round-trip of a given query. Defaults to the JDBC driver’s default fetch size.
snapshot.lock.timeout.ms	`10000`	An integer value that specifies the maximum amount of time (in milliseconds) to wait to obtain table locks when performing a snapshot. If table locks cannot be acquired in this time interval, the snapshot will fail (also see snapshots). When set to `0` the connector will fail immediately when it cannot obtain the lock. Value `-1` indicates infinite waiting.
snapshot.select.statement.overrides		Controls which rows from tables are included in snapshot. This property contains a comma-separated list of fully-qualified tables (SCHEMA_NAME.TABLE_NAME). Select statements for the individual tables are specified in further configuration properties, one for each table, identified by the id `snapshot.select.statement.overrides.[SCHEMA_NAME].[TABLE_NAME]`. The value of those properties is the SELECT statement to use when retrieving data from the specific table during snapshotting. A possible use case for large append-only tables is setting a specific point where to start (resume) snapshotting, in case a previous snapshotting was interrupted. Note: This setting has impact on snapshots only. Events captured during log reading are not affected by it.
source.struct.version	v2	Schema version for the `source` block in CDC events; Debezium 0.10 introduced a few breaking changes to the structure of the `source` block in order to unify the exposed structure across all the connectors. By setting this option to `v1` the structure used in earlier versions can be produced. Note that this setting is not recommended and is planned for removal in a future Debezium version.
sanitize.field.names	`true` when connector configuration explicitly specifies the `key.converter` or `value.converter` parameters to use Avro, otherwise defaults to `false`.	Whether field names are sanitized to adhere to Avro naming requirements. See Avro naming for more details.
database.server.timezone		Timezone of the server. This property defines the timezone of the transaction timestamp (`ts_ms`) that is retrieved from the server (which is actually not zoned). By default, the value is unset. Set a value for the property only when running on SQL Server 2014 or older, and the database server and the JVM running the Debezium connector use different timezones. When unset, default behavior is to use the timezone of the VM running the Debezium connector. In this case, when running on on SQL Server 2014 or older and using different timezones on server and the connector, incorrect ts_ms values may be produced. Possible values include “Z”, “UTC”, offset values like “+02:00”, short zone ids like “CET”, and long zone ids like “Europe/Paris”.
provide.transaction.metadata	`false`	When set to `true` Debezium generates events with transaction boundaries and enriches data events envelope with transaction metadata. See Transaction Metadata for additional details.
retriable.restart.connector.wait.ms	10000 (10 seconds)	The number of milli-seconds to wait before restarting a connector after a retriable error occurs.

The connector also supports pass-through configuration properties that are used when creating the Kafka producer and consumer. Specifically, all connector configuration properties that begin with the database.history.producer. prefix are used (without the prefix) when creating the Kafka producer that writes to the database history, and all those that begin with the prefix database.history.consumer. are used (without the prefix) when creating the Kafka consumer that reads the database history upon connector startup.

For example, the following connector configuration properties can be used to secure connections to the Kafka broker:

In addition to the pass-through to the Kafka producer and consumer, the properties starting with database., e.g. database.applicationName=debezium are passed to the JDBC URL.

database.history.producer.security.protocol=SSL
database.history.producer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks
database.history.producer.ssl.keystore.password=test1234
database.history.producer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks
database.history.producer.ssl.truststore.password=test1234
database.history.producer.ssl.key.password=test1234
database.history.consumer.security.protocol=SSL
database.history.consumer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks
database.history.consumer.ssl.keystore.password=test1234
database.history.consumer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks
database.history.consumer.ssl.truststore.password=test1234
database.history.consumer.ssl.key.password=test1234

Be sure to consult the Kafka documentation for all of the configuration properties for Kafka producers and consumers. (The SQL Server connector does use the new consumer.)

Database schema evolution

When change data capture is enabled for a SQL Server table, as changes occur in the table, event records are persisted to a capture table on the server. If you introduce a change in the structure of the source table change, for example, by adding a new column, that change is not dynamically reflected in the change table. For as long as the capture table continues to use the outdated schema, the Debezium connector is unable to emit data change events for the table correctly. You must intervene to refresh the capture table to enable the connector to resume processing change events.

Because of the way that CDC is implemented in SQL Server, you cannot use Debezium to update capture tables. To refresh capture tables, one must be a SQL Server database operator with elevated privileges. As a Debezium user, you must coordinate tasks with the SQL Server database operator to complete the schema refresh and restore streaming to Kafka topics.

You can use one of the following methods to update capture tables after a schema change:

Offline schema updates. In offline schema updates, capture tables are updated after you stop the Debezium connector.
Online schema updates. In online schema updates, capture tables are updated while the Debezium connector is running.

There are advantages and disadvantages to using each type of procedure.

Whether you use the online or offline update method, you must complete the entire schema update process before you apply subsequent schema updates on the same source table. The best practice is to execute all DDLs in a single batch so the procedure can be run only once.

Some schema changes are not supported on source tables that have CDC enabled. For example, if CDC is enabled on a table, SQL Server does not allow you to change the schema of the table if you renamed one of its columns or changed the column type.

