Debezium connector for MySQL :: Debezium Documentation (original) (raw)

Table of Contents

• How the connector works
  • Supported MySQL topologies
  • Schema history topic
  • Schema change topic
  • Snapshots
  • Ad hoc snapshots
  • Incremental snapshots
  • Operation type of snapshot events
  • Custom snapshotter SPI
  • Blocking snapshots
  • Setting the binlog position via a signal
• Format of a set-binlog-position signal
• Send a signal to reposition the stream
  • Topic names
  • Transaction metadata
• Data change events
  • Change event keys
  • Change event values
  • create events
  • update events
  • Primary key updates
  • delete events
  • Tombstone events
  • truncate events
• Data type mappings
  • Basic types
  • Temporal types
  • Decimal types
  • Boolean values
  • Spatial types
  • Vector types
• Custom converters
  • TINYINT(1) to Boolean
  • JDBC sink data types
• Setting up MySQL
  • Creating a user
  • Enabling the binlog
  • Enabling GTIDs
  • Configuring session timeouts
  • Enabling query log events
  • Validating binlog row value options
• Deployment
  • MySQL connector configuration example
  • Adding connector configuration
  • Connector properties
• Monitoring
  • Customized MBean names
  • Snapshot metrics
  • Streaming metrics
  • Schema history metrics
• Behavior when things go wrong

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.

Because 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 release overview.

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 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 detects changes only in 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

You can use a variety of solutions to provide redundancy for MySQL. Deploying redundant replica hosts makes it significantly easier to tolerate and almost immediately recover from problems and failures. Because most HA MySQL clusters use GTIDs, replicas are able to track all of the changes that occur on any primary server.

Multi-primary

Network Database (NDB) cluster replication uses one or more MySQL replica nodes that each replicate from multiple primary servers. Cluster replication provides 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, because 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

The Debezium MySQL connector can use hosted database options such as Amazon RDS and Amazon Aurora.

Because these hosted options do not permit the use of global read locks, the connector uses table-level locks when it creates a 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 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 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.

Schema change topic

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 schema for the schema change event has the following elements:

name

The name of the schema change event message.

type

The type of the change event message.

version

The version of the schema. The version is an integer that is incremented each time the schema is changed.

fields

The fields that are included in the change event message.

Example: Schema of the MySQL connector schema change topic

The following example shows a typical schema in JSON format.

{
  "schema": {
    "type": "struct",
    "fields": [
      {
        "type": "string",
        "optional": false,
        "field": "databaseName"
      }
    ],
    "optional": false,
    "name": "io.debezium.connector.mysql.SchemaChangeKey",
    "version": 1
  },
  "payload": {
    "databaseName": "inventory"
  }
}

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.

| | 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. | | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |

| | 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. | | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

| | 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": {
        "version": "3.5.2.Final",
        "connector": "mysql",
        "name": "mysql",
        "ts_ms": 1651535750218,
        "ts_us": 1651535750218000,
        "ts_ns": 1651535750218000000,
        "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",
      "schemaName": null,
      "ddl": "ALTER TABLE customers ADD middle_name varchar(255) AFTER first_name",
      "tableChanges": [
        {
          "type": "ALTER",
          "id": "\"inventory\".\"customers\"",
          "table": {
            "defaultCharsetName": "utf8mb4",
            "primaryKeyColumnNames": [
              "id"
            ],
            "columns": [
              {
                "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": [
            {
              "customAttribute": "attributeValue"
            }
          ]
        }
      }
    ]
  }
}

The following list describes select fields in the preceding example:

source

The structure of the payload.source field reflects the structure of standard data change events that the connector writes to table-specific topics. This field is useful to correlate events on different topics.

ts_ms, ts_us, ts_ns

Displays timestamps in milliseconds, microseconds, and nanoseconds that indicate when the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

databaseName, schemaName

These two fields provide the names of the database and schema that contain the change. The value of the databaseName field serves as the message key for the event record.

ddl

Provides the DDL that produced the schema change. The ddl field can contain multiple DDL statements. Each statement applies to the database that is specified 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 processes 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.

tableChanges

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

type

Describes the kind of change. This field can contain one of the following values:

CREATE

Table created.

ALTER

Table modified.

DROP

Table deleted.

id

Specifies the 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.

table

Represents table metadata after the applied change.

primaryKeyColumnNames

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

columns

Metadata for each column in the changed table.

attributes

Custom attribute metadata for each table change.

For more information about schema change events, see schema history topic.

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.

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.

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.

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.

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 table shows the steps in the workflow that Debezium follows 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.

Table 1. Settings for snapshot.mode connector configuration property

For more information, see snapshot.mode in the table of connector configuration properties.

Understanding 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.

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

1. Stop the connector.
2. Remove the internal database schema history topic that is specified by the schema.history.internal.kafka.topic property.
3. Apply the following changes to the connector configuration:
   1. Set the snapshot.mode to 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.
4. Restart the connector. The snapshot recovery process rebuilds the schema history based on the current structure of the tables.
5. (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.
6. (Optional) Reset the snapshot.mode back to no_data to prevent the connector from initiating recovery after a future restart.

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)

1. Edit the table.include.list property to specify the tables that you want to capture.
2. Restart the connector.
3. 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.

1. Stop the connector.
2. Remove the internal database schema history topic that is specified by the schema.history.internal.kafka.topic property.
3. Clear the offsets in the configured Kafka Connect offset.storage.topic. For more information about how to remove offsets, see the Debezium community FAQ.
   | | 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. |
   | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
4. Set values for properties in the connector configuration as described in the following steps:
   1. Set the value of the snapshot.mode property to no_data.
   2. Edit the table.include.list to add the tables that you want to capture.
5. Restart the connector.
6. 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.
7. 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.

1. Stop the connector.
2. Remove the internal database schema history topic that is specified by the schema.history.internal.kafka.topic property.
3. Clear the offsets in the configured Kafka Connect offset.storage.topic. For more information about how to remove offsets, see the Debezium community FAQ.
   | | 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. |
   | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
4. Edit the table.include.list to add the tables that you want to capture.
5. 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.
6. Restart the connector. The connector takes a full database snapshot. After the snapshot completes, the connector transitions to streaming.
7. (Optional) To capture any data that changed while the connector was off-line, initiate an incremental snapshot.

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. You might want to perform an ad hoc snapshot after any of the following changes occur in your Debezium environment:

• 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 or blocking, and provide the names of the tables to include in the snapshot, as described in the following table:

Table 2. Example of an ad hoc execute-snapshot signal record

Triggering an ad hoc incremental snapshot

You initiate an ad hoc incremental snapshot by adding an entry with the execute-snapshot signal type to the signaling table, or by sending a signal message to a Kafka signaling topic. 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.

For more information, see Incremental snapshots.

Triggering an ad hoc blocking snapshot

You initiate an ad hoc blocking snapshot by adding an entry with the execute-snapshot signal type to the signaling table or signaling topic. After the connector processes the message, it begins the snapshot operation. The connector temporarily stops streaming, and then initiates a snapshot of the specified table, following the same process that it uses during an initial snapshot. After the snapshot completes, the connector resumes streaming.

For more information, see Blocking 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 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 window 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.

| | To enable Debezium to perform incremental snapshots, you must grant the connector permission to write to the signaling table. Write permission is unnecessary only for connectors that can be configured to perform read-only incrementals snapshots (MariaDB, MySQL, or PostgreSQL). | | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

Currently, you can use either of the following methods to initiate an incremental snapshot:

• Send an ad hoc snapshot signal to the signaling table on the source database.
• Send a message to a configured Kafka signaling topic.

Triggering an incremental snapshot

To initiate an incremental snapshot, you can send an ad hoc snapshot signal to the signaling table on the source database. You submit snapshot signals 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 type of snapshot operation. Debezium currently supports the incremental and blocking snapshot types.

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"]}

Data collection names are case-sensitive. The data-collections array for an incremental snapshot signal has no default value. If the data-collections array is empty, Debezium interprets the empty array to mean that no action is required, and it does not perform a snapshot.

| | If the name of a table that you want to include in a snapshot contains a dot (.), a space, or some other non-alphanumeric character, you must escape the table name in double quotes.For example, to include a table that exists in the db1 database, and that has the name My.Table, use the following format: "db1.\"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

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

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

For example,

INSERT INTO db1.debezium_signal (id, type, data)  
values ('ad-hoc-1',  
    'execute-snapshot',  
    '{"data-collections": ["db1.table1", "db1.table2"],  
    "type":"incremental",  
    "additional-conditions":[{"data-collection": "db1.table1" ,"filter":"color=\'blue\'"}]}');  

The values of the id,type, and data parameters in the command correspond to the fields of the signaling table. The following list describes the parameters in the preceding example:
INSERT INTO database.debezium_signal
Specifies the fully-qualified name of the signaling table on the source database.
values
id
The id parameter contains the value ad-hoc, an arbitrary string that serves as the id identifier for the signal request.
type
The type parameter specifies the type of operation to perform, in this case, execute-snapshot.
data
The the data field of a signal contains the following fields:
data-collections
An array of table names or regular expressions to match table names to include in the snapshot.
type
An optional type component of the data field of a signal that specifies the type of snapshot operation to run.
Valid values are incremental and blocking.
If you do not specify a value, the connector defaults to performing an incremental snapshot.
additional-conditions
An optional array that specifies a set of additional conditions that the connector evaluates to determine the subset of records to include in a snapshot. Each additional condition in the array is an object with data-collection and filter properties.
Each data-collection in the additional-conditions array is specified by its fully-qualified name. You can specify one or more data collections. Each collection can be paired with an optional filter parameter. You can pair a unique filter with each data collection. The value of a filter property consists a column label and a value. When the snapshot runs, for each collection that is named in the array, it captures only the rows that contain the specified filter criteria.
In the example, for the table1 data collection, the signal results in a snapshot that captures only the rows that include the field color where the field value is 'blue'.
For more information about the additional-conditions parameter, see Running an ad hoc incremental snapshots with additional-conditions.

Using physical row identifiers as surrogate keys

Some databases provide physical row identifiers, which are pseudo-columns that represent the physical location of a row on disk. These identifiers can provide significant performance improvements for incremental snapshot chunking.

Physical row identifiers are particularly useful in the following scenarios:

Tables with composite primary keys

When a table has no single-column surrogate key and uses multiple columns as the primary key, chunking queries become complex and inefficient.

