tet123/documentation/modules/ROOT/pages/connectors/sqlserver.adoc

// Category: debezium-using
// Type: assembly
// ModuleID: debezium-connector-for-sql-server
[id="debezium-connector-for-sql-server"]
= {prodname} connector for SQL Server

:context: sqlserver
:mbean-name: sql_server
ifdef::community[]

:toc:
:toc-placement: macro
:linkattrs:
:icons: font
:source-highlighter: highlight.js

[NOTE]
====
Want to help us further hone and improve it? link:/docs/contribute/[Learn how].
====

toc::[]
endif::community[]

The {prodname} SQL Server connector captures row-level changes that occur in the schemas of a SQL Server database.

ifdef::product[]

For details about the {prodname} SQL Server connector and its use, see following topics:

* xref:overview-of-debezium-sql-server-connector[]
* xref:how-debezium-sql-server-connectors-work[]
* xref:descriptions-of-debezium-sql-server-connector-data-change-events[]
* xref:how-debezium-sql-server-connectors-map-data-types[]
* xref:setting-up-sql-server-for-use-with-the-debezium-sql-server-connector[]
* xref:deployment-of-debezium-sql-server-connectors[]
* xref:refreshing-capture-tables-after-a-schema-change[]
* xref:monitoring-debezium-sql-server-connector-performance[]

endif::product[]

The first time that the {prodname} SQL Server connector connects to a SQL Server database or cluster, it takes a consistent snapshot of the schemas in the database.
After the initial snapshot is complete, the connector continuously captures row-level changes for `INSERT`, `UPDATE`, or `DELETE` operations that are committed to the SQL Server databases that are enabled for CDC.
The connector produces events for each data change operation, and streams them to Kafka topics.
The connector streams all of the events for a table to a dedicated Kafka topic.
Applications and services can then consume data change event records from that topic.


// Type: concept
// Title: Overview of {prodname} SQL Server connector
// ModuleID: overview-of-debezium-sql-server-connector
[[sqlserver-overview]]
== Overview

The {prodname} SQL Server connector is based on the https://docs.microsoft.com/en-us/sql/relational-databases/track-changes/about-change-data-capture-sql-server?view=sql-server-2017[change data capture]
feature that is available in https://blogs.msdn.microsoft.com/sqlreleaseservices/sql-server-2016-service-pack-1-sp1-released/[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 {prodname} 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 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 {prodname} 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.

NOTE: 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.

// Type: assembly
// ModuleID: how-debezium-sql-server-connectors-work
// Title: How {prodname} SQL Server connectors work
[[how-the-sqlserver-connector-works]]
== How the SQL Server connector works

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

ifdef::product[]

For details about how the connector works, see the following sections:

* xref:how-debezium-sql-server-connectors-perform-database-snapshots[]
* xref:how-the-debezium-sql-server-connector-reads-change-data-tables[]
* xref:default-names-of-kafka-topics-that-receive-debezium-sql-server-change-event-records[]
* xref:how-the-debezium-sql-server-connector-uses-the-schema-change-topic[]
* xref:descriptions-of-debezium-sql-server-connector-data-change-events[]
* xref:debezium-sql-server-connector-generated-events-that-represent-transaction-boundaries[]

endif::product[]

// Type: concept
// Title: How {prodname} SQL Server connectors perform database snapshots
// ModuleID: how-debezium-sql-server-connectors-perform-database-snapshots
[[sqlserver-snapshots]]
=== Snapshots

SQL Server CDC is not designed to store a complete history of database changes.
For the {prodname} 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:

1. Determines the tables to be captured.
2. 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.
3. Reads the maximum log sequence number (LSN) position in the server's transaction log.
4. Captures the structure of all relevant tables.
5. Releases the locks obtained in Step 2, if necessary. In most cases, locks are held for only a short period of time.
6. 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.
7. 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.

include::{partialsdir}/modules/all-connectors/ref-connector-incremental-snapshot.adoc[leveloffset=+3]

// Type: concept
// ModuleID: how-the-debezium-sql-server-connector-reads-change-data-tables
// Title: How {prodname} SQL Server connectors read change data tables
=== 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.

1. 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.
2. 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 {prodname} in the same order in which they occurred in the database.
3. The connector passes the commit and change LSNs as offsets to Kafka Connect.
4. 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.

// Type: concept
// ModuleID: default-names-of-kafka-topics-that-receive-debezium-sql-server-change-event-records
// Title: Default names of Kafka topics that receive {prodname} SQL Server change event records
[[sqlserver-topic-names]]
=== 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 {link-prefix}:{link-topic-routing}#topic-routing[Topic routing].


// Type: concept
// ModuleID: how-the-debezium-sql-server-connector-uses-the-schema-change-topic
// Title: How the {prodname} SQL Server connector uses the schema change topic
=== Schema change topic

For each table for which CDC is enabled, the {prodname} 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.

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

{prodname} 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 {link-prefix}:{link-sqlserver-connector}#sqlserver-schema-evolution[schema evolution procedure].

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

[source,json,indent=0,subs="+attributes"]
----
{
  "schema": {
  ...
  },
  "payload": {
    "source": {
      "version": "{debezium-version}",
      "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
            }
          ]
        }
      }
    ]
  }
}
----

.Descriptions of fields in messages emitted to the schema change topic
[cols="1,4,5",options="header"]
|===
|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`
a|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:

[source,json,indent=0,subs="+attributes"]
----
{
  "schema": {
    "type": "struct",
    "fields": [
      {
        "type": "string",
        "optional": false,
        "field": "databaseName"
      }
    ],
    "optional": false,
    "name": "io.debezium.connector.sqlserver.SchemaChangeKey"
  },
  "payload": {
    "databaseName": "testDB"
  }
}
----

// Type: assembly
// ModuleID: descriptions-of-debezium-sql-server-connector-data-change-events
// Title: Descriptions of {prodname} SQL Server connector data change events
=== Data change events

The {prodname} 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.

