Chapter 3. Debezium connector for Db2

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 user-defined functions (UDFs) in C that you can compile and then use to do the following management tasks:

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

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

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

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

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

3.2. How Debezium Db2 connectors work

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

Details are in the following topics:

3.2.1. How Debezium Db2 connectors perform database snapshots

Db2`s replication feature is not designed to store the complete history of database changes. Consequently, when a Debezium Db2 connector connects to a database for the first time, it takes a consistent snapshot of tables that are in capture mode and streams this state to Kafka. This establishes the baseline for table content.

By default, when a Db2 connector performs a snapshot, it does the following:

Determines which tables are in capture mode, and thus must be included in the snapshot. By default, all non-system tables are in capture mode. Connector configuration properties, such as table.exclude.list and table.include.list let you specify which tables should be in capture mode.
Obtains a lock on each of the tables in capture mode. This ensures that no schema changes can occur in those tables during the snapshot. The level of the lock is determined by the snapshot.isolation.mode connector configuration property.
Reads the highest (most recent) LSN position in the server’s transaction log.
Captures the schema of all tables that are in capture mode. The connector persists this information in its internal database history topic.
Optional, releases the locks obtained in step 2. Typically, these locks are held for only a short time.
At the LSN position read in step 3, the connector scans the capture mode tables as well as their schemas. During the scan, the connector:
1. Confirms that the table was created before the start of the snapshot. If it was not, the snapshot skips that table. After the snapshot is complete, and the connector starts emitting change events, the connector produces change events for any tables that were created during the snapshot.
2. Produces a read event for each row in each table that is in capture mode. All read events contain the same LSN position, which is the LSN position that was obtained in step 3.
3. Emits each read event to the Kafka topic that has the same name as the table.
Records the successful completion of the snapshot in the connector offsets.

3.2.1.1. Ad hoc snapshots

Important

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

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

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

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

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

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

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

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

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

Triggering an ad hoc snapshot

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

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

3.2.1.2. Incremental snapshots

Important

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

To 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 1 KB.

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

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

Incremental snapshot process

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

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

How Debezium resolves collisions among records with the same primary key

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

Snapshot window

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

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

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

The connector repeats the process for each snapshot chunk.

Triggering an incremental snapshot

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

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

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

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

Prerequisites

Signaling is enabled.
- A signaling data collection exists on the source database and the connector is configured to capture it.
- The signaling data collection is specified in the signal.data.collection property.

Procedure

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>_"}');

For example,

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

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

The following table describes the these parameters:

Table 3.2. Descriptions of fields in a SQL command for sending an incremental snapshot signal to the signaling table
Value	Description
`myschema.debezium_signal`	Specifies the fully-qualified name of the signaling table on the source database
`ad-hoc-1`	The `id` parameter specifies an arbitrary string that is assigned as the `id` identifier for the signal request. Use this string to identify logging messages to entries in the signaling table. Debezium does not use this string. Rather, during the snapshot, Debezium generates its own `id` string as a watermarking signal.
`execute-snapshot`	Specifies `type` parameter specifies the operation that the signal is intended to trigger.
`data-collections`	A required component of the `data` field of a signal that specifies an array of table names to include in the snapshot. The array lists tables by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling table in the `signal.data.collection` configuration property.
`incremental`	An optional `type` component of the `data` field of a signal that specifies the kind of snapshot operation to run. Currently, the only valid option is the default value, `incremental`. Specifying a `type` value in the SQL query that you submit to the signaling table is optional. If you do not specify a value, the connector runs an incremental snapshot.

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

Example: Incremental snapshot event message

{
    "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.

Warning

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

3.2.2. 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.

3.2.3. 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:

databaseName.schemaName.tableName

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

databaseName: The logical name of the connector as specified by the database.server.name connector configuration property.
schemaName: The name of the schema in which the operation occurred.
tableName: The name of the table in which the operation occurred.

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 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.

3.2.4. 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 captured 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 <serverName> where <serverName> is the logical server name that is specified in the database.server.name connector configuration property. Messages that the connector sends to the schema change topic contain a payload 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 binlog where the statements appear.
tableChanges: A structured representation of the entire table schema after the schema change. The tableChanges field contains an array that includes entries for each column of the table. Because the structured representation presents data in JSON or Avro format, consumers can easily read messages without first processing them through a DDL parser.

