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Chapter 8. Debezium connector for SQL Server

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

For information about the SQL Server versions that are compatible with this connector, see the Debezium Supported Configurations page.

For details about the Debezium SQL Server connector and its use, see following topics:

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

8.1. Overview of Debezium SQL Server connector

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

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

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

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

Note

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

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

8.2. How Debezium SQL Server connectors work

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

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

8.2.1. How Debezium SQL Server connectors perform database snapshots

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

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

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

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

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

8.2.1.1. Ad hoc snapshots

Important

The use of ad hoc snapshots is a Technology Preview feature. Technology Preview features are not supported with Red Hat production service-level agreements (SLAs) and might not be functionally complete; therefore, Red Hat does not recommend implementing any Technology Preview features in production environments. This Technology Preview feature provides early access to upcoming product innovations, enabling you to test functionality and provide feedback during the development process. For more information about support scope, see Technology Preview Features Support Scope.

By default, a connector runs an initial snapshot operation only after it starts for the first time. Following this initial snapshot, under normal circumstances, the connector does not repeat the snapshot process. Any future change event data that the connector captures comes in through the streaming process only.

However, in some situations the data that the connector obtained during the initial snapshot might become stale, lost, or incomplete. To provide a mechanism for recapturing table data, Debezium includes an option to perform ad hoc snapshots. The following changes in a database might be cause for performing an ad hoc snapshot:

  • The connector configuration is modified to capture a different set of tables.
  • Kafka topics are deleted and must be rebuilt.
  • Data corruption occurs due to a configuration error or some other problem.

You can re-run a snapshot for a table for which you previously captured a snapshot by initiating a so-called ad-hoc snapshot. Ad hoc snapshots require the use of signaling tables. You initiate an ad hoc snapshot by sending a signal request to the Debezium signaling table.

When you initiate an ad hoc snapshot of an existing table, the connector appends content to the topic that already exists for the table. If a previously existing topic was removed, Debezium can create a topic automatically if automatic topic creation is enabled.

Ad hoc snapshot signals specify the tables to include in the snapshot. The snapshot can capture the entire contents of the database, or capture only a subset of the tables in the database.

You specify the tables to capture by sending an execute-snapshot message to the signaling table. Set the type of the execute-snapshot signal to incremental, and provide the names of the tables to include in the snapshot, as described in the following table:

Table 8.1. Example of an ad hoc execute-snapshot signal record
FieldDefaultValue

type

incremental

Specifies the type of snapshot that you want to run.
Setting the type is optional. Currently, you can request only incremental snapshots.

data-collections

N/A

An array that contains the fully-qualified names of the table to be snapshotted.
The format of the names is the same as for the signal.data.collection configuration option.

Triggering an ad hoc snapshot

You initiate an ad hoc snapshot by adding an entry with the execute-snapshot signal type to the signaling table. After the connector processes the message, it begins the snapshot operation. The snapshot process reads the first and last primary key values and uses those values as the start and end point for each table. Based on the number of entries in the table, and the configured chunk size, Debezium divides the table into chunks, and proceeds to snapshot each chunk, in succession, one at a time.

Currently, the execute-snapshot action type triggers incremental snapshots only. For more information, see Incremental snapshots.

8.2.1.2. Incremental snapshots

Important

The use of incremental snapshots is a Technology Preview feature. Technology Preview features are not supported with Red Hat production service-level agreements (SLAs) and might not be functionally complete; therefore, Red Hat does not recommend implementing any Technology Preview features in production environments. This Technology Preview feature provides early access to upcoming product innovations, enabling you to test functionality and provide feedback during the development process. For more information about support scope, see Technology Preview Features Support Scope.

To provide flexibility in managing snapshots, Debezium includes a supplementary snapshot mechanism, known as incremental snapshotting. Incremental snapshots rely on the Debezium mechanism for sending signals to a Debezium connector.

In an incremental snapshot, instead of capturing the full state of a database all at once, as in an initial snapshot, Debezium captures each table in phases, in a series of configurable chunks. You can specify the tables that you want the snapshot to capture and the size of each chunk. The chunk size determines the number of rows that the snapshot collects during each fetch operation on the database. The default chunk size for incremental snapshots is 1 KB.

As an incremental snapshot proceeds, Debezium uses watermarks to track its progress, maintaining a record of each table row that it captures. This phased approach to capturing data provides the following advantages over the standard initial snapshot process:

  • You can run incremental snapshots in parallel with streamed data capture, instead of postponing streaming until the snapshot completes. The connector continues to capture near real-time events from the change log throughout the snapshot process, and neither operation blocks the other.
  • If the progress of an incremental snapshot is interrupted, you can resume it without losing any data. After the process resumes, the snapshot begins at the point where it stopped, rather than recapturing the table from the beginning.
  • You can run an incremental snapshot on demand at any time, and repeat the process as needed to adapt to database updates. For example, you might re-run a snapshot after you modify the connector configuration to add a table to its table.include.list property.

Incremental snapshot process

When you run an incremental snapshot, Debezium sorts each table by primary key and then splits the table into chunks based on the configured chunk size. Working chunk by chunk, it then captures each table row in a chunk. For each row that it captures, the snapshot emits a READ event. That event represents the value of the row when the snapshot for the chunk began.

As a snapshot proceeds, it’s likely that other processes continue to access the database, potentially modifying table records. To reflect such changes, INSERT, UPDATE, or DELETE operations are committed to the transaction log as per usual. Similarly, the ongoing Debezium streaming process continues to detect these change events and emits corresponding change event records to Kafka.

How Debezium resolves collisions among records with the same primary key

In some cases, the UPDATE or DELETE events that the streaming process emits are received out of sequence. That is, the streaming process might emit an event that modifies a table row before the snapshot captures the chunk that contains the READ event for that row. When the snapshot eventually emits the corresponding READ event for the row, its value is already superseded. To ensure that incremental snapshot events that arrive out of sequence are processed in the correct logical order, Debezium employs a buffering scheme for resolving collisions. Only after collisions between the snapshot events and the streamed events are resolved does Debezium emit an event record to Kafka.

Snapshot window

To assist in resolving collisions between late-arriving READ events and streamed events that modify the same table row, Debezium employs a so-called snapshot window. The snapshot windows demarcates the interval during which an incremental snapshot captures data for a specified table chunk. Before the snapshot window for a chunk opens, Debezium follows its usual behavior and emits events from the transaction log directly downstream to the target Kafka topic. But from the moment that the snapshot for a particular chunk opens, until it closes, Debezium performs a de-duplication step to resolve collisions between events that have the same primary key..

For each data collection, the Debezium emits two types of events, and stores the records for them both in a single destination Kafka topic. The snapshot records that it captures directly from a table are emitted as READ operations. Meanwhile, as users continue to update records in the data collection, and the transaction log is updated to reflect each commit, Debezium emits UPDATE or DELETE operations for each change.

As the snapshot window opens, and Debezium begins processing a snapshot chunk, it delivers snapshot records to a memory buffer. During the snapshot windows, the primary keys of the READ events in the buffer are compared to the primary keys of the incoming streamed events. If no match is found, the streamed event record is sent directly to Kafka. If Debezium detects a match, it discards the buffered READ event, and writes the streamed record to the destination topic, because the streamed event logically supersede the static snapshot event. After the snapshot window for the chunk closes, the buffer contains only READ events for which no related transaction log events exist. Debezium emits these remaining READ events to the table’s Kafka topic.

The connector repeats the process for each snapshot chunk.

Triggering an incremental snapshot

Currently, the only way to initiate an incremental snapshot is to send an ad hoc snapshot signal to the signaling table on the source database. You submit signals to the table as SQL INSERT queries. After Debezium detects the change in the signaling table, it reads the signal, and runs the requested snapshot operation.

The query that you submit specifies the tables to include in the snapshot, and, optionally, specifies the kind of snapshot operation. Currently, the only valid option for snapshots operations is the default value, incremental.

To specify the tables to include in the snapshot, provide a data-collections array that lists the tables, for example,
{"data-collections": ["public.MyFirstTable", "public.MySecondTable"]}

The data-collections array for an incremental snapshot signal has no default value. If the data-collections array is empty, Debezium detects that no action is required and does not perform a snapshot.

Prerequisites

  • Signaling is enabled.

    • A signaling data collection exists on the source database and the connector is configured to capture it.
    • The signaling data collection is specified in the signal.data.collection property.

Procedure

  1. Send a SQL query to add the ad hoc incremental snapshot request to the signaling table:

    INSERT INTO _<signalTable>_ (id, type, data) VALUES (_'<id>'_, _'<snapshotType>'_, '{"data-collections": ["_<tableName>_","_<tableName>_"],"type":"_<snapshotType>_"}');

    For example,

    INSERT INTO myschema.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["schema1.table1", "schema2.table2"],"type":"incremental"}');

    The values of the id,type, and data parameters in the command correspond to the fields of the signaling table.

    The following table describes the these parameters:

    Table 8.2. Descriptions of fields in a SQL command for sending an incremental snapshot signal to the signaling table
    ValueDescription

    myschema.debezium_signal

    Specifies the fully-qualified name of the signaling table on the source database

    ad-hoc-1

    The id parameter specifies an arbitrary string that is assigned as the id identifier for the signal request.
    Use this string to identify logging messages to entries in the signaling table. Debezium does not use this string. Rather, during the snapshot, Debezium generates its own id string as a watermarking signal.

    execute-snapshot

    Specifies type parameter specifies the operation that the signal is intended to trigger.

    data-collections

    A required component of the data field of a signal that specifies an array of table names to include in the snapshot.
    The array lists tables by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling table in the signal.data.collection configuration property.

    incremental

    An optional type component of the data field of a signal that specifies the kind of snapshot operation to run.
    Currently, the only valid option is the default value, incremental.
    Specifying a type value in the SQL query that you submit to the signaling table is optional.
    If you do not specify a value, the connector runs an incremental snapshot.

The following example, shows the JSON for an incremental snapshot event that is captured by a connector.

Example: Incremental snapshot event message

{
    "before":null,
    "after": {
        "pk":"1",
        "value":"New data"
    },
    "source": {
        ...
        "snapshot":"incremental" 1
    },
    "op":"r", 2
    "ts_ms":"1620393591654",
    "transaction":null
}

ItemField nameDescription

1

snapshot

Specifies the type of snapshot operation to run.
Currently, the only valid option is the default value, incremental.
Specifying a type value in the SQL query that you submit to the signaling table is optional.
If you do not specify a value, the connector runs an incremental snapshot.

