[id="debezium-connector-for-sql-server"]
= {prodname} connector for SQL Server

:context: sqlserver
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[]

{prodname}'s SQL Server Connector can monitor and record the row-level changes in the schemas of a SQL Server database.

The first time it connects to a SQL Server database/cluster, it reads a consistent snapshot of all of the schemas.
When that snapshot is complete, the connector continuously streams the changes that were committed to SQL Server and generates corresponding insert, update and delete events.
All of the events for each table are recorded in a separate Kafka topic, where they can be easily consumed by applications and services.

[[sqlserver-overview]]
== Overview

The functionality of the connector is based upon 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 provided by SQL Server Standard (https://blogs.msdn.microsoft.com/sqlreleaseservices/sql-server-2016-service-pack-1-sp1-released/[since SQL Server 2016 SP1]) or Enterprise edition.
Using this mechanism a SQL Server capture process monitors all databases and tables the user is interested in and stores the changes into specifically created _CDC_ tables that have stored procedure facade.

The database operator must https://docs.microsoft.com/en-us/sql/relational-databases/track-changes/enable-and-disable-change-data-capture-sql-server?view=sql-server-2017[enable] _CDC_ for the table(s) that should be captured by the connector.
The connector then produces a _change event_ for every row-level insert, update, and delete operation that was published via the _CDC API_, recording all the change events for each table in a separate Kafka topic.
The client applications read the Kafka topics that correspond to the database tables they're interested in following, and react to every row-level event it sees in those topics.

The database operator normally enables _CDC_ in the mid-life of a database an/or table.
This means that the connector does not have the complete history of all changes that have been made to the database.
Therefore, when the SQL Server connector first connects to a particular SQL Server database, it starts by performing a _consistent snapshot_ of each of the database schemas.
After the connector completes the snapshot, it continues streaming changes from the exact point at which the snapshot was made.
This way, we start with a consistent view of all of the data, yet continue reading without having lost any of the changes made while the snapshot was taking place.

The connector is also tolerant of failures.
As the connector reads changes and produces events, it records the position in the database log (_LSN / Log Sequence Number_), that is associated with _CDC_ record, with each event.
If the connector stops for any reason (including communication failures, network problems, or crashes), upon restart it simply continues reading the _CDC_ tables where it last left off.
This includes snapshots: if the snapshot was not completed when the connector is stopped, upon restart it begins a new snapshot.

[[setting-up-sqlserver]]
== Setting up SQL Server

Before using the SQL Server connector to monitor the changes committed on SQL Server, first enable _CDC_ on a monitored database.
Please bear in mind that _CDC_ cannot be enabled for the `primary` database.

[source,sql]
----
-- ====
-- Enable Database for CDC template
-- ====
USE MyDB
GO
EXEC sys.sp_cdc_enable_db
GO
----

Then enable _CDC_ for each table that you plan to monitor.

[source,sql]
----
-- ====
-- Enable a Table Specifying Filegroup Option Template
-- ====
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
----

Verify that the user have access to the _CDC_ table.
[source, sql]
----
-- ====
-- Verify the user of the connector have access, this query should not have empty result
-- ====

EXEC sys.sp_cdc_help_change_data_capture
GO
----
If the result is empty then please make sure that the user has privileges to access both the capture instance and _CDC_ tables.

[[sqlserver-on-azure]]
=== SQL Server on Azure

The SQL Server plug-in has not been tested with SQL Server on Azure.
We welcome any feedback from a user to try the plug-in with database in managed environments.

ifdef::community[]
[[sqlserver-always-on-replica]]
=== SQL Server Always On

The SQL Server plug-in can capture changes from an Always On read-only replica.
A few pre-requisities are necessary to be fulfilled:

* 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` must be set to `ReadOnly`.
This is required by SQL Server.
When {prodname} detects this configuration option then it will:

** set `snapshot.isolation.mode` to `snapshot` as this is the only one transaction isolation mode supported by read-only replicas
** commit the (read-only) transaction in every execution of the streaming query loop, as this is necessary to get the latest view on CDC data
endif::community[]

[[how-the-sqlserver-connector-works]]
== How the SQL Server connector works

[[sqlserver-snapshots]]
=== Snapshots

SQL Server CDC is not designed to store the complete history of database changes.
It is thus necessary that {prodname} establishes the baseline of current database content and streams it to the Kafka.
This is achieved via a process called snapshotting.

