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2026-04-09
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Red Hat Integration
2023.Q4
Debezium User Guide
Debezium User Guide
Preface
1. High level overview of Debezium
1.1. Debezium Features
1.2. Description of Debezium architecture
2. Required custom resource upgrades
3. Debezium connector for Db2
3.1. Overview of Debezium Db2 connector
3.2. How Debezium Db2 connectors work
3.2.1. How Debezium Db2 connectors perform database snapshots
3.2.1.1. Description of why initial snapshots capture the schema history for all tables
3.2.1.2. Capturing data from tables not captured by the initial snapshot (no schema change)
3.2.1.3. Capturing data from tables not captured by the initial snapshot (schema change)
3.2.2. Ad hoc snapshots
3.2.3. Incremental snapshots
3.2.3.1. Triggering an incremental snapshot
3.2.3.2. Using the Kafka signaling channel to trigger an incremental snapshot
3.2.3.3. Stopping an incremental snapshot
3.2.3.4. Using the Kafka signaling channel to stop an incremental snapshot
3.2.4. How Debezium Db2 connectors read change-data tables
3.2.5. Default names of Kafka topics that receive Debezium Db2 change event records
3.2.6. How Debezium Db2 connectors handle database schema changes
3.2.7. About the Debezium Db2 connector schema change topic
3.2.8. Debezium Db2 connector-generated events that represent transaction boundaries
3.3. Descriptions of Debezium Db2 connector data change events
3.3.1. About keys in Debezium db2 change events
3.3.2. About values in Debezium Db2 change events
3.4. How Debezium Db2 connectors map data types
3.5. Setting up Db2 to run a Debezium connector
3.5.1. Configuring Db2 tables for change data capture
3.5.2. Effect of Db2 capture agent configuration on server load and latency
3.5.3. Db2 capture agent configuration parameters
3.6. Deployment of Debezium Db2 connectors
3.6.1. Obtaining the Db2 JDBC driver
3.6.2. Db2 connector deployment using AMQ Streams
3.6.3. Using AMQ Streams to deploy a Debezium Db2 connector
3.6.4. Deploying a Debezium Db2 connector by building a custom Kafka Connect container image from a Dockerfile
3.6.5. Verifying that the Debezium Db2 connector is running
3.6.6. Descriptions of Debezium Db2 connector configuration properties
3.7. Monitoring Debezium Db2 connector performance
3.7.1. Monitoring Debezium during snapshots of Db2 databases
3.7.2. Monitoring Debezium Db2 connector record streaming
3.7.3. Monitoring Debezium Db2 connector schema history
3.8. Managing Debezium Db2 connectors
3.9. Updating schemas for Db2 tables in capture mode for Debezium connectors
3.9.1. Performing offline schema updates for Debezium Db2 connectors
3.9.2. Performing online schema updates for Debezium Db2 connectors
4. Debezium connector for JDBC (Developer Preview)
4.1. How the Debezium JDBC connector works
4.1.1. Description of how the Debezium JDBC connector consumes complex change events
4.1.2. Description of Debezium JDBC connector at-least-once delivery
4.1.3. Description of Debezium JDBC use of multiple tasks
4.1.4. Description of Debezium JDBC connector data and column type mappings
4.1.5. Description of how the Debezium JDBC connector handles primary keys in source events
4.1.6. Configuring the Debezium JDBC connector to delete rows when consuming DELETE or tombstone events
4.1.7. Enabling the connector to perform idempotent writes
4.1.8. Schema evolution modes for the Debezium JDBC connector
4.1.9. Specifying options to define the letter case of destination table and column names
4.2. How the Debezium JDBC connector maps data types
4.3. Deployment of Debezium JDBC connectors
4.3.1. Debezium JDBC connector configuration
4.4. Descriptions of Debezium JDBC connector configuration properties
4.5. JDBC connector frequently asked questions
5. Debezium connector for MongoDB
5.1. Overview of Debezium MongoDB connector
5.1.1. Description of how the MongoDB connector uses change streams to capture event records
5.2. How Debezium MongoDB connectors work
5.2.1. MongoDB topologies supported by Debezium connectors
