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: Section 3.1, "Overview of Debezium Db2 connector" Section 3.2, "How Debezium Db2 connectors work" Section 3.3, "Descriptions of Debezium Db2 connector data change events" Section 3.4, "How Debezium Db2 connectors map data types" Section 3.5, "Setting up Db2 to run a Debezium connector" Section 3.6, "Deployment of Debezium Db2 connectors" Section 3.7, "Monitoring Debezium Db2 connector performance" Section 3.8, "Managing Debezium Db2 connectors" Section 3.9, "Updating schemas for Db2 tables in capture mode for Debezium connectors" 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: Section 3.2.1, "How Debezium Db2 connectors perform database snapshots" Section 3.2.2, "Ad hoc snapshots" Section 3.2.3, "Incremental snapshots" Section 3.2.4, "How Debezium Db2 connectors read change-data tables" Section 3.2.5, "Default names of Kafka topics that receive Debezium Db2 change event records" Section 3.2.7, "About the Debezium Db2 connector schema change topic" Section 3.2.8, "Debezium Db2 connector-generated events that represent transaction boundaries" 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: Section 3.2.2, "Ad hoc snapshots" Section 3.2.3, "Incremental snapshots" 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. Establish a connection to the database. 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 . 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. Read the highest (most recent) LSN position in the server's transaction log. 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 . Release any locks obtained in Step 3. Other database clients can now write to any previously locked tables. 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: Confirms that the table was created before the snapshot began. If the table was created after the snapshot began, the connector skips the table. After the snapshot is complete, and the connector transitions to streaming, it emits change events for any tables that were created after the snapshot began. Produces a read event for each row that is captured from a table. All read events contain the same LSN position, which is the LSN position that was obtained in step 4. Emits each read event to the Kafka topic for the source table. Releases data table locks, if applicable. 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 Capturing data from tables not captured by the initial snapshot (no schema change) Capturing data from tables not captured by the initial snapshot (schema change) Setting the schema.history.internal.store.only.captured.tables.ddl property to specify the tables from which to capture schema information. Setting the schema.history.internal.store.only.captured.databases.ddl property to specify the logical databases from which to capture schema changes. 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 You want to capture data from a table with a schema that the connector did not capture during the initial snapshot. No schema changes were applied to the table between the LSNs of the earliest and latest change table entry that the connector reads. For information about capturing data from a new table that has undergone structural changes, see Section 3.2.1.3, "Capturing data from tables not captured by the initial snapshot (schema change)" . Procedure Stop the connector. Remove the internal database schema history topic that is specified by the schema.history.internal.kafka.topic property . 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. Apply the following changes to the connector configuration: (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. Add the tables that you want the connector to capture to table.include.list . 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 ou want to restart the connector more quickly than with a full snapshot. Restart the connector. The connector completes the type of snapshot specified by the snapshot.mode . (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 ) Edit the table.include.list property to specify the tables that you want to capture. Restart the connector. Initiate an incremental snapshot if you want to capture existing data from the newly added tables. Initial snapshot did not capture the schema for all tables ( store.only.captured.tables.ddl was set to true ) If the initial snapshot did not save the schema of the table that you want to capture, complete one of the following procedures: Procedure 1: Schema snapshot, followed by incremental snapshot In this procedure, the connector first performs a schema snapshot. You can then initiate an incremental snapshot to enable the connector to synchronize data. Stop the connector. Remove the internal database schema history topic that is specified by the schema.history.internal.kafka.topic property . Clear the offsets in the configured Kafka Connect offset.storage.topic . For more information about how to remove offsets, see the Debezium community FAQ . Warning Removing offsets should be performed only by advanced users who have experience in manipulating internal Kafka Connect data. This operation is potentially destructive, and should be performed only as a last resort. Set values for properties in the connector configuration as described in the following steps: Set the value of the snapshot.mode property to schema_only . Edit the table.include.list to add the tables that you want to capture. Restart the connector. Wait for Debezium to capture the schema of the new and existing tables. Data changes that occurred any tables after the connector stopped are not captured. To ensure that no data is lost, initiate an incremental snapshot . Procedure 2: Initial snapshot, followed by optional incremental snapshot In this procedure the connector performs a full initial snapshot of the database. As with any initial snapshot, in a database with many large tables, running an initial snapshot can be a time-consuming operation. After the snapshot completes, you can optionally trigger an incremental snapshot to capture any changes that occur while the connector is off-line. Stop the connector. Remove the internal database schema history topic that is specified by the schema.history.internal.kafka.topic property . Clear the offsets in the configured Kafka Connect offset.storage.topic . For more information about how to remove offsets, see the Debezium community FAQ . Warning Removing offsets should be performed only by advanced users who have experience in manipulating internal Kafka Connect data. This operation is potentially destructive, and should be performed only as a last resort. Edit the table.include.list to add the tables that you want to capture. Set values for properties in the connector configuration as described in the following steps: Set the value of the snapshot.mode property to initial . (Optional) Set schema.history.internal.store.only.captured.tables.ddl to false . Restart the connector. The connector takes a full database snapshot. After the snapshot completes, the connector transitions to streaming. (Optional) To capture any data that changed while the connector was off-line, initiate an incremental snapshot . 