Debezium User Guide


Red Hat build of Debezium 2.3.7

For use with Red Hat build of Debezium 2.3.7

Red Hat build of Debezium Documentation Team

Abstract

This guide describes how to use the connectors provided with Red Hat build of Debezium.

Preface

Debezium is a set of distributed services that capture row-level changes in your databases so that your applications can see and respond to those changes. Debezium records all row-level changes committed to each database table. Each application reads the transaction logs of interest to view all operations in the order in which they occurred.

This guide provides details about using the following Debezium topics:

Making open source more inclusive

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Chapter 1. High level overview of Debezium

Debezium is a set of distributed services that capture changes in your databases. Your applications can consume and respond to those changes. Debezium captures each row-level change in each database table in a change event record and streams these records to Kafka topics. Applications read these streams, which provide the change event records in the same order in which they were generated.

More details are in the following sections:

1.1. Debezium Features

Debezium is a set of source connectors for Apache Kafka Connect. Each connector ingests changes from a different database by using that database’s features for change data capture (CDC). Unlike other approaches, such as polling or dual writes, log-based CDC as implemented by Debezium:

  • Ensures that all data changes are captured.
  • Produces change events with a very low delay while avoiding increased CPU usage required for frequent polling. For example, for MySQL or PostgreSQL, the delay is in the millisecond range.
  • Requires no changes to your data model, such as a "Last Updated" column.
  • Can capture deletes.
  • Can capture old record state and additional metadata such as transaction ID and causing query, depending on the database’s capabilities and configuration.

Five Advantages of Log-Based Change Data Capture is a blog post that provides more details.

Debezium connectors capture data changes with a range of related capabilities and options:

  • Snapshots: optionally, an initial snapshot of a database’s current state can be taken if a connector is started and not all logs still exist. Typically, this is the case when the database has been running for some time and has discarded transaction logs that are no longer needed for transaction recovery or replication. There are different modes for performing snapshots, including support for incremental snapshots, which can be triggered at connector runtime. For more details, see the documentation for the connector that you are using.
  • Filters: you can configure the set of captured schemas, tables and columns with include/exclude list filters.
  • Masking: the values from specific columns can be masked, for example, when they contain sensitive data.
  • Monitoring: most connectors can be monitored by using JMX.
  • Ready-to-use single message transformations (SMTs) for message routing, filtering, event flattening, and more. For more information about the SMTs that Debezium provides, see Applying transformations to modify messages exchanged with Apache Kafka.

The documentation for each connector provides details about the connectors features and configuration options.

1.2. Description of Debezium architecture

You deploy Debezium by means of Apache Kafka Connect. Kafka Connect is a framework and runtime for implementing and operating:

  • Source connectors such as Debezium that send records into Kafka
  • Sink connectors that propagate records from Kafka topics to other systems

The following image shows the architecture of a change data capture pipeline based on Debezium:

Debezium Architecture

As shown in the image, the Debezium connectors for MySQL and PostgresSQL are deployed to capture changes to these two types of databases. Each Debezium connector establishes a connection to its source database:

  • The MySQL connector uses a client library for accessing the binlog.
  • The PostgreSQL connector reads from a logical replication stream.

Kafka Connect operates as a separate service besides the Kafka broker.

By default, changes from one database table are written to a Kafka topic whose name corresponds to the table name. If needed, you can adjust the destination topic name by configuring Debezium’s topic routing transformation. For example, you can:

  • Route records to a topic whose name is different from the table’s name
  • Stream change event records for multiple tables into a single topic

After change event records are in Apache Kafka, different connectors in the Kafka Connect eco-system can stream the records to other systems and databases such as Elasticsearch, data warehouses and analytics systems, or caches such as Infinispan. Depending on the chosen sink connector, you might need to configure Debezium’s new record state extraction transformation. This Kafka Connect SMT propagates the after structure from Debezium’s change event to the sink connector. This is in place of the verbose change event record that is propagated by default.

Chapter 2. Required custom resource upgrades

Debezium is a Kafka connector plugin that is deployed to an Apache Kafka cluster that runs on AMQ Streams on OpenShift. To prepare for OpenShift CRD v1, in the current version of AMQ Streams the required version of the custom resource definitions (CRD) API is now set to v1beta2. The v1beta2 version of the API replaces the previously supported v1beta1 and v1alpha1 API versions. Support for the v1alpha1 and v1beta1 API versions is now deprecated in AMQ Streams. Those earlier versions are now removed from most AMQ Streams custom resources, including the KafkaConnect and KafkaConnector resources that you use to configure Debezium connectors.

The CRDs that are based on the v1beta2 API version use the OpenAPI structural schema. Custom resources based on the superseded v1alpha1 or v1beta1 APIs do not support structural schemas, and are incompatible with the current version of AMQ Streams. Before you upgrade to AMQ Streams2.5, you must upgrade existing custom resources to use API version kafka.strimzi.io/v1beta2. You can upgrade custom resources any time after you upgrade to AMQ Streams 1.7. You must complete the upgrade to the v1beta2 API before you upgrade to AMQ Streams2.5 or newer.

To facilitate the upgrade of CRDs and custom resources, AMQ Streams provides an API conversion tool that automatically upgrades them to a format that is compatible with v1beta2. For more information about the tool and about upgrading AMQ Streams, see Upgrading from an AMQ Streams version earlier than 1.7 in Deploying and Managing AMQ Streams on OpenShift.

Note

The requirement to update custom resources applies only to Debezium deployments that run on AMQ Streams on OpenShift. The requirement does not apply to Debezium on Red Hat Enterprise Linux.

Chapter 3. Debezium connector for Db2

Debezium’s Db2 connector can capture row-level changes in the tables of a Db2 database. For information about the Db2 Database versions that are compatible with this connector, see the Debezium Supported Configurations page.

This connector is strongly inspired by the Debezium implementation of SQL Server, which uses a SQL-based polling model that puts tables into "capture mode". When a table is in capture mode, the Debezium Db2 connector generates and streams a change event for each row-level update to that table.

A table that is in capture mode has an associated change-data table, which Db2 creates. For each change to a table that is in capture mode, Db2 adds data about that change to the table’s associated change-data table. A change-data table contains an entry for each state of a row. It also has special entries for deletions. The Debezium Db2 connector reads change events from change-data tables and emits the events to Kafka topics.

The first time a Debezium Db2 connector connects to a Db2 database, the connector reads a consistent snapshot of the tables for which the connector is configured to capture changes. By default, this is all non-system tables. There are connector configuration properties that let you specify which tables to put into capture mode, or which tables to exclude from capture mode.

When the snapshot is complete the connector begins emitting change events for committed updates to tables that are in capture mode. By default, change events for a particular table go to a Kafka topic that has the same name as the table. Applications and services consume change events from these topics.

Note

The connector requires the use of the abstract syntax notation (ASN) libraries, which are available as a standard part of Db2 for Linux. To use the ASN libraries, you must have a license for IBM InfoSphere Data Replication (IIDR). You do not have to install IIDR to use the ASN libraries.

Information and procedures for using a Debezium Db2 connector is organized as follows:

3.1. Overview of Debezium Db2 connector

The Debezium Db2 connector is based on the ASN Capture/Apply agents that enable SQL Replication in Db2. A capture agent:

  • Generates change-data tables for tables that are in capture mode.
  • Monitors tables in capture mode and stores change events for updates to those tables in their corresponding change-data tables.

The Debezium connector uses a SQL interface to query change-data tables for change events.

The database administrator must put the tables for which you want to capture changes into capture mode. For convenience and for automating testing, there are Debezium management user-defined functions (UDFs) in C that you can compile and then use to do the following management tasks:

  • Start, stop, and reinitialize the ASN agent
  • Put tables into capture mode
  • Create the replication (ASN) schemas and change-data tables
  • Remove tables from capture mode

Alternatively, you can use Db2 control commands to accomplish these tasks.

After the tables of interest are in capture mode, the connector reads their corresponding change-data tables to obtain change events for table updates. The connector emits a change event for each row-level insert, update, and delete operation to a Kafka topic that has the same name as the changed table. This is default behavior that you can modify. Client applications read the Kafka topics that correspond to the database tables of interest and can react to each row-level change event.

Typically, the database administrator puts a table into capture mode in the middle of the life of a table. This means that the connector does not have the complete history of all changes that have been made to the table. Therefore, when the Db2 connector first connects to a particular Db2 database, it starts by performing a consistent snapshot of each table that is in capture mode. After the connector completes the snapshot, the connector streams change events from the point at which the snapshot was made. In this way, the connector starts with a consistent view of the tables that are in capture mode, and does not drop any changes that were made while it was performing the snapshot.

Debezium connectors are tolerant of failures. As the connector reads and produces change events, it records the log sequence number (LSN) of the change-data table entry. The LSN is the position of the change event in the database log. If the connector stops for any reason, including communication failures, network problems, or crashes, upon restarting it continues reading the change-data tables where it left off. This includes snapshots. That is, if the snapshot was not complete when the connector stopped, upon restart the connector begins a new snapshot.

3.2. How Debezium Db2 connectors work

To optimally configure and run a Debezium Db2 connector, it is helpful to understand how the connector performs snapshots, streams change events, determines Kafka topic names, and handles schema changes.

Details are in the following topics:

3.2.1. How Debezium Db2 connectors perform database snapshots

Db2`s replication feature is not designed to store the complete history of database changes. As a result, the Debezium Db2 connector cannot retrieve the entire history of the database from the logs. To enable the connector to establish a baseline for the current state of the database, the first time that the connector starts, it performs an initial consistent snapshot of the tables that are in capture mode. For each change that the snapshot captures, the connector emits a read event to the Kafka topic for the captured table.

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

Default workflow that the Debezium Db2 connector uses to perform an initial snapshot

The following workflow lists the steps that Debezium takes to create a snapshot. These steps describe the process for a snapshot when the snapshot.mode configuration property is set to its default value, which is initial. 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.

  1. Establish a connection to the database.
  2. Determine which tables are in capture mode and should be included in the snapshot. By default, the connector captures the data for all non-system tables. After the snapshot completes, the connector continues to stream data for the specified tables. If you want the connector to capture data only from specific tables you can direct the connector to capture the data for only a subset of tables or table elements by setting properties such as table.include.list or table.exclude.list.
  3. Obtain a lock on each of the tables in capture mode. This lock ensures that no schema changes can occur in those tables until the snapshot completes. The level of the lock is determined by the snapshot.isolation.mode connector configuration property.
  4. Read the highest (most recent) LSN position in the server’s transaction log.
  5. Capture the schema of all tables or all tables that are designated for capture. The connector persists schema information in its internal database schema history topic. The schema history provides information about the structure that is in effect when a change event occurs.

    Note

    By default, the connector captures the schema of every table in the database that is in capture mode, including tables that are not configured for capture. If tables are not configured for capture, the initial snapshot captures only their structure; it does not capture any table data.

    For more information about why snapshots persist schema information for tables that you did not include in the initial snapshot, see Understanding why initial snapshots capture the schema for all tables.

  6. Release any locks obtained in Step 3. Other database clients can now write to any previously locked tables.
  7. At the LSN position read in Step 4, the connector scans the tables that are designated for capture. During the scan, the connector completes the following tasks:

    1. Confirms that the table was created before the snapshot began. If the table was created after the snapshot began, the connector skips the table. After the snapshot is complete, and the connector transitions to streaming, it emits change events for any tables that were created after the snapshot began.
    2. Produces a read event for each row that is captured from a table. All read events contain the same LSN position, which is the LSN position that was obtained in step 4.
    3. Emits each read event to the Kafka topic for the source table.
    4. Releases data table locks, if applicable.
  8. Record the successful completion of the snapshot in the connector offsets.

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

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

After the connector completes the initial snapshot, it continues streaming from the position that it read in Step 4 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.

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

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

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

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

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

Additional information

3.2.1.2. 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

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. Clear the offsets in the configured Kafka Connect offset.storage.topic. For more information about how to remove offsets, see the Debezium community FAQ.

    Warning

    Removing offsets should be performed only by advanced users who have experience in manipulating internal Kafka Connect data. This operation is potentially destructive, and should be performed only as a last resort.

  4. Apply the following changes to the connector configuration:

    1. (Optional) Set the value of schema.history.internal.captured.tables.ddl to false. This setting causes the snapshot to capture the schema for all tables, and guarantees that, in the future, the connector can reconstruct the schema history for all tables.

      Note

      Snapshots that capture the schema for all tables require more time to complete.

    2. Add the tables that you want the connector to capture to table.include.list.
    3. Set the snapshot.mode to one of the following values:

      initial
      When you restart the connector, it takes a full snapshot of the database that captures the table data and table structures.
      If you select this option, consider setting the value of the schema.history.internal.captured.tables.ddl property to false to enable the connector to capture the schema of all tables.
      schema_only
      When you restart the connector, it takes a snapshot that captures only the table schema. Unlike a full data snapshot, this option does not capture any table data. Use this option if you want to restart the connector more quickly than with a full snapshot.
  5. Restart the connector. The connector completes the type of snapshot specified by the snapshot.mode.
  6. (Optional) If the connector performed a schema_only snapshot, after the snapshot completes, initiate an incremental snapshot to capture data from the tables that you added. The connector runs the snapshot while it continues to stream real-time changes from the tables. Running an incremental snapshot captures the following data changes:

    • For tables that the connector previously captured, the incremental snapsot captures changes that occur while the connector was down, that is, in the interval between the time that the connector was stopped, and the current restart.
    • For newly added tables, the incremental snapshot captures all existing table rows.
3.2.1.3. 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.

    Warning

    Removing offsets should be performed only by advanced users who have experience in manipulating internal Kafka Connect data. This operation is potentially destructive, and should be performed only as a last resort.

  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 schema_only.
    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.

    Warning

    Removing offsets should be performed only by advanced users who have experience in manipulating internal Kafka Connect data. This operation is potentially destructive, and should be performed only as a last resort.

  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.

3.2.2. Ad hoc snapshots

By default, a connector runs an initial snapshot operation only after it starts for the first time. Following this initial snapshot, under normal circumstances, the connector does not repeat the snapshot process. Any future change event data that the connector captures comes in through the streaming process only.

However, in some situations the data that the connector obtained during the initial snapshot might become stale, lost, or incomplete. To provide a mechanism for recapturing table data, Debezium includes an option to perform ad hoc snapshots. The following changes in a database might be cause for performing an ad hoc snapshot:

  • The connector configuration is modified to capture a different set of tables.
  • Kafka topics are deleted and must be rebuilt.
  • Data corruption occurs due to a configuration error or some other problem.

You can re-run a snapshot for a table for which you previously captured a snapshot by initiating a so-called ad-hoc snapshot. Ad hoc snapshots require the use of signaling tables. You initiate an ad hoc snapshot by sending a signal request to the Debezium signaling table.

When you initiate an ad hoc snapshot of an existing table, the connector appends content to the topic that already exists for the table. If a previously existing topic was removed, Debezium can create a topic automatically if automatic topic creation is enabled.

Ad hoc snapshot signals specify the tables to include in the snapshot. The snapshot can capture the entire contents of the database, or capture only a subset of the tables in the database. Also, the snapshot can capture a subset of the contents of the table(s) in the database.

You specify the tables to capture by sending an execute-snapshot message to the signaling table. Set the type of the execute-snapshot signal to incremental, and provide the names of the tables to include in the snapshot, as described in the following table:

Table 3.1. Example of an ad hoc execute-snapshot signal record
FieldDefaultValue

type

incremental

Specifies the type of snapshot that you want to run.
Setting the type is optional. Currently, you can request only incremental snapshots.

data-collections

N/A

An array that contains regular expressions matching the fully-qualified names of the table to be snapshotted.
The format of the names is the same as for the signal.data.collection configuration option.

additional-condition

N/A

An optional string, which specifies a condition based on the column(s) of the table(s), to capture a subset of the contents of the table(s).

surrogate-key

N/A

An optional string that specifies the column name that the connector uses as the primary key of a table during the snapshot process.

Triggering an ad hoc snapshot

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

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

3.2.3. Incremental snapshots

To provide flexibility in managing snapshots, Debezium includes a supplementary snapshot mechanism, known as incremental snapshotting. Incremental snapshots rely on the Debezium mechanism for sending signals to a Debezium connector.

In an incremental snapshot, instead of capturing the full state of a database all at once, as in an initial snapshot, Debezium captures each table in phases, in a series of configurable chunks. You can specify the tables that you want the snapshot to capture and the size of each chunk. The chunk size determines the number of rows that the snapshot collects during each fetch operation on the database. The default chunk size for incremental snapshots is 1024 rows.

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

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

Incremental snapshot process

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

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

How Debezium resolves collisions among records with the same primary key

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

Snapshot window

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

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

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

The connector repeats the process for each snapshot chunk.

Warning

The Debezium connector for Db2 does not support schema changes while an incremental snapshot is running.

3.2.3.1. Triggering an incremental snapshot

Currently, the only way to initiate an incremental snapshot is to send an ad hoc snapshot signal to the signaling table on the source database.

You submit a signal to the signaling table as SQL INSERT queries.

After Debezium detects the change in the signaling table, it reads the signal, and runs the requested snapshot operation.

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

To specify the tables to include in the snapshot, provide a data-collections array that lists the tables or an array of regular expressions used to match tables, for example,

{"data-collections": ["public.MyFirstTable", "public.MySecondTable"]}

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

Note

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

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

Prerequisites

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": ["<tableName>","<tableName>"],"type":"<snapshotType>","additional-condition":"<additional-condition>"}');

    For example,

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

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

    The following table describes the parameters in the example:

    Table 3.2. Descriptions of fields in a SQL command for sending an incremental snapshot signal to the signaling table
    ItemValueDescription

    1

    myschema.debezium_signal

    Specifies the fully-qualified name of the signaling table on the source database.

    2

    ad-hoc-1

    The id parameter specifies an arbitrary string that is assigned as the id identifier for the signal request.
    Use this string to identify logging messages to entries in the signaling table. Debezium does not use this string. Rather, during the snapshot, Debezium generates its own id string as a watermarking signal.

    3

    execute-snapshot

    The type parameter specifies the operation that the signal is intended to trigger.

    4

    data-collections

    A required component of the data field of a signal that specifies an array of table names or regular expressions to match table names to include in the snapshot.
    The array lists regular expressions which match tables by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling table in the signal.data.collection configuration property.

    5

    incremental

    An optional type component of the data field of a signal that specifies the kind of snapshot operation to run.
    Currently, the only valid option is the default value, incremental.
    If you do not specify a value, the connector runs an incremental snapshot.

    6

    additional-condition

    An optional string, which specifies a condition based on the column(s) of the table(s), to capture a subset of the contents of the tables. For more information about the additional-condition parameter, see Ad hoc incremental snapshots with additional-condition.

Ad hoc incremental snapshots with additional-condition

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

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

SELECT * FROM <tableName> ....

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

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

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

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

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

  • id (primary key)
  • color
  • quantity

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

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

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

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

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

Example: Incremental snapshot event message

{
    "before":null,
    "after": {
        "pk":"1",
        "value":"New data"
    },
    "source": {
        ...
        "snapshot":"incremental" 1
    },
    "op":"r", 2
    "ts_ms":"1620393591654",
    "transaction":null
}

ItemField nameDescription

1

snapshot

Specifies the type of snapshot operation to run.
Currently, the only valid option is the default value, incremental.
Specifying a type value in the SQL query that you submit to the signaling table is optional.
If you do not specify a value, the connector runs an incremental snapshot.

2

op

Specifies the event type.
The value for snapshot events is r, signifying a READ operation.

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

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

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

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

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

Table 3.3. Execute snapshot data fields
FieldDefaultValue

type

incremental

The type of the snapshot to be executed. Currently Debezium supports only the incremental type.
See the next section for more details.

data-collections

N/A

An array of comma-separated regular expressions that match the fully-qualified names of tables to include in the snapshot.
Specify the names by using the same format as is required for the signal.data.collection configuration option.

additional-condition

N/A

An optional string that specifies a condition that the connector evaluates to designate a subset of columns to include in a snapshot.

An example of the execute-snapshot Kafka message:

Key = `test_connector`

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

Ad hoc incremental snapshots with additional-condition

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

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

SELECT * FROM <tableName> …​.

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

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

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

Key = `test_connector`

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

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

Key = `test_connector`

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

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

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

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

Prerequisites

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": ["<tableName>","<tableName>"],"type":"incremental"}');

    For example,

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

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

    The following table describes the parameters in the example:

    Table 3.4. Descriptions of fields in a SQL command for sending a stop incremental snapshot signal to the signaling table
    ItemValueDescription

    1

    myschema.debezium_signal

    Specifies the fully-qualified name of the signaling table on the source database.

    2

    ad-hoc-1

    The id parameter specifies an arbitrary string that is assigned as the id identifier for the signal request.
    Use this string to identify logging messages to entries in the signaling table. Debezium does not use this string.

    3

    stop-snapshot

    Specifies type parameter specifies the operation that the signal is intended to trigger.

    4

    data-collections

    An optional component of the data field of a signal that specifies an array of table names or regular expressions to match table names to remove from the snapshot.
    The array lists regular expressions which match tables by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling table in the signal.data.collection configuration property. If this component of the data field is omitted, the signal stops the entire incremental snapshot that is in progress.

    5

    incremental

    A required component of the data field of a signal that specifies the kind of snapshot operation that is to be stopped.
    Currently, the only valid option is incremental.
    If you do not specify a type value, the signal fails to stop the incremental snapshot.

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

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

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

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

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

Table 3.5. Execute snapshot data fields
FieldDefaultValue

type

incremental

The type of the snapshot to be executed. Currently Debezium supports only the incremental type.
See the next section for more details.

data-collections

N/A

An optional array of comma-separated regular expressions that match the fully-qualified names of the tables to include in the snapshot.
Specify the names by using the same format as is required for the signal.data.collection configuration option.

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

Key = `test_connector`

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

3.2.4. How Debezium Db2 connectors read change-data tables

After a complete snapshot, when a Debezium Db2 connector starts for the first time, the connector identifies the change-data table for each source table that is in capture mode. The connector does the following for each change-data table:

  1. Reads change events that were created between the last stored, highest LSN and the current, highest LSN.
  2. Orders the change events according to the commit LSN and the change LSN for each event. This ensures that the connector emits the change events in the order in which the table changes occurred.
  3. Passes commit and change LSNs as offsets to Kafka Connect.
  4. Stores the highest LSN that the connector passed to Kafka Connect.

After a restart, the connector resumes emitting change events from the offset (commit and change LSNs) where it left off. While the connector is running and emitting change events, if you remove a table from capture mode or add a table to capture mode, the connector detects the change, and modifies its behavior accordingly.

3.2.5. Default names of Kafka topics that receive Debezium Db2 change event records

By default, the Db2 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.schemaName.tableName

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.

For example, consider a Db2 installation with the mydatabase database, which contains four tables: PRODUCTS, PRODUCTS_ON_HAND, CUSTOMERS, and ORDERS that are in the MYSCHEMA schema. The connector would emit events to these four Kafka topics:

  • mydatabase.MYSCHEMA.PRODUCTS
  • mydatabase.MYSCHEMA.PRODUCTS_ON_HAND
  • mydatabase.MYSCHEMA.CUSTOMERS
  • mydatabase.MYSCHEMA.ORDERS

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.

3.2.6. How Debezium Db2 connectors handle database schema changes

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

To ensure correct processing of events that occur after a schema change, the Debezium Db2 connector stores a snapshot of the new schema based on the structures of the Db2 change data tables, which mirror the structures of their associated data tables. The connector stores the table schema information, together with the LSN of operations the result in schema changes, in the database schema history Kafka topic. The connector uses the stored schema representation to produce change events that correctly mirror the structure of tables at the time of each insert, update, or delete operation.

When the connector restarts after either a crash or a graceful stop, it resumes reading entries in the Db2 change data tables from the last position that it read. Based on the schema information that the connector reads from the database schema history topic, the connector applies the table structures that existed at the position where the connector restarts.