5.2.2. How Debezium MongoDB connectors use logical names for replica sets and sharded clusters
5.2.3. How Debezium MongoDB connectors perform snapshots
5.2.4. Ad hoc snapshots
5.2.5. Incremental snapshots
5.2.5.1. Triggering an incremental snapshot
5.2.5.2. Using the Kafka signaling channel to trigger an incremental snapshot
5.2.5.3. Stopping an incremental snapshot
5.2.5.4. Using the Kafka signaling channel to stop an incremental snapshot
5.2.6. How the Debezium MongoDB connector streams change event records
5.2.7. MongoDB support for populating the before field in Debezium change event
5.2.8. Default names of Kafka topics that receive Debezium MongoDB change event records
5.2.9. How event keys control topic partitioning for the Debezium MongoDB connector
5.2.10. Debezium MongoDB connector-generated events that represent transaction boundaries
5.3. Descriptions of Debezium MongoDB connector data change events
5.3.1. About keys in Debezium MongoDB change events
5.3.2. About values in Debezium MongoDB change events
5.4. Setting up MongoDB to work with a Debezium connector
5.5. Deployment of Debezium MongoDB connectors
5.5.1. MongoDB connector deployment using AMQ Streams
5.5.2. Using AMQ Streams to deploy a Debezium MongoDB connector
5.5.3. Deploying a Debezium MongoDB connector by building a custom Kafka Connect container image from a Dockerfile
5.5.4. Verifying that the Debezium MongoDB connector is running
5.5.5. Descriptions of Debezium MongoDB connector configuration properties
5.6. Monitoring Debezium MongoDB connector performance
5.6.1. Monitoring Debezium during MongoDB snapshots
5.6.2. Monitoring Debezium MongoDB connector record streaming
5.7. How Debezium MongoDB connectors handle faults and problems
6. Debezium connector for MySQL
6.1. How Debezium MySQL connectors work
6.1.1. MySQL topologies supported by Debezium connectors
6.1.2. How Debezium MySQL connectors handle database schema changes
6.1.3. How Debezium MySQL connectors expose database schema changes
6.1.4. How Debezium MySQL connectors perform database snapshots
6.1.4.1. Initial snapshots that use a global read lock
6.1.4.2. Initial snapshots that use table-level locks
6.1.4.3. Description of why initial snapshots capture the schema history for all tables
6.1.4.4. Capturing data from tables not captured by the initial snapshot (no schema change)
6.1.4.5. Capturing data from tables not captured by the initial snapshot (schema change)
6.1.5. Ad hoc snapshots
6.1.6. Incremental snapshots
6.1.6.1. Triggering an incremental snapshot
6.1.6.2. Using the Kafka signaling channel to trigger an incremental snapshot
6.1.6.3. Stopping an incremental snapshot
6.1.6.4. Using the Kafka signaling channel to stop an incremental snapshot
6.1.7. Default names of Kafka topics that receive Debezium MySQL change event records
6.2. Descriptions of Debezium MySQL connector data change events
6.2.1. About keys in Debezium MySQL change events
6.2.2. About values in Debezium MySQL change events
6.3. How Debezium MySQL connectors map data types
6.4. Setting up MySQL to run a Debezium connector
6.4.1. Creating a MySQL user for a Debezium connector
6.4.2. Enabling the MySQL binlog for Debezium
6.4.3. Enabling MySQL Global Transaction Identifiers for Debezium
6.4.4. Configuring MySQL session timesouts for Debezium
6.4.5. Enabling query log events for Debezium MySQL connectors
6.4.6. validate binlog row value options for Debezium MySQL connectors
6.5. Deployment of Debezium MySQL connectors
6.5.1. MySQL connector deployment using AMQ Streams
6.5.2. Using AMQ Streams to deploy a Debezium MySQL connector
6.5.3. Deploying Debezium MySQL connectors by building a custom Kafka Connect container image from a Dockerfile
6.5.4. Verifying that the Debezium MySQL connector is running
6.5.5. Descriptions of Debezium MySQL connector configuration properties
6.6. Monitoring Debezium MySQL connector performance
6.6.1. Monitoring Debezium during snapshots of MySQL databases
6.6.2. Monitoring Debezium MySQL connector record streaming
6.6.3. Monitoring Debezium MySQL connector schema history