After you change a column in a source table from NULL to NOT NULL or vice versa, the SQL Server connector cannot correctly capture the changed information until after you create a new capture instance. If you do not create a new capture table after a change to the column designation, change event records that the connector emits do not correctly indicate whether the column is optional. That is, columns that were previously defined as optional (or NULL) continue to be, despite now being defined as NOT NULL. Similarly, columns that had been defined as required (NOT NULL), retain that designation, although they are now defined as NULL.

Offline schema updates

Offline schema updates provide the safest method for updating capture tables. However, offline updates might not be feasible for use with applications that require high-availability.

Prerequisites

An update was committed to the schema of a SQL Server table that has CDC enabled.
You are a SQL Server database operator with elevated privileges.

Procedure

Suspend the application that updates the database.
Wait for the Debezium connector to stream all unstreamed change event records.
Stop the Debezium connector.
Apply all changes to the source table schema.
Create a new capture table for the update source table using sys.sp_cdc_enable_table procedure with a unique value for parameter @capture_instance.
Resume the application that you suspended in Step 1.
Start the Debezium connector.
After the Debezium connector starts streaming from the new capture table, drop the old capture table by running the stored procedure sys.sp_cdc_disable_table with the parameter @capture_instance set to the old capture instance name.

Online schema updates

The procedure for completing an online schema updates is simpler than the procedure for running an offline schema update, and you can complete it without requiring any downtime in application and data processing. However, with online schema updates, a potential processing gap can occur after you update the schema in the source database, but before you create the new capture instance. During that interval, change events continue to be captured by the old instance of the change table, Q and the change data that is saved to the old table retains the structure of the earlier schema. So, for example, if you added a new column to a source table, change events that are produced before the new capture table is ready, do not contain a field for the new column. If your application does not tolerate such a transition period, it is best to use the offline schema update procedure.

Prerequisites

An update was committed to the schema of a SQL Server table that has CDC enabled.
You are a SQL Server database operator with elevated privileges.

Procedure

Apply all changes to the source table schema.
Create a new capture table for the update source table by running the sys.sp_cdc_enable_table stored procedure with a unique value for the parameter @capture_instance.
When Debezium starts streaming from the new capture table, you can drop the old capture table by running the sys.sp_cdc_disable_table stored procedure with the parameter @capture_instance set to the old capture instance name.

Example: Running an online schema update after a database schema change

Let’s deploy the SQL Server based Debezium tutorial to demonstrate the online schema update.

In the following example, a column phone_number is added to the customers table.

Type the following command to start the database shell:

docker-compose -f docker-compose-sqlserver.yaml exec sqlserver bash -c '/opt/mssql-tools/bin/sqlcmd -U sa -P $SA_PASSWORD -d testDB'

Modify the schema of the customers source table by running the following query to add the phone_number field:
```
ALTER TABLE customers ADD phone_number VARCHAR(32);
```

Create the new capture instance by running the sys.sp_cdc_enable_table stored procedure.

EXEC sys.sp_cdc_enable_table @source_schema = 'dbo', @source_name = 'customers', @role_name = NULL, @supports_net_changes = 0, @capture_instance = 'dbo_customers_v2';
GO

Insert new data into the customers table by running the following query:

INSERT INTO customers(first_name,last_name,email,phone_number) VALUES ('John','Doe','john.doe@example.com', '+1-555-123456');
GO

The Kafka Connect log reports on configuration updates through entries similar to the following message:

connect_1    | 2019-01-17 10:11:14,924 INFO   ||  Multiple capture instances present for the same table: Capture instance "dbo_customers" [sourceTableId=testDB.dbo.customers, changeTableId=testDB.cdc.dbo_customers_CT, startLsn=00000024:00000d98:0036, changeTableObjectId=1525580473, stopLsn=00000025:00000ef8:0048] and Capture instance "dbo_customers_v2" [sourceTableId=testDB.dbo.customers, changeTableId=testDB.cdc.dbo_customers_v2_CT, startLsn=00000025:00000ef8:0048, changeTableObjectId=1749581271, stopLsn=NULL]   [io.debezium.connector.sqlserver.SqlServerStreamingChangeEventSource]
connect_1    | 2019-01-17 10:11:14,924 INFO   ||  Schema will be changed for ChangeTable [captureInstance=dbo_customers_v2, sourceTableId=testDB.dbo.customers, changeTableId=testDB.cdc.dbo_customers_v2_CT, startLsn=00000025:00000ef8:0048, changeTableObjectId=1749581271, stopLsn=NULL]   [io.debezium.connector.sqlserver.SqlServerStreamingChangeEventSource]
...
connect_1    | 2019-01-17 10:11:33,719 INFO   ||  Migrating schema to ChangeTable [captureInstance=dbo_customers_v2, sourceTableId=testDB.dbo.customers, changeTableId=testDB.cdc.dbo_customers_v2_CT, startLsn=00000025:00000ef8:0048, changeTableObjectId=1749581271, stopLsn=NULL]   [io.debezium.connector.sqlserver.SqlServerStreamingChangeEventSource]

Eventually, the phone_number field is added to the schema and its value appears in messages written to the Kafka topic.