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 history topic. The internal database 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 history topic. For the database 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 history topic manually, specify a partition count of 1.
If you use the Apache Kafka broker to create the database 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": "1.7.2.Final",
      "connector": "db2",
      "name": "db2",
      "ts_ms": 1588252618953,
      "snapshot": "true",
      "db": "testdb",
      "schema": "DB2INST1",
      "table": "CUSTOMERS",
      "change_lsn": null,
      "commit_lsn": "00000025:00000d98:00a2",
      "event_serial_no": null
    },
    "databaseName": "TESTDB", 1
    "schemaName": "DB2INST1",
    "ddl": null, 2
    "tableChanges": [ 3
      {
        "type": "CREATE", 4
        "id": "\"DB2INST1\".\"CUSTOMERS\"", 5
        "table": { 6
          "defaultCharsetName": null,
          "primaryKeyColumnNames": [ 7
            "ID"
          ],
          "columns": [ 8
            {
              "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
            }
          ]
        }
      }
    ]
  }
}

Table 3.3. Descriptions of fields in messages emitted to the schema change topic
Item	Field name	Description
1	`databaseName` `schemaName`	Identifies the database and the schema that contain the change.
2	`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.
3	`tableChanges`	An array of one or more items that contain the schema changes generated by a DDL command.
4	`type`	Describes the kind of change. The value is one of the following: `CREATE` - table created `ALTER` - table modified `DROP` - table deleted
5	`id`	Full identifier of the table that was created, altered, or dropped.
6	`table`	Represents table metadata after the applied change.
7	`primaryKeyColumnNames`	List of columns that compose the table’s primary key.
8	`columns`	Metadata for each column in the changed table.

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"
  }
}

3.2.5. 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.
event_count (for END events): Total number of events emitted by the transaction.
data_collections (for END events): An array of pairs of data_collection and event_count elements. that indicates the number of events that the connector emits for changes that originate from a data collection.

Example

{
  "status": "BEGIN",
  "id": "00000025:00000d08:0025",
  "event_count": null,
  "data_collections": null
}

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

The connector emits transaction events to the <database.server.name>.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 3.4. 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 the key identified by that property.
2	`payload`	The first `payload` field is part of the event key. It has the structure described by the previous `schema` field and it contains the key for the row that was changed.
3	`schema`	The second `schema` field is part of the event value. It specifies the Kafka Connect schema that describes what is in the event value’s `payload` portion. In other words, the second `schema` describes the structure of the row that was changed. Typically, this schema contains nested schemas.
4	`payload`	The second `payload` field is part of the event value. It has the structure described by the previous `schema` field and it contains the actual data for the row that was changed.

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

Warning

The Debezium Db2 connector ensures that all Kafka Connect schema names adhere to the Avro schema name format. This means that the logical server name must start with a Latin letter or an underscore, that is, a-z, A-Z, or _. Each remaining character in the logical server name and each character in the database and table names must be a Latin letter, a digit, or an underscore, that is, a-z, A-Z, 0-9, or \_. If there is an invalid character it is replaced with an underscore character.

This can lead to unexpected conflicts if the logical server name, a database name, or a table name contains invalid characters, and the only characters that distinguish names from one another are invalid and thus replaced with underscores.

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

Details are in the following topics:

3.3.1. About keys in Debezium db2 change events

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

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

Example table

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

Example change event key

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

{
    "schema": {  1
        "type": "struct",
        "fields": [  2
            {
                "type": "int32",
                "optional": false,
                "field": "ID"
            }
        ],
        "optional": false,  3
        "name": "mydatabase.MYSCHEMA.CUSTOMERS.Key"  4
    },
    "payload": {  5
        "ID": 1004
    }
}

Table 3.5. 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

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": "1.7.2.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 3.6. 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": "1.7.2.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 3.7. 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": "1.7.2.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 3.8. 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

Db2’s data types are described in Db2 SQL Data Types.

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.