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 Field Default Value type incremental Specifies the type of snapshot that you want to run. Setting the type is optional. Currently, you can request only incremental snapshots. data-collections N/A An array that contains regular expressions matching the fully-qualified names of the table to be snapshotted. The format of the names is the same as for the signal.data.collection configuration option. additional-condition N/A An optional string, which specifies a condition based on the column(s) of the table(s), to capture a subset of the contents of the table(s). surrogate-key N/A An optional string that specifies the column name that the connector uses as the primary key of a table during the snapshot process. Triggering an ad hoc snapshot You initiate an ad hoc snapshot by adding an entry with the execute-snapshot signal type to the signaling table. After the connector processes the message, it begins the snapshot operation. The snapshot process reads the first and last primary key values and uses those values as the start and end point for each table. Based on the number of entries in the table, and the configured chunk size, Debezium divides the table into chunks, and proceeds to snapshot each chunk, in succession, one at a time. Currently, the execute-snapshot action type triggers incremental snapshots only. For more information, see Incremental snapshots . 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 Signaling is enabled . A signaling data collection exists on the source database. The signaling data collection is specified in the signal.data.collection property. Using a source signaling channel to trigger an incremental snapshot Send a SQL query to add the ad hoc incremental snapshot request to the signaling table: INSERT INTO <signalTable> (id, type, data) VALUES ( '<id>' , '<snapshotType>' , '{"data-collections": [" <tableName> "," <tableName> "],"type":" <snapshotType> ","additional-condition":" <additional-condition> "}'); For example, INSERT INTO myschema.debezium_signal (id, type, data) 1 values ('ad-hoc-1', 2 'execute-snapshot', 3 '{"data-collections": ["schema1.table1", "schema2.table2"], 4 "type":"incremental"}, 5 "additional-condition":"color=blue"}'); 6 The values of the id , type , and data parameters in the command correspond to the fields of the signaling table . The following table describes the parameters in the example: Table 3.2. Descriptions of fields in a SQL command for sending an incremental snapshot signal to the signaling table Item Value Description 1 myschema.debezium_signal Specifies the fully-qualified name of the signaling table on the source database. 2 ad-hoc-1 The id parameter specifies an arbitrary string that is assigned as the id identifier for the signal request. Use this string to identify logging messages to entries in the signaling table. Debezium does not use this string. Rather, during the snapshot, Debezium generates its own id string as a watermarking signal. 3 execute-snapshot The type parameter specifies the operation that the signal is intended to trigger. 4 data-collections A required component of the data field of a signal that specifies an array of table names or regular expressions to match table names to include in the snapshot. The array lists regular expressions which match tables by their fully-qualified names, using the same format as you use to specify the name of the connector's signaling table in the signal.data.collection configuration property. 5 incremental An optional type component of the data field of a signal that specifies the kind of snapshot operation to run. Currently, the only valid option is the default value, incremental . If you do not specify a value, the connector runs an incremental snapshot. 6 additional-condition An optional string, which specifies a condition based on the column(s) of the table(s), to capture a subset of the contents of the tables. For more information about the additional-condition parameter, see Ad hoc incremental snapshots with additional-condition . Ad hoc incremental snapshots with additional-condition If you want a snapshot to include only a subset of the content in a table, you can modify the signal request by appending an additional-condition parameter to the snapshot signal. The SQL query for a typical snapshot takes the following form: SELECT * FROM <tableName> .... By adding an additional-condition parameter, you append a WHERE condition to the SQL query, as in the following example: SELECT * FROM <tableName> WHERE <additional-condition> .... The following example shows a SQL query to send an ad hoc incremental snapshot request with an additional condition to the signaling table: INSERT INTO <signalTable> (id, type, data) VALUES ( '<id>' , '<snapshotType>' , '{"data-collections": [" <tableName> "," <tableName> "],"type":" <snapshotType> ","additional-condition":" <additional-condition> "}'); For example, suppose you have a products table that contains the following columns: id (primary key) color quantity If you want an incremental snapshot of the products table to include only the data items where color=blue , you can use the following SQL statement to trigger the snapshot: INSERT INTO myschema.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["schema1.products"],"type":"incremental", "additional-condition":"color=blue"}'); The additional-condition parameter also enables you to pass conditions that are based on more than one column. For example, using the products table from the example, you can submit a query that triggers an incremental snapshot that includes the data of only those items for which color=blue and quantity>10 : INSERT INTO myschema.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["schema1.products"],"type":"incremental", "additional-condition":"color=blue AND quantity>10"}'); The following example, shows the JSON for an incremental snapshot event that is captured by a connector. Example: Incremental snapshot event message { "before":null, "after": { "pk":"1", "value":"New data" }, "source": { ... "snapshot":"incremental" 1 }, "op":"r", 2 "ts_ms":"1620393591654", "transaction":null } Item Field name Description 1 snapshot Specifies the type of snapshot operation to run. Currently, the only valid option is the default value, incremental . Specifying a type value in the SQL query that you submit to the signaling table is optional. If you do not specify a value, the connector runs an incremental snapshot. 