Chapter 6. Debezium connector for MySQL

MySQL has a binary log (binlog) that records all operations in the order in which they are committed to the database. This includes changes to table schemas as well as changes to the data in tables. MySQL uses the binlog for replication and recovery.

The Debezium MySQL connector reads the binlog, produces change events for row-level INSERT, UPDATE, and DELETE operations, and emits the change events to Kafka topics. Client applications read those Kafka topics.

As MySQL is typically set up to purge binlogs after a specified period of time, the MySQL connector performs an initial consistent snapshot of each of your databases. The MySQL connector reads the binlog from the point at which the snapshot was made.

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

Information and procedures for using a Debezium MySQL connector are organized as follows:

6.1. How Debezium MySQL connectors work

An overview of the MySQL topologies that the connector supports is useful for planning your application. To optimally configure and run a Debezium MySQL connector, it is helpful to understand how the connector tracks the structure of tables, exposes schema changes, performs snapshots, and determines Kafka topic names.

Details are in the following topics:

6.1.1. MySQL topologies supported by Debezium connectors

The Debezium MySQL connector supports the following MySQL topologies:

Standalone

When a single MySQL server is used, the server must have the binlog enabled (and optionally GTIDs enabled) so the Debezium MySQL connector can monitor the server. This is often acceptable, since the binary log can also be used as an incremental backup. In this case, the MySQL connector always connects to and follows this standalone MySQL server instance.

Primary and replica

The Debezium MySQL connector can follow one of the primary servers or one of the replicas (if that replica has its binlog enabled), but the connector sees changes in only the cluster that is visible to that server. Generally, this is not a problem except for the multi-primary topologies.

The connector records its position in the server’s binlog, which is different on each server in the cluster. Therefore, the connector must follow just one MySQL server instance. If that server fails, that server must be restarted or recovered before the connector can continue.

High available clusters

A variety of high availability solutions exist for MySQL, and they make it significantly easier to tolerate and almost immediately recover from problems and failures. Most HA MySQL clusters use GTIDs so that replicas are able to keep track of all changes on any of the primary servers.

Multi-primary

Network Database (NDB) cluster replication uses one or more MySQL replica nodes that each replicate from multiple primary servers. This is a powerful way to aggregate the replication of multiple MySQL clusters. This topology requires the use of GTIDs.

A Debezium MySQL connector can use these multi-primary MySQL replicas as sources, and can fail over to different multi-primary MySQL replicas as long as the new replica is caught up to the old replica. That is, the new replica has all transactions that were seen on the first replica. This works even if the connector is using only a subset of databases and/or tables, as the connector can be configured to include or exclude specific GTID sources when attempting to reconnect to a new multi-primary MySQL replica and find the correct position in the binlog.

Hosted

There is support for the Debezium MySQL connector to use hosted options such as Amazon RDS and Amazon Aurora.

Because these hosted options do not allow a global read lock, table-level locks are used to create the consistent snapshot.

6.1.2. How Debezium MySQL connectors handle database schema changes

When a database client queries a database, the client uses the database’s current schema. However, the database schema can be changed at any time, which means that the connector must be able to identify what the schema was at the time each insert, update, or delete operation was recorded. Also, a connector cannot necessarily apply the current schema to every event. If an event is relatively old, it’s possible that it was recorded before the current schema was applied.

To ensure correct processing of events that occur after a schema change, MySQL includes in the transaction log not only the row-level changes that affect the data, but also the DDL statements that are applied to the database. As the connector encounters these DDL statements in the binlog, it parses them and updates an in-memory representation of each table’s schema. The connector uses this schema representation to identify the structure of the tables at the time of each insert, update, or delete operation and to produce the appropriate change event. In a separate database schema history Kafka topic, the connector records all DDL statements along with the position in the binlog where each DDL statement appeared.

When the connector restarts after either a crash or a graceful stop, it starts reading the binlog from a specific position, that is, from a specific point in time. The connector rebuilds the table structures that existed at this point in time by reading the database schema history Kafka topic and parsing all DDL statements up to the point in the binlog where the connector is starting.

This database schema history topic is for internal connector use only. Optionally, the connector can also emit schema change events to a different topic that is intended for consumer applications.

When the MySQL connector captures changes in a table to which a schema change tool such as gh-ost or pt-online-schema-change is applied, there are helper tables created during the migration process. You must configure the connector to capture changes that occur in these helper tables. If consumers do not need the records the the connector generates for helper tables, configure a single message transform (SMT) to remove these records from the messages that the connector emits.

Additional resources

Default names for topics that receive Debezium event records.

6.1.3. How Debezium MySQL connectors expose database schema changes

You can configure a Debezium MySQL connector to produce schema change events that describe schema changes that are applied to tables in the database. The connector writes schema change events to a Kafka topic named <topicPrefix>, where topicPrefix is the namespace specified in the topic.prefix connector 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:

ddl: Provides the SQL CREATE, ALTER, or DROP statement that results in the schema change.
databaseName: The name of the database to which the DDL statements are applied. The value of databaseName serves as the message key.
pos: The position in the binlog where the statements appear.
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

For a table that is in capture mode, the connector not only stores the history of schema changes in the schema change topic, but also in an internal database schema history topic. The internal database schema 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.

Important

Never partition the database schema history topic. For the database schema history topic to function correctly, it must maintain a consistent, global order of the event records that the connector emits to it.

To ensure that the topic is not split among partitions, set the partition count for the topic by using one of the following methods:

If you create the database schema history topic manually, specify a partition count of 1.
If you use the Apache Kafka broker to create the database schema history topic automatically, the topic is created, set the value of the Kafka num.partitions configuration option to 1.

Warning

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

Example: Message emitted to the MySQL connector schema change topic

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

{
  "schema": { },
  "payload": {
      "source": {  1
        "version": "2.3.4.Final",
        "connector": "mysql",
        "name": "mysql",
        "ts_ms": 1651535750218, 2
        "snapshot": "false",
        "db": "inventory",
        "sequence": null,
        "table": "customers",
        "server_id": 223344,
        "gtid": null,
        "file": "mysql-bin.000003",
        "pos": 570,
        "row": 0,
        "thread": null,
        "query": null
      },
      "databaseName": "inventory", 3
      "schemaName": null,
      "ddl": "ALTER TABLE customers ADD middle_name varchar(255) AFTER first_name", 4
      "tableChanges": [  5
        {
          "type": "ALTER", 6
          "id": "\"inventory\".\"customers\"", 7
          "table": {    8
            "defaultCharsetName": "utf8mb4",
            "primaryKeyColumnNames": [  9
              "id"
            ],
            "columns": [  10
              {
                "name": "id",
                "jdbcType": 4,
                "nativeType": null,
                "typeName": "INT",
                "typeExpression": "INT",
                "charsetName": null,
                "length": null,
                "scale": null,
                "position": 1,
                "optional": false,
                "autoIncremented": true,
                "generated": true
              },
              {
                "name": "first_name",
                "jdbcType": 12,
                "nativeType": null,
                "typeName": "VARCHAR",
                "typeExpression": "VARCHAR",
                "charsetName": "utf8mb4",
                "length": 255,
                "scale": null,
                "position": 2,
                "optional": false,
                "autoIncremented": false,
                "generated": false
              },
              {
                "name": "middle_name",
                "jdbcType": 12,
                "nativeType": null,
                "typeName": "VARCHAR",
                "typeExpression": "VARCHAR",
                "charsetName": "utf8mb4",
                "length": 255,
                "scale": null,
                "position": 3,
                "optional": true,
                "autoIncremented": false,
                "generated": false
              },
              {
                "name": "last_name",
                "jdbcType": 12,
                "nativeType": null,
                "typeName": "VARCHAR",
                "typeExpression": "VARCHAR",
                "charsetName": "utf8mb4",
                "length": 255,
                "scale": null,
                "position": 4,
                "optional": false,
                "autoIncremented": false,
                "generated": false
              },
              {
                "name": "email",
                "jdbcType": 12,
                "nativeType": null,
                "typeName": "VARCHAR",
                "typeExpression": "VARCHAR",
                "charsetName": "utf8mb4",
                "length": 255,
                "scale": null,
                "position": 5,
                "optional": false,
                "autoIncremented": false,
                "generated": false
            }
          ],
          "attributes": [ 11
            {
              "customAttribute": "attributeValue"
            }
          ]
        }
      }
    ]
  }
}

Table 6.1. Descriptions of fields in messages emitted to the schema change topic
Item	Field name	Description
1	`source`	The `source` field is structured exactly as standard data change events that the connector writes to table-specific topics. This field is useful to correlate events on different topics.
2	`ts_ms`	Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task. In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.
3	`databaseName` `schemaName`	Identifies the database and the schema that contains the change. The value of the `databaseName` field is used as the message key for the record.
4	`ddl`	This field contains the DDL that is responsible for the schema change. The `ddl` field can contain multiple DDL statements. Each statement applies to the database in the `databaseName` field. Multiple DDL statements appear in the order in which they were applied to the database. Clients can submit multiple DDL statements that apply to multiple databases. If MySQL applies them atomically, the connector takes the DDL statements in order, groups them by database, and creates a schema change event for each group. If MySQL applies them individually, the connector creates a separate schema change event for each statement.
5	`tableChanges`	An array of one or more items that contain the schema changes generated by a DDL command.
6	`type`	Describes the kind of change. The value is one of the following: `CREATE` Table created. `ALTER` Table modified. `DROP` Table deleted.
7	`id`	Full identifier of the table that was created, altered, or dropped. In the case of a table rename, this identifier is a concatenation of `<old>,<new>` table names.
8	`table`	Represents table metadata after the applied change.
9	`primaryKeyColumnNames`	List of columns that compose the table’s primary key.
10	`columns`	Metadata for each column in the changed table.
11	`attributes`	Custom attribute metadata for each table change.

For more information, see schema history topic.

6.1.4. How Debezium MySQL connectors perform database snapshots

When a Debezium MySQL connector is first started, it performs an initial consistent snapshot of your database. This snapshot enables the connector to establish a baseline for the current state of the database.

Debezium can use different modes when it runs a snapshot. The snapshot mode is determined by the snapshot.mode configuration property. The default value of the property is initial. You can customize the way that the connector creates snapshots by changing the value of the snapshot.mode property.

You can find more information about snapshots in the following sections:

The connector completes a series of tasks when it performs the snapshot. The exact steps vary with the snapshot mode and with the table locking policy that is in effect for the database. The Debezium MySQL connector completes different steps when it performs an initial snapshot that uses a global read lock or table-level locks.

6.1.4.1. Initial snapshots that use a global read lock

You can customize the way that the connector creates snapshots by changing the value of the snapshot.mode property. If you configure a different snapshot mode, the connector completes the snapshot by using a modified version of this workflow. For information about the snapshot process in environments that do not permit global read locks, see the snapshot workflow for table-level locks.

Default workflow that the Debezium MySQL connector uses to perform an initial snapshot with a global read lock

The following table shows the steps in the workflow that Debezium follows to create a snapshot with a global read lock.

Step	Action
1	Establish a connection to the database.
2	Determine the tables to be captured. By default, the connector captures the data for all non-system tables. After the snapshot completes, the connector continues to stream data for the specified tables. If you want the connector to capture data only from specific tables you can direct the connector to capture the data for only a subset of tables or table elements by setting properties such as `table.include.list` or `table.exclude.list`.
3	Obtain a global read lock on the tables to be captured to block writes by other database clients. The snapshot itself does not prevent other clients from applying DDL that might interfere with the connector’s attempt to read the binlog position and table schemas. The connector retains the global read lock while it reads the binlog position, and releases the lock as described in a later step.
4	Start a transaction with repeatable read semantics to ensure that all subsequent reads within the transaction are done against the consistent snapshot. Note The use of these isolation semantics can slow the progress of the snapshot. If the snapshot takes too long to complete, consider using a different isolation configuration, or skip the initial snapshot and run an incremental snapshot instead.
5	Read the current binlog position.
6	Capture the structure of all tables in the database, or all tables that are designated for capture. The connector persists schema information in its internal database schema history topic, including all necessary `DROP…` and `CREATE…` DDL statements. The schema history provides information about the structure that is in effect when a change event occurs. Note By default, the connector captures the schema of every table in the database, including tables that are not configured for capture. If tables are not configured for capture, the initial snapshot captures only their structure; it does not capture any table data. For more information about why snapshots persist schema information for tables that you did not include in the initial snapshot, see Understanding why initial snapshots capture the schema for all tables.
7	Release the global read lock obtained in Step 3. Other database clients can now write to the database.
8	At the binlog position that the connector read in Step 5, the connector begins to scan the tables that are designated for capture. During the scan, the connector completes the following tasks: Confirms that the table was created before the snapshot began. If the table was created after the snapshot began, the connector skips the table. After the snapshot is complete, and the connector transitions to streaming, it emits change events for any tables that were created after the snapshot began. Produces a `read` event for each row that is captured from a table. All `read` events contain the same binlog position, which is the position that was obtained in step 5. Emits each `read` event to the Kafka topic for the source table. Releases data table locks, if applicable.
9	Commit the transaction.
10	Record the successful completion of the snapshot in the connector offsets.

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

After the snapshot process begins, if the process is interrupted due to connector failure, rebalancing, or other reasons, the process restarts after the connector restarts.

After the connector completes the initial snapshot, it continues streaming from the position that it read in Step 5 so that it does not miss any updates.

If the connector stops again for any reason, after it restarts, it resumes streaming changes from where it previously left off.

After the connector restarts, if the logs have been pruned, the connector’s position in the logs might no longer available. The connector then fails, and returns an error that indicates that a new snapshot is required. To configure the connector to automatically initiate a snapshot in this situation, set the value of the snapshot.mode property to when_needed. For more tips on troubleshooting the Debezium MySQL connector, see behavior when things go wrong.

6.1.4.2. Initial snapshots that use table-level locks

In some database environments administrators do not permit global read locks. If the Debezium MySQL connector detects that global read locks are not permitted, the connector uses table-level locks when it performs snapshots. For the connector to perform a snapshot that uses table-level locks, the database account that the Debezium connector uses to connect to MySQL must have LOCK TABLES privileges.

Default workflow that the Debezium MySQL connector uses to perform an initial snapshot with table-level locks

The following workflow lists the steps that Debezium takes to create a snapshot with table-level read locks. For information about the snapshot process in environments that do not permit global read locks, see the snapshot workflow for global read locks.