By default (snapshotting mode *initial*) the connector will upon the first startup perform an initial _consistent snapshot_ of the database
(meaning the structure and data within any tables to be captured as per the connector's filter configuration).

Each snapshot consists of the following steps:

1. Determine the tables to be captured
2. Obtain a lock on each of the monitored tables to ensure that no structural changes can occur to any of the tables.
The level of the lock is determined by `snapshot.isolation.mode` configuration option.
3. Read the maximum LSN ("log sequence number") position in the server's transaction log.
4. Capture the structure of all relevant tables.
5. Optionally release the locks obtained in step 2, i.e. the locks are held usually only for a short period of time.
6. Scan all of the relevant database tables and schemas as valid at the LSN position read in step 3, and generate a `READ` event for each row and write that event to the appropriate table-specific Kafka topic.
7. Record the successful completion of the snapshot in the connector offsets.

=== Reading the change data tables

Upon first start-up, the connector takes a structural snapshot of the structure of the captured tables
and persists this information in its internal database history topic.
Then the connector identifies a change table for each of the source tables and executes the main loop

1. For each change table read all changes that were created between last stored maximum LSN and current maximum LSN
2. Order the read changes incrementally according to commit LSN and change LSN.
This ensures that the changes are replayed by {prodname} in the same order as were made to the database.
3. Pass commit and change LSNs as offsets to Kafka Connect.
4. Store the maximum LSN and repeat the loop.

After a restart, the connector will resume from the offset (commit and change LSNs) where it left off before.

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

[[sqlserver-topic-names]]
=== Topic names

The SQL Server connector writes events for all insert, update, and delete operations on a single table to a single Kafka topic. The name of the Kafka topics always takes the form _serverName_._schemaName_._tableName_, where _serverName_ is the logical name of the connector as specified with the `database.server.name` configuration property, _schemaName_ is the name of the schema where the operation occurred, and _tableName_ is the name of the database table on which the operation occurred.

For example, consider a SQL Server installation with an `inventory` database that contains four tables: `products`, `products_on_hand`, `customers`, and `orders` in schema `dbo`. If the connector monitoring this database were given a logical server name of `fulfillment`, then the connector would produce events on these four Kafka topics:

* `fulfillment.dbo.products`
* `fulfillment.dbo.products_on_hand`
* `fulfillment.dbo.customers`
* `fulfillment.dbo.orders`

=== Schema change topic

For a table for which CDC is enabled, the {prodname} SQL Server connector stores the history of schema changes to that table in a database history topic. This topic reflects an internal connector state and you should not use it. If your application needs to track schema changes, there is a public schema change topic. The name of the schema change topic is the same as the logical server name specified in the connector configuration.

[WARNING]
====
The format of 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:

* 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 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"
  }
}
----

=== Change data 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 converver 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.
====

[[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{zwsp}.customers{zwsp}.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[]

[[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. 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}.

|===

[[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. 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}.


|===

[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. 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.

[[sqlserver-transaction-metadata]]
=== Transaction Metadata

{prodname} can generate events that represents tranaction metadata boundaries and enrich data messages.

==== Transaction boundaries
{prodname} generates events for every transaction `BEGIN` and `END`.
Every event contains

* `status` - `BEGIN` or `END`
* `id` - string representation of unique transaction identifier
* `event_count` (for `END` events) - total number of events emmitted by the transaction
* `data_collections` (for `END` events) - an array of pairs of `data_collection` and `event_count` that provides number of events emitted by changes originating from given data collection

Following is an example of what a message looks like:

[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`.

==== Data events 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

Following is an example of what a 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"
  }
}
----

[[sqlserver-schema-evolution]]
=== Database schema evolution

{prodname} is able to capture schema changes over time.
Due to the way CDC is implemented in SQL Server, it is necessary to work in co-operation with a database operator in order to ensure the connector continues to produce data change events when the schema is updated.

