Debezium connector for MySQL

How the connector works

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.

Supported MySQL topologies

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.

Schema history topic

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 just use the current schema because the connector might be processing events that are relatively old that were recorded before the tables’ schemas were changed.

To ensure correct processing of changes that occur after a schema change, MySQL includes in the binlog not only the row-level changes to the data, but also the DDL statements that are applied to the database. As the connector reads the binlog and comes across these DDL statements, 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 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 having crashed or been stopped gracefully, the connector 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 history Kafka topic and parsing all DDL statements up to the point in the binlog where the connector is starting.

This database history topic is for connector use only. The connector can optionally 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. The connector needs to be configured to capture change to these helper tables. If consumers do not need the records generated for helper tables, then a single message transform can be applied to filter them out.

See default names for topics that receive Debezium event records.

Schema change topic

You can configure a Debezium MySQL connector to produce schema change events that describe schema changes that are applied to captured tables in the database. The connector writes schema change events to a Kafka topic named *<serverName>*, where *serverName* is the logical server name that is specified in the database.server.name 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.

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.

1. {
2. "schema": {
3. ...
4. },
5. "payload": {
6. "source": {  // (1)
7. "version": "1.9.6.Final",
8. "connector": "mysql",
9. "name": "dbserver1",
10. "ts_ms": 0,
11. "snapshot": "false",
12. "db": "inventory",
13. "sequence": null,
14. "table": "customers",
15. "server_id": 0,
16. "gtid": null,
17. "file": "mysql-bin.000003",
18. "pos": 219,
19. "row": 0,
20. "thread": null,
21. "query": null
22. },
23. "databaseName": "inventory", // (2)
24. "schemaName": null,
25. "ddl": "ALTER TABLE customers ADD COLUMN middle_name VARCHAR(2000)", // (3)
26. "tableChanges": [ // (4)
27. {
28. "type": "ALTER", // (5)
29. "id": "\"inventory\".\"customers\"",  // (6)
30. "table": { // (7)
31. "defaultCharsetName": "latin1",
32. "primaryKeyColumnNames": [  // (8)
33. "id"
34. ],
35. "columns": [ // (9)
36. {
37. "name": "id",
38. "jdbcType": 4,
39. "nativeType": null,
40. "typeName": "INT",
41. "typeExpression": "INT",
42. "charsetName": null,
43. "length": 11,
44. "scale": null,
45. "position": 1,
46. "optional": false,
47. "autoIncremented": true,
48. "generated": true
49. },
50. {
51. "name": "first_name",
52. "jdbcType": 12,
53. "nativeType": null,
54. "typeName": "VARCHAR",
55. "typeExpression": "VARCHAR",
56. "charsetName": "latin1",
57. "length": 255,
58. "scale": null,
59. "position": 2,
60. "optional": false,
61. "autoIncremented": false,
62. "generated": false
63. },                        {
64. "name": "last_name",
65. "jdbcType": 12,
66. "nativeType": null,
67. "typeName": "VARCHAR",
68. "typeExpression": "VARCHAR",
69. "charsetName": "latin1",
70. "length": 255,
71. "scale": null,
72. "position": 3,
73. "optional": false,
74. "autoIncremented": false,
75. "generated": false
76. },
77. {
78. "name": "email",
79. "jdbcType": 12,
80. "nativeType": null,
81. "typeName": "VARCHAR",
82. "typeExpression": "VARCHAR",
83. "charsetName": "latin1",
84. "length": 255,
85. "scale": null,
86. "position": 4,
87. "optional": false,
88. "autoIncremented": false,
89. "generated": false
90. },
91. {
92. "name": "middle_name",
93. "jdbcType": 12,
94. "nativeType": null,
95. "typeName": "VARCHAR",
96. "typeExpression": "VARCHAR",
97. "charsetName": "latin1",
98. "length": 2000,
99. "scale": null,
100. "position": 5,
101. "optional": true,
102. "autoIncremented": false,
103. "generated": false
104. }
105. ]
106. }
107. }
108. ]
109. },
110. "payload": {
111. "databaseName": "inventory",
112. "ddl": "CREATE TABLE products ( id INTEGER NOT NULL AUTO_INCREMENT PRIMARY KEY, name VARCHAR(255) NOT NULL, description VARCHAR(512), weight FLOAT ); ALTER TABLE products AUTO_INCREMENT = 101;",
113. "source" : {
114. "version": "1.9.6.Final",
115. "name": "mysql-server-1",
116. "server_id": 0,
117. "ts_ms": 0,
118. "gtid": null,
119. "file": "mysql-bin.000003",
120. "pos": 154,
121. "row": 0,
122. "snapshot": true,
123. "thread": null,
124. "db": null,
125. "table": null,
126. "query": null
127. }
128. }
129. }