2 op Specifies the event type. The value for snapshot events is r , signifying a READ operation. 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 Field Default Value type incremental The type of the snapshot to be executed. Currently Debezium supports only the incremental type. See the 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: 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: You can use the additional-condition statement to pass conditions based on multiple columns. For example, using the same products table as in the 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: 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 Signaling is enabled . A signaling data collection exists on the source database. The signaling data collection is specified in the signal.data.collection property. Using a source signaling channel to stop an incremental snapshot Send a SQL query to stop the ad hoc incremental snapshot to the signaling table: INSERT INTO <signalTable> (id, type, data) values ( '<id>' , 'stop-snapshot', '{"data-collections": [" <tableName> "," <tableName> "],"type":"incremental"}'); For example, INSERT INTO myschema.debezium_signal (id, type, data) 1 values ('ad-hoc-1', 2 'stop-snapshot', 3 '{"data-collections": ["schema1.table1", "schema2.table2"], 4 "type":"incremental"}'); 5 The values of the id , type , and data parameters in the signal command correspond to the fields of the signaling table . The following table describes the parameters in the example: Table 3.4. Descriptions of fields in a SQL command for sending a stop incremental snapshot signal to the signaling table Item Value Description 1 myschema.debezium_signal Specifies the fully-qualified name of the signaling table on the source database. 2 ad-hoc-1 The id parameter specifies an arbitrary string that is assigned as the id identifier for the signal request. Use this string to identify logging messages to entries in the signaling table. Debezium does not use this string. 3 stop-snapshot Specifies type parameter specifies the operation that the signal is intended to trigger. 4 data-collections An optional component of the data field of a signal that specifies an array of table names or regular expressions to match table names to remove from the snapshot. The array lists regular expressions which match tables by their fully-qualified names, using the same format as you use to specify the name of the connector's signaling table in the signal.data.collection configuration property. If this component of the data field is omitted, the signal stops the entire incremental snapshot that is in progress. 5 incremental A required component of the data field of a signal that specifies the kind of snapshot operation that is to be stopped. Currently, the only valid option is incremental . If you do not specify a type value, the signal fails to stop the incremental snapshot. 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 Field Default Value type incremental The type of the snapshot to be executed. Currently Debezium supports only the incremental type. See the 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: 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: Reads change events that were created between the last stored, highest LSN and the current, highest LSN. 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. Passes commit and change LSNs as offsets to Kafka Connect. 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 Default names for topics that receive Debezium event records. 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": "2.3.4.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 Item Field name Description 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 Item Field name Description 1 schema The first schema field is part of the event key. It specifies a Kafka Connect schema that describes what is in the event key's payload portion. In other words, the first schema field describes the structure of the primary key, or the unique key if the table does not have a primary key, for the table that was changed. It is possible to override the table's primary key by setting the message.key.columns connector configuration property . In this case, the first schema field describes the structure of the key identified by that property. 2 payload The first payload field is part of the event key. It has the structure described by the 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 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: Section 3.3.1, "About keys in Debezium db2 change events" Section 3.3.2, "About values in Debezium Db2 change events" 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 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 Item Field name Description 1 schema The schema portion of the key specifies a Kafka Connect schema that describes what is in the key's payload portion. 2 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 update events delete events create events The following example shows the value portion of a change event that the connector generates for an operation that creates data in the customers table: { "schema": { 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": "2.3.4.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 Item Field name Description 1 schema The value's schema, which describes the structure of the value's payload. A change event's value schema is the same in every change event that the connector generates for a particular table. 2 name In the schema section, each name field specifies the schema for a field in the value's payload. 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": "2.3.4.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 Item Field name Description 1 before An optional field that specifies the state of the row before the event occurred. In an update event value, the before field contains a field for each table column and the value that was in that column before the database commit. In this example, 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": "2.3.4.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 Item Field name Description 1 before Optional field that specifies the state of the row before the event occurred. In a delete event value, the before field contains the values that were in the row before it was deleted with the database commit. 2 after Optional field that specifies the state of the row after the event occurred. In a delete event value, the after field is null , signifying that the row no longer exists. 