Debezium Connector for SQL Server

Overview

The functionality of the connector is based upon change data capture feature provided by SQL Server Standard (since SQL Server 2016 SP1) or Enterprise edition. Using this mechanism a SQL Server capture process monitors all databases and tables the user is interested in and stores the changes into specifically created CDC tables that have stored procedure facade. The connector has been tested with SQL Server 2017, but community members have reportedly used it successfully with earlier versions up to 2014, too (as long as the CDC feature is provided).

The database operator must enable CDC for the table(s) that should be captured by the connector. The connector then produces a change event for every row-level insert, update, and delete operation that was published via the CDC API, recording all the change events for each table in a separate Kafka topic. The client applications read the Kafka topics that correspond to the database tables they’re interested in following, and react to every row-level event it sees in those topics.

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

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

Setting up SQL Server

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

-- ====
-- Enable Database for CDC template
-- ====
USE MyDB
GO
EXEC sys.sp_cdc_enable_db
GO

Then enable CDC for each table that you plan to monitor

-- =========
-- Enable a Table Specifying Filegroup Option Template
-- =========
USE MyDB
GO
EXEC sys.sp_cdc_enable_table
@source_schema = N'dbo',
@source_name   = N'MyTable',
@role_name     = N'MyRole',
@filegroup_name = N'MyDB_CT',
@supports_net_changes = 0
GO

Verify that the user have access to the CDC table.

-- =========
-- Verify the user of the connector have access, this query should not have empty result
-- =========
EXEC sys.sp_cdc_help_change_data_capture
GO

If the result is empty then please make sure that the user has privileges to access both the capture instance and CDC tables.

SQL Server on Azure

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

SQL Server Always On

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

• Change data capture is configured and enabled on the master node. SQL Server does not support CDC directly on replicas.

• The configuration option database.applicationIntent must be set to ReadOnly. This is required by SQL Server. When Debezium detects this configuration option then it will:

  • set snapshot.isolation.mode to snapshot as this is the only one transaction isolation mode supported by raed-only replicas

  • commit the (read-only) transaction in every execution of the streaming query loop, as this is necessary to get the latest view on CDC data

How the SQL Server connector works

Snapshots

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

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

Each snapshot consists of the following steps:

1. Determine the tables to be captured

2. Obtain a lock on each of the monitored tables to ensure that no structural changes can occur to any of the tables. The level of the lock is determined by snapshot.isolation.mode configuration option.

3. Read the maximum LSN (“log sequence number”) position in the server’s transaction log.

4. Capture the structure of all relevant tables.

5. Optionally release the locks obtained in step 2, i.e. the locks are held usually only for a short period of time.

6. Scan all of the relevant database tables and schemas as valid at the LSN position read in step 3, and generate a READ event for each row and write that event to the appropriate table-specific Kafka topic.

7. Record the successful completion of the snapshot in the connector offsets.

Reading the change data tables

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

1. For each change table read all changes that were created between last stored maximum LSN and current maximum LSN

2. Order the read changes incrementally according to commit LSN and change LSN. This ensures that the changes are replayed by Debezium in the same order as were made to the database.

3. Pass commit and change LSNs as offsets to Kafka Connect.

4. Store the maximum LSN and repeat the loop.

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

The connector is able to detect whether the CDC is enabled or disabled for whitelisted source table during the runtime and modify its behaviour.

Topic names

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

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

• fulfillment.dbo.products

• fulfillment.dbo.products_on_hand

• fulfillment.dbo.customers

• fulfillment.dbo.orders

Schema change topic

The user-facing schema change topic is not implemented yet (see DBZ-1904).

Events

All data change events produced by the SQL Server connector have a key and a value, although the structure of the key and value depend on the table from which the change events originated (see Topic names).

Debezium and Kafka Connect are designed around continuous streams of event messages, and the structure of these events may change over time. This could be difficult for consumers to deal with, so to make it easy Kafka Connect makes each event self-contained. Every message key and value has two parts: a schema and payload. The schema describes the structure of the payload, while the payload contains the actual data.

Change Event Keys

For a given table, the change event’s key will have a structure that contains a field for each column in the primary key (or unique key constraint) of the table at the time the event was created.

Consider a customers table defined in the inventory database’s schema dbo:

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
);

If the database.server.name configuration property has the value server1, every change event for the customers table while it has this definition will feature the same key structure, which in JSON looks like this:

{
    "schema": {
        "type": "struct",
        "fields": [
            {
                "type": "int32",
                "optional": false,
                "field": "id"
            }
        ],
        "optional": false,
        "name": "server1.dbo.customers.Key"
    },
    "payload": {
        "id": 1004
    }
}

The schema portion of the key contains a Kafka Connect schema describing what is in the key portion, and in our case that means that the payload value is not optional, is a structure defined by a schema named server1.dbo.customers.Key, and has one required field named id of type int32. If we look at the value of the key’s payload field, we’ll see that it is indeed a structure (which in JSON is just an object) with a single id field, whose value is 1004.