{prodname} 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:

[source,json,index=0]
----
{
 "schema": { // <1>
   ...
  },
 "payload": { // <2>
   ...
 },
 "schema": { // <3>
   ...
 },
 "payload": { // <4>
   ...
 },
}
----

.Overview of change event basic content
[cols="1,2,7",options="header"]
|===
|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 {link-prefix}:{link-sqlserver-connector}#sqlserver-property-message-key-columns[`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 {link-prefix}:{link-sqlserver-connector}#sqlserver-topic-names[topic names].

[WARNING]
====
The SQL Server connector ensures that all Kafka Connect schema names adhere to the link:http://avro.apache.org/docs/current/spec.html#names[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.
====

ifdef::product[]

For details about change events, see the following topics:

* xref:about-keys-in-debezium-sql-server-change-events[]
* xref:about-values-in-debezium-sql-server-change-events[]

endif::product[]

// Type: concept
// ModuleID: about-keys-in-debezium-sql-server-change-events
// Title: About keys in {prodname} SQL Server change events
[[sqlserver-change-event-keys]]
==== 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
[source,sql,indent=0]
----
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:

[source,json,indent=0]
----
{
    "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
    }
}
----

.Description of change event key
[cols="1,2,7",options="header"]
|===
|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`
a|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`.

|===

ifdef::community[]
[NOTE]
====
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.
====

[WARNING]
====
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.
====
endif::community[]


// Type: concept
// ModuleID: about-values-in-debezium-sql-server-change-events
// Title: About values in {prodname} SQL Server change events
[[sqlserver-change-event-values]]
==== 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:

[source,sql,indent=0]
----
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.

ifdef::product[]

* <<sqlserver-create-events,_create_ events>>
* <<sqlserver-update-events,_update_ events>>
* <<sqlserver-delete-events,_delete_ events>>

endif::product[]

[[sqlserver-create-events]]
===== _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:

[source,json,indent=0,subs="+attributes"]
----
{
  "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": "{debezium-version}",
      "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>
  }
}
----


.Descriptions of _create_ event value fields
[cols="1,2,7",options="header"]
|===
|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`
a|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 {link-prefix}:{link-avro-serialization}[Avro converter], the resulting Avro schema for each table in each logical source has its own evolution and history.

|3
|`name`
a|`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`
a|`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 {link-prefix}:{link-avro-serialization}[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`
a|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:

* {prodname} 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`
a|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`
a| 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 {prodname}.

|===

[[sqlserver-update-events]]
===== _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:

[source,json,indent=0,subs="+attributes"]
----
{
  "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": "{debezium-version}",
      "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>
  }
}
----


.Descriptions of _update_ event value fields
[cols="1,2,7",options="header"]
|===
|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`
a|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:

* {prodname} 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 (link:https://docs.microsoft.com/en-us/sql/relational-databases/system-tables/cdc-capture-instance-ct-transact-sql?view=sql-server-2017[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`
a|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`
a| 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 {prodname}.

|===

[NOTE]
====
Updating the columns for a row's primary/unique key changes the value of the row's key. When a key changes, {prodname} outputs _three_ events: a _delete_ event and a {link-prefix}:{link-sqlserver-connector}#sqlserver-tombstone-events[tombstone event] with the old key for the row, followed by a _create_ event with the new key for the row.
====

[[sqlserver-delete-events]]
===== _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:

[source,json,indent=0,subs="+attributes"]
----
{
  "schema": { ... },
  },
  "payload": {
    "before": { <>
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "noreply@example.org"
    },
    "after": null, <2>
    "source": { <3>
      "version": "{debezium-version}",
      "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>
  }
}
----

.Descriptions of _delete_ event value fields
[cols="1,2,7",options="header"]
|===
|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`
a|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:

* {prodname} 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`
a|Mandatory string that describes the type of operation. The `op` field value is `d`, signifying that this row was deleted.

|5
|`ts_ms`
a| 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 {prodname}.

|===

SQL Server connector events are designed to work with link:{link-kafka-docs}/#compaction[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.

[[sqlserver-tombstone-events]]
.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 {prodname}’s SQL Server connector emits a _delete_ event, the connector emits a special tombstone event that has the same key but a `null` value.

// Type: assembly
// ModuleID: debezium-sql-server-connector-generated-events-that-represent-transaction-boundaries
// Title: {prodname} SQL Server connector-generated events that represent transaction boundaries
[[sqlserver-transaction-metadata]]
=== Transaction metadata

{prodname} can generate events that represent transaction boundaries and that enrich data change event messages.

[NOTE]
.Limits on when {prodname} receives transaction metadata
====
{prodname} registers and receives metadata only for transactions that occur after you deploy the connector.
Metadata for transactions that occur before you deploy the connector is not available.
====

Database transactions are represented by a statement block that is enclosed between the `BEGIN` and `END` keywords.
{prodname} 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.

[WARNING]
====
There is no way for {prodname} to reliably identify when a transaction has ended.
The transaction `END` marker is thus emitted only after the first event of another transaction arrives.
This can lead to the delayed delivery of `END` marker in case of a low-traffic system.
====

The following example shows a typical transaction boundary message:

.Example: SQL Server connector transaction boundary event
[source,json,indent=0,subs="+attributes"]
----
{
  "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`.