If you update the schema of a Db2 table that is in capture mode, it’s important that you also update the schema of the corresponding change table. You must be a Db2 database administrator with elevated privileges to update database schema. For more information about how to update Db2 database schema in Debezium environments, see Schema history eveolution.

The 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.

Additional resources

3.2.7. About the Debezium Db2 connector schema change topic

You can configure a Debezium Db2 connector to produce schema change events that describe schema changes that are applied to tables in the database.

Debezium emits a message to the schema change topic when:

  • A new table goes into capture mode.
  • A table is removed from capture mode.
  • During a database schema update, there is a change in the schema for a table that is in capture mode.

The connector writes schema change events to a Kafka schema change topic that has the name <topicPrefix> where <topicPrefix> is the topic prefix that is specified in the topic.prefix connector configuration property. Messages that the connector sends to the schema change topic contain a payload that includes the following elements:

databaseName
The name of the database to which the statements are applied. The value of databaseName serves as the message key.
pos
The position in the transaction log where the statements appear.
tableChanges
A structured representation of the entire table schema after the schema change. The tableChanges field contains an array that includes entries for each column of the table. Because the structured representation presents data in JSON or Avro format, consumers can easily read messages without first processing them through a DDL parser.
Important

For a table that is in capture mode, the connector not only stores the history of schema changes in the schema change topic, but also in an internal database schema history topic. The internal database schema history topic is for connector use only and it is not intended for direct use by consuming applications. Ensure that applications that require notifications about schema changes consume that information only from the schema change topic.

Important

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

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

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

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

Example: Message emitted to the Db2 connector schema change topic

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

{
  "schema": {
  ...
  },
  "payload": {
    "source": {
      "version": "2.3.7.Final",
      "connector": "db2",
      "name": "db2",
      "ts_ms": 0,
      "snapshot": "true",
      "db": "testdb",
      "schema": "DB2INST1",
      "table": "CUSTOMERS",
      "change_lsn": null,
      "commit_lsn": "00000025:00000d98:00a2",
      "event_serial_no": null
    },
    "ts_ms": 1588252618953, 1
    "databaseName": "TESTDB", 2
    "schemaName": "DB2INST1",
    "ddl": null, 3
    "tableChanges": [ 4
      {
        "type": "CREATE", 5
        "id": "\"DB2INST1\".\"CUSTOMERS\"", 6
        "table": { 7
          "defaultCharsetName": null,
          "primaryKeyColumnNames": [ 8
            "ID"
          ],
          "columns": [ 9
            {
              "name": "ID",
              "jdbcType": 4,
              "nativeType": null,
              "typeName": "int identity",
              "typeExpression": "int identity",
              "charsetName": null,
              "length": 10,
              "scale": 0,
              "position": 1,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            },
            {
              "name": "FIRST_NAME",
              "jdbcType": 12,
              "nativeType": null,
              "typeName": "varchar",
              "typeExpression": "varchar",
              "charsetName": null,
              "length": 255,
              "scale": null,
              "position": 2,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            },
            {
              "name": "LAST_NAME",
              "jdbcType": 12,
              "nativeType": null,
              "typeName": "varchar",
              "typeExpression": "varchar",
              "charsetName": null,
              "length": 255,
              "scale": null,
              "position": 3,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            },
            {
              "name": "EMAIL",
              "jdbcType": 12,
              "nativeType": null,
              "typeName": "varchar",
              "typeExpression": "varchar",
              "charsetName": null,
              "length": 255,
              "scale": null,
              "position": 4,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            }
          ],
          "attributes": [ 10
            {
              "customAttribute": "attributeValue"
            }
          ]
        }
      }
    ]
  }
}
Table 3.6. Descriptions of fields in messages emitted to the schema change topic
ItemField nameDescription

1

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

2

databaseName
schemaName

Identifies the database and the schema that contain the change.

3

ddl

Always null for the Db2 connector. For other connectors, this field contains the DDL responsible for the schema change. This DDL is not available to Db2 connectors.

4

tableChanges

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

5

type

Describes the kind of change. The value is one of the following:

  • CREATE - table created
  • ALTER - table modified
  • DROP - table deleted

6

id

Full identifier of the table that was created, altered, or dropped.

7

table

Represents table metadata after the applied change.

8

primaryKeyColumnNames

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

9

columns

Metadata for each column in the changed table.

10

attributes

Custom attribute metadata for each table change.

In messages that the connector sends to the schema change topic, the message key is the name of the database that contains the schema change. In the following example, the payload field contains the key:

{
  "schema": {
    "type": "struct",
    "fields": [
      {
        "type": "string",
        "optional": false,
        "field": "databaseName"
      }
    ],
    "optional": false,
    "name": "io.debezium.connector.db2.SchemaChangeKey"
  },
  "payload": {
    "databaseName": "TESTDB"
  }
}

3.2.8. Debezium Db2 connector-generated events that represent transaction boundaries

Debezium can generate events that represent transaction boundaries and that enrich change data 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 emmitted 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": "00000025:00000d08:0025",
  "ts_ms": 1486500577125,
  "event_count": null,
  "data_collections": null
}

{
  "status": "END",
  "id": "00000025:00000d08:0025",
  "ts_ms": 1486500577691,
  "event_count": 2,
  "data_collections": [
    {
      "data_collection": "testDB.dbo.tablea",
      "event_count": 1
    },
    {
      "data_collection": "testDB.dbo.tableb",
      "event_count": 1
    }
  ]
}

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

Data change event enrichment

When transaction metadata is enabled the connector enriches the change event Envelope with a new transaction field. This field provides information about every event in the form of a composite of fields:

id
String representation of unique transaction identifier.
total_order
The absolute position of the event among all events generated by the transaction.
data_collection_order
The per-data collection position of the event among all events that were emitted by the transaction.

Following is an example of a message:

{
  "before": null,
  "after": {
    "pk": "2",
    "aa": "1"
  },
  "source": {
...
  },
  "op": "c",
  "ts_ms": "1580390884335",
  "transaction": {
    "id": "00000025:00000d08:0025",
    "total_order": "1",
    "data_collection_order": "1"
  }
}

3.3. Descriptions of Debezium Db2 connector data change events

The Debezium Db2 connector generates a data change event for each row-level INSERT, UPDATE, and DELETE operation. Each event contains a key and a value. The structure of the key and the value depends on the table that was changed.

Debezium and Kafka Connect are designed around continuous streams of event messages. However, the structure of these events may change over time, which can be difficult for consumers to handle. To address this, each event contains the schema for its content or, if you are using a schema registry, a schema ID that a consumer can use to obtain the schema from the registry. This makes each event self-contained.

The following skeleton JSON shows the basic four parts of a change event. However, how you configure the Kafka Connect converter that you choose to use in your application determines the representation of these four parts in change events. A schema field is in a change event only when you configure the converter to produce it. Likewise, the event key and event payload are in a change event only if you configure a converter to produce it. If you use the JSON converter and you configure it to produce all four basic change event parts, change events have this structure:

{
 "schema": { 1
   ...
  },
 "payload": { 2
   ...
 },
 "schema": { 3
   ...
 },
 "payload": { 4
   ...
 },
}
Table 3.7. Overview of change event basic content
ItemField nameDescription

1

schema

The first schema field is part of the event key. It specifies a Kafka Connect schema that describes what is in the event key’s payload portion. In other words, the first schema field describes the structure of the primary key, or the unique key if the table does not have a primary key, for the table that was changed.

It is possible to override the table’s primary key by setting the message.key.columns connector configuration property. In this case, the first schema field describes the structure of the key identified by that property.

2

payload

The first payload field is part of the event key. It has the structure described by the previous schema field and it contains the key for the row that was changed.

3

schema

The second schema field is part of the event value. It specifies the Kafka Connect schema that describes what is in the event value’s payload portion. In other words, the second schema describes the structure of the row that was changed. Typically, this schema contains nested schemas.

4

payload

The second payload field is part of the event value. It has the structure described by the previous schema field and it contains the actual data for the row that was changed.

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

Warning

The Debezium Db2 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.

Also, Db2 names for databases, schemas, and tables can be case sensitive. This means that the connector could emit event records for more than one table to the same Kafka topic.

Details are in the following topics:

3.3.1. About keys in Debezium db2 change events

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

Consider the following customers table, which is followed by an example of a change event key for this table.

Example table

CREATE TABLE customers (
 ID INTEGER IDENTITY(1001,1) NOT NULL PRIMARY KEY,
 FIRST_NAME VARCHAR(255) NOT NULL,
 LAST_NAME VARCHAR(255) NOT NULL,
 EMAIL VARCHAR(255) NOT NULL UNIQUE
);

Example change event key

Every change event that captures a change to the customers table has the same event key schema. For as long as the customers table has the previous definition, every change event that captures a change to the customers table has the following key structure. In JSON, it looks like this:

{
    "schema": {  1
        "type": "struct",
        "fields": [  2
            {
                "type": "int32",
                "optional": false,
                "field": "ID"
            }
        ],
        "optional": false,  3
        "name": "mydatabase.MYSCHEMA.CUSTOMERS.Key"  4
    },
    "payload": {  5
        "ID": 1004
    }
}
Table 3.8. Description of change event key
ItemField nameDescription

1

schema

The schema portion of the key specifies a Kafka Connect schema that describes what is in the key’s payload portion.

2

fields

Specifies each field that is expected in the payload, including each field’s name, type, and whether it is required.

3

optional

Indicates whether the event key must contain a value in its payload field. In this example, a value in the key’s payload is required. A value in the key’s payload field is optional when a table does not have a primary key.

4

mydatabase.MYSCHEMA.CUSTOMERS.Key

Name of the schema that defines the structure of the key’s payload. This schema describes the structure of the primary key for the table that was changed. Key schema names have the format connector-name.database-name.table-name.Key. In this example:

  • mydatabase is the name of the connector that generated this event.
  • MYSCHEMA is the database schema that contains the table that was changed.
  • CUSTOMERS is the table that was updated.

5

payload

Contains the key for the row for which this change event was generated. In this example, the key, contains a single ID field whose value is 1004.

3.3.2. About values in Debezium Db2 change events

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

Consider the same sample table that was used to show an example of a change event key:

Example table

CREATE TABLE customers (
 ID INTEGER IDENTITY(1001,1) NOT NULL PRIMARY KEY,
 FIRST_NAME VARCHAR(255) NOT NULL,
 LAST_NAME VARCHAR(255) NOT NULL,
 EMAIL VARCHAR(255) NOT NULL UNIQUE
);

The event value portion of every change event for the customers table specifies the same schema. The event value’s payload varies according to the event type:

create events

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

{
  "schema": {  1
    "type": "struct",
    "fields": [
      {
        "type": "struct",
        "fields": [
          {
            "type": "int32",
            "optional": false,
            "field": "ID"
          },
          {
            "type": "string",
            "optional": false,
            "field": "FIRST_NAME"
          },
          {
            "type": "string",
            "optional": false,
            "field": "LAST_NAME"
          },
          {
            "type": "string",
            "optional": false,
            "field": "EMAIL"
          }
        ],
        "optional": true,
        "name": "mydatabase.MYSCHEMA.CUSTOMERS.Value",  2
        "field": "before"
      },
      {
        "type": "struct",
        "fields": [
          {
            "type": "int32",
            "optional": false,
            "field": "ID"
          },
          {
            "type": "string",
            "optional": false,
            "field": "FIRST_NAME"
          },
          {
            "type": "string",
            "optional": false,
            "field": "LAST_NAME"
          },
          {
            "type": "string",
            "optional": false,
            "field": "EMAIL"
          }
        ],
        "optional": true,
        "name": "mydatabase.MYSCHEMA.CUSTOMERS.Value",
        "field": "after"
      },
      {
        "type": "struct",
        "fields": [
          {
            "type": "string",
            "optional": false,
            "field": "version"
          },
          {
            "type": "string",
            "optional": false,
            "field": "connector"
          },
          {
            "type": "string",
            "optional": false,
            "field": "name"
          },
          {
            "type": "int64",
            "optional": false,
            "field": "ts_ms"
          },
          {
            "type": "boolean",
            "optional": true,
            "default": false,
            "field": "snapshot"
          },
          {
            "type": "string",
            "optional": false,
            "field": "db"
          },
          {
            "type": "string",
            "optional": false,
            "field": "schema"
          },
          {
            "type": "string",
            "optional": false,
            "field": "table"
          },
          {
            "type": "string",
            "optional": true,
            "field": "change_lsn"
          },
          {
            "type": "string",
            "optional": true,
            "field": "commit_lsn"
          },
        ],
        "optional": false,
        "name": "io.debezium.connector.db2.Source",  3
        "field": "source"
      },
      {
        "type": "string",
        "optional": false,
        "field": "op"
      },
      {
        "type": "int64",
        "optional": true,
        "field": "ts_ms"
      }
    ],
    "optional": false,
    "name": "mydatabase.MYSCHEMA.CUSTOMERS.Envelope"  4
  },
  "payload": {  5
    "before": null,  6
    "after": {  7
      "ID": 1005,
      "FIRST_NAME": "john",
      "LAST_NAME": "doe",
      "EMAIL": "john.doe@example.org"
    },
    "source": {  8
      "version": "2.3.7.Final",
      "connector": "db2",
      "name": "myconnector",
      "ts_ms": 1559729468470,
      "snapshot": false,
      "db": "mydatabase",
      "schema": "MYSCHEMA",
      "table": "CUSTOMERS",
      "change_lsn": "00000027:00000758:0003",
      "commit_lsn": "00000027:00000758:0005",
    },
    "op": "c",  9
    "ts_ms": 1559729471739  10
  }
}
Table 3.9. Descriptions of create event value fields
ItemField nameDescription

1

schema

The value’s schema, which describes the structure of the value’s payload. A change event’s value schema is the same in every change event that the connector generates for a particular table.

2

name

In the schema section, each name field specifies the schema for a field in the value’s payload.

mydatabase.MYSCHEMA.CUSTOMERS.Value is the schema for the payload’s before and after fields. This schema is specific to the customers table. The connector uses this schema for all rows in the MYSCHEMA.CUSTOMERS table.

Names of schemas for before and after fields are of the form logicalName.schemaName.tableName.Value, which ensures that the schema name is unique in the database. This means that when using the Avro converter, the resulting Avro schema for each table in each logical source has its own evolution and history.

3

name

io.debezium.connector.db2.Source is the schema for the payload’s source field. This schema is specific to the Db2 connector. The connector uses it for all events that it generates.

4

name

mydatabase.MYSCHEMA.CUSTOMERS.Envelope is the schema for the overall structure of the payload, where mydatabase is the database, MYSCHEMA is the schema, and CUSTOMERS is the table.

5

payload

The value’s actual data. This is the information that the change event is providing.

It may appear that JSON representations of events are much larger than the rows they describe. This is because a JSON representation must include the schema portion and the payload portion of the message. However, by using the Avro converter, you can significantly decrease the size of the messages that the connector streams to Kafka topics.

6

before

An optional field that specifies the state of the row before the event occurred. When the op field is c for create, as it is in this example, the before field is null since this change event is for new content.

7

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.

8

source

Mandatory field that describes the source metadata for the event. The source structure shows Db2 information about this change, which provides traceability. It also has information you can use to compare to other events in the same topic or in other topics to know whether this event occurred before, after, or as part of the same commit as other events. The source metadata includes:

  • Debezium version
  • Connector type and name
  • Timestamp for when the change was made in the database
  • Whether the event is part of an ongoing snapshot
  • Name of the database, schema, and table that contain the new row
  • Change LSN
  • Commit LSN (omitted if this event is part of a snapshot)

9

op

Mandatory string that describes the type of operation that caused the connector to generate the event. In this example, c indicates that the operation created a row. Valid values are:

  • c = create
  • u = update
  • d = delete
  • r = read (applies to only snapshots)

10

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

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 update event value’s payload has the same structure. However, the event value payload contains different values in an update event. Here is an example of a change event value in an event that the connector generates for an update in the customers table:

{
  "schema": { ... },
  "payload": {
    "before": {  1
      "ID": 1005,
      "FIRST_NAME": "john",
      "LAST_NAME": "doe",
      "EMAIL": "john.doe@example.org"
    },
    "after": {  2
      "ID": 1005,
      "FIRST_NAME": "john",
      "LAST_NAME": "doe",
      "EMAIL": "noreply@example.org"
    },
    "source": {  3
      "version": "2.3.7.Final",
      "connector": "db2",
      "name": "myconnector",
      "ts_ms": 1559729995937,
      "snapshot": false,
      "db": "mydatabase",
      "schema": "MYSCHEMA",
      "table": "CUSTOMERS",
      "change_lsn": "00000027:00000ac0:0002",
      "commit_lsn": "00000027:00000ac0:0007",
    },
    "op": "u",  4
    "ts_ms": 1559729998706  5
  }
}
Table 3.10. Descriptions of update event value fields
ItemField nameDescription

1

before

An optional field that specifies the state of the row before the event occurred. In an update event value, the before field contains a field for each table column and the value that was in that column before the database commit. In this example, note that the EMAIL value is john.doe@example.com.

2

after

An optional field that specifies the state of the row after the event occurred. You can compare the before and after structures to determine what the update to this row was. In the example, the EMAIL value is now noreply@example.com.

3

source

Mandatory field that describes the source metadata for the event. The source field structure contains the same fields as in a create event, but some values are different, for example, the sample update event has different LSNs. You can use this information to compare this event to other events to know whether this event occurred before, after, or as part of the same commit as other events. The source metadata includes:

  • Debezium version
  • Connector type and name
  • Timestamp for when the change was made in the database
  • Whether the event is part of an ongoing snapshot
  • Name of the database, schema, and table that contain the new row
  • Change LSN
  • Commit LSN (omitted if this event is part of a snapshot)

4

op

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

5

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

Note

Updating the columns for a row’s primary/unique key changes the value of the row’s key. When a key changes, Debezium outputs three events: a DELETE event and a tombstone event with the old key for the row, followed by an event with the new key for the row.

delete events

The value in a delete change event has the same schema portion as create and update events for the same table. The event value payload in a delete event for the sample customers table looks like this:

{
  "schema": { ... },
  },
  "payload": {
    "before": {  1
      "ID": 1005,
      "FIRST_NAME": "john",
      "LAST_NAME": "doe",
      "EMAIL": "noreply@example.org"
    },
    "after": null,  2
    "source": {  3
      "version": "2.3.7.Final",
      "connector": "db2",
      "name": "myconnector",
      "ts_ms": 1559730445243,
      "snapshot": false,
      "db": "mydatabase",
      "schema": "MYSCHEMA",
      "table": "CUSTOMERS",
      "change_lsn": "00000027:00000db0:0005",
      "commit_lsn": "00000027:00000db0:0007"
    },
    "op": "d",  4
    "ts_ms": 1559730450205  5
  }
}
Table 3.11. Descriptions of delete event value fields
ItemField nameDescription

1

before

Optional field that specifies the state of the row before the event occurred. In a delete event value, the before field contains the values that were in the row before it was deleted with the database commit.

2

after

Optional field that specifies the state of the row after the event occurred. In a delete event value, the after field is null, signifying that the row no longer exists.

3

source

Mandatory field that describes the source metadata for the event. In a delete event value, the source field structure is the same as for create and update events for the same table. Many source field values are also the same. In a delete event value, the ts_ms and LSN field values, as well as other values, might have changed. But the source field in a delete event value provides the same metadata:

  • Debezium version
  • Connector type and name
  • Timestamp for when the change was made in the database
  • Whether the event is part of an ongoing snapshot
  • Name of the database, schema, and table that contain the new row
  • Change LSN
  • Commit LSN (omitted if this event is part of a snapshot)

4

op

Mandatory string that describes the type of operation. The op field value is d, signifying that this row was deleted.

5

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

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

Db2 connector events are designed to work with Kafka log compaction. Log compaction enables removal of some older messages as long as at least the most recent message for every key is kept. This lets Kafka reclaim storage space while ensuring that the topic contains a complete data set and can be used for reloading key-based state.

When a row is deleted, the delete event value still works with log compaction, because Kafka can remove all earlier messages that have that same key. However, for Kafka to remove all messages that have that same key, the message value must be null. To make this possible, after Debezium’s Db2 connector emits a delete event, the connector emits a special tombstone event that has the same key but a null value.

3.4. How Debezium Db2 connectors map data types

For a complete description of the data types that Db2 supports, see Data Types in the Db2 documentation.

The Db2 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. How that value is represented in the event depends on the Db2 data type of the column. This section describes these mappings. If the default data type conversions do not meet your needs, you can create a custom converter for the connector.

Details are in the following sections:

Basic types

The following table describes how the connector maps each Db2 data type to a literal type and a semantic type in event fields.

  • literal type describes how the value is represented using Kafka Connect schema types: INT8, INT16, INT32, INT64, FLOAT32, FLOAT64, BOOLEAN, STRING, BYTES, ARRAY, MAP, and STRUCT.
  • semantic type describes how the Kafka Connect schema captures the meaning of the field using the name of the Kafka Connect schema for the field.
Table 3.12. Mappings for Db2 basic data types
Db2 data typeLiteral type (schema type)Semantic type (schema name) and Notes

BOOLEAN

BOOLEAN

Only snapshots can be taken from tables with BOOLEAN type columns. Currently SQL Replication on Db2 does not support BOOLEAN, so Debezium can not perform CDC on those tables. Consider using a different type.

BIGINT

INT64

n/a

BINARY

BYTES

n/a

BLOB

BYTES

n/a

CHAR[(N)]

STRING

n/a

CLOB

STRING

n/a

DATE

INT32

io.debezium.time.Date

String representation of a timestamp without timezone information

DECFLOAT

BYTES

org.apache.kafka.connect.data.Decimal

DECIMAL

BYTES

org.apache.kafka.connect.data.Decimal

DBCLOB

STRING

n/a

DOUBLE

FLOAT64

n/a

INTEGER

INT32

n/a

REAL

FLOAT32

n/a

SMALLINT

INT16

n/a

TIME

INT32

io.debezium.time.Time

String representation of a time without timezone information

TIMESTAMP

INT64

io.debezium.time.MicroTimestamp

String representation of a timestamp without timezone information

VARBINARY

BYTES

n/a

VARCHAR[(N)]

STRING

n/a

VARGRAPHIC

STRING

n/a

XML

STRING

io.debezium.data.Xml

String representation of an XML document

If present, a column’s default value is propagated to the corresponding field’s Kafka Connect schema. Change events contain the field’s default value unless an explicit column value had been given. Consequently, there is rarely a need to obtain the default value from the schema.

Temporal types

Except for the DATETIMEOFFSET data type, which contains time zone information, Db2 maps temporal types based on the value of the time.precision.mode connector configuration property. The following sections describe these mappings:

time.precision.mode=adaptive

When the time.precision.mode configuration property is set to adaptive, the default, the connector determines the literal type and semantic type based on the column’s data type definition. This ensures that events exactly represent the values in the database.

Table 3.13. Mappings when time.precision.mode is adaptive
Db2 data typeLiteral type (schema type)Semantic type (schema name) and Notes

DATE

INT32

io.debezium.time.Date

Represents the number of days since the epoch.

TIME(0), TIME(1), TIME(2), TIME(3)

INT32

io.debezium.time.Time

Represents the number of milliseconds past midnight, and does not include timezone information.

TIME(4), TIME(5), TIME(6)

INT64

io.debezium.time.MicroTime

Represents the number of microseconds past midnight, and does not include timezone information.

TIME(7)

INT64

io.debezium.time.NanoTime

Represents the number of nanoseconds past midnight, and does not include timezone information.

DATETIME

INT64

io.debezium.time.Timestamp

Represents the number of milliseconds since the epoch, and does not include timezone information.

time.precision.mode=connect

When the time.precision.mode configuration property is set to connect, the connector uses Kafka Connect logical types. This may be useful when consumers can handle only the built-in Kafka Connect logical types and are unable to handle variable-precision time values. However, since Db2 supports tenth of a microsecond precision, the events generated by a connector with the connect time precision results in a loss of precision when the database column has a fractional second precision value that is greater than 3.