Table 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 | 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. | | 3 | 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. | | 4 | tableChanges | An array of one or more items that contain the schema changes generated by a DDL command. | | 5 | type | Describes the kind of change. The value is one of the following: CREATE Table created. ALTER Table modified. DROP Table deleted. | | 6 | 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. | | 7 | table | Represents table metadata after the applied change. | | 8 | primaryKeyColumnNames | List of columns that compose the table’s primary key. | | 9 | columns | Metadata for each column in the changed table. |

See also: schema history topic.

Snapshots

When a Debezium MySQL connector is first started, it performs an initial consistent snapshot of your database. The following flow describes how the connector creates this snapshot. This flow is for the default snapshot mode, which is initial. For information about other snapshot modes, see the MySQL connector snapshot.mode configuration property.

Table 2. Workflow for performing an initial snapshot with a global read lock

Connector restarts

If the connector fails, stops, or is rebalanced while performing the initial snapshot, then after the connector restarts, it performs a new snapshot. After that intial snapshot is completed, the Debezium MySQL connector restarts from the same position in the binlog so it does not miss any updates.

If the connector stops for long enough, MySQL could purge old binlog files and the connector’s position would be lost. If the position is lost, the connector reverts to the initial snapshot for its starting position. For more tips on troubleshooting the Debezium MySQL connector, see behavior when things go wrong.

Global read locks not allowed

Some environments do not allow global read locks. If the Debezium MySQL connector detects that global read locks are not permitted, the connector uses table-level locks instead and performs a snapshot with this method. This requires the database user for the Debezium connector to have LOCK TABLES privileges.

Table 3. Workflow for performing an initial snapshot with table-level locks

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.

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 4. 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 the fully-qualified names of the table to be snapshotted. The format of the names is the same as for the signal.data.collection configuration option. |

Triggering an ad hoc snapshot

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

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

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. Incremental snapshots are based on the DDD-3 design document.

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

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

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

Incremental snapshot process

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

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

How Debezium resolves collisions among records with the same primary key

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

Snapshot window

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

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

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

The connector repeats the process for each snapshot chunk.

Triggering an incremental snapshot

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

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

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

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

Prerequisites

• Signaling is enabled.

• A signaling data collection exists on the source database and the connector is configured to capture it.

• The signaling data collection is specified in the signal.data.collection property.

Procedure

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

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

For example,

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

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

The following table describes the these parameters:

Table 5. Descriptions of fields in a SQL command for sending an incremental snapshot signal to the signaling table

  | Value | Description |

| --- | --- | | myschema.debezium_signal | Specifies the fully-qualified name of the signaling table on the source database | | ad-hoc-1 | The id parameter specifies an arbitrary string that is assigned as the id identifier for the signal request. Use this string to identify logging messages to entries in the signaling table. Debezium does not use this string. Rather, during the snapshot, Debezium generates its own id string as a watermarking signal. | | execute-snapshot | Specifies type parameter specifies the operation that the signal is intended to trigger. | | data-collections | A required component of the data field of a signal that specifies an array of table names to include in the snapshot. The array lists tables by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling table in the signal.data.collection configuration property. | | incremental | An optional type component of the data field of a signal that specifies the kind of snapshot operation to run. Currently, the only valid option is the default value, incremental. Specifying a type value in the SQL query that you submit to the signaling table is optional. If you do not specify a value, the connector runs an incremental snapshot. |

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

Example: Incremental snapshot event message

1. {
2. "before":null,
3. "after": {
4. "pk":"1",
5. "value":"New data"
6. },
7. "source": {
8. ...
9. "snapshot":"incremental" (1)
10. },
11. "op":"r", (2)
12. "ts_ms":"1620393591654",
13. "transaction":null
14. }

Read-only incremental snapshots

The MySQL connector allows for running incremental snapshots with a read-only connection to the database. To run an incremental snapshot with read-only access, the connector uses the executed global transaction IDs (GTID) set as high and low watermarks. The state of a chunk’s window is updated by comparing the GTIDs of binary log (binlog) events or the server’s heartbeats against low and high watermarks.