Details are in the following sections:

Basic types

The following table describes how the connector maps each of the Db2 data types 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 3.9. 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

Other than Db2’s DATETIMEOFFSET data type, which contains time zone information, how temporal types are mapped depends on the value of the time.precision.mode connector configuration property. The following sections describe these mappings:

time.precision.mode=adaptive

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

Table 3.10. 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.
`SMALLDATETIME`	`INT64`	`io.debezium.time.Timestamp` Represents the number of milliseconds since the epoch, and does not include timezone information.
`DATETIME2(0)`, `DATETIME2(1)`, `DATETIME2(2)`, `DATETIME2(3)`	`INT64`	`io.debezium.time.Timestamp` Represents the number of milliseconds since the epoch, and does not include timezone information.
`DATETIME2(4)`, `DATETIME2(5)`, `DATETIME2(6)`	`INT64`	`io.debezium.time.MicroTimestamp` Represents the number of microseconds since the epoch, and does not include timezone information.
`DATETIME2(7)`	`INT64`	`io.debezium.time.NanoTimestamp` Represents the number of nanoseconds past 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 3.11. 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.
`SMALLDATETIME`	`INT64`	`org.apache.kafka.connect.data.Timestamp` Represents the number of milliseconds since the epoch, and does not include timezone information.
`DATETIME2`	`INT64`	`org.apache.kafka.connect.data.Timestamp` Represents the number of milliseconds since the epoch, 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.

Timestamp types

The DATETIME, SMALLDATETIME and DATETIME2 types represent a timestamp without time zone information. Such columns are converted into an equivalent Kafka Connect value based on UTC. For example, the DATETIME2 value "2018-06-20 15:13:16.945104" is represented by an io.debezium.time.MicroTimestamp with the value "1529507596945104".

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

Table 3.12. 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.
`SMALLMONEY`	`BYTES`	`org.apache.kafka.connect.data.Decimal` The `scale` schema parameter contains an integer that represents how many digits the decimal point iss shifted. The `connect.decimal.precision` schema parameter contains an integer that represents the precision of the given decimal value.
`MONEY`	`BYTES`	`org.apache.kafka.connect.data.Decimal` The `scale` schema parameter contains an integer that represents how many digits the decimal point is shifted. The `connect.decimal.precision` schema parameter contains an integer that represents the precision of the given decimal value.

3.5. Setting up Db2 to run a Debezium connector

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

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

3.5.1. Configuring Db2 tables for change data capture

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

Prerequisites

You are logged in to Db2 as the db2instl user.
On the Db2 host, the Debezium management UDFs are available in the $HOME/asncdctools/src directory. UDFs are available from the Debezium examples repository.

Procedure

Compile the Debezium management UDFs on the Db2 server host by using the bldrtn command provided with Db2:
```
cd $HOME/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 $HOME/sqllib/bnd

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.
1. Back up the database. Replace DB_NAME and BACK_UP_LOCATION with appropriate values:
```
db2 backup db DB_NAME to BACK_UP_LOCATION
```
2. Restart the database:
```
db2 restart db DB_NAME
```
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 $HOME/asncdctools/src/asncdc $HOME/sqllib/function

chmod 777 $HOME/sqllib/function

Enable the Debezium UDF that starts and stops the ASN capture agent:
```
db2 -tvmf $HOME/asncdctools/src/asncdc_UDF.sql
```

Create the ASN control tables:

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

Enable the Debezium UDF that adds tables to capture mode and removes tables from capture mode:
```
$ db2 -tvmf $HOME/asncdctools/src/asncdcaddremove.sql
```
After you set up the Db2 server, use the UDFs to control Db2 replication (ASN) with SQL commands. Some of the UDFs expect a return value in which case you use the SQL VALUE statement to invoke them. For other UDFs, use the SQL CALL statement.
Start the ASN agent:
```
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 resources

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.

The Debezium Db2 connector requires the Db2 JDBC driver to connect to Db2 databases. For information about how to obtain the driver, see Obtaining the Db2 JDBC driver.

Additional resources

Section 3.6.6, “Description of Debezium Db2 connector configuration properties”

3.6.1. Obtaining the Db2 JDBC driver

Due to licensing requirements, the Db2 JDBC driver file is not included in the Debezium Db2 connector archive. Regardless of the deployment method that you use, you must download the driver file to complete the deployment.

The following steps describe how to obtain the driver and use it your your environment.