Therefore, we interpret this key as describing the row in the dbo.customers table (output from the connector named server1) whose id primary key column had a value of 1004.

Change Event Values

Like the message key, the value of a change event message has a schema section and payload section. The payload section of every change event value produced by the SQL Server connector has an envelope structure with the following fields:

• op is a mandatory field that contains a string value describing the type of operation. Values for the SQL Server connector are c for create (or insert), u for update, d for delete, and r for read (in the case of a snapshot).

• before is an optional field that if present contains the state of the row before the event occurred. The structure is described by the server1.dbo.customers.Value Kafka Connect schema, which the server1 connector uses for all rows in the dbo.customers table.

• after is an optional field that if present contains the state of the row after the event occurred. The structure is described by the same server1.dbo.customers.Value Kafka Connect schema used in before.

• source is a mandatory field that contains a structure describing the source metadata for the event, which in the case of SQL Server contains these fields: the Debezium version, the connector name, whether the event is part of an ongoing snapshot or not, the commit LSN (not while snapshotting), the LSN of the change, database, schema and table where the change happened, and a timestamp representing the point in time when the record was changed in the source database (during snapshotting, it’ll be the point in time of snapshotting).

  Also a field event_serial_no is present during streaming. This is used to differentiate among events that have the same commit and change LSN. There are mostly two situations when you can see it present with value different from 1:

  • update events will have the value set to 2, this is because the update generates two events in the CDC change table of SQL Server (source documentation). The first one contains the old values and the second one contains new values. So the first one is dropped and the values from it are used with the second one to create the Debezium change event.

  • when a primary key is updated, then SQL Server emits two records - delete to remove the record with the old primary key value and insert to create the record with the new primary key. Both operations share the same commit and change LSN and their event numbers are 1 and 2.

• ts_ms is optional and if present contains the time (using the system clock in the JVM running the Kafka Connect task) at which the connector processed the event.

And of course, the schema portion of the event message’s value contains a schema that describes this envelope structure and the nested fields within it.

Create events

Let’s look at what a create event value might look like for our customers table:

{
  "schema": {
    "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",
        "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",
        "field": "source"
      },
      {
        "type": "string",
        "optional": false,
        "field": "op"
      },
      {
        "type": "int64",
        "optional": true,
        "field": "ts_ms"
      }
    ],
    "optional": false,
    "name": "server1.dbo.customers.Envelope"
  },
  "payload": {
    "before": null,
    "after": {
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "john.doe@example.org"
    },
    "source": {
      "version": "1.0.3.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",
    "ts_ms": 1559729471739
  }
}

If we look at the schema portion of this event’s value, we can see the schema for the envelope, the schema for the source structure (which is specific to the SQL Server connector and reused across all events), and the table-specific schemas for the before and after fields.

If we look at the payload portion of this event’s value, we can see the information in the event, namely that it is describing that the row was created (since op=c), and that the after field value contains the values of the new inserted row’s’ id, first_name, last_name, and email columns.

Update events

The value of an update change event on this table will actually have the exact same schema, and its payload is structured the same but will hold different values. Here’s an example:

{
  "schema": { ... },
  "payload": {
    "before": {
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "john.doe@example.org"
    },
    "after": {
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "noreply@example.org"
    },
    "source": {
      "version": "1.0.3.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",
    "ts_ms": 1559729998706
  }
}

When we compare this to the value in the insert event, we see a couple of differences in the payload section:

• The op field value is now u, signifying that this row changed because of an update

• The before field now has the state of the row with the values before the database commit

• The after field now has the updated state of the row, and here was can see that the email value is now noreply@example.org.

• The source field structure has the same fields as before, but the values are different since this event is from a different position in the transaction log.

• The event_serial_no field has value 2. That is due to the update event composed of two events behind the scenes and we are exposing only the second one. If you are interested in details please check the source documentation and refer to the field $operation.

• The ts_ms shows the timestamp that Debezium processed this event.

There are several things we can learn by just looking at this payload section. We can compare the before and after structures to determine what actually changed in this row because of the commit. The source structure tells us information about SQL Server’s record of this change (providing traceability), but more importantly this has information we can compare to other events in this and other topics to know whether this event occurred before, after, or as part of the same SQL Server commit as other events.

Delete events

So far we’ve seen samples of create and update events. Now, let’s look at the value of a delete event for the same table. Once again, the schema portion of the value is exactly the same as with the create and update events:

{
  "schema": { ... },
  },
  "payload": {
    "before": {
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "noreply@example.org"
    },
    "after": null,
    "source": {
      "version": "1.0.3.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",
    "ts_ms": 1559730450205
  }
}

If we look at the payload portion, we see a number of differences compared with the create or update event payloads:

• The op field value is now d, signifying that this row was deleted

• The before field now has the state of the row that was deleted with the database commit.