Step	Action
1	Establish a connection to the database.
2	Determine the tables to be captured. By default, the connector captures all non-system tables. To have the connector capture a subset of tables or table elements, you can set a number of `include` and `exclude` properties to filter the data, for example, `table.include.list` or `table.exclude.list`.
3	Obtain table-level locks.
4	Start a transaction with repeatable read semantics to ensure that all subsequent reads within the transaction are done against the consistent snapshot.
5	Read the current binlog position.
6	Read the schema of the databases and tables for which the connector is configured to capture changes. The connector persists schema information in its internal database schema history topic, including all necessary `DROP…` and `CREATE…` DDL statements. The schema history provides information about the structure that is in effect when a change event occurs. Note By default, the connector captures the schema of every table in the database, including tables that are not configured for capture. If tables are not configured for capture, the initial snapshot captures only their structure; it does not capture any table data. For more information about why snapshots persist schema information for tables that you did not include in the initial snapshot, see Understanding why initial snapshots capture the schema for all tables.
7	At the binlog position that the connector read in Step 5, the connector begins to scan the tables that are designated for capture. During the scan, the connector completes the following tasks: Confirms that the table was created before the snapshot began. If the table was created after the snapshot began, the connector skips the table. After the snapshot is complete, and the connector transitions to streaming, it emits change events for any tables that were created after the snapshot began. Produces a `read` event for each row that is captured from a table. All `read` events contain the same binlog position, which is the position that was obtained in step 5. Emits each `read` event to the Kafka topic for the source table. Releases data table locks, if applicable.
8	Commit the transaction.
9	Release the table-level locks. Other database clients can now write to any previously locked tables.
10	Record the successful completion of the snapshot in the connector offsets.

6.1.4.3. Description of why initial snapshots capture the schema history for all tables

The initial snapshot that a connector runs captures two types of information:

Table data: Information about INSERT, UPDATE, and DELETE operations in tables that are named in the connector’s table.include.list property.
Schema data: DDL statements that describe the structural changes that are applied to tables. Schema data is persisted to both the internal schema history topic, and to the connector’s schema change topic, if one is configured.

After you run an initial snapshot, you might notice that the snapshot captures schema information for tables that are not designated for capture. By default, initial snapshots are designed to capture schema information for every table that is present in the database, not only from tables that are designated for capture. Connectors require that the table’s schema is present in the schema history topic before they can capture a table. By enabling the initial snapshot to capture schema data for tables that are not part of the original capture set, Debezium prepares the connector to readily capture event data from these tables should that later become necessary. If the initial snapshot does not capture a table’s schema, you must add the schema to the history topic before the connector can capture data from the table.

In some cases, you might want to limit schema capture in the initial snapshot. This can be useful when you want to reduce the time required to complete a snapshot. Or when Debezium connects to the database instance through a user account that has access to multiple logical databases, but you want the connector to capture changes only from tables in a specific logic database.

Additional information

Capturing data from tables not captured by the initial snapshot (no schema change)
Capturing data from tables not captured by the initial snapshot (schema change)
Setting the schema.history.internal.store.only.captured.tables.ddl property to specify the tables from which to capture schema information.
Setting the schema.history.internal.store.only.captured.databases.ddl property to specify the logical databases from which to capture schema changes.

6.1.4.4. Capturing data from tables not captured by the initial snapshot (no schema change)

In some cases, you might want the connector to capture data from a table whose schema was not captured by the initial snapshot. Depending on the connector configuration, the initial snapshot might capture the table schema only for specific tables in the database. If the table schema is not present in the history topic, the connector fails to capture the table, and reports a missing schema error.

You might still be able to capture data from the table, but you must perform additional steps to add the table schema.

Prerequisites

You want to capture data from a table with a schema that the connector did not capture during the initial snapshot.
In the transaction log, all entries for the table use the same schema. For information about capturing data from a new table that has undergone structural changes, see Capturing data from tables not captured by the initial snapshot (schema change).

Procedure

Stop the connector.
Remove the internal database schema history topic that is specified by the schema.history.internal.kafka.topic property.
Apply the following changes to the connector configuration:
1. Set the snapshot.mode to schema_only_recovery.
2. Set the value of schema.history.internal.store.only.captured.tables.ddl to false.
3. Add the tables that you want the connector to capture to table.include.list. This guarantees that in the future, the connector can reconstruct the schema history for all tables.
Restart the connector. The snapshot recovery process rebuilds the schema history based on the current structure of the tables.
(Optional) After the snapshot completes, initiate an incremental snapshot to capture existing data for newly added tables along with changes to other tables that occurred while that connector was off-line.
(Optional) Reset the snapshot.mode back to schema_only to prevent the connector from initiating recovery after a future restart.

6.1.4.5. Capturing data from tables not captured by the initial snapshot (schema change)

If a schema change is applied to a table, records that are committed before the schema change have different structures than those that were committed after the change. When Debezium captures data from a table, it reads the schema history to ensure that it applies the correct schema to each event. If the schema is not present in the schema history topic, the connector is unable to capture the table, and an error results.

If you want to capture data from a table that was not captured by the initial snapshot, and the schema of the table was modified, you must add the schema to the history topic, if it is not already available. You can add the schema by running a new schema snapshot, or by running an initial snapshot for the table.

Prerequisites

You want to capture data from a table with a schema that the connector did not capture during the initial snapshot.
A schema change was applied to the table so that the records to be captured do not have a uniform structure.

Procedure

Initial snapshot captured the schema for all tables (store.only.captured.tables.ddl was set to false)

Edit the table.include.list property to specify the tables that you want to capture.
Restart the connector.
Initiate an incremental snapshot if you want to capture existing data from the newly added tables.

Initial snapshot did not capture the schema for all tables (store.only.captured.tables.ddl was set to true)

If the initial snapshot did not save the schema of the table that you want to capture, complete one of the following procedures:

Procedure 1: Schema snapshot, followed by incremental snapshot

In this procedure, the connector first performs a schema snapshot. You can then initiate an incremental snapshot to enable the connector to synchronize data.

Stop the connector.
Remove the internal database schema history topic that is specified by the schema.history.internal.kafka.topic property.
Clear the offsets in the configured Kafka Connect offset.storage.topic. For more information about how to remove offsets, see the Debezium community FAQ.
Warning
Removing offsets should be performed only by advanced users who have experience in manipulating internal Kafka Connect data. This operation is potentially destructive, and should be performed only as a last resort.
Set values for properties in the connector configuration as described in the following steps:
1. Set the value of the snapshot.mode property to schema_only.
2. Edit the table.include.list to add the tables that you want to capture.
Restart the connector.
Wait for Debezium to capture the schema of the new and existing tables. Data changes that occurred any tables after the connector stopped are not captured.
To ensure that no data is lost, initiate an incremental snapshot.

Procedure 2: Initial snapshot, followed by optional incremental snapshot

In this procedure the connector performs a full initial snapshot of the database. As with any initial snapshot, in a database with many large tables, running an initial snapshot can be a time-consuming operation. After the snapshot completes, you can optionally trigger an incremental snapshot to capture any changes that occur while the connector is off-line.

Stop the connector.
Remove the internal database schema history topic that is specified by the schema.history.internal.kafka.topic property.
Clear the offsets in the configured Kafka Connect offset.storage.topic. For more information about how to remove offsets, see the Debezium community FAQ.
Warning
Removing offsets should be performed only by advanced users who have experience in manipulating internal Kafka Connect data. This operation is potentially destructive, and should be performed only as a last resort.
Edit the table.include.list to add the tables that you want to capture.
Set values for properties in the connector configuration as described in the following steps:
1. Set the value of the snapshot.mode property to initial.
2. (Optional) Set schema.history.internal.store.only.captured.tables.ddl to false.
Restart the connector. The connector takes a full database snapshot. After the snapshot completes, the connector transitions to streaming.
(Optional) To capture any data that changed while the connector was off-line, initiate an incremental snapshot.

6.1.5. Ad hoc snapshots

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. Also, the snapshot can capture a subset of the contents of the table(s) 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 6.2. Example of an ad hoc execute-snapshot signal record
Field	Default	Value
`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 regular expressions matching 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.
`additional-condition`	N/A	An optional string, which specifies a condition based on the column(s) of the table(s), to capture a subset of the contents of the table(s).
`surrogate-key`	N/A	An optional string that specifies the column name that the connector uses as the primary key of a table during the snapshot process.

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.

6.1.6. Incremental snapshots

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 1024 rows.

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.

6.1.6.1. 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 a signal to the signaling 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 or an array of regular expressions used to match 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.

Note

If the name of a table that you want to include in a snapshot contains a dot (.) in the name of the database, schema, or table, to add the table to the data-collections array, you must escape each part of the name in double quotes.

For example, to include a table that exists in the public schema and that has the name My.Table, use the following format: "public"."My.Table".

Prerequisites

Signaling is enabled.
- A signaling data collection exists on the source database.
- The signaling data collection is specified in the signal.data.collection property.

Using a source signaling channel to trigger an incremental snapshot

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>","additional-condition":"<additional-condition>"}');

For example,

INSERT INTO myschema.debezium_signal (id, type, data) 1
values ('ad-hoc-1',   2
    'execute-snapshot',  3
    '{"data-collections": ["schema1.table1", "schema2.table2"], 4
    "type":"incremental"}, 5
    "additional-condition":"color=blue"}'); 6

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 parameters in the example:

Table 6.3. Descriptions of fields in a SQL command for sending an incremental snapshot signal to the signaling table
Item	Value	Description
1	`myschema.debezium_signal`	Specifies the fully-qualified name of the signaling table on the source database.
2	`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.
3	`execute-snapshot`	The `type` parameter specifies the operation that the signal is intended to trigger.
4	`data-collections`	A required component of the `data` field of a signal that specifies an array of table names or regular expressions to match table names to include in the snapshot. The array lists regular expressions which match 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.
5	`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`. If you do not specify a value, the connector runs an incremental snapshot.
6	`additional-condition`	An optional string, which specifies a condition based on the column(s) of the table(s), to capture a subset of the contents of the tables. For more information about the `additional-condition` parameter, see Ad hoc incremental snapshots with `additional-condition`.

Ad hoc incremental snapshots with additional-condition

If you want a snapshot to include only a subset of the content in a table, you can modify the signal request by appending an additional-condition parameter to the snapshot signal.

The SQL query for a typical snapshot takes the following form:

SELECT * FROM <tableName> ....

By adding an additional-condition parameter, you append a WHERE condition to the SQL query, as in the following example:

SELECT * FROM <tableName> WHERE <additional-condition> ....

The following example shows a SQL query to send an ad hoc incremental snapshot request with an additional condition to the signaling table:

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

For example, suppose you have a products table that contains the following columns:

id (primary key)
color
quantity

If you want an incremental snapshot of the products table to include only the data items where color=blue, you can use the following SQL statement to trigger the snapshot:

INSERT INTO myschema.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["schema1.products"],"type":"incremental", "additional-condition":"color=blue"}');

The additional-condition parameter also enables you to pass conditions that are based on more than one column. For example, using the products table from the previous example, you can submit a query that triggers an incremental snapshot that includes the data of only those items for which color=blue and quantity>10:

INSERT INTO myschema.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["schema1.products"],"type":"incremental", "additional-condition":"color=blue AND quantity>10"}');

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
}

Item	Field name	Description
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.

6.1.6.2. Using the Kafka signaling channel to trigger an incremental snapshot

You can send a message to the configured Kafka topic to request the connector to run an ad hoc incremental snapshot.

The key of the Kafka message must match the value of the topic.prefix connector configuration option.

The value of the message is a JSON object with type and data fields.

The signal type is execute-snapshot, and the data field must have the following fields:

Table 6.4. Execute snapshot data fields
Field	Default	Value
`type`	`incremental`	The type of the snapshot to be executed. Currently Debezium supports only the `incremental` type. See the next section for more details.
`data-collections`	N/A	An array of comma-separated regular expressions that match the fully-qualified names of tables to include in the snapshot. Specify the names by using the same format as is required for the signal.data.collection configuration option.
`additional-condition`	N/A	An optional string that specifies a condition that the connector evaluates to designate a subset of columns to include in a snapshot.

An example of the execute-snapshot Kafka message:

Key = `test_connector`

Value = `{"type":"execute-snapshot","data": {"data-collections": ["schema1.table1", "schema1.table2"], "type": "INCREMENTAL"}}`

Ad hoc incremental snapshots with additional-condition

Debezium uses the additional-condition field to select a subset of a table’s content.

Typically, when Debezium runs a snapshot, it runs a SQL query such as:

SELECT * FROM <tableName> ….

When the snapshot request includes an additional-condition, the additional-condition is appended to the SQL query, for example:

SELECT * FROM <tableName> WHERE <additional-condition> ….

For example, given a products table with the columns id (primary key), color, and brand, if you want a snapshot to include only content for which color='blue', when you request the snapshot, you could append an additional-condition statement to filter the content:

Key = `test_connector`

Value = `{"type":"execute-snapshot","data": {"data-collections": ["schema1.products"], "type": "INCREMENTAL", "additional-condition":"color='blue'"}}`

You can use the additional-condition statement to pass conditions based on multiple columns. For example, using the same products table as in the previous example, if you want a snapshot to include only the content from the products table for which color='blue', and brand='MyBrand', you could send the following request:

Key = `test_connector`

Value = `{"type":"execute-snapshot","data": {"data-collections": ["schema1.products"], "type": "INCREMENTAL", "additional-condition":"color='blue' AND brand='MyBrand'"}}`

6.1.6.3. Stopping an incremental snapshot

You can also stop an incremental snapshot by sending a signal to the table on the source database. You submit a stop snapshot signal to the table by sending a SQL INSERT query.

After Debezium detects the change in the signaling table, it reads the signal, and stops the incremental snapshot operation if it’s in progress.

The query that you submit specifies the snapshot operation of incremental, and, optionally, the tables of the current running snapshot to be removed.

Prerequisites

Signaling is enabled.
- A signaling data collection exists on the source database.
- The signaling data collection is specified in the signal.data.collection property.