To switch to a read-only implementation, set the value of the read.only property to true.

Prerequisites

• Enable MySQL GTIDs.

• If the connector reads from a multi-threaded replica (that is, a replica for which the value of replica_parallel_workers is greater than 0) you must set one of the following options:

• replica_preserve_commit_order=ON

• slave_preserve_commit_order=ON

Ad hoc read-only incremental snapshots

When the MySQL connection is read-only, the signaling table mechanism can also run a snapshot by sending a message to the Kafka topic that is specified in the signal.kafka.topic property.

The key of the Kafka message must match the value of the database.server.name connector configuration option.

The value 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. Execute snapshot data fields

| Field | Default | Value |

| --- | --- | --- | | type | incremental | The type of the snapshot to be executed. Currently only incremental is supported. See the next section for more details. | | data-collections | N/A | An array of qualified names of table to be snapshotted. The format of the names is the same as for signal.data.collection configuration option. |

An example of the execute-snapshot Kafka message:

1. Key = `test_connector`

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

Operation type of snapshot events

The MySQL connector emits snapshot events as READ operations ("op" : "r"). If you prefer that the connector emits snapshot events as CREATE (c) events, configure the Debezium ReadToInsertEvent single message transform (SMT) to modify the event type.

The following example shows how to configure the SMT:

Example: Using the ReadToInsertEvent SMT to change the type of snapshot events

1. transforms=snapshotasinsert,...
2. transforms.snapshotasinsert.type=io.debezium.connector.mysql.transforms.ReadToInsertEvent

Topic names

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:

serverName.databaseName.tableName

Suppose that fulfillment is the server name, 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:

1. fulfillment.inventory.orders
2. fulfillment.inventory.customers
3. fulfillment.inventory.products

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

serverName

The logical name of the server as specified by the database.server.name 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 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.

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.

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

1. {
2. "status": "BEGIN",
3. "id": "0e4d5dcd-a33b-11ea-80f1-02010a22a99e:10",
4. "event_count": null,
5. "data_collections": null
6. }

8. {
9. "status": "END",
10. "id": "0e4d5dcd-a33b-11ea-80f1-02010a22a99e:10",
11. "event_count": 2,
12. "data_collections": [
13. {
14. "data_collection": "s1.a",
15. "event_count": 1
16. },
17. {
18. "data_collection": "s2.a",
19. "event_count": 1
20. }
21. ]
22. }

Unless overridden via the transaction.topic option, the connector emits transaction events to the .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 - 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:

1. {
2. "before": null,
3. "after": {
4. "pk": "2",
5. "aa": "1"
6. },
7. "source": {
8. ...
9. },
10. "op": "c",
11. "ts_ms": "1580390884335",
12. "transaction": {
13. "id": "0e4d5dcd-a33b-11ea-80f1-02010a22a99e:10",
14. "total_order": "1",
15. "data_collection_order": "1"
16. }
17. }

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.

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:

1. {
2. "schema": { (1)
3. ...
4. },
5. "payload": { (2)
6. ...
7. },
8. "schema": { (3)
9. ...
10. },
11. "payload": { (4)
12. ...
13. },
14. }

Table 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.

Change event keys

A change event’s key contains the schema for the changed table’s key and the changed row’s actual key. Both the schema and its corresponding payload contain a field for each column in the changed table’s PRIMARY KEY (or unique 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.

1. CREATE TABLE customers (
2. id INTEGER NOT NULL AUTO_INCREMENT PRIMARY KEY,
3. first_name VARCHAR(255) NOT NULL,
4. last_name VARCHAR(255) NOT NULL,
5. email VARCHAR(255) NOT NULL UNIQUE KEY
6. ) 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:

1. {
2. "schema": { (1)
3. "type": "struct",
4. "name": "mysql-server-1.inventory.customers.Key", (2)
5. "optional": false, (3)
6. "fields": [ (4)
7. {
8. "field": "id",
9. "type": "int32",
10. "optional": false
11. }
12. ]
13. },
14. "payload": { (5)
15. "id": 1001
16. }
17. }

Table 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. |

Change event values

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

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