6.7. How Debezium MySQL connectors handle faults and problems
7. Debezium Connector for Oracle
7.1. How Debezium Oracle connectors work
7.1.1. How Debezium Oracle connectors perform database snapshots
7.1.1.1. Description of why initial snapshots capture the schema history for all tables
7.1.1.2. Capturing data from tables not captured by the initial snapshot (no schema change)
7.1.1.3. Capturing data from tables not captured by the initial snapshot (schema change)
7.1.2. Ad hoc snapshots
7.1.3. Incremental snapshots
7.1.3.1. Triggering an incremental snapshot
7.1.3.2. Using the Kafka signaling channel to trigger an incremental snapshot
7.1.3.3. Stopping an incremental snapshot
7.1.3.4. Using the Kafka signaling channel to stop an incremental snapshot
7.1.4. Default names of Kafka topics that receive Debezium Oracle change event records
7.1.5. How Debezium Oracle connectors handle database schema changes
7.1.6. How Debezium Oracle connectors expose database schema changes
7.1.7. Debezium Oracle connector-generated events that represent transaction boundaries
7.1.7.1. How the Debezium Oracle connector enriches change event messages with transaction metadata
7.1.8. How the Debezium Oracle connector uses event buffering
7.1.9. How the Debezium Oracle connector detects gaps in SCN values
7.1.10. How Debezium manages offsets in databases that change infrequently
7.2. Descriptions of Debezium Oracle connector data change events
7.2.1. About keys in Debezium Oracle connector change events
7.2.2. About values in Debezium Oracle connector change events
7.3. How Debezium Oracle connectors map data types
7.4. Setting up Oracle to work with Debezium
7.4.1. Compatibility of the Debezium Oracle connector with Oracle installation types
7.4.2. Schemas that the Debezium Oracle connector excludes when capturing change events
7.4.3. Tables that the Debezium Oracle connector excludes when capturing change events
7.4.4. Preparing Oracle databases for use with Debezium
7.4.5. Resizing Oracle redo logs to accommodate the data dictionary
7.4.6. Creating an Oracle user for the Debezium Oracle connector
7.4.7. Support for Oracle standby databases
7.5. Deployment of Debezium Oracle connectors
7.5.1. Obtaining the Oracle JDBC driver
7.5.2. Debezium Oracle connector deployment using AMQ Streams
7.5.3. Using AMQ Streams to deploy a Debezium Oracle connector
7.5.4. Deploying a Debezium Oracle connector by building a custom Kafka Connect container image from a Dockerfile
7.5.5. Configuration of container databases and non-container-databases
7.5.6. Verifying that the Debezium Oracle connector is running
7.6. Descriptions of Debezium Oracle connector configuration properties
7.7. Monitoring Debezium Oracle connector performance
7.7.1. Debezium Oracle connector snapshot metrics
7.7.2. Debezium Oracle connector streaming metrics
7.7.3. Debezium Oracle connector schema history metrics
7.8. Oracle connector frequently asked questions
8. Debezium connector for PostgreSQL
8.1. Overview of Debezium PostgreSQL connector
8.2. How Debezium PostgreSQL connectors work
8.2.1. Security for PostgreSQL connector
8.2.2. How Debezium PostgreSQL connectors perform database snapshots
8.2.3. Ad hoc snapshots
8.2.4. Incremental snapshots
8.2.4.1. Triggering an incremental snapshot
8.2.4.2. Using the Kafka signaling channel to trigger an incremental snapshot
8.2.4.3. Stopping an incremental snapshot
8.2.4.4. Using the Kafka signaling channel to stop an incremental snapshot
8.2.5. How Debezium PostgreSQL connectors stream change event records
8.2.6. Default names of Kafka topics that receive Debezium PostgreSQL change event records
8.2.7. Debezium PostgreSQL connector-generated events that represent transaction boundaries
8.3. Descriptions of Debezium PostgreSQL connector data change events
8.3.1. About keys in Debezium PostgreSQL change events
8.3.2. About values in Debezium PostgreSQL change events
8.4. How Debezium PostgreSQL connectors map data types
8.5. Setting up PostgreSQL to run a Debezium connector
8.5.1. Configuring a replication slot for the Debezium pgoutput plug-in