//Type: concept
//ModuleID: change-data-event-enrichment
==== 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:

[source,json,indent=0,subs="+attributes"]
----
{
  "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"
  }
}
----

// Type: reference
// ModuleID: how-debezium-sql-server-connectors-map-data-types
// Title: How {prodname} SQL Server connectors map data types
[[sqlserver-data-types]]
=== Data type mappings

The {prodname} 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.

ifdef::product[]

For more information about data type mappings, see the following sections:

* xref:sql-server-basic-values[]
* xref:sql-server-temporal-values[]
* xref:sql-server-decimal-values[]
* xref:sql-server-timestamp-values[]

endif::product[]

[id="sql-server-basic-values"]
==== Basic types

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

.Data type mappings used by the SQL Server connector
[cols="30%a,25%a,45%a",options="header"]
|===
|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.
ifdef::community[]
Passing the default value helps though with satisfying the compatibility rules when {link-prefix}:{link-avro-serialization}[using Avro] as serialization format together with the Confluent schema registry.
endif::community[]

[[sql-server-temporal-values]]
==== 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:

[cols="30%a,25%a,45%a",options="header"]
|===
|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:

[cols="25%a,20%a,55%a",options="header"]
|===
|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.

|===

[[sql-server-timestamp-values]]
===== 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 {prodname} does not affect this conversion.

[id="sql-server-decimal-values"]
==== Decimal values

{prodname} connectors handle decimals according to the setting of the {link-prefix}:{link-sqlserver-connector}#sqlserver-property-decimal-handling-mode[`decimal.handling.mode` connector configuration property].

decimal.handling.mode=precise::
+
.Mappings when `decimal.handing.mode=precise`
[cols="30%a,15%a,55%a",options="header",subs="+attributes"]
|===
|SQL Server type
|Literal type (schema type)
|Semantic type (schema name)

|`NUMERIC[(P[,S])]`
|`BYTES`
a|`org.apache.kafka.connect.data.Decimal` +
The `scale` schema parameter contains an integer that represents how many digits the decimal point shifted.

|`DECIMAL[(P[,S])]`
|`BYTES`
a|`org.apache.kafka.connect.data.Decimal` +
The `scale` schema parameter contains an integer that represents how many digits the decimal point shifted.

|`SMALLMONEY`
|`BYTES`
a|`org.apache.kafka.connect.data.Decimal` +
The `scale` schema parameter contains an integer that represents how many digits the decimal point shifted.

|`MONEY`
|`BYTES`
a|`org.apache.kafka.connect.data.Decimal` +
The `scale` schema parameter contains an integer that represents how many digits the decimal point shifted.

|===

decimal.handling.mode=double::
+
.Mappings when `decimal.handing.mode=double`
[cols="30%a,30%a,40%a",options="header",subs="+attributes"]
|===
|SQL Server type |Literal type |Semantic type

|`NUMERIC[(M[,D])]`
|`FLOAT64`
a|_n/a_

|`DECIMAL[(M[,D])]`
|`FLOAT64`
a|_n/a_

|`SMALLMONEY[(M[,D])]`
|`FLOAT64`
a|_n/a_

|`MONEY[(M[,D])]`
|`FLOAT64`
a|_n/a_

|===

decimal.handling.mode=string::
+
.Mappings when `decimal.handing.mode=string`
[cols="30%a,30%a,40%a",options="header",subs="+attributes"]
|===
|SQL Server type |Literal type |Semantic type

|`NUMERIC[(M[,D])]`
|`STRING`
a|_n/a_

|`DECIMAL[(M[,D])]`
|`STRING`
a|_n/a_

|`SMALLMONEY[(M[,D])]`
|`STRING`
a|_n/a_

|`MONEY[(M[,D])]`
|`STRING`
a|_n/a_

|===

// Type: assembly
// ModuleID: setting-up-sql-server-for-use-with-the-debezium-sql-server-connector
//Title: Setting up SQL Server to run a {prodname} connector
[[setting-up-sqlserver]]
== Setting up SQL Server

For {prodname} 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.

ifdef::product[]

For details about setting up SQL Server for use with the {prodname} connector, see the following sections:

* xref:enabling-cdc-on-the-sql-server-database[]
* xref:enabling-cdc-on-a-sql-server-table[]
* xref:verifying-debezium-connector-access-to-the-cdc-table[]
* xref:debezium-sql-server-connector-on-azure[]
* xref:effect-of-sql-server-capture-job-agent-configuration-on-server-load-and-latency[]
* xref:sql-server-capture-job-agent-configuration-parameters[]

endif::product[]

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 {prodname} connector can then capture these events and emit them to Kafka topics.

// Type: procedure
// ModuleID: enabling-cdc-on-the-sql-server-database
=== 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.

NOTE: 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
[source,sql]
----
USE MyDB
GO
EXEC sys.sp_cdc_enable_db
GO
----

// Type: procedure
// ModuleID: enabling-cdc-on-a-sql-server-table
=== 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
[source,sql]
----
USE MyDB
GO

EXEC sys.sp_cdc_enable_table
@source_schema = N'dbo',
@source_name   = N'MyTable', //<.>
@role_name     = N'MyRole',  //<.>
@filegroup_name = N'MyDB_CT',//<.>
@supports_net_changes = 0
GO
----
<.> Specifies the name of the table that you want to capture.
<.> 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.
<.> 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.


// Type: procedure
// ModuleID: verifying-debezium-connector-access-to-the-cdc-table
=== 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
[source, sql]
----
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.

// Type: concept
// ModuleID: debezium-sql-server-connector-on-azure
[[sqlserver-on-azure]]
=== SQL Server on Azure

The {prodname} SQL Server connector has not been tested with SQL Server on Azure.

ifdef::community[]

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

endif::community[]

ifdef::community[]

[[sqlserver-always-on-replica]]
=== 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 {prodname} 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.

endif::community[]

// Type: concept
// ModuleID: effect-of-sql-server-capture-job-agent-configuration-on-server-load-and-latency
=== 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 {prodname} 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.

// Type: reference
// ModuleID: sql-server-capture-job-agent-configuration-parameters
=== 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 link:https://docs.microsoft.com/en-us/sql/relational-databases/system-tables/dbo-cdc-jobs-transact-sql?view=latest[`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 link:https://docs.microsoft.com/en-us/sql/relational-databases/system-stored-procedures/sys-sp-cdc-change-job-transact-sql?view=latest[`sys.sp_cdc_change_job`] stored procedure and supplying new values.