Procedure

From a browser, navigate to the IBM Support site and download the JDBC driver that matches your version of Db2.
- If you use a Dockerfile to build the connector, copy the downloaded file to the directory that contains the Debezium Db2 connector files, for example, <kafka_home>/libs directory.
- If you use AMQ Streams to add the connector to your Kafka Connect image:
  1. Deploy the driver to a Maven repository or to another HTTP server that is available to your OpenShift cluster.
  2. Add the artifact URL to the KafkaConnect custom resource.

After you apply the KafkaConnector resource to deploy the connector, the connector is configured to use the specified driver.

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 Upgrading 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, it was necessary 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 pod. 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.
You have a Red Hat Integration license.
Kafka Connect is deployed on AMQ Streams.
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. You must explicitly create an ImageStream for the cluster. ImageStreams are not available by default.

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 that specifies the metadata.annotations and spec.build properties, as shown in the following example. Save the file with a name such as dbz-connect.yaml.

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

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: debezium-kafka-connect-cluster
  annotations:
    strimzi.io/use-connector-resources: "true" 1
spec:
  version: 3.00
  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-db2/1.7.2.Final-redhat-<build_number>/debezium-connector-db2-1.7.2.Final-redhat-<build_number>-plugin.zip  7
          - type: zip
            url: https://maven.repository.redhat.com/ga/io/apicurio/apicurio-registry-distro-connect-converter/2.0-redhat-<build-number>/apicurio-registry-distro-connect-converter-2.0-redhat-<build-number>.zip
          - type: zip
            url: https://maven.repository.redhat.com/ga/io/debezium/debezium-scripting/1.7.2.Final/debezium-scripting-1.7.2.Final.zip

  bootstrapServers: debezium-kafka-cluster-kafka-bootstrap:9093

Table 3.13. 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 like 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 documentation.
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.

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. A 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
    database.history.kafka.bootstrap.servers: 'debezium-kafka-cluster-kafka-bootstrap.debezium.svc.cluster.local:9092'
    database.history.kafka.topic: schema-changes.inventory
    database.hostname: db2.debezium-db2.svc.cluster.local 5
    database.port: 3306   6
    database.user: debezium  7
    database.password: dbz  8
    database.dbname: mydatabase 9
    database.server.name: inventory_connector_db2 10
    database.include.list: public.inventory  11