...
     {
        "type": "string",
        "optional": true,
        "field": "phone_number"
     }
...
    "after": {
      "id": 1005,
      "first_name": "John",
      "last_name": "Doe",
      "email": "john.doe@example.com",
      "phone_number": "+1-555-123456"
    },

Drop the old capture instance by running the sys.sp_cdc_disable_table stored procedure.

EXEC sys.sp_cdc_disable_table @source_schema = 'dbo', @source_name = 'dbo_customers', @capture_instance = 'dbo_customers';
GO

Monitoring

The Debezium SQL Server connector provides three types of metrics that are in addition to the built-in support for JMX metrics that Zookeeper, Kafka, and Kafka Connect provide. The connector provides the following metrics:

snapshot metrics; for monitoring the connector when performing snapshots.
streaming metrics; for monitoring the connector when reading CDC table data.
schema history metrics; for monitoring the status of the connector’s schema history.

For information about how to expose the preceding metrics through JMX, see the Debezium monitoring documentation.

Snapshot metrics

The MBean is debezium.sql_server:type=connector-metrics,context=snapshot,server=*<database.server.name>*.

Attributes	Type	Description
LastEvent	`string`	The last snapshot event that the connector has read.
MilliSecondsSinceLastEvent	`long`	The number of milliseconds since the connector has read and processed the most recent event.
TotalNumberOfEventsSeen	`long`	The total number of events that this connector has seen since last started or reset.
NumberOfEventsFiltered	`long`	The number of events that have been filtered by include/exclude list filtering rules configured on the connector.
MonitoredTables	`string[]`	The list of tables that are monitored by the connector.
QueueTotalCapacity	`int`	The length the queue used to pass events between the snapshotter and the main Kafka Connect loop.
QueueRemainingCapacity	`int`	The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop.
TotalTableCount	`int`	The total number of tables that are being included in the snapshot.
RemainingTableCount	`int`	The number of tables that the snapshot has yet to copy.
SnapshotRunning	`boolean`	Whether the snapshot was started.
SnapshotAborted	`boolean`	Whether the snapshot was aborted.
SnapshotCompleted	`boolean`	Whether the snapshot completed.
SnapshotDurationInSeconds	`long`	The total number of seconds that the snapshot has taken so far, even if not complete.
RowsScanned	`Map<String, Long>`	Map containing the number of rows scanned for each table in the snapshot. Tables are incrementally added to the Map during processing. Updates every 10,000 rows scanned and upon completing a table.
MaxQueueSizeInBytes	`long`	The maximum buffer of the queue in bytes. It will be enabled if `max.queue.size.in.bytes` is passed with a positive long value.
CurrentQueueSizeInBytes	`long`	The current data of records in the queue in bytes.

Streaming metrics

The MBean is debezium.sql_server:type=connector-metrics,context=streaming,server=*<database.server.name>*.

Attributes	Type	Description
LastEvent	`string`	The last streaming event that the connector has read.
MilliSecondsSinceLastEvent	`long`	The number of milliseconds since the connector has read and processed the most recent event.
TotalNumberOfEventsSeen	`long`	The total number of events that this connector has seen since last started or reset.
NumberOfEventsFiltered	`long`	The number of events that have been filtered by include/exclude list filtering rules configured on the connector.
MonitoredTables	`string[]`	The list of tables that are monitored by the connector.
QueueTotalCapacity	`int`	The length the queue used to pass events between the streamer and the main Kafka Connect loop.
QueueRemainingCapacity	`int`	The free capacity of the queue used to pass events between the streamer and the main Kafka Connect loop.
Connected	`boolean`	Flag that denotes whether the connector is currently connected to the database server.
MilliSecondsBehindSource	`long`	The number of milliseconds between the last change event’s timestamp and the connector processing it. The values will incoporate any differences between the clocks on the machines where the database server and the connector are running.
NumberOfCommittedTransactions	`long`	The number of processed transactions that were committed.
SourceEventPosition	`Map<String, String>`	The coordinates of the last received event.
LastTransactionId	`string`	Transaction identifier of the last processed transaction.
MaxQueueSizeInBytes	`long`	The maximum buffer of the queue in bytes.
CurrentQueueSizeInBytes	`long`	The current data of records in the queue in bytes.

Schema history metrics

The MBean is debezium.sql_server:type=connector-metrics,context=schema-history,server=*<database.server.name>*.

Attributes	Type	Description
Status	`string`	One of `STOPPED`, `RECOVERING` (recovering history from the storage), `RUNNING` describing the state of the database history.
RecoveryStartTime	`long`	The time in epoch seconds at what recovery has started.
ChangesRecovered	`long`	The number of changes that were read during recovery phase.
ChangesApplied	`long`	the total number of schema changes applied during recovery and runtime.
MilliSecondsSinceLastRecoveredChange	`long`	The number of milliseconds that elapsed since the last change was recovered from the history store.
MilliSecondsSinceLastAppliedChange	`long`	The number of milliseconds that elapsed since the last change was applied.
LastRecoveredChange	`string`	The string representation of the last change recovered from the history store.
LastAppliedChange	`string`	The string representation of the last applied change.