As was already mentioned before, {prodname} uses SQL Server's change data capture functionality.
This means that SQL Server creates a capture table that contains all changes executed on the source table.
Unfortunately, the capture table is static and needs to be updated when the source table structure changes.
This update is not done by the connector itself but must be executed by an operator with elevated privileges.

There are generally two procedures how to execute the schema change:

* cold - this is executed when {prodname} is stopped
* hot - executed while {prodname} is running

Both approaches have their own advantages and disadvantages.

[WARNING]
====
In both cases, it is critically important to execute the procedure completely before a new schema update on the same source table is made.
It is thus recommended to execute all DDLs in a single batch so the procedure is done only once.
====

[NOTE]
====
Not all schema changes are supported when CDC is enabled for a source table.
One such exception identified is renaming a column or changing its type, SQL Server will not allow executing the operation.
====

[NOTE]
====
Although not required by SQL Server's CDC mechanism itself, a new capture instance must be created when altering a column from `NULL` to `NOT NULL` or vice versa.
This is required so that the SQL Server connector can pick up that changed information.
Otherwise, emitted change events will have the `optional` value for the corresponding field (`true` or `false`) set to match the original value.
====

==== Cold schema update

This is the safest procedure but might not be feasible for applications with high-availability requirements.
The operator should follow this sequence of steps

1. Suspend the application that generates the database records
2. Wait for {prodname} to stream all unstreamed changes
3. Stop the 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
7. Start the connector
8. When {prodname} starts streaming from the new capture table it is possible to drop the old one using `sys.sp_cdc_disable_table` stored procedure with parameter `@capture_instance` set to the old capture instance name

==== Hot schema update

The hot schema update does not require any downtime in application and data processing.
The procedure itself is also much simpler than in case of cold schema update

1. Apply all changes to the source table schema
2. 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`
3. When {prodname} starts streaming from the new capture table it is possible to drop the old one using `sys.sp_cdc_disable_table` stored procedure with parameter `@capture_instance` set to the old capture instance name

The hot schema update has one drawback.
There is a period of time between the database schema update and creating the new capture instance.
All changes that will arrive during this period are captured by the old instance with the old structure.
For instance this means that in case of a newly added column any change event produced during this time will not yet contain a field for that new column.
If your application does not tolerate such a transition period we recommend to follow the cold schema update.

==== Example
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 hot schema update.
endif::community[]

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

[source,shell]
----
# Start the database shell
docker-compose -f docker-compose-sqlserver.yaml exec sqlserver bash -c '/opt/mssql-tools/bin/sqlcmd -U sa -P $SA_PASSWORD -d testDB'
----

[source,sql]
----
-- Modify the source table schema
ALTER TABLE customers ADD phone_number VARCHAR(32);

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

Kafka Connect log will contain messages like these:
[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, there is a new field in the schema and value of the 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"
    },
----

[source,sql]
----
-- Drop the old capture instance
EXEC sys.sp_cdc_disable_table @source_schema = 'dbo', @source_name = 'dbo_customers', @capture_instance = 'dbo_customers';
GO
----

[[sqlserver-data-types]]
=== Data types

As described above, the SQL Server connector represents the changes to rows with events that are structured like the table in which the row exist.
The event contains a field for each column value, and how that value is represented in the event depends on the SQL data type of the column. This section describes this mapping.

The following table describes how the connector maps each of the SQL Server data types to a _literal type_ and _semantic type_ within the events' fields.
Here, the _literal type_ describes how the value is literally represented using Kafka Connect schema types, namely `INT8`, `INT16`, `INT32`, `INT64`, `FLOAT32`, `FLOAT64`, `BOOLEAN`, `STRING`, `BYTES`, `ARRAY`, `MAP`, and `STRUCT`.
The _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.

[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[]

[[sqlserver-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.

|===

[[sqlserver-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.