Table 3.14. 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 user account through which Debezium connects to the database.
8	The password for the database user account.
9	The name of the database to capture changes from.
10	The logical name of the database instance or cluster. The specified name must be formed only from alphanumeric characters or underscores. Because the logical name 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. The 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.
You obtained the required JDBC driver for Db2.
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:
1. Download the Debezium Db2 connector archive.
2. Extract the Debezium Db2 connector archive to create a directory structure for the connector plug-in, for example:
```
./my-plugins/
├── debezium-connector-db2
│   ├── ...
```
3. Create a Dockerfile that uses registry.redhat.io/amq7/amq-streams-kafka-30-rhel8:2.0.0 as the base image. For example, from a terminal window, enter the following, replacing my-plugins with the name of your plug-ins directory:
```
cat <<EOF >debezium-container-for-db2.yaml 1
FROM registry.redhat.io/amq7/amq-streams-kafka-30-rhel8:2.0.0
USER root:root
COPY ./<my-plugins>/ /opt/kafka/plugins/ 2
USER 1001
EOF
```
  1 1 1 1 1 1 1 1 1 1
  You can specify any file name that you want.
  2 2 2 2 2 2 2 2 2 2
  Replace my-plugins with the name of your plug-ins directory.
  The command creates a Dockerfile with the name debezium-container-for-db2.yaml in the current directory.
4. Build the container image from the debezium-container-for-db2.yaml Docker file that you created in the previous step. From the directory that contains the file, open a terminal window and enter one of the following commands:
```
podman build -t debezium-container-for-db2:latest .
```
```
docker build -t debezium-container-for-db2:latest .
```
  The preceding commands build a container image with the name debezium-container-for-db2.
5. 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
```
6. 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 as shown in the following example:
```
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
```
  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.
7. 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 fulfillment is the server’s logical name.

Db2 inventory-connector.yaml

apiVersion: kafka.strimzi.io/v1beta2
  kind: KafkaConnector
  metadata:
    name: inventory-connector  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
      database.server.name: fullfillment   10
      database.include.list: public.inventory   11

Table 3.15. 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	A list of all tables whose changes Debezium should capture.

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

From a terminal window:

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

From a terminal window:

Enter the following command:

oc get kafkatopics

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

Example 3.4. KafkaTopic resource status

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

Check topic content.

From a terminal window, enter the following command:

oc exec -n <project>  -it <kafka-cluster> -- /opt/kafka/bin/kafka-console-consumer.sh \
>     --bootstrap-server localhost:9092 \
>     --from-beginning \
>     --property print.key=true \
>     --topic=<topic-name>

For example,

 oc exec -n debezium  -it debezium-kafka-cluster-kafka-0 -- /opt/kafka/bin/kafka-console-consumer.sh \
>     --bootstrap-server localhost:9092 \
>     --from-beginning \
>     --property print.key=true \
>     --topic=inventory_connector_db2.inventory.products_on_hand

The format for specifying the topic name is the same as the oc describe command returns in Step 1, for example, inventory_connector_db2.inventory.addresses.

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

Example 3.5. Content of a Debezium change event

{"schema":{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"}],"optional":false,"name":"inventory_connector_db2.inventory.products_on_hand.Key"},"payload":{"product_id":101}}	{"schema":{"type":"struct","fields":[{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"},{"type":"int32","optional":false,"field":"quantity"}],"optional":true,"name":"inventory_connector_db2.inventory.products_on_hand.Value","field":"before"},{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"},{"type":"int32","optional":false,"field":"quantity"}],"optional":true,"name":"inventory_connector_db2.inventory.products_on_hand.Value","field":"after"},{"type":"struct","fields":[{"type":"string","optional":false,"field":"version"},{"type":"string","optional":false,"field":"connector"},{"type":"string","optional":false,"field":"name"},{"type":"int64","optional":false,"field":"ts_ms"},{"type":"string","optional":true,"name":"io.debezium.data.Enum","version":1,"parameters":{"allowed":"true,last,false"},"default":"false","field":"snapshot"},{"type":"string","optional":false,"field":"db"},{"type":"string","optional":true,"field":"sequence"},{"type":"string","optional":true,"field":"table"},{"type":"int64","optional":false,"field":"server_id"},{"type":"string","optional":true,"field":"gtid"},{"type":"string","optional":false,"field":"file"},{"type":"int64","optional":false,"field":"pos"},{"type":"int32","optional":false,"field":"row"},{"type":"int64","optional":true,"field":"thread"},{"type":"string","optional":true,"field":"query"}],"optional":false,"name":"io.debezium.connector.db2.Source","field":"source"},{"type":"string","optional":false,"field":"op"},{"type":"int64","optional":true,"field":"ts_ms"},{"type":"struct","fields":[{"type":"string","optional":false,"field":"id"},{"type":"int64","optional":false,"field":"total_order"},{"type":"int64","optional":false,"field":"data_collection_order"}],"optional":true,"field":"transaction"}],"optional":false,"name":"inventory_connector_db2.inventory.products_on_hand.Envelope"},"payload":{"before":null,"after":{"product_id":101,"quantity":3},"source":{"version":"1.7.2.Final-redhat-00001","connector":"db2","name":"inventory_connector_db2","ts_ms":1638985247805,"snapshot":"true","db":"inventory","sequence":null,"table":"products_on_hand","server_id":0,"gtid":null,"file":"db2-bin.000003","pos":156,"row":0,"thread":null,"query":null},"op":"r","ts_ms":1638985247805,"transaction":null}}

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