Table 3.14. Mappings when time.precision.mode is connect
Db2 data typeLiteral type (schema type)Semantic type (schema name) and Notes

DATE

INT32

org.apache.kafka.connect.data.Date

Represents the number of days since the epoch.

TIME([P])

INT64

org.apache.kafka.connect.data.Time

Represents the number of milliseconds since midnight, and does not include timezone information. Db2 allows P to be in the range 0-7 to store up to tenth of a microsecond precision, though this mode results in a loss of precision when P is greater than 3.

DATETIME

INT64

org.apache.kafka.connect.data.Timestamp

Represents the number of milliseconds since the epoch, and does not include timezone information.

Timestamp types

The DATETIME type represents a timestamp without time zone information. Such columns are converted into an equivalent Kafka Connect value based on UTC. For example, the DATETIME value "2018-06-20 15:13:16.945104" is represented by an io.debezium.time.Timestamp with the value "1529507596000".

The timezone of the JVM running Kafka Connect and Debezium does not affect this conversion.

Table 3.15. Decimal types
Db2 data typeLiteral type (schema type)Semantic type (schema name) and Notes

NUMERIC[(P[,S])]

BYTES

org.apache.kafka.connect.data.Decimal

The scale schema parameter contains an integer that represents how many digits the decimal point is shifted. The connect.decimal.precision schema parameter contains an integer that represents the precision of the given decimal value.

DECIMAL[(P[,S])]

BYTES

org.apache.kafka.connect.data.Decimal

The scale schema parameter contains an integer that represents how many digits the decimal point is shifted. The connect.decimal.precision schema parameter contains an integer that represents the precision of the given decimal value.

3.5. Setting up Db2 to run a Debezium connector

For Debezium to capture change events that are committed to Db2 tables, a Db2 database administrator with the necessary privileges must configure tables in the database for change data capture. After you begin to run Debezium you can adjust the configuration of the capture agent to optimize performance.

For details about setting up Db2 for use with the Debezium connector, see the following sections:

3.5.1. Configuring Db2 tables for change data capture

To put tables into capture mode, Debezium provides a set of user-defined functions (UDFs) for your convenience. The procedure here shows how to install and run these management UDFs. Alternatively, you can run Db2 control commands to put tables into capture mode. The administrator must then enable CDC for each table that you want Debezium to capture.

Prerequisites

  • You are logged in to Db2 as the db2instl user.
  • On the Db2 host, the Debezium management UDFs are available in the $HOME/asncdctools/src directory. UDFs are available from the Debezium examples repository.
  • The Db2 command bldrtn is on PATH, e.g. by running export PATH=$PATH:/opt/ibm/db2/V11.5.0.0/samples/c/ with Db2 11.5

Procedure

  1. Compile the Debezium management UDFs on the Db2 server host by using the bldrtn command provided with Db2:

    cd $HOME/asncdctools/src
    bldrtn asncdc
  2. Start the database if it is not already running. Replace DB_NAME with the name of the database that you want Debezium to connect to.

    db2 start db DB_NAME
  3. Ensure that JDBC can read the Db2 metadata catalog:

    cd $HOME/sqllib/bnd
    db2 connect to DB_NAME
    db2 bind db2schema.bnd blocking all grant public sqlerror continue
  4. Ensure that the database was recently backed-up. The ASN agents must have a recent starting point to read from. If you need to perform a backup, run the following commands, which prune the data so that only the most recent version is available. If you do not need to retain the older versions of the data, specify dev/null for the backup location.

    1. Back up the database. Replace DB_NAME and BACK_UP_LOCATION with appropriate values:

      db2 backup db DB_NAME to BACK_UP_LOCATION
    2. Restart the database:

      db2 restart db DB_NAME
  5. Connect to the database to install the Debezium management UDFs. It is assumed that you are logged in as the db2instl user so the UDFs should be installed on the db2inst1 user.

    db2 connect to DB_NAME
  6. Copy the Debezium management UDFs and set permissions for them:

    cp $HOME/asncdctools/src/asncdc $HOME/sqllib/function
    chmod 777 $HOME/sqllib/function
  7. Enable the Debezium UDF that starts and stops the ASN capture agent:

    db2 -tvmf $HOME/asncdctools/src/asncdc_UDF.sql
  8. Create the ASN control tables:

    $ db2 -tvmf $HOME/asncdctools/src/asncdctables.sql
  9. Enable the Debezium UDF that adds tables to capture mode and removes tables from capture mode:

    $ db2 -tvmf $HOME/asncdctools/src/asncdcaddremove.sql

    After you set up the Db2 server, use the UDFs to control Db2 replication (ASN) with SQL commands. Some of the UDFs expect a return value in which case you use the SQL VALUE statement to invoke them. For other UDFs, use the SQL CALL statement.

  10. Start the ASN agent from an SQL client:

    VALUES ASNCDC.ASNCDCSERVICES('start','asncdc');

    or from the shell:

    db2 "VALUES ASNCDC.ASNCDCSERVICES('start','asncdc');"

    The preceding statement returns one of the following results:

    • asncap is already running
    • start --> <COMMAND>

      In this case, enter the specified <COMMAND> in the terminal window as shown in the following example:

      /database/config/db2inst1/sqllib/bin/asncap capture_schema=asncdc capture_server=SAMPLE &
  11. Put tables into capture mode. Invoke the following statement for each table that you want to put into capture. Replace MYSCHEMA with the name of the schema that contains the table you want to put into capture mode. Likewise, replace MYTABLE with the name of the table to put into capture mode:

    CALL ASNCDC.ADDTABLE('MYSCHEMA', 'MYTABLE');
  12. Reinitialize the ASN service:

    VALUES ASNCDC.ASNCDCSERVICES('reinit','asncdc');

3.5.2. Effect of Db2 capture agent configuration on server load and latency

When a database administrator enables change data capture for a source table, the capture agent begins to run. The agent reads new change event records from the transaction log and replicates the event records to a capture table. Between the time that a change is committed in the source table, and the time that the change appears in the corresponding change table, there is always a small latency interval. This latency interval represents a gap between when changes occur in the source table and when they become available for Debezium to stream to Apache Kafka.

Ideally, for applications that must respond quickly to changes in data, you want to maintain close synchronization between the source and capture tables. You might imagine that running the capture agent to continuously process change events as rapidly as possible might result in increased throughput and reduced latency — populating change tables with new event records as soon as possible after the events occur, in near real time. However, this is not necessarily the case. There is a performance penalty to pay in the pursuit of more immediate synchronization. Each time that the change agent queries the database for new event records, it increases the CPU load on the database host. The additional load on the server can have a negative effect on overall database performance, and potentially reduce transaction efficiency, especially during times of peak database use.

It’s important to monitor database metrics so that you know if the database reaches the point where the server can no longer support the capture agent’s level of activity. If you experience performance issues while running the capture agent, adjust capture agent settings to reduce CPU load.

3.5.3. Db2 capture agent configuration parameters

On Db2, the IBMSNAP_CAPPARMS table contains parameters that control the behavior of the capture agent. You can adjust the values for these parameters to balance the configuration of the capture process to reduce CPU load and still maintain acceptable levels of latency.

Note

Specific guidance about how to configure Db2 capture agent parameters is beyond the scope of this documentation.

In the IBMSNAP_CAPPARMS table, the following parameters have the greatest effect on reducing CPU load:

COMMIT_INTERVAL
  • Specifies the number of seconds that the capture agent waits to commit data to the change data tables.
  • A higher value reduces the load on the database host and increases latency.
  • The default value is 30.
SLEEP_INTERVAL
  • Specifies the number of seconds that the capture agent waits to start a new commit cycle after it reaches the end of the active transaction log.
  • A higher value reduces the load on the server, and increases latency.
  • The default value is 5.

Additional resources

  • For more information about capture agent parameters, see the Db2 documentation.

3.6. Deployment of Debezium Db2 connectors

You can use either of the following methods to deploy a Debezium Db2 connector:

Important

Due to licensing requirements, the Debezium Db2 connector archive does not include the Db2 JDBC driver that Debezium requires to connect to a Db2 database. To enable the connector to access the database, you must add the driver to your connector environment. For information about how to obtain the driver, see Obtaining the Db2 JDBC driver.

3.6.1. Obtaining the Db2 JDBC driver

Due to licensing requirements, the Db2 JDBC driver file that Debezium requires to connect to an Db2 database is not included in the Debezium Db2 connector archive. The driver is available for download from Maven Central. Depending on the deployment method that you use, you retrieve the driver by adding a command to the Kafka Connect custom resource or to the Dockerfile that you use to build the connector image.

3.6.2. Db2 connector deployment using AMQ Streams

Beginning with Debezium 1.7, the preferred method for deploying a Debezium connector is to use AMQ Streams to build a Kafka Connect container image that includes the connector plug-in.

During the deployment process, you create and use the following custom resources (CRs):

  • A KafkaConnect CR that defines your Kafka Connect instance and includes information about the connector artifacts needs to include in the image.
  • A KafkaConnector CR that provides details that include information the connector uses to access the source database. After AMQ Streams starts the Kafka Connect pod, you start the connector by applying the KafkaConnector CR.

In the build specification for the Kafka Connect image, you can specify the connectors that are available to deploy. For each connector plug-in, you can also specify other components that you want to make available for deployment. For example, you can add Apicurio Registry artifacts, or the Debezium scripting component. When AMQ Streams builds the Kafka Connect image, it downloads the specified artifacts, and incorporates them into the image.

The spec.build.output parameter in the KafkaConnect CR specifies where to store the resulting Kafka Connect container image. Container images can be stored in a Docker registry, or in an OpenShift ImageStream. To store images in an ImageStream, you must create the ImageStream before you deploy Kafka Connect. ImageStreams are not created automatically.

Note

If you use a KafkaConnect resource to create a cluster, afterwards you cannot use the Kafka Connect REST API to create or update connectors. You can still use the REST API to retrieve information.

Additional resources

3.6.3. Using AMQ Streams to deploy a Debezium Db2 connector

With earlier versions of AMQ Streams, to deploy Debezium connectors on OpenShift, you were required to first build a Kafka Connect image for the connector. The current preferred method for deploying connectors on OpenShift is to use a build configuration in AMQ Streams to automatically build a Kafka Connect container image that includes the Debezium connector plug-ins that you want to use.

During the build process, the AMQ Streams Operator transforms input parameters in a KafkaConnect custom resource, including Debezium connector definitions, into a Kafka Connect container image. The build downloads the necessary artifacts from the Red Hat Maven repository or another configured HTTP server.

The newly created container is pushed to the container registry that is specified in .spec.build.output, and is used to deploy a Kafka Connect cluster. After AMQ Streams builds the Kafka Connect image, you create KafkaConnector custom resources to start the connectors that are included in the build.

Prerequisites

  • You have access to an OpenShift cluster on which the cluster Operator is installed.
  • The AMQ Streams Operator is running.
  • An Apache Kafka cluster is deployed as documented in Deploying and Managing AMQ Streams on OpenShift.
  • Kafka Connect is deployed on AMQ Streams
  • You have a Red Hat build of Debezium license.
  • The OpenShift oc CLI client is installed or you have access to the OpenShift Container Platform web console.
  • Depending on how you intend to store the Kafka Connect build image, you need registry permissions or you must create an ImageStream resource:

    To store the build image in an image registry, such as Red Hat Quay.io or Docker Hub
    • An account and permissions to create and manage images in the registry.
    To store the build image as a native OpenShift ImageStream

Procedure

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

    Example 3.1. A dbz-connect.yaml file that defines a KafkaConnect custom resource that includes a Debezium connector

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

    • The Debezium Db2 connector archive.
    • The Red Hat build of Apicurio Registry archive. The Apicurio Registry is an optional component. Add the Apicurio Registry component only if you intend to use Avro serialization with the connector.
    • The Debezium scripting SMT archive and the associated language dependencies that you want to use with the Debezium connector. The SMT archive and language dependencies are optional components. Add these components only if you intend to use the Debezium content-based routing SMT or filter SMT.
    • The Db2 JDBC driver, which is required to connect to Db2 databases, but is not included in the connector archive.
    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnect
    metadata:
      name: debezium-kafka-connect-cluster
      annotations:
        strimzi.io/use-connector-resources: "true" 1
    spec:
      version: 3.5.0
      build: 2
        output: 3
          type: imagestream  4
          image: debezium-streams-connect:latest
        plugins: 5
          - name: debezium-connector-db2
            artifacts:
              - type: zip 6
                url: https://maven.repository.redhat.com/ga/io/debezium/debezium-connector-db2/2.3.7.Final-redhat-00001/debezium-connector-db2-2.3.7.Final-redhat-00001-plugin.zip  7
              - type: zip
                url: https://maven.repository.redhat.com/ga/io/apicurio/apicurio-registry-distro-connect-converter/2.4.4.Final-redhat-<build-number>/apicurio-registry-distro-connect-converter-2.4.4.Final-redhat-<build-number>.zip  8
              - type: zip
                url: https://maven.repository.redhat.com/ga/io/debezium/debezium-scripting/2.3.7.Final-redhat-00001/debezium-scripting-2.3.7.Final-redhat-00001.zip 9
              - type: jar
                url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy/3.0.11/groovy-3.0.11.jar  10
              - type: jar
                url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy-jsr223/3.0.11/groovy-jsr223-3.0.11.jar
              - type: jar
                url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy-json3.0.11/groovy-json-3.0.11.jar
              - type: jar          11
                url: https://repo1.maven.org/maven2/com/ibm/db2/jcc/11.5.0.0/jcc-11.5.0.0.jar
    
      bootstrapServers: debezium-kafka-cluster-kafka-bootstrap:9093
    
      ...
    Table 3.16. Descriptions of Kafka Connect configuration settings
    ItemDescription

    1

    Sets the strimzi.io/use-connector-resources annotation to "true" to enable the Cluster Operator to use KafkaConnector resources to configure connectors in this Kafka Connect cluster.

    2

    The spec.build configuration specifies where to store the build image and lists the plug-ins to include in the image, along with the location of the plug-in artifacts.

    3

    The build.output specifies the registry in which the newly built image is stored.

    4

    Specifies the name and image name for the image output. Valid values for output.type are docker to push into a container registry such as Docker Hub or Quay, or imagestream to push the image to an internal OpenShift ImageStream. To use an ImageStream, an ImageStream resource must be deployed to the cluster. For more information about specifying the build.output in the KafkaConnect configuration, see the AMQ Streams Build schema reference in {NameConfiguringStreamsOpenShift}.

    5

    The plugins configuration lists all of the connectors that you want to include in the Kafka Connect image. For each entry in the list, specify a plug-in name, and information for about the artifacts that are required to build the connector. Optionally, for each connector plug-in, you can include other components that you want to be available for use with the connector. For example, you can add Service Registry artifacts, or the Debezium scripting component.

    6

    The value of artifacts.type specifies the file type of the artifact specified in the artifacts.url. Valid types are zip, tgz, or jar. Debezium connector archives are provided in .zip file format. JDBC driver files are in .jar format. The type value must match the type of the file that is referenced in the url field.

    7

    The value of artifacts.url specifies the address of an HTTP server, such as a Maven repository, that stores the file for the connector artifact. The OpenShift cluster must have access to the specified server.

    8

    (Optional) Specifies the artifact type and url for downloading the Apicurio Registry component. Include the Apicurio Registry artifact, only if you want the connector to use Apache Avro to serialize event keys and values with the Red Hat build of Apicurio Registry, instead of using the default JSON converter.

    9

    (Optional) Specifies the artifact type and url for the Debezium scripting SMT archive to use with the Debezium connector. Include the scripting SMT only if you intend to use the Debezium content-based routing SMT or filter SMT To use the scripting SMT, you must also deploy a JSR 223-compliant scripting implementation, such as groovy.

    10

    (Optional) Specifies the artifact type and url for the JAR files of a JSR 223-compliant scripting implementation, which is required by the Debezium scripting SMT.

    Important

    If you use AMQ Streams to incorporate the connector plug-in into your Kafka Connect image, for each of the required scripting language components, artifacts.url must specify the location of a JAR file, and the value of artifacts.type must also be set to jar. Invalid values cause the connector fails at runtime.

    To enable use of the Apache Groovy language with the scripting SMT, the custom resource in the example retrieves JAR files for the following libraries:

    • groovy
    • groovy-jsr223 (scripting agent)
    • groovy-json (module for parsing JSON strings)

    The Debezium scripting SMT also supports the use of the JSR 223 implementation of GraalVM JavaScript.

    11

    Specifies the location of the Db2 JDBC driver in Maven Central. The required driver is not included in the Debezium Db2 connector archive.

  3. Apply the KafkaConnect build specification to the OpenShift cluster by entering the following command:

    oc create -f dbz-connect.yaml

    Based on the configuration specified in the custom resource, the Streams Operator prepares a Kafka Connect image to deploy.
    After the build completes, the Operator pushes the image to the specified registry or ImageStream, and starts the Kafka Connect cluster. The connector artifacts that you listed in the configuration are available in the cluster.

  4. Create a KafkaConnector resource to define an instance of each connector that you want to deploy.
    For example, create the following KafkaConnector CR, and save it as db2-inventory-connector.yaml

    Example 3.2. db2-inventory-connector.yaml file that defines the KafkaConnector custom resource for a Debezium connector

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnector
    metadata:
      labels:
        strimzi.io/cluster: debezium-kafka-connect-cluster
      name: inventory-connector-db2 1
    spec:
      class: io.debezium.connector.db2.Db2ConnectorConnector 2
      tasksMax: 1  3
      config:  4
        schema.history.internal.kafka.bootstrap.servers: debezium-kafka-cluster-kafka-bootstrap.debezium.svc.cluster.local:9092
        schema.history.internal.kafka.topic: schema-changes.inventory
        database.hostname: db2.debezium-db2.svc.cluster.local 5
        database.port: 50000   6
        database.user: debezium  7
        database.password: dbz  8
        database.dbname: mydatabase 9
        topic.prefix: inventory-connector-db2 10
        table.include.list: public.inventory  11
    
        ...
    Table 3.17. Descriptions of connector configuration settings
    ItemDescription

    1

    The name of the connector to register with the Kafka Connect cluster.

    2

    The name of the connector class.

    3

    The number of tasks that can operate concurrently.

    4

    The connector’s configuration.

    5

    The address of the host database instance.

    6

    The port number of the database instance.

    7

    The name of the account that Debezium uses to connect to the database.

    8

    The password that Debezium uses to connect to the database user account.

    9

    The name of the database to capture changes from.

    10

    The topic prefix for the database instance or cluster.
    The specified name must be formed only from alphanumeric characters or underscores.
    Because the topic prefix is used as the prefix for any Kafka topics that receive change events from this connector, the name must be unique among the connectors in the cluster.
    This namespace is also used in the names of related Kafka Connect schemas, and the namespaces of a corresponding Avro schema if you integrate the connector with the Avro connector.

    11

    The list of tables from which the connector captures change events.

  5. Create the connector resource by running the following command:

    oc create -n <namespace> -f <kafkaConnector>.yaml

    For example,

    oc create -n debezium -f {context}-inventory-connector.yaml

    The connector is registered to the Kafka Connect cluster and starts to run against the database that is specified by spec.config.database.dbname in the KafkaConnector CR. After the connector pod is ready, Debezium is running.

You are now ready to verify the Debezium Db2 deployment.

3.6.4. Deploying a Debezium Db2 connector by building a custom Kafka Connect container image from a Dockerfile

To deploy a Debezium Db2 connector, you must build a custom Kafka Connect container image that contains the Debezium connector archive, and then push this container image to a container registry. You then need to create the following custom resources (CRs):

  • A KafkaConnect CR that defines your Kafka Connect instance. The image property in the CR specifies the name of the container image that you create to run your Debezium connector. You apply this CR to the OpenShift instance where Red Hat AMQ Streams is deployed. AMQ Streams offers operators and images that bring Apache Kafka to OpenShift.
  • A KafkaConnector CR that defines your Debezium Db2 connector. Apply this CR to the same OpenShift instance where you applied the KafkaConnect CR.

Prerequisites

  • Db2 is running and you completed the steps to set up Db2 to work with a Debezium connector.
  • AMQ Streams is deployed on OpenShift and is running Apache Kafka and Kafka Connect. For more information, see Deploying and Managing AMQ Streams on OpenShift.
  • Podman or Docker is installed.
  • The Kafka Connect server has access to Maven Central to download the required JDBC driver for Db2. You can also use a local copy of the driver, or one that is available from a local Maven repository or other HTTP server.
  • You have an account and permissions to create and manage containers in the container registry (such as quay.io or docker.io) to which you plan to add the container that will run your Debezium connector.

Procedure

  1. Create the Debezium Db2 container for Kafka Connect:

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

      cat <<EOF >debezium-container-for-db2.yaml 1
      FROM registry.redhat.io/amq-streams-kafka-35-rhel8:2.5.0
      USER root:root
      RUN mkdir -p /opt/kafka/plugins/debezium 2
      RUN cd /opt/kafka/plugins/debezium/ \
      && curl -O https://maven.repository.redhat.com/ga/io/debezium/debezium-connector-db2/2.3.7.Final-redhat-00001/debezium-connector-db2-2.3.7.Final-redhat-00001-plugin.zip \
      && unzip debezium-connector-db2-2.3.7.Final-redhat-00001-plugin.zip \
      && rm debezium-connector-db2-2.3.7.Final-redhat-00001-plugin.zip
      RUN cd /opt/kafka/plugins/debezium/ \
      && curl -O https://repo1.maven.org/maven2/com/ibm/db2/jcc/11.5.0.0/jcc-11.5.0.0.jar
      USER 1001
      EOF
      ItemDescription

      1

      You can specify any file name that you want.

      2

      Specifies the path to your Kafka Connect plug-ins directory. If your Kafka Connect plug-ins directory is in a different location, replace this path with the actual path of your directory.

      The command creates a Dockerfile with the name debezium-container-for-db2.yaml in the current directory.

    2. Build the container image from the debezium-container-for-db2.yaml Docker file that you created in the previous step. From the directory that contains the file, open a terminal window and enter one of the following commands:

      podman build -t debezium-container-for-db2:latest .
      docker build -t debezium-container-for-db2:latest .

      The preceding commands build a container image with the name debezium-container-for-db2.

    3. Push your custom image to a container registry, such as quay.io or an internal container registry. The container registry must be available to the OpenShift instance where you want to deploy the image. Enter one of the following commands:

      podman push <myregistry.io>/debezium-container-for-db2:latest
      docker push <myregistry.io>/debezium-container-for-db2:latest
    4. Create a new Debezium Db2 KafkaConnect custom resource (CR). For example, create a KafkaConnect CR with the name dbz-connect.yaml that specifies annotations and image properties. The following example shows an excerpt from a dbz-connect.yaml file that describes a KafkaConnect custom resource.

      apiVersion: kafka.strimzi.io/v1beta2
      kind: KafkaConnect
      metadata:
        name: my-connect-cluster
        annotations:
          strimzi.io/use-connector-resources: "true" 1
      spec:
        #...
        image: debezium-container-for-db2  2
      
        ...
      ItemDescription

      1

      metadata.annotations indicates to the Cluster Operator that KafkaConnector resources are used to configure connectors in this Kafka Connect cluster.

      2

      spec.image specifies the name of the image that you created to run your Debezium connector. This property overrides the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE variable in the Cluster Operator.

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

      oc create -f dbz-connect.yaml

      The command adds a Kafka Connect instance that specifies the name of the image that you created to run your Debezium connector.

  2. Create a KafkaConnector custom resource that configures your Debezium Db2 connector instance.

    You configure a Debezium Db2 connector in a .yaml file that specifies the configuration properties for the connector. The connector configuration might instruct Debezium to produce events for a subset of the schemas and tables, or it might set properties so that Debezium ignores, masks, or truncates values in specified columns that are sensitive, too large, or not needed.

    The following example configures a Debezium connector that connects to a Db2 server host, 192.168.99.100, on port 50000. This host has a database named mydatabase, a table with the name inventory, and inventory-connector-db2 is the server’s logical name.