3 source Mandatory field that describes the source metadata for the event. In a delete event value, the source field structure is the same as for create and update events for the same table. Many source field values are also the same. In a delete event value, the ts_ms and 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 Temporal types Timestamp types Table 3.15, "Decimal types" 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 type Literal 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 time.precision.mode=connect 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 type Literal 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 type Literal 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 type Literal 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: Section 3.5.1, "Configuring Db2 tables for change data capture" Section 3.5.2, "Effect of Db2 capture agent configuration on server load and latency" Section 3.5.3, "Db2 capture agent configuration parameters" 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 USDHOME/asncdctools/src directory. UDFs are available from the Debezium examples repository . The Db2 command bldrtn is on PATH, e.g. by running export PATH=USDPATH:/opt/ibm/db2/V11.5.0.0/samples/c/ with Db2 11.5 Procedure Compile the Debezium management UDFs on the Db2 server host by using the bldrtn command provided with Db2: cd USDHOME/asncdctools/src bldrtn asncdc 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 Ensure that JDBC can read the Db2 metadata catalog: cd USDHOME/sqllib/bnd db2 connect to DB_NAME db2 bind db2schema.bnd blocking all grant public sqlerror continue 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. Back up the database. Replace DB_NAME and BACK_UP_LOCATION with appropriate values: db2 backup db DB_NAME to BACK_UP_LOCATION Restart the database: db2 restart db DB_NAME 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 Copy the Debezium management UDFs and set permissions for them: cp USDHOME/asncdctools/src/asncdc USDHOME/sqllib/function chmod 777 USDHOME/sqllib/function Enable the Debezium UDF that starts and stops the ASN capture agent: db2 -tvmf USDHOME/asncdctools/src/asncdc_UDF.sql Create the ASN control tables: USD db2 -tvmf USDHOME/asncdctools/src/asncdctables.sql Enable the Debezium UDF that adds tables to capture mode and removes tables from capture mode: USD db2 -tvmf USDHOME/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. 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 & 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'); Reinitialize the ASN service: VALUES ASNCDC.ASNCDCSERVICES('reinit','asncdc'); Additional resource Reference table for Debezium Db2 management UDFs 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: Use AMQ Streams to automatically create an image that includes the connector plug-in . This is the preferred method. Build a custom Kafka Connect container image from a Dockerfile . 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 . Additional resources Section 3.6.6, "Descriptions of Debezium Db2 connector configuration properties" 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. If you use AMQ Streams to add the connector to your Kafka Connect image, add the Maven Central location for the driver to builds.plugins.artifact.url in the KafkaConnect custom resource as shown in Section 3.6.3, "Using AMQ Streams to deploy a Debezium Db2 connector" . If you use a Dockerfile to build a container image for the connector, insert a curl command in the Dockerfile to specify the URL for downloading the required driver file from Maven Central. For more information, see Section 3.6.4, "Deploying a Debezium Db2 connector by building a custom Kafka Connect container image from a Dockerfile" . 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 Service Registry artifacts, or the Debezium scripting component. When AMQ Streams builds the Kafka Connect image, it downloads the specified artifacts, and incorporates them into the image. The spec.build.output parameter in the KafkaConnect CR specifies where to store the resulting Kafka Connect container image. Container images can be stored in a Docker registry, or in an OpenShift ImageStream. To store images in an ImageStream, you must create the ImageStream before you deploy Kafka Connect. ImageStreams are not created automatically. Note If you use a KafkaConnect resource to create a cluster, afterwards you cannot use the Kafka Connect REST API to create or update connectors. You can still use the REST API to retrieve information. Additional resources Configuring Kafka Connect in Using AMQ Streams on OpenShift. Creating a new container image automatically using AMQ Streams in Deploying and Managing AMQ Streams on OpenShift. 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 Upgrading AMQ Streams on OpenShift . Kafka Connect is deployed on AMQ Streams You have a Red Hat Integration license. The OpenShift oc CLI client is installed or you have access to the OpenShift Container Platform web console. Depending on how you intend to store the Kafka Connect build image, you need registry permissions or you must create an ImageStream resource: To store the build image in an image registry, such as Red Hat Quay.io or Docker Hub An account and permissions to create and manage images in the registry. To store the build image as a native OpenShift ImageStream An ImageStream resource is deployed to the cluster for storing new container images. You must explicitly create an ImageStream for the cluster. ImageStreams are not available by default. For more information about ImageStreams, see Managing image streams on OpenShift Container Platform . Procedure Log in to the OpenShift cluster. 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 Service Registry archive. The Service Registry is an optional component. Add the Service Registry component only if you intend to use Avro serialization with the connector. The Debezium scripting SMT archive and the associated 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-db2/2.3.4.Final-redhat-00001/debezium-connector-db2-2.3.4.Final-redhat-00001-plugin.zip 7 - type: zip url: https://maven.repository.redhat.com/ga/io/apicurio/apicurio-registry-distro-connect-converter/2.4.4.Final-redhat- <build-number> /apicurio-registry-distro-connect-converter-2.4.4.Final-redhat- <build-number> .zip 8 - type: zip url: https://maven.repository.redhat.com/ga/io/debezium/debezium-scripting/2.3.4.Final-redhat-00001/debezium-scripting-2.3.4.Final-redhat-00001.zip 9 - type: jar url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy/3.0.11/groovy-3.0.11.jar 10 - type: jar url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy-jsr223/3.0.11/groovy-jsr223-3.0.11.jar - type: jar url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy-json3.0.11/groovy-json-3.0.11.jar - 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 Item Description 1 Sets the strimzi.io/use-connector-resources annotation to "true" to enable the Cluster Operator to use KafkaConnector resources to configure connectors in this Kafka Connect cluster. 