Inefficient index utilization

Database query optimizers often cannot use indexes effectively with the disjunction of conditions generated by incremental snapshot chunking queries, resulting in full table scans.

By using a physical row identifier as the surrogate key, Debezium can generate simpler range-based queries that leverage the inherent ordering of row identifiers, significantly improving performance.

Debezium supports the following physical row identifiers as surrogate keys:

The following example shows a SQL query to trigger an incremental snapshot using Oracle’s ROWID as the surrogate key:

INSERT INTO db1.myschema.debezium_signal (id, type, data)
VALUES ('ad-hoc-1',
    'execute-snapshot',
    '{"data-collections": ["db1.myschema.mytable"],
      "type": "incremental",
      "surrogate-key": "ROWID"}');

| | Physical row identifiers can change under certain circumstances, which may affect snapshot consistency. Oracle ROWID can change during table reorganization operations such as ALTER TABLE MOVE, partition maintenance, or SHRINK SPACE operations. To ensure data consistency, when an incremental snapshot that uses a physical row identifier is in progress, do not perform table maintenance operations, such as table moves, partition management, or shrink operations. | | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

Running an ad hoc incremental snapshots with additional-conditions

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-conditions parameter to the snapshot signal.

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

SELECT * FROM <tableName> ....

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

SELECT * FROM <data-collection> WHERE <filter> ....

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": ["<fullyQualfiedTableName>","<fullyQualfiedTableName>"],"type":"<snapshotType>","additional-conditions":[{"data-collection": "<fullyQualfiedTableName>", "filter": "<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 db1.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["db1.products"],"type":"incremental", "additional-conditions":[{"data-collection": "db1.products", "filter": "color=blue"}]}');

The additional-conditions 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 db1.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["db1.products"],"type":"incremental", "additional-conditions":[{"data-collection": "db1.products", "filter": "color=blue AND quantity>10"}]}');

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

Example 1. Incremental snapshot event message

{
    "before":null,
    "after": {
        "pk":"1",
        "value":"New data"
    },
    "source": {
        ...
        "snapshot":"incremental"
    },
    "op":"r",
    "ts_ms":"1620393591654",
    "ts_us":"1620393591654547",
    "ts_ns":"1620393591654547920",
    "transaction":null
}

The following list describes select fields in the preceding example of an incremental snapshot event message:

snapshot

Specifies the type of the snapshot.

op

Specifies the type of operation. For snapshot events, the value of the op field value is r, because a snapshot is a READ operation.

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 3. Execute snapshot data fields

Example 2. An execute-snapshot Kafka message

Key = test_connector

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

Ad hoc incremental snapshots with additional-conditions

Debezium uses the additional-conditions 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-conditions property, the data-collection and filter parameters of the property are appended to the SQL query, for example:

SELECT * FROM _<data-collection>_ WHERE _<filter>_ …​.

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 add the additional-conditions property to filter the content:

Key = `test_connector`

Value = `{"type":"execute-snapshot","data": {"data-collections": ["db1.products"], "type": "INCREMENTAL", "additional-conditions": [{"data-collection": "db1.products" ,"filter":"color='blue'"}]}}`

You can also use the additional-conditions property 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": ["db1.products"], "type": "INCREMENTAL", "additional-conditions": [{"data-collection": "db1.products" ,"filter":"color='blue' AND brand='MyBrand'"}]}}`

Stopping an incremental snapshot

In some situations, it might be necessary to stop an incremental snapshot. For example, you might realize that snapshot was not configured correctly, or maybe you want to ensure that resources are available for other database operations. You can stop a snapshot that is already running by sending a signal to the signaling table on the source database.

You submit a stop snapshot signal to the signaling table by sending it in a SQL INSERT query. The stop-snapshot signal specifies the type of the snapshot operation as incremental, and optionally specifies the tables that you want to omit from the currently running snapshot. After Debezium detects the change in the signaling table, it reads the signal, and stops the incremental snapshot operation if it’s in progress.

Additional resources

You can also stop an incremental snapshot by sending a JSON message to the Kafka signaling topic.

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

1. 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": ["<fullyQualfiedTableName>","<fullyQualfiedTableName>"],"type":"incremental"}');  

For example,

INSERT INTO db1.debezium_signal (id, type, data) (1)  
values ('ad-hoc-1',   (2)  
    'stop-snapshot',  (3)  
    '{"data-collections": ["db1.table1", "db1.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 4. Descriptions of fields in a SQL command for sending a stop incremental snapshot signal to the signaling table

Using the Kafka signaling channel to stop an 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 5. Execute snapshot data fields

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

Key = `test_connector`

Value = `{"type":"stop-snapshot","data": {"data-collections": ["db1.table1", "db1.table2"], "type": "INCREMENTAL"}}`

Read-only incremental snapshots

The Debezium 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, you can use any of the available signaling channels without the requirement to use the source channel.

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

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

Custom snapshotter SPI

For more advanced uses, you can fine-tune control of the snapshot by implementing one of the following interfaces:

io.debezium.snapshot.spi.Snapshotter

Controls whether the connector takes a snapshot.

io.debezium.snapshot.spi.SnapshotQuery

Controls how data is queried during a snapshot.

io.debezium.snapshot.spi.SnapshotLock

Controls whether the connector locks tables when taking a snapshot.

io.debezium.snapshot.spi.Snapshotter interface. All built-in snapshot modes implement this interface.

/**
 * {@link Snapshotter} is used to determine the following details about the snapshot process:
 * <p>
 * - Whether a snapshot occurs. <br>
 * - Whether streaming continues during the snapshot. <br>
 * - Whether the snapshot includes schema (if supported). <br>
 * - Whether to snapshot data or schema following an error.
 * <p>
 * Although Debezium provides many default snapshot modes,
 * to provide more advanced functionality, such as partial snapshots,
 * you can customize implementation of the interface.
 * For more information, see the documentation.
 *
 *
 *
 */
@Incubating
public interface Snapshotter extends Configurable {

    /**
     * @return the name of the snapshotter.
     *
     *
     */
    String name();

    /**
     * @param offsetExists is {@code true} when the connector has an offset context (i.e. restarted)
     * @param snapshotInProgress is {@code true} when the connector is started, but a snapshot is already in progress
     *
     * @return {@code true} if the snapshotter should take a data snapshot
     */
    boolean shouldSnapshotData(boolean offsetExists, boolean snapshotInProgress);

    /**
     * @param offsetExists is {@code true} when the connector has an offset context (i.e. restarted)
     * @param snapshotInProgress is {@code true} when the connector is started, but a snapshot is already in progress
     *
     * @return {@code true} if the snapshotter should take a schema snapshot
     */
    boolean shouldSnapshotSchema(boolean offsetExists, boolean snapshotInProgress);

    /**
     * @return {@code true} if the snapshotter should stream after taking a snapshot
     */
    boolean shouldStream();

    /**
     * @return {@code true} whether the schema can be recovered if database schema history is corrupted.
     */
    boolean shouldSnapshotOnSchemaError();

    /**
     * @return {@code true} whether the snapshot should be re-executed when there is a gap in data stream.
     */
    boolean shouldSnapshotOnDataError();

    /**
     *
     * @return {@code true} if streaming should resume from the start of the snapshot
     * transaction, or {@code false} for when a connector resumes and takes a snapshot,
     * streaming should resume from where streaming previously left off.
     */
    default boolean shouldStreamEventsStartingFromSnapshot() {
        return true;
    }

    /**
     * Lifecycle hook called after the snapshot phase is successful.
     */
    default void snapshotCompleted() {
        // no operation
    }

    /**
     * Lifecycle hook called after the snapshot phase is aborted.
     */
    default void snapshotAborted() {
        // no operation
    }
}

io.debezium.snapshot.spi.SnapshotQuery interface. All built-in snapshot query modes implement this interface.

/**
 * {@link SnapshotQuery} is used to determine the query used during a data snapshot
 *
 *
 */
public interface SnapshotQuery extends Configurable, Service {

    /**
     * @return the name of the snapshot lock.
     *
     *
     */
    String name();

    /**
     * Generate a valid query string for the specified table, or an empty {@link Optional}
     * to skip snapshotting this table (but that table will still be streamed from)
     *
     * @param tableId the table to generate a query for
     * @param snapshotSelectColumns the columns to be used in the snapshot select based on the column
     *                              include/exclude filters
     * @return a valid query string, or none to skip snapshotting this table
     */
    Optional<String> snapshotQuery(String tableId, List<String> snapshotSelectColumns);

}

io.debezium.snapshot.spi.SnapshotLock interface. All built-in snapshot lock modes implement this interface.

/**
 * {@link SnapshotLock} is used to determine the table lock mode used during schema snapshot
 *
 *
 */
public interface SnapshotLock extends Configurable, Service {

    /**
     * @return the name of the snapshot lock.
     *
     *
     */
    String name();

    /**
     * Returns a SQL statement for locking the given table during snapshotting, if required by the specific snapshotter
     * implementation.
     */
    Optional<String> tableLockingStatement(Duration lockTimeout, String tableId);

}

Blocking snapshots

To provide more flexibility in managing snapshots, Debezium includes a supplementary ad hoc snapshot mechanism, known as a blocking snapshot. Blocking snapshots rely on the Debezium mechanism for sending signals to a Debezium connector.

A blocking snapshot behaves just like an initial snapshot, except that you can trigger it at run time.

You might want to run a blocking snapshot rather than use the standard initial snapshot process in the following situations:

• You add a new table and you want to complete the snapshot while the connector is running.
• You add a large table, and you want the snapshot to complete in less time than is possible with an incremental snapshot.

Blocking snapshot process

When you run a blocking snapshot, Debezium stops streaming, and then initiates a snapshot of the specified table, following the same process that it uses during an initial snapshot. After the snapshot completes, the streaming is resumed.

Configure snapshot

You can set the following properties in the data component of a signal:

• data-collections: to specify which tables must be snapshot.
• data-collections: Specifies the tables that you want the snapshot to include.
  This property accepts a comma-separated list of regular expressions that match fully-qualified table names. The behavior of the property is similar to the behavior of the table.include.list property, which specifies the tables to capture in a blocking snapshot.
• additional-conditions: You can specify different filters for different table.
  • The data-collection property is the fully-qualified name of the table for which the filter will be applied, and can be case-sensitive or case-insensitive depending on the database.
  • The filter property will have the same value used in the snapshot.select.statement.overrides, the fully-qualified name of the table that should match by case.