    Db2 inventory-connector.yaml

    apiVersion: kafka.strimzi.io/v1beta2
      kind: KafkaConnector
      metadata:
        name: inventory-connector-db2  1
        labels:
          strimzi.io/cluster: my-connect-cluster
        annotations:
          strimzi.io/use-connector-resources: 'true'
      spec:
        class: io.debezium.connector.db2.Db2Connector 2
        tasksMax: 1  3
        config:  4
          database.hostname: 192.168.99.100   5
          database.port: 50000 6
          database.user: db2inst1 7
          database.password: Password! 8
          database.dbname: mydatabase 9
          topic.prefix: inventory-connector-db2   10
          table.include.list: public.inventory   11
    
          ...

    Table 3.18. Descriptions of connector configuration settings
    ItemDescription

    1

    The name of the connector when we register it with a Kafka Connect cluster.

    2

    The name of this Db2 connector class.

    3

    Only one task should operate at any one time.

    4

    The connector’s configuration.

    5

    The database host, which is the address of the Db2 instance.

    6

    The port number of the Db2 instance.

    7

    The name of the Db2 user.

    8

    The password for the Db2 user.

    9

    The name of the database to capture changes from.

    10

    The logical name of the Db2 instance/cluster, which forms a namespace and is used in the names of the Kafka topics to which the connector writes, the names of Kafka Connect schemas, and the namespaces of the corresponding Avro schema when the Avro Connector is used.

    11

    The connector captures changes from the public.inventory table only.

  3. Create your connector instance with Kafka Connect. For example, if you saved your KafkaConnector resource in the inventory-connector.yaml file, you would run the following command:

    oc apply -f inventory-connector.yaml

    The preceding command registers inventory-connector and the connector starts to run against the mydatabase database as defined in the KafkaConnector CR.

For the complete list of the configuration properties that you can set for the Debezium Db2 connector, see Db2 connector properties.

Results

After the connector starts, it performs a consistent snapshot of the Db2 database tables that the connector is configured to capture changes for. The connector then starts generating data change events for row-level operations and streaming change event records to Kafka topics.

3.6.5. Verifying that the Debezium Db2 connector is running

If the connector starts correctly without errors, it creates a topic for each table that the connector is configured to capture. Downstream applications can subscribe to these topics to retrieve information events that occur in the source database.

To verify that the connector is running, you perform the following operations from the OpenShift Container Platform web console, or through the OpenShift CLI tool (oc):

  • Verify the connector status.
  • Verify that the connector generates topics.
  • Verify that topics are populated with events for read operations ("op":"r") that the connector generates during the initial snapshot of each table.

Prerequisites

  • A Debezium connector is deployed to AMQ Streams on OpenShift.
  • The OpenShift oc CLI client is installed.
  • You have access to the OpenShift Container Platform web console.

Procedure

  1. Check the status of the KafkaConnector resource by using one of the following methods:

    • From the OpenShift Container Platform web console:

      1. Navigate to Home → Search.
      2. On the Search page, click Resources to open the Select Resource box, and then type KafkaConnector.
      3. From the KafkaConnectors list, click the name of the connector that you want to check, for example inventory-connector-db2.
      4. In the Conditions section, verify that the values in the Type and Status columns are set to Ready and True.
    • From a terminal window:

      1. Enter the following command:

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

        For example,

        oc describe KafkaConnector inventory-connector-db2 -n debezium

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

        Example 3.3. KafkaConnector resource status

        Name:         inventory-connector-db2
        Namespace:    debezium
        Labels:       strimzi.io/cluster=debezium-kafka-connect-cluster
        Annotations:  <none>
        API Version:  kafka.strimzi.io/v1beta2
        Kind:         KafkaConnector
        
        ...
        
        Status:
          Conditions:
            Last Transition Time:  2021-12-08T17:41:34.897153Z
            Status:                True
            Type:                  Ready
          Connector Status:
            Connector:
              State:      RUNNING
              worker_id:  10.131.1.124:8083
            Name:         inventory-connector-db2
            Tasks:
              Id:               0
              State:            RUNNING
              worker_id:        10.131.1.124:8083
            Type:               source
          Observed Generation:  1
          Tasks Max:            1
          Topics:
            inventory-connector-db2.inventory
            inventory-connector-db2.inventory.addresses
            inventory-connector-db2.inventory.customers
            inventory-connector-db2.inventory.geom
            inventory-connector-db2.inventory.orders
            inventory-connector-db2.inventory.products
            inventory-connector-db2.inventory.products_on_hand
        Events:  <none>
  2. Verify that the connector created Kafka topics:

    • From the OpenShift Container Platform web console.

      1. Navigate to Home → Search.
      2. On the Search page, click Resources to open the Select Resource box, and then type KafkaTopic.
      3. From the KafkaTopics list, click the name of the topic that you want to check, for example, inventory-connector-db2.inventory.orders---ac5e98ac6a5d91e04d8ec0dc9078a1ece439081d.
      4. In the Conditions section, verify that the values in the Type and Status columns are set to Ready and True.
    • From a terminal window:

      1. Enter the following command:

        oc get kafkatopics

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

        Example 3.4. KafkaTopic resource status

        NAME                                                                    CLUSTER               PARTITIONS   REPLICATION FACTOR   READY
        connect-cluster-configs                                                 debezium-kafka-cluster   1            1                    True
        connect-cluster-offsets                                                 debezium-kafka-cluster   25           1                    True
        connect-cluster-status                                                  debezium-kafka-cluster   5            1                    True
        consumer-offsets---84e7a678d08f4bd226872e5cdd4eb527fadc1c6a             debezium-kafka-cluster   50           1                    True
        inventory-connector-db2--a96f69b23d6118ff415f772679da623fbbb99421                               debezium-kafka-cluster   1            1                    True
        inventory-connector-db2.inventory.addresses---1b6beaf7b2eb57d177d92be90ca2b210c9a56480          debezium-kafka-cluster   1            1                    True
        inventory-connector-db2.inventory.customers---9931e04ec92ecc0924f4406af3fdace7545c483b          debezium-kafka-cluster   1            1                    True
        inventory-connector-db2.inventory.geom---9f7e136091f071bf49ca59bf99e86c713ee58dd5               debezium-kafka-cluster   1            1                    True
        inventory-connector-db2.inventory.orders---ac5e98ac6a5d91e04d8ec0dc9078a1ece439081d             debezium-kafka-cluster   1            1                    True
        inventory-connector-db2.inventory.products---df0746db116844cee2297fab611c21b56f82dcef           debezium-kafka-cluster   1            1                    True
        inventory-connector-db2.inventory.products_on_hand---8649e0f17ffcc9212e266e31a7aeea4585e5c6b5   debezium-kafka-cluster   1            1                    True
        schema-changes.inventory                                                debezium-kafka-cluster   1            1                    True
        strimzi-store-topic---effb8e3e057afce1ecf67c3f5d8e4e3ff177fc55          debezium-kafka-cluster   1            1                    True
        strimzi-topic-operator-kstreams-topic-store-changelog---b75e702040b99be8a9263134de3507fc0cc4017b  debezium-kafka-cluster  1   1    True
  3. Check topic content.

    • From a terminal window, enter the following command:
    oc exec -n <project>  -it <kafka-cluster> -- /opt/kafka/bin/kafka-console-consumer.sh \
    >     --bootstrap-server localhost:9092 \
    >     --from-beginning \
    >     --property print.key=true \
    >     --topic=<topic-name>

    For example,

    oc exec -n debezium  -it debezium-kafka-cluster-kafka-0 -- /opt/kafka/bin/kafka-console-consumer.sh \
    >     --bootstrap-server localhost:9092 \
    >     --from-beginning \
    >     --property print.key=true \
    >     --topic=inventory-connector-db2.inventory.products_on_hand

    The format for specifying the topic name is the same as the oc describe command returns in Step 1, for example, inventory-connector-db2.inventory.addresses.

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

    Example 3.5. Content of a Debezium change event

    {"schema":{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"}],"optional":false,"name":"inventory-connector-db2.inventory.products_on_hand.Key"},"payload":{"product_id":101}} {"schema":{"type":"struct","fields":[{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"},{"type":"int32","optional":false,"field":"quantity"}],"optional":true,"name":"inventory-connector-db2.inventory.products_on_hand.Value","field":"before"},{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"},{"type":"int32","optional":false,"field":"quantity"}],"optional":true,"name":"inventory-connector-db2.inventory.products_on_hand.Value","field":"after"},{"type":"struct","fields":[{"type":"string","optional":false,"field":"version"},{"type":"string","optional":false,"field":"connector"},{"type":"string","optional":false,"field":"name"},{"type":"int64","optional":false,"field":"ts_ms"},{"type":"string","optional":true,"name":"io.debezium.data.Enum","version":1,"parameters":{"allowed":"true,last,false"},"default":"false","field":"snapshot"},{"type":"string","optional":false,"field":"db"},{"type":"string","optional":true,"field":"sequence"},{"type":"string","optional":true,"field":"table"},{"type":"int64","optional":false,"field":"server_id"},{"type":"string","optional":true,"field":"gtid"},{"type":"string","optional":false,"field":"file"},{"type":"int64","optional":false,"field":"pos"},{"type":"int32","optional":false,"field":"row"},{"type":"int64","optional":true,"field":"thread"},{"type":"string","optional":true,"field":"query"}],"optional":false,"name":"io.debezium.connector.db2.Source","field":"source"},{"type":"string","optional":false,"field":"op"},{"type":"int64","optional":true,"field":"ts_ms"},{"type":"struct","fields":[{"type":"string","optional":false,"field":"id"},{"type":"int64","optional":false,"field":"total_order"},{"type":"int64","optional":false,"field":"data_collection_order"}],"optional":true,"field":"transaction"}],"optional":false,"name":"inventory-connector-db2.inventory.products_on_hand.Envelope"},"payload":{"before":null,"after":{"product_id":101,"quantity":3},"source":{"version":"2.3.7.Final-redhat-00001","connector":"db2","name":"inventory-connector-db2","ts_ms":1638985247805,"snapshot":"true","db":"inventory","sequence":null,"table":"products_on_hand","server_id":0,"gtid":null,"file":"db2-bin.000003","pos":156,"row":0,"thread":null,"query":null},"op":"r","ts_ms":1638985247805,"transaction":null}}

    In the preceding example, the payload value shows that the connector snapshot generated a read ("op" ="r") event from the table inventory.products_on_hand. The "before" state of the product_id record is null, indicating that no previous value exists for the record. The "after" state shows a quantity of 3 for the item with product_id 101.

3.6.6. Descriptions of Debezium Db2 connector configuration properties

The Debezium Db2 connector has numerous configuration properties that you can use to achieve the right connector behavior for your application. Many properties have default values. Information about the properties is organized as follows:

Required Debezium Db2 connector configuration properties

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

PropertyDefaultDescription

name

No default

Unique name for the connector. Attempting to register again with the same name will fail. This property is required by all Kafka Connect connectors.

connector.class

No default

The name of the Java class for the connector. Always use a value of io.debezium.connector.db2.Db2Connector for the Db2 connector.

tasks.max

1

The maximum number of tasks that should be created for this connector. The Db2 connector always uses a single task and therefore does not use this value, so the default is always acceptable.

database.hostname

No default

IP address or hostname of the Db2 database server.

database.port

50000

Integer port number of the Db2 database server.

database.user

No default

Name of the Db2 database user for connecting to the Db2 database server.

database.password

No default

Password to use when connecting to the Db2 database server.

database.dbname

No default

The name of the Db2 database from which to stream the changes

topic.prefix

No default

Topic prefix which provides a namespace for the particular Db2 database server that hosts the database for which Debezium is capturing changes. Only alphanumeric characters, hyphens, dots and underscores must be used in the topic prefix name. The topic prefix should be unique across all other connectors, since this topic prefix is used for all Kafka topics that receive records from this connector.

Warning

Do not change the value of this property. If you change the name value, after a restart, instead of continuing to emit events to the original topics, the connector emits subsequent events to topics whose names are based on the new value. The connector is also unable to recover its database schema history topic.

table.include.list

No default

An optional, comma-separated list of regular expressions that match fully-qualified table identifiers for tables whose changes you want the connector to capture. When this property is set, the connector captures changes only from the specified tables. Each identifier is of the form schemaName.tableName. By default, the connector captures changes in every non-system table.

To match the name of a table, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the table it does not match substrings that might be present in a table name.
If you include this property in the configuration, do not also set the table.exclude.list property.

table.exclude.list

No default

An optional, comma-separated list of regular expressions that match fully-qualified table identifiers for tables whose changes you do not want the connector to capture. The connector captures changes in each non-system table that is not included in the exclude list. Each identifier is of the form schemaName.tableName.

To match the name of a table, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the table it does not match substrings that might be present in a table name.
If you include this property in the configuration, do not also set the table.include.list property.

column.include.list

empty string

An optional, comma-separated list of regular expressions that match the fully-qualified names of columns to include in change event record values. Fully-qualified names for columns are of the form 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; 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.exclude.list property.

column.exclude.list

empty string

An optional, comma-separated list of regular expressions that match the fully-qualified names of columns to exclude from change event values. Fully-qualified names for columns are of the form 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; it does not match substrings that might be present in a column name. Primary key columns are always included in the event’s key, even if they are excluded from the value. If you include this property in the configuration, do not set the column.include.list property.

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

n/a

An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns. Fully-qualified names for columns are of the form 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. 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.

time.precision.mode

adaptive

Time, date, and timestamps can be represented with different kinds of precision:

adaptive captures the time and timestamp values exactly as in the database using either millisecond, microsecond, or nanosecond precision values based on the database column’s type.

connect always represents time and timestamp values by using Kafka Connect’s built-in representations for Time, Date, and Timestamp, which uses millisecond precision regardless of the database columns' precision. For more information, see temporal types.

tombstones.on.delete

true

Controls whether a delete event is followed by a tombstone event.

true - a delete operation is represented by a delete event and a subsequent tombstone event.

false - only a delete event is emitted.

After a source record is deleted, emitting a tombstone event (the default behavior) allows Kafka to completely delete all events that pertain to the key of the deleted row in case log compaction is enabled for the topic.

include.schema.changes

true

Boolean value that specifies whether the connector should publish changes in the database schema to a Kafka topic with the same name as the database server ID. Each schema change is recorded with a key that contains the database name and a value that is a JSON structure that describes the schema update. This is independent of how the connector internally records database schema history.

column.truncate.to.length.chars

n/a

An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns. Set this property if you want to truncate the data in a set of columns when it exceeds the number of characters specified by the length in the property name. Set length to a positive integer value, for example, column.truncate.to.20.chars.

The fully-qualified name of a column observes the following format: 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.

You can specify multiple properties with different lengths in a single configuration.

column.mask.with.length.chars

n/a

An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns. Set this property if you want the connector to mask the values for a set of columns, for example, if they contain sensitive data. Set length to a positive integer to replace data in the specified columns with the number of asterisk (*) characters specified by the length in the property name. Set length to 0 (zero) to replace data in the specified columns with an empty string.

The fully-qualified name of a column observes the following format: 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.

You can specify multiple properties with different lengths in a single configuration.

column.propagate.source.type

n/a

An optional, comma-separated list of regular expressions that match the fully-qualified names of columns for which you want the connector to emit extra parameters that represent column metadata. When this property is set, the connector adds the following fields to the schema of event records:

  • __debezium.source.column.type
  • __debezium.source.column.length
  • __debezium.source.column.scale

These parameters propagate a column’s original type name and length (for variable-width types), respectively.
Enabling the connector to emit this extra data can assist in properly sizing specific numeric or character-based columns in sink databases.

The fully-qualified name of a column observes one of the following formats: databaseName.tableName.columnName, or databaseName.schemaName.tableName.columnName.
To match the name of a column, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the column; the expression does not match substrings that might be present in a column name.

datatype.propagate.source.type

n/a

An optional, comma-separated list of regular expressions that specify the fully-qualified names of data types that are defined for columns in a database. When this property is set, for columns with matching data types, the connector emits event records that include the following extra fields in their schema:

  • __debezium.source.column.type
  • __debezium.source.column.length
  • __debezium.source.column.scale

These parameters propagate a column’s original type name and length (for variable-width types), respectively.
Enabling the connector to emit this extra data can assist in properly sizing specific numeric or character-based columns in sink databases.

The fully-qualified name of a column observes one of the following formats: databaseName.tableName.typeName, or databaseName.schemaName.tableName.typeName.
To match the name of a data type, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the data type; the expression does not match substrings that might be present in a type name.

For the list of Db2-specific data type names, see the Db2 data type mappings .

message.key.columns

empty string

A list of expressions that specify the columns that the connector uses to form custom message keys for change event records that it publishes to the Kafka topics for specified tables.

By default, Debezium uses the primary key column of a table as the message key for records that it emits. In place of the default, or to specify a key for tables that lack a primary key, you can configure custom message keys based on one or more columns.

To establish a custom message key for a table, list the table, followed by the columns to use as the message key. Each list entry takes the following format:

<fully-qualified_tableName>:<keyColumn>,<keyColumn>

To base a table key on multiple column names, insert commas between the column names.
Each fully-qualified table name is a regular expression in the following format:

<schemaName>.<tableName>

The property can list entries for multiple tables. Use a semicolon to separate entries for different tables in the list.

The following example sets the message key for the tables inventory.customers and purchaseorders:

inventory.customers:pk1,pk2;(.*).purchaseorders:pk3,pk4

In the preceding example, the columns pk1 and pk2 are specified as the message key for the table inventory.customer. For purchaseorders tables in any schema, the columns pk3 and pk4 serve as the message key.

schema.name.adjustment.mode

none

Specifies how schema names should be adjusted for compatibility with the message converter used by the connector. Possible settings:

  • none does not apply any adjustment.
  • avro replaces the characters that cannot be used in the Avro type name with underscore.
  • avro_unicode replaces the underscore or characters that cannot be used in the Avro type name with corresponding unicode like _uxxxx. Note: _ is an escape sequence like backslash in Java

field.name.adjustment.mode

none

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

  • none does not apply any adjustment.
  • avro replaces the characters that cannot be used in the Avro type name with underscore.
  • avro_unicode replaces the underscore or characters that cannot be used in the Avro type name with corresponding unicode like _uxxxx. Note: _ is an escape sequence like backslash in Java

See Avro naming for more details.

Advanced connector configuration properties

The following advanced configuration properties have defaults that work in most situations and therefore rarely need to be specified in the connector’s configuration.

PropertyDefaultDescription

converters

No default

Enumerates a comma-separated list of the symbolic names of the custom converter instances that the connector can use. For example,

isbn

You must set the converters property to enable the connector to use a custom converter.

For each converter that you configure for a connector, you must also add a .type property, which specifies the fully-qualifed name of the class that implements the converter interface. The .type property uses the following format:

<converterSymbolicName>.type

For example,

isbn.type: io.debezium.test.IsbnConverter

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 any additional configuration parameter with a converter, prefix the parameter names with the symbolic name of the converter.
For example,

isbn.schema.name: io.debezium.db2.type.Isbn

snapshot.mode

initial

Specifies the criteria for performing a snapshot when the connector starts:

initial - For tables in capture mode, the connector takes a snapshot of the schema for the table and the data in the table. This is useful for populating Kafka topics with a complete representation of the data.

initial_only - Takes a snapshot of structure and data like initial but instead does not transition into streaming changes once the snapshot has completed.

schema_only - For tables in capture mode, the connector takes a snapshot of only the schema for the table. This is useful when only the changes that are happening from now on need to be emitted to Kafka topics. After the snapshot is complete, the connector continues by reading change events from the database’s redo logs.

snapshot.isolation.mode

repeatable_read

During a snapshot, controls the transaction isolation level and how long the connector locks the tables that are in capture mode. The possible values are:

read_uncommitted - Does not prevent other transactions from updating table rows during an initial snapshot. This mode has no data consistency guarantees; some data might be lost or corrupted.

read_committed - Does not prevent other transactions from updating table rows during an initial snapshot. It is possible for a new record to appear twice: once in the initial snapshot and once in the streaming phase. However, this consistency level is appropriate for data mirroring.

repeatable_read - Prevents other transactions from updating table rows during an initial snapshot. It is possible for a new record to appear twice: once in the initial snapshot and once in the streaming phase. However, this consistency level is appropriate for data mirroring.

exclusive - Uses repeatable read isolation level but takes an exclusive lock for all tables to be read. This mode prevents other transactions from updating table rows during an initial snapshot. Only exclusive mode guarantees full consistency; the initial snapshot and streaming logs constitute a linear history.

event.processing.failure.handling.mode

fail

Specifies how the connector handles exceptions during processing of events. The possible values are:

fail - The connector logs the offset of the problematic event and stops processing.

warn - The connector logs the offset of the problematic event and continues processing with the next event.

skip - The connector skips the problematic event and continues processing with the next event.

poll.interval.ms

500

Positive integer value that specifies the number of milliseconds the connector should wait for new change events to appear before it starts processing a batch of events. Defaults to 500 milliseconds, or 0.5 second.

max.batch.size

2048

Positive integer value that specifies the maximum size of each batch of events that the connector processes.

max.queue.size

8192

Positive integer value that specifies the maximum number of records that the blocking queue can hold. When Debezium reads events streamed from the database, it places the events in the blocking queue before it writes them to Kafka. The blocking queue can provide backpressure for reading change events from the database in cases where the connector ingests messages faster than it can write them to Kafka, or when Kafka becomes unavailable. Events that are held in the queue are disregarded when the connector periodically records offsets. Always set the value of max.queue.size to be larger than the value of max.batch.size.

max.queue.size.in.bytes

0

A long integer value that specifies the maximum volume of the blocking queue in bytes. By default, volume limits are not specified for the blocking queue. To specify the number of bytes that the queue can consume, set this property to a positive long value.
If max.queue.size is also set, writing to the queue is blocked when the size of the queue reaches the limit specified by either property. For example, if you set max.queue.size=1000, and max.queue.size.in.bytes=5000, writing to the queue is blocked after the queue contains 1000 records, or after the volume of the records in the queue reaches 5000 bytes.

heartbeat.interval.ms

0

Controls how frequently the connector sends heartbeat messages to a Kafka topic. The default behavior is that the connector does not send heartbeat messages.

Heartbeat messages are useful for monitoring whether the connector is receiving change events from the database. Heartbeat messages might help decrease the number of change events that need to be re-sent when a connector restarts. To send heartbeat messages, set this property to a positive integer, which indicates the number of milliseconds between heartbeat messages.

Heartbeat messages are useful when there are many updates in a database that is being tracked but only a tiny number of updates are in tables that are in capture mode. In this situation, the connector reads from the database transaction log as usual but rarely emits change records to Kafka. This means that the connector has few opportunities to send the latest offset to Kafka. Sending heartbeat messages enables the connector to send the latest offset to Kafka.

snapshot.delay.ms

No default

An interval in milliseconds that the connector should wait before performing a snapshot when the connector starts. If you are starting multiple connectors in a cluster, this property is useful for avoiding snapshot interruptions, which might cause re-balancing of connectors.

snapshot.include.collection.list

All tables specified in table.include.list

An optional, comma-separated list of regular expressions that match the fully-qualified names (<schemaName>.<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.fetch.size

2000

During a snapshot, the connector reads table content in batches of rows. This property specifies the maximum number of rows in a batch.

snapshot.lock.timeout.ms

10000

Positive integer value 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 interval, the snapshot fails. How the connector performs snapshots provides details. Other possible settings are:

0 - The connector immediately fails when it cannot obtain a lock.

-1 - The connector waits infinitely.

snapshot.select.statement.overrides

No default

Specifies the table rows to include in a snapshot. Use the property if you want a snapshot to include only a subset of the rows in a table. This property affects snapshots only. It does not apply to events that the connector reads from the log.