• The after field is null, signifying that the row no longer exists

• The source field structure has many of the same values as before, except the ts_ms, commit_lsn and change_lsn fields have changed

• The ts_ms shows the timestamp that Debezium processed this event.

This event gives a consumer all kinds of information that it can use to process the removal of this row.

The SQL Server connector’s events are designed to work with Kafka log compaction, which allows for the removal of some older messages as long as at least the most recent message for every key is kept. This allows Kafka to reclaim storage space while ensuring the topic contains a complete dataset and can be used for reloading key-based state.

When a row is deleted, the delete event value listed above still works with log compaction, since Kafka can still remove all earlier messages with that same key. But only if the message value is null will Kafka know that it can remove all messages with that same key. To make this possible, the SQL Server connector always follows the delete event with a special tombstone event that has the same key but null value.

Database schema evolution

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

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

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

• cold - this is executed when Debezium is stopped

• hot - executed while Debezium is running

Both approaches have their own advantages and disadvantages.

Cold schema update

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

1. Suspend the application that generates the database records

2. Wait for Debezium to stream all unstreamed changes

3. Stop the connector

4. Apply all changes to the source table schema

5. Create a new capture table for the update source table using sys.sp_cdc_enable_table procedure with a unique value for parameter @capture_instance

6. Resume the application

7. Start the connector

8. When Debezium starts streaming from the new capture table it is possible to drop the old one using sys.sp_cdc_disable_table stored procedure with parameter @capture_instance set to the old capture instance name

Hot schema update

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

1. Apply all changes to the source table schema

2. Create a new capture table for the update source table using sys.sp_cdc_enable_table procedure with a unique value for parameter @capture_instance

3. When Debezium starts streaming from the new capture table it is possible to drop the old one using sys.sp_cdc_disable_table stored procedure with parameter @capture_instance set to the old capture instance name

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

Example

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

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

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

-- Modify the source table schema
ALTER TABLE customers ADD phone_number VARCHAR(32);
-- Create the new capture instance
EXEC sys.sp_cdc_enable_table @source_schema = 'dbo', @source_name = 'customers', @role_name = NULL, @supports_net_changes = 0, @capture_instance = 'dbo_customers_v2';
GO
-- Insert new data
INSERT INTO customers(first_name,last_name,email,phone_number) VALUES ('John','Doe','john.doe@example.com', '+1-555-123456');
GO

Kafka Connect log will contain messages like these:

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

Eventually, there is a new field in the schema and value of the messages written to the Kafka topic.

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

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

Data types

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

The following table describes how the connector maps each of the SQL Server data types to a literal type and semantic type within the events’ fields. Here, the literal type describes how the value is literally represented using Kafka Connect schema types, namely INT8, INT16, INT32, INT64, FLOAT32, FLOAT64, BOOLEAN, STRING, BYTES, ARRAY, MAP, and STRUCT. The semantic type describes how the Kafka Connect schema captures the meaning of the field using the name of the Kafka Connect schema for the field.

Other data type mappings are described in the following sections.

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

Temporal values

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

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:

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

Deploying the SQL Server connector

If you’ve already installed Zookeeper, Kafka, and Kafka Connect, then using Debezium’s SQL Server` connector is easy. Simply download the connector’s plugin archive, extract the JARs into your Kafka Connect environment, and add the directory with the JARs to Kafka Connect’s plugin.path. Restart your Kafka Connect process to pick up the new JARs.

If immutable containers are your thing, then check out Debezium’s Docker images for Zookeeper, Kafka and Kafka Connect with the SQL Server connector already pre-installed and ready to go. You can even run Debezium on OpenShift.

Example configuration

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

1. Enable the CDC on SQL Server to publish the CDC events in the database.

2. Create a configuration file for the SQL Server connector.

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

Following is an example of the configuration for a connector instance that monitors a SQL Server server at port 1433 on 192.168.99.100, which we logically name fullfillment. Typically, you configure the Debezium SQL Server connector in a .json file using the configuration properties available for the connector.

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

See the complete list of connector properties that can be specified in these configurations.

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

Monitoring

Kafka, Zookeeper, and Kafka Connect all have built-in support for JMX metrics. The SQL Server connector also publishes a number of metrics about the connector’s activities that can be monitored through JMX. The connector has two types of metrics. Snapshot metrics help you monitor the snapshot activity and are available when the connector is performing a snapshot. Streaming metrics help you monitor the progress and activity while the connector reads CDC table data.

Snapshot Metrics

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

Streaming Metrics

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

Schema History Metrics

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

Connector properties

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

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.

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

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

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

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

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