3.6.6. Description 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 history connector configuration properties that control how Debezium processes events that it reads from the database history topic.
- Pass-through database 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
`database.server.name`	No default	Logical name that identifies and 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 database server logical name. The logical name should be unique across all other connectors, since it is used as a topic name prefix for all Kafka topics that receive records from this connector.
`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. Any table not included in the include list does not have its changes captured. Each identifier is of the form schemaName.tableName. By default, the connector captures changes in every non-system table. 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. Do not also set the `table.include.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. Primary key columns are always included in the event’s key, even if they are excluded from the value.
`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. In the resulting change event record, the values for the specified columns are replaced with pseudonyms. A pseudonym consists of the hashed value that results from applying the specified hashAlgorithm and salt. Based on the hash function that is used, referential integrity is maintained, while column values are replaced with pseudonyms. Supported hash functions are described in the MessageDigest section of the Java Cryptography Architecture Standard Algorithm Name Documentation. In the following example, `CzQMA0cB5K` is a randomly selected salt. column.mask.hash.SHA-256.with.salt.CzQMA0cB5K = inventory.orders.customerName, inventory.shipment.customerName If necessary, the pseudonym is automatically shortened to the length of the column. The connector configuration can include multiple properties that specify different hash algorithms and salts. Depending on the hashAlgorithm used, the salt selected, and the actual data set, the resulting data set might not be completely masked.
`time.precision.mode`	`adaptive`	Time, date, and timestamps can be represented with different kinds of precision: `adaptive` captures the time and timestamp values exactly as in the database using either millisecond, microsecond, or nanosecond precision values based on the database column’s type. `connect` always represents time and timestamp values by using Kafka Connect’s built-in representations for `Time`, `Date`, and `Timestamp`, which uses millisecond precision regardless of the database columns' precision. See temporal values.
`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 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. Fully-qualified names for columns are of the form schemaName.tableName.columnName. In change event records, values in these columns are truncated if they are longer than the number of characters specified by length in the property name. You can specify multiple properties with different lengths in a single configuration. Length must be a positive integer, for example, `column.truncate.to.20.chars`.
`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. Fully-qualified names for columns are of the form schemaName.tableName.columnName. In change event values, the values in the specified table columns are replaced with length number of asterisk (`*`) characters. You can specify multiple properties with different lengths in a single configuration. Length must be a positive integer or zero. When you specify zero, the connector replaces a value with an empty string.
`column.propagate.source.type`	n/a	An optional, comma-separated list of regular expressions that match the fully-qualified names of columns. Fully-qualified names for columns are of the form databaseName.tableName.columnName, or databaseName.schemaName.tableName.columnName. For each specified column, the connector adds the column’s original type and original length as parameters to the corresponding field schemas in the emitted change records. Add the following schema parameters to propagate the original type name and the original length for variable-width types: `__debezium.source.column.type` `__debezium.source.column.length` `__debezium.source.column.scale` This property is useful for properly sizing corresponding columns in sink databases.
`datatype.propagate.source.type`	n/a	An optional, comma-separated list of regular expressions that match the database-specific data type name for some columns. Fully-qualified data type names are of the form databaseName.tableName.typeName, or databaseName.schemaName.tableName.typeName. For these data types, the connector adds parameters to the corresponding field schemas in emitted change records. The added parameters specify the original type and length of the column: `__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. This property is useful for properly sizing corresponding columns in sink databases. See Db2 data types for the list of Db2-specific data type names.
`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.

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
`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. `schema_only` - For tables in capture mode, the connector takes a snapshot of only the schema for the table. This is useful when only the changes that are happening from now on need to be emitted to Kafka topics. After the snapshot is complete, the connector continues by reading change events from the database’s redo logs.
`snapshot.isolation.mode`	`repeatable_read`	During a snapshot, controls the transaction isolation level and how long the connector locks the tables that are in capture mode. The possible values are: `read_uncommitted` - Does not prevent other transactions from updating table rows during an initial snapshot. This mode has no data consistency guarantees; some data might be lost or corrupted. `read_committed` - Does not prevent other transactions from updating table rows during an initial snapshot. It is possible for a new record to appear twice: once in the initial snapshot and once in the streaming phase. However, this consistency level is appropriate for data mirroring. `repeatable_read` - Prevents other transactions from updating table rows during an initial snapshot. It is possible for a new record to appear twice: once in the initial snapshot and once in the streaming phase. However, this consistency level is appropriate for data mirroring. `exclusive` - Uses repeatable read isolation level but takes an exclusive lock for all tables to be read. This mode prevents other transactions from updating table rows during an initial snapshot. Only `exclusive` mode guarantees full consistency; the initial snapshot and streaming logs constitute a linear history.