For example:

  {"type": "blocking", "data-collections": ["schema1.table1", "schema1.table2"], "additional-conditions": [{"data-collection": "schema1.table1", "filter": "SELECT * FROM [schema1].[table1] WHERE column1 = 0 ORDER BY column2 DESC"}, {"data-collection": "schema1.table2", "filter": "SELECT * FROM [schema1].[table2] WHERE column2 > 0"}]}

Possible duplicates

A delay might exist between the time that you send the signal to trigger the snapshot, and the time when streaming stops and the snapshot starts. As a result of this delay, after the snapshot completes, the connector might emit some event records that duplicate records captured by the snapshot.

Setting the binlog position via a signal

In certain situations it can be useful to precisely control the point in the binlog from which the connector begins to capture database changes. You can send a Debezium set-binlog-position signal at run time to dynamically specify where the MySQL connector begins reading from the database binlog (binary log).

| | Depending on the position that you specify in the set-binlog-position signal, the connector either excludes events from the log, or sends duplicate events. If you specify a binlog position that is later than the current position, the connector skips change events in the history between the current position and the new position. The connector does not capture or stream changes in the skipped range. On the other hand, if the signal directs the connector to resume reading from a point earlier than the current position, the connector replays log entries that it previously sent, emitting duplicate events to the destination topic. | | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

You might want to reset the connector’s binlog position under the following circumstances:

Disaster recovery

Direct the connector to resume streaming from the last known good position in the log after a system failure. Restarting from a specified position helps to maintain data consistency across the pipeline by ensuring that data is not lost or duplicated.

Corrupted data

You detect corrupted or incorrect data in the database or in the destination Kafka topics, and want to skip the corrupted data and resume from a known good position.

Avoid replaying old data after a database refresh

After you refresh a database from another source, such as a backup, the connector might attempt to replay all historical events from an old binlog position, which can result in duplicate topic data, and lead to heavy resource consumption. By resuming streaming from a specific recent point, you skip historical events that you don’t need, and can help save cost and time.

Partial replication

When diverting captured data to a new Kafka topic, you want the connector to process the log from a specific point to exclude the full event history and send only a specific subset of database changes. By using this approach, you prevent the connector from processing obsolete, older data so that the connector captures only the relevant recent changes.

Testing

Test the connector with a specific subset of data, excluding the full history captured by snapshots.

Format of a set-binlog-position signal

A Debezium set-binlog-position signal message includes the standard id, type, and data properties. The exact format of the message depends on which signaling channel you use. For more information about the format of Debezium signals, see Sending signals to a Debezium connector.

To designate the binlog position in a set-binlog-position signal, in the data portion of the signal, you specify either the binlog file and position, or a GTID set, depending on whether GTID (Global Transaction Identifier) mode is enabled. You cannot use both formats in the same signal.

The following examples illustrate the two formats for specifying the binlog position in a signal:

Binlog file and position format

When GTID mode is disabled, specify the binlog filename and position in the signal, as in the following example:

{"binlog_filename": "mysql-bin.000003", "binlog_position": 1234}

The binlog_filename must match the MySQL binlog naming pattern (for example, mysql-bin.000001).

The binlog_position must be a non-negative integer.

GTID set format

When GTID mode is enabled, specify the GTID set, for example:

{"gtid_set": "3E11FA47-71CA-11E1-9E33-C80AA9429562:23"}

The value of the gtid_set attribute must be a valid GTID set string.

Send a signal to reposition the stream

Send a Debezium set-binlog-position signal at run time to specify the binlog reading position for the MySQL connector.

Prerequisites

• The connector is configured to use one of the available Debezium signaling channels.
• A signaling data collection exists on the source database.
• The connector configuration property heartbeat.interval.ms is set to ensure that offset changes are persisted.

Procedure

1. Determine the target position in your database history (binlog file and position, or GTID set).
2. Send a set-binlog-position signal that includes the standard id, type, and data properties in the format that is required for your preferred signaling channel.
   In the data portion of the signal specify the target position, as determined in step 1.
   For example, for the source signaling channel, add one of the following SQL commands to the data component of the signal, depending on whether GTID is enabled:
   SQL to use when GTID is disabled (uses the binlog file and position)

INSERT INTO debezium_signal (id, type, data) VALUES (  
  'set-position-001',  
  'set-binlog-position',  
  '{"binlog_filename": "mysql-bin.000003", "binlog_position": 1234}'  
);  

SQL to use when GTID is enabled (specifies GTID set)

INSERT INTO debezium_signal (id, type, data) VALUES (  
  'set-gtid-001',  
  'set-binlog-position',  
  '{"gtid_set": "3E11FA47-71CA-11E1-9E33-C80AA9429562:1-100"}'  
);  

1. Restart the connector to begin streaming from the new position.

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:

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",
  "ts_us": "1580390884335472",
  "ts_ns": "1580390884335472987",
  "transaction": {
    "id": "0e4d5dcd-a33b-11ea-80f1-02010a22a99e:10",
    "total_order": "1",
    "data_collection_order": "1"
  }
}

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 four basic components of a change event. The exact way that Debezium represents these components in a change event message depends on how you configure the Kafka Connect converter in your application. Change events only contain schema fields if you configure the converter to produce them. Likewise, change events only contain the event key and event payload if you configure the converter to produce them. If you use the JSON converter and you configure it to produce all four of the basic change event components, a change event has the following structure:

{
 "schema": {
   ...
  },
 "payload": {
   ...
 },
 "schema": {
   ...
 },
 "payload": {
   ...
 },
}

The following list provides details about the components that are shown in the preceding change event structure:

schema

This first schema field in the event message is part of the event key. The event key schema is a Kafka Connect schema that defines the structure and data types that are present in the payload portion of the event key. The event key structure reflects the structure of the primary key for the table that was changed by the database operation. If the source table has no primary key, the event key structure maps represents the structure of the source table’s unique key.

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.

payload

The first payload field is part of the event key. The payload structure reflects the structure of the preceding event key schema, and it contains the key for the row that was changed.

schema

The second schema field is part of the event value. This event value schema represents the Kafka Connect schema that defines the structure and data types present in the payload portion of the event value. This schema values reflects the structure of the row that the database operation changed. Typically, the event value schema contains nested schemas.

payload

The second payload field is part of the event value. The payload structure reflects the structure of the preceding event value schema, 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. For more information, see topic names.

| | 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. | | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

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.

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. Thus, for every change event that Debezium emits for the table, the event message has the same key structure. Key consistency remains stable as long as the schema of the customers table remains unchanged. The preceding table definiton results in a key structure such as the one in the following JSON example:

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

The following list describes some of the elements in the event key of a change event message that Debezium emits from the customers table:

schema

The schema element of the key specifies the Kafka Connect schema that defines the structure and data types that are present in the payload portion of the event key.

name

Specifies the 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. The schema name of an event key has the following format: connector-name.database-name.table-name.Key.

In the preceding event message, the schema name is made up of the following elements:

mysql-server-1

Specifies the name of the connector that generated this event.

inventory

Specifies the database that contains the table that was changed.

customers

Specifies the table that was changed.

fields

Specifies the required fields in the payload. The fields element includes the following attributes:

field

The name of the field, for example, id.

type

The data type of the field, for example, int32.

optional

A Boolean value that specifies whether a value is required in the payload field of the event key A value in the key’s payload field is optional when a table does not have a primary key.

payload

Specifies the key of the row for which this change event was generated. In the preceding example, the payload contains a single field, id, which has the value 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 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
• update events
• Primary key updates
• delete events
• Tombstone events
• truncate events

create events

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

{
  "schema": {
    "type": "struct",
    "fields": [
      {
        "type": "struct",
        "fields": [
          {
            "type": "int32",
            "optional": false,
            "field": "id"
          },
          {
            "type": "string",
            "optional": false,
            "field": "first_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "last_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "email"
          }
        ],
        "optional": true,
        "name": "mysql-server-1.inventory.customers.Value",
        "field": "before"
      },
      {
        "type": "struct",
        "fields": [
          {
            "type": "int32",
            "optional": false,
            "field": "id"
          },
          {
            "type": "string",
            "optional": false,
            "field": "first_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "last_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "email"
          }
        ],
        "optional": true,
        "name": "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": "int64",
            "optional": false,
            "field": "ts_us"
          },
          {
            "type": "int64",
            "optional": false,
            "field": "ts_ns"
          },
          {
            "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",
        "field": "source"
      },
      {
        "type": "string",
        "optional": false,
        "field": "op"
      },
      {
        "type": "int64",
        "optional": true,
        "field": "ts_ms"
      },
      {
        "type": "int64",
        "optional": true,
        "field": "ts_us"
      },
      {
        "type": "int64",
        "optional": true,
        "field": "ts_ns"
      }
    ],
    "optional": false,
    "name": "mysql-server-1.inventory.customers.Envelope"
  },
  "payload": {
    "op": "c",
    "ts_ms": 1465491411815,
    "ts_us": 1465491411815437,
    "ts_ns": 1465491411815437158,
    "before": null,
    "after": {
      "id": 1004,
      "first_name": "Anne",
      "last_name": "Kretchmar",
      "email": "[email protected]"
    },
    "source": {
      "version": "3.5.2.Final",
      "connector": "mysql",
      "name": "mysql-server-1",
      "ts_ms": 0,
      "ts_us": 0,
      "ts_ns": 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', '[email protected]')"
    }
  }
}

The following list describes select fields in the value portion of the preceding create event message:

schema

Specifies the schema of the event value. The schema describes the structure of the value’s payload. Every change event that Debezium emits for a table uses the same value schema, as long as the table schema remains unchanged.

name

The schema section can contain multiple name fields. Each name field specifies the schema for a field in the payload of the event value.

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.

The schema names of the before and after fields are of the form _logicalName_._tableName_.Value. This format ensures that schema names are unique within the database. In environments that use the Avro converter, having unique schema names ensures that the Avro schema for each table in each logical source has its own evolution and history.

"name": "io.debezium.connector.mysql.Source"

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.