The property contains a comma-separated list of fully-qualified table names in the form <schemaName>.<tableName>. For example,

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

For each table in the list, add a further configuration property that specifies the SELECT statement for the connector to run on the table when it takes a snapshot. The specified SELECT statement determines the subset of table rows to include in the snapshot. Use the following format to specify the name of this SELECT statement property:

snapshot.select.statement.overrides.<schemaName>.<tableName>. For example, snapshot.select.statement.overrides.customers.orders.

Example:

From a customers.orders table that includes the soft-delete column, delete_flag, add the following properties if you want a snapshot to include only those records that are not soft-deleted:

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

In the resulting snapshot, the connector includes only the records for which delete_flag = 0.

provide.transaction.metadata

false

Determines whether the connector generates events with transaction boundaries and enriches change event envelopes with transaction metadata. Specify true if you want the connector to do this. See Transaction metadata for details.

skipped.operations

t

A comma-separated list of operation types that will be skipped during streaming. The operations include: c for inserts/create, u for updates, d for deletes, t for truncates, and none to not skip any operations. By default, truncate operations are skipped (not emitted by this connector).

signal.data.collection

No default

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:
<schemaName>.<tableName>

signal.enabled.channels

source

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

  • source
  • kafka
  • file
  • jmx

notification.enabled.channels

No default

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

  • sink
  • log
  • jmx

incremental.snapshot.chunk.size

1024

The maximum number of rows that the connector fetches and reads into memory during an incremental snapshot chunk. Increasing the chunk size provides greater efficiency, because the snapshot runs fewer snapshot queries of a greater size. However, larger chunk sizes also require more memory to buffer the snapshot data. Adjust the chunk size to a value that provides the best performance in your environment.

topic.naming.strategy

io.debezium.schema.SchemaTopicNamingStrategy

The name of the TopicNamingStrategy class that should be used to determine the topic name for data change, schema change, transaction, heartbeat event etc., defaults to SchemaTopicNamingStrategy.

topic.delimiter

.

Specify the delimiter for topic name, defaults to ..

topic.cache.size

10000

The size used for holding the topic names in bounded concurrent hash map. This cache will help to determine the topic name corresponding to a given data collection.

topic.heartbeat.prefix

__debezium-heartbeat

Controls the name of the topic to which the connector sends heartbeat messages. The topic name has this pattern:

topic.heartbeat.prefix.topic.prefix

For example, if the topic prefix is fulfillment, the default topic name is __debezium-heartbeat.fulfillment.

topic.transaction

transaction

Controls the name of the topic to which the connector sends transaction metadata messages. The topic name has this pattern:

topic.prefix.topic.transaction

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

snapshot.max.threads

1

Specifies the number of threads that the connector uses when performing an initial snapshot. To enable parallel initial snapshots, set the property to a value greater than 1. In a parallel initial snapshot, the connector processes multiple tables concurrently.

Important

Parallel initial snapshots is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process. For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

errors.max.retries

-1

The maximum number of retries on retriable errors (e.g. connection errors) before failing (-1 = no limit, 0 = disabled, > 0 = num of retries).

Debezium connector database schema history configuration properties

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

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

Table 3.19. Connector database schema history configuration properties
PropertyDefaultDescription

schema.history.internal.kafka.topic

No default

The full name of the Kafka topic where the connector stores the database schema history.

schema.history.internal.kafka.bootstrap.servers

No default

A list of host/port pairs that the connector uses for establishing an initial connection to the Kafka cluster. This connection is used for retrieving the database schema history previously stored by the connector, and for writing each DDL statement read from the source database. Each pair should point to the same Kafka cluster used by the Kafka Connect process.

schema.history.internal.kafka.recovery.poll.interval.ms

100

An integer value that specifies the maximum number of milliseconds the connector should wait during startup/recovery while polling for persisted data. The default is 100ms.

schema.history.internal.kafka.query.timeout.ms

3000

An integer value that specifies the maximum number of milliseconds the connector should wait while fetching cluster information using Kafka admin client.

schema.history.internal.kafka.create.timeout.ms

30000

An integer value that specifies the maximum number of milliseconds the connector should wait while create kafka history topic using Kafka admin client.

schema.history.internal.kafka.recovery.attempts

100

The maximum number of times that the connector should try to read persisted history data before the connector recovery fails with an error. The maximum amount of time to wait after receiving no data is recovery.attempts × recovery.poll.interval.ms.

schema.history.internal.skip.unparseable.ddl

false

A Boolean value that specifies whether the connector should ignore malformed or unknown database statements or stop processing so a human can fix the issue. The safe default is false. Skipping should be used only with care as it can lead to data loss or mangling when the binlog is being processed.

schema.history.internal.store.only.captured.tables.ddl

false

A Boolean value that specifies whether the connector records schema structures from all tables in a schema or database, or only from tables that are designated for capture.
Specify one of the following values:

false (default)
During a database snapshot, the connector records the schema data for all non-system tables in the database, including tables that are not designated for capture. It’s best to retain the default setting. If you later decide to capture changes from tables that you did not originally designate for capture, the connector can easily begin to capture data from those tables, because their schema structure is already stored in the schema history topic. Debezium requires the schema history of a table so that it can identify the structure that was present at the time that a change event occurred.
true
During a database snapshot, the connector records the table schemas only for the tables from which Debezium captures change events. If you change the default value, and you later configure the connector to capture data from other tables in the database, the connector lacks the schema information that it requires to capture change events from the tables.

schema.history.internal.store.only.captured.databases.ddl

false

A Boolean value that specifies whether the connector records schema structures from all logical databases in the database instance.
Specify one of the following values:

true
The connector records schema structures only for tables in the logical database and schema from which Debezium captures change events.
false
The connector records schema structures for all logical databases.
Note

The default value is true for MySQL Connector

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


Debezium relies on a Kafka producer to write schema changes to database schema history topics. Similarly, it relies on a Kafka consumer to read from database schema history topics when a connector starts. You define the configuration for the Kafka producer and consumer clients by assigning values to a set of pass-through configuration properties that begin with the schema.history.internal.producer.* and schema.history.internal.consumer.* prefixes. The pass-through producer and consumer database schema history properties control a range of behaviors, such as how these clients secure connections with the Kafka broker, as shown in the following example:

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

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

Debezium strips the prefix from the property name before it passes the property to the Kafka client.

See the Kafka documentation for more details about Kafka producer configuration properties and Kafka consumer configuration properties.

Debezium connector Kafka signals configuration properties

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

The following table describes the Kafka signal properties.

Table 3.20. Kafka signals configuration properties
PropertyDefaultDescription

signal.kafka.topic

<topic.prefix>-signal

The name of the Kafka topic that the connector monitors for ad hoc signals.

Note

If automatic topic creation is disabled, you must manually create the required signaling topic. A signaling topic is required to preserve signal ordering. The signaling topic must have a single partition.

signal.kafka.groupId

kafka-signal

The name of the group ID that is used by Kafka consumers.

signal.kafka.bootstrap.servers

No default

A list of host/port pairs that the connector uses for establishing an initial connection to the Kafka cluster. Each pair references the Kafka cluster that is used by the Debezium Kafka Connect process.

signal.kafka.poll.timeout.ms

100

An integer value that specifies the maximum number of milliseconds that the connector waits when polling signals.

Debezium connector pass-through signals Kafka consumer client configuration properties

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

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

Debezium connector sink notifications configuration properties

The following table describes the notification properties.

Table 3.21. Sink notification configuration properties
PropertyDefaultDescription

notification.sink.topic.name

No default

The name of the topic that receives notifications from Debezium. This property is required when you configure the notification.enabled.channels property to include sink as one of the enabled notification channels.

Debezium connector pass-through database driver configuration properties

The Debezium connector provides for pass-through configuration of the database driver. Pass-through database properties begin with the prefix driver.*. For example, the connector passes properties such as driver.foobar=false to the JDBC URL.

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

3.7. Monitoring Debezium Db2 connector performance

The Debezium Db2 connector provides three types of metrics that are in addition to the built-in support for JMX metrics that Apache ZooKeeper, Apache 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 capturing changes and streaming change event records.
  • Schema history metrics provide information about the status of the connector’s schema history.

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

3.7.1. Monitoring Debezium during snapshots of Db2 databases

The MBean is debezium.db2:type=connector-metrics,context=snapshot,server=<topic.prefix>.

Snapshot metrics are not exposed unless a snapshot operation is active, or if a snapshot has occurred since the last connector start.

The following table lists the shapshot metrics that are available.

AttributesTypeDescription

LastEvent

string

The last snapshot event that the connector has read.

MilliSecondsSinceLastEvent

long

The number of milliseconds since the connector has read and processed the most recent event.

TotalNumberOfEventsSeen

long

The total number of events that this connector has seen since last started or reset.

NumberOfEventsFiltered

long

The number of events that have been filtered by include/exclude list filtering rules configured on the connector.

CapturedTables

string[]

The list of tables that are captured by the connector.

QueueTotalCapacity

int

The length the queue used to pass events between the snapshotter and the main Kafka Connect loop.

QueueRemainingCapacity

int

The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop.

TotalTableCount

int

The total number of tables that are being included in the snapshot.

RemainingTableCount

int

The number of tables that the snapshot has yet to copy.

SnapshotRunning

boolean

Whether the snapshot was started.

SnapshotPaused

boolean

Whether the snapshot was paused.

SnapshotAborted

boolean

Whether the snapshot was aborted.

SnapshotCompleted

boolean

Whether the snapshot completed.

SnapshotDurationInSeconds

long

The total number of seconds that the snapshot has taken so far, even if not complete. Includes also time when snapshot was paused.

SnapshotPausedDurationInSeconds

long

The total number of seconds that the snapshot was paused. If the snapshot was paused several times, the paused time adds up.

RowsScanned

Map<String, Long>

Map containing the number of rows scanned for each table in the snapshot. Tables are incrementally added to the Map during processing. Updates every 10,000 rows scanned and upon completing a table.

MaxQueueSizeInBytes

long

The maximum buffer of the queue in bytes. This metric is available if max.queue.size.in.bytes is set to a positive long value.

CurrentQueueSizeInBytes

long

The current volume, in bytes, of records in the queue.

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

AttributesTypeDescription

ChunkId

string

The identifier of the current snapshot chunk.

ChunkFrom

string

The lower bound of the primary key set defining the current chunk.

ChunkTo

string

The upper bound of the primary key set defining the current chunk.

TableFrom

string

The lower bound of the primary key set of the currently snapshotted table.

TableTo

string

The upper bound of the primary key set of the currently snapshotted table.

3.7.2. Monitoring Debezium Db2 connector record streaming

The MBean is debezium.db2:type=connector-metrics,context=streaming,server=<topic.prefix>.

The following table lists the streaming metrics that are available.

AttributesTypeDescription

LastEvent

string

The last streaming event that the connector has read.

MilliSecondsSinceLastEvent

long

The number of milliseconds since the connector has read and processed the most recent event.

TotalNumberOfEventsSeen

long

The total number of events that this connector has seen since the last start or metrics reset.

TotalNumberOfCreateEventsSeen

long

The total number of create events that this connector has seen since the last start or metrics reset.

TotalNumberOfUpdateEventsSeen

long

The total number of update events that this connector has seen since the last start or metrics reset.

TotalNumberOfDeleteEventsSeen

long

The total number of delete events that this connector has seen since the last start or metrics reset.

NumberOfEventsFiltered

long

The number of events that have been filtered by include/exclude list filtering rules configured on the connector.

CapturedTables

string[]

The list of tables that are captured by the connector.

QueueTotalCapacity

int

The length the queue used to pass events between the streamer and the main Kafka Connect loop.

QueueRemainingCapacity

int

The free capacity of the queue used to pass events between the streamer and the main Kafka Connect loop.

Connected

boolean

Flag that denotes whether the connector is currently connected to the database server.

MilliSecondsBehindSource

long

The number of milliseconds between the last change event’s timestamp and the connector processing it. The values will incoporate any differences between the clocks on the machines where the database server and the connector are running.

NumberOfCommittedTransactions

long

The number of processed transactions that were committed.

SourceEventPosition

Map<String, String>

The coordinates of the last received event.

LastTransactionId

string

Transaction identifier of the last processed transaction.

MaxQueueSizeInBytes

long

The maximum buffer of the queue in bytes. This metric is available if max.queue.size.in.bytes is set to a positive long value.

CurrentQueueSizeInBytes

long

The current volume, in bytes, of records in the queue.

3.7.3. Monitoring Debezium Db2 connector schema history

The MBean is debezium.db2:type=connector-metrics,context=schema-history,server=<topic.prefix>.

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

AttributesTypeDescription

Status

string

One of STOPPED, RECOVERING (recovering history from the storage), RUNNING describing the state of the database schema history.

RecoveryStartTime

long

The time in epoch seconds at what recovery has started.

ChangesRecovered

long

The number of changes that were read during recovery phase.

ChangesApplied

long

the total number of schema changes applied during recovery and runtime.

MilliSecondsSinceLast​RecoveredChange

long

The number of milliseconds that elapsed since the last change was recovered from the history store.

MilliSecondsSinceLast​AppliedChange

long

The number of milliseconds that elapsed since the last change was applied.

LastRecoveredChange

string

The string representation of the last change recovered from the history store.

LastAppliedChange

string

The string representation of the last applied change.

3.8. Managing Debezium Db2 connectors

After you deploy a Debezium Db2 connector, use the Debezium management UDFs to control Db2 replication (ASN) with SQL commands. Some of the UDFs expect a return value in which case you use the SQL VALUE statement to invoke them. For other UDFs, use the SQL CALL statement.

Table 3.22. Descriptions of Debezium management UDFs
TaskCommand and notes

Start the ASN agent

VALUES ASNCDC.ASNCDCSERVICES('start','asncdc');

Stop the ASN agent

VALUES ASNCDC.ASNCDCSERVICES('stop','asncdc');

Check the status of the ASN agent

VALUES ASNCDC.ASNCDCSERVICES('status','asncdc');

Put a table into capture mode

CALL ASNCDC.ADDTABLE('MYSCHEMA', 'MYTABLE');

Replace MYSCHEMA with the name of the schema that contains the table you want to put into capture mode. Likewise, replace MYTABLE with the name of the table to put into capture mode.

Remove a table from capture mode

CALL ASNCDC.REMOVETABLE('MYSCHEMA', 'MYTABLE');

Reinitialize the ASN service

VALUES ASNCDC.ASNCDCSERVICES('reinit','asncdc');

Do this after you put a table into capture mode or after you remove a table from capture mode.

3.9. Updating schemas for Db2 tables in capture mode for Debezium connectors

While a Debezium Db2 connector can capture schema changes, to update a schema, you must collaborate with a database administrator to ensure that the connector continues to produce change events. This is required by the way that Db2 implements replication.

For each table in capture mode, the replication feature in Db2 creates a change-data table that contains all changes to that source table. However, change-data table schemas are static. If you update the schema for a table in capture mode then you must also update the schema of its corresponding change-data table. A Debezium Db2 connector cannot do this. A database administrator with elevated privileges must update schemas for tables that are in capture mode.

Warning

It is vital to execute a schema update procedure completely before there is a new schema update on the same table. Consequently, the recommendation is to execute all DDLs in a single batch so the schema update procedure is done only once.

There are generally two procedures for updating table schemas:

Each approach has advantages and disadvantages.

3.9.1. Performing offline schema updates for Debezium Db2 connectors

You stop the Debezium Db2 connector before you perform an offline schema update. While this is the safer schema update procedure, it might not be feasible for applications with high-availability requirements.

Prerequisites

  • One or more tables that are in capture mode require schema updates.

Procedure

  1. Suspend the application that updates the database.
  2. Wait for the Debezium connector to stream all unstreamed change event records.
  3. Stop the Debezium connector.
  4. Apply all changes to the source table schema.
  5. In the ASN register table, mark the tables with updated schemas as INACTIVE.
  6. Reinitialize the ASN capture service.
  7. Remove the source table with the old schema from capture mode by running the Debezium UDF for removing tables from capture mode.
  8. Add the source table with the new schema to capture mode by running the Debezium UDF for adding tables to capture mode.
  9. In the ASN register table, mark the updated source tables as ACTIVE.
  10. Reinitialize the ASN capture service.
  11. Resume the application that updates the database.
  12. Restart the Debezium connector.

3.9.2. Performing online schema updates for Debezium Db2 connectors

An online schema update does not require application and data processing downtime. That is, you do not stop the Debezium Db2 connector before you perform an online schema update. Also, an online schema update procedure is simpler than the procedure for an offline schema update.

However, when a table is in capture mode, after a change to a column name, the Db2 replication feature continues to use the old column name. The new column name does not appear in Debezium change events. You must restart the connector to see the new column name in change events.

Prerequisites

  • One or more tables that are in capture mode require schema updates.

Procedure when adding a column to the end of a table

  1. Lock the source tables whose schema you want to change.
  2. In the ASN register table, mark the locked tables as INACTIVE.
  3. Reinitialize the ASN capture service.
  4. Apply all changes to the schemas for the source tables.
  5. Apply all changes to the schemas for the corresponding change-data tables.
  6. In the ASN register table, mark the source tables as ACTIVE.
  7. Reinitialize the ASN capture service.
  8. Optional. Restart the connector to see updated column names in change events.

Procedure when adding a column to the middle of a table

  1. Lock the source table(s) to be changed.
  2. In the ASN register table, mark the locked tables as INACTIVE.
  3. Reinitialize the ASN capture service.
  4. For each source table to be changed:

    1. Export the data in the source table.
    2. Truncate the source table.
    3. Alter the source table and add the column.
    4. Load the exported data into the altered source table.
    5. Export the data in the source table’s corresponding change-data table.
    6. Truncate the change-data table.
    7. Alter the change-data table and add the column.
    8. Load the exported data into the altered change-data table.
  5. In the ASN register table, mark the tables as INACTIVE. This marks the old change-data tables as inactive, which allows the data in them to remain but they are no longer updated.
  6. Reinitialize the ASN capture service.
  7. Optional. Restart the connector to see updated column names in change events.

Chapter 4. Debezium connector for JDBC (Developer Preview)

The Debezium JDBC connector is a Kafka Connect sink connector implementation that can consume events from multiple source topics, and then write those events to a relational database by using a JDBC driver. This connector supports a wide variety of database dialects, including Db2, MySQL, Oracle, PostgreSQL, and SQL Server.

Important

The Debezium JDBC connector is Developer Preview software only. Developer Preview software is not supported by Red Hat in any way and is not functionally complete or production-ready. Do not use Developer Preview software for production or business-critical workloads. Developer Preview software provides early access to upcoming product software in advance of its possible inclusion in a Red Hat product offering. Customers can use this software to test functionality and provide feedback during the development process. This software is subject to change or removal at any time, and has received limited testing. Red Hat might provide ways to submit feedback on Developer Preview software without an associated SLA.

For more information about the support scope of Red Hat Developer Preview software, see Developer Preview Support Scope.

4.1. How the Debezium JDBC connector works

The Debezium JDBC connector is a Kafka Connect sink connector, and therefore requires the Kafka Connect runtime. The connector periodically polls the Kafka topics that it subscribes to, consumes events from those topics, and then writes the events to the configured relational database. The connector supports idempotent write operations by using upsert semantics and basic schema evolution.

The Debezium JDBC connector provides the following features:

4.1.1. Description of how the Debezium JDBC connector consumes complex change events

By default, Debezium source connectors produce complex, hierarchical change events. When Debezium connectors are used with other JDBC sink connector implementations, you might need to apply the ExtractNewRecordState single message transformation (SMT) to flatten the payload of change events, so that they can be consumed by the sink implementation. If you run the Debezium JDBC sink connector, it’s not necessary to deploy the SMT, because the Debezium sink connector can consume native Debezium change events directly, without the use of a transformation.

When the JDBC sink connector consumes a complex change event from a Debezium source connector, it extracts the values from the after section of the original insert or update event. When a delete event is consumed by the sink connector, no part of the event’s payload is consulted.

Important

The Debezium JDBC sink connector is not designed to read from schema change topics. If your source connector is configured to capture schema changes, in the JDBC connector configuration, set the topics or topics.regex properties so that the connector does not consume from schema change topics.

4.1.2. Description of Debezium JDBC connector at-least-once delivery

The Debezium JDBC sink connector guarantees that events that is consumes from Kafka topics are processed at least once.

4.1.3. Description of Debezium JDBC use of multiple tasks

You can run the Debezium JDBC sink connector across multiple Kafka Connect tasks. To run the connector across multiple tasks, set the tasks.max configuration property to the number of tasks that you want the connector to use. The Kafka Connect runtime starts the specified number of tasks, and runs one instance of the connector per task. Multiple tasks can improve performance by reading and processing changes from multiple source topics in parallel.

4.1.4. Description of Debezium JDBC connector data and column type mappings

To enable the Debezium JDBC sink connector to correctly map the data type from an inbound message field to an outbound message field, the connector requires information about the data type of each field that is present in the source event. The connector supports a wide range of column type mappings across different database dialects. To correctly convert the destination column type from the type metadata in an event field, the connector applies the data type mappings that are defined for the source database. You can enhance the way that the connector resolves data types for a column by setting the column.propagate.source.type or datatype.propagate.source.type options in the source connector configuration. When you enable these options, Debezium includes extra parameter metadata, which assists the JDBC sink connector in more accurately resolving the data type of destination columns.

For the Debezium JDBC sink connector to process events from a Kafka topic, the Kafka topic message key, when present, must be a primitive data type or a Struct. In addition, the payload of the source message must be a Struct that has either a flattened structure with no nested struct types, or a nested struct layout that conforms to Debezium’s complex, hierarchical structure.

If the structure of the events in the Kafka topic do not adhere to these rules, you must implement a custom single message transformation to convert the structure of the source events into a usable format.

4.1.5. Description of how the Debezium JDBC connector handles primary keys in source events

By default, the Debezium JDBC sink connector does not transform any of the fields in the source event into the primary key for the event. Unfortunately, the lack of a stable primary key can complicate event processing, depending on your business requirements, or when the sink connector uses upsert semantics. To define a consistent primary key, you can configure the connector to use one of the primary key modes described in the following table:

ModeDescription

none

No primary key fields are specified when creating the table.

kafka

The primary key consists of the following three columns:

  • __connect_topic
  • __connect_partition
  • __connect_offset

The values for these columns are sourced from the coordinates of the Kafka event.

record_key

The primary key is composed of the Kafka event’s key.

If the primary key is a primitive type, specify the name of the column to be used by setting the primary.key.fields property. If the primary key is a struct type, the fields in the struct are mapped as columns of the primary key. You can use the primary.key.fields property to restrict the primary key to a subset of columns.

record_value

The primary key is composed of the Kafka event’s value.

Because the value of a Kafka event is always a Struct, by default, all of the fields in the value become columns of the primary key. To use a subset of fields in the primary key, set the primary.key.fields property to specify a comma-separated list of fields in the value from which you want to derive the primary key columns.

Important

Some database dialects might throw an exception if you set the primary.key.mode to kafka and set schema.evolution to basic. This exception occurs when a dialect maps a STRING data type mapping to a variable length string data type such as TEXT or CLOB, and the dialect does not allow primary key columns to have unbounded lengths. To avoid this problem, apply the following settings in your environment:

  • Do not set schema.evolution to basic.
  • Create the database table and primary key mappings in advance.

4.1.6. Configuring the Debezium JDBC connector to delete rows when consuming DELETE or tombstone events

The Debezium JDBC sink connector can delete rows in the destination database when a DELETE or tombstone event is consumed. By default, the JDBC sink connector does not enable delete mode.

If you want to the connector to remove rows, you must explicitly set delete.enabled=true in the connector configuration. To use this mode you must also set primary.key.fields to a value other than none. The preceding configuration is necessary, because deletes are executed based on the primary key mapping, so if a destination table has no primary key mapping, the connector is unable to delete rows.

4.1.7. Enabling the connector to perform idempotent writes

The Debezium JDBC sink connector can perform idempotent writes, enabling it to replay the same records repeatedly and not change the final database state.

To enable the connector to perform idempotent writes, you must be explicitly set the insert.mode for the connector to upsert. An upsert operation is applied as either an update or an insert, depending on whether the specified primary key already exists.