Using a source signaling channel to stop an incremental snapshot

Send a SQL query to stop the ad hoc incremental snapshot to the signaling table:

INSERT INTO <signalTable> (id, type, data) values ('<id>', 'stop-snapshot', '{"data-collections": ["<tableName>","<tableName>"],"type":"incremental"}');

For example,

INSERT INTO myschema.debezium_signal (id, type, data) 1
values ('ad-hoc-1',   2
    'stop-snapshot',  3
    '{"data-collections": ["schema1.table1", "schema2.table2"], 4
    "type":"incremental"}'); 5

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

The following table describes the parameters in the example:

Table 6.5. Descriptions of fields in a SQL command for sending a stop incremental snapshot signal to the signaling table
Item	Value	Description
1	`myschema.debezium_signal`	Specifies the fully-qualified name of the signaling table on the source database.
2	`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.
3	`stop-snapshot`	Specifies `type` parameter specifies the operation that the signal is intended to trigger.
4	`data-collections`	An optional component of the `data` field of a signal that specifies an array of table names or regular expressions to match table names to remove from the snapshot. The array lists regular expressions which match 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. If this component of the `data` field is omitted, the signal stops the entire incremental snapshot that is in progress.
5	`incremental`	A required component of the `data` field of a signal that specifies the kind of snapshot operation that is to be stopped. Currently, the only valid option is `incremental`. If you do not specify a `type` value, the signal fails to stop the incremental snapshot.

6.1.6.4. Using the Kafka signaling channel to stop an incremental snapshot

You can send a signal message to the configured Kafka signaling topic to stop an ad hoc incremental snapshot.

The key of the Kafka message must match the value of the topic.prefix connector configuration option.

The value of the message is a JSON object with type and data fields.

The signal type is stop-snapshot, and the data field must have the following fields:

Table 6.6. Execute snapshot data fields
Field	Default	Value
`type`	`incremental`	The type of the snapshot to be executed. Currently Debezium supports only the `incremental` type. See the next section for more details.
`data-collections`	N/A	An optional array of comma-separated regular expressions that match the fully-qualified names of the tables to include in the snapshot. Specify the names by using the same format as is required for the signal.data.collection configuration option.

The following example shows a typical stop-snapshot Kafka message:

Key = `test_connector`

Value = `{"type":"stop-snapshot","data": {"data-collections": ["schema1.table1", "schema1.table2"], "type": "INCREMENTAL"}}`

6.1.7. Default names of Kafka topics that receive Debezium MySQL change event records

By default, the MySQL connector writes change events for all of the 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:

topicPrefix.databaseName.tableName

Suppose that fulfillment is the topic prefix, inventory is the database name, and the database contains tables named orders, customers, and products. The Debezium MySQL connector emits events to three Kafka topics, one for each table in the database:

fulfillment.inventory.orders
fulfillment.inventory.customers
fulfillment.inventory.products

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

topicPrefix: The topic prefix as specified by the topic.prefix connector configuration property.
schemaName: The name of the schema in which the operation occurred.
tableName: The name of the table in which the operation occurred.

The connector applies similar naming conventions to label its internal database schema 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.

Transaction metadata

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.

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 the unique transaction identifier.
ts_ms: The time of a transaction boundary event (BEGIN or END event) at the data source. If the data source does not provide Debezium with the event time, then the field instead represents the time at which Debezium processes the event.
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 elements that indicates the number of events that the connector emits for changes that originate from a data collection.

Example

{
  "status": "BEGIN",
  "id": "0e4d5dcd-a33b-11ea-80f1-02010a22a99e:10",
  "ts_ms": 1486500577125,
  "event_count": null,
  "data_collections": null
}

{
  "status": "END",
  "id": "0e4d5dcd-a33b-11ea-80f1-02010a22a99e:10",
  "ts_ms": 1486500577691,
  "event_count": 2,
  "data_collections": [
    {
      "data_collection": "s1.a",
      "event_count": 1
    },
    {
      "data_collection": "s2.a",
      "event_count": 1
    }
  ]
}

Unless overridden via the topic.transaction option, the connector emits transaction events to the <topic.prefix>.transaction topic.

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.

Following is an example of a message:

{
  "before": null,
  "after": {
    "pk": "2",
    "aa": "1"
  },
  "source": {
...
  },
  "op": "c",
  "ts_ms": "1580390884335",
  "transaction": {
    "id": "0e4d5dcd-a33b-11ea-80f1-02010a22a99e:10",
    "total_order": "1",
    "data_collection_order": "1"
  }
}

For systems which don’t have GTID enabled, the transaction identifier is constructed using the combination of binlog filename and binlog position. For example, if the binlog filename and position corresponding to the transaction BEGIN event are mysql-bin.000002 and 1913 respectively then the Debezium constructed transaction identifier would be file=mysql-bin.000002,pos=1913.

6.2. Descriptions of Debezium MySQL connector data change events

The Debezium MySQL 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 6.7. Overview of change event basic content
Item	Field name	Description
1	`schema`	The first `schema` field is part of the event key. It specifies a Kafka Connect schema that describes what is in the event key’s `payload` portion. In other words, the first `schema` field describes the structure of the primary key, or the unique key if the table does not have a primary key, for the table that was changed. It is possible to override the table’s primary key by setting the `message.key.columns` connector configuration property. In this case, the first schema field describes the structure of the key identified by that property.
2	`payload`	The first `payload` field is part of the event key. It has the structure described by the previous `schema` field and it contains the key for the row that was changed.
3	`schema`	The second `schema` field is part of the event value. It specifies the Kafka Connect schema that describes what is in the event value’s `payload` portion. In other words, the second `schema` describes the structure of the row that was changed. Typically, this schema contains nested schemas.
4	`payload`	The second `payload` field is part of the event value. It has the structure described by the previous `schema` field and it contains the actual data for the row that was changed.

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

Warning

The MySQL 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.

More details are in the following topics:

6.2.1. About keys in Debezium MySQL 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 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.

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

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. In JSON, it looks like this:

{
 "schema": { 1
    "type": "struct",
    "name": "mysql-server-1.inventory.customers.Key", 2
    "optional": false, 3
    "fields": [ 4
      {
        "field": "id",
        "type": "int32",
        "optional": false
      }
    ]
  },
 "payload": { 5
    "id": 1001
  }
}

Table 6.8. Description of change event key
Item	Field name	Description
1	`schema`	The schema portion of the key specifies a Kafka Connect schema that describes what is in the key’s `payload` portion.
2	`mysql-server-1.inventory.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-name.table-name.`Key`. In this example: `mysql-server-1` is the name of the connector that generated this event. `inventory` is the database that contains the table that was changed. `customers` is the table that was updated.
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	`fields`	Specifies each field that is expected in the `payload`, including each field’s name, type, and whether it is required.
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 `1001`.

6.2.2. About values in Debezium MySQL 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 NOT NULL AUTO_INCREMENT PRIMARY KEY,
  first_name VARCHAR(255) NOT NULL,
  last_name VARCHAR(255) NOT NULL,
  email VARCHAR(255) NOT NULL UNIQUE KEY
) AUTO_INCREMENT=1001;

The value portion of a change event for a change to this table is described for:

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": "mysql-server-1.inventory.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": "mysql-server-1.inventory.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": 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.mysql.Source", 3
        "field": "source"
      },
      {
        "type": "string",
        "optional": false,
        "field": "op"
      },
      {
        "type": "int64",
        "optional": true,
        "field": "ts_ms"
      }
    ],
    "optional": false,
    "name": "mysql-server-1.inventory.customers.Envelope" 4
  },
  "payload": { 5
    "op": "c", 6
    "ts_ms": 1465491411815, 7
    "before": null, 8
    "after": { 9
      "id": 1004,
      "first_name": "Anne",
      "last_name": "Kretchmar",
      "email": "annek@noanswer.org"
    },
    "source": { 10
      "version": "2.3.4.Final",
      "connector": "mysql",
      "name": "mysql-server-1",
      "ts_ms": 0,
      "snapshot": false,
      "db": "inventory",
      "table": "customers",
      "server_id": 0,
      "gtid": null,
      "file": "mysql-bin.000003",
      "pos": 154,
      "row": 0,
      "thread": 7,
      "query": "INSERT INTO customers (first_name, last_name, email) VALUES ('Anne', 'Kretchmar', 'annek@noanswer.org')"
    }
  }
}

Table 6.9. Descriptions of create event value fields
Item	Field name	Description
1	`schema`	The value’s schema, which describes the structure of the value’s payload. A change event’s value schema is the same in every change event that the connector generates for a particular table.
2	`name`	In the `schema` section, each `name` field specifies the schema for a field in the value’s payload. `mysql-server-1.inventory.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.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.mysql.Source` is the schema for the payload’s `source` field. This schema is specific to the MySQL connector. The connector uses it for all events that it generates.
4	`name`	`mysql-server-1.inventory.customers.Envelope` is the schema for the overall structure of the payload, where `mysql-server-1` is the connector name, `inventory` is the database, 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	`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)
7	`ts_ms`	Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task. In the `source` object, `ts_ms` indicates the time that the change was made in the database. By comparing the value for `payload.source.ts_ms` with the value for `payload.ts_ms`, you can determine the lag between the source database update and Debezium.
8	`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.
9	`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.
10	`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 name binlog name where the event was recorded binlog position Row within the event If the event was part of a snapshot Name of the database and table that contain the new row ID of the MySQL thread that created the event (non-snapshot only) MySQL server ID (if available) Timestamp for when the change was made in the database If the `binlog_rows_query_log_events` MySQL configuration option is enabled and the connector configuration `include.query` property is enabled, the `source` field also provides the `query` field, which contains the original SQL statement that caused the change event.

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": 1004,
      "first_name": "Anne",
      "last_name": "Kretchmar",
      "email": "annek@noanswer.org"
    },
    "after": { 2
      "id": 1004,
      "first_name": "Anne Marie",
      "last_name": "Kretchmar",
      "email": "annek@noanswer.org"
    },
    "source": { 3
      "version": "2.3.4.Final",
      "name": "mysql-server-1",
      "connector": "mysql",
      "name": "mysql-server-1",
      "ts_ms": 1465581029100,
      "snapshot": false,
      "db": "inventory",
      "table": "customers",
      "server_id": 223344,
      "gtid": null,
      "file": "mysql-bin.000003",
      "pos": 484,
      "row": 0,
      "thread": 7,
      "query": "UPDATE customers SET first_name='Anne Marie' WHERE id=1004"
    },
    "op": "u", 4
    "ts_ms": 1465581029523 5
  }
}

Table 6.10. Descriptions of update event value fields
Item	Field name	Description
1	`before`	An optional field that specifies the state of the row before the event occurred. In an update event value, the `before` field contains a field for each table column and the value that was in that column before the database commit. In this example, the `first_name` value is `Anne.`
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 `first_name` value is now `Anne Marie`.
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 is from a different position in the binlog. The source metadata includes: Debezium version Connector name binlog name where the event was recorded binlog position Row within the event If the event was part of a snapshot Name of the database and table that contain the updated row ID of the MySQL thread that created the event (non-snapshot only) MySQL server ID (if available) Timestamp for when the change was made in the database If the `binlog_rows_query_log_events` MySQL configuration option is enabled and the connector configuration `include.query` property is enabled, the `source` field also provides the `query` field, which contains the original SQL statement that caused the change event.
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. 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.

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 an event with the new key for the row. Details are in the next section.

Primary key updates

An UPDATE operation that changes a row’s primary key field(s) is known as a primary key change. For a primary key change, in place of an UPDATE event record, the connector emits a DELETE event record for the old key and a CREATE event record for the new (updated) key. These events have the usual structure and content, and in addition, each one has a message header related to the primary key change:

The DELETE event record has __debezium.newkey as a message header. The value of this header is the new primary key for the updated row.
The CREATE event record has __debezium.oldkey as a message header. The value of this header is the previous (old) primary key that the updated row had.

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": { 1
      "id": 1004,
      "first_name": "Anne Marie",
      "last_name": "Kretchmar",
      "email": "annek@noanswer.org"
    },
    "after": null, 2
    "source": { 3
      "version": "2.3.4.Final",
      "connector": "mysql",
      "name": "mysql-server-1",
      "ts_ms": 1465581902300,
      "snapshot": false,
      "db": "inventory",
      "table": "customers",
      "server_id": 223344,
      "gtid": null,
      "file": "mysql-bin.000003",
      "pos": 805,
      "row": 0,
      "thread": 7,
      "query": "DELETE FROM customers WHERE id=1004"
    },
    "op": "d", 4
    "ts_ms": 1465581902461 5
  }
}

Table 6.11. Descriptions of delete event value fields
Item	Field name	Description
1	`before`	Optional field that specifies the state of the row before the event occurred. In a delete event value, the `before` field contains the values that were in the row before it was deleted with the database commit.
2	`after`	Optional field that specifies the state of the row after the event occurred. In a delete event value, the `after` field is `null`, signifying that the row no longer exists.
3	`source`	Mandatory field that describes the source metadata for the event. In a delete event value, the `source` field structure is the same as for create and update events for the same table. Many `source` field values are also the same. In a delete event value, the `ts_ms` and `pos` field values, as well as other values, might have changed. But the `source` field in a delete event value provides the same metadata: Debezium version Connector name binlog name where the event was recorded binlog position Row within the event If the event was part of a snapshot Name of the database and table that contain the updated row ID of the MySQL thread that created the event (non-snapshot only) MySQL server ID (if available) Timestamp for when the change was made in the database If the `binlog_rows_query_log_events` MySQL configuration option is enabled and the connector configuration `include.query` property is enabled, the `source` field also provides the `query` field, which contains the original SQL statement that caused the change event.
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. 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.

A delete change event record provides a consumer with the information it needs to process the removal of this row. The old values are included because some consumers might require them in order to properly handle the removal.