"name": "mysql-server-1.inventory.customers.Envelope"

Specifies the name of the schema for the overall structure of the payload. The schema name is made up of the following elements:

mysql-server-1

Specifies the name of the connector that generated this event.

inventory

Specifies the database that contains the table that was changed.

customers

Specifies the table that was changed.

payload

Specifies the actual data for the row that was changed.

Because the JSON representation of an event includes both the schema and the payload portions of the message, it is often larger than the row that it describes. To reduce the size of the messages that the connector streams to Kafka topics, you can use the Avro converter.

op

Specifies the type of operation that caused the connector to generate the event. In this example, c indicates that a create operation was performed resulting in a new row. This field can contain one of the following values:

ts_ms, ts_us, ts_ns

Displays timestamps in milliseconds, microseconds, and nanoseconds that indicate when the connector processed the event. The time is based on the system clock in the JVM that runs the Kafka Connect task.

before

An optional field that specifies the state of the row before the event occurred. Because the op field in the preceding example is c (create), this change event is describes new data, so the value of the before field is null.

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.

source

Mandatory field that describes the source metadata for the event. This field contains information that you can use to compare this event to 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 provides the following information:

version

Debezium version.

name

Connector name.

connector

The type of connector.

ts_ms, ts_us, ts_ns

Displays timestamps in milliseconds, microseconds, and nanoseconds that indicate when the change was made in the database.

snapshot

Specifies whether the event resulted from a snapshot operation.

db, table

Names of the database and table that contain the new row.

server_id

MySQL server ID (if available).

file

Binary log name where the event was recorded.

pos

Binary log position.

row

Row within the event.

thread

ID of the MySQL thread that created the event (non-snapshot only).

query

SQL command that performed the operation on the source table.

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": {
      "id": 1004,
      "first_name": "Anne",
      "last_name": "Kretchmar",
      "email": "[email protected]"
    },
    "after": {
      "id": 1004,
      "first_name": "Anne Marie",
      "last_name": "Kretchmar",
      "email": "[email protected]"
    },
    "source": {
      "version": "3.5.2.Final",
      "name": "mysql-server-1",
      "connector": "mysql",
      "ts_ms": 1465581029100,
      "ts_us": 1465581029100000,
      "ts_ns": 1465581029100000000,
      "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",
    "ts_ms": 1465581029523,
    "ts_us": 1465581029523758,
    "ts_ns": 1465581029523758914
  }
}

The following list describes select fields in the value portion of the preceding update event message:

before

Shows 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.

after

Shows 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.

source

Represents 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 fields in an update event value provides the following metadata:

version

Debezium version.

name

Connector name.

connector

The type of connector.

ts_ms, ts_us, ts_ns

Displays timestamps in milliseconds, microseconds, and nanoseconds that indicate when the change was made in the database.

snapshot

Specifies whether the event resulted from a snapshot operation.

db, table

Names of the database and table that contain the new row.

server_id

MySQL server ID (if available).

file

Binary log name where the event was recorded.

pos

Binary log position.

row

Row within the event.

thread

ID of the MySQL thread that created the event (non-snapshot only).

query

SQL command that performed the operation on the source table.

op

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

ts_ms, ts_us, ts_ns

Displays timestamps in milliseconds, microseconds, and nanoseconds that indicate when the connector processed the event. The time is based on the system clock in the JVM that runs 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 of payload.source.ts_ms with the value of payload.ts_ms, you can determine the lag between the source database update and Debezium.

| | Updating the columns for a row’s primary or unique key changes the value of the row’s key. After a key changes, Debezium generates the following three events: A DELETE event. A Tombstone event that specifies the old key for the row. A CREATE event that provides the new primary key for the row. For more information, see Primary key updates. | | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

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": {
      "id": 1004,
      "first_name": "Anne Marie",
      "last_name": "Kretchmar",
      "email": "[email protected]"
    },
    "after": null,
    "source": {
      "version": "3.5.2.Final",
      "connector": "mysql",
      "name": "mysql-server-1",
      "ts_ms": 1465581902300,
      "ts_us": 1465581902300000,
      "ts_ns": 1465581902300000000,
      "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",
    "ts_ms": 1465581902461,
    "ts_us": 1465581902461842,
    "ts_ns": 1465581902461842579
  }
}

The following list describes select fields in the value portion of the preceding delete event message:

before

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.

after

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.

source

Specifies 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. The source field in a delete event value provides the following metadata:

version

Debezium version.

name

Connector name.

connector

The type of connector.

ts_ms, ts_us, ts_ns

Displays timestamps in milliseconds, microseconds, and nanoseconds that indicate when the change was made in the database.

snapshot

Specifies whether the event resulted from a snapshot operation.

db, table

Names of the database and table that contain the new row.

server_id

MySQL server ID (if available).

file

Binary log name where the event was recorded.

pos

Binary log position.

row

Row within the event.

thread

ID of the MySQL thread that created the event (non-snapshot only).

query

SQL command that performed the operation on the source table.

op

Specifies the type of operation. In this example, the d value indicates that a DELETE operation was performed, resulting in removal of a row.

ts_ms, ts_us, ts_ns

Displays timestamps in milliseconds, microseconds, and nanoseconds that indicate when the connector processed the event. The time is based on the system clock in the JVM that runs the Kafka Connect task.

In the source object, these fields indicate the time when 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. Kafka can thus reclaim storage space while still ensuring that the topic contains a complete data set that 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 the Debezium 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 of a truncate event is null. The message value resembles the following example:

{
    "schema": { ... },
    "payload": {
        "source": {
            "version": "3.5.2.Final",
            "name": "mysql-server-1",
            "connector": "mysql",
            "name": "mysql-server-1",
            "ts_ms": 1465581029100,
            "ts_us": 1465581029100000,
            "ts_ns": 1465581029100000000,
            "snapshot": false,
            "db": "inventory",
            "table": "customers",
            "server_id": 223344,
            "gtid": null,
            "file": "mysql-bin.000003",
            "pos": 484,
            "row": 0,
            "thread": 7,
        },
        "op": "t",
        "ts_ms": 1465581029523,
        "ts_us": 1465581029523468,
        "ts_ns": 1465581029523468471
    }
}

The following list describes select fields in the value portion of the preceding truncate event message:

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. The source field provides the following metadata:

version

Debezium version.

name

Connector name.

connector

The type of connector.

ts_ms, ts_us, ts_ns

Displays timestamps in milliseconds, microseconds, and nanoseconds that indicate when the change was made in the database.

snapshot

Specifies whether the event resulted from a snapshot operation.

db, table

Names of the database and table that contain the new row.

server_id

MySQL server ID (if available).

file

Binary log name where the event was recorded.

pos

Binary log position.

row

Row within the event.

thread

ID of the MySQL thread that created the event (non-snapshot only).

op

Specifies the type of operation. In this example, the t value indicates that a TRUNCATE operation was performed, resulting in removal of a row.op: Mandatory string that describes the type of operation. The op field value is t, signifying that this table was truncated.

ts_ms, ts_us, ts_ns

Displays timestamps in milliseconds, microseconds, and nanoseconds that indicate when the connector processed the event. The time is based on the system clock in the JVM that runs the Kafka Connect task.

In the source object, these fields indicate the time when 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.

If a single TRUNCATE statement applies to multiple tables, the connector emits one truncate change event record for each truncated table.

| | A truncate event represents a change that is made to an entire table and it has no message key. As a result, for topics with multiple partitions, there is no ordering guarantee for the change events (create, update, and so forth), or truncate events that pertain to a table. For example, if a consumer reads events for a table from multiple partitions, it might receive an update event for a table from one partition after it receives a truncate event that deletes all of the data in the table from another partition. Ordering is guaranteed only for topics that use a single partition. | | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |

If you do not want the connector to capture truncate events, use the skipped.operations option to filter them out.

Data type mappings

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.

Basic types

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

Table 6. Descriptions of basic type mappings

.

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.

The time zone setting for the JVM that runs 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 7. Mappings when time.precision.mode=adaptive_time_microseconds

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 8. Mappings when time.precision.mode=connect

Decimal types

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

decimal.handling.mode=precise

Table 9. Mappings when decimal.handling.mode=precise

decimal.handling.mode=double

Table 10. Mappings when decimal.handling.mode=double

decimal.handling.mode=string

Table 11. Mappings when decimal.handling.mode=string

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.binlog.converters.TinyIntOneToBooleanConverter  
boolean.selector=db1.table1.*, db1.table2.column1  

• NOTE: In some cases, the database may not show the length of tinyint unsigned when the 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 selected property instead of converting all columns based on type, as shown in the following example:

converters=boolean  
boolean.type=io.debezium.connector.binlog.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 12. Description of spatial type mappings

Vector types

Vector types

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

Table 13. Description of vector type mappings

Custom converters

By default, the Debezium MySQL connector provides several CustomConverter implementations for MySQL data types. These custom converters provide alternative mappings for specific data types based on the connector configuration. To add a CustomConverter to the connector, follow the instructions in the Custom Converters documentation.

TINYINT(1) to Boolean

By default, during a connector snapshot, the Debezium MySQL connector obtains column types from the JDBC driver, which assigns the TINYINT(1) type to BOOLEAN columns. Debezium then uses these JDBC column types to define the schema for the snapshot events. After the connector transitions from the snapshot to the streaming phase, the change event schema that results from the default mapping can lead to inconsistent mappings for BOOLEAN columns. To help ensure that MySQL emits BOOLEAN columns uniformly, you can apply the custom TinyIntOneToBooleanConverter, as shown in the following configuration example.

Example: TinyIntOneToBooleanConverter configuration

converters=tinyint-one-to-boolean
tinyint-one-to-boolean.type=io.debezium.connector.binlog.converters.TinyIntOneToBooleanConverter
tinyint-one-to-boolean.selector=.*.MY_TABLE.DATA
tinyint-one-to-boolean.length.checker=false

In the preceding example, the selector and length.checker properties are optional. By default, the converter checks that TINYINT data types conform to a length of 1. If length.checker to false, the converter does not explicitly confirm that the TINYINT data type conforms to a length of 1. The selector designates the tables or columns to convert, based on the supplied regular expression. If you omit the selector property, the converter maps all TINYINT columns to logical BOOL field types. If you do not configure a selector option, and you want to map TINYINT columns to TINYINT(1), omit the length.checker property, or set its value to true.