==== Decimal values

[cols="30%a,17%a,53%a",options="header"]
|===
|SQL Server data type
|Literal type (schema type)
|Semantic type (schema name)

|`NUMERIC[(P[,S])]`
|`BYTES`
|`org.apache.kafka.connect.data.Decimal` 

|`DECIMAL[(P[,S])]`
|`BYTES`
|`org.apache.kafka.connect.data.Decimal` 

|`SMALLMONEY`
|`BYTES`
|`org.apache.kafka.connect.data.Decimal`

|`MONEY`
|`BYTES`
|`org.apache.kafka.connect.data.Decimal`

|===

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

[[sqlserver-deploying-a-connector]]
== Deployment

ifdef::community[]
With link:https://zookeeper.apache.org[Zookeeper], http://kafka.apache.org/[Kafka], and {link-kafka-docs}.html#connect[Kafka Connect] installed, the remaining tasks to deploy a {prodname} SQL Server connector are to download the https://repo1.maven.org/maven2/io/debezium/debezium-connector-sqlserver/{debezium-version}/debezium-connector-sqlserver-{debezium-version}-plugin.tar.gz[connector's plug-in archive], extract the JAR files into your Kafka Connect environment, and add the directory with the JAR files to {link-kafka-docs}/#connectconfigs[Kafka Connect's `plugin.path`].
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 Zookeeper, Kafka, SQL Server and Kafka Connect with the SQL Server connector already installed and ready to run. You can also xref:operations/openshift.adoc[run {prodname} on Kubernetes and OpenShift].
endif::community[]

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

To install the SQL Server connector, follow the procedures in {LinkDebeziumInstallOpenShift}[{NameDebeziumInstallOpenShift}]. The main steps are:

. Use link:https://access.redhat.com/products/red-hat-amq#streams[Red Hat AMQ Streams] to set up Apache Kafka and Kafka Connect on OpenShift. AMQ Streams offers operators and images that bring Kafka to OpenShift. 

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

. Extract the connector files into your Kafka Connect environment.
. Add the connector plug-in's parent directory to your Kafka Connect `plugin.path`, for example: 
+
[source]
----
plugin.path=/kafka/connect
----
+
The above example assumes that you extracted the {prodname} SQL Server connector to the `/kafka/connect/{prodname}-connector-sqlserver` path.

. Restart your Kafka Connect process to ensure that the new JAR files are picked up.

You also need to {LinkDebeziumUserGuide}#{LinkDebeziumUserGuide}#setting-up-sqlserver[set up SQL Server] to run a {prodname} connector.

.Additional resources

For more information about the deployment process, and deploying connectors with AMQ Streams, see the {prodname} installation guides.

* {LinkDebeziumInstallOpenShift}[{NameDebeziumInstallOpenShift}]
* {LinkDebeziumInstallRHEL}[{NameDebeziumInstallRHEL}]
endif::product[]

[[sqlserver-example-configuration]]
=== Example configuration

To use the connector to produce change events for a particular SQL Server database or cluster:

. Enable the {link-prefix}:{link-sqlserver-connector}#setting-up-sqlserver[CDC on SQL Server] to publish the _CDC_ events in the database.
. Create a configuration file for the SQL Server connector.

When the connector starts, it will grab a consistent snapshot of the schemas in your SQL Server database and start streaming changes, producing events for every inserted, updated, and deleted row.
You can also choose to produce events for a subset of the schemas and tables.
Optionally ignore, mask, or truncate columns that are sensitive, too large, or not needed.

ifdef::community[]
Following is an example of the configuration for a connector instance that monitors 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 using the configuration properties available for the connector.

[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[]

ifdef::product[]
Following is an example of the configuration for a connector instance that monitors 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 `.yaml` file using the configuration properties available for the connector.

[source,yaml,options="nowrap"]
----
apiVersion: kafka.strimzi.io/v1beta1 
  kind: KafkaConnector
  metadata:
    name: inventory-connector // <1>
    labels: strimzi.io/cluster: my-connect-cluster
  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.

|===

endif::product[]

See the {link-prefix}:{link-sqlserver-connector}#sqlserver-connector-properties[complete list of connector properties] that can be specified in these configurations.

This configuration can be sent via POST to a running Kafka Connect service, which will then record the configuration and start up the one connector task that will connect to the SQL Server database, read the transaction log, and record events to Kafka topics.


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

ifdef::community[]
To run a {prodname} SQL Server connector, create a connector configuration and add the configuration to your Kafka Connect cluster. 