2 The spec.build configuration specifies where to store the build image and lists the plug-ins to include in the image, along with the location of the plug-in artifacts. 3 The build.output specifies the registry in which the newly built image is stored. 4 Specifies the name and image name for the image output. Valid values for output.type are docker to push into a container registry such as Docker Hub or Quay, or imagestream to push the image to an internal OpenShift ImageStream. To use an ImageStream, an ImageStream resource must be deployed to the cluster. For more information about specifying the build.output in the KafkaConnect configuration, see the AMQ Streams Build schema reference in Configuring AMQ Streams on OpenShift. 5 The plugins configuration lists all of the connectors that you want to include in the Kafka Connect image. For each entry in the list, specify a plug-in name , and information for about the artifacts that are required to build the connector. Optionally, for each connector plug-in, you can include other components that you want to be available for use with the connector. For example, you can add Service Registry artifacts, or the Debezium scripting component. 6 The value of artifacts.type specifies the file type of the artifact specified in the artifacts.url . Valid types are zip , tgz , or jar . Debezium connector archives are provided in .zip file format. 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 Service Registry component. Include the Service Registry artifact, only if you want the connector to use Apache Avro to serialize event keys and values with the Service Registry, instead of using the default JSON converter. 9 (Optional) Specifies the artifact type and url for the Debezium scripting SMT archive to use with the Debezium connector. Include the scripting SMT only if you intend to use the Debezium content-based routing SMT or filter SMT To use the scripting SMT, you must also deploy a JSR 223-compliant scripting implementation, such as groovy. 10 (Optional) Specifies the artifact type and url for the JAR files of a JSR 223-compliant scripting implementation, which is required by the Debezium scripting SMT. Important If you use AMQ Streams to incorporate the connector plug-in into your Kafka Connect image, for each of the required scripting language components, artifacts.url must specify the location of a JAR file, and the value of artifacts.type must also be set to jar . Invalid values cause the connector fails at runtime. To enable use of the Apache Groovy language with the scripting SMT, the custom resource in the example retrieves JAR files for the following libraries: groovy groovy-jsr223 (scripting agent) groovy-json (module for parsing JSON strings) 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. Apply the KafkaConnect build specification to the OpenShift cluster by entering the following command: oc create -f dbz-connect.yaml Based on the configuration specified in the custom resource, the Streams Operator prepares a Kafka Connect image to deploy. After the build completes, the Operator pushes the image to the specified registry or ImageStream, and starts the Kafka Connect cluster. The connector artifacts that you listed in the configuration are available in the cluster. Create a KafkaConnector resource to define an instance of each connector that you want to deploy. For example, create the following KafkaConnector CR, and save it as 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 Item Description 1 The name of the connector to register with the Kafka Connect cluster. 2 The name of the connector class. 3 The number of tasks that can operate concurrently. 4 The connector's configuration. 5 The address of the host database instance. 6 The port number of the database instance. 7 The name of the account that Debezium uses to connect to the database. 8 The password that Debezium uses to connect to the database user account. 9 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. 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 Upgrading 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 Create the Debezium Db2 container for Kafka Connect: Create a Dockerfile that uses registry.redhat.io/amq-streams-kafka-35-rhel8:2.5.0 as the base image. For example, from a terminal window, enter the following command: cat <<EOF >debezium-container-for-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-db2/2.3.4.Final-redhat-00001/debezium-connector-db2-2.3.4.Final-redhat-00001-plugin.zip \ && unzip debezium-connector-db2-2.3.4.Final-redhat-00001-plugin.zip \ && rm debezium-connector-db2-2.3.4.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 Item Description 1 You can specify any file name that you want. 2 Specifies the path to your Kafka Connect plug-ins directory. If your Kafka Connect plug-ins directory is in a different location, replace this path with the actual path of your directory. The command creates a Dockerfile with the name debezium-container-for-db2.yaml in the current directory. Build the container image from the debezium-container-for-db2.yaml Docker file that you created in the 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 . 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 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 ... Item Description 1 metadata.annotations indicates to the Cluster Operator that KafkaConnector resources are used to configure connectors in this Kafka Connect cluster. 2 spec.image specifies the name of the image that you created to run your Debezium connector. This property overrides the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE variable in the Cluster Operator. Apply the KafkaConnect CR to the OpenShift Kafka Connect environment by entering the following command: oc create -f dbz-connect.yaml The command adds a Kafka Connect instance that specifies the name of the image that you created to run your Debezium connector. Create a KafkaConnector custom resource that configures your Debezium 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 Item Description 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. 