JDBC sink data types

If you integrate the Debezium JDBC sink connector with a Debezium MySQL source connector, the MySQL connector emits some column attributes differently during the snapshot and streaming phases. For the JDBC sink connector to consistently consume changes from both the snapshot and streaming phase, you must include the JdbcSinkDataTypesConverter converter as part of the MySQL source connector configuration, as shown in the following example:

Example: JdbcSinkDataTypesConverter configuration

converters=jdbc-sink
jdbc-sink.type=io.debezium.connector.binlog.converters.JdbcSinkDataTypesConverter
jdbc-sink.selector.boolean=.*.MY_TABLE.BOOL_COL
jdbc-sink.selector.real=.*.MY_TABLE.REAL_COL
jdbc-sink.selector.string=.*.MY_TABLE.STRING_COL
jdbc-sink.treat.real.as.double=true

In the preceding example, the selector.* and treat.real.as.double configuration properties are optional.

The selector.* properties specify comma-separated lists of regular expressions that specify which tables and columns that the converter applies to. By default, the converter applies the following rules apply to all Boolean, real, and string-based column data types, across all tables:

• BOOLEAN data types are always emitted as INT16 logical types, with 1 representing true and 0 representing false
• REAL data types are always emitted as FLOAT64 logical types.
• String-based columns always include the __debezium.source.column.character_set schema parameter that contains the column’s character set.

For each data type, you can configure a selector rule to override the default scope and apply the selector to specific tables and columns only. For example, to set the scope of the Boolean converter, add the following rule to the connector configuration, as in the preceding example: converters.jdbc-sink.selector.boolean=.*.MY_TABLE.BOOL_COL

Setting up MySQL

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

Creating a user

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

1. Create the MySQL user:

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

1. Grant the required permissions to the user:

mysql> GRANT SELECT, RELOAD, SHOW DATABASES, REPLICATION SLAVE, REPLICATION CLIENT ON *.* TO 'user' IDENTIFIED BY 'password';  

For a description of the required permissions, see Descriptions of user permissions.

| | 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. |
| --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | 3. Finalize the user’s permissions:

Descriptions of user permissions

Table 14. Descriptions of user permissions

Enabling the binlog

You must enable binary logging for MySQL replication. The binary logs record transaction updates in a way that enables replicas to propagate those changes.

Prerequisites

• A MySQL server.
• Appropriate MySQL user privileges.

Procedure

1. Check whether the log-bin option is enabled:

// 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';  

1. If the binlog is OFF, add the properties in the following table to the configuration file for the MySQL server:

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  
binlog_expire_logs_seconds  = 864000  

1. 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';  

1. If you run MySQL on Amazon RDS, you must enable automated backups for your database instance for binary logging to occur. If the database instance is not configured to perform automated backups, the binlog is disabled, even if you apply the settings described in the previous steps.

Descriptions of MySQL binlog configuration properties

Table 15. Descriptions of MySQL binlog configuration properties

Enabling GTIDs

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

1. Enable gtid_mode:
2. Enable enforce_gtid_consistency:

mysql> enforce_gtid_consistency=ON  

1. Confirm the changes:

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

Result

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

Table 16. Descriptions of GTID options

Configuring session timeouts

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

1. Configure interactive_timeout:

mysql> interactive_timeout=<duration-in-seconds>  

1. Configure wait_timeout:

mysql> wait_timeout=<duration-in-seconds>  

Table 17. Descriptions of MySQL session timeout options

Enabling query log events

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 in MySQL:

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

Validating binlog row value options

Verify the setting of the binlog_row_value_options variable in the database. To enable the connector to consume UPDATE events, this variable must be set to a value other than PARTIAL_JSON.

Prerequisites

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

Procedure

1. Check current variable value

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

Result

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

1. If the value of the variable is set to PARTIAL_JSON, run the following command to unset it:

mysql> set @@global.binlog_row_value_options="" ;  

Deployment

To deploy a Debezium MySQL connector, you install the Debezium MySQL connector archive, configure the connector, and start the connector by adding its configuration to Kafka Connect.

Procedure

1. Download the Debezium MySQL connector plug-in.
2. Extract the files into your Kafka Connect environment.
3. Add the directory with the JAR files to Kafka Connect’s plugin.path.
4. Configure the connector and add the configuration to your Kafka Connect cluster.
5. Restart your Kafka Connect process to pick up the new JAR files.

If you are working with immutable containers, see Debezium’s container images for Apache Kafka MySQL and Kafka Connect with the MySQL connector already installed and ready to run.

| | The Debezium container images that you obtain from quay.io do not undergo rigorous testing or security analysis, and are provided for testing and evaluation purposes only. These images are not intended for use in production environments. To mitigate risk in production deployments, deploy only containers that are actively maintained by trusted vendors, and thoroughly tested for potential vulnerabilities. | | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

MySQL connector configuration example

The following example shows the configuration for a connector instance that captures data from a MySQL database server on port 3306 at 192.168.99.100, which we logically name fullfillment. Typically, you configure the Debezium MySQL connector in a JSON file by setting the configuration properties that are available for the connector.

You can choose to produce events for a subset of the schemas and tables in a database. Optionally, you can ignore, mask, or truncate columns that contain sensitive data, those that are larger than a specified size, or those that you do not need to support downstream applications.

{
    "name": "inventory-connector",
    "config": {
        "connector.class": "io.debezium.connector.{context}.{connector-name}Connector",
        "database.hostname": "192.168.99.100",
        "database.port": "3306",
        "database.user": "debezium-user",
        "database.password": "debezium-user-pw",
        "database.server.id": "184054",
        "topic.prefix": "fullfillment",
        "database.include.list": "inventory",
        "schema.history.internal.kafka.bootstrap.servers": "kafka:9092",
        "schema.history.internal.kafka.topic": "schemahistory.fullfillment",
        "include.schema.changes": "true"
    }
}

The following list describes each of the fields in the preceding configuration example:

name

Specifies the connector name (inventory-connector) to register with the Kafka Connect service.

connector.class

Specifies the name of the Java connector class.

database.hostname

Specifies the address or hostname of the MySQL server.

database.port

Species the port number that the connector uses to access the MySQL server.

database.user

Specifies the name of the MySQL user account that the connector uses to access the database. The specified account must have sufficient privileges, as described in the table Descriptions of user permissions.

database.password

Specifies the password for the MySQL user account that Debezium uses to access the database.

database.server.id

Specifies the unique ID of the connector.

topic.prefix

Speciies the topic prefix for the MySQL server or cluster.

database.include.list

Specifies databases on the specified server that you want the connector to capture from.

schema.history.internal.kafka.bootstrap.servers

Specifies the Kafka brokers that the connector uses to write and recover DDL statements to the database schema history topic.

schema.history.internal.kafka.topic

Specifies the name of the database schema history topic. This topic is for internal use only and should not be used by consumers.

include.schema.changes

Specifies whether the connector should generate events for DDL changes and emit them to the fulfillment schema change topic for use by consumers.

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

You can send this configuration with a POST command to a running Kafka Connect service. The service records the configuration and starts one connector task that performs the following actions:

• Connects to the MySQL database.
• Reads change-data tables for tables in capture mode.
• Streams change event records to Kafka topics.

Adding connector configuration

To start running a MySQL connector, configure a connector configuration, and add the configuration to your Kafka Connect cluster.

Prerequisites

• MySQL is set up to work with a Debezium connector.
• The Debezium MySQL connector is installed.

Procedure

1. Create a configuration for the MySQL connector.
2. Use the Kafka Connect REST API to add that connector configuration to your Kafka Connect cluster.

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.

Connector 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 MySQL connector configuration 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 MySQL connector configuration properties
  • Pass-through database schema history properties for configuring producer and consumer clients
  • Pass-through Kafka signals configuration properties
  • Pass-through Kafka signals consumer client configuration properties
  • Pass-through sink notification configuration properties
  • Pass-through database driver configuration properties

Required Debezium MySQL connector configuration properties

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

bigint.unsigned.handling.mode

Default value

long

Description

Specifies how the connector represents BIGINT UNSIGNED columns in change events.

Set one of the following options:

long

Uses Java long data types to represent BIGINT UNSIGNED column values. Although the long type does not offer the greatest precision, it is easy implement in most consumers. In most environments, this is the preferred setting.

precise

Uses java.math.BigDecimal data types to represent values. The connector uses the Kafka Connect org.apache.kafka.connect.data.Decimal data type to represent values in encoded binary format. Set this option if the connector typically works with values larger than 2^63. The long data type cannot convey values of that size.

binary.handling.mode

Default value

bytes

Description

Specifies how the connector represents values for binary columns, such as, blob, binary, varbinary, in change events.

Set one of the following options:

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.

column.exclude.list

Default value

empty string

Description

An optional, comma-separated list of regular expressions that match the fully-qualified names of columns to exclude from change event record values. Other columns in the source record are captured as usual. 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

Default value

empty string

Description

An optional, comma-separated list of regular expressions that match the fully-qualified names of columns to include in change event record values. Other columns are omitted from the event record. 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.

column.mask.hash.v2.hashAlgorithm.with.salt.salt

Default value

No default

Description

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 ensures fidelity of values that are hashed in different places or systems.

column.mask.with.length.chars

Default value

No default

Description

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.propagate.source.type

Default value

No default

Description

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.

column.truncate.to.length.chars

Default value

No default

Description

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.

connect.timeout.ms

Default value

30000 (30 seconds)

Description

A positive integer value that specifies the maximum time in milliseconds that the connector waits to establish a connection to the MySQL database server before the connection request times out.

connector.class

Default value

No default

Description

The name of the Java class for the connector. Always specifyio.debezium.connector.mysql.MySqlConnectorfor the MySQL connector.

database.exclude.list

Default value

empty string

Description

An optional, comma-separated list of regular expressions that match the names of databases from which you do not want the connector to capture changes. The connector captures changes in any database that is not named 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.

database.hostname

Default value

No default

Description

The IP address or hostname of the MySQL database server.

database.include.list

Default value

empty string

Description

An optional, comma-separated list of regular expressions that match the names of the databases from which the connector captures 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.jdbc.driver

Default value

com.mysql.cj.jdbc.Driver

Description

Specifies the name of the driver class that the connector uses.