.Prerequisites

* SQL Server is set up to run a {prodname} connector.

* A {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[]

ifdef::product[]
You can use a provided {prodname} container to deploy a {prodname} SQL Server connector. In this procedure, you build a custom Kafka Connect container image for {prodname}, configure the {prodname} connector as needed, and then add your connector configuration to your Kafka Connect environment. 

.Prerequisites

* Podman or Docker is installed and you have sufficient rights to create and manage containers.
* You installed the {prodname} SQL Server connector archive. 

.Procedure

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

. Create and publish a custom image for running your {prodname} connector:

.. Create a new `Dockerfile` by using `{DockerKafkaConnect}` as the base image. In the following example, you would replace _my-plugins_ with the name of your plug-ins directory:
+
[subs="+macros,+attributes"]
----
FROM {DockerKafkaConnect}
USER root:root
pass:quotes[COPY _./my-plugins/_ /opt/kafka/plugins/]
USER 1001
----
+
Before Kafka Connect starts running the connector, Kafka Connect loads any third-party plug-ins that are in the `/opt/kafka/plugins` directory.

.. Build the container image. For example, if you saved the `Dockerfile` that you created in the previous step as `debezium-container-for-sqlserver`, and if the `Dockerfile` is in the current directory, then you would run the following command:
+
`podman build -t debezium-container-for-sqlserver:latest .`

.. Push your custom image to your container registry, for example:
+
`podman push debezium-container-for-sqlserver:latest`

.. Point to the new container image. Do one of the following:
+
* Edit the `spec.image` property of the `KafkaConnector` custom resource. If set, this property overrides the `STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE` variable in the Cluster Operator. For example:
+
[source,yaml,subs="+attributes"]
----
apiVersion: {KafkaConnectApiVersion}
kind: KafkaConnector
metadata:
  name: my-connect-cluster
spec:
  #...
  image: debezium-container-for-sqlserver
----
+
* In the `install/cluster-operator/050-Deployment-strimzi-cluster-operator.yaml` file, edit the `STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE` variable to point to the new container image and reinstall the Cluster Operator. If you edit this file you must apply it to your OpenShift cluster.

. Create a `KafkaConnector` custom resource that defines your {prodname} SQL Server connector instance. See {LinkDebeziumUserGuide}#sqlserver-example-configuration[the connector configuration example].

. Apply the connector instance, for example: 
+
`oc apply -f inventory-connector.yaml`
+
This registers `inventory-connector` and the connector starts to run against the `inventory` database.

. Verify that the connector was created and has started to capture changes in the specified database. You can verify the connector instance by watching the Kafka Connect log output as, for example, `inventory-connector` starts.

.. Display the Kafka Connect log output:
+
[source,shell,options="nowrap"]
----
oc logs $(oc get pods -o name -l strimzi.io/name=my-connect-cluster-connect)
----

.. Review the log output to verify that the initial snapshot has been executed. You should see something like the following lines: 
+
[source,shell,options="nowrap"]
----
... INFO Starting snapshot for ...
... INFO Snapshot is using user 'debezium' ... 
----

endif::product[]

.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 change event records to Kafka topics. 

[[sqlserver-monitoring]]
=== Monitoring

The {prodname} SQL Server connector has three metric types in addition to the built-in support for JMX metrics that Zookeeper, Kafka, and Kafka Connect have.

* <<sqlserver-snapshot-metrics, snapshot metrics>>; for monitoring the connector when performing snapshots
* <<sqlserver-streaming-metrics, streaming metrics>>; for monitoring the connector when reading CDC table data
* <<sqlserver-schema-history-metrics, schema history metrics>>; for monitoring the status of the connector's schema history

Please refer to the {link-prefix}:{link-debezium-monitoring}[monitoring documentation] for details of how to expose these metrics via JMX.