[NOTE]
====
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 {prodname} 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.

// Type: assembly
// ModuleID: deployment-of-debezium-sql-server-connectors
// Title: Deployment of {prodname} SQL Server connectors
[[sqlserver-deploying-a-connector]]
== Deployment

ifdef::community[]
To deploy a {prodname} SQL Server connector, you install the {prodname} SQL Server connector archive, configure the connector, and start the connector by adding its configuration to Kafka Connect.

.Prerequisites
* link:https://zookeeper.apache.org/[Apache ZooKeeper], link:http://kafka.apache.org/[Apache Kafka], and link:{link-kafka-docs}.html#connect[Kafka Connect] are installed.
* SQL Server is installed, is {link-prefix}:{link-sqlserver-connector}#setting-up-sqlserver[configured for CDC], and is ready to be used with the {prodname} connector].

.Procedure
. Download the {prodname} https://repo1.maven.org/maven2/io/debezium/debezium-connector-sqlserver/{debezium-version}/debezium-connector-sqlserver-{debezium-version}-plugin.tar.gz[SQL Server connector plug-in archive]
. Extract the files into your Kafka Connect environment.
. Add the directory with the JAR files to {link-kafka-docs}/#connectconfigs[Kafka Connect's `plugin.path`].
. {link-prefix}:{link-sqlserver-connector}#sqlserver-example-configuration[Configure the connector] and {link-prefix}:{link-sqlserver-connector}#sqlserver-adding-connector-configuration[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 link:https://hub.docker.com/r/debezium/[{prodname}'s container images] for Åpache ZooKeeper, Apache Kafka, and Kafka Connect.
You can pull the official link:https://hub.docker.com/_/microsoft-mssql-server[container images for Microsoft SQL Server on Linux] from Docker Hub.

You can also xref:operations/openshift.adoc[run {prodname} on Kubernetes and OpenShift].
endif::community[]

ifdef::product[]
To deploy a {prodname} SQL Server connector, you add the connector files to Kafka Connect, create a custom container to run the connector, and then add connector configuration to your container.
For details about deploying the {prodname} SQL Server connector, see the following topics:

* xref:deploying-debezium-sql-server-connectors[]
* xref:descriptions-of-debezium-sql-server-connector-configuration-properties[]

// Type: procedure
// ModuleID: deploying-debezium-sql-server-connectors
[[sql-server-deploying-a-connector]]
=== Deploying {prodname} SQL Server connectors

To deploy a {prodname} SQL Server connector, you must build a custom Kafka Connect container image that contains the {prodname} connector archive, and then push this container image to a container registry.
You then need to create the following custom resources (CRs):

* A `KafkaConnect` CR that defines your Kafka Connect instance.
  The `image` property in the CR specifies the name of the container image that you create to run your {prodname} connector.
  You apply this CR to the OpenShift instance where link:https://access.redhat.com/products/red-hat-amq#streams[Red Hat {StreamsName}] is deployed.
  {StreamsName} offers operators and images that bring Apache Kafka to OpenShift.

* A `KafkaConnector` CR that defines your {prodname} SQL Server connector.
  Apply this CR to the same OpenShift instance where you apply the `KafkaConnect` CR.

.Prerequisites

* SQL Server is running and you completed the steps to {LinkDebeziumUserGuide}#setting-up-sql-server-for-use-with-the-debezium-sql-server-connector[set up SQL Server to work with a {prodname} connector].

* {StreamsName} is deployed on OpenShift and is running Apache Kafka and Kafka Connect.
  For more information, see link:{LinkDeployStreamsOpenShift}[{NameDeployStreamsOpenShift}]

* Podman or Docker is installed.

* You have an account and permissions to create and manage containers in the container registry (such as `quay.io` or `docker.io`) to which you plan to add the container that will run your Debezium connector.

.Procedure

. Create the {prodname} SQL Server container for Kafka Connect:
.. Download the {prodname} link:https://access.redhat.com/jbossnetwork/restricted/listSoftware.html?product=red.hat.integration&downloadType=distributions[SQL Server connector archive].

.. Extract the {prodname} SQL Server connector archive to create a directory structure for the connector plug-in, for example:
+
[subs="+macros"]
----
./my-plugins/
├── debezium-connector-sqlserver
│   ├── ...
----

.. Create a Docker file that uses `{DockerKafkaConnect}` as the base image.
For example, from a terminal window, enter the following, replacing `my-plugins` with the name of your plug-ins directory:
+
[source,shell,subs="+attributes,+quotes"]
----
cat <<EOF >debezium-container-for-sqlserver.yaml // <1>
FROM {DockerKafkaConnect}
USER root:root
COPY ./_<my-plugins>_/ /opt/kafka/plugins/ // <2>
USER 1001
EOF
----
<1> You can specify any file name that you want.
<2> Replace `my-plugins` with the name of your plug-ins directory.
+
The command creates a Docker file with the name `debezium-container-for-sqlserver.yaml` in the current directory.

.. Build the container image from the `debezium-container-for-sqlserver.yaml` Docker file that you created in the previous step.
From the directory that contains the file, open a terminal window and enter one of the following commands:
+
[source,shell,options="nowrap"]
----
podman build -t debezium-container-for-sqlserver:latest .
----
+
[source,shell,options="nowrap"]
----
docker build -t debezium-container-for-sqlserver:latest .
----
The preceding commands build a container image with the name `debezium-container-for-sqlserver`.