If the primary key value already exists, the operation updates values in the row. If the specified primary key value doesn’t exist, an insert adds a new row.

Each database dialect handles idempotent writes differently, because there is no SQL standard for upsert operations. The following table shows the upsert DML syntax for the database dialects that Debezium supports:

DialectUpsert Syntax

Db2

MERGE …​

MySQL

INSERT …​ ON DUPLICATE KEY UPDATE …​

Oracle

MERGE …​

PostgreSQL

INSERT …​ ON CONFLICT …​ DO UPDATE SET …​

SQL Server

MERGE …​

4.1.8. Schema evolution modes for the Debezium JDBC connector

You can use the following schema evolution modes with the Debezium JDBC sink connector:

ModeDescription

none

The connector does not perform any DDL schema evolution.

basic

The connector automatically detects fields that are in the event payload but that do not exist in the destination table. The connector alters the destination table to add the new fields.

When schema.evolution is set to basic, the connector automatically creates or alters the destination database table according to the structure of the incoming event.

When an event is received from a topic for the first time, and the destination table does not yet exist, the Debezium JDBC sink connector uses the event’s key, or the schema structure of the record to resolve the column structure of the table. If schema evolution is enabled, the connector prepares and executes a CREATE TABLE SQL statement before it applies the DML event to the destination table.

When the Debezium JDBC connector receives an event from a topic, if the schema structure of the record differs from the schema structure of the destination table, the connector uses either the event’s key or its schema structure to identify which columns are new, and must be added to the database table. If schema evolution is enabled, the connector prepares and executes an ALTER TABLE SQL statement before it applies the DML event to the destination table. Because changing column data types, dropping columns, and adjusting primary keys can be considered dangerous operations, the connector is prohibited from performing these operations.

The schema of each field determines whether a column is NULL or NOT NULL. The schema also defines the default values for each column. If the connector attempts to create a table with a nullability setting or a default value that don’t want, you must either create the table manually, ahead of time, or adjust the schema of the associated field before the sink connector processes the event. To adjust nullability settings or default values, you can introduce a custom single message transformation that applies changes in the pipeline, or modifies the column state defined in the source database.

A field’s data type is resolved based on a predefined set of mappings. For more information, see Section 4.2, “How the Debezium JDBC connector maps data types”.

Important

When you introduce new fields to the event structure of tables that already exist in the destination database, you must define the new fields as optional, or the fields must have a default value specified in the database schema. If you want a field to be removed from the destination table, use one of the following options:

  • Remove the field manually.
  • Drop the column.
  • Assign a default value to the field.
  • Define the field a nullable.

4.1.9. Specifying options to define the letter case of destination table and column names

The Debezium JDBC sink connector consumes Kafka messages by constructing either DDL (schema changes) or DML (data changes) SQL statements that are executed on the destination database. By default, the connector uses the names of the source topic and the event fields as the basis for the table and column names in the destination table. The constructed SQL does not automatically delimit identifiers with quotes to preserve the case of the original strings. As a result, by default, the text case of table or column names in the destination database depends entirely on how the database handles name strings when the case is not specified.

For example, if the destination database dialect is Oracle and the event’s topic is orders, the destination table will be created as ORDERS because Oracle defaults to upper-case names when the name is not quoted. Similarly, if the destination database dialect is PostgreSQL and the event’s topic is ORDERS, the destination table will be created as orders because PostgreSQL defaults to lower-case names when the name is not quoted.

To explicitly preserve the case of the table and field names that are present in a Kafka event, in the connector configuration, set the value of the quote.identifiers property to true. When this options is set, when an incoming event is for a topic called orders, and the destination database dialect is Oracle, the connector creates a table with the name orders, because the constructed SQL defines the name of the table as "orders". Enabling quoting results in the same behavior when the connector creates column names.

4.2. How the Debezium JDBC connector maps data types

The Debezium JDBC sink connector resolves a column’s data type by using a logical or primitive type-mapping system. Primitive types include values such as integers, floating points, Booleans, strings, and bytes. Typically, these types are represented with a specific Kafka Connect Schema type code only. Logical data types are more often complex types, including values such as Struct-based types that have a fixed set of field names and schema, or values that are represented with a specific encoding, such as number of days since epoch.

The following examples show representative structures of primitive and logical data types:

Primitive field schema

{
  "schema": {
    "type": "INT64"
  }
}

Logical field schema

[
  "schema": {
    "type": "INT64",
    "name": "org.apache.kafka.connect.data.Date"
  }
]

Kafka Connect is not the only source for these complex, logical types. In fact, Debezium source connectors generate change events that have fields with similar logical types to represent a variety of different data types, including but not limited to, timestamps, dates, and even JSON data.

The Debezium JDBC sink connector uses these primitive and logical types to resolve a column’s type to a JDBC SQL code, which represents a column’s type. These JDBC SQL codes are then used by the underlying Hibernate persistence framework to resolve the column’s type to a logical data type for the dialect in use. The following tables illustrate the primitive and logical mappings between Kafka Connect and JDBC SQL types, and between Debezium and JDBC SQL types. The actual final column type varies with for each database type.

Table 4.1. Mappings between Kafka Connect Primitives and Column Data Types
Primitive TypeJDBC SQL Type

INT8

Types.TINYINT

INT16

Types.SMALLINT

INT32

Types.INTEGER

INT64

Types.BIGINT

FLOAT32

Types.FLOAT

FLOAT64

Types.DOUBLE

BOOLEAN

Types.BOOLEAN

STRING

Types.CHAR, Types.NCHAR, Types.VARCHAR, Types.NVARCHAR

BYTES

Types.VARBINARY

Table 4.2. Mappings between Kafka Connect Logical Types and Column Data Types
Logical TypeJDBC SQL Type

org.apache.kafka.connect.data.Decimal

Types.DECIMAL

org.apache.kafka.connect.data.Date

Types.DATE

org.apache.kafka.connect.data.Time

Types.TIMESTAMP

org.apache.kafka.connect.data.Timestamp

Types.TIMESTAMP

Table 4.3. Mappings between Debezium Logical Types and Column Data Types
Logical TypeJDBC SQL Type

io.debezium.time.Date

Types.DATE

io.debezium.time.Time

Types.TIMESTAMP

io.debezium.time.MicroTime

Types.TIMESTAMP

io.debezium.time.NanoTime

Types.TIMESTAMP

io.debezium.time.ZonedTime

Types.TIME_WITH_TIMEZONE

io.debezium.time.Timestamp

Types.TIMESTAMP

io.debezium.time.MicroTimestamp

Types.TIMESTAMP

io.debezium.time.NanoTimestamp

Types.TIMESTAMP

io.debezium.time.ZonedTimestamp

Types.TIMESTAMP_WITH_TIMEZONE

io.debezium.data.VariableScaleDecimal

Types.DOUBLE

Important

If the database does not support time or timestamps with time zones, the mapping resolves to its equivalent without timezones.

Table 4.4. Mappings between Debezium dialect-specific Logical Types and Column Data Types
Logical TypeMySQL SQL TypePostgreSQL SQL TypeSQL Server SQL Type

io.debezium.data.Bits

bit(n)

bit(n) or bit varying

varbinary(n)

io.debezium.data.Enum

enum

Types.VARCHAR

n/a

io.debezium.data.Json

json

json

n/a

io.debezium.data.EnumSet

set

n/a

n/a

io.debezium.time.Year

year(n)

n/a

n/a

io.debezium.time.MicroDuration

n/a

interval

n/a

io.debezium.data.Ltree

n/a

ltree

n/a

io.debezium.data.Uuid

n/a

uuid

n/a

io.debezium.data.Xml

n/a

xml

xml

In addition to the primitive and logical mappings above, if the source of the change events is a Debezium source connector, the resolution of the column type, along with its length, precision, and scale, can be further influenced by enabling column or data type propagation. To enforce propagation, one of the following properties must be set in the source connector configuration:

  • column.propagate.source.type
  • datatype.propagate.source.type

The Debezium JDBC sink connector applies the values with the higher precedence.

For example, let’s say the following field schema is included in a change event:

Debezium change event field schema with column or data type propagation enabled

{
  "schema": {
    "type": "INT8",
    "parameters": {
      "__debezium.source.column.type": "TINYINT",
      "__debezium.source.column.length": "1"
    }
  }
}

In the preceding example, if no schema parameters are set, the Debezium JDBC sink connector maps this field to a column type of Types.SMALLINT. Types.SMALLINT can have different logical database types, depending on the database dialect. For MySQL, the column type in the example converts to a TINYINT column type with no specified length. If column or data type propagation is enabled for the source connector, the Debezium JDBC sink connector uses the mapping information to refine the data type mapping process and create a column with the type TINYINT(1).

Note

Typically, the effect of using column or data type propagation is much greater when the same type of database is used for both the source and sink database.

4.3. Deployment of Debezium JDBC connectors

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

Prerequisites

Procedure

  1. Download the Debezium JDBC connector plug-in archive.
  2. Extract the files into your Kafka Connect environment.
  3. Optionally download the JDBC driver from Maven Central and extract the downloaded driver file to the directory that contains the JDBC sink connector JAR file.

    Note

    Drivers for Oracle and Db2 are not included with the JDBC sink connector. You must download the drivers and install them manually.

  4. Add the driver JAR files to the path where the JDBC sink connector has been installed.
  5. Make sure that the path where you install the JDBC sink connector is part of the Kafka Connect plugin.path.
  6. Restart the Kafka Connect process to pick up the new JAR files.

4.3.1. Debezium JDBC connector configuration

Typically, you register a Debezium JDBC connector by submitting a JSON request that specifies the configuration properties for the connector. The following example shows a JSON request for registering an instance of the Debezium JDBC sink connector that consumes events from a topic called orders with the most common configuration settings:

Example: Debezium JDBC connector configuration

{
    "name": "jdbc-connector",  1
    "config": {
        "connector.class": "io.debezium.connector.jdbc.JdbcSinkConnector",  2
        "tasks.max": "1",  3
        "connection.url": "jdbc:postgresql://localhost/db",  4
        "connection.username": "pguser",  5
        "connection.password": "pgpassword",  6
        "insert.mode": "upsert",  7
        "delete.enabled": "true",  8
        "primary.key.mode": "record_key",  9
        "schema.evolution": "basic",  10
        "database.time_zone": "UTC"  11
    }
}

Table 4.5. Descriptions of JDBC connector configuration settings
ItemDescription

1

The name that is assigned to the connector when you register it with Kafka Connect service.

2

The name of the JDBC sink connector class.

3

The maximum number of tasks to create for this connector.

4

The JDBC URL that the connector uses to connect to the sink database that it writes to.

5

The name of the database user that is used for authentication.

6

The password of the database user used for authentication.

7

The insert.mode that the connector uses.

8

Enables the deletion of records in the database. For more information, see the delete.enabled configuration property.

9

Specifies the method used to resolve primary key columns. For more information, see the primary.key.mode configuration property.

10

Enables the connector to evolve the destination database’s schema. For more information, see the schema.evolution configuration property.

11

Specifies the timezone used when writing temporal field types.

12

List of topics to consume, separated by commas.

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

You can send this configuration with a POST command to a running Kafka Connect service. The service records the configuration and starts a sink connector task(s) that performs the following operations:

  • Connects to the database.
  • Consumes events from subscribed Kafka topics.
  • Writes the events to the configured database.

4.4. Descriptions of Debezium JDBC connector configuration properties

The Debezium JDBC sink connector has several configuration properties that you can use to achieve the connector behavior that meets your needs. Many properties have default values. Information about the properties is organized as follows:

Table 4.6. Generic properties
PropertyDefaultDescription

name

No default

Unique name for the connector. A failure results if you attempt to reuse this name when registering a connector. This property is required by all Kafka Connect connectors.

connector.class

No default

The name of the Java class for the connector. For the Debezium JDBC connector, specify the value io.debezium.connector.jdbc.JdbcSinkConnector.

tasks.max

1

Maximum number of tasks to use for this connector.

topics

No default

List of topics to consume, separated by commas. Do not use this property in combination with the topics.regex property.

topics.regex

No default

A regular expression that specifies the topics to consume. Internally, the regular expression is compiled to a java.util.regex.Pattern. Do not use this property in combination with the topics property.

Table 4.7. JDBC connector connection properties
PropertyDefaultDescription

connection.url

No default

The JDBC connection URL used to connect to the database.

connection.username

No default

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

connection.password

No default

The password that the connector uses to connect to the database.

connection.pool.min_size

5

Specifies the minimum number of connections in the pool.

connection.pool.min_size

32

Specifies the maximum number of concurrent connections that the pool maintains.

connection.pool.acquire_increment

32

Specifies the number of connections that the connector attempts to acquire if the connection pool exceeds its maximum size.

connection.pool.timeout

1800

Specifies the number of seconds that an unused connection is kept before it is discarded.

Table 4.8. JDBC connector runtime properties
PropertyDefaultDescription

database.time_zone

UTC

Specifies the timezone used when inserting JDBC temporal values.

delete.enabled

false

Specifies whether the connector processes DELETE or tombstone events and removes the corresponding row from the database. Use of this option requires that you set the primary.key.mode to record.key.

insert.mode

insert

Specifies the strategy used to insert events into the database. The following options are available:

insert
Specifies that all events should construct INSERT-based SQL statements. Use this option only when no primary key is used, or when you can be certain that no updates can occur to rows with existing primary key values.
update
Specifies that all events should construct UPDATE-based SQL statements. Use this option only when you can be certain that the connector receives only events that apply to existing rows.
upsert
Specifies that the connector adds events to the table using upsert semantics. That is, if the primary key does not exist, the connector performs an INSERT operation, and if the key does exist, the connector performs an UPDATE operation. When idempotent writes are required, the connector should be configured to use this option.

primary.key.mode

none

Specifies how the connector resolves the primary key columns from the event.

none
Specifies that no primary key columns are created.
kafka

Specifies that the connector uses Kafka coordinates as the primary key columns. The key coordinates are defined from the topic name, partition, and offset of the event, and are mapped to columns with the following names:

  • __connect_topic
  • __connect_partition
  • __connect_offset
record_key
Specifies that the primary key columns are sourced from the event’s record key. If the record key is a primitive type, the primary.key.fields property is required to specify the name of the primary key column. If the record key is a struct type, the primary.key.fields property is optional, and can be used to specify a subset of columns from the event’s key as the table’s primary key.
record_value
Specifies that the primary key columns is sourced from the event’s value. You can set the primary.key.fields property to define the primary key as a subset of fields from the event’s value; otherwise all fields are used by default.

primary.key.fields

No default

Either the name of the primary key column or a comma-separated list of fields to derive the primary key from.

When primary.key.mode is set to record_key and the event’s key is a primitive type, it is expected that this property specifies the column name to be used for the key.

When the primary.key.mode is set to record_key with a non-primitive key, or record_value, it is expected that this property specifies a comma-separated list of field names from either the key or value. If the primary.key.mode is set to record_key with a non-primitive key, or record_value, and this property is not specifies, the connector derives the primary key from all fields of either the record key or record value, depending on the specified mode.

quote.identifiers

false

Specifies whether generated SQL statements use quotation marks to delimit table and column names. See the Section 4.1.9, “Specifying options to define the letter case of destination table and column names” section for more details.

schema.evolution

none

Specifies how the connector evolves the destination table schemas. For more information, see Section 4.1.8, “Schema evolution modes for the Debezium JDBC connector”. The following options are available:

none
Specifies that the connector does not evolve the destination schema.
basic
Specifies that basic evolution occurs. The connector adds missing columns to the table by comparing the incoming event’s record schema to the database table structure.

table.name.format

${topic}

Specifies a string that determines how the destination table name is formatted, based on the topic name of the event. The placeholder, ${topic}, is replaced by the topic name.

Table 4.9. JDBC connector extendable properties
PropertyDefaultDescription

column.naming.strategy

i.d.c.j.n.DefaultColumnNamingStrategy

Specifies the fully-qualified class name of a ColumnNamingStrategy implementation that the connector uses to resolve column names from event field names.

By default, the connector uses the field name as the column name.

table.naming.strategy

i.d.c.j.n.DefaultTableNamingStrategy

Specifies the fully-qualified class name of a TableNamingStrategy implementation that the connector uses to resolve table names from incoming event topic names.

The default behavior is to:

  • Replace the ${topic} placeholder in the table.name.format configuration property with the event’s topic.
  • Sanitize the table name by replacing dots (.) with underscores (_).

4.5. JDBC connector frequently asked questions

Is the ExtractNewRecordState single message transformation required?
No, that is actually one of the differentiating factors of the Debezium JDBC connector from other implementations. While the connector is capable of ingesting flattened events like its competitors, it can also ingest Debezium’s complex change event structure natively, without requiring any specific type of transformation.
If a column’s type is changed, or if a column is renamed or dropped, is this handled by schema evolution?
No, the Debezium JDBC connector does not make any changes to existing columns. The schema evolution supported by the connector is quite basic. It simply compares the fields in the event structure to the table’s column list, and then adds any fields that are not yet defined as columns in the table. If a column’s type or default value change, the connector does not adjust them in the destination database. If a column is renamed, the old column is left as-is, and the connector appends a column with the new name to the table; however existing rows with data in the old column remain unchanged. These types of schema changes should be handled manually.
If a column’s type does not resolve to the type that I want, how can I enforce mapping to a different data type?
The Debezium JDBC connector uses a sophisticated type system to resolve a column’s data type. For details about how this type system resolves a specific field’s schema definition to a JDBC type, see the Section 4.1.4, “Description of Debezium JDBC connector data and column type mappings” section. If you want to apply a different data type mapping, define the table manually to explicitly obtain the preferred column type.
How do you specify a prefix or a suffix to the table name without changing the Kafka topic name?
In order to add a prefix or a suffix to the destination table name, adjust the table.name.format connector configuration property to apply the prefix or suffix that you want. For example, to prefix all table names with jdbc_, specify the table.name.format configuration property with a value of jdbc_${topic}. If the connector is subscribed to a topic called orders, the resulting table is created as jdbc_orders.
Why are some columns automatically quoted, even though identifier quoting is not enabled?
In some situations, specific column or table names might be explicitly quoted, even when quote.identifiers is not enabled. This is often necessary when the column or table name starts with or uses a specific convention that would otherwise be considered illegal syntax. For example, when the primary.key.mode is set to kafka, some databases only permit column names to begin with an underscore if the column’s name is quoted. Quoting behavior is dialect-specific, and varies among different types of database.

Chapter 5. Debezium connector for MongoDB

Debezium’s MongoDB connector tracks a MongoDB replica set or a MongoDB sharded cluster for document changes in databases and collections, recording those changes as events in Kafka topics. The connector automatically handles the addition or removal of shards in a sharded cluster, changes in membership of each replica set, elections within each replica set, and awaiting the resolution of communications problems.

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

Information and procedures for using a Debezium MongoDB connector is organized as follows:

5.1. Overview of Debezium MongoDB connector

MongoDB’s replication mechanism provides redundancy and high availability, and is the preferred way to run MongoDB in production. MongoDB connector captures the changes in a replica set or sharded cluster.

A MongoDB replica set consists of a set of servers that all have copies of the same data, and replication ensures that all changes made by clients to documents on the replica set’s primary are correctly applied to the other replica set’s servers, called secondaries. MongoDB replication works by having the primary record the changes in its oplog (or operation log), and then each of the secondaries reads the primary’s oplog and applies in order all of the operations to their own documents. When a new server is added to a replica set, that server first performs an snapshot of all of the databases and collections on the primary, and then reads the primary’s oplog to apply all changes that might have been made since it began the snapshot. This new server becomes a secondary (and able to handle queries) when it catches up to the tail of the primary’s oplog.

5.1.1. Description of how the MongoDB connector uses change streams to capture event records

Although the Debezium MongoDB connector does not become part of a replica set, it uses a similar replication mechanism to obtain oplog data. The main difference is that the connector does not read the oplog directly. Instead, it delegates the capture and decoding of oplog data to the MongoDB change streams feature. With change streams, the MongoDB server exposes the changes that occur in a collection as an event stream. The Debezium connector monitors the stream and then delivers the changes downstream. The first time that the connector detects a replica set, it examines the oplog to obtain the last recorded transaction, and then performs a snapshot of the primary’s databases and collections. After the connector finishes copying the data, it creates a change stream beginning from the oplog position that it read earlier.

As the MongoDB connector processes changes, it periodically records the position at which the event originated in the oplog stream. When the connector stops, it records the last oplog stream position that it processed, so that after a restart it can resume streaming from that position. In other words, the connector can be stopped, upgraded or maintained, and restarted some time later, and always pick up exactly where it left off without losing a single event. Of course, MongoDB oplogs are usually capped at a maximum size, so if the connector is stopped for long periods, operations in the oplog might be purged before the connector has a chance to read them. In this case, after a restart the connector detects the missing oplog operations, performs a snapshot, and then proceeds to stream changes.

The MongoDB connector is also quite tolerant of changes in membership and leadership of the replica sets, of additions or removals of shards within a sharded cluster, and network problems that might cause communication failures. The connector always uses the replica set’s primary node to stream changes, so when the replica set undergoes an election and a different node becomes primary, the connector will immediately stop streaming changes, connect to the new primary, and start streaming changes using the new primary node. Similarly, if connector is unable to communicate with the replica set primary, it attempts to reconnect (using exponential backoff so as to not overwhelm the network or replica set). After connection is reestablished, the connector continues to stream changes from the last event that it captured. In this way the connector dynamically adjusts to changes in replica set membership, and automatically handles communication disruptions.

5.2. How Debezium MongoDB connectors work

An overview of the MongoDB topologies that the connector supports is useful for planning your application.

When a MongoDB connector is configured and deployed, it starts by connecting to the MongoDB servers at the seed addresses, and determines the details about each of the available replica sets. Since each replica set has its own independent oplog, the connector will try to use a separate task for each replica set. The connector can limit the maximum number of tasks it will use, and if not enough tasks are available the connector will assign multiple replica sets to each task, although the task will still use a separate thread for each replica set.

Note

When running the connector against a sharded cluster, use a value of tasks.max that is greater than the number of replica sets. This will allow the connector to create one task for each replica set, and will let Kafka Connect coordinate, distribute, and manage the tasks across all of the available worker processes.

The following topics provide details about how the Debezium MongoDB connector works:

5.2.1. MongoDB topologies supported by Debezium connectors

The MongoDB connector supports the following MongoDB topologies:

MongoDB replica set

The Debezium MongoDB connector can capture changes from a single MongoDB replica set. Production replica sets require a minimum of at least three members.

To use the MongoDB connector with a replica set, you must set the value of the mongodb.connection.string property in the connector configuration to the replica set connection string. When the connector is ready to begin capturing changes from a MongoDB change stream, it starts a connection task. The connection task then uses the specified connection string to establish a connection to an available replica set member.

Warning

Due to changes in the way that the connector manages database connections, this release of Debezium no longer supports use of the mongodb.members.auto.discover property to prevent the connector from performing membership discovery.

MongoDB sharded cluster

A MongoDB sharded cluster consists of:

  • One or more shards, each deployed as a replica set;
  • A separate replica set that acts as the cluster’s configuration server
  • One or more routers (also called mongos) to which clients connect and that routes requests to the appropriate shards

    To use the MongoDB connector with a sharded cluster, in the connector configuration, set the value of the mongodb.connection.string property to the sharded cluster connection string.

Warning

The mongodb.connection.string property replaces the deprecated mongodb.hosts property that was used to provide earlier versions of the connector with the host address of the configuration server replica. In the current release, use mongodb.connection.string to provide the connector with the addresses of MongoDB routers, also known as mongos.

Note

When the connector connects to sharded cluster, it discovers the information about each replica set that represents a shard in the cluster. The connector uses a separate task to capture changes from each shard. As shards are added or removed from the cluster, the connector dynamically adjusts the numbers of tasks to compensate for the change.