[[sqlserver-snapshot-metrics]]
==== Snapshot Metrics

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

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

[[sqlserver-streaming-metrics]]
==== Streaming Metrics

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

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

[[sqlserver-schema-history-metrics]]
==== Schema History Metrics

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

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

[[sqlserver-connector-properties]]
=== Connector 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`>>
|
|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`>>
|
|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`>>
|
|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`>>
|
|Username to use when connecting to the SQL Server database server.

|[[sqlserver-property-database-password]]<<sqlserver-property-database-password, `database.password`>>
|
|Password to use when connecting to the SQL Server database server.

|[[sqlserver-property-database-dbname]]<<sqlserver-property-database-dbname, `database.dbname`>>
|
|The name of the SQL Server database from which to stream the changes

|[[sqlserver-property-database-server-name]]<<sqlserver-property-database-server-name, `database.server{zwsp}.name`>>
|
|Logical name that identifies and provides a namespace for the particular SQL Server database server being monitored. The logical name should be unique across all other connectors, since it is used as a prefix for all Kafka topic names emanating from this connector.
Only alphanumeric characters and underscores should be used.

|[[sqlserver-property-database-history-kafka-topic]]<<sqlserver-property-database-history-kafka-topic, `database.history{zwsp}.kafka.topic`>>
|
|The full name of the Kafka topic where the connector will store the database schema history.

|[[sqlserver-property-database-history-kafka-bootstrap-servers]]<<sqlserver-property-database-history-kafka-bootstrap-servers, `database.history{zwsp}.kafka.bootstrap{zwsp}.servers`>>
|
|A list of host/port pairs that the connector will use for establishing an initial connection to the Kafka cluster.
This connection is used for retrieving database schema history previously stored by the connector, and for writing each DDL statement read from the source database. This should point to the same Kafka cluster used by the Kafka Connect process.

|[[sqlserver-property-table-whitelist]]
[[sqlserver-property-table-include-list]]<<sqlserver-property-table-include-list, `table.include.list`>>
|
|An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be monitored; any table not included in `table.include.list` is excluded from monitoring. Each identifier is of the form _schemaName_._tableName_. By default the connector will monitor every non-system table in each monitored schema.
Must not be used with `table.exclude.list`.

|[[sqlserver-property-table-blacklist]]
[[sqlserver-property-table-exclude-list]]<<sqlserver-property-table-exclude-list, `table.exclude.list`>>
|
|An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be excluded from monitoring; any table not included in `table.exclude.list` is monitored.
Each identifier is of the form _schemaName_._tableName_. Must not be used with `table.include.list`.

|[[sqlserver-property-column-whitelist]]
[[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-blacklist]]
[[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{zwsp}.hash._hashAlgorithm_{zwsp}.with.salt._salt_`>>
|_n/a_
|An optional comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be pseudonyms in the change event message values with a field value consisting of the hashed value using the algorithm `_hashAlgorithm_` and salt `_salt_`.
Based on the used hash function referential integrity is kept while data is pseudonymized. Supported hash functions are described in the {link-java7-standard-names}[MessageDigest section] of the Java Cryptography Architecture Standard Algorithm Name Documentation.
The hash is automatically shortened to the length of the column.

Multiple properties with different lengths can be used in a single configuration, although in each the length must be a positive integer or zero. Fully-qualified names for columns are of the form _schemaName_._tableName_._columnName_.

Example:

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

where `CzQMA0cB5K` is a randomly selected salt.

Note: Depending on the `_hashAlgorithm_` used, the `_salt_` selected and the actual data set, the resulting masked data set may not be completely anonymized.

|[[sqlserver-property-time-precision-mode]]<<sqlserver-property-time-precision-mode, `time.precision{zwsp}.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-include-schema-changes]]<<sqlserver-property-include-schema-changes, `include.schema{zwsp}.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{zwsp}.delete`>>
|`true`
| Controls whether a tombstone event should be generated after a delete event. +
When `true` the delete operations are represented by a delete event and a subsequent tombstone event. When `false` only a delete event is sent. +
Emitting the tombstone event (the default behavior) allows Kafka to completely delete all events pertaining to the given key once the source record got deleted.

|[[sqlserver-property-column-truncate-to-length-chars]]<<sqlserver-property-column-truncate-to-length-chars, `column.truncate.to{zwsp}._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{zwsp}._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{zwsp}.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{zwsp}.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`>>
|_empty string_
| 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_.
|===