2

op

Specifies the event type.
The value for snapshot events is r, signifying a READ operation.

Warning

The Debezium connector for SQL Server does not support schema changes while an incremental snapshot is running.

8.2.2. How Debezium SQL Server connectors read change data tables

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

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

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

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

8.2.3. Default names of Kafka topics that receive Debezium SQL Server change event records

By default, the SQL Server connector writes events for all INSERT, UPDATE, and DELETE operations that occur in a table to a single Apache Kafka topic that is specific to that table. The connector uses the following convention to name change event topics: <serverName>.<schemaName>.<tableName>

The following list provides definitions for the components of the default name:

serverName
The logical name of the server, as specified by the database.server.name configuration property.
schemaName
The name of the database schema in which the change event occurred.
tableName
The name of the database table in which the change event occurred.

For example, if fulfillment is the server name, and dbo is the schema name, and the database contains tables with the names products, products_on_hand, customers, and orders, the connector would stream change event records to the following Kafka topics:

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

The connector applies similar naming conventions to label its internal database history topics, schema change topics, and transaction metadata topics.

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

8.2.4. How the Debezium SQL Server connector uses the schema change topic

For each table for which CDC is enabled, the Debezium SQL Server connector stores a history of the schema change events that are applied to captured tables in the database. The connector writes schema change events to a Kafka topic named <serverName>, where serverName is the logical server name that is specified in the database.server.name configuration property.

Messages that the connector sends to the schema change topic contain a payload, and, optionally, also contain the schema of the change event message. The payload of a schema change event message includes the following elements:

databaseName
The name of the database to which the statements are applied. The value of databaseName serves as the message key.
tableChanges
A structured representation of the entire table schema after the schema change. The tableChanges field contains an array that includes entries for each column of the table. Because the structured representation presents data in JSON or Avro format, consumers can easily read messages without first processing them through a DDL parser.
Important

When the connector is configured to capture a table, it stores the history of the table’s schema changes not only in the schema change topic, but also in an internal database history topic. The internal database history topic is for connector use only and it is not intended for direct use by consuming applications. Ensure that applications that require notifications about schema changes consume that information only from the schema change topic.

Warning

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

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

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

Example: Message emitted to the SQL Server connector schema change topic

The following example shows a message in the schema change topic. The message contains a logical representation of the table schema.

{
  "schema": {
  ...
  },
  "payload": {
    "source": {
      "version": "1.7.2.Final",
      "connector": "sqlserver",
      "name": "server1",
      "ts_ms": 1588252618953,
      "snapshot": "true",
      "db": "testDB",
      "schema": "dbo",
      "table": "customers",
      "change_lsn": null,
      "commit_lsn": "00000025:00000d98:00a2",
      "event_serial_no": null
    },
    "databaseName": "testDB", 1
    "schemaName": "dbo",
    "ddl": null, 2
    "tableChanges": [ 3
      {
        "type": "CREATE", 4
        "id": "\"testDB\".\"dbo\".\"customers\"", 5
        "table": { 6
          "defaultCharsetName": null,
          "primaryKeyColumnNames": [ 7
            "id"
          ],
          "columns": [ 8
            {
              "name": "id",
              "jdbcType": 4,
              "nativeType": null,
              "typeName": "int identity",
              "typeExpression": "int identity",
              "charsetName": null,
              "length": 10,
              "scale": 0,
              "position": 1,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            },
            {
              "name": "first_name",
              "jdbcType": 12,
              "nativeType": null,
              "typeName": "varchar",
              "typeExpression": "varchar",
              "charsetName": null,
              "length": 255,
              "scale": null,
              "position": 2,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            },
            {
              "name": "last_name",
              "jdbcType": 12,
              "nativeType": null,
              "typeName": "varchar",
              "typeExpression": "varchar",
              "charsetName": null,
              "length": 255,
              "scale": null,
              "position": 3,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            },
            {
              "name": "email",
              "jdbcType": 12,
              "nativeType": null,
              "typeName": "varchar",
              "typeExpression": "varchar",
              "charsetName": null,
              "length": 255,
              "scale": null,
              "position": 4,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            }
          ]
        }
      }
    ]
  }
}
Table 8.3. Descriptions of fields in messages emitted to the schema change topic
ItemField nameDescription

1

databaseName
schemaName

Identifies the database and the schema that contain the change.

2

ddl

Always null for the SQL Server connector. For other connectors, this field contains the DDL responsible for the schema change. This DDL is not available to SQL Server connectors.

3

tableChanges

An array of one or more items that contain the schema changes generated by a DDL command.

4

type

Describes the kind of change. The value is one of the following:

  • CREATE - table created
  • ALTER - table modified
  • DROP - table deleted

5

id

Full identifier of the table that was created, altered, or dropped.

6

table

Represents table metadata after the applied change.

7

primaryKeyColumnNames

List of columns that compose the table’s primary key.

8

columns

Metadata for each column in the changed table.

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

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

8.2.5. Descriptions of Debezium SQL Server connector data change events

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

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

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

{
 "schema": { 1
   ...
  },
 "payload": { 2
   ...
 },
 "schema": { 3
   ...
 },
 "payload": { 4
   ...
 },
}
Table 8.4. Overview of change event basic content
ItemField nameDescription

1

schema

The first schema field is part of the event key. It specifies a Kafka Connect schema that describes what is in the event key’s payload portion. In other words, the first schema field describes the structure of the primary key, or the unique key if the table does not have a primary key, for the table that was changed.

It is possible to override the table’s primary key by setting the message.key.columns connector configuration property. In this case, the first schema field describes the structure of the key identified by that property.

2

payload

The first payload field is part of the event key. It has the structure described by the previous schema field and it contains the key for the row that was changed.

3

schema

The second schema field is part of the event value. It specifies the Kafka Connect schema that describes what is in the event value’s payload portion. In other words, the second schema describes the structure of the row that was changed. Typically, this schema contains nested schemas.

4

payload

The second payload field is part of the event value. It has the structure described by the previous schema field and it contains the actual data for the row that was changed.

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

Warning

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

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

For details about change events, see the following topics:

8.2.5.1. About keys in Debezium SQL Server change events

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

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

Example table

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

Example change event key

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

{
    "schema": { 1
        "type": "struct",
        "fields": [ 2
            {
                "type": "int32",
                "optional": false,
                "field": "id"
            }
        ],
        "optional": false, 3
        "name": "server1.dbo.customers.Key" 4
    },
    "payload": { 5
        "id": 1004
    }
}
Table 8.5. Description of change event key
ItemField nameDescription

1

schema

The schema portion of the key specifies a Kafka Connect schema that describes what is in the key’s payload portion.

2

fields

Specifies each field that is expected in the payload, including each field’s name, type, and whether it is required. In this example, there is one required field named id of type int32.

3

optional

Indicates whether the event key must contain a value in its payload field. In this example, a value in the key’s payload is required. A value in the key’s payload field is optional when a table does not have a primary key.

4

server1.dbo.customers.Key

Name of the schema that defines the structure of the key’s payload. This schema describes the structure of the primary key for the table that was changed. Key schema names have the format connector-name.database-schema-name.table-name.Key. In this example:

  • server1 is the name of the connector that generated this event.
  • dbo is the database schema for the table that was changed.
  • customers is the table that was updated.

5

payload

Contains the key for the row for which this change event was generated. In this example, the key, contains a single id field whose value is 1004.

8.2.5.2. About values in Debezium SQL Server change events

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

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

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

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

create events

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

{
  "schema": { 1
    "type": "struct",
    "fields": [
      {
        "type": "struct",
        "fields": [
          {
            "type": "int32",
            "optional": false,
            "field": "id"
          },
          {
            "type": "string",
            "optional": false,
            "field": "first_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "last_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "email"
          }
        ],
        "optional": true,
        "name": "server1.dbo.customers.Value", 2
        "field": "before"
      },
      {
        "type": "struct",
        "fields": [
          {
            "type": "int32",
            "optional": false,
            "field": "id"
          },
          {
            "type": "string",
            "optional": false,
            "field": "first_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "last_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "email"
          }
        ],
        "optional": true,
        "name": "server1.dbo.customers.Value",
        "field": "after"
      },
      {
        "type": "struct",
        "fields": [
          {
            "type": "string",
            "optional": false,
            "field": "version"
          },
          {
            "type": "string",
            "optional": false,
            "field": "connector"
          },
          {
            "type": "string",
            "optional": false,
            "field": "name"
          },
          {
            "type": "int64",
            "optional": false,
            "field": "ts_ms"
          },
          {
            "type": "boolean",
            "optional": true,
            "default": false,
            "field": "snapshot"
          },
          {
            "type": "string",
            "optional": false,
            "field": "db"
          },
          {
            "type": "string",
            "optional": false,
            "field": "schema"
          },
          {
            "type": "string",
            "optional": false,
            "field": "table"
          },
          {
            "type": "string",
            "optional": true,
            "field": "change_lsn"
          },
          {
            "type": "string",
            "optional": true,
            "field": "commit_lsn"
          },
          {
            "type": "int64",
            "optional": true,
            "field": "event_serial_no"
          }
        ],
        "optional": false,
        "name": "io.debezium.connector.sqlserver.Source", 3
        "field": "source"
      },
      {
        "type": "string",
        "optional": false,
        "field": "op"
      },
      {
        "type": "int64",
        "optional": true,
        "field": "ts_ms"
      }
    ],
    "optional": false,
    "name": "server1.dbo.customers.Envelope" 4
  },
  "payload": { 5
    "before": null, 6
    "after": { 7
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "john.doe@example.org"
    },
    "source": { 8
      "version": "1.7.2.Final",
      "connector": "sqlserver",
      "name": "server1",
      "ts_ms": 1559729468470,
      "snapshot": false,
      "db": "testDB",
      "schema": "dbo",
      "table": "customers",
      "change_lsn": "00000027:00000758:0003",
      "commit_lsn": "00000027:00000758:0005",
      "event_serial_no": "1"
    },
    "op": "c", 9
    "ts_ms": 1559729471739 10
  }
}
Table 8.6. Descriptions of create event value fields
ItemField nameDescription

1

schema

The value’s schema, which describes the structure of the value’s payload. A change event’s value schema is the same in every change event that the connector generates for a particular table.