MySQL 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 MySQL connector emits a delete event, the connector emits a special tombstone event that has the same key but a null value.

truncate events

A truncate change event signals that a table has been truncated. The message key is null in this case, the message value looks like this:

{
    "schema": { ... },
    "payload": {
        "source": { 1
            "version": "2.3.4.Final",
            "name": "mysql-server-1",
            "connector": "mysql",
            "name": "mysql-server-1",
            "ts_ms": 1465581029100,
            "snapshot": false,
            "db": "inventory",
            "table": "customers",
            "server_id": 223344,
            "gtid": null,
            "file": "mysql-bin.000003",
            "pos": 484,
            "row": 0,
            "thread": 7,
            "query": "UPDATE customers SET first_name='Anne Marie' WHERE id=1004"
        },
        "op": "t", 2
        "ts_ms": 1465581029523 3
    }
}

Table 6.12. Descriptions of truncate event value fields
Item	Field name	Description
1	`source`	Mandatory field that describes the source metadata for the event. In a truncate event value, the `source` field structure is the same as for create, update, and delete events for the same table, provides this metadata: Debezium version Connector type and name Binlog name where the event was recorded Binlog position Row within the event If the event was part of a snapshot Name of the database and table ID of the MySQL thread that truncated the event (non-snapshot only) MySQL server ID (if available) Timestamp for when the change was made in the database
2	`op`	Mandatory string that describes the type of operation. The `op` field value is `t`, signifying that this table was truncated.
3	`ts_ms`	Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task. + In the `source` object, `ts_ms` indicates the time that the change was made in the database. By comparing the value for `payload.source.ts_ms` with the value for `payload.ts_ms`, you can determine the lag between the source database update and Debezium.

In case a single TRUNCATE statement applies to multiple tables, one truncate change event record for each truncated table will be emitted.

Note that since truncate events represent a change made to an entire table and don’t have a message key, unless you’re working with topics with a single partition, there are no ordering guarantees for the change events pertaining to a table (create, update, etc.) and truncate events for that table. For instance a consumer may receive an update event only after a truncate event for that table, when those events are read from different partitions.

6.3. How Debezium MySQL connectors map data types

The Debezium MySQL connector represents changes to rows with events that are structured like the table in which the row exists. The event contains a field for each column value. The MySQL data type of that column dictates how Debezium represents the value in the event.

Columns that store strings are defined in MySQL with a character set and collation. The MySQL connector uses the column’s character set when reading the binary representation of the column values in the binlog events.

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

Literal type: how the value is represented using Kafka Connect schema types.
Semantic type: how the Kafka Connect schema captures the meaning of the field (schema name).

If the default data type conversions do not meet your needs, you can create a custom converter for the connector.

Details are in the following sections:

Basic types

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

Table 6.13. Descriptions of basic type mappings
MySQL type	Literal type	Semantic type
`BOOLEAN, BOOL`	`BOOLEAN`	n/a
`BIT(1)`	`BOOLEAN`	n/a
`BIT(>1)`	`BYTES`	`io.debezium.data.Bits` The `length` schema parameter contains an integer that represents the number of bits. The `byte[]` contains the bits in little-endian form and is sized to contain the specified number of bits. For example, where `n` is bits: `numBytes = n/8 + (n%8== 0 ? 0 : 1)`
`TINYINT`	`INT16`	n/a
`SMALLINT[(M)]`	`INT16`	n/a
`MEDIUMINT[(M)]`	`INT32`	n/a
`INT, INTEGER[(M)]`	`INT32`	n/a
`BIGINT[(M)]`	`INT64`	n/a
`REAL[(M,D)]`	`FLOAT32`	n/a
`FLOAT[(P)]`	`FLOAT32` or `FLOAT64`	The precision is used only to determine storage size. A precision `P` from 0 to 23 results in a 4-byte single-precision `FLOAT32` column. A precision `P` from 24 to 53 results in an 8-byte double-precision `FLOAT64` column.
`FLOAT(M,D)`	`FLOAT64`	As of MySQL 8.0.17, the nonstandard FLOAT(M,D) and DOUBLE(M,D) syntax is deprecated, and should expect support for it be removed in a future version of MySQL, set `FLOAT64` as default.
`DOUBLE[(M,D)]`	`FLOAT64`	n/a
`CHAR(M)]`	`STRING`	n/a
`VARCHAR(M)]`	`STRING`	n/a
`BINARY(M)]`	`BYTES` or `STRING`	n/a Either the raw bytes (the default), a base64-encoded String, or a base64-url-safe-encoded String, or a hex-encoded String, based on the `binary.handling.mode` connector configuration property setting.
`VARBINARY(M)]`	`BYTES` or `STRING`	n/a Either the raw bytes (the default), a base64-encoded String, or a base64-url-safe-encoded String, or a hex-encoded String, based on the `binary.handling.mode` connector configuration property setting.
`TINYBLOB`	`BYTES` or `STRING`	n/a Either the raw bytes (the default), a base64-encoded String, or a base64-url-safe-encoded String, or a hex-encoded String, based on the `binary.handling.mode` connector configuration property setting.
`TINYTEXT`	`STRING`	n/a
`BLOB`	`BYTES` or `STRING`	n/a Either the raw bytes (the default), a base64-encoded String, or a base64-url-safe-encoded String, or a hex-encoded String, based on the `binary.handling.mode` connector configuration property setting. Only values with a size of up to 2GB are supported. It is recommended to externalize large column values, using the claim check pattern.
`TEXT`	`STRING`	n/a Only values with a size of up to 2GB are supported. It is recommended to externalize large column values, using the claim check pattern.
`MEDIUMBLOB`	`BYTES` or `STRING`	n/a Either the raw bytes (the default), a base64-encoded String, or a base64-url-safe-encoded String, or a hex-encoded String, based on the `binary.handling.mode` connector configuration property setting.
`MEDIUMTEXT`	`STRING`	n/a
`LONGBLOB`	`BYTES` or `STRING`	n/a Either the raw bytes (the default), a base64-encoded String, or a base64-url-safe-encoded String, or a hex-encoded String, based on the `binary.handling.mode` connector configuration property setting. Only values with a size of up to 2GB are supported. It is recommended to externalize large column values, using the claim check pattern.
`LONGTEXT`	`STRING`	n/a Only values with a size of up to 2GB are supported. It is recommended to externalize large column values, using the claim check pattern.
`JSON`	`STRING`	`io.debezium.data.Json` Contains the string representation of a `JSON` document, array, or scalar.
`ENUM`	`STRING`	`io.debezium.data.Enum` The `allowed` schema parameter contains the comma-separated list of allowed values.
`SET`	`STRING`	`io.debezium.data.EnumSet` The `allowed` schema parameter contains the comma-separated list of allowed values.
`YEAR[(2\|4)]`	`INT32`	`io.debezium.time.Year`
`TIMESTAMP[(M)]`	`STRING`	`io.debezium.time.ZonedTimestamp` In ISO 8601 format with microsecond precision. MySQL allows `M` to be in the range of `0-6`.

Temporal types

Excluding the TIMESTAMP data type, MySQL temporal types depend on the value of the time.precision.mode connector configuration property. For TIMESTAMP columns whose default value is specified as CURRENT_TIMESTAMP or NOW, the value 1970-01-01 00:00:00 is used as the default value in the Kafka Connect schema.

MySQL allows zero-values for DATE, DATETIME, and TIMESTAMP columns because zero-values are sometimes preferred over null values. The MySQL connector represents zero-values as null values when the column definition allows null values, or as the epoch day when the column does not allow null values.

Temporal values without time zones

The DATETIME type represents a local date and time such as "2018-01-13 09:48:27". As you can see, there is no time zone information. Such columns are converted into epoch milliseconds or microseconds based on the column’s precision by using UTC. The TIMESTAMP type represents a timestamp without time zone information. It is converted by MySQL from the server (or session’s) current time zone into UTC when writing and from UTC into the server (or session’s) current time zone when reading back the value. For example:

DATETIME with a value of 2018-06-20 06:37:03 becomes 1529476623000.
TIMESTAMP with a value of 2018-06-20 06:37:03 becomes 2018-06-20T13:37:03Z.

Such columns are converted into an equivalent io.debezium.time.ZonedTimestamp in UTC based on the server (or session’s) current time zone. The time zone will be queried from the server by default. If this fails, it must be specified explicitly by the database connectionTimeZone MySQL configuration option. For example, if the database’s time zone (either globally or configured for the connector by means of the connectionTimeZone option) is "America/Los_Angeles", the TIMESTAMP value "2018-06-20 06:37:03" is represented by a ZonedTimestamp with the value "2018-06-20T13:37:03Z".

The time zone of the JVM running Kafka Connect and Debezium does not affect these conversions.

More details about properties related to temporal values are in the documentation for MySQL connector configuration properties.

time.precision.mode=adaptive_time_microseconds(default)

The MySQL connector determines the literal type and semantic type based on the column’s data type definition so that events represent exactly the values in the database. All time fields are in microseconds. Only positive TIME field values in the range of 00:00:00.000000 to 23:59:59.999999 can be captured correctly.

Table 6.14. Mappings when time.precision.mode=adaptive_time_microseconds
MySQL type	Literal type	Semantic type
`DATE`	`INT32`	`io.debezium.time.Date` Represents the number of days since the epoch.
`TIME[(M)]`	`INT64`	`io.debezium.time.MicroTime` Represents the time value in microseconds and does not include time zone information. MySQL allows `M` to be in the range of `0-6`.
`DATETIME, DATETIME(0), DATETIME(1), DATETIME(2), DATETIME(3)`	`INT64`	`io.debezium.time.Timestamp` Represents the number of milliseconds past the epoch and does not include time zone information.
`DATETIME(4), DATETIME(5), DATETIME(6)`	`INT64`	`io.debezium.time.MicroTimestamp` Represents the number of microseconds past the epoch and does not include time zone information.

time.precision.mode=connect

The MySQL connector uses defined Kafka Connect logical types. This approach is less precise than the default approach and the events could be less precise if the database column has a fractional second precision value of greater than 3. Values in only the range of 00:00:00.000 to 23:59:59.999 can be handled. Set time.precision.mode=connect only if you can ensure that the TIME values in your tables never exceed the supported ranges. The connect setting is expected to be removed in a future version of Debezium.

Table 6.15. Mappings when time.precision.mode=connect
MySQL type	Literal type	Semantic type
`DATE`	`INT32`	`org.apache.kafka.connect.data.Date` Represents the number of days since the epoch.
`TIME[(M)]`	`INT64`	`org.apache.kafka.connect.data.Time` Represents the time value in microseconds since midnight and does not include time zone information.
`DATETIME[(M)]`	`INT64`	`org.apache.kafka.connect.data.Timestamp` Represents the number of milliseconds since the epoch, and does not include time zone information.

Decimal types

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

decimal.handling.mode=precise

Table 6.16. Mappings when decimal.handling.mode=precise
MySQL type	Literal type	Semantic type
`NUMERIC[(M[,D])]`	`BYTES`	`org.apache.kafka.connect.data.Decimal` The `scale` schema parameter contains an integer that represents how many digits the decimal point shifted.
`DECIMAL[(M[,D])]`	`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 6.17. Mappings when decimal.handling.mode=double
MySQL type	Literal type	Semantic type
`NUMERIC[(M[,D])]`	`FLOAT64`	n/a
`DECIMAL[(M[,D])]`	`FLOAT64`	n/a

decimal.handling.mode=string

Table 6.18. Mappings when decimal.handling.mode=string
MySQL type	Literal type	Semantic type
`NUMERIC[(M[,D])]`	`STRING`	n/a
`DECIMAL[(M[,D])]`	`STRING`	n/a

Boolean values

MySQL handles the BOOLEAN value internally in a specific way. The BOOLEAN column is internally mapped to the TINYINT(1) data type. When the table is created during streaming then it uses proper BOOLEAN mapping as Debezium receives the original DDL. During snapshots, Debezium executes SHOW CREATE TABLE to obtain table definitions that return TINYINT(1) for both BOOLEAN and TINYINT(1) columns. Debezium then has no way to obtain the original type mapping and so maps to TINYINT(1).

To enable you to convert source columns to Boolean data types, Debezium provides a TinyIntOneToBooleanConverter custom converter that you can use in one of the following ways:

Map all TINYINT(1) or TINYINT(1) UNSIGNED columns to BOOLEAN types.
Enumerate a subset of columns by using a comma-separated list of regular expressions.
To use this type of conversion, you must set the converters configuration property with the selector parameter, as shown in the following example:
```
converters=boolean
boolean.type=io.debezium.connector.mysql.converters.TinyIntOneToBooleanConverter
boolean.selector=db1.table1.*, db1.table2.column1
```
NOTE: MySQL8 not showing the length of tinyint unsigned type when snapshot executes SHOW CREATE TABLE, which means this converter doesn’t work. The new option length.checker can solve this issue, the default value is true. Disable the length.checker and specify the columns that need to be converted to selector property instead of converting all columns based on type, as shown in the following example:
```
converters=boolean
boolean.type=io.debezium.connector.mysql.converters.TinyIntOneToBooleanConverter
boolean.length.checker=false
boolean.selector=db1.table1.*, db1.table2.column1
```

Spatial types

Currently, the Debezium MySQL connector supports the following spatial data types.

Table 6.19. Description of spatial type mappings
MySQL type	Literal type	Semantic type
`GEOMETRY, LINESTRING, POLYGON, MULTIPOINT, MULTILINESTRING, MULTIPOLYGON, GEOMETRYCOLLECTION`	`STRUCT`	`io.debezium.data.geometry.Geometry` Contains a structure with two fields: `srid (INT32`: spatial reference system ID that defines the type of geometry object stored in the structure `wkb (BYTES)`: binary representation of the geometry object encoded in the Well-Known-Binary (wkb) format. See the Open Geospatial Consortium for more details.

6.4. Setting up MySQL to run a Debezium connector

Some MySQL setup tasks are required before you can install and run a Debezium connector.

Details are in the following sections:

6.4.1. Creating a MySQL user for a Debezium connector

A Debezium MySQL connector requires a MySQL user account. This MySQL user must have appropriate permissions on all databases for which the Debezium MySQL connector captures changes.

Prerequisites

A MySQL server.
Basic knowledge of SQL commands.