Set this property to configure a driver other than the one that is packaged with the connector.

database.password

Default value

No default

Description

The password of the MySQL user that the connector uses to connect to the MySQL database server.

database.port

Default value

3306

Description

Integer port number of the MySQL database server.

database.protocol

Default value

jdbc:mysql

Description

Specifies the JDBC protocol that the driver connection string uses to connect to the database.

database.server.id

Default value

No default

Description

The numeric ID of this database client. The specified ID must be unique across all currently running database processes in the MySQL cluster. To enable reading from the binlog, the connector uses this unique ID to join the MySQL database cluster as another server.

database.user

Default value

No default

Description

The name of the MySQL user that the connector uses to connect to the MySQL database server.

decimal.handling.mode

Default value

precise

Description

Specifies how the connector handles values for DECIMAL and NUMERIC columns in change events.

Set one of the following options:

precise

Uses java.math.BigDecimal values in binary form to represent values precisely.

double

Uses the double data type to represent values. This option can result in a loss of precision, but it is easier for most consumers to use.

string

Encodes values as formatted strings. This option is easy to consume, but can result in the loss of semantic information about the real type.

event.deserialization.failure.handling.mode Deprecated

Default value

fail

Description

Specifies how the connector reacts after an exception occurs during deserialization of binlog events.

| | This option is deprecated. Use the event.processing.failure.handling.mode property instead. | | -------------------------------------------------------------------------------------------------------------------------------------------------------- |

This property accepts the following options:

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.

field.name.adjustment.mode

Default value

No default

Description

Specifies how field names should be adjusted for compatibility with the message converter used by the connector.

Set one of the following options:

none

No adjustment.

avro

Replaces characters that are not valid in Avro names with underscore characters.

avro_unicode

Replaces underscore characters or characters that cannot be used in Avro names with corresponding unicode, such as _uxxxx.

| | The underscore character (_) represents an escape sequence, similar to a backslash in Java | | ---------------------------------------------------------------------------------------------- |

gtid.source.excludes

Default value

No default

Description

A comma-separated list of regular expressions that match source domain IDs 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 GTID’s domain identifier.

If you set this property, do not also set the gtid.source.includes property.

gtid.source.includes

Default value

No default

Description

A comma-separated list of regular expressions that match source domain IDs 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 GTID’s domain identifier.

If you set this property, do not also set the gtid.source.excludes property.

include.query

Default value

false

Description

Boolean value that specifies whether the change event that the connector emits includes the SQL query that generated the change.

| | Setting this property to true might expose information about tables or fields that you explicitly excluded or masked via other settings. | | ------------------------------------------------------------------------------------------------------------------------------------------- |

To enable this property, the database property binlog_annotate_row_events must be set to ON.

Setting this property has no effect on events that the snapshot process generates. Snapshot events do not include the original SQL query.

For more information about configuring the database to return the original SQL statement for each log event, see Enabling query log events.

include.schema.changes

Default value

true

Description

Boolean value that specifies whether the connector publishes changes in the database schema to a Kafka topic with the same name as the topic prefix. The connector records each schema change with a key that contains the database name, and a value that is a JSON structure that describes the schema update. This mechanism for recording schema changes is independent of the connector’s internal recording of changes to the database schema history.

inconsistent.schema.handling.mode

Default value

fail

Description

Specifies how the connector responds to binlog events that refer to tables that are not present in the internal schema representation. That is, the internal representation is not consistent with the database.

Set one of the following options:

fail

The connector throws an exception that reports the problematic event and its binlog offset. The connector then stops.

warn

The connector logs the problematic event and its binlog offset, and then skips the event.

skip

The connector skips the problematic event and does not report it in the log.

message.key.columns

Default value

No default

Description

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-qualifiedtableName>_:_<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.

name

Default value

No default

Description

Unique name for the connector. If you attempt to use the same name to register multiple connectors, registration fails. This property is required by all Kafka Connect connectors.

schema.name.adjustment.mode

Default value

No default

Description

Specifies how the connector adjusts schema names for compatibility with the message converter used by the connector.

Set one of the following options:

none

No adjustment.

avro

Replaces characters that are not valid in Avro names with underscore characters.

avro_unicode

Replaces underscore characters or characters that cannot be used in Avro names with corresponding unicode, such as _uxxxx.

| | _ is an escape sequence, similar to a backslash in Java | | ----------------------------------------------------------- |

skip.messages.without.change

Default value

false

Description

Specifies whether the connector emits messages for records when it does not detect a change in the included columns. Columns are considered to be included if they are listed in the column.include.list, or are not listed in the column.exclude.list. Set the value to true to prevent the connector from capturing records when no changes are present in the included columns.

table.exclude.list

Default value

empty string

Description

An optional, comma-separated list of regular expressions that match fully-qualified table identifiers of tables from which you do not want the connector to capture changes. 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 set this property, do not also set the table.include.list property.

table.include.list

Default value

empty string

Description

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 all non-system tables in every database from which it is configured to captures changes.

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 set this property, do not also set the table.exclude.list property.

tasks.max

Default value

1

Description

The maximum number of tasks to create for this connector. Because the MySQL connector always uses a single task, changing the default value has no effect.

time.precision.mode

Default value

adaptive_time_microseconds

Description

Specifies the type of precision that the connector uses to represent time, date, and timestamps values.

Set one of the following options:

adaptive_time_microseconds

The connector 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.

adaptive

(deprecated) The connector captures time and timestamp values exactly as in the database using either millisecond, microsecond, or nanosecond precision values based on the data type of the column.

connect

The connector 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.

tombstones.on.delete

Default value

true

Description

Specifies whether a delete event is followed by a tombstone event. After a source record is deleted, the connector can emit a tombstone event (the default behavior) to enable Kafka to completely delete all events that pertain to the key of the deleted row in case log compaction is enabled for the topic.

Set one of the following options:

true

The connector represents delete operations by emitting a delete event and a subsequent tombstone event.

false

The connector emits only delete events.

topic.prefix

Default value

No default

Description

A string that specifies the namespace for the MySQL database server or cluster from which Debezium captures changes. Because the topic prefix is used to name all of the Kafka topics that receive events that this connector emits, it’s important that the topic prefix is unique across all connectors. Values must contain only alphanumeric characters, hyphens, dots, and underscores.

| | After you set this property, do not change its value. If you change the value, after the connector restarts, 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. | | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

Advanced Debezium MySQL connector configuration properties

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

binlog.buffer.size

Default value

0

Description

The size of a look-ahead buffer used by the binlog reader. The default setting of 0 disables buffering.

Under specific conditions, it is possible that the MySQL binlog contains uncommitted data finished by a ROLLBACK statement. Typical examples are using savepoints or mixing temporary and regular table changes in a single transaction.

When a beginning of a transaction is detected then Debezium tries to roll forward the binlog position and find either COMMIT or ROLLBACK so it can determine whether to stream the changes from the transaction. The size of the binlog buffer defines the maximum number of changes in the transaction that Debezium can buffer while searching for transaction boundaries. If the size of the transaction is larger than the buffer then Debezium must rewind and re-read the events that have not fit into the buffer while streaming.

| | This feature is incubating. Feedback is encouraged. It is expected that this feature is not completely polished. | | ------------------------------------------------------------------------------------------------------------------- |

binlog.net.read.timeout

Default value

0

Description

The number of seconds to wait for a read from the binlog connection to complete before the server times out. A value of 0 means that the connector uses the MySQL server default.

In high-latency network environments, the default server value for net_read_timeout might be too low, causing the server to close the binlog streaming connection prematurely with an EOFException. Increase this value to prevent unexpected disconnects during binlog streaming.

| | This value is set at the session level on the binlog streaming connection using SET net_read_timeout=. It does not affect the global server setting. | | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |

binlog.net.write.timeout

Default value

0

Description

The number of seconds to wait for a write to the binlog connection to complete before the server times out. A value of 0 means that the connector uses the MySQL server default.

When the server transfers large data volumes through the binlog, the default server value for net_write_timeout might be too low, causing the server to close the connection with an EOFException. Increase this value to prevent unexpected disconnects during streaming of large transactions.

| | This value is set at the session level on the binlog streaming connection using SET net_write_timeout=. It does not affect the global server setting. | | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |

connect.keep.alive

Default value

true

Description

A Boolean value that specifies whether a separate thread should be used to ensure that the connection to the MySQL server or cluster is kept alive.

converters

Default value

No default

Description

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-qualified 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.binlog.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 parameter 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

custom.sanitize.pattern

Default value

.*secret$|.*password$|.*sasl\.jaas\.config$|.*basic\.auth\.user\.info|.*registry\.auth\.client-secret

Description

An optional regular expression that specifies a custom pattern for masking sensitive configuration keys.

By default, Debezium masks the value of configuration keys that match a predefined pattern for commonly known sensitive properties such as passwords and authentication tokens. To customize the way that the connector masks configuration key values, set this property to a regular expression that matches specific configuration key values that you want to mask.

For example, to mask keys the contain the strings api.key or token, set this property to the following value:"custom.sanitize.pattern": ".**api\\.key.**\\|.**token.**"

If you set this property for the connector, Debezium masks any matching instances of configuration key values that it returns for display, logging, or in response to API calls. A string of asterisks replaces the original values, for example, ********.

| | The pattern that you specify for the custom.sanitize.pattern property overrides the default Debezium masking pattern; the specified value does not extend or augment the default pattern. | | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

database.initial.statements

Default value

No default

Description

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.

database.query.timeout.ms

Default value

600000 (10 minutes)

Description

Specifies the time, in milliseconds, that the connector waits for a query to complete.

Set the value to 0 (zero) to remove the timeout limit.

database.ssl.keystore

Default value

No default

Description

An optional setting that specifies the location of the key store file. A key store file can be used for two-way authentication between the client and the MySQL server.

database.ssl.keystore.password

Default value

No default

Description

The password for the key store file. Specify a password only if the database.ssl.keystore is configured.

database.ssl.mode

Default value

preferred

Description

Specifies whether the connector uses an encrypted connection.