2

name

In the schema section, each name field specifies the schema for a field in the value’s payload.

server1.dbo.customers.Value is the schema for the payload’s before and after fields. This schema is specific to the customers table.

Names of schemas for before and after fields are of the form logicalName.database-schemaName.tableName.Value, which ensures that the schema name is unique in the database. This means that when using the Avro converter, the resulting Avro schema for each table in each logical source has its own evolution and history.

3

name

io.debezium.connector.sqlserver.Source is the schema for the payload’s source field. This schema is specific to the SQL Server connector. The connector uses it for all events that it generates.

4

name

server1.dbo.customers.Envelope is the schema for the overall structure of the payload, where server1 is the connector name, dbo is the database schema name, and customers is the table.

5

payload

The value’s actual data. This is the information that the change event is providing.

It may appear that the JSON representations of the events are much larger than the rows they describe. This is because the JSON representation must include the schema and the payload portions of the message. However, by using the Avro converter, you can significantly decrease the size of the messages that the connector streams to Kafka topics.

6

before

An optional field that specifies the state of the row before the event occurred. When the op field is c for create, as it is in this example, the before field is null since this change event is for new content.

7

after

An optional field that specifies the state of the row after the event occurred. In this example, the after field contains the values of the new row’s id, first_name, last_name, and email columns.

8

source

Mandatory field that describes the source metadata for the event. This field contains information that you can use to compare this event with other events, with regard to the origin of the events, the order in which the events occurred, and whether events were part of the same transaction. The source metadata includes:

  • Debezium version
  • Connector type and name
  • Database and schema names
  • Timestamp for when the change was made in the database
  • If the event was part of a snapshot
  • Name of the table that contains the new row
  • Server log offsets

9

op

Mandatory string that describes the type of operation that caused the connector to generate the event. In this example, c indicates that the operation created a row. Valid values are:

  • c = create
  • u = update
  • d = delete
  • r = read (applies to only snapshots)

10

ts_ms

Optional field that displays the time at which the connector processed the event. In the event message envelope, the time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time when a change was committed in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

update events

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

{
  "schema": { ... },
  "payload": {
    "before": { 1
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "john.doe@example.org"
    },
    "after": { 2
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "noreply@example.org"
    },
    "source": { 3
      "version": "1.7.2.Final",
      "connector": "sqlserver",
      "name": "server1",
      "ts_ms": 1559729995937,
      "snapshot": false,
      "db": "testDB",
      "schema": "dbo",
      "table": "customers",
      "change_lsn": "00000027:00000ac0:0002",
      "commit_lsn": "00000027:00000ac0:0007",
      "event_serial_no": "2"
    },
    "op": "u", 4
    "ts_ms": 1559729998706  5
  }
}
Table 8.7. Descriptions of update event value fields
ItemField nameDescription

1

before

An optional field that specifies the state of the row before the event occurred. In an update event value, the before field contains a field for each table column and the value that was in that column before the database commit. In this example, the email value is john.doe@example.org.

2

after

An optional field that specifies the state of the row after the event occurred. You can compare the before and after structures to determine what the update to this row was. In the example, the email value is now noreply@example.org.

3

source

Mandatory field that describes the source metadata for the event. The source field structure has the same fields as in a create event, but some values are different, for example, the sample update event has a different offset. The source metadata includes:

  • Debezium version
  • Connector type and name
  • Database and schema names
  • Timestamp for when the change was made in the database
  • If the event was part of a snapshot
  • Name of the table that contains the new row
  • Server log offsets

The event_serial_no field differentiates events that have the same commit and change LSN. Typical situations for when this field has a value other than 1:

  • update events have the value set to 2 because the update generates two events in the CDC change table of SQL Server (see the source documentation for details). The first event contains the old values and the second contains contains new values. The connector uses values in the first event to create the second event. The connector drops the first event.
  • When a primary key is updated SQL Server emits two evemts. A delete event for the removal of the record with the old primary key value and a create event for the addition of the record with the new primary key. Both operations share the same commit and change LSN and their event numbers are 1 and 2, respectively.

4

op

Mandatory string that describes the type of operation. In an update event value, the op field value is u, signifying that this row changed because of an update.

5

ts_ms

Optional field that displays the time at which the connector processed the event. In the event message envelope, the time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time when the change was committed to the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

Note

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

delete events

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

{
  "schema": { ... },
  },
  "payload": {
    "before": { <>
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "noreply@example.org"
    },
    "after": null, 1
    "source": { 2
      "version": "1.7.2.Final",
      "connector": "sqlserver",
      "name": "server1",
      "ts_ms": 1559730445243,
      "snapshot": false,
      "db": "testDB",
      "schema": "dbo",
      "table": "customers",
      "change_lsn": "00000027:00000db0:0005",
      "commit_lsn": "00000027:00000db0:0007",
      "event_serial_no": "1"
    },
    "op": "d", 3
    "ts_ms": 1559730450205 4
  }
}
Table 8.8. Descriptions of delete event value fields
ItemField nameDescription

1

before

Optional field that specifies the state of the row before the event occurred. In a delete event value, the before field contains the values that were in the row before it was deleted with the database commit.

2

after

Optional field that specifies the state of the row after the event occurred. In a delete event value, the after field is null, signifying that the row no longer exists.

3

source

Mandatory field that describes the source metadata for the event. In a delete event value, the source field structure is the same as for create and update events for the same table. Many source field values are also the same. In a delete event value, the ts_ms and pos field values, as well as other values, might have changed. But the source field in a delete event value provides the same metadata:

  • Debezium version
  • Connector type and name
  • Database and schema names
  • Timestamp for when the change was made in the database
  • If the event was part of a snapshot
  • Name of the table that contains the new row
  • Server log offsets

4

op

Mandatory string that describes the type of operation. The op field value is d, signifying that this row was deleted.

5

ts_ms

Optional field that displays the time at which the connector processed the event. In the event message envelope, the time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

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

Tombstone events

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

8.2.6. Debezium SQL Server connector-generated events that represent transaction boundaries

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

Limits on when Debezium receives transaction metadata

Debezium registers and receives metadata only for transactions that occur after you deploy the connector. Metadata for transactions that occur before you deploy the connector is not available.

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

status
BEGIN or END
id
String representation of unique transaction identifier.
event_count (for END events)
Total number of events emitted by the transaction.
data_collections (for END events)
An array of pairs of data_collection and event_count that provides the number of events emitted by changes originating from given data collection.
Warning

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

The following example shows a typical transaction boundary message:

Example: SQL Server connector transaction boundary event

{
  "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.

8.2.6.1. Change data event enrichment

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

id
String representation of unique transaction identifier
total_order
The absolute position of the event among all events generated by the transaction
data_collection_order
The per-data collection position of the event among all events that were emitted by the transaction

The following example shows what a typical message looks like:

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

8.2.7. How Debezium SQL Server connectors map data types

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

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

Literal type
Describes how the value is literally represented by using Kafka Connect schema types, namely INT8, INT16, INT32, INT64, FLOAT32, FLOAT64, BOOLEAN, STRING, BYTES, ARRAY, MAP, and STRUCT.
Semantic type
Describes how the Kafka Connect schema captures the meaning of the field using the name of the Kafka Connect schema for the field.

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

Basic types

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

Table 8.9. Data type mappings used by the SQL Server connector
SQL Server data typeLiteral 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.

Temporal values

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

SQL Server data typeLiteral type (schema type)Semantic type (schema name) and Notes

DATE

INT32

io.debezium.time.Date

Represents the number of days since the epoch.

TIME(0), TIME(1), TIME(2), TIME(3)

INT32

io.debezium.time.Time

Represents the number of milliseconds past midnight, and does not include timezone information.

TIME(4), TIME(5), TIME(6)

INT64

io.debezium.time.MicroTime

Represents the number of microseconds past midnight, and does not include timezone information.

TIME(7)

INT64

io.debezium.time.NanoTime

Represents the number of nanoseconds past midnight, and does not include timezone information.

DATETIME

INT64

io.debezium.time.Timestamp

Represents the number of milliseconds past the epoch, and does not include timezone information.

SMALLDATETIME

INT64

io.debezium.time.Timestamp

Represents the number of milliseconds past the epoch, and does not include timezone information.

DATETIME2(0), DATETIME2(1), DATETIME2(2), DATETIME2(3)

INT64

io.debezium.time.Timestamp

Represents the number of milliseconds past the epoch, and does not include timezone information.

DATETIME2(4), DATETIME2(5), DATETIME2(6)

INT64

io.debezium.time.MicroTimestamp

Represents the number of microseconds past the epoch, and does not include timezone information.

DATETIME2(7)

INT64

io.debezium.time.NanoTimestamp

Represents the number of nanoseconds past the epoch, and does not include timezone information.

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

SQL Server data typeLiteral type (schema type)Semantic type (schema name) and Notes

DATE

INT32

org.apache.kafka.connect.data.Date

Represents the number of days since the epoch.

TIME([P])

INT64

org.apache.kafka.connect.data.Time

Represents the number of milliseconds since midnight, and does not include timezone information. SQL Server allows P to be in the range 0-7 to store up to tenth of a microsecond precision, though this mode results in a loss of precision when P > 3.

DATETIME

INT64

org.apache.kafka.connect.data.Timestamp

Represents the number of milliseconds since the epoch, and does not include timezone information.

SMALLDATETIME

INT64

org.apache.kafka.connect.data.Timestamp

Represents the number of milliseconds past the epoch, and does not include timezone information.

DATETIME2

INT64

org.apache.kafka.connect.data.Timestamp

Represents the number of milliseconds since the epoch, and does not include timezone information. SQL Server allows P to be in the range 0-7 to store up to tenth of a microsecond precision, though this mode results in a loss of precision when P > 3.

Timestamp values

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

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

Decimal values

Debezium connectors handle decimals according to the setting of the decimal.handling.mode connector configuration property.

decimal.handling.mode=precise
Table 8.10. Mappings when decimal.handing.mode=precise
SQL Server typeLiteral type (schema type)Semantic type (schema name)

NUMERIC[(P[,S])]

BYTES

org.apache.kafka.connect.data.Decimal
The scale schema parameter contains an integer that represents how many digits the decimal point shifted.