Procedure

Create the MySQL user:

mysql> CREATE USER 'user'@'localhost' IDENTIFIED BY 'password';

Grant the required permissions to the user:
```
mysql> GRANT SELECT, RELOAD, SHOW DATABASES, REPLICATION SLAVE, REPLICATION CLIENT ON *.* TO 'user' IDENTIFIED BY 'password';
```
The table below describes the permissions.
Important
If using a hosted option such as Amazon RDS or Amazon Aurora that does not allow a global read lock, table-level locks are used to create the consistent snapshot. In this case, you need to also grant LOCK TABLES permissions to the user that you create. See snapshots for more details.
Finalize the user’s permissions:
```
mysql> FLUSH PRIVILEGES;
```

Table 6.20. Descriptions of user permissions
Keyword	Description
`SELECT`	Enables the connector to select rows from tables in databases. This is used only when performing a snapshot.
`RELOAD`	Enables the connector the use of the `FLUSH` statement to clear or reload internal caches, flush tables, or acquire locks. This is used only when performing a snapshot.
`SHOW DATABASES`	Enables the connector to see database names by issuing the `SHOW DATABASE` statement. This is used only when performing a snapshot.
`REPLICATION SLAVE`	Enables the connector to connect to and read the MySQL server binlog.
`REPLICATION CLIENT`	Enables the connector the use of the following statements: `SHOW MASTER STATUS` `SHOW SLAVE STATUS` `SHOW BINARY LOGS` The connector always requires this.
`ON`	Identifies the database to which the permissions apply.
`TO 'user'`	Specifies the user to grant the permissions to.
`IDENTIFIED BY 'password'`	Specifies the user’s MySQL password.

6.4.2. Enabling the MySQL binlog for Debezium

You must enable binary logging for MySQL replication. The binary logs record transaction updates for replication tools to propagate changes.

Prerequisites

A MySQL server.
Appropriate MySQL user privileges.

Procedure

Check whether the log-bin option is already on:

// for MySql 5.x
mysql> SELECT variable_value as "BINARY LOGGING STATUS (log-bin) ::"
FROM information_schema.global_variables WHERE variable_name='log_bin';
// for MySql 8.x
mysql> SELECT variable_value as "BINARY LOGGING STATUS (log-bin) ::"
FROM performance_schema.global_variables WHERE variable_name='log_bin';

If it is OFF, configure your MySQL server configuration file with the following properties, which are described in the table below:

server-id         = 223344 # Querying variable is called server_id, e.g. SELECT variable_value FROM information_schema.global_variables WHERE variable_name='server_id';
log_bin           = mysql-bin
binlog_format     = ROW
binlog_row_image  = FULL
expire_logs_days  = 10

Confirm your changes by checking the binlog status once more:

// for MySql 5.x
mysql> SELECT variable_value as "BINARY LOGGING STATUS (log-bin) ::"
FROM information_schema.global_variables WHERE variable_name='log_bin';
// for MySql 8.x
mysql> SELECT variable_value as "BINARY LOGGING STATUS (log-bin) ::"
FROM performance_schema.global_variables WHERE variable_name='log_bin';

Table 6.21. Descriptions of MySQL binlog configuration properties
Property	Description
`server-id`	The value for the `server-id` must be unique for each server and replication client in the MySQL cluster. During MySQL connector set up, Debezium assigns a unique server ID to the connector.
`log_bin`	The value of `log_bin` is the base name of the sequence of binlog files.
`binlog_format`	The `binlog-format` must be set to `ROW` or `row`.
`binlog_row_image`	The `binlog_row_image` must be set to `FULL` or `full`.
`expire_logs_days`	This is the number of days for automatic binlog file removal. The default is `0`, which means no automatic removal. Set the value to match the needs of your environment. See MySQL purges binlog files.

6.4.3. Enabling MySQL Global Transaction Identifiers for Debezium

Global transaction identifiers (GTIDs) uniquely identify transactions that occur on a server within a cluster. Though not required for a Debezium MySQL connector, using GTIDs simplifies replication and enables you to more easily confirm if primary and replica servers are consistent.

GTIDs are available in MySQL 5.6.5 and later. See the MySQL documentation for more details.

Prerequisites

A MySQL server.
Basic knowledge of SQL commands.
Access to the MySQL configuration file.

Procedure

Enable gtid_mode:
```
mysql> gtid_mode=ON
```
Enable enforce_gtid_consistency:
```
mysql> enforce_gtid_consistency=ON
```

Confirm the changes:

mysql> show global variables like '%GTID%';

Result

+--------------------------+-------+
| Variable_name            | Value |
+--------------------------+-------+
| enforce_gtid_consistency | ON    |
| gtid_mode                | ON    |
+--------------------------+-------+

Table 6.22. Descriptions of GTID options
Option	Description
`gtid_mode`	Boolean that specifies whether GTID mode of the MySQL server is enabled or not. `ON` = enabled `OFF` = disabled
`enforce_gtid_consistency`	Boolean that specifies whether the server enforces GTID consistency by allowing the execution of statements that can be logged in a transactionally safe manner. Required when using GTIDs. `ON` = enabled `OFF` = disabled

6.4.4. Configuring MySQL session timesouts for Debezium

When an initial consistent snapshot is made for large databases, your established connection could timeout while the tables are being read. You can prevent this behavior by configuring interactive_timeout and wait_timeout in your MySQL configuration file.

Prerequisites

A MySQL server.
Basic knowledge of SQL commands.
Access to the MySQL configuration file.

Procedure

Configure interactive_timeout:

mysql> interactive_timeout=<duration-in-seconds>

Configure wait_timeout:

mysql> wait_timeout=<duration-in-seconds>

Table 6.23. Descriptions of MySQL session timeout options
Option	Description
`interactive_timeout`	The number of seconds the server waits for activity on an interactive connection before closing it. See MySQL’s documentation for more details.
`wait_timeout`	The number of seconds the server waits for activity on a non-interactive connection before closing it. See MySQL’s documentation for more details.

6.4.5. Enabling query log events for Debezium MySQL connectors

You might want to see the original SQL statement for each binlog event. Enabling the binlog_rows_query_log_events option in the MySQL configuration file allows you to do this.

This option is available in MySQL 5.6 and later.

Prerequisites

A MySQL server.
Basic knowledge of SQL commands.
Access to the MySQL configuration file.

Procedure

Enable binlog_rows_query_log_events:
```
mysql> binlog_rows_query_log_events=ON
```
binlog_rows_query_log_events is set to a value that enables/disables support for including the original SQL statement in the binlog entry.
- ON = enabled
- OFF = disabled

6.4.6. validate binlog row value options for Debezium MySQL connectors

Check binlog_row_value_options variable, and make sure that value is not set to PARTIAL_JSON, since in such case connector might fail to consume UPDATE events.

Prerequisites

A MySQL server.
Basic knowledge of SQL commands.
Access to the MySQL configuration file.

Procedure

Check current variable value

mysql> show global variables where variable_name = 'binlog_row_value_options';

Result

+--------------------------+-------+
| Variable_name            | Value |
+--------------------------+-------+
| binlog_row_value_options |       |
+--------------------------+-------+

In case value is PARTIAL_JSON, unset this variable by:
```
mysql> set @@global.binlog_row_value_options="" ;
```

6.5. Deployment of Debezium MySQL connectors

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

Use AMQ Streams to automatically create an image that includes the connector plug-in.
This is the preferred method.
Build a custom Kafka Connect container image from a Dockerfile.

Additional resources

Section 6.5.5, “Descriptions of Debezium MySQL connector configuration properties”

6.5.1. MySQL 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.

Additional resources

Configuring Kafka Connect in Using AMQ Streams on OpenShift.
Creating a new container image automatically using AMQ Streams in Deploying and Managing AMQ Streams on OpenShift.

6.5.2. Using AMQ Streams to deploy a Debezium MySQL connector

With earlier versions of AMQ Streams, to deploy Debezium connectors on OpenShift, you were required 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 cluster. 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.
Kafka Connect is deployed on AMQ Streams
You have a Red Hat Integration license.
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 for storing new container images. You must explicitly create an ImageStream for the cluster. ImageStreams are not available by default. For more information about ImageStreams, see Managing image streams on OpenShift Container Platform.

Procedure

Create a Debezium KafkaConnect custom resource (CR) for the connector, or modify an existing one. For example, create a KafkaConnect CR with the name dbz-connect.yaml that specifies the metadata.annotations and spec.build properties. The following example shows an excerpt from a dbz-connect.yaml file that describes a KafkaConnect custom resource.

Example 6.1. A dbz-connect.yaml file that defines a KafkaConnect custom resource that includes a Debezium connector

In the example that follows, the custom resource is configured to download the following artifacts:

The Debezium MySQL connector archive.
The Service Registry archive. The Service Registry is an optional component. Add the Service Registry component only if you intend to use Avro serialization with the connector.
The Debezium scripting SMT archive and the associated scripting engine that you want to use with the Debezium connector. The SMT archive and scripting language dependencies are optional components. Add these components only if you intend to use the Debezium content-based routing SMT or filter SMT.

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: debezium-kafka-connect-cluster
  annotations:
    strimzi.io/use-connector-resources: "true" 1
spec:
  version: 3.5.0
  build: 2
    output: 3
      type: imagestream  4
      image: debezium-streams-connect:latest
    plugins: 5
      - name: debezium-connector-mysql
        artifacts:
          - type: zip 6
            url: https://maven.repository.redhat.com/ga/io/debezium/debezium-connector-mysql/2.3.4.Final-redhat-00001/debezium-connector-mysql-2.3.4.Final-redhat-00001-plugin.zip  7
          - type: zip
            url: https://maven.repository.redhat.com/ga/io/apicurio/apicurio-registry-distro-connect-converter/2.4.4.Final-redhat-<build-number>/apicurio-registry-distro-connect-converter-2.4.4.Final-redhat-<build-number>.zip  8
          - type: zip
            url: https://maven.repository.redhat.com/ga/io/debezium/debezium-scripting/2.3.4.Final-redhat-00001/debezium-scripting-2.3.4.Final-redhat-00001.zip 9
          - type: jar
            url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy/3.0.11/groovy-3.0.11.jar  10
          - type: jar
            url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy-jsr223/3.0.11/groovy-jsr223-3.0.11.jar
          - type: jar
            url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy-json3.0.11/groovy-json-3.0.11.jar

  bootstrapServers: debezium-kafka-cluster-kafka-bootstrap:9093

  ...

Table 6.24. Descriptions of Kafka Connect configuration settings
Item	Description
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 such as 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 in Configuring AMQ Streams on OpenShift.
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. 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. Debezium connector artifacts are available in the Red Hat Maven repository. The OpenShift cluster must have access to the specified server.
8	(Optional) Specifies the artifact `type` and `url` for downloading the Service Registry component. Include the Service Registry artifact, only if you want the connector to use Apache Avro to serialize event keys and values with the Service Registry, instead of using the default JSON converter.
9	(Optional) Specifies the artifact `type` and `url` for the Debezium scripting SMT archive to use with the Debezium connector. Include the scripting SMT only if you intend to use the Debezium content-based routing SMT or filter SMT To use the scripting SMT, you must also deploy a JSR 223-compliant scripting implementation, such as groovy.
10	(Optional) Specifies the artifact `type` and `url` for the JAR files of a JSR 223-compliant scripting implementation, which is required by the Debezium scripting SMT. Important If you use AMQ Streams to incorporate the connector plug-in into your Kafka Connect image, for each of the required scripting language components `artifacts.url` must specify the location of a JAR file, and the value of `artifacts.type` must also be set to `jar`. Invalid values cause the connector fails at runtime. To enable use of the Apache Groovy language with the scripting SMT, the custom resource in the example retrieves JAR files for the following libraries: `groovy` `groovy-jsr223` (scripting agent) `groovy-json` (module for parsing JSON strings) As an alternative, the Debezium scripting SMT also supports the use of the JSR 223 implementation of GraalVM JavaScript.

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.

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 mysql-inventory-connector.yaml

Example 6.2. mysql-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-mysql 1
    spec:
      class: io.debezium.connector.mysql.MySqlConnector 2
      tasksMax: 1  3
      config:  4
        schema.history.internal.kafka.bootstrap.servers: debezium-kafka-cluster-kafka-bootstrap.debezium.svc.cluster.local:9092
        schema.history.internal.kafka.topic: schema-changes.inventory
        database.hostname: mysql.debezium-mysql.svc.cluster.local 5
        database.port: 3306   6
        database.user: debezium  7
        database.password: dbz  8
        database.server.id: 184054 9
        topic.prefix: inventory-connector-mysql 10
        table.include.list: inventory.*  11

        ...

Table 6.25. Descriptions of connector configuration settings
Item	Description
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 account that Debezium uses to connect to the database.
8	The password that Debezium uses to connect to the database user account.
9	Unique numeric ID of the connector.
10	The topic prefix for the database instance or cluster. The specified name must be formed only from alphanumeric characters or underscores. Because the topic prefix 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. This 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.

Create the connector resource by running the following command:
```
oc create -n <namespace> -f <kafkaConnector>.yaml
```
For example,
```
oc create -n debezium -f mysql-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 MySQL deployment.

6.5.3. Deploying Debezium MySQL connectors by building a custom Kafka Connect container image from a Dockerfile

To deploy a Debezium MySQL 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 MySQL connector. Apply this CR to the same OpenShift instance where you apply the KafkaConnect CR.

Prerequisites

MySQL is running and you completed the steps to set up MySQL 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

Create the Debezium MySQL container for Kafka Connect:

Create a Dockerfile that uses registry.redhat.io/amq-streams-kafka-35-rhel8:2.5.0 as the base image. For example, from a terminal window, enter the following command:

cat <<EOF >debezium-container-for-mysql.yaml 1
FROM registry.redhat.io/amq-streams-kafka-35-rhel8:2.5.0
USER root:root
RUN mkdir -p /opt/kafka/plugins/debezium 2
RUN cd /opt/kafka/plugins/debezium/ \
&& curl -O https://maven.repository.redhat.com/ga/io/debezium/debezium-connector-mysql/2.3.4.Final-redhat-00001/debezium-connector-mysql-2.3.4.Final-redhat-00001-plugin.zip \
&& unzip debezium-connector-mysql-2.3.4.Final-redhat-00001-plugin.zip \
&& rm debezium-connector-mysql-2.3.4.Final-redhat-00001-plugin.zip
RUN cd /opt/kafka/plugins/debezium/
USER 1001
EOF

Item	Description
1	You can specify any file name that you want.
2	Specifies the path to your Kafka Connect plug-ins directory. If your Kafka Connect plug-ins directory is in a different location, replace this path with the actual path of your directory.

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

Build the container image from the debezium-container-for-mysql.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-mysql:latest .
```
```
docker build -t debezium-container-for-mysql:latest .
```
The preceding commands build a container image with the name debezium-container-for-mysql.
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-mysql:latest
```
```
docker push <myregistry.io>/debezium-container-for-mysql:latest
```

Create a new Debezium MySQL KafkaConnect custom resource (CR). For example, create a KafkaConnect CR with the name dbz-connect.yaml that specifies annotations and image properties. The following example shows an excerpt from a dbz-connect.yaml file that describes a KafkaConnect custom resource.

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-mysql  2

  ...

Item	Description
1	`metadata.annotations` indicates to the Cluster Operator that `KafkaConnector` resources are used to configure connectors in this Kafka Connect cluster.
2	`spec.image` specifies the name of the image that you created to run your Debezium connector. This property overrides the `STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE` variable in the Cluster Operator.

Apply the KafkaConnect CR to the OpenShift Kafka Connect environment by entering the following command:
```
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.