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 Check the status of the KafkaConnector resource by using one of the following methods: From the OpenShift Container Platform web console: Navigate to Home Search . On the Search page, click Resources to open the Select Resource box, and then type KafkaConnector . From the KafkaConnectors list, click the name of the connector that you want to check, for example inventory-connector-db2 . In the Conditions section, verify that the values in the Type and Status columns are set to Ready and True . From a terminal window: Enter the following command: oc describe KafkaConnector <connector-name> -n <project> For example, oc describe KafkaConnector inventory-connector-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> Verify that the connector created Kafka topics: From the OpenShift Container Platform web console. Navigate to Home Search . On the Search page, click Resources to open the Select Resource box, and then type KafkaTopic . From the KafkaTopics list, click the name of the topic that you want to check, for example, inventory-connector-db2.inventory.orders---ac5e98ac6a5d91e04d8ec0dc9078a1ece439081d . In the Conditions section, verify that the values in the Type and Status columns are set to Ready and True . From a terminal window: Enter the following command: oc get kafkatopics The command returns status information that is similar to the following output: Example 3.4. KafkaTopic resource status 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 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 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 configuration properties Advanced configuration properties Database schema history connector configuration properties that control how Debezium processes events that it reads from the database schema history topic. Pass-through database schema history properties Pass-through database driver properties that control the behavior of the database driver. Required Debezium Db2 connector configuration properties The following configuration properties are required unless a default value is available. Property Default Description 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. 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. Property Default Description 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, 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, 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 event. skip - The connector skips the problematic event and continues processing with the 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: 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 Property Default Description schema.history.internal.kafka.topic No default The full name of the Kafka topic where the connector stores the database schema history. schema.history.internal.kafka.bootstrap.servers No default A list of host/port pairs that the connector uses for establishing an initial connection to the Kafka cluster. This connection is used for retrieving the database schema history previously stored by the connector, and for writing each DDL statement read from the source database. Each pair should point to the same Kafka cluster used by the Kafka Connect process. schema.history.internal.kafka.recovery.poll.interval.ms 100 An integer value that specifies the maximum number of milliseconds the connector should wait during startup/recovery while polling for persisted data. The default is 100ms. schema.history.internal.kafka.query.timeout.ms 3000 An integer value that specifies the maximum number of milliseconds the connector should wait while fetching cluster information using Kafka admin client. schema.history.internal.kafka.create.timeout.ms 30000 An integer value that specifies the maximum number of milliseconds the connector should wait while create kafka history topic using Kafka admin client. schema.history.internal.kafka.recovery.attempts 100 The maximum number of times that the connector should try to read persisted history data before the connector recovery fails with an error. The maximum amount of time to wait after receiving no data is recovery.attempts x 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: 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 Property Default Description signal.kafka.topic <topic.prefix>-signal The name of the Kafka topic that the connector monitors for ad hoc signals. Note If automatic topic creation is disabled, you must manually create the required signaling topic. A signaling topic is required to preserve signal ordering. The signaling topic must have a single partition. signal.kafka.groupId kafka-signal The name of the group ID that is used by Kafka consumers. signal.kafka.bootstrap.servers No default A list of host/port pairs that the connector uses for establishing an initial connection to the Kafka cluster. Each pair references the Kafka cluster that is used by the Debezium Kafka Connect process. signal.kafka.poll.timeout.ms 100 An integer value that specifies the maximum number of milliseconds that the connector waits when polling signals. Debezium connector pass-through signals Kafka consumer client configuration properties The Debezium connector provides for pass-through configuration of the signals Kafka consumer. Pass-through signals properties begin with the prefix signals.consumer.* . For example, the connector passes properties such as signal.consumer.security.protocol=SSL to the Kafka consumer. Debezium strips the prefixes from the properties before it passes the properties to the Kafka signals consumer. Debezium connector sink notifications configuration properties The following table describes the notification properties. Table 3.21. Sink notification configuration properties Property Default Description notification.sink.topic.name No default The name of the topic that receives notifications from Debezium. This property is required when you configure the notification.enabled.channels property to include sink as one of the enabled notification channels. Debezium connector pass-through database driver configuration properties The Debezium connector provides for pass-through configuration of the database driver. Pass-through database properties begin with the prefix driver.* . For example, the connector passes properties such as driver.foobar=false to the JDBC URL. As is the case with the pass-through properties for database schema history clients , Debezium strips the prefixes from the properties before it passes them to the database driver. 