DECIMAL[(P[,S])]

BYTES

org.apache.kafka.connect.data.Decimal
The scale schema parameter contains an integer that represents how many digits the decimal point shifted.

SMALLMONEY

BYTES

org.apache.kafka.connect.data.Decimal
The scale schema parameter contains an integer that represents how many digits the decimal point shifted.

MONEY

BYTES

org.apache.kafka.connect.data.Decimal
The scale schema parameter contains an integer that represents how many digits the decimal point shifted.

decimal.handling.mode=double
Table 8.11. Mappings when decimal.handing.mode=double
SQL Server typeLiteral typeSemantic type

NUMERIC[(M[,D])]

FLOAT64

n/a

DECIMAL[(M[,D])]

FLOAT64

n/a

SMALLMONEY[(M[,D])]

FLOAT64

n/a

MONEY[(M[,D])]

FLOAT64

n/a

decimal.handling.mode=string
Table 8.12. Mappings when decimal.handing.mode=string
SQL Server typeLiteral typeSemantic type

NUMERIC[(M[,D])]

STRING

n/a

DECIMAL[(M[,D])]

STRING

n/a

SMALLMONEY[(M[,D])]

STRING

n/a

MONEY[(M[,D])]

STRING

n/a

8.3. Setting up SQL Server to run a Debezium connector

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

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

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

8.3.1. Enabling CDC on the SQL Server database

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

Prerequisites

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

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

Procedure

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

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

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

    Example: Enabling a SQL Server database for the CDC template

    USE MyDB
    GO
    EXEC sys.sp_cdc_enable_db
    GO

8.3.2. Enabling CDC on a SQL Server table

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

Prerequisites

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

Procedure

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

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

    Example: Enabling CDC for a SQL Server table

    USE MyDB
    GO
    
    EXEC sys.sp_cdc_enable_table
    @source_schema = N'dbo',
    @source_name   = N'MyTable', //<.>
    @role_name     = N'MyRole',  //<.>
    @filegroup_name = N'MyDB_CT',//<.>
    @supports_net_changes = 0
    GO

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

8.3.3. Verifying that the user has access to the CDC table

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

Prerequisites

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

Procedure

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

    Queries should not return empty results.

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

    Example: Querying a table for CDC configuration information

    USE MyDB;
    GO
    EXEC sys.sp_cdc_help_change_data_capture
    GO

    The query returns configuration information for each table in the database that is enabled for CDC and that contains change data that the caller is authorized to access. If the result is empty, verify that the user has privileges to access both the capture instance and the CDC tables.

8.3.4. SQL Server on Azure

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

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

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

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

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

8.3.6. SQL Server capture job agent configuration parameters

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

Note

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

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

pollinginterval
  • Specifies the number of seconds that the capture agent waits between log scan cycles.
  • A higher value reduces the load on the database host and increases latency.
  • A value of 0 specifies no wait between scans.
  • The default value is 5.
maxtrans
  • Specifies the maximum number of transactions to process during each log scan cycle. After the capture job processes the specified number of transactions, it pauses for the length of time that the pollinginterval specifies before the next scan begins.
  • A lower value reduces the load on the database host and increases latency.
  • The default value is 500.
maxscans
  • Specifies a limit on the number of scan cycles that the capture job can attempt in capturing the full contents of the database transaction log. If the continuous parameter is set to 1, the job pauses for the length of time that the pollinginterval specifies before it resumes scanning.
  • A lower values reduces the load on the database host and increases latency.
  • The default value is 10.

Additional resources

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

8.4. Deployment of Debezium SQL Server connectors

You can use either of the following methods to deploy a Debezium SQL Server connector:

8.4.1. SQL Server connector deployment using AMQ Streams

Beginning with Debezium 1.7, the preferred method for deploying a Debezium connector is to use AMQ Streams to build a Kafka Connect container image that includes the connector plug-in.

During the deployment process, you create and use the following custom resources (CRs):

  • A KafkaConnect CR that defines your Kafka Connect instance and includes information about the connector artifacts needs to include in the image.
  • A KafkaConnector CR that provides details that include information the connector uses to access the source database. After AMQ Streams starts the Kafka Connect pod, you start the connector by applying the KafkaConnector CR.

In the build specification for the Kafka Connect image, you can specify the connectors that are available to deploy. For each connector plug-in, you can also specify other components that you want to make available for deployment. For example, you can add Service Registry artifacts, or the Debezium scripting component. When AMQ Streams builds the Kafka Connect image, it downloads the specified artifacts, and incorporates them into the image.

The spec.build.output parameter in the KafkaConnect CR specifies where to store the resulting Kafka Connect container image. Container images can be stored in a Docker registry, or in an OpenShift ImageStream. To store images in an ImageStream, you must create the ImageStream before you deploy Kafka Connect. ImageStreams are not created automatically.

Note

If you use a KafkaConnect resource to create a cluster, afterwards you cannot use the Kafka Connect REST API to create or update connectors. You can still use the REST API to retrieve information.

8.4.2. Using AMQ Streams to deploy a Debezium SQL Server connector

With earlier versions of AMQ Streams, to deploy Debezium connectors on OpenShift, it was necessary to first build a Kafka Connect image for the connector. The current preferred method for deploying connectors on OpenShift is to use a build configuration in AMQ Streams to automatically build a Kafka Connect container image that includes the Debezium connector plug-ins that you want to use.

During the build process, the AMQ Streams Operator transforms input parameters in a KafkaConnect custom resource, including Debezium connector definitions, into a Kafka Connect container image. The build downloads the necessary artifacts from the Red Hat Maven repository or another configured HTTP server. The newly created container is pushed to the container registry that is specified in .spec.build.output, and is used to deploy a Kafka Connect pod. After AMQ Streams builds the Kafka Connect image, you create KafkaConnector custom resources to start the connectors that are included in the build.

Prerequisites

  • You have access to an OpenShift cluster on which the cluster Operator is installed.
  • The AMQ Streams Operator is running.
  • An Apache Kafka cluster is deployed as documented in Deploying and Upgrading AMQ Streams on OpenShift.
  • You have a Red Hat Integration license.
  • Kafka Connect is deployed on AMQ Streams.
  • The OpenShift oc CLI client is installed or you have access to the OpenShift Container Platform web console.
  • Depending on how you intend to store the Kafka Connect build image, you need registry permissions or you must create an ImageStream resource:

    To store the build image in an image registry, such as Red Hat Quay.io or Docker Hub
    • An account and permissions to create and manage images in the registry.
    To store the build image as a native OpenShift ImageStream
    • An ImageStream resource is deployed to the cluster. You must explicitly create an ImageStream for the cluster. ImageStreams are not available by default.

Procedure

  1. Log in to the OpenShift cluster.
  2. Create a Debezium KafkaConnect custom resource (CR) for the connector, or modify an existing one. For example, create a KafkaConnect CR that specifies the metadata.annotations and spec.build properties, as shown in the following example. Save the file with a name such as dbz-connect.yaml.

    Example 8.1. A dbz-connect.yaml file that defines a KafkaConnect custom resource that includes a Debezium connector

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnect
    metadata:
      name: debezium-kafka-connect-cluster
      annotations:
        strimzi.io/use-connector-resources: "true" 1
    spec:
      version: 3.00
      build: 2
        output: 3
          type: imagestream  4
          image: debezium-streams-connect:latest
        plugins: 5
          - name: debezium-connector-sqlserver
            artifacts:
              - type: zip 6
                url: https://maven.repository.redhat.com/ga/io/debezium/debezium-connector-sqlserver/1.7.2.Final-redhat-<build_number>/debezium-connector-sqlserver-1.7.2.Final-redhat-<build_number>-plugin.zip  7
              - type: zip
                url: https://maven.repository.redhat.com/ga/io/apicurio/apicurio-registry-distro-connect-converter/2.0-redhat-<build-number>/apicurio-registry-distro-connect-converter-2.0-redhat-<build-number>.zip
              - type: zip
                url: https://maven.repository.redhat.com/ga/io/debezium/debezium-scripting/1.7.2.Final/debezium-scripting-1.7.2.Final.zip
    
      bootstrapServers: debezium-kafka-cluster-kafka-bootstrap:9093
    Table 8.13. Descriptions of Kafka Connect configuration settings
    ItemDescription

    1

    Sets the strimzi.io/use-connector-resources annotation to "true" to enable the Cluster Operator to use KafkaConnector resources to configure connectors in this Kafka Connect cluster.

    2

    The spec.build configuration specifies where to store the build image and lists the plug-ins to include in the image, along with the location of the plug-in artifacts.

    3

    The build.output specifies the registry in which the newly built image is stored.

    4

    Specifies the name and image name for the image output. Valid values for output.type are docker to push into a container registry like Docker Hub or Quay, or imagestream to push the image to an internal OpenShift ImageStream. To use an ImageStream, an ImageStream resource must be deployed to the cluster. For more information about specifying the build.output in the KafkaConnect configuration, see the AMQ Streams Build schema reference documentation.

    5

    The plugins configuration lists all of the connectors that you want to include in the Kafka Connect image. For each entry in the list, specify a plug-in name, and information for about the artifacts that are required to build the connector. Optionally, for each connector plug-in, you can include other components that you want to be available for use with the connector. For example, you can add Service Registry artifacts, or the Debezium scripting component.

    6

    The value of artifacts.type specifies the file type of the artifact specified in the artifacts.url. Valid types are zip, tgz, or jar. Debezium connector archives are provided in .zip file format. JDBC driver files are in .jar format. The type value must match the type of the file that is referenced in the url field.

    7

    The value of artifacts.url specifies the address of an HTTP server, such as a Maven repository, that stores the file for the connector artifact. The OpenShift cluster must have access to the specified server.

  3. Apply the KafkaConnect build specification to the OpenShift cluster by entering the following command:

    oc create -f dbz-connect.yaml

    Based on the configuration specified in the custom resource, the Streams Operator prepares a Kafka Connect image to deploy.
    After the build completes, the Operator pushes the image to the specified registry or ImageStream, and starts the Kafka Connect cluster. The connector artifacts that you listed in the configuration are available in the cluster.