`event.processing.failure.handling.mode`	`fail`	Specifies how the connector handles exceptions during processing of events. The possible values are: `fail` - The connector logs the offset of the problematic event and stops processing. `warn` - The connector logs the offset of the problematic event and continues processing with the next event. `skip` - The connector skips the problematic event and continues processing with the next event.
`poll.interval.ms`	`1000`	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 1000 milliseconds, or 1 second.
`max.queue.size`	`8192`	Positive integer value for the maximum size of the blocking queue. The connector places change events that it reads from the database log into the blocking queue before writing them to Kafka. This queue can provide backpressure for reading change-data tables when, for example, writing records to Kafka is slower than it should be or Kafka is not available. Events that appear in the queue are not included in the offsets that are periodically recorded by the connector. The `max.queue.size` value should always be larger than the value of the `max.batch.size` connector configuration property.
`max.batch.size`	`2048`	Positive integer value that specifies the maximum size of each batch of events that the connector processes.
`max.queue.size.in.bytes`	`0`	Long value for the maximum size in bytes of the blocking queue. The feature is disabled by default, it will be active if it’s set with a positive long value.
`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.
`heartbeat.topics.prefix`	`__debezium-heartbeat`	Specifies the prefix for the name of the topic to which the connector sends heartbeat messages. The format for this topic name is `<heartbeat.topics.prefix>.<server.name>`.
`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.fetch.size`	`2000`	During a snapshot, the connector reads table content in batches of rows. This property specifies the maximum number of rows in a batch.
`snapshot.lock.timeout.ms`	`10000`	Positive integer value that specifies the maximum amount of time (in milliseconds) to wait to obtain table locks when performing a snapshot. If the connector cannot acquire table locks in this interval, the snapshot fails. How the connector performs snapshots provides details. Other possible settings are: `0` - The connector immediately fails when it cannot obtain a lock. `-1` - The connector waits infinitely.
`snapshot.select.statement.overrides`	No default	Specifies the table rows to include in a snapshot. Use the property if you want a snapshot to include only a subset of the rows in a table. This property affects snapshots only. It does not apply to events that the connector reads from the log. The property contains a comma-separated list of fully-qualified table names in the form `<schemaName>.<tableName>`. For example, `"snapshot.select.statement.overrides": "inventory.products,customers.orders"` For each table in the list, add a further configuration property that specifies the `SELECT` statement for the connector to run on the table when it takes a snapshot. The specified `SELECT` statement determines the subset of table rows to include in the snapshot. Use the following format to specify the name of this `SELECT` statement property: `snapshot.select.statement.overrides.<schemaName>.<tableName>`. For example, `snapshot.select.statement.overrides.customers.orders`. Example: From a `customers.orders` table that includes the soft-delete column, `delete_flag`, add the following properties if you want a snapshot to include only those records that are not soft-deleted: "snapshot.select.statement.overrides": "customer.orders", "snapshot.select.statement.overrides.customer.orders": "SELECT * FROM [customers].[orders] WHERE delete_flag = 0 ORDER BY id DESC" In the resulting snapshot, the connector includes only the records for which `delete_flag = 0`.
`sanitize.field.names`	`true` if connector configuration sets the `key.converter` or `value.converter` property to the Avro converter. `false` if not.	Indicates whether field names are sanitized to adhere to Avro naming requirements.
`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`	No default	comma-separated list of operation types that will be skipped during streaming. The operations include: `c` for inserts/create, `u` for updates, and `d` for deletes. By default, no operations are skipped.
`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>` Signaling is a Technology Preview feature.
`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. Incremental snapshots is a Technology Preview feature.

Debezium connector database history configuration properties

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

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

Table 3.16. Connector database history configuration properties
Property	Default	Description
`database.history.kafka.topic`		The full name of the Kafka topic where the connector stores the database schema history.
`database.history.kafka.bootstrap.servers`		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.
`database.history.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.
`database.history.kafka.recovery.attempts`	`4`	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`.
`database.history.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.
`database.history.store.only.monitored.tables.ddl` Deprecated and scheduled for removal in a future release; use `database.history.store.only.captured.tables.ddl` instead.	`false`	A Boolean value that specifies whether the connector should record all DDL statements `true` records only those DDL statements that are relevant to tables whose changes are being captured by Debezium. Set to `true` with care because missing data might become necessary if you change which tables have their changes captured. The safe default is `false`.