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. Attributes Type Description LastEvent string The last snapshot event that the connector has read. MilliSecondsSinceLastEvent long The number of milliseconds since the connector has read and processed the most recent event. TotalNumberOfEventsSeen long The total number of events that this connector has seen since last started or reset. NumberOfEventsFiltered long The number of events that have been filtered by include/exclude list filtering rules configured on the connector. CapturedTables string[] The list of tables that are captured by the connector. QueueTotalCapacity int The length the queue used to pass events between the snapshotter and the main Kafka Connect loop. QueueRemainingCapacity int The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop. TotalTableCount int The total number of tables that are being included in the snapshot. RemainingTableCount int The number of tables that the snapshot has yet to copy. SnapshotRunning boolean Whether the snapshot was started. SnapshotPaused boolean Whether the snapshot was paused. SnapshotAborted boolean Whether the snapshot was aborted. SnapshotCompleted boolean Whether the snapshot completed. SnapshotDurationInSeconds long The total number of seconds that the snapshot has taken so far, even if not complete. Includes also time when snapshot was paused. SnapshotPausedDurationInSeconds long The total number of seconds that the snapshot was paused. If the snapshot was paused several times, the paused time adds up. RowsScanned Map<String, Long> Map containing the number of rows scanned for each table in the snapshot. Tables are incrementally added to the Map during processing. Updates every 10,000 rows scanned and upon completing a table. MaxQueueSizeInBytes long The maximum buffer of the queue in bytes. This metric is available if max.queue.size.in.bytes is set to a positive long value. CurrentQueueSizeInBytes long The current volume, in bytes, of records in the queue. The connector also provides the following additional snapshot metrics when an incremental snapshot is executed: Attributes Type Description ChunkId string The identifier of the current snapshot chunk. ChunkFrom string The lower bound of the primary key set defining the current chunk. ChunkTo string The upper bound of the primary key set defining the current chunk. TableFrom string The lower bound of the primary key set of the currently snapshotted table. TableTo string The upper bound of the primary key set of the currently snapshotted table. 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. Attributes Type Description LastEvent string The last streaming event that the connector has read. MilliSecondsSinceLastEvent long The number of milliseconds since the connector has read and processed the most recent event. TotalNumberOfEventsSeen long The total number of events that this connector has seen since the last start or metrics reset. TotalNumberOfCreateEventsSeen long The total number of create events that this connector has seen since the last start or metrics reset. TotalNumberOfUpdateEventsSeen long The total number of update events that this connector has seen since the last start or metrics reset. TotalNumberOfDeleteEventsSeen long The total number of delete events that this connector has seen since the last start or metrics reset. NumberOfEventsFiltered long The number of events that have been filtered by include/exclude list filtering rules configured on the connector. CapturedTables string[] The list of tables that are captured by the connector. QueueTotalCapacity int The length the queue used to pass events between the streamer and the main Kafka Connect loop. QueueRemainingCapacity int The free capacity of the queue used to pass events between the streamer and the main Kafka Connect loop. Connected boolean Flag that denotes whether the connector is currently connected to the database server. MilliSecondsBehindSource long The number of milliseconds between the last change event's timestamp and the connector processing it. The values will incoporate any differences between the clocks on the machines where the database server and the connector are running. NumberOfCommittedTransactions long The number of processed transactions that were committed. SourceEventPosition Map<String, String> The coordinates of the last received event. LastTransactionId string Transaction identifier of the last processed transaction. MaxQueueSizeInBytes long The maximum buffer of the queue in bytes. This metric is available if max.queue.size.in.bytes is set to a positive long value. CurrentQueueSizeInBytes long The current volume, in bytes, of records in the queue. 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. Attributes Type Description Status string One of STOPPED , RECOVERING (recovering history from the storage), RUNNING describing the state of the database schema history. RecoveryStartTime long The time in epoch seconds at what recovery has started. ChangesRecovered long The number of changes that were read during recovery phase. ChangesApplied long the total number of schema changes applied during recovery and runtime. 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 Task Command 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: Offline - executed while Debezium is stopped Online - executed while Debezium is running 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 Suspend the application that updates the database. Wait for the Debezium connector to stream all unstreamed change event records. Stop the Debezium connector. Apply all changes to the source table schema. In the ASN register table, mark the tables with updated schemas as INACTIVE . Reinitialize the ASN capture service . Remove the source table with the old schema from capture mode by running the Debezium UDF for removing tables from capture mode . Add the source table with the new schema to capture mode by running the Debezium UDF for adding tables to capture mode . In the ASN register table, mark the updated source tables as ACTIVE . Reinitialize the ASN capture service. Resume the application that updates the database. 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 Lock the source tables whose schema you want to change. In the ASN register table, mark the locked tables as INACTIVE . Reinitialize the ASN capture service. Apply all changes to the schemas for the source tables. Apply all changes to the schemas for the corresponding change-data tables. In the ASN register table, mark the source tables as ACTIVE . Reinitialize the ASN capture service. Optional. Restart the connector to see updated column names in change events. Procedure when adding a column to the middle of a table Lock the source table(s) to be changed. In the ASN register table, mark the locked tables as INACTIVE . Reinitialize the ASN capture service. For each source table to be changed: Export the data in the source table. Truncate the source table. Alter the source table and add the column. Load the exported data into the altered source table. Export the data in the source table's corresponding change-data table. Truncate the change-data table. Alter the change-data table and add the column. Load the exported data into the altered change-data table. 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. Reinitialize the ASN capture service. Optional. Restart the connector to see updated column names in change events.

Commands:
INSERT INTO <signalTable> (id, type, data) VALUES ( '<id>' , '<snapshotType>' , '{"data-collections": [" <tableName> "," <tableName> "],"type":" <snapshotType> ","additional-condition":" <additional-condition> "}');
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
SELECT * FROM <tableName> .
SELECT * FROM <tableName> WHERE <additional-condition> .