  4. Create a KafkaConnector resource to define an instance of each connector that you want to deploy.
    For example, create the following KafkaConnector CR, and save it as sqlserver-inventory-connector.yaml

    Example 8.2. A sqlserver-inventory-connector.yaml file that defines the KafkaConnector custom resource for a Debezium connector

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnector
    metadata:
      labels:
        strimzi.io/cluster: debezium-kafka-connect-cluster
      name: inventory-connector-sqlserver 1
    spec:
      class: io.debezium.connector.sqlserver.SqlServerConnector 2
      tasksMax: 1  3
      config:  4
        database.history.kafka.bootstrap.servers: 'debezium-kafka-cluster-kafka-bootstrap.debezium.svc.cluster.local:9092'
        database.history.kafka.topic: schema-changes.inventory
        database.hostname: sqlserver.debezium-sqlserver.svc.cluster.local 5
        database.port: 3306   6
        database.user: debezium  7
        database.password: dbz  8
        database.dbname: mydatabase 9
        database.server.name: inventory_connector_sqlserver 10
        database.include.list: public.inventory  11
    Table 8.14. Descriptions of connector configuration settings
    ItemDescription

    1

    The name of the connector to register with the Kafka Connect cluster.

    2

    The name of the connector class.

    3

    The number of tasks that can operate concurrently.

    4

    The connector’s configuration.

    5

    The address of the host database instance.

    6

    The port number of the database instance.

    7

    The name of the user account through which Debezium connects to the database.

    8

    The password for the database user account.

    9

    The name of the database to capture changes from.

    10

    The logical name of the database instance or cluster.
    The specified name must be formed only from alphanumeric characters or underscores.
    Because the logical name is used as the prefix for any Kafka topics that receive change events from this connector, the name must be unique among the connectors in the cluster.
    The namespace is also used in the names of related Kafka Connect schemas, and the namespaces of a corresponding Avro schema if you integrate the connector with the Avro connector.

    11

    The list of tables from which the connector captures change events.

  5. Create the connector resource by running the following command:

    oc create -n <namespace> -f <kafkaConnector>.yaml

    For example,

    oc create -n debezium -f {context}-inventory-connector.yaml

    The connector is registered to the Kafka Connect cluster and starts to run against the database that is specified by spec.config.database.dbname in the KafkaConnector CR. After the connector pod is ready, Debezium is running.

You are now ready to verify the Debezium SQL Server deployment.

8.4.3. Deploying a Debezium SQL Server connector by building a custom Kafka Connect container image from a Dockerfile

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

  • A KafkaConnect CR that defines your Kafka Connect instance. The image property in the CR specifies the name of the container image that you create to run your Debezium connector. You apply this CR to the OpenShift instance where Red Hat AMQ Streams is deployed. AMQ Streams offers operators and images that bring Apache Kafka to OpenShift.
  • A KafkaConnector CR that defines your Debezium SQL Server connector. Apply this CR to the same OpenShift instance where you apply the KafkaConnect CR.

Prerequisites

  • SQL Server is running and you completed the steps to set up SQL Server to work with a Debezium connector.
  • AMQ Streams is deployed on OpenShift and is running Apache Kafka and Kafka Connect. For more information, see Deploying and Upgrading AMQ Streams on OpenShift
  • Podman or Docker is installed.
  • You have an account and permissions to create and manage containers in the container registry (such as quay.io or docker.io) to which you plan to add the container that will run your Debezium connector.

Procedure

  1. Create the Debezium SQL Server container for Kafka Connect:

    1. Download the Debezium SQL Server connector archive.
    2. Extract the Debezium SQL Server connector archive to create a directory structure for the connector plug-in, for example:

      ./my-plugins/
      ├── debezium-connector-sqlserver
      │   ├── ...
    3. Create a Dockerfile that uses registry.redhat.io/amq7/amq-streams-kafka-30-rhel8:2.0.0 as the base image. For example, from a terminal window, enter the following, replacing my-plugins with the name of your plug-ins directory:

      cat <<EOF >debezium-container-for-sqlserver.yaml 1
      FROM registry.redhat.io/amq7/amq-streams-kafka-30-rhel8:2.0.0
      USER root:root
      COPY ./<my-plugins>/ /opt/kafka/plugins/ 2
      USER 1001
      EOF
      1 1 1 1 1 1 1 1 1
      You can specify any file name that you want.
      2 2 2 2 2 2 1 2 2 2
      Replace my-plugins with the name of your plug-ins directory.

      The command creates a Dockerfile with the name debezium-container-for-sqlserver.yaml in the current directory.

    4. Build the container image from the debezium-container-for-sqlserver.yaml Docker file that you created in the previous step. From the directory that contains the file, open a terminal window and enter one of the following commands:

      podman build -t debezium-container-for-sqlserver:latest .
      docker build -t debezium-container-for-sqlserver:latest .

      The preceding commands build a container image with the name debezium-container-for-sqlserver.

    5. Push your custom image to a container registry, such as quay.io or an internal container registry. The container registry must be available to the OpenShift instance where you want to deploy the image. Enter one of the following commands:

      podman push <myregistry.io>/debezium-container-for-sqlserver:latest
      docker push <myregistry.io>/debezium-container-for-sqlserver:latest
    6. Create a new Debezium SQL Server KafkaConnect custom resource (CR). For example, create a KafkaConnect CR with the name dbz-connect.yaml that specifies annotations and image properties as shown in the following example:

      apiVersion: kafka.strimzi.io/v1beta2
      kind: KafkaConnect
      metadata:
        name: my-connect-cluster
        annotations:
          strimzi.io/use-connector-resources: "true" 1
      spec:
        #...
        image: debezium-container-for-sqlserver  2
      1
      metadata.annotations indicates to the Cluster Operator that KafkaConnector resources are used to configure connectors in this Kafka Connect cluster.
      2
      spec.image specifies the name of the image that you created to run your Debezium connector. This property overrides the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE variable in the Cluster Operator
    7. Apply the KafkaConnect CR to the OpenShift Kafka Connect environment by entering the following command:

      oc create -f dbz-connect.yaml

      The command adds a Kafka Connect instance that specifies the name of the image that you created to run your Debezium connector.

  2. Create a KafkaConnector custom resource that configures your Debezium SQL Server connector instance.

    You configure a Debezium SQL Server connector in a .yaml file that specifies the configuration properties for the connector. The connector configuration might instruct Debezium to produce events for a subset of the schemas and tables, or it might set properties so that Debezium ignores, masks, or truncates values in specified columns that are sensitive, too large, or not needed.

    The following example configures a Debezium connector that connects to a SQL server host, 192.168.99.100, on port 1433. This host has a database named testDB, a table with the name customers, and fulfillment is the server’s logical name.

    SQL Server fulfillment-connector.yaml

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnector
    metadata:
      name: fulfillment-connector 1
      labels:
        strimzi.io/cluster: my-connect-cluster
      annotations:
        strimzi.io/use-connector-resources: 'true'
    spec:
      class: io.debezium.connector.sqlserver.SqlServerConnector 2
      config:
        database.hostname: 192.168.99.100 3
        database.port: 1433 4
        database.user: debezium 5
        database.password: dbz 6
        database.dbname: testDB 7
        database.server.name: fullfullment 8
        database.include.list: dbo.customers 9
        database.history.kafka.bootstrap.servers: my-cluster-kafka-bootstrap:9092 10
        database.history.kafka.topic: dbhistory.fullfillment 11

    Table 8.15. Descriptions of connector configuration settings
    ItemDescription

    1

    The name of our connector when we register it with a Kafka Connect service.

    2

    The name of this SQL Server connector class.

    3

    The address of the SQL Server instance.

    4

    The port number of the SQL Server instance.

    5

    The name of the SQL Server user.

    6

    The password for the SQL Server user.

    7

    The name of the database to capture changes from.

    8

    The logical name of the SQL Server instance/cluster, which forms a namespace and is used in all the names of the Kafka topics to which the connector writes, the Kafka Connect schema names, and the namespaces of the corresponding Avro schema when the Avro converter is used.

    9

    A list of all tables whose changes Debezium should capture.

    10

    The list of Kafka brokers that this connector will use to write and recover DDL statements to the database history topic.

    11

    The name of the database history topic where the connector will write and recover DDL statements. This topic is for internal use only and should not be used by consumers.

  3. Create your connector instance with Kafka Connect. For example, if you saved your KafkaConnector resource in the fulfillment-connector.yaml file, you would run the following command:

    oc apply -f fulfillment-connector.yaml

    The preceding command registers fulfillment-connector and the connector starts to run against the testDB database as defined in the KafkaConnector CR.

Verifying that the Debezium SQL Server connector is running

If the connector starts correctly without errors, it creates a topic for each table that the connector is configured to capture. Downstream applications can subscribe to these topics to retrieve information events that occur in the source database.

To verify that the connector is running, you perform the following operations from the OpenShift Container Platform web console, or through the OpenShift CLI tool (oc):

  • Verify the connector status.
  • Verify that the connector generates topics.
  • Verify that topics are populated with events for read operations ("op":"r") that the connector generates during the initial snapshot of each table.

Prerequisites

  • A Debezium connector is deployed to AMQ Streams on OpenShift.
  • The OpenShift oc CLI client is installed.
  • You have access to the OpenShift Container Platform web console.

Procedure

  1. Check the status of the KafkaConnector resource by using one of the following methods:

    • From the OpenShift Container Platform web console:

      1. Navigate to Home Search.
      2. On the Search page, click Resources to open the Select Resource box, and then type KafkaConnector.
      3. From the KafkaConnectors list, click the name of the connector that you want to check, for example inventory-connector-sqlserver.
      4. In the Conditions section, verify that the values in the Type and Status columns are set to Ready and True.
    • From a terminal window:

      1. Enter the following command:

        oc describe KafkaConnector <connector-name> -n <project>

        For example,

        oc describe KafkaConnector inventory-connector-sqlserver -n debezium

        The command returns status information that is similar to the following output:

        Example 8.3. KafkaConnector resource status

        Name:         inventory-connector-sqlserver
        Namespace:    debezium
        Labels:       strimzi.io/cluster=debezium-kafka-connect-cluster
        Annotations:  <none>
        API Version:  kafka.strimzi.io/v1beta2
        Kind:         KafkaConnector
        
        ...
        