`database.history.store.only.captured.tables.ddl`	`false`	A Boolean value that specifies whether the connector should record all DDL statements `true` records only those DDL statements that are relevant to tables whose changes are being captured by Debezium. Set to `true` with care because missing data might become necessary if you change which tables have their changes captured. The safe default is `false`.

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

Debezium relies on a Kafka producer to write schema changes to database history topics. Similarly, it relies on a Kafka consumer to read from database 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 database.history.producer.* and database.history.consumer.* prefixes. The pass-through producer and consumer database history properties control a range of behaviors, such as how these clients secure connections with the Kafka broker, as shown in the following example:

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

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

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

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

Debezium connector 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 database.*. For example, the connector passes properties such as database.foobar=false to the JDBC URL.

As is the case with the pass-through properties for database 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=<db2.server.name>.

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.
`MonitoredTables` Deprecated and scheduled for removal in a future release; use the `CapturedTables` metric instead.	`string[]`	The list of tables that are monitored by 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.
`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.
`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. It will be enabled if `max.queue.size.in.bytes` is passed with a positive long value.
`CurrentQueueSizeInBytes`	`long`	The current data of records in the queue in bytes.

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.

Important

Incremental 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 https://access.redhat.com/support/offerings/techpreview.

3.7.2. Monitoring Debezium Db2 connector record streaming

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

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 last started or reset.
`NumberOfEventsFiltered`	`long`	The number of events that have been filtered by include/exclude list filtering rules configured on the connector.
`MonitoredTables` Deprecated and scheduled for removal in a future release; use the 'CapturedTables' metric instead	`string[]`	The list of tables that are monitored by 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.
`CurrentQueueSizeInBytes`	`long`	The current data of records in the queue in bytes.

3.7.3. Monitoring Debezium Db2 connector schema history

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

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 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.
`MilliSecondsSinceLastRecoveredChange`	`long`	The number of milliseconds that elapsed since the last change was recovered from the history store.
`MilliSecondsSinceLastAppliedChange`	`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 3.17. 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, Db2’s replication feature creates a change-data table that contains all changes to that source table. However, change-data table schemas are static. If you update the schema for a table in capture mode then you must also update the schema of its corresponding change-data table. A Debezium Db2 connector cannot do this. A database administrator with elevated privileges must update schemas for tables that are in capture mode.

Warning

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

There are generally two procedures for updating table schemas:

Each approach has advantages and disadvantages.

3.9.1. Performing offline schema updates for Debezium Db2 connectors

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

Prerequisites

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

Procedure

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:
1. Export the data in the source table.
2. Truncate the source table.
3. Alter the source table and add the column.
4. Load the exported data into the altered source table.
5. Export the data in the source table’s corresponding change-data table.
6. Truncate the change-data table.
7. Alter the change-data table and add the column.
8. Load the exported data into the altered change-data table.
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.

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3.1. Overview of Debezium Db2 connector

3.2. How Debezium Db2 connectors work

3.2.1. How Debezium Db2 connectors perform database snapshots

3.2.1.1. Ad hoc snapshots

3.2.1.2. Incremental snapshots

3.2.2. How Debezium Db2 connectors read change-data tables

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

3.2.4. About the Debezium Db2 connector schema change topic

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

3.3. Descriptions of Debezium Db2 connector data change events

3.3.1. About keys in Debezium db2 change events

3.3.2. About values in Debezium Db2 change events

3.4. How Debezium Db2 connectors map data types

3.5. Setting up Db2 to run a Debezium connector

3.5.1. Configuring Db2 tables for change data capture

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

3.5.3. Db2 capture agent configuration parameters

3.6. Deployment of Debezium Db2 connectors

3.6.1. Obtaining the Db2 JDBC driver

3.6.2. Db2 connector deployment using AMQ Streams

3.6.3. Using AMQ Streams to deploy a Debezium Db2 connector

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

3.6.5. Verifying that the Debezium Db2 connector is running

3.6.6. Description of Debezium Db2 connector configuration properties

3.7. Monitoring Debezium Db2 connector performance

3.7.1. Monitoring Debezium during snapshots of Db2 databases

3.7.2. Monitoring Debezium Db2 connector record streaming

3.7.3. Monitoring Debezium Db2 connector schema history

3.8. Managing Debezium Db2 connectors

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

3.9.1. Performing offline schema updates for Debezium Db2 connectors

3.9.2. Performing online schema updates for Debezium Db2 connectors

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