8.5.2. Setting up PostgreSQL permissions for the Debezium connector
8.5.3. Setting privileges to enable Debezium to create PostgreSQL publications
8.5.4. Configuring PostgreSQL to allow replication with the Debezium connector host
8.5.5. Configuring PostgreSQL to manage Debezium WAL disk space consumption
8.5.6. Upgrading PostgreSQL databases that Debezium captures from
8.6. Deployment of Debezium PostgreSQL connectors
8.6.1. PostgreSQL connector deployment using AMQ Streams
8.6.2. Using AMQ Streams to deploy a Debezium PostgreSQL connector
8.6.3. Deploying a Debezium PostgreSQL connector by building a custom Kafka Connect container image from a Dockerfile
8.6.4. Verifying that the Debezium PostgreSQL connector is running
8.6.5. Descriptions of Debezium PostgreSQL connector configuration properties
8.7. Monitoring Debezium PostgreSQL connector performance
8.7.1. Monitoring Debezium during snapshots of PostgreSQL databases
8.7.2. Monitoring Debezium PostgreSQL connector record streaming
8.8. How Debezium PostgreSQL connectors handle faults and problems
9. Debezium connector for SQL Server
9.1. Overview of Debezium SQL Server connector
9.2. How Debezium SQL Server connectors work
9.2.1. How Debezium SQL Server connectors perform database snapshots
9.2.1.1. Description of why initial snapshots capture the schema history for all tables
9.2.1.2. Capturing data from tables not captured by the initial snapshot (no schema change)
9.2.1.3. Capturing data from tables not captured by the initial snapshot (schema change)
9.2.2. Ad hoc snapshots
9.2.3. Incremental snapshots
9.2.3.1. Triggering an incremental snapshot
9.2.3.2. Using the Kafka signaling channel to trigger an incremental snapshot
9.2.3.3. Stopping an incremental snapshot
9.2.3.4. Using the Kafka signaling channel to stop an incremental snapshot
9.2.4. How Debezium SQL Server connectors read change data tables
9.2.5. No maximum LSN recorded in the database
9.2.6. Limitations of Debezium SQL Server connector
9.2.7. Default names of Kafka topics that receive Debezium SQL Server change event records
9.2.8. How Debezium SQL Server connectors handle database schema changes
9.2.9. How the Debezium SQL Server connector uses the schema change topic
9.2.10. Descriptions of Debezium SQL Server connector data change events
9.2.10.1. About keys in Debezium SQL Server change events
9.2.10.2. About values in Debezium SQL Server change events
9.2.11. Debezium SQL Server connector-generated events that represent transaction boundaries
9.2.11.1. Change data event enrichment
9.2.12. How Debezium SQL Server connectors map data types
9.3. Setting up SQL Server to run a Debezium connector
9.3.1. Enabling CDC on the SQL Server database
9.3.2. Enabling CDC on a SQL Server table
9.3.3. Verifying that the user has access to the CDC table
9.3.4. SQL Server on Azure
9.3.5. Effect of SQL Server capture job agent configuration on server load and latency
9.3.6. SQL Server capture job agent configuration parameters
9.4. Deployment of Debezium SQL Server connectors
9.4.1. SQL Server connector deployment using AMQ Streams
9.4.2. Using AMQ Streams to deploy a Debezium SQL Server connector
9.4.3. Deploying a Debezium SQL Server connector by building a custom Kafka Connect container image from a Dockerfile
9.4.4. Descriptions of Debezium SQL Server connector configuration properties
9.5. Refreshing capture tables after a schema change
9.5.1. Running an offline update after a schema change
9.5.2. Running an online update after a schema change
9.6. Monitoring Debezium SQL Server connector performance
9.6.1. Debezium SQL Server connector snapshot metrics
9.6.2. Debezium SQL Server connector streaming metrics
9.6.3. Debezium SQL Server connector schema history metrics
10. Monitoring Debezium
10.1. Metrics for monitoring Debezium connectors
10.2. Enabling JMX in local installations
10.2.1. Zookeeper JMX environment variables
10.2.2. Kafka JMX environment variables
10.2.3. Kafka Connect JMX environment variables
10.3. Monitoring Debezium on OpenShift
11. Debezium logging
11.1. Debezium logging concepts