Create a KafkaConnector custom resource that configures your Debezium MySQL connector instance.

You configure a Debezium MySQL 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 MySQL host, 192.168.99.100, on port 3306, and captures changes to the inventory database. dbserver1 is the server’s logical name.

MySQL inventory-connector.yaml

  apiVersion: kafka.strimzi.io/v1beta2
  kind: KafkaConnector
  metadata:
    name: inventory-connector-mysql  1
    labels:
      strimzi.io/cluster: my-connect-cluster
  spec:
    class: io.debezium.connector.mysql.MySqlConnector
    tasksMax: 1  2
    config:  3
      database.hostname: mysql  4
      database.port: 3306
      database.user: debezium
      database.password: dbz
      database.server.id: 184054  5
      topic.prefix: inventory-connector-mysql 6
      table.include.list: inventory  7
      schema.history.internal.kafka.bootstrap.servers: my-cluster-kafka-bootstrap:9092  8
      schema.history.internal.kafka.topic: schema-changes.inventory  9

Table 6.26. Descriptions of connector configuration settings
Item	Description
1	The name of the connector.
2	Only one task should operate at any one time. Because the MySQL connector reads the MySQL server’s `binlog`, using a single connector task ensures proper order and event handling. The Kafka Connect service uses connectors to start one or more tasks that do the work, and it automatically distributes the running tasks across the cluster of Kafka Connect services. If any of the services stop or crash, those tasks will be redistributed to running services.
3	The connector’s configuration.
4	The database host, which is the name of the container running the MySQL server (`mysql`).
5	Unique ID of the connector.
6	Topic prefix for the MySQL server or cluster. This name is used as the prefix for all Kafka topics that receive change event records.
7	The connector captures changes from the `inventory` table only.
8	The list of Kafka brokers that this connector will use to write and recover DDL statements to the database schema history topic. Upon restart, the connector recovers the schemas of the database that existed at the point in time in the binlog when the connector should begin reading.
9	The name of the database schema history topic. This topic is for internal use only and should not be used by consumers.

Create your connector instance with Kafka Connect. For example, if you saved your KafkaConnector resource in the inventory-connector.yaml file, you would run the following command:
```
oc apply -f inventory-connector.yaml
```
The preceding command registers inventory-connector and the connector starts to run against the inventory database as defined in the KafkaConnector CR.

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

Results

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

6.5.4. Verifying that the Debezium MySQL 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

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-mysql.
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:

Enter the following command:

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

For example,

oc describe KafkaConnector inventory-connector-mysql -n debezium

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

Example 6.3. KafkaConnector resource status

Name:         inventory-connector-mysql
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-mysql
    Tasks:
      Id:               0
      State:            RUNNING
      worker_id:        10.131.1.124:8083
    Type:               source
  Observed Generation:  1
  Tasks Max:            1
  Topics:
    inventory-connector-mysql.inventory
    inventory-connector-mysql.inventory.addresses
    inventory-connector-mysql.inventory.customers
    inventory-connector-mysql.inventory.geom
    inventory-connector-mysql.inventory.orders
    inventory-connector-mysql.inventory.products
    inventory-connector-mysql.inventory.products_on_hand
Events:  <none>

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-mysql.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:

Enter the following command:

oc get kafkatopics

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

Example 6.4. 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-mysql--a96f69b23d6118ff415f772679da623fbbb99421                               debezium-kafka-cluster   1            1                    True
inventory-connector-mysql.inventory.addresses---1b6beaf7b2eb57d177d92be90ca2b210c9a56480          debezium-kafka-cluster   1            1                    True
inventory-connector-mysql.inventory.customers---9931e04ec92ecc0924f4406af3fdace7545c483b          debezium-kafka-cluster   1            1                    True
inventory-connector-mysql.inventory.geom---9f7e136091f071bf49ca59bf99e86c713ee58dd5               debezium-kafka-cluster   1            1                    True
inventory-connector-mysql.inventory.orders---ac5e98ac6a5d91e04d8ec0dc9078a1ece439081d             debezium-kafka-cluster   1            1                    True
inventory-connector-mysql.inventory.products---df0746db116844cee2297fab611c21b56f82dcef           debezium-kafka-cluster   1            1                    True
inventory-connector-mysql.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

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-mysql.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-mysql.inventory.addresses.

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

Example 6.5. Content of a Debezium change event

{"schema":{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"}],"optional":false,"name":"inventory-connector-mysql.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-mysql.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-mysql.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.mysql.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-mysql.inventory.products_on_hand.Envelope"},"payload":{"before":null,"after":{"product_id":101,"quantity":3},"source":{"version":"2.3.4.Final-redhat-00001","connector":"mysql","name":"inventory-connector-mysql","ts_ms":1638985247805,"snapshot":"true","db":"inventory","sequence":null,"table":"products_on_hand","server_id":0,"gtid":null,"file":"mysql-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.

6.5.5. Descriptions of Debezium MySQL connector configuration properties

The Debezium MySQL 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 connector configuration properties
Advanced connector configuration properties
Database schema history connector configuration properties that control how Debezium processes events that it reads from the database schema history topic.
- Pass-through database schema history properties
Pass-through database driver properties that control the behavior of the database driver.

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

Table 6.27. Required Debezium MySQL connector configuration properties
Property	Default	Description
`name`	No default	Unique name for the connector. Attempting to register again with the same name fails. This property is required by all Kafka Connect connectors.
`connector.class`	No default	The name of the Java class for the connector. Always specify `io.debezium.connector.mysql.MySqlConnector` for the MySQL connector.
`tasks.max`	`1`	The maximum number of tasks that should be created for this connector. The MySQL 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 host name of the MySQL database server.
`database.port`	`3306`	Integer port number of the MySQL database server.
`database.user`	No default	Name of the MySQL user to use when connecting to the MySQL database server.
`database.password`	No default	Password to use when connecting to the MySQL database server.
`topic.prefix`	No default	Topic prefix that provides a namespace for the particular MySQL database server/cluster in which Debezium is capturing changes. The topic prefix should be unique across all other connectors, since it is used as a prefix for all Kafka topic names that receive events emitted by this connector. Only alphanumeric characters, hyphens, dots and underscores must be used in the database server logical name. Warning Do not change the value of this property. If you change the name value, after a restart, instead of continuing to emit events to the original topics, the connector emits subsequent events to topics whose names are based on the new value. The connector is also unable to recover its database schema history topic.
`database.server.id`	No default	A numeric ID of this database client, which must be unique across all currently-running database processes in the MySQL cluster. This connector joins the MySQL database cluster as another server (with this unique ID) so it can read the binlog.
`database.include.list`	empty string	An optional, comma-separated list of regular expressions that match the names of the databases for which to capture changes. The connector does not capture changes in any database whose name is not in `database.include.list`. By default, the connector captures changes in all databases. To match the name of a database, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the database; it does not match substrings that might be present in a database name. If you include this property in the configuration, do not also set the `database.exclude.list` property.
`database.exclude.list`	empty string	An optional, comma-separated list of regular expressions that match the names of databases for which you do not want to capture changes. The connector captures changes in any database whose name is not in the `database.exclude.list`. To match the name of a database, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the database; it does not match substrings that might be present in a database name. If you include this property in the configuration, do not also set the `database.include.list` property.
`table.include.list`	empty string	An optional, comma-separated list of regular expressions that match fully-qualified table identifiers of tables whose changes you want to capture. The connector does not capture changes in any table that is not included in `table.include.list`. Each identifier is of the form databaseName.tableName. By default, the connector captures changes in every non-system table in each database whose changes are being captured. To match the name of a table, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the table; it does not match substrings that might be present in a table name. If you include this property in the configuration, do not also set the `table.exclude.list` property.
`table.exclude.list`	empty string	An optional, comma-separated list of regular expressions that match fully-qualified table identifiers for tables whose changes you do not want to capture. The connector captures changes in any table that is not included in `table.exclude.list`. Each identifier is of the form databaseName.tableName. To match the name of a column, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the table; it does not match substrings that might be present in a table name. If you include this property in the configuration, do not also set the `table.include.list` property.
`column.exclude.list`	empty string	An optional, comma-separated list of regular expressions that match the fully-qualified names of columns to exclude from change event record values. Fully-qualified names for columns are of the form databaseName.tableName.columnName. To match the name of a column, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the column; it does not match substrings that might be present in a column name. If you include this property in the configuration, do not also set the `column.include.list` property.
`column.include.list`	empty string	An optional, comma-separated list of regular expressions that match the fully-qualified names of columns to include in change event record values. Fully-qualified names for columns are of the form databaseName.tableName.columnName. To match the name of a column, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the column; it does not match substrings that might be present in a column name. If you include this property in the configuration, do not set the `column.exclude.list` property.
`skip.messages.without.change`	`false`	Specifies whether to skip publishing messages when there is no change in included columns. This would essentially filter messages if there is no change in columns included as per `column.include.list` or `column.exclude.list` properties.
`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. Set this property if you want to truncate the data in a set of columns when it exceeds the number of characters specified by the length in the property name. Set `length` to a positive integer value, for example, `column.truncate.to.20.chars`. The fully-qualified name of a column observes the following format: databaseName.tableName.columnName. To match the name of a column, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the column; the expression does not match substrings that might be present in a column name. You can specify multiple properties with different lengths in a single configuration.
`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. Set this property if you want the connector to mask the values for a set of columns, for example, if they contain sensitive data. Set `length` to a positive integer to replace data in the specified columns with the number of asterisk (``) characters specified by the length* in the property name. Set length to `0` (zero) to replace data in the specified columns with an empty string. The fully-qualified name of a column observes the following format: databaseName.tableName.columnName. To match the name of a column, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the column; the expression does not match substrings that might be present in a column name. You can specify multiple properties with different lengths in a single configuration.
`column.mask.hash.hashAlgorithm.with.salt.salt`; `column.mask.hash.v2.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 `<databaseName>.<tableName>.<columnName>`. To match the name of a column Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the column; the expression does not match substrings that might be present in a column name. 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. Hashing strategy version 2 should be used to ensure fidelity if the value is being hashed in different places or systems.
`column.propagate.source.type`	n/a	An optional, comma-separated list of regular expressions that match the fully-qualified names of columns for which you want the connector to emit extra parameters that represent column metadata. When this property is set, the connector adds the following fields to the schema of event records: `__debezium.source.column.type` `__debezium.source.column.length` `__debezium.source.column.scale` These parameters propagate a column’s original type name and length (for variable-width types), respectively. Enabling the connector to emit this extra data can assist in properly sizing specific numeric or character-based columns in sink databases. The fully-qualified name of a column observes one of the following formats: databaseName.tableName.columnName, or databaseName.schemaName.tableName.columnName. To match the name of a column, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the column; the expression does not match substrings that might be present in a column name.
`datatype.propagate.source.type`	n/a	An optional, comma-separated list of regular expressions that specify the fully-qualified names of data types that are defined for columns in a database. When this property is set, for columns with matching data types, the connector emits event records that include the following extra fields in their schema: `__debezium.source.column.type` `__debezium.source.column.length` `__debezium.source.column.scale` These parameters propagate a column’s original type name and length (for variable-width types), respectively. Enabling the connector to emit this extra data can assist in properly sizing specific numeric or character-based columns in sink databases. The fully-qualified name of a column observes one of the following formats: databaseName.tableName.typeName, or databaseName.schemaName.tableName.typeName. To match the name of a data type, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the data type; the expression does not match substrings that might be present in a type name. For the list of MySQL-specific data type names, see the MySQL data type mappings.
`time.precision.mode`	`adaptive_time_microseconds`	Time, date, and timestamps can be represented with different kinds of precision, including: `adaptive_time_microseconds` (the default) captures the date, datetime and timestamp values exactly as in the database using either millisecond, microsecond, or nanosecond precision values based on the database column’s type, with the exception of TIME type fields, which are always captured as microseconds. `connect` always represents time and timestamp values using Kafka Connect’s built-in representations for Time, Date, and Timestamp, which use millisecond precision regardless of the database columns' precision.
`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.
`bigint.unsigned.handling.mode`	`long`	Specifies how BIGINT UNSIGNED columns should be represented in change events. Possible settings are: `long` represents values by using Java’s `long`, which might not offer the precision but which is easy to use in consumers. `long` is usually the preferred setting. `precise` uses `java.math.BigDecimal` to represent values, which are encoded in the change events by using a binary representation and Kafka Connect’s `org.apache.kafka.connect.data.Decimal` type. Use this setting when working with values larger than 2^63, because these values cannot be conveyed by using `long`.
`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 by using a key that contains the database name and whose value includes the DDL statement(s). This is independent of how the connector internally records database schema history.
`include.schema.comments`	`false`	Boolean value that specifies whether the connector should parse and publish table and column comments on metadata objects. Enabling this option will bring the implications on memory usage. The number and size of logical schema objects is what largely impacts how much memory is consumed by the Debezium connectors, and adding potentially large string data to each of them can potentially be quite expensive.
`include.query`	`false`	Boolean value that specifies whether the connector should include the original SQL query that generated the change event. If you set this option to `true` then you must also configure MySQL with the `binlog_rows_query_log_events` option set to `ON`. When `include.query` is `true`, the query is not present for events that the snapshot process generates. Setting `include.query` to `true` might expose tables or fields that are explicitly excluded or masked by including the original SQL statement in the change event. For this reason, the default setting is `false`.
`event.deserialization.failure.handling.mode`	`fail`	Specifies how the connector should react to exceptions during deserialization of binlog events. This option is deprecated, please use `event.processing.failure.handling.mode` option instead. `fail` propagates the exception, which indicates the problematic event and its binlog offset, and causes the connector to stop. `warn` logs the problematic event and its binlog offset and then skips the event. `ignore` passes over the problematic event and does not log anything.
`inconsistent.schema.handling.mode`	`fail`	Specifies how the connector should react to binlog events that relate to tables that are not present in internal schema representation. That is, the internal representation is not consistent with the database. `fail` throws an exception that indicates the problematic event and its binlog offset, and causes the connector to stop. `warn` logs the problematic event and its binlog offset and skips the event. `skip` passes over the problematic event and does not log anything.
`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.
`max.queue.size`	`8192`	Positive integer value that specifies the maximum number of records that the blocking queue can hold. When Debezium reads events streamed from the database, it places the events in the blocking queue before it writes them to Kafka. The blocking queue can provide backpressure for reading change events from the database in cases where the connector ingests messages faster than it can write them to Kafka, or when Kafka becomes unavailable. Events that are held in the queue are disregarded when the connector periodically records offsets. Always set the value of `max.queue.size` to be larger than the value of `max.batch.size`.
`max.queue.size.in.bytes`	`0`	A long integer value that specifies the maximum volume of the blocking queue in bytes. By default, volume limits are not specified for the blocking queue. To specify the number of bytes that the queue can consume, set this property to a positive long value. If `max.queue.size` is also set, writing to the queue is blocked when the size of the queue reaches the limit specified by either property. For example, if you set `max.queue.size=1000`, and `max.queue.size.in.bytes=5000`, writing to the queue is blocked after the queue contains 1000 records, or after the volume of the records in the queue reaches 5000 bytes.
`poll.interval.ms`	`500`	Positive integer value that specifies the number of milliseconds the connector should wait for new change events to appear before it starts processing a batch of events. Defaults to 500 milliseconds, or 0.5 second.
`connect.timeout.ms`	`30000`	A positive integer value that specifies the maximum time in milliseconds this connector should wait after trying to connect to the MySQL database server before timing out. Defaults to 30 seconds.
`gtid.source.includes`	No default	A comma-separated list of regular expressions that match source UUIDs in the GTID set used that the connector uses to find the binlog position on the MySQL server. When this property is set, the connector uses only the GTID ranges that have source UUIDs that match one of the specified `include` patterns. To match the value of a GTID, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire UUID string; it does not match substrings that might be present in the UUID. If you include this property in the configuration, do not also set the `gtid.source.excludes` property.
`gtid.source.excludes`	No default	A comma-separated list of regular expressions that match source UUIDs in the GTID set that the connector uses to find the binlog position on the MySQL server. When this property is set, the connector uses only the GTID ranges that have source UUIDs that do not match any of the specified `exclude` patterns. To match the value of a GTID, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire UUID string; it does not match substrings that might be present in the UUID. If you include this property in the configuration, do not also set the `gtid.source.includes` property.
`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.
`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: `<databaseName>.<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 database, 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 columns, for example, `blob`, `binary`, `varbinary`, should be represented in change events. Possible settings: `bytes` represents binary data as a byte array. `base64` represents binary data as a base64-encoded String. `base64-url-safe` represents binary data as a base64-url-safe-encoded String. `hex` represents binary data as a hex-encoded (base16) String.
`schema.name.adjustment.mode`	none	Specifies how schema names should be adjusted for compatibility with the message converter used by the connector. Possible settings: `none` does not apply any adjustment. `avro` replaces the characters that cannot be used in the Avro type name with underscore. `avro_unicode` replaces the underscore or characters that cannot be used in the Avro type name with corresponding unicode like _uxxxx. Note: _ is an escape sequence like backslash in Java
`field.name.adjustment.mode`	none	Specifies how field names should be adjusted for compatibility with the message converter used by the connector. Possible settings: `none` does not apply any adjustment. `avro` replaces the characters that cannot be used in the Avro type name with underscore. `avro_unicode` replaces the underscore or characters that cannot be used in the Avro type name with corresponding unicode like _uxxxx. Note: _ is an escape sequence like backslash in Java See Avro naming for more details.