MongoDB standalone server
The MongoDB connector is not capable of monitoring the changes of a standalone MongoDB server, since standalone servers do not have an oplog. The connector will work if the standalone server is converted to a replica set with one member.
Note

MongoDB does not recommend running a standalone server in production. For more information, see the MongoDB documentation.

5.2.2. How Debezium MongoDB connectors use logical names for replica sets and sharded clusters

The connector configuration property topic.prefix serves as a logical name for the MongoDB replica set or sharded cluster. The connector uses the logical name in a number of ways: as the prefix for all topic names, and as a unique identifier when recording the change stream position of each replica set.

You should give each MongoDB connector a unique logical name that meaningfully describes the source MongoDB system. We recommend logical names begin with an alphabetic or underscore character, and remaining characters that are alphanumeric or underscore.

5.2.3. How Debezium MongoDB connectors perform snapshots

When a Debezium task starts to use a replica set, it uses the connector’s logical name and the replica set name to find an offset that describes the position where the connector previously stopped reading changes. If an offset can be found and it still exists in the oplog, then the task immediately proceeds with streaming changes, starting at the recorded offset position.

However, if no offset is found, or if the oplog no longer contains that position, the task must first obtain the current state of the replica set contents by performing a snapshot. This process starts by recording the current position of the oplog and recording that as the offset (along with a flag that denotes a snapshot has been started). The task then proceeds to copy each collection, spawning as many threads as possible (up to the value of the snapshot.max.threads configuration property) to perform this work in parallel. The connector records a separate read event for each document it sees. Each read event contains the object’s identifier, the complete state of the object, and source information about the MongoDB replica set where the object was found. The source information also includes a flag that denotes that the event was produced during a snapshot.

This snapshot will continue until it has copied all collections that match the connector’s filters. If the connector is stopped before the tasks' snapshots are completed, upon restart the connector begins the snapshot again.

Note

Try to avoid task reassignment and reconfiguration while the connector performs snapshots of any replica sets. The connector generates log messages to report on the progress of the snapshot. To provide for the greatest control, run a separate Kafka Connect cluster for each connector.

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

5.2.4. Ad hoc snapshots

By default, a connector runs an initial snapshot operation only after it starts for the first time. Following this initial snapshot, under normal circumstances, the connector does not repeat the snapshot process. Any future change event data that the connector captures comes in through the streaming process only.

However, in some situations the data that the connector obtained during the initial snapshot might become stale, lost, or incomplete. To provide a mechanism for recapturing collection data, Debezium includes an option to perform ad hoc snapshots. The following changes in a database might be cause for performing an ad hoc snapshot:

  • The connector configuration is modified to capture a different set of collections.
  • 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 collection for which you previously captured a snapshot by initiating a so-called ad-hoc snapshot. Ad hoc snapshots require the use of signaling collections. You initiate an ad hoc snapshot by sending a signal request to the Debezium signaling collection.

When you initiate an ad hoc snapshot of an existing collection, the connector appends content to the topic that already exists for the collection. 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 collections to include in the snapshot. The snapshot can capture the entire contents of the database, or capture only a subset of the collections in the database. Also, the snapshot can capture a subset of the contents of the collection(s) in the database.

You specify the collections to capture by sending an execute-snapshot message to the signaling collection. Set the type of the execute-snapshot signal to incremental, and provide the names of the collections to include in the snapshot, as described in the following table:

Table 5.1. Example of an ad hoc execute-snapshot signal record
FieldDefaultValue

type

incremental

Specifies the type of snapshot that you want to run.
Setting the type is optional. Currently, you can request only incremental snapshots.

data-collections

N/A

An array that contains regular expressions matching the fully-qualified names of the collection to be snapshotted.
The format of the names is the same as for the signal.data.collection configuration option.

additional-condition

N/A

An optional string, which specifies a condition based on the column(s) of the collection(s), to capture a subset of the contents of the collection(s).

surrogate-key

N/A

An optional string that specifies the column name that the connector uses as the primary key of a collection during the snapshot process.

Triggering an ad hoc snapshot

You initiate an ad hoc snapshot by adding an entry with the execute-snapshot signal type to the signaling collection. 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 collection. Based on the number of entries in the collection, and the configured chunk size, Debezium divides the collection into chunks, and proceeds to snapshot each chunk, in succession, one at a time.

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

5.2.5. Incremental snapshots

To provide flexibility in managing snapshots, Debezium includes a supplementary snapshot mechanism, known as incremental snapshotting. Incremental snapshots rely on the Debezium mechanism for sending signals to a Debezium connector.

In an incremental snapshot, instead of capturing the full state of a database all at once, as in an initial snapshot, Debezium captures each collection in phases, in a series of configurable chunks. You can specify the collections 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 collection 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 collection 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 collection to its collection.include.list property.

Incremental snapshot process

When you run an incremental snapshot, Debezium sorts each collection by primary key and then splits the collection into chunks based on the configured chunk size. Working chunk by chunk, it then captures each collection 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 collection 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 collection 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 collection row, Debezium employs a so-called snapshot window. The snapshot windows demarcates the interval during which an incremental snapshot captures data for a specified collection 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 collection’s Kafka topic.

The connector repeats the process for each snapshot chunk.

Warning

Incremental snapshots requires the primary key to be stably ordered. However, String may not guarantees stable ordering as encodings and special characters can lead to unexpected behaviour (Mongo sort String). Please consider using other types for the primary key when performing incremental snapshots.

Incremental snapshots for sharded clusters

Incremental snapshots for sharded clusters is a Technology Preview feature for the Debezium MongoDB connector. Technology Preview features are not supported with Red Hat production service-level agreements (SLAs) and might not be functionally complete; therefore, Red Hat does not recommend implementing any Technology Preview features in production environments. This Technology Preview feature provides early access to upcoming product innovations, enabling you to test functionality and provide feedback during the development process. For more information about support scope, see Technology Preview Features Support Scope.

To use incremental snapshots with sharded MongoDB clusters, you must set specific values for the following properties:

5.2.5.1. Triggering an incremental snapshot

Currently, the only way to initiate an incremental snapshot is to send an ad hoc snapshot signal to the signaling collection on the source database.

You submit a signal to the signaling collection by using the MongoDB insert() method.

After Debezium detects the change in the signaling collection, it reads the signal, and runs the requested snapshot operation.

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

To specify the collections to include in the snapshot, provide a data-collections array that lists the collections or an array of regular expressions used to match collections, for example,
{"data-collections": ["public.Collection1", "public.Collection2"]}

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

Note

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

For example, to include a data collection that exists in the public database, and that has the name My.Collection, use the following format: "public"."My.Collection".

Prerequisites

Using a source signaling channel to trigger an incremental snapshot

  1. Insert a snapshot signal document into the signaling collection:

    <signalDataCollection>.insert({"id" : _<idNumber>,"type" : <snapshotType>, "data" : {"data-collections" ["<collectionName>", "<collectionName>"],"type": <snapshotType>}});

    For example,

    db.debeziumSignal.insert({ 1
    "type" : "execute-snapshot", 2 3
    "data" : {
    "data-collections" ["\"public\".\"Collection1\"", "\"public\".\"Collection2\""], 4
    "type": "incremental"} 5
    });

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

    The following table describes the parameters in the example:

    Table 5.2. Descriptions of fields in a MongoDB insert() command for sending an incremental snapshot signal to the signaling collection
    ItemValueDescription

    1

    db.debeziumSignal

    Specifies the fully-qualified name of the signaling collection on the source database.

    2

    null

    The _id parameter specifies an arbitrary string that is assigned as the id identifier for the signal request.
    The insert method in the preceding example omits use of the optional _id parameter. Because the document does not explicitly assign a value for the parameter, the arbitrary id that MongoDB automatically assigns to the document becomes the id identifier for the signal request.
    Use this string to identify logging messages to entries in the signaling collection. Debezium does not use this identifier string. Rather, during the snapshot, Debezium generates its own id string as a watermarking signal.

    3

    execute-snapshot

    Specifies type parameter specifies the operation that the signal is intended to trigger.

    4

    data-collections

    A required component of the data field of a signal that specifies an array of collection names or regular expressions to match collection names to include in the snapshot.
    The array lists regular expressions which match collections by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling collection in the signal.data.collection configuration property.

    5

    incremental

    An optional type component of the data field of a signal that specifies the kind of snapshot operation to run.
    Currently, the only valid option is the default value, incremental.
    If you do not specify a value, the connector runs an incremental snapshot.

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

Example: Incremental snapshot event message

{
    "before":null,
    "after": {
        "pk":"1",
        "value":"New data"
    },
    "source": {
        ...
        "snapshot":"incremental" 1
    },
    "op":"r", 2
    "ts_ms":"1620393591654",
    "transaction":null
}

ItemField nameDescription

1

snapshot

Specifies the type of snapshot operation to run.
Currently, the only valid option is the default value, incremental.
Specifying a type value in the SQL query that you submit to the signaling collection is optional.
If you do not specify a value, the connector runs an incremental snapshot.

2

op

Specifies the event type.
The value for snapshot events is r, signifying a READ operation.

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

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

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

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

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

Table 5.3. Execute snapshot data fields
FieldDefaultValue

type

incremental

The type of the snapshot to be executed. Currently Debezium supports only the incremental type.
See the next section for more details.

data-collections

N/A

An array of comma-separated regular expressions that match the fully-qualified names of tables to include in the snapshot.
Specify the names by using the same format as is required for the signal.data.collection configuration option.

additional-condition

N/A

An optional string that specifies a condition that the connector evaluates to designate a subset of columns to include in a snapshot.

An example of the execute-snapshot Kafka message:

Key = `test_connector`

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

Ad hoc incremental snapshots with additional-condition

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

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

SELECT * FROM <tableName> …​.

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

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

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

Key = `test_connector`

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

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

Key = `test_connector`

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

You can also stop an incremental snapshot by sending a signal to the collection on the source database. You submit a stop snapshot signal by inserting a document into the to the signaling collection. After Debezium detects the change in the signaling collection, it reads the signal, and stops the incremental snapshot operation if it’s in progress.

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

Prerequisites

Using a source signaling channel to stop an incremental snapshot

  1. Insert a stop snapshot signal document into the signaling collection:

    <signalDataCollection>.insert({"id" : _<idNumber>,"type" : "stop-snapshot", "data" : {"data-collections" ["<collectionName>", "<collectionName>"],"type": "incremental"}});

    For example,

    db.debeziumSignal.insert({ 1
    "type" : "stop-snapshot", 2 3
    "data" : {
    "data-collections" ["\"public\".\"Collection1\"", "\"public\".\"Collection2\""], 4
    "type": "incremental"} 5
    });

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

    The following table describes the parameters in the example:

    Table 5.4. Descriptions of fields in an insert command for sending a stop incremental snapshot document to the signaling collection
    ItemValueDescription

    1

    db.debeziumSignal

    Specifies the fully-qualified name of the signaling collection on the source database.

    2

    null

    The insert method in the preceding example omits use of the optional _id parameter. Because the document does not explicitly assign a value for the parameter, the arbitrary id that MongoDB automatically assigns to the document becomes the id identifier for the signal request.
    Use this string to identify logging messages to entries in the signaling collection. Debezium does not use this identifier string.

    3

    stop-snapshot

    The type parameter specifies the operation that the signal is intended to trigger.

    4

    data-collections

    An optional component of the data field of a signal that specifies an array of collection names or regular expressions to match collection names to remove from the snapshot.
    The array lists regular expressions which match collections by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling collection in the signal.data.collection configuration property. If this component of the data field is omitted, the signal stops the entire incremental snapshot that is in progress.

    5

    incremental

    A required component of the data field of a signal that specifies the kind of snapshot operation that is to be stopped.
    Currently, the only valid option is incremental.
    If you do not specify a type value, the signal fails to stop the incremental snapshot.

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

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

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

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

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

Table 5.5. Execute snapshot data fields
FieldDefaultValue

type

incremental

The type of the snapshot to be executed. Currently Debezium supports only the incremental type.
See the next section for more details.

data-collections

N/A

An optional array of comma-separated regular expressions that match the fully-qualified names of the tables to include in the snapshot.
Specify the names by using the same format as is required for the signal.data.collection configuration option.

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

Key = `test_connector`

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

5.2.6. How the Debezium MongoDB connector streams change event records

After the connector task for a replica set records an offset, it uses the offset to determine the position in the oplog where it should start streaming changes. The task then (depending on the configuration) either connects to the replica set’s primary node or connects to a replica-set-wide change stream and starts streaming changes from that position. It processes all of create, insert, and delete operations, and converts them into Debezium change events. Each change event includes the position in the oplog where the operation was found, and the connector periodically records this as its most recent offset. The interval at which the offset is recorded is governed by offset.flush.interval.ms, which is a Kafka Connect worker configuration property.

When the connector is stopped gracefully, the last offset processed is recorded so that, upon restart, the connector will continue exactly where it left off. If the connector’s tasks terminate unexpectedly, however, then the tasks may have processed and generated events after it last records the offset but before the last offset is recorded; upon restart, the connector begins at the last recorded offset, possibly generating some the same events that were previously generated just prior to the crash.

Note

When all components in a Kafka pipeline operate nominally, Kafka consumers receive every message exactly once. However, when things go wrong, Kafka can only guarantee that consumers receive every message at least once. To avoid unexpected results, consumers must be able to handle duplicate messages.

As mentioned earlier, the connector tasks always use the replica set’s primary node to stream changes from the oplog, ensuring that the connector sees the most up-to-date operations as possible and can capture the changes with lower latency than if secondaries were to be used instead. When the replica set elects a new primary, the connector immediately stops streaming changes, connects to the new primary, and starts streaming changes from the new primary node at the same position. Likewise, if the connector experiences any problems communicating with the replica set members, it tries to reconnect, by using exponential backoff so as to not overwhelm the replica set, and once connected it continues streaming changes from where it last left off. In this way, the connector is able to dynamically adjust to changes in replica set membership and automatically handle communication failures.

To summarize, the MongoDB connector continues running in most situations. Communication problems might cause the connector to wait until the problems are resolved.

5.2.7. MongoDB support for populating the before field in Debezium change event

In MongoDB 6.0 and later, you can configure change streams to emit the pre-image state of a document to populate the before field for MongoDB change events. To enable the use of pre-images in MongoDB, you must set the changeStreamPreAndPostImages for a collection by using db.createCollection(), create, or collMod. To enable the Debezium MongoDB to include pre-images in change events, set the capture.mode for the connector to one of the *_with_pre_image options.

Size limits on MongoDB change stream events

The size of a MongoDB change stream event is limited to 16 megabytes. The use of pre-images thus increases the likelihood of exceeding this threshold, which can lead to failures. For information about how to avoid exceeding the change stream limit, see the MongoDB documentation.

5.2.8. Default names of Kafka topics that receive Debezium MongoDB change event records

The MongoDB connector writes events for all insert, update, and delete operations to documents in each collection to a single Kafka topic. The name of the Kafka topics always takes the form logicalName.databaseName.collectionName, where logicalName is the logical name of the connector as specified with the topic.prefix configuration property, databaseName is the name of the database where the operation occurred, and collectionName is the name of the MongoDB collection in which the affected document existed.

For example, consider a MongoDB replica set with an inventory database that contains four collections: products, products_on_hand, customers, and orders. If the connector monitoring this database were given a logical name of fulfillment, then the connector would produce events on these four Kafka topics:

  • fulfillment.inventory.products
  • fulfillment.inventory.products_on_hand
  • fulfillment.inventory.customers
  • fulfillment.inventory.orders

Notice that the topic names do not incorporate the replica set name or shard name. As a result, all changes to a sharded collection (where each shard contains a subset of the collection’s documents) all go to the same Kafka topic.

You can set up Kafka to auto-create the topics as they are needed. If not, then you must use Kafka administration tools to create the topics before starting the connector.

5.2.9. How event keys control topic partitioning for the Debezium MongoDB connector

The MongoDB connector does not make any explicit determination about how to partition topics for events. Instead, it allows Kafka to determine how to partition topics based on event keys. You can change Kafka’s partitioning logic by defining the name of the Partitioner implementation in the Kafka Connect worker configuration.

Kafka maintains total order only for events written to a single topic partition. Partitioning the events by key does mean that all events with the same key always go to the same partition. This ensures that all events for a specific document are always totally ordered.

5.2.10. Debezium MongoDB connector-generated events that represent transaction boundaries

Debezium can generate events that represents transaction metadata boundaries and enrich change data 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.

For every transaction BEGIN and END, Debezium generates an event that contains the following fields:

status
BEGIN or END
id
String representation of unique transaction identifier.
event_count (for END events)
Total number of events emitted by the transaction.
data_collections (for END events)
An array of pairs of data_collection and event_count that provides number of events emitted by changes originating from given data collection.

The following example shows a typical message:

{
  "status": "BEGIN",
  "id": "1462833718356672513",
  "event_count": null,
  "data_collections": null
}

{
  "status": "END",
  "id": "1462833718356672513",
  "event_count": 2,
  "data_collections": [
    {
      "data_collection": "rs0.testDB.collectiona",
      "event_count": 1
    },
    {
      "data_collection": "rs0.testDB.collectionb",
      "event_count": 1
    }
  ]
}

Unless overridden via the topic.transaction option, transaction events are written to the topic named <topic.prefix>.transaction.

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 what a message looks like:

{
  "after": "{\"_id\" : {\"$numberLong\" : \"1004\"},\"first_name\" : \"Anne\",\"last_name\" : \"Kretchmar\",\"email\" : \"annek@noanswer.org\"}",
  "source": {
...
  },
  "op": "c",
  "ts_ms": "1580390884335",
  "transaction": {
    "id": "1462833718356672513",
    "total_order": "1",
    "data_collection_order": "1"
  }
}

5.3. Descriptions of Debezium MongoDB connector data change events

The Debezium MongoDB connector generates a data change event for each document-level operation that inserts, updates, or deletes data. Each event contains a key and a value. The structure of the key and the value depends on the collection that was changed.

Debezium and Kafka Connect are designed around continuous streams of event messages. However, the structure of these events may change over time, which can be difficult for consumers to handle. To address this, each event contains the schema for its content or, if you are using a schema registry, a schema ID that a consumer can use to obtain the schema from the registry. This makes each event self-contained.

The following skeleton JSON shows the basic four parts of a change event. However, how you configure the Kafka Connect converter that you choose to use in your application determines the representation of these four parts in change events. A schema field is in a change event only when you configure the converter to produce it. Likewise, the event key and event payload are in a change event only if you configure a converter to produce it. If you use the JSON converter and you configure it to produce all four basic change event parts, change events have this structure:

{
 "schema": { 1
   ...
  },
 "payload": { 2
   ...
 },
 "schema": { 3
   ...
 },
 "payload": { 4
   ...
 },
}
Table 5.6. Overview of change event basic content
ItemField nameDescription

1

schema

The first schema field is part of the event key. It specifies a Kafka Connect schema that describes what is in the event key’s payload portion. In other words, the first schema field describes the structure of the key for the document that was changed.

2

payload

The first payload field is part of the event key. It has the structure described by the previous schema field and it contains the key for the document that was changed.

3

schema

The second schema field is part of the event value. It specifies the Kafka Connect schema that describes what is in the event value’s payload portion. In other words, the second schema describes the structure of the document that was changed. Typically, this schema contains nested schemas.

4

payload

The second payload field is part of the event value. It has the structure described by the previous schema field and it contains the actual data for the document that was changed.

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

Warning

The MongoDB 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 collection 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 collection name contains invalid characters, and the only characters that distinguish names from one another are invalid and thus replaced with underscores.

For more information, see the following topics:

5.3.1. About keys in Debezium MongoDB change events

A change event’s key contains the schema for the changed document’s key and the changed document’s actual key. For a given collection, both the schema and its corresponding payload contain a single id field. The value of this field is the document’s identifier represented as a string that is derived from MongoDB extended JSON serialization strict mode.

Consider a connector with a logical name of fulfillment, a replica set containing an inventory database, and a customers collection that contains documents such as the following.

Example document

{
  "_id": 1004,
  "first_name": "Anne",
  "last_name": "Kretchmar",
  "email": "annek@noanswer.org"
}

Example change event key

Every change event that captures a change to the customers collection has the same event key schema. For as long as the customers collection has the previous definition, every change event that captures a change to the customers collection has the following key structure. In JSON, it looks like this:

{
  "schema": { 1
    "type": "struct",
    "name": "fulfillment.inventory.customers.Key", 2
    "optional": false, 3
    "fields": [ 4
      {
        "field": "id",
        "type": "string",
        "optional": false
      }
    ]
  },
  "payload": { 5
    "id": "1004"
  }
}
Table 5.7. Description of change event key
ItemField nameDescription

1

schema

The schema portion of the key specifies a Kafka Connect schema that describes what is in the key’s payload portion.

2

fulfillment.inventory.customers.Key

Name of the schema that defines the structure of the key’s payload. This schema describes the structure of the key for the document that was changed. Key schema names have the format connector-name.database-name.collection-name.Key. In this example:

  • fulfillment is the name of the connector that generated this event.
  • inventory is the database that contains the collection that was changed.
  • customers is the collection that contains the document that was updated.

3

optional

Indicates whether the event key must contain a value in its payload field. In this example, a value in the key’s payload is required. A value in the key’s payload field is optional when a document does not have a key.

4

fields

Specifies each field that is expected in the payload, including each field’s name, type, and whether it is required.

5

payload

Contains the key for the document for which this change event was generated. In this example, the key contains a single id field of type string whose value is 1004.

This example uses a document with an integer identifier, but any valid MongoDB document identifier works the same way, including a document identifier. For a document identifier, an event key’s payload.id value is a string that represents the updated document’s original _id field as a MongoDB extended JSON serialization that uses strict mode. The following table provides examples of how different types of _id fields are represented.

Table 5.8. Examples of representing document _id fields in event key payloads
TypeMongoDB _id ValueKey’s payload

Integer

1234

{ "id" : "1234" }

Float

12.34

{ "id" : "12.34" }

String

"1234"

{ "id" : "\"1234\"" }

Document

{ "hi" : "kafka", "nums" : [10.0, 100.0, 1000.0] }

{ "id" : "{\"hi\" : \"kafka\", \"nums\" : [10.0, 100.0, 1000.0]}" }

ObjectId

ObjectId("596e275826f08b2730779e1f")

{ "id" : "{\"$oid\" : \"596e275826f08b2730779e1f\"}" }

Binary

BinData("a2Fma2E=",0)

{ "id" : "{\"$binary\" : \"a2Fma2E=\", \"$type\" : \"00\"}" }

5.3.2. About values in Debezium MongoDB change events

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

Consider the same sample document that was used to show an example of a change event key:

Example document

{
  "_id": 1004,
  "first_name": "Anne",
  "last_name": "Kretchmar",
  "email": "annek@noanswer.org"
}

The value portion of a change event for a change to this document is described for each event type:

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 collection:

{
    "schema": { 1
      "type": "struct",
      "fields": [
        {
          "type": "string",
          "optional": true,
          "name": "io.debezium.data.Json", 2
          "version": 1,
          "field": "after"
        },
        {
          "type": "string",
          "optional": true,
          "name": "io.debezium.data.Json",
          "version": 1,
          "field": "patch"
        },
        {
          "type": "struct",
          "fields": [
            {
              "type": "string",
              "optional": false,
              "field": "version"
            },
            {
              "type": "string",
              "optional": false,
              "field": "connector"
            },
            {
              "type": "string",
              "optional": false,
              "field": "name"
            },
            {
              "type": "int64",
              "optional": false,
              "field": "ts_ms"
            },
            {
              "type": "boolean",
              "optional": true,
              "default": false,
              "field": "snapshot"
            },
            {
              "type": "string",
              "optional": false,
              "field": "db"
            },
            {
              "type": "string",
              "optional": false,
              "field": "rs"
            },
            {
              "type": "string",
              "optional": false,
              "field": "collection"
            },
            {
              "type": "int32",
              "optional": false,
              "field": "ord"
            },
            {
              "type": "int64",
              "optional": true,
              "field": "h"
            }
          ],
          "optional": false,
          "name": "io.debezium.connector.mongo.Source", 3
          "field": "source"
        },
        {
          "type": "string",
          "optional": true,
          "field": "op"
        },
        {
          "type": "int64",
          "optional": true,
          "field": "ts_ms"
        }
      ],
      "optional": false,
      "name": "dbserver1.inventory.customers.Envelope" 4
      },
    "payload": { 5
      "after": "{\"_id\" : {\"$numberLong\" : \"1004\"},\"first_name\" : \"Anne\",\"last_name\" : \"Kretchmar\",\"email\" : \"annek@noanswer.org\"}", 6
      "source": { 7
        "version": "2.3.7.Final",
        "connector": "mongodb",
        "name": "fulfillment",
        "ts_ms": 1558965508000,
        "snapshot": false,
        "db": "inventory",
        "rs": "rs0",
        "collection": "customers",
        "ord": 31,
        "h": 1546547425148721999
      },
      "op": "c", 8
      "ts_ms": 1558965515240 9
    }
  }
Table 5.9. Descriptions of create event value fields
ItemField nameDescription

1

schema

The value’s schema, which describes the structure of the value’s payload. A change event’s value schema is the same in every change event that the connector generates for a particular collection.