11.2. Default Debezium logging configuration
11.3. Configuring Debezium logging
11.3.1. Changing the Debezium logging level by configuring loggers
11.3.2. Dynamically changing the Debezium logging level with the Kafka Connect API
11.3.3. Changing the Debezium logging levely by adding mapped diagnostic contexts
11.4. Debezium logging on OpenShift
12. Configuring Debezium connectors for your application
12.1. Customization of Kafka Connect automatic topic creation
12.1.1. Disabling automatic topic creation for the Kafka broker
12.1.2. Configuring automatic topic creation in Kafka Connect
12.1.3. Configuration of automatically created topics
12.1.3.1. Topic creation groups
12.1.3.2. Topic creation group configuration properties
12.1.3.3. Specifying the configuration for the Debezium default topic creation group
12.1.3.4. Specifying the configuration for Debezium custom topic creation groups
12.1.3.5. Registering Debezium custom topic creation groups
12.2. Configuring Debezium connectors to use Avro serialization
12.2.1. About the Service Registry
12.2.2. Overview of deploying a Debezium connector that uses Avro serialization
12.2.3. Deploying connectors that use Avro in Debezium containers
12.2.4. About Avro name requirements
12.3. Emitting Debezium change event records in CloudEvents format
12.3.1. Example Debezium change event records in CloudEvents format
12.3.2. Example of configuring Debezium CloudEvents converter
12.3.3. Debezium CloudEvents converter configuration options
12.4. Configuring notifications to report connector status
12.4.1. Description of the format of Debezium notifications
12.4.2. Types of Debezium notifications
12.4.2.1. Example: Debezium notifications that report on the progress of incremental snapshots
12.4.3. Enabling Debezium to emit events to notification channels
12.4.3.1. Enabling Debezium notifications to report events exposed through JMX beans
12.5. Sending signals to a Debezium connector
12.5.1. Enabling Debezium source signaling channel
12.5.1.1. Required structure of a Debezium signaling data collection
12.5.1.2. Creating a Debezium signaling data collection
12.5.2. Enabling the Debezium Kafka signaling channel
12.5.3. Enabling the Debezium JMX signaling channel
12.5.4. Types of Debezium signal actions
12.5.4.1. Logging signals
12.5.4.2. Ad hoc snapshot signals
12.5.4.3. Incremental snapshots
13. Applying transformations to modify messages exchanged with Apache Kafka
13.1. Applying transformations selectively with SMT predicates
13.1.1. About SMT predicates
13.1.2. Defining SMT predicates
13.1.3. Ignoring tombstone events
13.2. Routing Debezium event records to topics that you specify
13.2.1. Use case for routing Debezium records to topics that you specify
13.2.2. Example of routing Debezium records for multiple tables to one topic
13.2.3. Ensuring unique keys across Debezium records routed to the same topic
13.2.4. Options for applying the topic routing transformation selectively
13.2.5. Options for configuring Debezium topic routing transformation
13.3. Routing change event records to topics according to event content
13.3.1. Setting up the Debezium content-based-routing SMT
13.3.2. Example: Debezium basic content-based routing configuration
13.3.3. Variables for use in Debezium content-based routing expressions
13.3.4. Options for applying the content-based routing transformation selectively
13.3.5. Configuration of content-based routing conditions for other scripting languages
13.3.6. Options for configuring the content-based routing transformation
13.4. Extracting field-level changes from Debezium event records
13.4.1. Description of Debezium change event structure
13.4.2. Behavior of the Debezium event changes SMT
13.4.3. Configuration of the Debezium event changes SMT
13.4.4. Options for applying the event changes transformation selectively
13.4.5. Descriptions of the configuration options for the Debezium event changes SMT
13.5. Filtering Debezium change event records
13.5.1. Setting up the Debezium filter SMT
13.5.2. Example: Debezium basic filter SMT configuration
13.5.3. Variables for use in filter expressions