Advanced MySQL connector configuration properties

The following table describes advanced MySQL connector properties. The default values for these properties rarely need to be changed. Therefore, you do not need to specify them in the connector configuration.

Table 6.28. Descriptions of MySQL connector advanced configuration properties
Property	Default	Description
`connect.keep.alive`	`true`	A Boolean value that specifies whether a separate thread should be used to ensure that the connection to the MySQL server/cluster is kept alive.
`converters`	No default	Enumerates a comma-separated list of the symbolic names of the custom converter instances that the connector can use. For example, `boolean`. This property is required to enable the connector to use a custom converter. For each converter that you configure for a connector, you must also add a `.type` property, which specifies the fully-qualifed name of the class that implements the converter interface. The `.type` property uses the following format: `<converterSymbolicName>.type` For example, boolean.type: io.debezium.connector.mysql.converters.TinyIntOneToBooleanConverter If you want to further control the behavior of a configured converter, you can add one or more configuration parameters to pass values to the converter. To associate these additional configuration parameter with a converter, prefix the paraemeter name with the symbolic name of the converter. For example, to define a `selector` parameter that specifies the subset of columns that the `boolean` converter processes, add the following property: boolean.selector=db1.table1.*, db1.table2.column1
`table.ignore.builtin`	`true`	A Boolean value that specifies whether built-in system tables should be ignored. This applies regardless of the table include and exclude lists. By default, system tables are excluded from having their changes captured, and no events are generated when changes are made to any system tables.
`database.ssl.mode`	`preferred`	Specifies whether to use an encrypted connection. Possible settings are: `disabled` specifies the use of an unencrypted connection. `preferred` establishes an encrypted connection if the server supports secure connections. If the server does not support secure connections, falls back to an unencrypted connection. `required` establishes an encrypted connection or fails if one cannot be made for any reason. `verify_ca` behaves like `required` but additionally it verifies the server TLS certificate against the configured Certificate Authority (CA) certificates and fails if the server TLS certificate does not match any valid CA certificates. `verify_identity` behaves like `verify_ca` but additionally verifies that the server certificate matches the host of the remote connection.
`snapshot.mode`	`initial`	Specifies the criteria for running a snapshot when the connector starts. Possible settings are: `initial` - the connector runs a snapshot only when no offsets have been recorded for the logical server name. `initial_only` - the connector runs a snapshot only when no offsets have been recorded for the logical server name and then stops; i.e. it will not read change events from the binlog. `when_needed` - the connector runs a snapshot upon startup whenever it deems it necessary. That is, when no offsets are available, or when a previously recorded offset specifies a binlog location or GTID that is not available in the server. `never` - the connector never uses snapshots. Upon first startup with a logical server name, the connector reads from the beginning of the binlog. Configure this behavior with care. It is valid only when the binlog is guaranteed to contain the entire history of the database. `schema_only` - the connector runs a snapshot of the schemas and not the data. This setting is useful when you do not need the topics to contain a consistent snapshot of the data but need them to have only the changes since the connector was started. `schema_only_recovery` - this is a recovery setting for a connector that has already been capturing changes. When you restart the connector, this setting enables recovery of a corrupted or lost database schema history topic. You might set it periodically to "clean up" a database schema history topic that has been growing unexpectedly. Database schema history topics require infinite retention.
`snapshot.locking.mode`	`minimal`	Controls whether and how long the connector holds the global MySQL read lock, which prevents any updates to the database, while the connector is performing a snapshot. Possible settings are: `minimal` - the connector holds the global read lock for only the initial portion of the snapshot during which the connector reads the database schemas and other metadata. The remaining work in a snapshot involves selecting all rows from each table. The connector can do this in a consistent fashion by using a REPEATABLE READ transaction. This is the case even when the global read lock is no longer held and other MySQL clients are updating the database. `minimal_percona` - the connector holds the global backup lock for only the initial portion of the snapshot during which the connector reads the database schemas and other metadata. The remaining work in a snapshot involves selecting all rows from each table. The connector can do this in a consistent fashion by using a REPEATABLE READ transaction. This is the case even when the global backup lock is no longer held and other MySQL clients are updating the database. This mode does not flush tables to disk, is not blocked by long-running reads, and is available only in Percona Server. `extended` - blocks all writes for the duration of the snapshot. Use this setting if there are clients that are submitting operations that MySQL excludes from REPEATABLE READ semantics. `none` - prevents the connector from acquiring any table locks during the snapshot. While this setting is allowed with all snapshot modes, it is safe to use if and only if no schema changes are happening while the snapshot is running. For tables defined with MyISAM engine, the tables would still be locked despite this property being set as MyISAM acquires a table lock. This behavior is unlike InnoDB engine, which acquires row level locks.
`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 (`<databaseName>.<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. To match the name of a table, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the table; it does not match substrings that might be present in a table name.
`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 `<databaseName>.<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.<databaseName>.<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`.
`min.row.count.to.stream.results`	`1000`	During a snapshot, the connector queries each table for which the connector is configured to capture changes. The connector uses each query result to produce a read event that contains data for all rows in that table. This property determines whether the MySQL connector puts results for a table into memory, which is fast but requires large amounts of memory, or streams the results, which can be slower but work for very large tables. The setting of this property specifies the minimum number of rows a table must contain before the connector streams results. To skip all table size checks and always stream all results during a snapshot, set this property to `0`.
`heartbeat.interval.ms`	`0`	Controls how frequently the connector sends heartbeat messages to a Kafka topic. The default behavior is that the connector does not send heartbeat messages. Heartbeat messages are useful for monitoring whether the connector is receiving change events from the database. Heartbeat messages might help decrease the number of change events that need to be re-sent when a connector restarts. To send heartbeat messages, set this property to a positive integer, which indicates the number of milliseconds between heartbeat messages.
`heartbeat.action.query`	No default	Specifies a query that the connector executes on the source database when the connector sends a heartbeat message. For example, this can be used to periodically capture the state of the executed GTID set in the source database. `INSERT INTO gtid_history_table (select * from mysql.gtid_executed)`
`database.initial.statements`	No default	A semicolon separated list of SQL statements to be executed when a JDBC connection, not the connection that is reading the transaction log, to the database is established. To specify a semicolon as a character in a SQL statement and not as a delimiter, use two semicolons, (`;;`). The connector might establish JDBC connections at its own discretion, so this property is ony for configuring session parameters. It is not for executing DML statements.
`snapshot.delay.ms`	No default	An interval in milliseconds that the connector should wait before performing a snapshot when the connector starts. If you are starting multiple connectors in a cluster, this property is useful for avoiding snapshot interruptions, which might cause re-balancing of connectors.
`snapshot.fetch.size`	No default	During a snapshot, the connector reads table content in batches of rows. This property specifies the maximum number of rows in a batch.
`snapshot.lock.timeout.ms`	`10000`	Positive integer that specifies the maximum amount of time (in milliseconds) to wait to obtain table locks when performing a snapshot. If the connector cannot acquire table locks in this time interval, the snapshot fails. See how MySQL connectors perform database snapshots.
`enable.time.adjuster`	`true`	Boolean value that indicates whether the connector converts a 2-digit year specification to 4 digits. Set to `false` when conversion is fully delegated to the database. MySQL allows users to insert year values with either 2-digits or 4-digits. For 2-digit values, the value gets mapped to a year in the range 1970 - 2069. The default behavior is that the connector does the conversion.
`skipped.operations`	`t`	A comma-separated list of operation types that will be skipped during streaming. The operations include: `c` for inserts/create, `u` for updates, `d` for deletes, `t` for truncates, and `none` to not skip any operations. By default, truncate 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>.<tableName>`
`signal.enabled.channels`	source	List of the signaling channel names that are enabled for the connector. By default, the following channels are available: `source` `kafka` `file` `jmx`
`notification.enabled.channels`	No default	List of notification channel names that are enabled for the connector. By default, the following channels are available: `sink` `log` `jmx`
`incremental.snapshot.allow.schema.changes`	`false`	Allow schema changes during an incremental snapshot. When enabled the connector will detect schema change during an incremental snapshot and re-select a current chunk to avoid locking DDLs. Note that changes to a primary key are not supported and can cause incorrect results if performed during an incremental snapshot. Another limitation is that if a schema change affects only columns' default values, then the change won’t be detected until the DDL is processed from the binlog stream. This doesn’t affect the snapshot events' values, but the schema of snapshot events may have outdated defaults.
`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.
`provide.transaction.metadata`	`false`	Determines whether the connector generates events with transaction boundaries and enriches change event envelopes with transaction metadata. Specify `true` if you want the connector to do this. See Transaction metadata for details.
`event.processing.failure.handling.mode`	`fail`	Specify how failures during processing of events (i.e. when encountering a corrupted event) should be handled. By default, `fail` mode raises an exception indicating the problematic event and its position, causing the connector to be stopped. `warn` mode does not raise the exception, instead the problematic event and its position will be logged and the event will be skipped. `ignore` mode ignores the problematic event completely with no logging.
`topic.naming.strategy`	`io.debezium.schema.DefaultTopicNamingStrategy`	The name of the TopicNamingStrategy class that should be used to determine the topic name for data change, schema change, transaction, heartbeat event etc., defaults to `DefaultTopicNamingStrategy`.
`topic.delimiter`	`.`	Specify the delimiter for topic name, defaults to `.`.
`topic.cache.size`	`10000`	The size used for holding the topic names in bounded concurrent hash map. This cache will help to determine the topic name corresponding to a given data collection.
`topic.heartbeat.prefix`	`__debezium-heartbeat`	Controls the name of the topic to which the connector sends heartbeat messages. The topic name has this pattern: topic.heartbeat.prefix.topic.prefix For example, if the topic prefix is `fulfillment`, the default topic name is `__debezium-heartbeat.fulfillment`.
`topic.transaction`	`transaction`	Controls the name of the topic to which the connector sends transaction metadata messages. The topic name has this pattern: topic.prefix.topic.transaction For example, if the topic prefix is `fulfillment`, the default topic name is `fulfillment.transaction`.
`snapshot.max.threads`	`1`	Specifies the number of threads that the connector uses when performing an initial snapshot. To enable parallel initial snapshots, set the property to a value greater than 1. In a parallel initial snapshot, the connector processes multiple tables concurrently. Important Parallel initial 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 Technology Preview Features Support Scope.
`snapshot.tables.order.by.row.count`	`disabled`	Controls the order in which the connector processes tables when it performs an initial snapshot. Specify one of the following options: `descending` The connector snapshots tables in order, based on the number of rows from the highest to the lowest. `ascending` The connector snapshots tables in order, based on the number of rows, from lowest to highest. `disabled` The connector disregards row count when performing an initial snapshot.
`errors.max.retries`	`-1`	The maximum number of retries on retriable errors (e.g. connection errors) before failing (-1 = no limit, 0 = disabled, > 0 = num of retries).