The following settings are available:

disabled

Specifies the use of an unencrypted connection.

preferred

The connector establishes an encrypted connection if the server supports secure connections. If the server does not support secure connections, the connector falls back to using an unencrypted connection.

required

The connector establishes an encrypted connection. If it is unable to establish an encrypted connection, the connector fails.

verify_ca

The connector behaves as when you set the required option, but it also verifies the server TLS certificate against the configured Certificate Authority (CA) certificates. If the server TLS certificate does not match any valid CA certificates, the connector fails.

verify_identity

The connector behaves as when you set the verify_ca option, but it also verifies that the server certificate matches the host of the remote connection.

database.ssl.truststore

Default value

No default

Description

The location of the trust store file for the server certificate verification.

database.ssl.truststore.password

Default value

No default

Description

The password for the trust store file. Used to check the integrity of the truststore, and unlock the truststore.

enable.time.adjuster

Default value

true

Description

Boolean value that indicates whether the connector converts a 2-digit year specification to 4 digits. Set the value to false when conversion is fully delegated to the database.

MySQL users can insert year values with either 2-digits or 4-digits. 2-digit values are mapped to a year in the range 1970 - 2069. By default, the connector performs the conversion.

errors.max.retries

Default value

-1

Description

Specifies how the connector responds after an operation that results in a retriable error, such as a connection error.

Set one of the following options:

-1

No limit. The connector always restarts automatically, and retries the operation, regardless of the number of previous failures.

0

Disabled. The connector fails immediately, and never retries the operation. User intervention is required to restart the connector.

>0

The connector restarts automatically until it reaches the specified maximum number of retries. After the next failure, the connector stops, and user intervention is required to restart it.

event.converting.failure.handling.mode

Default value

warn

Description

Specifies how the connector responds when it cannot convert a table record due to a mismatch between the data type of a column and the type specified by the Debezium internal schema. Set one of the following options:

fail

An exception reports that conversion failed because the data type of the field did not match the schema type, and indicates that it might be necessary to restart the connector in schema _only_recovery mode to enable a successful conversion.

warn

The connector writes a null value to the event field for the column that failed conversion, writes a message to the warning log .

skip

The connector writes a null value to the event field for the column that failed conversion, and writes a message to the debug log.

event.processing.failure.handling.mode

Default value

fail

Description

Specifies how the connector handles failures that occur when processing events, for example, if it encounters a corrupted event. The following settings are available:

fail

The connector raises an exception that reports the problematic event and its position. The connector then stops.

warn

The connector does not raise an exception. Instead, it logs the problematic event and its position, and then skips the event.

ignore

The connector ignores the problematic event, and does not generate a log entry.

heartbeat.action.query

Default value

No default

Description

Specifies a query that the connector executes on the source database when the connector sends a heartbeat message.

For example, the following query periodically captures the state of the executed GTID set in the source database.

INSERT INTO gtid_history_table (select @gtid_executed)

heartbeat.interval.ms

Default value

0

Description

Specifies how frequently the connector sends heartbeat messages to a Kafka topic. By default, 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.

incremental.snapshot.allow.schema.changes

Default value

false

Description

Specifies whether the connector allows schema changes during an incremental snapshot. When the value is set to true, the connector detects schema change during an incremental snapshot, and re-select a current chunk to avoid locking DDLs.

Changes to a primary key are not supported. Changing the primary during an incremental snapshot, can lead to incorrect results. A further limitation is that if a schema change affects only the default values of columns, then the change is not detected until the DDL is processed from the binlog stream. This does not affect the values of snapshot events, but the schema of these snapshot events may have outdated defaults.

incremental.snapshot.chunk.size

Default value

1024

Description

The maximum number of rows that the connector fetches and reads into memory when it retrieves 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.

incremental.snapshot.watermarking.strategy

Default value

insert_insert

Description

Specifies the watermarking mechanism that the connector uses during an incremental snapshot to deduplicate events that might be captured by an incremental snapshot and then recaptured after streaming resumes.

You can specify one of the following options:

insert_insert (default)

When you send a signal to initiate an incremental snapshot, for every chunk that Debezium reads during the snapshot, it writes an entry to the signaling data collection to record the signal to open the snapshot window. After the snapshot completes, Debezium inserts a second entry that records the signal to close the window.

insert_delete

When you send a signal to initiate an incremental snapshot, for every chunk that Debezium reads, it writes a single entry to the signaling data collection to record the signal to open the snapshot window. After the snapshot completes, this entry is removed. No entry is created for the signal to close the snapshot window. Set this option to prevent rapid growth of the signaling data collection.

max.batch.size

Default value

2048

Description

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

max.queue.size

Default value

8192

Description

A 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 max.queue.size to a value that is larger than the value of max.batch.size.

max.queue.size.in.bytes

Default value

0

Description

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.

min.row.count.to.stream.results

Default value

1000

Description

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.

notification.enabled.channels

Default value

No default

Description

List of notification channel names that are enabled for the connector. By default, the following channels are available:

• sink
• log
• jmx

poll.interval.ms

Default value

500 (0.5 seconds)

Description

Positive integer value that specifies the number of milliseconds the connector waits for new change events to appear before it starts processing a batch of events.

read.only

Default value

false

Description

Specifies whether a connector writes watermarks to the signal data collection to track the progress of an incremental snapshot. Set the value to true to enable a connector that has a read-only connection to the database to use an incremental snapshot watermarking strategy that does not require writing to the signal data collection.

signal.data.collection

Default value

No default

Description

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>_

Collection names are case-sensitive.

signal.enabled.channels

Default value

No default

Description

List of the signaling channel names that are enabled for the connector. By default, the following channels are available:

• source
• kafka
• file
• jmx

skipped.operations

Default value

t

Description

A comma-separated list of the operation types that you want the connector to skip during streaming.

Set one of the following options to specify the operations to skip:

c

Insert/create operations.

u

Update operations.

d

Delete operations.

t

Truncate operations.

none

The connector does not skip any operations.

snapshot.delay.ms

Default value

No default

Description

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

Default value

Unset

Description

By default, during a snapshot, the connector reads table content in batches of rows. Set this property to specify the maximum number of rows in a batch.

| | To maintain connector performance, it’s best to preserve the unset default of this property. This default configuration enables MySQL to stream the result set to Debezium one row at a time. By contrast, if you set this property, performance problems can result, because Debezium attempts to fetch the entire result set into memory at once. | | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |

snapshot.include.collection.list

Default value

All tables specified in the table.include.list.

Description

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.lock.timeout.ms

Default value

10000

Description

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.

For more information, see the documentation that describes how MySQL connectors perform database snapshots.

snapshot.locking.mode

Default value

minimal

Description

Specifies whether and for how long the connector holds the global MySQL read lock, which prevents any updates to the database while the connector is performing a snapshot.

The following settings are available:

minimal

The connector holds the global read lock for only the initial phase of the snapshot during which it reads the database schemas and other metadata. During the next phase of the snapshot, the connector releases the lock as it selects all rows from each table. To perform the SELECT operation in a consistent fashion, the connector uses a REPEATABLE READ transaction. Although the release of the global read lock permits other MySQL clients to update the database, use of REPEATABLE READ isolation ensures a consistent snapshot, because the connector continues to read the same data for the duration of the transaction.

extended

Blocks all write operations for the duration of the snapshot. Use this setting if clients submit concurrent operations that are incompatible with the REPEATABLE READ isolation level in MySQL.

none

Prevents the connector from acquiring any table locks during the snapshot. Although this option is allowed with all snapshot modes, it is safe to use only if no schema changes occur while the snapshot is running. Tables that are defined with the MyISAM engine always acquire a table lock. As a result, such tables are locked even if you set this option. This behavior differs from tables that are defined by the InnoDB engine, which acquire row-level locks.

custom

The connector performs a snapshot according to the implementation specified by the snapshot.locking.mode.custom.name property, which is a custom implementation of the io.debezium.spi.snapshot.SnapshotLock interface.

snapshot.locking.mode.custom.name

Default value

No default

Description

When snapshot.locking.mode is set to custom, use this setting to specify the name of the custom implementation provided in the name() method that is defined by the 'io.debezium.spi.snapshot.SnapshotLock' interface.

For more information, see custom snapshotter SPI.

snapshot.max.threads

Default value

1

Description

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.

| | Parallel initial snapshots is an incubating feature. | | ------------------------------------------------------- |

snapshot.mode

Default value

initial

Description

Specifies the criteria for running a snapshot when the connector starts.

The following settings are available:

always

The connector performs a snapshot every time that it starts. The snapshot includes the structure and data of the captured tables. Specify this value to populate topics with a complete representation of the data from the captured tables every time that the connector starts.

initial

The connector runs a snapshot only when no offsets have been recorded for the logical server name, or if it detects that an earlier snapshot failed to complete. After the snapshot completes, the connector begins to stream event records for subsequent database changes.

initial_only

The connector runs a snapshot only when no offsets have been recorded for the logical server name. After the snapshot completes, the connector stops. It does not transition to streaming to read change events from the binlog.

schema_only

Deprecated, see no_data.

no_data

The connector runs a snapshot that captures only the schema, but not any table data. Set this option if you do not need the topics to contain a consistent snapshot of the data, but you want to capture any schema changes that were applied after the last connector restart.

schema_only_recovery

Deprecated, see recovery.

recovery

Set this option to restore a database schema history topic that is lost or corrupted. After a restart, the connector runs a snapshot that rebuilds the topic from the source tables. You can also set the property to periodically prune a database schema history topic that experiences unexpected growth.

| | Do not use this mode to perform a snapshot if schema changes were committed to the database after the last connector shutdown. | | --------------------------------------------------------------------------------------------------------------------------------- |

never

When the connector starts, rather than performing a snapshot, it immediately begins to stream event records for subsequent database changes. This option is under consideration for future deprecation, in favor of the no_data option.

when_needed

After the connector starts, it performs a snapshot only if it detects one of the following circumstances:

• It cannot detect any topic offsets.
• A previously recorded offset specifies a binlog position or GTID that is not available on the server.

configuration_based

With this option, you control snapshot behavior through a set of connector properties that have the prefix 'snapshot.mode.configuration.based'.

custom

The connector performs a snapshot according to the implementation specified by the snapshot.mode.custom.name property, which defines a custom implementation of the io.debezium.spi.snapshot.Snapshotter interface.

snapshot.mode.configuration.based.snapshot.data

Default value

false

Description

If the snapshot.mode is set to configuration_based, set this property to specify whether the connector includes table data when it performs a snapshot.

snapshot.mode.configuration.based.snapshot.on.data.error

Default value

false

Description

If the snapshot.mode is set to configuration_based, set this property to specify whether the connector includes table data in a snapshot in the event that data is no longer available in the transaction log.

snapshot.mode.configuration.based.snapshot.on.schema.error

Default value

false

Description

If the snapshot.mode is set to configuration_based, set this property to specify whether the connector includes table schema in a snapshot if the schema history topic is not available.

snapshot.mode.configuration.based.snapshot.schema

Default value

false

Description

If the snapshot.mode is set to configuration_based, set this property to specify whether the connector includes the table schema when it performs a snapshot.

snapshot.mode.configuration.based.start.stream

Default value

false

Description

If the snapshot.mode is set to configuration_based, set this property to specify whether the connector begins to stream change events after a snapshot completes.

snapshot.mode.custom.name

Default value

No default

Description

If snapshot.mode is set to custom, use this setting to specify the name of the custom implementation that is provided in the name() method that is defined in the 'io.debezium.spi.snapshot.Snapshotter' interface. After a connector restart, Debezium calls the specified custom implementation to determine whether to perform a snapshot. For more information, see custom snapshotter SPI.

snapshot.query.mode

Default value

select_all

Description

Specifies how the connector queries data while performing a snapshot.