13.5.4. Options for applying the filter transformation selectively
13.5.5. Filter condition configuration for other scripting languages
13.5.6. Options for configuring filter transformation
13.6. Converting message headers into event record values
13.6.1. Example: Basic configuration of the Debezium HeaderToValue SMT
13.6.2. Options for configuring the HeaderToValue transformation
13.7. Extracting source record after state from Debezium change events
13.7.1. Description of Debezium change event structure
13.7.2. Behavior of Debezium event flattening transformation
13.7.3. Configuration of Debezium event flattening transformation
13.7.4. Example of adding Debezium metadata to the Kafka record
13.7.5. Options for applying the event flattening transformation selectively
13.7.6. Options for configuring Debezium event flattening transformation
13.8. Extracting the source document after state from Debezium MongoDB change events
13.8.1. Description of Debezium MongoDB change event structure
13.8.2. Behavior of the Debezium MongoDB event flattening transformation
13.8.3. Configuration of the Debezium MongoDB event flattening transformation
13.8.3.1. Example: Basic configuration of the Debezium MongoDB event flattening-transformation
13.8.4. Options for encoding arrays in MongoDB event messages
13.8.5. Flattening nested structures in a MongoDB event message
13.8.6. How the Debezium MongoDB connector reports the names of fields removed by $unset operations
13.8.7. Determining the type of the original database operation
13.8.8. Using the MongoDB event flattening SMT to add Debezium metadata to Kafka records
13.8.9. Options for applying the MongoDB extract new document state transformation selectively
13.8.10. Configuration options for the Debezium event flattening transformation for MongoDB
13.9. Configuring Debezium connectors to use the outbox pattern
13.9.1. Example of a Debezium outbox message
13.9.2. Outbox table structure expected by Debezium outbox event router SMT
13.9.3. Basic Debezium outbox event router SMT configuration
13.9.4. Options for applying the Outbox event router transformation selectively
13.9.5. Using Avro as the payload format in Debezium outbox messages
13.9.6. Emitting additional fields in Debezium outbox messages
13.9.7. Expanding escaped JSON String as JSON
13.9.8. Options for configuring outbox event router transformation
13.10. Configuring Debezium MongoDB connectors to use the outbox pattern
13.10.1. Example of a Debezium MongoDB outbox message
13.10.2. Outbox collection structure expected by Debezium mongodb outbox event router SMT
13.10.3. Basic Debezium MongoDB outbox event router SMT configuration
13.10.4. Options for applying the MongoDB outbox event router transformation selectively
13.10.5. Using Avro as the payload format in Debezium MongoDB outbox messages
13.10.6. Emitting additional fields in Debezium MongoDB outbox messages
13.10.7. Expanding escaped JSON String as JSON
13.10.8. Options for configuring outbox event router transformation
13.11. Routing records to partitions based on payload fields
13.11.1. Example: Basic configuration of the Debezium partition routing SMT
13.11.2. Example: Advanced configuration of the Debezium partition routing SMT
13.11.3. Migrating from the Debezium ComputePartition SMT
13.11.4. Options for configuring the partition routing transformation
14. Developing Debezium custom data type converters
14.1. Creating a Debezium custom data type converter
14.1.1. Debezium custom converter example
14.1.2. Debezium and Kafka Connect API module dependencies
14.2. Using custom converters with Debezium connectors
14.2.1. Deploying a custom converter
14.2.2. Configuring a connector to use a custom converter
Legal Notice
Debezium User Guide
Red Hat Integration
2023.q4
For use with Red Hat Integration 2.3.4
Red Hat Integration Documentation Team
This content is not included.
fuse-docs-support@redhat.com
Legal Notice
Abstract
This guide describes how to use the connectors provided with Red Hat Integration.
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