.. Push your custom image to a container registry, such as quay.io or an internal container registry.
The container registry must be available to the OpenShift instance where you want to deploy the image.
Enter one of the following commands:
+
[source,shell,subs="+quotes"]
----
podman push _<myregistry.io>_/debezium-container-for-sqlserver:latest
----
+
[source,shell,subs="+quotes"]
----
docker push _<myregistry.io>_/debezium-container-for-sqlserver:latest
----

.. Create a new {prodname} SQL Server KafkaConnect custom resource (CR).
For example, create a KafkaConnect CR with the name `dbz-connect.yaml` that specifies `annotations` and `image` properties as shown in the following example:
+
[source,yaml,subs="+attributes"]
----
apiVersion: {KafkaConnectApiVersion}
kind: KafkaConnect
metadata:
  name: my-connect-cluster
  annotations:
    strimzi.io/use-connector-resources: "true" // <1>
spec:
  #...
  image: debezium-container-for-sqlserver  // <2>
----
<1>  `metadata.annotations` indicates to the Cluster Operator that KafkaConnector resources are used to configure connectors in this Kafka Connect cluster.
<2>  `spec.image` specifies the name of the image that you created to run your Debezium connector.
This property overrides the `STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE` variable in the Cluster Operator

.. Apply the `KafkaConnect` CR to the OpenShift Kafka Connect environment by entering the following command:
+
[source,shell,options="nowrap"]
----
oc create -f dbz-connect.yaml
----
+
The command adds a Kafka Connect instance that specifies the name of the image that you created to run your {prodname} connector.

. Create a `KafkaConnector` custom resource that configures your {prodname} SQL Server connector instance.
+
You configure a {prodname} SQL Server connector in a `.yaml` file that specifies the configuration properties for the connector.
The connector configuration might instruct {prodname} to produce events for a subset of the schemas and tables, or it might set properties so that {prodname} ignores, masks, or truncates values in specified columns that are sensitive, too large, or not needed.
+
The following example configures a {prodname} connector that connects to a SQL server host, `192.168.99.100`, on port `1433`.
This host has a database named `testDB`, a table with the name `customers`, and `fulfillment` is the server's logical name.
+
.SQL Server `fulfillment-connector.yaml`
[source,yaml,subs="+attributes",options="nowrap"]
----
apiVersion: {KafkaConnectorApiVersion}
kind: KafkaConnector
metadata:
  name: fulfillment-connector // <1>
  labels:
    strimzi.io/cluster: my-connect-cluster
  annotations:
    strimzi.io/use-connector-resources: 'true'
spec:
  class: io.debezium.connector.sqlserver.SqlServerConnector // <2>
  config:
    database.hostname: 192.168.99.100 // <3>
    database.port: 1433 // <4>
    database.user: debezium // <5>
    database.password: dbz // <6>
    database.dbname: testDB // <7>
    database.server.name: fullfullment // <8>
    database.include.list: dbo.customers // <9>
    database.history.kafka.bootstrap.servers: my-cluster-kafka-bootstrap:9092 // <10>
    database.history.kafka.topic: dbhistory.fullfillment // <11>
----
+
.Descriptions of connector configuration settings
[cols="1,7",options="header",subs="+attributes"]
|===
|Item |Description

|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 {prodname} 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.

|===

. Create your connector instance with Kafka Connect.
  For example, if you saved your `KafkaConnector` resource in the `fulfillment-connector.yaml` file, you would run the following command:
+
[source,shell,options="nowrap"]
----
oc apply -f fulfillment-connector.yaml
----
+
The preceding command registers `fulfillment-connector` and the connector starts to run against the `testDB` database as defined in the `KafkaConnector` CR.

. Verify that the connector was created and has started:
.. Display the Kafka Connect log output to verify that the connector was created and has started to capture changes in the specified database:
+
[source,shell,options="nowrap"]
----
oc logs $(oc get pods -o name -l strimzi.io/cluster=my-connect-cluster)
----

.. Review the log output to verify that {prodname} performs the initial snapshot.
The log displays output that is similar to the following messages:
+
[source,shell,options="nowrap"]
----
... INFO Starting snapshot for ...
... INFO Snapshot is using user 'debezium' ...
----
+
If the connector starts correctly without errors, it creates a topic for each table whose changes the connector is capturing.
For the example CR, there would be a topic for the table specified in the `include.list` property.
Downstream applications can subscribe to these topics.

.. Verify that the connector created the topics by running the following command:
+
[source,shell,options="nowrap"]
----
oc get kafkatopics
----

endif::product[]

ifdef::community[]
[[sqlserver-example-configuration]]
=== 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 {prodname} 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.

[source,json]
----
{
    "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 {prodname} 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.

endif::community[]

For the complete list of the configuration properties that you can set for the {prodname} SQL Server connector, see {link-prefix}:{link-sqlserver-connector}#sqlserver-connector-properties[SQL Server connector properties].

ifdef::community[]
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.

[[sqlserver-adding-connector-configuration]]
=== Adding connector configuration

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

.Prerequisites

* {link-prefix}:{link-sqlserver-connector}#setting-up-sqlserver[CDC is enabled on SQL Server].
* The {prodname} SQL Server connector is installed.

.Procedure

. Create a configuration for the SQL Server connector.

. Use the link:{link-kafka-docs}/#connect_rest[Kafka Connect REST API] to add that connector configuration to your Kafka Connect cluster.
endif::community[]

.Results

When the connector starts, it {link-prefix}:{link-sqlserver-connector}#sqlserver-snapshots[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.

// Type: reference
// Title: Descriptions of {prodname} SQL Server connector configuration properties
// ModuleID: descriptions-of-debezium-sql-server-connector-configuration-properties
[[sqlserver-connector-properties]]
=== Connector properties

The {prodname} 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.

Information about the properties is organized as follows:

* xref:{context}-required-connector-configuration-properties[Required connector configuration properties]
* xref:{context}-advanced-connector-configuration-properties[Advanced connector configuration properties]
* xref:debezium-{context}-connector-database-history-configuration-properties[Database history connector configuration properties] that control how {prodname} processes events that it reads from the database history topic.
** xref:{context}-pass-through-database-history-properties-for-configuring-producer-and-consumer-clients[Pass-through database history properties]
* xref:debezium-{context}-connector-pass-through-database-driver-configuration-properties[Pass-through database driver properties] that control the behavior of the database driver.