        Status:
          Conditions:
            Last Transition Time:  2021-12-08T17:41:34.897153Z
            Status:                True
            Type:                  Ready
          Connector Status:
            Connector:
              State:      RUNNING
              worker_id:  10.131.1.124:8083
            Name:         inventory-connector-sqlserver
            Tasks:
              Id:               0
              State:            RUNNING
              worker_id:        10.131.1.124:8083
            Type:               source
          Observed Generation:  1
          Tasks Max:            1
          Topics:
            inventory_connector_sqlserver
            inventory_connector_sqlserver.inventory.addresses
            inventory_connector_sqlserver.inventory.customers
            inventory_connector_sqlserver.inventory.geom
            inventory_connector_sqlserver.inventory.orders
            inventory_connector_sqlserver.inventory.products
            inventory_connector_sqlserver.inventory.products_on_hand
        Events:  <none>
  2. Verify that the connector created Kafka topics:

    • From the OpenShift Container Platform web console.

      1. Navigate to Home Search.
      2. On the Search page, click Resources to open the Select Resource box, and then type KafkaTopic.
      3. From the KafkaTopics list, click the name of the topic that you want to check, for example, inventory-connector-sqlserver.inventory.orders---ac5e98ac6a5d91e04d8ec0dc9078a1ece439081d.
      4. In the Conditions section, verify that the values in the Type and Status columns are set to Ready and True.
    • From a terminal window:

      1. Enter the following command:

        oc get kafkatopics

        The command returns status information that is similar to the following output:

        Example 8.4. KafkaTopic resource status

        NAME                                                                                                   CLUSTER             PARTITIONS   REPLICATION FACTOR   READY
        connect-cluster-configs                                                                           debezium-kafka-cluster   1            1                    True
        connect-cluster-offsets                                                                           debezium-kafka-cluster   25           1                    True
        connect-cluster-status                                                                            debezium-kafka-cluster   5            1                    True
        consumer-offsets---84e7a678d08f4bd226872e5cdd4eb527fadc1c6a                                       debezium-kafka-cluster   50           1                    True
        inventory-connector-sqlserver---a96f69b23d6118ff415f772679da623fbbb99421                              debezium-kafka-cluster   1            1                    True
        inventory-connector-sqlserver.inventory.addresses---1b6beaf7b2eb57d177d92be90ca2b210c9a56480          debezium-kafka-cluster   1            1                    True
        inventory-connector-sqlserver.inventory.customers---9931e04ec92ecc0924f4406af3fdace7545c483b          debezium-kafka-cluster   1            1                    True
        inventory-connector-sqlserver.inventory.geom---9f7e136091f071bf49ca59bf99e86c713ee58dd5               debezium-kafka-cluster   1            1                    True
        inventory-connector-sqlserver.inventory.orders---ac5e98ac6a5d91e04d8ec0dc9078a1ece439081d             debezium-kafka-cluster   1            1                    True
        inventory-connector-sqlserver.inventory.products---df0746db116844cee2297fab611c21b56f82dcef           debezium-kafka-cluster   1            1                    True
        inventory-connector-sqlserver.inventory.products-on-hand---8649e0f17ffcc9212e266e31a7aeea4585e5c6b5   debezium-kafka-cluster   1            1                    True
        schema-changes.inventory                                                                          debezium-kafka-cluster   1            1                    True
        strimzi-store-topic---effb8e3e057afce1ecf67c3f5d8e4e3ff177fc55                                    debezium-kafka-cluster   1            1                    True
        strimzi-topic-operator-kstreams-topic-store-changelog---b75e702040b99be8a9263134de3507fc0cc4017b  debezium-kafka-cluster   1            1                    True
  3. Check topic content.

    • From a terminal window, enter the following command:
    oc exec -n <project>  -it <kafka-cluster> -- /opt/kafka/bin/kafka-console-consumer.sh \
    >     --bootstrap-server localhost:9092 \
    >     --from-beginning \
    >     --property print.key=true \
    >     --topic=<topic-name>

    For example,

     oc exec -n debezium  -it debezium-kafka-cluster-kafka-0 -- /opt/kafka/bin/kafka-console-consumer.sh \
    >     --bootstrap-server localhost:9092 \
    >     --from-beginning \
    >     --property print.key=true \
    >     --topic=inventory_connector_sqlserver.inventory.products_on_hand

    The format for specifying the topic name is the same as the oc describe command returns in Step 1, for example, inventory_connector_sqlserver.inventory.addresses.

    For each event in the topic, the command returns information that is similar to the following output:

    Example 8.5. Content of a Debezium change event

    {"schema":{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"}],"optional":false,"name":"inventory_connector_sqlserver.inventory.products_on_hand.Key"},"payload":{"product_id":101}}	{"schema":{"type":"struct","fields":[{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"},{"type":"int32","optional":false,"field":"quantity"}],"optional":true,"name":"inventory_connector_sqlserver.inventory.products_on_hand.Value","field":"before"},{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"},{"type":"int32","optional":false,"field":"quantity"}],"optional":true,"name":"inventory_connector_sqlserver.inventory.products_on_hand.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":"string","optional":true,"name":"io.debezium.data.Enum","version":1,"parameters":{"allowed":"true,last,false"},"default":"false","field":"snapshot"},{"type":"string","optional":false,"field":"db"},{"type":"string","optional":true,"field":"sequence"},{"type":"string","optional":true,"field":"table"},{"type":"int64","optional":false,"field":"server_id"},{"type":"string","optional":true,"field":"gtid"},{"type":"string","optional":false,"field":"file"},{"type":"int64","optional":false,"field":"pos"},{"type":"int32","optional":false,"field":"row"},{"type":"int64","optional":true,"field":"thread"},{"type":"string","optional":true,"field":"query"}],"optional":false,"name":"io.debezium.connector.sqlserver.Source","field":"source"},{"type":"string","optional":false,"field":"op"},{"type":"int64","optional":true,"field":"ts_ms"},{"type":"struct","fields":[{"type":"string","optional":false,"field":"id"},{"type":"int64","optional":false,"field":"total_order"},{"type":"int64","optional":false,"field":"data_collection_order"}],"optional":true,"field":"transaction"}],"optional":false,"name":"inventory_connector_sqlserver.inventory.products_on_hand.Envelope"},"payload":{"before":null,"after":{"product_id":101,"quantity":3},"source":{"version":"1.7.2.Final-redhat-00001","connector":"sqlserver","name":"inventory_connector_sqlserver","ts_ms":1638985247805,"snapshot":"true","db":"inventory","sequence":null,"table":"products_on_hand","server_id":0,"gtid":null,"file":"sqlserver-bin.000003","pos":156,"row":0,"thread":null,"query":null},"op":"r","ts_ms":1638985247805,"transaction":null}}

    In the preceding example, the payload value shows that the connector snapshot generated a read ("op" ="r") event from the table inventory.products_on_hand. The "before" state of the product_id record is null, indicating that no previous value exists for the record. The "after" state shows a quantity of 3 for the item with product_id 101.

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

Results

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

8.4.4. Descriptions of Debezium SQL Server connector configuration properties

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

Information about the properties is organized as follows:

Required Debezium SQL Server connector configuration properties

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

PropertyDefaultDescription

name

No default

Unique name for the connector. Attempting to register again with the same name will fail. (This property is required by all Kafka Connect connectors.)

connector.class

No default

The name of the Java class for the connector. Always use a value of io.debezium.connector.sqlserver.SqlServerConnector for the SQL Server connector.

tasks.max

1

The maximum number of tasks that should be created for this connector. The SQL Server connector always uses a single task and therefore does not use this value, so the default is always acceptable.

database.hostname

No default

IP address or hostname of the SQL Server database server.

database.port

1433

Integer port number of the SQL Server database server.

database.user

No default

Username to use when connecting to the SQL Server database server.

database.password

No default

Password to use when connecting to the SQL Server database server.

database.dbname

No default

The name of the SQL Server database from which to stream the changes Must not be used with database.names.

database.names

No default

The comma-separated list of the SQL Server database names from which to stream the changes. Currently, only one database name is supported. Must not be used with database.dbname.

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

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

database.server.name

No default

Logical name that identifies and provides a namespace for the SQL Server database server that you want Debezium to capture. The logical name should be unique across all other connectors, since it is used as a prefix for all Kafka topic names emanating from this connector. Only alphanumeric characters, hyphens, dots and underscores must be used.

table.include.list

No default

An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables that you want Debezium to capture; any table that is not included in table.include.list is excluded from capture. Each identifier is of the form schemaName.tableName. By default, the connector captures all non-system tables for the designated schemas. Must not be used with table.exclude.list.

table.exclude.list

No default

An optional comma-separated list of regular expressions that match fully-qualified table identifiers for the tables that you want to exclude from being captured; Debezium captures all tables that are not included in table.exclude.list. Each identifier is of the form schemaName.tableName. Must not be used with table.include.list.

column.include.list

empty string

An optional comma-separated list of regular expressions that match the fully-qualified names of columns that should be included in the change event message values. Fully-qualified names for columns are of the form schemaName.tableName.columnName. Note that primary key columns are always included in the event’s key, even if not included in the value. Do not also set the column.exclude.list property.

column.exclude.list

empty string

An optional comma-separated list of regular expressions that match the fully-qualified names of columns that should be excluded from change event message values. Fully-qualified names for columns are of the form schemaName.tableName.columnName. Note that primary key columns are always included in the event’s key, also if excluded from the value. Do not also set the column.include.list property.

column.mask.hash.hashAlgorithm.with.salt.salt

n/a

An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns. Fully-qualified names for columns are of the form `<schemaName>.<tableName>._<columnName>`. In the resulting change event record, the values for the specified columns are replaced with pseudonyms.

A pseudonym consists of the hashed value that results from applying the specified hashAlgorithm and salt. Based on the hash function that is used, referential integrity is maintained, while column values are replaced with pseudonyms. Supported hash functions are described in the MessageDigest section of the Java Cryptography Architecture Standard Algorithm Name Documentation.

In the following example, CzQMA0cB5K is a randomly selected salt.

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

If necessary, the pseudonym is automatically shortened to the length of the column. The connector configuration can include multiple properties that specify different hash algorithms and salts.