Debezium connector database schema history configuration properties

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

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

Table 6.29. Connector database schema history configuration properties
Property	Default	Description
`schema.history.internal.kafka.topic`	No default	The full name of the Kafka topic where the connector stores the database schema history.
`schema.history.internal.kafka.bootstrap.servers`	No default	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.
`schema.history.internal.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.
`schema.history.internal.kafka.query.timeout.ms`	`3000`	An integer value that specifies the maximum number of milliseconds the connector should wait while fetching cluster information using Kafka admin client.
`schema.history.internal.kafka.create.timeout.ms`	`30000`	An integer value that specifies the maximum number of milliseconds the connector should wait while create kafka history topic using Kafka admin client.
`schema.history.internal.kafka.recovery.attempts`	`100`	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` × `recovery.poll.interval.ms`.
`schema.history.internal.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.
`schema.history.internal.store.only.captured.tables.ddl`	`false`	A Boolean value that specifies whether the connector records schema structures from all tables in a schema or database, or only from tables that are designated for capture. Specify one of the following values: `false` (default) During a database snapshot, the connector records the schema data for all non-system tables in the database, including tables that are not designated for capture. It’s best to retain the default setting. If you later decide to capture changes from tables that you did not originally designate for capture, the connector can easily begin to capture data from those tables, because their schema structure is already stored in the schema history topic. Debezium requires the schema history of a table so that it can identify the structure that was present at the time that a change event occurred. `true` During a database snapshot, the connector records the table schemas only for the tables from which Debezium captures change events. If you change the default value, and you later configure the connector to capture data from other tables in the database, the connector lacks the schema information that it requires to capture change events from the tables.
`schema.history.internal.store.only.captured.databases.ddl`	`false`	A Boolean value that specifies whether the connector records schema structures from all logical databases in the database instance. Specify one of the following values: `true` The connector records schema structures only for tables in the logical database and schema from which Debezium captures change events. `false` The connector records schema structures for all logical databases. Note The default value is `true` for MySQL Connector

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

Debezium relies on a Kafka producer to write schema changes to database schema history topics. Similarly, it relies on a Kafka consumer to read from database schema 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 schema.history.internal.producer.* and schema.history.internal.consumer.* prefixes. The pass-through producer and consumer database schema history properties control a range of behaviors, such as how these clients secure connections with the Kafka broker, as shown in the following example:

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

schema.history.internal.consumer.security.protocol=SSL
schema.history.internal.consumer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks
schema.history.internal.consumer.ssl.keystore.password=test1234
schema.history.internal.consumer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks
schema.history.internal.consumer.ssl.truststore.password=test1234
schema.history.internal.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 connector Kafka signals configuration properties

Debezium provides a set of signal.* properties that control how the connector interacts with the Kafka signals topic.

The following table describes the Kafka signal properties.

Table 6.30. Kafka signals configuration properties
Property	Default	Description
`signal.kafka.topic`	<topic.prefix>-signal	The name of the Kafka topic that the connector monitors for ad hoc signals. Note If automatic topic creation is disabled, you must manually create the required signaling topic. A signaling topic is required to preserve signal ordering. The signaling topic must have a single partition.
`signal.kafka.groupId`	kafka-signal	The name of the group ID that is used by Kafka consumers.
`signal.kafka.bootstrap.servers`	No default	A list of host/port pairs that the connector uses for establishing an initial connection to the Kafka cluster. Each pair references the Kafka cluster that is used by the Debezium Kafka Connect process.
`signal.kafka.poll.timeout.ms`	`100`	An integer value that specifies the maximum number of milliseconds that the connector waits when polling signals.

Debezium connector pass-through signals Kafka consumer client configuration properties

The Debezium connector provides for pass-through configuration of the signals Kafka consumer. Pass-through signals properties begin with the prefix signals.consumer.*. For example, the connector passes properties such as signal.consumer.security.protocol=SSL to the Kafka consumer.

Debezium strips the prefixes from the properties before it passes the properties to the Kafka signals consumer.

Debezium connector sink notifications configuration properties

The following table describes the notification properties.

Table 6.31. Sink notification configuration properties
Property	Default	Description
`notification.sink.topic.name`	No default	The name of the topic that receives notifications from Debezium. This property is required when you configure the `notification.enabled.channels` property to include `sink` as one of the enabled notification channels.

Debezium 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 driver.*. For example, the connector passes properties such as driver.foobar=false to the JDBC URL.

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

6.6. Monitoring Debezium MySQL connector performance

The Debezium MySQL 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.

Snapshot metrics provide information about connector operation while performing a snapshot.
Streaming metrics provide information about connector operation when the connector is reading the binlog.
Schema history metrics provide information about the status of the connector’s schema history.

Debezium monitoring documentation provides details for how to expose these metrics by using JMX.

6.6.1. Monitoring Debezium during snapshots of MySQL databases

The MBean is debezium.mysql:type=connector-metrics,context=snapshot,server=<topic.prefix>.

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.

Attributes	Type	Description
`LastEvent`	`string`	The last snapshot event that the connector has read.
`MilliSecondsSinceLastEvent`	`long`	The number of milliseconds since the connector has read and processed the most recent event.
`TotalNumberOfEventsSeen`	`long`	The total number of events that this connector has seen since last started or reset.
`NumberOfEventsFiltered`	`long`	The number of events that have been filtered by include/exclude list filtering rules configured on the connector.
`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.
`SnapshotPaused`	`boolean`	Whether the snapshot was paused.
`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. Includes also time when snapshot was paused.
`SnapshotPausedDurationInSeconds`	`long`	The total number of seconds that the snapshot was paused. If the snapshot was paused several times, the paused time adds up.
`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. This metric is available if `max.queue.size.in.bytes` is set to a positive long value.
`CurrentQueueSizeInBytes`	`long`	The current volume, in bytes, of records in the queue.

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

Attributes	Type	Description
`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.

The Debezium MySQL connector also provides the HoldingGlobalLock custom snapshot metric. This metric is set to a Boolean value that indicates whether the connector currently holds a global or table write lock.

6.6.2. Monitoring Debezium MySQL connector record streaming

Transaction-related attributes are available only if binlog event buffering is enabled. The MBean is debezium.mysql:type=connector-metrics,context=streaming,server=<topic.prefix>.

The following table lists the streaming metrics that are available.

Attributes	Type	Description
`LastEvent`	`string`	The last streaming event that the connector has read.
`MilliSecondsSinceLastEvent`	`long`	The number of milliseconds since the connector has read and processed the most recent event.
`TotalNumberOfEventsSeen`	`long`	The total number of events that this connector has seen since the last start or metrics reset.
`TotalNumberOfCreateEventsSeen`	`long`	The total number of create events that this connector has seen since the last start or metrics reset.
`TotalNumberOfUpdateEventsSeen`	`long`	The total number of update events that this connector has seen since the last start or metrics reset.
`TotalNumberOfDeleteEventsSeen`	`long`	The total number of delete events that this connector has seen since the last start or metrics reset.
`NumberOfEventsFiltered`	`long`	The number of events that have been filtered by include/exclude list filtering rules configured on 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. This metric is available if `max.queue.size.in.bytes` is set to a positive long value.
`CurrentQueueSizeInBytes`	`long`	The current volume, in bytes, of records in the queue.

The Debezium MySQL connector also provides the following additional streaming metrics:

Table 6.32. Descriptions of additional streaming metrics
Attribute	Type	Description
`BinlogFilename`	`string`	The name of the binlog file that the connector has most recently read.
`BinlogPosition`	`long`	The most recent position (in bytes) within the binlog that the connector has read.
`IsGtidModeEnabled`	`boolean`	Flag that denotes whether the connector is currently tracking GTIDs from MySQL server.
`GtidSet`	`string`	The string representation of the most recent GTID set processed by the connector when reading the binlog.
`NumberOfSkippedEvents`	`long`	The number of events that have been skipped by the MySQL connector. Typically events are skipped due to a malformed or unparseable event from MySQL’s binlog.
`NumberOfDisconnects`	`long`	The number of disconnects by the MySQL connector.
`NumberOfRolledBackTransactions`	`long`	The number of processed transactions that were rolled back and not streamed.
`NumberOfNotWellFormedTransactions`	`long`	The number of transactions that have not conformed to the expected protocol of `BEGIN` + `COMMIT`/`ROLLBACK`. This value should be `0` under normal conditions.
`NumberOfLargeTransactions`	`long`	The number of transactions that have not fit into the look-ahead buffer. For optimal performance, this value should be significantly smaller than `NumberOfCommittedTransactions` and `NumberOfRolledBackTransactions`.

6.6.3. Monitoring Debezium MySQL connector schema history

The MBean is debezium.mysql:type=connector-metrics,context=schema-history,server=<topic.prefix>.

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

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

6.7. How Debezium MySQL connectors handle faults and problems

Debezium is a distributed system that captures all changes in multiple upstream databases; it never misses or loses an event. When the system is operating normally or being managed carefully then Debezium provides exactly once delivery of every change event record.

If a fault does happen then the system does not lose any events. However, while it is recovering from the fault, it might repeat some change events. In these abnormal situations, Debezium, like Kafka, provides at least once delivery of change events.

Details are in the following sections:

Configuration and startup errors

In the following situations, the connector fails when trying to start, reports an error or exception in the log, and stops running:

The connector’s configuration is invalid.
The connector cannot successfully connect to the MySQL server by using the specified connection parameters.
The connector is attempting to restart at a position in the binlog for which MySQL no longer has the history available.

In these cases, the error message has details about the problem and possibly a suggested workaround. After you correct the configuration or address the MySQL problem, restart the connector.

MySQL becomes unavailable

If your MySQL server becomes unavailable, the Debezium MySQL connector fails with an error and the connector stops. When the server is available again, restart the connector.

However, if GTIDs are enabled for a highly available MySQL cluster, you can restart the connector immediately. It will connect to a different MySQL server in the cluster, find the location in the server’s binlog that represents the last transaction, and begin reading the new server’s binlog from that specific location.

If GTIDs are not enabled, the connector records the binlog position of only the MySQL server to which it was connected. To restart from the correct binlog position, you must reconnect to that specific server.

Kafka Connect stops gracefully

When Kafka Connect stops gracefully, there is a short delay while the Debezium MySQL connector tasks are stopped and restarted on new Kafka Connect processes.

Kafka Connect process crashes

If Kafka Connect crashes, the process stops and any Debezium MySQL connector tasks terminate without their most recently-processed offsets being recorded. In distributed mode, Kafka Connect restarts the connector tasks on other processes. However, the MySQL connector resumes from the last offset recorded by the earlier processes. This means that the replacement tasks might generate some of the same events processed prior to the crash, creating duplicate events.

Each change event message includes source-specific information that you can use to identify duplicate events, for example:

Event origin
MySQL server’s event time
The binlog file name and position
GTIDs (if used)

Kafka becomes unavailable

The Kafka Connect framework records Debezium change events in Kafka by using the Kafka producer API. If the Kafka brokers become unavailable, the Debezium MySQL connector pauses until the connection is reestablished and the connector resumes where it left off.

MySQL purges binlog files

If the Debezium MySQL connector stops for too long, the MySQL server purges older binlog files and the connector’s last position may be lost. When the connector is restarted, the MySQL server no longer has the starting point and the connector performs another initial snapshot. If the snapshot is disabled, the connector fails with an error.

See snapshots for details about how MySQL connectors perform initial snapshots.

Chapter 6. Debezium connector for MySQL

6.1. How Debezium MySQL connectors work

6.1.1. MySQL topologies supported by Debezium connectors

6.1.2. How Debezium MySQL connectors handle database schema changes

6.1.3. How Debezium MySQL connectors expose database schema changes

6.1.4. How Debezium MySQL connectors perform database snapshots

6.1.4.1. Initial snapshots that use a global read lock

6.1.4.2. Initial snapshots that use table-level locks

6.1.4.3. Description of why initial snapshots capture the schema history for all tables

6.1.4.4. Capturing data from tables not captured by the initial snapshot (no schema change)

6.1.4.5. Capturing data from tables not captured by the initial snapshot (schema change)

6.1.5. Ad hoc snapshots

6.1.6. Incremental snapshots

6.1.6.1. Triggering an incremental snapshot

6.1.6.2. Using the Kafka signaling channel to trigger an incremental snapshot

6.1.6.3. Stopping an incremental snapshot

6.1.6.4. Using the Kafka signaling channel to stop an incremental snapshot

6.1.7. Default names of Kafka topics that receive Debezium MySQL change event records

6.2. Descriptions of Debezium MySQL connector data change events

6.2.1. About keys in Debezium MySQL change events

6.2.2. About values in Debezium MySQL change events

6.3. How Debezium MySQL connectors map data types

6.4. Setting up MySQL to run a Debezium connector

6.4.1. Creating a MySQL user for a Debezium connector

6.4.2. Enabling the MySQL binlog for Debezium

6.4.3. Enabling MySQL Global Transaction Identifiers for Debezium

6.4.4. Configuring MySQL session timesouts for Debezium

6.4.5. Enabling query log events for Debezium MySQL connectors

6.4.6. validate binlog row value options for Debezium MySQL connectors

6.5. Deployment of Debezium MySQL connectors

6.5.1. MySQL connector deployment using AMQ Streams

6.5.2. Using AMQ Streams to deploy a Debezium MySQL connector

6.5.3. Deploying Debezium MySQL connectors by building a custom Kafka Connect container image from a Dockerfile

6.5.4. Verifying that the Debezium MySQL connector is running

6.5.5. Descriptions of Debezium MySQL connector configuration properties

6.6. Monitoring Debezium MySQL connector performance

6.6.1. Monitoring Debezium during snapshots of MySQL databases

6.6.2. Monitoring Debezium MySQL connector record streaming

6.6.3. Monitoring Debezium MySQL connector schema history

6.7. How Debezium MySQL connectors handle faults and problems

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