[id="{context}-required-connector-configuration-properties"]
==== Required {prodname} SQL Server connector configuration properties

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

[cols="30%a,25%a,45%a",options="header"]
|===
|Property
|Default
|Description

|[[sqlserver-property-name]]<<sqlserver-property-name, `+name+`>>
|No default
|Unique name for the connector. Attempting to register again with the same name will fail. (This property is required by all Kafka Connect connectors.)

|[[sqlserver-property-connector-class]]<<sqlserver-property-connector-class, `+connector.class+`>>
|No default
|The name of the Java class for the connector. Always use a value of `io.debezium.connector.sqlserver.SqlServerConnector` for the SQL Server connector.

|[[sqlserver-property-tasks-max]]<<sqlserver-property-tasks-max, `+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.

|[[sqlserver-property-database-hostname]]<<sqlserver-property-database-hostname, `+database.hostname+`>>
|No default
|IP address or hostname of the SQL Server database server.

|[[sqlserver-property-database-port]]<<sqlserver-property-database-port, `+database.port+`>>
|`1433`
|Integer port number of the SQL Server database server.

|[[sqlserver-property-database-user]]<<sqlserver-property-database-user, `+database.user+`>>
|No default
|Username to use when connecting to the SQL Server database server.

|[[sqlserver-property-database-password]]<<sqlserver-property-database-password, `+database.password+`>>
|No default
|Password to use when connecting to the SQL Server database server.

|[[sqlserver-property-database-dbname]]<<sqlserver-property-database-dbname, `+database.dbname+`>>
|No default
|The name of the SQL Server database from which to stream the changes
Must not be used with `database.names`.

|[[sqlserver-property-database-names]]<<sqlserver-property-database-names, `+database.names+`>>
|No default
|The comma-separated list of the SQL Server database names from which to stream the changes.
Currently, only one database name is supported. Must not be used with `database.dbname`.

This option is *experimental* and must not be used in production. Using it will make the behavior of the connector
incompatible with the default configuration with no upgrade or downgrade path:

* The connector will use different keys for its committed offset messages.
* The SQL statements used in `snapshot.select.statement.overrides` will have to use the database name
  as part of the fully-qualified table name.

|[[sqlserver-property-database-server-name]]<<sqlserver-property-database-server-name, `+database.server.name+`>>
|No default
|Logical name that identifies and provides a namespace for the SQL Server database server that you want {prodname} 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, hyphens, dots and underscores must be used.

|[[sqlserver-property-table-include-list]]<<sqlserver-property-table-include-list, `+table.include.list+`>>
|No default
|An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables that you want {prodname} 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`.

|[[sqlserver-property-table-exclude-list]]<<sqlserver-property-table-exclude-list, `+table.exclude.list+`>>
|No default
|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; {prodname} 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`.

|[[sqlserver-property-column-include-list]]<<sqlserver-property-column-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.

|[[sqlserver-property-column-exclude-list]]<<sqlserver-property-column-exclude-list, `+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.


|[[sqlserver-property-column-mask-hash]]<<sqlserver-property-column-mask-hash, `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.
Fully-qualified names for columns are of the form _schemaName_._tableName_._columnName_.
In the resulting change event record, the values for the specified columns are replaced with pseudonyms. +

A pseudonym consists of the hashed value that results from applying the specified _hashAlgorithm_ and _salt_.
Based on the hash function that is used, referential integrity is maintained, while column values are replaced with pseudonyms.
Supported hash functions are described in the {link-java7-standard-names}[MessageDigest section] of the Java Cryptography Architecture Standard Algorithm Name Documentation. +
 +
In the following example, `CzQMA0cB5K` is a randomly selected salt. +

----
column.mask.hash.SHA-256.with.salt.CzQMA0cB5K = inventory.orders.customerName, inventory.shipment.customerName
----

If necessary, the pseudonym is automatically shortened to the length of the column.
The connector configuration can include multiple properties that specify different hash algorithms and salts. +
 +
Depending on the _hashAlgorithm_ used, the _salt_ selected, and the actual data set, the resulting data set might not be completely masked.

|[[sqlserver-property-time-precision-mode]]<<sqlserver-property-time-precision-mode, `+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 {link-prefix}:{link-sqlserver-connector}#sqlserver-temporal-values[temporal values].

|[[sqlserver-property-decimal-handling-mode]]<<sqlserver-property-decimal-handling-mode,`+decimal.handling.mode+`>>
|`precise`
|Specifies how the connector should handle values for `DECIMAL` and `NUMERIC` columns: +
 +
`precise` (the default) represents them precisely using `java.math.BigDecimal` values represented in change events in a binary form. +
 +
`double` represents them using `double` values, which may result in a loss of precision but is easier to use. +
 +
`string` encodes values as formatted strings, which is easy to consume but  semantic information about the real type is lost.

|[[sqlserver-property-include-schema-changes]]<<sqlserver-property-include-schema-changes, `+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`.

|[[sqlserver-property-tombstones-on-delete]]<<sqlserver-property-tombstones-on-delete, `+tombstones.on.delete+`>>
|`true`
|Controls whether a _delete_ event is followed by a tombstone event. +
 +
`true` - a delete operation is represented by a _delete_ event and a subsequent tombstone event.  +
 +
`false` - only a _delete_ event is emitted. +
 +
After a source record is deleted, emitting a tombstone event (the default behavior) allows Kafka to completely delete all events that pertain to the key of the deleted row in case {link-kafka-docs}/#compaction[log compaction] is enabled for the topic.

|[[sqlserver-property-column-truncate-to-length-chars]]<<sqlserver-property-column-truncate-to-length-chars, `+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_.