2

name

In the schema section, each name field specifies the schema for a field in the value’s payload.

io.debezium.data.Json is the schema for the payload’s after, patch, and filter fields. This schema is specific to the customers collection. A create event is the only kind of event that contains an after field. An update event contains a filter field and a patch field. A delete event contains a filter field, but not an after field nor a patch field.

3

name

io.debezium.connector.mongo.Source is the schema for the payload’s source field. This schema is specific to the MongoDB connector. The connector uses it for all events that it generates.

4

name

dbserver1.inventory.customers.Envelope is the schema for the overall structure of the payload, where dbserver1 is the connector name, inventory is the database, and customers is the collection. This schema is specific to the collection.

5

payload

The value’s actual data. This is the information that the change event is providing.

It may appear that the JSON representations of the events are much larger than the documents they describe. This is because the JSON representation must include the schema and the payload portions of the message. However, by using the Avro converter, you can significantly decrease the size of the messages that the connector streams to Kafka topics.

6

after

An optional field that specifies the state of the document after the event occurred. In this example, the after field contains the values of the new document’s _id, first_name, last_name, and email fields. The after value is always a string. By convention, it contains a JSON representation of the document. MongoDB oplog entries contain the full state of a document only for _create_ events and also for update events, when the capture.mode option is set to change_streams_update_full; in other words, a create event is the only kind of event that contains an after field regardless of capture.mode option.

7

source

Mandatory field that describes the source metadata for the event. This field contains information that you can use to compare this event with other events, with regard to the origin of the events, the order in which the events occurred, and whether events were part of the same transaction. The source metadata includes:

  • Debezium version.
  • Name of the connector that generated the event.
  • Logical name of the MongoDB replica set, which forms a namespace for generated events and is used in Kafka topic names to which the connector writes.
  • Names of the collection and database that contain the new document.
  • If the event was part of a snapshot.
  • Timestamp for when the change was made in the database and ordinal of the event within the timestamp.
  • Unique identifier of the MongoDB operation (the h field in the oplog event).
  • Unique identifiers of the MongoDB session lsid and transaction number txnNumber in case the change was executed inside a transaction (change streams capture mode only).

8

op

Mandatory string that describes the type of operation that caused the connector to generate the event. In this example, c indicates that the operation created a document. Valid values are:

  • c = create
  • u = update
  • d = delete
  • r = read (applies to only snapshots)

9

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

Change streams capture mode

The value of a change event for an update in the sample customers collection has the same schema as a create event for that collection. Likewise, the event value’s payload has the same structure. However, the event value payload contains different values in an update event. An update event includes an after value only if the capture.mode option is set to change_streams_update_full. A before value is provided if the capture.mode option is set to one of the *_with_pre_image option. There is a new structured field updateDescription with a few additional fields in this case:

  • updatedFields is a string field that contains the JSON representation of the updated document fields with their values
  • removedFields is a list of field names that were removed from the document
  • truncatedArrays is a list of arrays in the document that were truncated

Here is an example of a change event value in an event that the connector generates for an update in the customers collection:

{
    "schema": { ... },
    "payload": {
      "op": "u", 1
      "ts_ms": 1465491461815, 2
      "before":"{\"_id\": {\"$numberLong\": \"1004\"},\"first_name\": \"unknown\",\"last_name\": \"Kretchmar\",\"email\": \"annek@noanswer.org\"}", 3
      "after":"{\"_id\": {\"$numberLong\": \"1004\"},\"first_name\": \"Anne Marie\",\"last_name\": \"Kretchmar\",\"email\": \"annek@noanswer.org\"}", 4
      "updateDescription": {
        "removedFields": null,
        "updatedFields": "{\"first_name\": \"Anne Marie\"}", 5
        "truncatedArrays": null
      },
      "source": { 6
        "version": "2.3.7.Final",
        "connector": "mongodb",
        "name": "fulfillment",
        "ts_ms": 1558965508000,
        "snapshot": false,
        "db": "inventory",
        "rs": "rs0",
        "collection": "customers",
        "ord": 1,
        "h": null,
        "tord": null,
        "stxnid": null,
        "lsid":"{\"id\": {\"$binary\": \"FA7YEzXgQXSX9OxmzllH2w==\",\"$type\": \"04\"},\"uid\": {\"$binary\": \"47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=\",\"$type\": \"00\"}}",
        "txnNumber":1
      }
    }
  }
Table 5.10. Descriptions of update event value fields
ItemField nameDescription

1

op

Mandatory string that describes the type of operation that caused the connector to generate the event. In this example, u indicates that the operation updated a document.

2

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

3

before

Contains the JSON string representation of the actual MongoDB document before change. + An update event value does not contain an before field if the capture mode is not set to one of the *_with_preimage options.

4

after

Contains the JSON string representation of the actual MongoDB document.
An update event value does not contain an after field if the capture mode is not set to change_streams_update_full

5

updatedFields

Contains the JSON string representation of the updated field values of the document. In this example, the update changed the first_name field to a new value.

6

source

Mandatory field that describes the source metadata for the event. This field contains the same information as a create event for the same collection, but the values are different since this event is from a different position in the oplog. The source metadata includes:

  • Debezium version.
  • Name of the connector that generated the event.
  • Logical name of the MongoDB replica set, which forms a namespace for generated events and is used in Kafka topic names to which the connector writes.
  • Names of the collection and database that contain the updated document.
  • If the event was part of a snapshot.
  • Timestamp for when the change was made in the database and ordinal of the event within the timestamp.
  • Unique identifiers of the MongoDB session lsid and transaction number txnNumber in case the change was executed inside a transaction.
Warning

The after value in the event should be handled as the at-point-of-time value of the document. The value is not calculated dynamically but is obtained from the collection. It is thus possible if multiple updates are closely following one after the other, that all update updates events will contain the same after value which will be representing the last value stored in the document.

If your application depends on gradual change evolution then you should rely on updateDescription only.

delete events

The value in a delete change event has the same schema portion as create and update events for the same collection. The payload portion in a delete event contains values that are different from create and update events for the same collection. In particular, a delete event contains neither an after value nor a updateDescription value. Here is an example of a delete event for a document in the customers collection:

{
    "schema": { ... },
    "payload": {
      "op": "d", 1
      "ts_ms": 1465495462115, 2
      "before":"{\"_id\": {\"$numberLong\": \"1004\"},\"first_name\": \"Anne Marie\",\"last_name\": \"Kretchmar\",\"email\": \"annek@noanswer.org\"}",3
      "source": { 4
        "version": "2.3.7.Final",
        "connector": "mongodb",
        "name": "fulfillment",
        "ts_ms": 1558965508000,
        "snapshot": true,
        "db": "inventory",
        "rs": "rs0",
        "collection": "customers",
        "ord": 6,
        "h": 1546547425148721999
      }
    }
  }
Table 5.11. Descriptions of delete event value fields
ItemField nameDescription

1

op

Mandatory string that describes the type of operation. The op field value is d, signifying that this document was deleted.

2

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

3

before

Contains the JSON string representation of the actual MongoDB document before change. + An update event value does not contain an before field if the capture mode is not set to one of the *_with_preimage options.

4

source

Mandatory field that describes the source metadata for the event. This field contains the same information as a create or update event for the same collection, but the values are different since this event is from a different position in the oplog. The source metadata includes:

  • Debezium version.
  • Name of the connector that generated the event.
  • Logical name of the MongoDB replica set, which forms a namespace for generated events and is used in Kafka topic names to which the connector writes.
  • Names of the collection and database that contained the deleted document.
  • If the event was part of a snapshot.
  • Timestamp for when the change was made in the database and ordinal of the event within the timestamp.
  • Unique identifier of the MongoDB operation (the h field in the oplog event).
  • Unique identifiers of the MongoDB session lsid and transaction number txnNumber in case the change was executed inside a transaction (change streams capture mode only).

MongoDB connector events are designed to work with Kafka log compaction. Log compaction enables removal of some older messages as long as at least the most recent message for every key is kept. This lets Kafka reclaim storage space while ensuring that the topic contains a complete data set and can be used for reloading key-based state.

Tombstone events

All MongoDB connector events for a uniquely identified document have exactly the same key. When a document 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 key, the message value must be null. To make this possible, after Debezium’s MongoDB connector emits a delete event, the connector emits a special tombstone event that has the same key but a null value. A tombstone event informs Kafka that all messages with that same key can be removed.

5.4. Setting up MongoDB to work with a Debezium connector

The MongoDB connector uses MongoDB’s change streams to capture the changes, so the connector works only with MongoDB replica sets or with sharded clusters where each shard is a separate replica set. See the MongoDB documentation for setting up a replica set or sharded cluster. Also, be sure to understand how to enable access control and authentication with replica sets.

You must also have a MongoDB user that has the appropriate roles to read the admin database where the oplog can be read. Additionally, the user must also be able to read the config database in the configuration server of a sharded cluster and must have listDatabases privilege action. When change streams are used (the default) the user also must have cluster-wide privilege actions find and changeStream.

When you intend to utilize pre-image and populate the before field, you need to first enable changeStreamPreAndPostImages for a collection using db.createCollection(), create, or collMod.

5.5. Deployment of Debezium MongoDB connectors

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

5.5.1. MongoDB connector deployment using AMQ Streams

Beginning with Debezium 1.7, the preferred method for deploying a Debezium connector is to use AMQ Streams to build a Kafka Connect container image that includes the connector plug-in.

During the deployment process, you create and use the following custom resources (CRs):

  • A KafkaConnect CR that defines your Kafka Connect instance and includes information about the connector artifacts needs to include in the image.
  • A KafkaConnector CR that provides details that include information the connector uses to access the source database. After AMQ Streams starts the Kafka Connect pod, you start the connector by applying the KafkaConnector CR.

In the build specification for the Kafka Connect image, you can specify the connectors that are available to deploy. For each connector plug-in, you can also specify other components that you want to make available for deployment. For example, you can add Apicurio Registry artifacts, or the Debezium scripting component. When AMQ Streams builds the Kafka Connect image, it downloads the specified artifacts, and incorporates them into the image.

The spec.build.output parameter in the KafkaConnect CR specifies where to store the resulting Kafka Connect container image. Container images can be stored in a Docker registry, or in an OpenShift ImageStream. To store images in an ImageStream, you must create the ImageStream before you deploy Kafka Connect. ImageStreams are not created automatically.

Note

If you use a KafkaConnect resource to create a cluster, afterwards you cannot use the Kafka Connect REST API to create or update connectors. You can still use the REST API to retrieve information.

Additional resources

5.5.2. Using AMQ Streams to deploy a Debezium MongoDB connector

With earlier versions of AMQ Streams, to deploy Debezium connectors on OpenShift, you were required to first build a Kafka Connect image for the connector. The current preferred method for deploying connectors on OpenShift is to use a build configuration in AMQ Streams to automatically build a Kafka Connect container image that includes the Debezium connector plug-ins that you want to use.

During the build process, the AMQ Streams Operator transforms input parameters in a KafkaConnect custom resource, including Debezium connector definitions, into a Kafka Connect container image. The build downloads the necessary artifacts from the Red Hat Maven repository or another configured HTTP server.

The newly created container is pushed to the container registry that is specified in .spec.build.output, and is used to deploy a Kafka Connect cluster. After AMQ Streams builds the Kafka Connect image, you create KafkaConnector custom resources to start the connectors that are included in the build.

Prerequisites

  • You have access to an OpenShift cluster on which the cluster Operator is installed.
  • The AMQ Streams Operator is running.
  • An Apache Kafka cluster is deployed as documented in Deploying and Managing AMQ Streams on OpenShift.
  • Kafka Connect is deployed on AMQ Streams
  • You have a Red Hat build of Debezium license.
  • The OpenShift oc CLI client is installed or you have access to the OpenShift Container Platform web console.
  • Depending on how you intend to store the Kafka Connect build image, you need registry permissions or you must create an ImageStream resource:

    To store the build image in an image registry, such as Red Hat Quay.io or Docker Hub
    • An account and permissions to create and manage images in the registry.
    To store the build image as a native OpenShift ImageStream

Procedure

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

    Example 5.1. A dbz-connect.yaml file that defines a KafkaConnect custom resource that includes a Debezium connector

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

    • The Debezium MongoDB connector archive.
    • The Red Hat build of Apicurio Registry archive. The Apicurio Registry is an optional component. Add the Apicurio Registry component only if you intend to use Avro serialization with the connector.
    • The Debezium scripting SMT archive and the associated scripting engine that you want to use with the Debezium connector. The SMT archive and scripting language dependencies are optional components. Add these components only if you intend to use the Debezium content-based routing SMT or filter SMT.
    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnect
    metadata:
      name: debezium-kafka-connect-cluster
      annotations:
        strimzi.io/use-connector-resources: "true" 1
    spec:
      version: 3.5.0
      build: 2
        output: 3
          type: imagestream  4
          image: debezium-streams-connect:latest
        plugins: 5
          - name: debezium-connector-mongodb
            artifacts:
              - type: zip 6
                url: https://maven.repository.redhat.com/ga/io/debezium/debezium-connector-mongodb/2.3.7.Final-redhat-00001/debezium-connector-mongodb-2.3.7.Final-redhat-00001-plugin.zip  7
              - type: zip
                url: https://maven.repository.redhat.com/ga/io/apicurio/apicurio-registry-distro-connect-converter/2.4.4.Final-redhat-<build-number>/apicurio-registry-distro-connect-converter-2.4.4.Final-redhat-<build-number>.zip  8
              - type: zip
                url: https://maven.repository.redhat.com/ga/io/debezium/debezium-scripting/2.3.7.Final-redhat-00001/debezium-scripting-2.3.7.Final-redhat-00001.zip 9
              - type: jar
                url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy/3.0.11/groovy-3.0.11.jar  10
              - type: jar
                url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy-jsr223/3.0.11/groovy-jsr223-3.0.11.jar
              - type: jar
                url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy-json3.0.11/groovy-json-3.0.11.jar
    
      bootstrapServers: debezium-kafka-cluster-kafka-bootstrap:9093
    
      ...
    Table 5.12. Descriptions of Kafka Connect configuration settings
    ItemDescription

    1

    Sets the strimzi.io/use-connector-resources annotation to "true" to enable the Cluster Operator to use KafkaConnector resources to configure connectors in this Kafka Connect cluster.

    2

    The spec.build configuration specifies where to store the build image and lists the plug-ins to include in the image, along with the location of the plug-in artifacts.

    3

    The build.output specifies the registry in which the newly built image is stored.

    4

    Specifies the name and image name for the image output. Valid values for output.type are docker to push into a container registry such as Docker Hub or Quay, or imagestream to push the image to an internal OpenShift ImageStream. To use an ImageStream, an ImageStream resource must be deployed to the cluster. For more information about specifying the build.output in the KafkaConnect configuration, see the AMQ Streams Build schema reference in {NameConfiguringStreamsOpenShift}.

    5

    The plugins configuration lists all of the connectors that you want to include in the Kafka Connect image. For each entry in the list, specify a plug-in name, and information for about the artifacts that are required to build the connector. Optionally, for each connector plug-in, you can include other components that you want to be available for use with the connector. For example, you can add Service Registry artifacts, or the Debezium scripting component.

    6

    The value of artifacts.type specifies the file type of the artifact specified in the artifacts.url. Valid types are zip, tgz, or jar. Debezium connector archives are provided in .zip file format. The type value must match the type of the file that is referenced in the url field.

    7

    The value of artifacts.url specifies the address of an HTTP server, such as a Maven repository, that stores the file for the connector artifact. Debezium connector artifacts are available in the Red Hat Maven repository. The OpenShift cluster must have access to the specified server.

    8

    (Optional) Specifies the artifact type and url for downloading the Apicurio Registry component. Include the Apicurio Registry artifact, only if you want the connector to use Apache Avro to serialize event keys and values with the Red Hat build of Apicurio Registry, instead of using the default JSON converter.

    9

    (Optional) Specifies the artifact type and url for the Debezium scripting SMT archive to use with the Debezium connector. Include the scripting SMT only if you intend to use the Debezium content-based routing SMT or filter SMT To use the scripting SMT, you must also deploy a JSR 223-compliant scripting implementation, such as groovy.

    10

    (Optional) Specifies the artifact type and url for the JAR files of a JSR 223-compliant scripting implementation, which is required by the Debezium scripting SMT.

    Important

    If you use AMQ Streams to incorporate the connector plug-in into your Kafka Connect image, for each of the required scripting language components artifacts.url must specify the location of a JAR file, and the value of artifacts.type must also be set to jar. Invalid values cause the connector fails at runtime.

    To enable use of the Apache Groovy language with the scripting SMT, the custom resource in the example retrieves JAR files for the following libraries:

    • groovy
    • groovy-jsr223 (scripting agent)
    • groovy-json (module for parsing JSON strings)

    As an alternative, the Debezium scripting SMT also supports the use of the JSR 223 implementation of GraalVM JavaScript.

  3. Apply the KafkaConnect build specification to the OpenShift cluster by entering the following command:

    oc create -f dbz-connect.yaml

    Based on the configuration specified in the custom resource, the Streams Operator prepares a Kafka Connect image to deploy.
    After the build completes, the Operator pushes the image to the specified registry or ImageStream, and starts the Kafka Connect cluster. The connector artifacts that you listed in the configuration are available in the cluster.

  4. Create a KafkaConnector resource to define an instance of each connector that you want to deploy.
    For example, create the following KafkaConnector CR, and save it as mongodb-inventory-connector.yaml

    Example 5.2. mongodb-inventory-connector.yaml file that defines the KafkaConnector custom resource for a Debezium connector

    apiVersion: kafka.strimzi.io/v1beta2
    kind: KafkaConnector
    metadata:
      labels:
        strimzi.io/cluster: debezium-kafka-connect-cluster
      name: inventory-connector-mongodb 1
    spec:
      class: io.debezium.connector.mongodb.MongoDbConnector 2
      tasksMax: 1  3
      config:  4
        mongodb.hosts: rs0/192.168.99.100:27017 5
        mongodb.user: debezium  6
        mongodb.password: dbz  7
        topic.prefix: inventory-connector-mongodb 8
        collection.include.list: inventory[.]*  9
    Table 5.13. Descriptions of connector configuration settings
    ItemDescription

    1

    The name of the connector to register with the Kafka Connect cluster.

    2

    The name of the connector class.

    3

    The number of tasks that can operate concurrently.

    4

    The connector’s configuration.

    5

    The address and port number of the host database instance.

    7

    The name of the account that Debezium uses to connect to the database.

    8

    The password that Debezium uses to connect to the database user account.

    8

    The topic prefix for the database instance or cluster.
    The specified name must be formed only from alphanumeric characters or underscores.
    Because the topic prefix is used as the prefix for any Kafka topics that receive change events from this connector, the name must be unique among the connectors in the cluster.
    This namespace is also used in the names of related Kafka Connect schemas, and the namespaces of a corresponding Avro schema if you integrate the connector with the Avro connector.

    9

    The names of the collections that the connector captures changes from.

  5. Create the connector resource by running the following command:

    oc create -n <namespace> -f <kafkaConnector>.yaml

    For example,

    oc create -n debezium -f {context}-inventory-connector.yaml

    The connector is registered to the Kafka Connect cluster and starts to run against the database that is specified by spec.config.database.dbname in the KafkaConnector CR. After the connector pod is ready, Debezium is running.

You are now ready to verify the Debezium MongoDB deployment.

5.5.3. Deploying a Debezium MongoDB connector by building a custom Kafka Connect container image from a Dockerfile

To deploy a Debezium MongoDB connector, you must build a custom Kafka Connect container image that contains the Debezium connector archive and then push this container image to a container registry. You then create two custom resources (CRs):

  • A KafkaConnect CR that defines your Kafka Connect instance. The image property in the CR specifies the name of the container image that you create to run your Debezium connector. You apply this CR to the OpenShift instance where Red Hat AMQ Streams is deployed. AMQ Streams offers operators and images that bring Apache Kafka to OpenShift.
  • A KafkaConnector CR that defines your Debezium MongoDB connector. Apply this CR to the same OpenShift instance where you apply the KafkaConnect CR.

Prerequisites

  • MongoDB is running and you completed the steps to set up MongoDB to work with a Debezium connector.
  • AMQ Streams is deployed on OpenShift and is running Apache Kafka and Kafka Connect. For more information, see Deploying and Managing AMQ Streams on OpenShift.
  • Podman or Docker is installed.
  • You have an account and permissions to create and manage containers in the container registry (such as quay.io or docker.io) to which you plan to add the container that will run your Debezium connector.

Procedure

  1. Create the Debezium MongoDB container for Kafka Connect:

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

      cat <<EOF >debezium-container-for-mongodb.yaml 1
      FROM registry.redhat.io/amq-streams-kafka-35-rhel8:2.5.0
      USER root:root
      RUN mkdir -p /opt/kafka/plugins/debezium 2
      RUN cd /opt/kafka/plugins/debezium/ \
      && curl -O https://maven.repository.redhat.com/ga/io/debezium/debezium-connector-mongodb/2.3.7.Final-redhat-00001/debezium-connector-mongodb-2.3.7.Final-redhat-00001-plugin.zip \
      && unzip debezium-connector-mongodb-2.3.7.Final-redhat-00001-plugin.zip \
      && rm debezium-connector-mongodb-2.3.7.Final-redhat-00001-plugin.zip
      RUN cd /opt/kafka/plugins/debezium/
      USER 1001
      EOF
      ItemDescription

      1

      You can specify any file name that you want.

      2

      Specifies the path to your Kafka Connect plug-ins directory. If your Kafka Connect plug-ins directory is in a different location, replace this path with the actual path of your directory.

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

    2. Build the container image from the debezium-container-for-mongodb.yaml Docker file that you created in the previous step. From the directory that contains the file, open a terminal window and enter one of the following commands:

      podman build -t debezium-container-for-mongodb:latest .
      docker build -t debezium-container-for-mongodb:latest .

      The preceding commands build a container image with the name debezium-container-for-mongodb.

    3. Push your custom image to a container registry, such as quay.io or an internal container registry. The container registry must be available to the OpenShift instance where you want to deploy the image. Enter one of the following commands:

      podman push <myregistry.io>/debezium-container-for-mongodb:latest
      docker push <myregistry.io>/debezium-container-for-mongodb:latest
    4. Create a new Debezium MongoDB KafkaConnect custom resource (CR). For example, create a KafkaConnect CR with the name dbz-connect.yaml that specifies annotations and image properties. The following example shows an excerpt from a dbz-connect.yaml file that describes a KafkaConnect custom resource.

      apiVersion: kafka.strimzi.io/v1beta2
      kind: KafkaConnect
      metadata:
        name: my-connect-cluster
        annotations:
          strimzi.io/use-connector-resources: "true" 1
      spec:
        #...
        image: debezium-container-for-mongodb  2
      
        ...
      ItemDescription

      1

      metadata.annotations indicates to the Cluster Operator that