Depending on the hashAlgorithm used, the salt selected, and the actual data set, the resulting data set might not be completely masked.

time.precision.mode

adaptive

Time, date, and timestamps can be represented with different kinds of precision, including: adaptive (the default) captures the time and timestamp values exactly as in the database using either millisecond, microsecond, or nanosecond precision values based on the database column’s type; or connect always represents time and timestamp values using Kafka Connect’s built-in representations for Time, Date, and Timestamp, which uses millisecond precision regardless of the database columns' precision. See temporal values.

decimal.handling.mode

precise

Specifies how the connector should handle values for DECIMAL and NUMERIC columns:

precise (the default) represents them precisely using java.math.BigDecimal values represented in change events in a binary form.

double represents them using double values, which may result in a loss of precision but is easier to use.

string encodes values as formatted strings, which is easy to consume but semantic information about the real type is lost.

include.schema.changes

true

Boolean value that specifies whether the connector should publish changes in the database schema to a Kafka topic with the same name as the database server ID. Each schema change is recorded with a key that contains the database name and a value that is a JSON structure that describes the schema update. This is independent of how the connector internally records database history. The default is true.

tombstones.on.delete

true

Controls whether a delete event is followed by a tombstone event.

true - a delete operation is represented by a delete event and a subsequent tombstone event.

false - only a delete event is emitted.

After a source record is deleted, emitting a tombstone event (the default behavior) allows Kafka to completely delete all events that pertain to the key of the deleted row in case log compaction is enabled for the topic.

column.truncate.to._length_.chars

n/a

An optional comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be truncated in the change event message values if the field values are longer than the specified number of characters. Multiple properties with different lengths can be used in a single configuration, although in each the length must be a positive integer. Fully-qualified names for columns are of the form schemaName.tableName.columnName.

column.mask.with._length_.chars

n/a

An optional comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be replaced in the change event message values with a field value consisting of the specified number of asterisk (*) characters. Multiple properties with different lengths can be used in a single configuration, although in each the length must be a positive integer or zero. Fully-qualified names for columns are of the form schemaName.tableName.columnName.

column.propagate.source.type

n/a

An optional comma-separated list of regular expressions that match the fully-qualified names of columns whose original type and length should be added as a parameter to the corresponding field schemas in the emitted change messages. The schema parameters __debezium.source.column.type, __debezium.source.column.length and __debezium.source.column.scale is used to propagate the original type name and length (for variable-width types), respectively. Useful to properly size corresponding columns in sink databases. Fully-qualified names for columns are of the form schemaName.tableName.columnName.

datatype.propagate.source.type+

n/a

An optional comma-separated list of regular expressions that match the database-specific data type name of columns whose original type and length should be added as a parameter to the corresponding field schemas in the emitted change messages. The schema parameters __debezium.source.column.type, __debezium.source.column.length and __debezium.source.column.scale will be used to propagate the original type name and length (for variable-width types), respectively. Useful to properly size corresponding columns in sink databases. Fully-qualified data type names are of the form schemaName.tableName.typeName. See SQL Server data types for the list of SQL Server-specific data type names.

message.key.columns

n/a

A list of expressions that specify the columns that the connector uses to form custom message keys for change event records that it publishes to the Kafka topics for specified tables.

By default, Debezium uses the primary key column of a table as the message key for records that it emits. In place of the default, or to specify a key for tables that lack a primary key, you can configure custom message keys based on one or more columns.

To establish a custom message key for a table, list the table, followed by the columns to use as the message key. Each list entry takes the following format:

<fully-qualified_tableName>:_<keyColumn>_,<keyColumn>

To base a table key on multiple column names, insert commas between the column names.

Each fully-qualified table name is a regular expression in the following format:

<schemaName>.<tableName>

The property can include entries for multiple tables. Use a semicolon to separate table entries in the list.

The following example sets the message key for the tables inventory.customers and purchase.orders:

inventory.customers:pk1,pk2;(.*).purchaseorders:pk3,pk4

For the table inventory.customer, the columns pk1 and pk2 are specified as the message key. For the purchaseorders tables in any schema, the columns pk3 and pk4 server as the message key.

There is no limit to the number of columns that you use to create custom message keys. However, it’s best to use the minimum number that are required to specify a unique key.

binary.handling.mode

bytes

Specifies how binary (binary, varbinary) columns should be represented in change events, including: bytes represents binary data as byte array (default), base64 represents binary data as base64-encoded String, hex represents binary data as hex-encoded (base16) String

Advanced SQL Server connector configuration properties

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

PropertyDefaultDescription

snapshot.mode

initial

A mode for taking an initial snapshot of the structure and optionally data of captured tables. Once the snapshot is complete, the connector will continue reading change events from the database’s redo logs. The following values are supported:

  • initial: Takes a snapshot of structure and data of captured tables; useful if topics should be populated with a complete representation of the data from the captured tables.
  • initial_only: Takes a snapshot of structure and data like initial but instead does not transition into streaming changes once the snapshot has completed.
  • schema_only: Takes a snapshot of the structure of captured tables only; useful if only changes happening from now onwards should be propagated to topics.

snapshot.include.collection.list

All tables specified in table.include.list

An optional, comma-separated list of regular expressions that match the fully-qualified names (<dbName>.<schemaName>.<tableName>) of the tables to include in a snapshot. The specified items must be named in the connector’s table.include.list property. This property takes effect only if the connector’s snapshot.mode property is set to a value other than never.

This property does not affect the behavior of incremental snapshots.

snapshot.isolation.mode

repeatable_read

Mode to control which transaction isolation level is used and how long the connector locks tables that are designated for capture. The following values are supported:

  • read_uncommitted
  • read_committed
  • repeatable_read
  • snapshot
  • exclusive (exclusive mode uses repeatable read isolation level, however, it takes the exclusive lock on all tables to be read).

The snapshot, read_committed and read_uncommitted modes do not prevent other transactions from updating table rows during initial snapshot. The exclusive and repeatable_read modes do prevent concurrent updates.

Mode choice also affects data consistency. Only exclusive and snapshot modes guarantee full consistency, that is, initial snapshot and streaming logs constitute a linear history. In case of repeatable_read and read_committed modes, it might happen that, for instance, a record added appears twice - once in initial snapshot and once in streaming phase. Nonetheless, that consistency level should do for data mirroring. For read_uncommitted there are no data consistency guarantees at all (some data might be lost or corrupted).

event.processing.failure.handling.mode

fail

Specifies how the connector should react to exceptions during processing of events. fail will propagate the exception (indicating the offset of the problematic event), causing the connector to stop.
warn will cause the problematic event to be skipped and the offset of the problematic event to be logged.
skip will cause the problematic event to be skipped.

poll.interval.ms

1000

Positive integer value that specifies the number of milliseconds the connector should wait during each iteration for new change events to appear. Defaults to 1000 milliseconds, or 1 second.

max.queue.size

8192

Positive integer value that specifies the maximum size of the blocking queue into which change events read from the database log are placed before they are written to Kafka. This queue can provide backpressure to the CDC table reader when, for example, writes to Kafka are slower or if Kafka is not available. Events that appear in the queue are not included in the offsets periodically recorded by this connector. Defaults to 8192, and should always be larger than the maximum batch size specified in the max.batch.size property.

max.batch.size

2048

Positive integer value that specifies the maximum size of each batch of events that should be processed during each iteration of this connector. Defaults to 2048.

heartbeat.interval.ms

0

Controls how frequently heartbeat messages are sent.
This property contains an interval in milliseconds that defines how frequently the connector sends messages to a heartbeat topic. The property can be used to confirm whether the connector is still receiving change events from the database. You also should leverage heartbeat messages in cases where only records in non-captured tables are changed for a longer period of time. In such situation the connector would proceed to read the log from the database but never emit any change messages into Kafka, which in turn means that no offset updates are committed to Kafka. This may result in more change events to be re-sent after a connector restart. Set this parameter to 0 to not send heartbeat messages at all.
Disabled by default.

heartbeat.topics.prefix

__debezium-heartbeat

Controls the naming of the topic to which heartbeat messages are sent.
The topic is named according to the pattern <heartbeat.topics.prefix>.<server.name>.

snapshot.delay.ms

No default

An interval in milli-seconds that the connector should wait before taking a snapshot after starting up;
Can be used to avoid snapshot interruptions when starting multiple connectors in a cluster, which may cause re-balancing of connectors.

snapshot.fetch.size

2000

Specifies the maximum number of rows that should be read in one go from each table while taking a snapshot. The connector will read the table contents in multiple batches of this size. Defaults to 2000.

query.fetch.size

No default

Specifies the number of rows that will be fetched for each database round-trip of a given query. Defaults to the JDBC driver’s default fetch size.

snapshot.lock.timeout.ms

10000

An integer value that specifies the maximum amount of time (in milliseconds) to wait to obtain table locks when performing a snapshot. If table locks cannot be acquired in this time interval, the snapshot will fail (also see snapshots).
When set to 0 the connector will fail immediately when it cannot obtain the lock. Value -1 indicates infinite waiting.

snapshot.select.statement.overrides

No default

Specifies the table rows to include in a snapshot. Use the property if you want a snapshot to include only a subset of the rows in a table. This property affects snapshots only. It does not apply to events that the connector reads from the log.

The property contains a comma-separated list of fully-qualified table names in the form <schemaName>.<tableName>. For example,

"snapshot.select.statement.overrides": "inventory.products,customers.orders"

For each table in the list, add a further configuration property that specifies the SELECT statement for the connector to run on the table when it takes a snapshot. The specified SELECT statement determines the subset of table rows to include in the snapshot. Use the following format to specify the name of this SELECT statement property:

snapshot.select.statement.overrides.<schemaName>.<tableName>. For example, snapshot.select.statement.overrides.customers.orders.

Example:

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

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

In the resulting snapshot, the connector includes only the records for which delete_flag = 0.

sanitize.field.names

true when connector configuration explicitly specifies the key.converter or value.converter parameters to use Avro, otherwise defaults to false.

Whether field names are sanitized to adhere to Avro naming requirements.

provide.transaction.metadata

false

When set to true Debezium generates events with transaction boundaries and enriches data events envelope with transaction metadata.