Set one of the following options:

select_all (default)

The connector uses a select all query to retrieve rows from captured tables, optionally adjusting the columns selected based on the column include and exclude list configurations.

custom

The connector performs a snapshot query according to the implementation specified by the snapshot.query.mode.custom.name property, which defines a custom implementation of the io.debezium.spi.snapshot.SnapshotQuery interface.

This setting enables you to manage snapshot content in a more flexible manner compared to using the snapshot.select.statement.overrides property.

snapshot.query.mode.custom.name

Default value

No default

Description

When snapshot.query.mode is set as custom, use this setting to specify the name of the custom implementation provided in the name() method that is defined by the 'io.debezium.spi.snapshot.SnapshotQuery' interface. For more information, see custom snapshotter SPI.

snapshot.select.statement.overrides

Default value

No default

Description

Specifies the tables for which the connector uses custom SELECT statements to determine which rows to include in a snapshot.

This property affects snapshots only. It does not apply to events that the connector reads from the log during the streaming phase.

This property consists of two parts that work together:

Main property

A comma-separated list of fully-qualified table names in the format _<databaseName>.<tableName>_. This list identifies the tables for which you want to specify custom snapshot queries.

For example:

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

| | If the fully qualified schema or table name contains special characters, such as spaces, square brackets ([ or ]), or period characters (.), enclose the string in double quotes to prevent the connector from interpreting the special characters as delimiters. Double quotes around table names are optional if the fully qualified table name does not include spaces or special characters. | | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

Secondary properties

For each table listed in the main property, you must define a corresponding snapshot.select.statement.overrides._<databaseName>_._<tableName>_ property that specifies the custom SELECT statement to run during the snapshot.

The SELECT statement determines which rows from the table to include in the snapshot.

For example, to specify the SELECT statement for the customers.orders table:, add the following property:

snapshot.select.statement.overrides.customers.orders

| | If a table is listed in the main property but its corresponding secondary property is missing, the connector logs a warning and uses the default snapshot behavior for that table. | | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

Example configuration

The following example shows how to configure the snapshot.select.statement.overrides property to perform a snapshot of the customers.orders table that includes only records that are not soft-deleted; that is, the value of the soft-delete field, delete_flag, is set to 0.

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

snapshot.tables.order.by.row.count

Default value

disabled

Description

Specifies the order in which the connector processes tables when it performs an initial snapshot.

Set 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.

source.struct.version

Default value

v2

Description

Schema version for the source block in Debezium events. Debezium 0.10 introduced a few breaking changes to the structure of the source block in order to unify the exposed structure across all the connectors.

By setting this option to v1, the structure used in earlier versions can be produced. However, this setting is not recommended and is planned for removal in a future Debezium version.

streaming.delay.ms

Default value

0

Description

Specifies the time, in milliseconds, that the connector delays the start of the streaming process after it completes a snapshot. Setting a delay interval helps to prevent the connector from restarting snapshots in the event that a failure occurs immediately after the snapshot completes, but before the streaming process begins. Set a delay value that is higher than the value of the offset.flush.interval.ms property that is set for the Kafka Connect worker.

table.ignore.builtin

Default value

true

Description

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, changes that occur to the values in system tables are excluded from capture, and Debezium does not generate events for system table changes.

topic.cache.size

Default value

10000

Description

Specifies the number of topic names that can be stored in memory in a bounded concurrent hash map. The connector uses the cache to help determine the topic name that corresponds to a data collection.

topic.delimiter

Default value

. (period)

Description

Specifies the delimiter that the connector inserts between components of the topic name.

topic.heartbeat.prefix

Default value

__debezium-heartbeat

Description

Specifies the name of the topic to which the connector sends heartbeat messages. The topic name takes the following format:

topic.heartbeat.prefix.topic.prefix

For example, when you set the default value for this property, and the topic prefix is fulfillment, the topic name is __debezium-heartbeat.fulfillment.

topic.naming.strategy

Default value

io.debezium.schema.DefaultTopicNamingStrategy

Description

The name of the TopicNamingStrategy class that the connector uses. The specified strategy determines how the connector names the topics that store event records for data changes, schema changes, transactions, heartbeats, and so forth.

topic.transaction

Default value

transaction

Description

Specifies the name of the topic to which the connector sends transaction metadata messages. The topic name takes the following pattern:

topic.prefix.topic.transaction

For example, if the topic prefix is fulfillment, the default topic name is fulfillment.transaction.

use.nongraceful.disconnect

Default value

false

Description

A Boolean value that specifies whether the binary log client’s keepalive thread sets the SO_LINGER socket option to 0 to immediately close stale TCP connections. Set the value to true if the connector experiences deadlocks in SSLSocketImpl.close. For more information, see Issue 133 in the mysql-binlog-connector-java GitHub repository.

guardrail.collections.max

Default value

0

Description

Specifies the maximum number of tables that the connector can capture. This limit applies not only to tables being captured for data changes but also to any tables whose schemas are tracked as part of schema history. Exceeding this limit triggers the action specified by guardrail.collections.limit.action. Set this property to 0 to prevent the connector from triggering guardrail actions.

guardrail.collections.limit.action

Default value

warn

Description

Specifies the action to trigger if the number of tables that the connector captures exceeds the number that you specify in the guardrail.collections.max property. Set the property to one of the following values:

fail

The connector fails and reports an exception.

warn

The connector logs a warning.

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 18. Connector database schema history configuration properties

Pass-through MySQL connector configuration properties

You can set pass-through properties in the connector configuration to customize the behavior of the Apache Kafka producer and consumer. For information about the full range of configuration properties for Kafka producers and consumers, see the Kafka documentation.

Pass-through properties for configuring how producer and consumer clients interact with schema history topics

Debezium relies on an Apache 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.

Pass-through properties for configuring how the MySQL connector interacts with the Kafka signaling topic

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 19. Kafka signals configuration properties

Pass-through properties for configuring the Kafka consumer client for the signaling channel

The Debezium connector provides for pass-through configuration of the signals Kafka consumer. Pass-through signals properties begin with the prefix signal.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.

Pass-through properties for configuring the MySQL connector sink notification channel

The following table describes properties that you can use to configure the Debezium sink notification channel.

Table 20. Sink notification configuration properties

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.

Debezium strips the prefixes from the properties before it passes the properties to the database driver.

Monitoring

The Debezium MySQL connector provides three types of metrics that are in addition to the built-in support for JMX metrics that 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.

Customized MBean names

Debezium connectors expose metrics via the MBean name for the connector. These metrics, which are specific to each connector instance, provide data about the behavior of the connector’s snapshot, streaming, and schema history processes.

By default, when you deploy a correctly configured connector, Debezium generates a unique MBean name for each of the different connector metrics. To view the metrics for a connector process, you configure your observability stack to monitor its MBean. But these default MBean names depend on the connector configuration; configuration changes can result in changes to the MBean names. A change to the MBean name breaks the linkage between the connector instance and the MBean, disrupting monitoring activity. In this scenario, you must reconfigure the observability stack to use the new MBean name if you want to resume monitoring.

To prevent monitoring disruptions that result from MBean name changes, you can configure custom metrics tags. You configure custom metrics by adding the custom.metric.tags property to the connector configuration. The property accepts key-value pairs in which each key represents a tag for the MBean object name, and the corresponding value represents the value of that tag. For example: k1=v1,k2=v2. Debezium appends the specified tags to the MBean name of the connector.

After you configure the custom.metric.tags property for a connector, you can configure the observability stack to retrieve metrics associated with the specified tags. The observability stack then uses the specified tags, rather than the mutable MBean names to uniquely identify connectors. Later, if Debezium redefines how it constructs MBean names, or if the topic.prefix in the connector configuration changes, metrics collection is uninterrupted, because the metrics scrape task uses the specified tag patterns to identify the connector.

A further benefit of using custom tags, is that you can use tags that reflect the architecture of your data pipeline, so that metrics are organized in a way that suits you operational needs. For example, you might specify tags with values that declare the type of connector activity, the application context, or the data source, for example, db1-streaming-for-application-abc. If you specify multiple key-value pairs, all of the specified pairs are appended to the connector’s MBean name.

The following example illustrates how tags modify the default MBean name.

Example 3. How custom tags modify the connector MBean name

By default, the MySQL connector uses the following MBean name for streaming metrics:

debezium.mysql:type=connector-metrics,context=streaming,server=<topic.prefix>

If you set the value of custom.metric.tags to database=salesdb-streaming,table=inventory, Debezium generates the following custom MBean name:

debezium.mysql:type=connector-metrics,context=streaming,server=<topic.prefix>,database=salesdb-streaming,table=inventory

Snapshot metrics

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 snapshot metrics that are available.

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

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.

Streaming metrics

The Debezium MySQL connector provides three types of metrics that are in addition to the built-in support for JMX metrics that 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.

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

The following table lists the streaming metrics that are available.

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

Table 21. Descriptions of additional MySQL streaming metrics

Schema history metrics

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.

Behavior when things go wrong

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

The rest of this section describes how Debezium handles various kinds of faults and problems.

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 you are connecting to 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.

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. As a result, the replacement tasks might regenerate some events that were processed before 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 :leveloffset: +1
• 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 then 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.