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. +
 +
Supported values are: +
`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 schemas specified in `table.include.list` for which you *want* to take the snapshot.

|[[sqlserver-property-snapshot-isolation-mode]]<<sqlserver-property-snapshot-isolation-mode, `snapshot.isolation{zwsp}.mode`>>
|_repeatable_read_
|Mode to control which transaction isolation level is used and how long the connector locks the monitored tables.
There are five possible values: `read_uncommitted`, `read_committed`, `repeatable_read`, `snapshot`, and `exclusive` (
in fact, `exclusive` mode uses repeatable read isolation level, however, it takes the exclusive lock on all tables
to be read). +

It is worth documenting that `snapshot`, `read_committed` and `read_uncommitted` modes do not prevent other
transactions from updating table rows during initial snapshot, while `exclusive` and `repeatable_read` do. +

Another aspect is 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-source-timestamp-mode]]<<sqlserver-property-source-timestamp-mode, `source.timestamp{zwsp}.mode`>>
|_commit_
|String representing the criteria of the attached timestamp within the source record (ts_ms).
`commit` will set the source timestamp to the instant where the record was committed in the database (default and current behavior).
`processing` will set the source timestamp to the instant where the record was processed by {prodname}. This option could be used when either we want to set the top level `ts_ms` value here or when we want to skip the query to extract the timestamp of that LSN.

|[[sqlserver-property-event-processing-failure-handling-mode]]<<sqlserver-property-event-processing-failure-handling-mode, `event.processing{zwsp}.failure.handling{zwsp}.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{zwsp}.ms`>>
|`0`
|Controls how frequently heartbeat messages are sent. +
This property contains an interval in milli-seconds that defines how frequently the connector sends messages into a heartbeat topic.
This can be used to monitor 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{zwsp}.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`>>
|
|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`>>
|
|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{zwsp}.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{zwsp}.statement{zwsp}.overrides`>>
|
|Controls which rows from tables are included in snapshot. +
This property contains a comma-separated list of fully-qualified tables _(SCHEMA_NAME.TABLE_NAME)_. Select statements for the individual tables are specified in further configuration properties, one for each table, identified by the id `snapshot.select.statement.overrides.[SCHEMA_NAME].[TABLE_NAME]`. The value of those properties is the SELECT statement to use when retrieving data from the specific table during snapshotting. _A possible use case for large append-only tables is setting a specific point where to start (resume) snapshotting, in case a previous snapshotting was interrupted._ +
*Note*: This setting has impact on snapshots only. Events captured during log reading are not affected by it.

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{zwsp}.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-database-server-timezone]]<<sqlserver-property-database-server-timezone, `database.server{zwsp}.timezone`>>
|
| Timezone of the server.

This is used to define the timezone of the transaction timestamp (ts_ms) retrieved from the server (which is actually not zoned). Default value is unset. Should only be specified when running on SQL Server 2014 or older and using different timezones for the database server and the JVM running the {prodname} connector. +
When unset, default behavior is to use the timezone of the VM running the {prodname} connector. In this case, when running on on SQL Server 2014 or older and using different timezones on server and the connector, incorrect ts_ms values may be produced. +
Possible values include "Z", "UTC", offset values like "+02:00", short zone ids like "CET", and long zone ids like "Europe/Paris".

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

|===

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

For example, the following connector configuration properties can be used to {link-kafka-docs}.html#security_configclients[secure connections to the Kafka broker]:

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

[source,properties,indent=0]
----
database.history.producer.security.protocol=SSL
database.history.producer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks
database.history.producer.ssl.keystore.password=test1234
database.history.producer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks
database.history.producer.ssl.truststore.password=test1234
database.history.producer.ssl.key.password=test1234
database.history.consumer.security.protocol=SSL
database.history.consumer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks
database.history.consumer.ssl.keystore.password=test1234
database.history.consumer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks
database.history.consumer.ssl.truststore.password=test1234
database.history.consumer.ssl.key.password=test1234
----

Be sure to consult the {link-kafka-docs}.html[Kafka documentation] for all of the configuration properties for Kafka producers and consumers. (The SQL Server connector does use the {link-kafka-docs}.html#newconsumerconfigs[new consumer].)