See Transaction Metadata for additional details.

retriable.restart.connector.wait.ms

10000 (10 seconds)

The number of milli-seconds to wait before restarting a connector after a retriable error occurs.

skipped.operations

No default

comma-separated list of operation types that will be skipped during streaming. The operations include: c for inserts/create, u for updates, and d for deletes. By default, no operations are skipped.

signal.data.collection

No default value

Fully-qualified name of the data collection that is used to send signals to the connector.
Use the following format to specify the collection name:
<databaseName>.<schemaName>.<tableName>

Signaling is a Technology Preview feature.

incremental.snapshot.chunk.size

1024

The maximum number of rows that the connector fetches and reads into memory during an incremental snapshot chunk. Increasing the chunk size provides greater efficiency, because the snapshot runs fewer snapshot queries of a greater size. However, larger chunk sizes also require more memory to buffer the snapshot data. Adjust the chunk size to a value that provides the best performance in your environment.

max.iteration.transactions

0

Specifies the maximum number of transactions per iteration to be used to reduce the memory footprint when streaming changes from multiple tables in a database. When set to 0 (the default), the connector uses the current maximum LSN as the range to fetch changes from. When set to a value greater than zero, the connector uses the n-th LSN specified by this setting as the range to fetch changes from.

Debezium SQL Server connector database history configuration properties

Debezium provides a set of database.history.* properties that control how the connector interacts with the schema history topic.

The following table describes the database.history properties for configuring the Debezium connector.

Table 8.16. Connector database history configuration properties
PropertyDefaultDescription

database.history.kafka.topic

 

The full name of the Kafka topic where the connector stores the database schema history.

database.history.kafka.bootstrap.servers

 

A list of host/port pairs that the connector uses for establishing an initial connection to the Kafka cluster. This connection is used for retrieving the database schema history previously stored by the connector, and for writing each DDL statement read from the source database. Each pair should point to the same Kafka cluster used by the Kafka Connect process.

database.history.kafka.recovery.poll.interval.ms

100

An integer value that specifies the maximum number of milliseconds the connector should wait during startup/recovery while polling for persisted data. The default is 100ms.

database.history.kafka.recovery.attempts

4

The maximum number of times that the connector should try to read persisted history data before the connector recovery fails with an error. The maximum amount of time to wait after receiving no data is recovery.attempts x recovery.poll.interval.ms.

database.history.skip.unparseable.ddl

false

A Boolean value that specifies whether the connector should ignore malformed or unknown database statements or stop processing so a human can fix the issue. The safe default is false. Skipping should be used only with care as it can lead to data loss or mangling when the binlog is being processed.

database.history.store.only.monitored.tables.ddl

Deprecated and scheduled for removal in a future release; use database.history.store.only.captured.tables.ddl instead.

false

A Boolean value that specifies whether the connector should record all DDL statements

true records only those DDL statements that are relevant to tables whose changes are being captured by Debezium. Set to true with care because missing data might become necessary if you change which tables have their changes captured.

The safe default is false.

database.history.store.only.captured.tables.ddl

false

A Boolean value that specifies whether the connector should record all DDL statements

true records only those DDL statements that are relevant to tables whose changes are being captured by Debezium. Set to true with care because missing data might become necessary if you change which tables have their changes captured.

The safe default is false.

Pass-through database history properties for configuring producer and consumer clients


Debezium relies on a Kafka producer to write schema changes to database history topics. Similarly, it relies on a Kafka consumer to read from database history topics when a connector starts. You define the configuration for the Kafka producer and consumer clients by assigning values to a set of pass-through configuration properties that begin with the database.history.producer.* and database.history.consumer.* prefixes. The pass-through producer and consumer database history properties control a range of behaviors, such as how these clients secure connections with the Kafka broker, as shown in the following example:

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

Debezium strips the prefix from the property name before it passes the property to the Kafka client.

See the Kafka documentation for more details about Kafka producer configuration properties and Kafka consumer configuration properties.

Debezium SQL Server connector pass-through database driver configuration properties

The Debezium connector provides for pass-through configuration of the database driver. Pass-through database properties begin with the prefix database.*. For example, the connector passes properties such as database.foobar=false to the JDBC URL.

As is the case with the pass-through properties for database history clients, Debezium strips the prefixes from the properties before it passes them to the database driver.

8.5. Refreshing capture tables after a schema change

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

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

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

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

Warning

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

Note

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

Note

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

8.5.1. Running an offline update after a schema change

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

Prerequisites

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

Procedure

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

8.5.2. Running an online update after a schema change

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

Prerequisites

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

Procedure

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

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

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

  1. Modify the schema of the customers source table by running the following query to add the phone_number field:

    ALTER TABLE customers ADD phone_number VARCHAR(32);
  2. Create the new capture instance by running the sys.sp_cdc_enable_table stored procedure.

    EXEC sys.sp_cdc_enable_table @source_schema = 'dbo', @source_name = 'customers', @role_name = NULL, @supports_net_changes = 0, @capture_instance = 'dbo_customers_v2';
    GO
  3. Insert new data into the customers table by running the following query:

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

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

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

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

    ...
         {
            "type": "string",
            "optional": true,
            "field": "phone_number"
         }
    ...
        "after": {
          "id": 1005,
          "first_name": "John",
          "last_name": "Doe",
          "email": "john.doe@example.com",
          "phone_number": "+1-555-123456"
        },
  4. Drop the old capture instance by running the sys.sp_cdc_disable_table stored procedure.

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

8.6. Monitoring Debezium SQL Server connector performance

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

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

8.6.1. Debezium SQL Server connector snapshot metrics

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

Snapshot metrics are not exposed unless a snapshot operation is active, or if a snapshot has occurred since the last connector start.

The following table lists the shapshot metrics that are available.

AttributesTypeDescription

LastEvent

string

The last snapshot event that the connector has read.

MilliSecondsSinceLastEvent

long

The number of milliseconds since the connector has read and processed the most recent event.

TotalNumberOfEventsSeen

long

The total number of events that this connector has seen since last started or reset.

NumberOfEventsFiltered

long

The number of events that have been filtered by include/exclude list filtering rules configured on the connector.

MonitoredTables
Deprecated and scheduled for removal in a future release; use the CapturedTables metric instead.

string[]

The list of tables that are monitored by the connector.

CapturedTables

string[]

The list of tables that are captured by the connector.

QueueTotalCapacity

int

The length the queue used to pass events between the snapshotter and the main Kafka Connect loop.

QueueRemainingCapacity

int

The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop.

TotalTableCount

int

The total number of tables that are being included in the snapshot.

RemainingTableCount

int

The number of tables that the snapshot has yet to copy.

SnapshotRunning

boolean

Whether the snapshot was started.

SnapshotAborted

boolean

Whether the snapshot was aborted.

SnapshotCompleted

boolean

Whether the snapshot completed.

SnapshotDurationInSeconds

long

The total number of seconds that the snapshot has taken so far, even if not complete.

RowsScanned

Map<String, Long>

Map containing the number of rows scanned for each table in the snapshot. Tables are incrementally added to the Map during processing. Updates every 10,000 rows scanned and upon completing a table.

MaxQueueSizeInBytes

long

The maximum buffer of the queue in bytes. It will be enabled if max.queue.size.in.bytes is passed with a positive long value.

CurrentQueueSizeInBytes

long

The current data of records in the queue in bytes.

The connector also provides the following additional snapshot metrics when an incremental snapshot is executed:

AttributesTypeDescription

ChunkId

string

The identifier of the current snapshot chunk.

ChunkFrom

string

The lower bound of the primary key set defining the current chunk.

ChunkTo

string

The upper bound of the primary key set defining the current chunk.

TableFrom

string

The lower bound of the primary key set of the currently snapshotted table.

TableTo

string

The upper bound of the primary key set of the currently snapshotted table.

Important

Incremental snapshots is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process. For more information about the support scope of Red Hat Technology Preview features, see https://access.redhat.com/support/offerings/techpreview.

8.6.2. Debezium SQL Server connector streaming metrics

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

The following table lists the streaming metrics that are available.

AttributesTypeDescription

LastEvent

string

The last streaming event that the connector has read.

MilliSecondsSinceLastEvent

long

The number of milliseconds since the connector has read and processed the most recent event.

TotalNumberOfEventsSeen

long

The total number of events that this connector has seen since last started or reset.

NumberOfEventsFiltered

long

The number of events that have been filtered by include/exclude list filtering rules configured on the connector.

MonitoredTables
Deprecated and scheduled for removal in a future release; use the 'CapturedTables' metric instead

string[]

The list of tables that are monitored by the connector.

CapturedTables

string[]

The list of tables that are captured by the connector.

QueueTotalCapacity

int

The length the queue used to pass events between the streamer and the main Kafka Connect loop.

QueueRemainingCapacity

int

The free capacity of the queue used to pass events between the streamer and the main Kafka Connect loop.

Connected

boolean

Flag that denotes whether the connector is currently connected to the database server.

MilliSecondsBehindSource

long

The number of milliseconds between the last change event’s timestamp and the connector processing it. The values will incoporate any differences between the clocks on the machines where the database server and the connector are running.

NumberOfCommittedTransactions

long

The number of processed transactions that were committed.

SourceEventPosition

Map<String, String>

The coordinates of the last received event.

LastTransactionId

string

Transaction identifier of the last processed transaction.

MaxQueueSizeInBytes

long

The maximum buffer of the queue in bytes.

CurrentQueueSizeInBytes

long

The current data of records in the queue in bytes.

8.6.3. Debezium SQL Server connector schema history metrics

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

The following table lists the schema history metrics that are available.

AttributesTypeDescription

Status

string

One of STOPPED, RECOVERING (recovering history from the storage), RUNNING describing the state of the database history.

RecoveryStartTime

long

The time in epoch seconds at what recovery has started.

ChangesRecovered

long

The number of changes that were read during recovery phase.

ChangesApplied

long

the total number of schema changes applied during recovery and runtime.

MilliSecondsSinceLast​RecoveredChange

long

The number of milliseconds that elapsed since the last change was recovered from the history store.

MilliSecondsSinceLast​AppliedChange

long

The number of milliseconds that elapsed since the last change was applied.

LastRecoveredChange

string

The string representation of the last change recovered from the history store.

LastAppliedChange

string

The string representation of the last applied change.

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