|[[sqlserver-property-column-mask-with-length-chars]]<<sqlserver-property-column-mask-with-length-chars, `+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_.

|[[sqlserver-property-column-propagate-source-type]]<<sqlserver-property-column-propagate-source-type, `+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 `pass:[_]pass:[_]debezium.source.column.type`, `pass:[_]pass:[_]debezium.source.column.length` and `pass:[_]pass:[_]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_.

|[[sqlserver-property-datatype-propagate-source-type]]<<sqlserver-property-datatype-propagate-source-type,`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 `pass:[_]pass:[_]debezium.source.column.type`, `pass:[_]pass:[_]debezium.source.column.length` and `pass:[_]pass:[_]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 {link-prefix}:{link-sqlserver-connector}#sqlserver-data-types[SQL Server data types] for the list of SQL Server-specific data type names.

|[[sqlserver-property-message-key-columns]]<<sqlserver-property-message-key-columns, `+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_.

|[[sqlserver-property-binary-handling-mode]]<<sqlserver-property-binary-handling-mode, `+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
|===

[id="sqlserver-advanced-connector-configuration-properties"]
==== Advanced SQL Server connector configuration properties

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.

[cols="30%a,25%a,45%a",options="header"]
|===
|Property
|Default
|Description

|[[sqlserver-property-snapshot-mode]]<<sqlserver-property-snapshot-mode, `+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.

|[[sqlserver-property-snapshot-include-collection-list]]<<sqlserver-property-snapshot-include-collection-list, `+snapshot.include.collection.list+`>>
| All tables specified in `table.include.list`
|An optional, comma-separated list of regular expressions that match names of *fully-qualified* table names (`<db-name>.<schema-name>.<name>`) included in `table.include.list` for which you *want* to take the snapshot.

|[[sqlserver-property-snapshot-isolation-mode]]<<sqlserver-property-snapshot-isolation-mode, `+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).

|[[sqlserver-property-event-processing-failure-handling-mode]]<<sqlserver-property-event-processing-failure-handling-mode, `+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.

|[[sqlserver-property-poll-interval-ms]]<<sqlserver-property-poll-interval-ms, `+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.

|[[sqlserver-property-max-queue-size]]<<sqlserver-property-max-queue-size, `+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.

|[[sqlserver-property-max-batch-size]]<<sqlserver-property-max-batch-size, `+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.

|[[sqlserver-property-heartbeat-interval-ms]]<<sqlserver-property-heartbeat-interval-ms, `+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.

|[[sqlserver-property-heartbeat-topics-prefix]]<<sqlserver-property-heartbeat-topics-prefix, `+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>`.

|[[sqlserver-property-snapshot-delay-ms]]<<sqlserver-property-snapshot-delay-ms, `+snapshot.delay.ms+`>>
|No default
|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.

|[[sqlserver-property-snapshot-fetch-size]]<<sqlserver-property-snapshot-fetch-size, `+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.

|[[sqlserver-property-query-fetch-size]]<<sqlserver-property-query-fetch-size, `+query.fetch.size+`>>
|No default
|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.

|[[sqlserver-property-snapshot-lock-timeout-ms]]<<sqlserver-property-snapshot-lock-timeout-ms, `+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 {link-prefix}:{link-sqlserver-connector}#sqlserver-snapshots[snapshots]). +
When set to `0` the connector will fail immediately when it cannot obtain the lock. Value `-1` indicates infinite waiting.

|[[sqlserver-property-snapshot-select-statement-overrides]]<<sqlserver-property-snapshot-select-statement-overrides, `+snapshot.select.statement.overrides+`>>
|No default
|Specifies the table rows to include in a snapshot.
Use the property if you want a snapshot to include only a subset of the rows in a table.
This property affects snapshots only.
It does not apply to events that the connector reads from the log.

The property contains a comma-separated list of fully-qualified table names in the form `_<schemaName>.<tableName>_`. For example, +
 +
`+"snapshot.select.statement.overrides": "inventory.products,customers.orders"+` +
 +
For each table in the list, add a further configuration property that specifies the `SELECT` statement for the connector to run on the table when it takes a snapshot.
The specified `SELECT` statement determines the subset of table rows to include in the snapshot.
Use the following format to specify the name of this `SELECT` statement property: +
 +
`snapshot.select.statement.overrides._<schemaName>_._<tableName>_`.
For example,
`snapshot.select.statement.overrides.customers.orders`. +
 +
Example:

From a `customers.orders` table that includes the soft-delete column, `delete_flag`, add the following properties if you want a snapshot to include only those records that are not soft-deleted:

----
"snapshot.select.statement.overrides": "customer.orders",
"snapshot.select.statement.overrides.customer.orders": "SELECT * FROM [customers].[orders] WHERE delete_flag = 0 ORDER BY id DESC"
----

In the resulting snapshot, the connector includes only the records for which `delete_flag = 0`.
ifdef::community[]
|[[sqlserver-property-source-struct-version]]<<sqlserver-property-source-struct-version, `+source.struct.version+`>>
|v2
|Schema version for the `source` block in CDC events; {prodname} 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 {prodname} version.
endif::community[]

|[[sqlserver-property-sanitize-field-names]]<<sqlserver-property-sanitize-field-names, `+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.
ifdef::community[]
See {link-prefix}:{link-avro-serialization}#avro-naming[Avro naming] for more details.
endif::community[]

|[[sqlserver-property-provide-transaction-metadata]]<<sqlserver-property-provide-transaction-metadata, `+provide.transaction.metadata+`>>
|`false`
|When set to `true` {prodname} generates events with transaction boundaries and enriches data events envelope with transaction metadata.