INSERT INTO <signalTable> (id, type, data) VALUES ( '<id>' , '<snapshotType>' , '{"data-collections": [" <tableName> "," <tableName> "],"type":" <snapshotType> ","additional-condition":" <additional-condition> "}');
INSERT INTO myschema.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["schema1.products"],"type":"incremental", "additional-condition":"color=blue"}');
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"}');
{ "before":null, "after": { "pk":"1", "value":"New data" }, "source": { "snapshot":"incremental" 1 }, "op":"r", 2 "ts_ms":"1620393591654", "transaction":null }
Key = `test_connector` Value = `{"type":"execute-snapshot","data": {"data-collections": ["schema1.table1", "schema1.table2"], "type": "INCREMENTAL"}}`
Key = `test_connector` Value = `{"type":"execute-snapshot","data": {"data-collections": ["schema1.products"], "type": "INCREMENTAL", "additional-condition":"color='blue'"}}`
Key = `test_connector` Value = `{"type":"execute-snapshot","data": {"data-collections": ["schema1.products"], "type": "INCREMENTAL", "additional-condition":"color='blue' AND brand='MyBrand'"}}`
INSERT INTO <signalTable> (id, type, data) values ( '<id>' , 'stop-snapshot', '{"data-collections": [" <tableName> "," <tableName> "],"type":"incremental"}');
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
Key = `test_connector` Value = `{"type":"stop-snapshot","data": {"data-collections": ["schema1.table1", "schema1.table2"], "type": "INCREMENTAL"}}`
{ "schema": { }, "payload": { "source": { "version": "2.3.4.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" } ] } } ] } }
{ "schema": { "type": "struct", "fields": [ { "type": "string", "optional": false, "field": "databaseName" } ], "optional": false, "name": "io.debezium.connector.db2.SchemaChangeKey" }, "payload": { "databaseName": "TESTDB" } }
{ "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 } ] }
{ "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" } }
{ "schema": { 1 }, "payload": { 2 }, "schema": { 3 }, "payload": { 4 }, }
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 );
{ "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 } }
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 );
{ "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": "2.3.4.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 } }
{ "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": "2.3.4.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 } }
{ "schema": { ... }, }, "payload": { "before": { 1 "ID": 1005, "FIRST_NAME": "john", "LAST_NAME": "doe", "EMAIL": "noreply@example.org" }, "after": null, 2 "source": { 3 "version": "2.3.4.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 } }
cd USDHOME/asncdctools/src
bldrtn asncdc
db2 start db DB_NAME
cd USDHOME/sqllib/bnd
db2 connect to DB_NAME db2 bind db2schema.bnd blocking all grant public sqlerror continue
db2 backup db DB_NAME to BACK_UP_LOCATION
db2 restart db DB_NAME
db2 connect to DB_NAME
cp USDHOME/asncdctools/src/asncdc USDHOME/sqllib/function
chmod 777 USDHOME/sqllib/function
db2 -tvmf USDHOME/asncdctools/src/asncdc_UDF.sql
db2 -tvmf USDHOME/asncdctools/src/asncdctables.sql
db2 -tvmf USDHOME/asncdctools/src/asncdcaddremove.sql
VALUES ASNCDC.ASNCDCSERVICES('start','asncdc');
db2 "VALUES ASNCDC.ASNCDCSERVICES('start','asncdc');"
/database/config/db2inst1/sqllib/bin/asncap capture_schema=asncdc capture_server=SAMPLE &
CALL ASNCDC.ADDTABLE('MYSCHEMA', 'MYTABLE');
VALUES ASNCDC.ASNCDCSERVICES('reinit','asncdc');
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-db2/2.3.4.Final-redhat-00001/debezium-connector-db2-2.3.4.Final-redhat-00001-plugin.zip 7 - type: zip url: https://maven.repository.redhat.com/ga/io/apicurio/apicurio-registry-distro-connect-converter/2.4.4.Final-redhat- <build-number> /apicurio-registry-distro-connect-converter-2.4.4.Final-redhat- <build-number> .zip 8 - type: zip url: https://maven.repository.redhat.com/ga/io/debezium/debezium-scripting/2.3.4.Final-redhat-00001/debezium-scripting-2.3.4.Final-redhat-00001.zip 9 - type: jar url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy/3.0.11/groovy-3.0.11.jar 10 - type: jar url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy-jsr223/3.0.11/groovy-jsr223-3.0.11.jar - type: jar url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy-json3.0.11/groovy-json-3.0.11.jar - 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
create -f dbz-connect.yaml
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
create -n <namespace> -f <kafkaConnector> .yaml
create -n debezium -f {context}-inventory-connector.yaml
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-db2/2.3.4.Final-redhat-00001/debezium-connector-db2-2.3.4.Final-redhat-00001-plugin.zip && unzip debezium-connector-db2-2.3.4.Final-redhat-00001-plugin.zip && rm debezium-connector-db2-2.3.4.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
build -t debezium-container-for-db2:latest .
docker build -t debezium-container-for-db2:latest .
push <myregistry.io> /debezium-container-for-db2:latest
docker push <myregistry.io> /debezium-container-for-db2:latest
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
create -f dbz-connect.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
apply -f inventory-connector.yaml
describe KafkaConnector <connector-name> -n <project>
describe KafkaConnector inventory-connector-db2 -n debezium
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>
get kafkatopics
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
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 >
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
{"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":"2.3.4.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}}
column.mask.hash.SHA-256.with.salt.CzQMA0cB5K = inventory.orders.customerName, inventory.shipment.customerName
isbn.type: io.debezium.test.IsbnConverter
isbn.schema.name: io.debezium.db2.type.Isbn
"snapshot.select.statement.overrides": "customer.orders", "snapshot.select.statement.overrides.customer.orders": "SELECT * FROM [customers].[orders] WHERE delete_flag = 0 ORDER BY id DESC"
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