See {link-prefix}:{link-sqlserver-connector}#sqlserver-transaction-metadata[Transaction Metadata] for additional details.

|[[sqlserver-property-retriable-restart-connector-wait-ms]]<<sqlserver-property-retriable-restart-connector-wait-ms, `+retriable.restart.connector.wait.ms+`>> +
|10000 (10 seconds)
|The number of milli-seconds to wait before restarting a connector after a retriable error occurs.

|[[sqlserver-property-skipped-operations]]<<sqlserver-property-skipped-operations, `+skipped.operations+`>>
|No default
| comma-separated list of operation types that will be skipped during streaming.
The operations include: `c` for inserts/create, `u` for updates, and `d` for deletes.
By default, no operations are skipped.

|[[sqlserver-property-signal-data-collection]]<<sqlserver-property-signal-data-collection, `+signal.data.collection+`>>
|No default
| Fully-qualified name of the data collection that is used to send {link-prefix}:{link-signalling}[signals] to the connector.
The name format is _database_name.schema-name.table-name_.

|===

[id="debezium-{context}-connector-database-history-configuration-properties"]
==== {prodname} connector database history configuration properties

include::{partialsdir}/modules/all-connectors/ref-connector-configuration-database-history-properties.adoc[leveloffset=+1]

[id="debezium-{context}-connector-pass-through-database-driver-configuration-properties"]
==== {prodname} connector pass-through database driver configuration properties

include::{partialsdir}/modules/all-connectors/ref-connector-pass-through-database-driver-configuration-properties.adoc[leveloffset=+1]


// Type: assembly
// ModuleID: refreshing-capture-tables-after-a-schema-change
// Title: Refreshing capture tables after a schema change
[[sqlserver-schema-evolution]]
== 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 {prodname} 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 {prodname} to update capture tables.
To refresh capture tables, one must be a SQL Server database operator with elevated privileges.
As a {prodname} 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:

* {link-prefix}:{link-sqlserver-connector}#offline-schema-updates[Offline schema updates] require you to stop the {prodname} connector before you can update capture tables.
* {link-prefix}:{link-sqlserver-connector}#online-schema-updates[Online schema updates] can update capture tables while the {prodname} connector is running.

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

[WARNING]
====
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.
====

[NOTE]
====
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.
====

[NOTE]
====
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`.
====

// Type: procedure
// ModuleID: debezium-sql-server-connector-running-an-offline-update-after-a-schema-change
// Title: Running an offline update after a schema change
[id="offline-schema-updates"]
=== 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

1. Suspend the application that updates the database.
2. Wait for the {prodname} connector to stream all unstreamed change event records.
3. Stop the {prodname} connector.
4. Apply all changes to the source table schema.
5. 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`.
6. Resume the application that you suspended in Step 1.
7. Start the {prodname} connector.
8. After the {prodname} 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.

// Type: procedure
// ModuleID: debezium-sql-server-connector-running-an-online-update-after-a-schema-change
// Title: Running an online update after a schema change
[id="online-schema-updates"]
=== 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
1. Apply all changes to the source table schema.
2. 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`.
3. When {prodname} 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
ifdef::community[]
Let's deploy the SQL Server based https://github.com/debezium/debezium-examples/tree/master/tutorial#using-sql-server[{prodname} 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:
[source,shell]
----
docker-compose -f docker-compose-sqlserver.yaml exec sqlserver bash -c '/opt/mssql-tools/bin/sqlcmd -U sa -P $SA_PASSWORD -d testDB'
----
endif::community[]

ifdef::product[]

The following example shows how to complete an online schema update in the change table after the column `phone_number` is added to the `customers` source table.

endif::product[]

. Modify the schema of the `customers` source table by running the following query to add the `phone_number` field:
+
[source,sql]
----
ALTER TABLE customers ADD phone_number VARCHAR(32);
----

. Create the new capture instance by running the `sys.sp_cdc_enable_table` stored procedure.
+
[source,sql]
----
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:
[source,sql]
+
----
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:
+
[source,shell]
----
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.
+
[source,json]
----
...
     {
        "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.
+
[source,sql]
----
EXEC sys.sp_cdc_disable_table @source_schema = 'dbo', @source_name = 'dbo_customers', @capture_instance = 'dbo_customers';
GO
----

// Type: assembly
// ModuleID: monitoring-debezium-sql-server-connector-performance
// Title: Monitoring {prodname} SQL Server connector performance
[[sqlserver-monitoring]]
== Monitoring

The {prodname} 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:

* {link-prefix}:{link-sqlserver-connector}#sqlserver-snapshot-metrics[Snapshot metrics] for monitoring the connector when performing snapshots.
* {link-prefix}:{link-sqlserver-connector}#sqlserver-streaming-metrics[Streaming metrics] for monitoring the connector when reading CDC table data.
* {link-prefix}:{link-sqlserver-connector}#sqlserver-schema-history-metrics[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 {link-prefix}:{link-debezium-monitoring}[{prodname} monitoring documentation].

// Type: reference
// ModuleID: debezium-sql-server-connector-snapshot-metrics
// Title: {prodname} SQL Server connector snapshot metrics
[[sqlserver-snapshot-metrics]]
=== Snapshot metrics

include::{partialsdir}/modules/all-connectors/ref-connector-monitoring-snapshot-metrics.adoc[leveloffset=+1]

include::{partialsdir}/modules/all-connectors/ref-connector-monitoring-incremental-snapshot-metrics.adoc[leveloffset=+1]

// Type: reference
// ModuleID: debezium-sql-server-connector-streaming-metrics
// Title: {prodname} SQL Server connector streaming metrics
[[sqlserver-streaming-metrics]]
=== Streaming metrics

include::{partialsdir}/modules/all-connectors/ref-connector-monitoring-streaming-metrics.adoc[leveloffset=+1]

// Type: reference
// ModuleID: debezium-sql-server-connector-schema-history-metrics
// Title: {prodname} SQL Server connector schema history metrics
[[sqlserver-schema-history-metrics]]
=== Schema history metrics

include::{partialsdir}/modules/all-connectors/ref-connector-monitoring-schema-history-metrics.adoc[leveloffset=+1]