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Debezium connector for Db2

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

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

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

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

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

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

The Db2 connector has been tested with Db2 for Linux. It is expected that the connector would also work on other platforms such as Windows, and we’d love to get your feedback if you can confirm this to be the case.

Overview

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.

How the connector works

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.

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:

  1. 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.

  2. 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.

  3. Reads the highest (most recent) LSN position in the server’s transaction log.

  4. Captures the schema of all tables that are in capture mode. The connector persists this information in its internal database history topic.

  5. Optional, releases the locks obtained in step 2. Typically, these locks are held for only a short time.

  6. 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.

  7. Records the successful completion of the snapshot in the connector offsets.

Ad hoc snapshots

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

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

  • The connector configuration is modified to capture a different set of tables.

  • Kafka topics are deleted and must be rebuilt.

  • Data corruption occurs due to a configuration error or some other problem.

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

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

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

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 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.

Incremental snapshots

To provide flexibility in managing snapshots, Debezium includes a supplementary snapshot mechanism, known as incremental snapshotting. Incremental snapshots rely on the Debezium mechanism for sending signals to a Debezium connector. Incremental snapshots are based on the DDD-3 design document.

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

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

Triggering an incremental snapshot

Currently, the only way to initiate an incremental snapshot is to send an ad hoc snapshot signal to the signaling table on the source database. You submit a signal to the signaling table as SQL INSERT queries.

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

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

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

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

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

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

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

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

    For example,

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

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

    The following table describes the parameters in the example:

    Table 2. Descriptions of fields in a SQL command for sending an incremental snapshot signal to the signaling table
    Item Value Description

    1

    myschema.debezium_signal

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

    2

    ad-hoc-1

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

    3

    execute-snapshot

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

    4

    data-collections

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

    5

    incremental

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

    6

    additional-condition

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

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

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

SELECT * FROM <tableName> ....

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

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

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

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

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

  • id (primary key)

  • color

  • quantity

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

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

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

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

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

Example: Incremental snapshot event message
{
    "before":null,
    "after": {
        "pk":"1",
        "value":"New data"
    },
    "source": {
        ...
        "snapshot":"incremental" (1)
    },
    "op":"r", (2)
    "ts_ms":"1620393591654",
    "transaction":null
}
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.

Stopping an incremental snapshot

You can also stop an incremental snapshot by sending a signal to the table on the source database. You submit a stop snapshot signal to the table by sending a SQL INSERT query. After Debezium detects the change in the signaling table, it reads the signal, and stops the incremental snapshot operation if it’s in progress.

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

Prerequisites
Procedure

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

    INSERT INTO <signalTable> (id, type, data) values ('<id>', 'stop-snapshot', '{"data-collections": ["<tableName>","<tableName>"],"type":"incremental"}');

    For example,

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

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

    The following table describes the parameters in the example:

    Table 3. Descriptions of fields in a SQL command for sending a stop incremental snapshot signal to the signaling table
    Item Value Description

    1

    myschema.debezium_signal

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

    2

    ad-hoc-1

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

    3

    stop-snapshot

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

    4

    data-collections

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

    5

    incremental

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

Change-data tables

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

  1. Reads change events that were created between the last stored, highest LSN and the current, highest LSN.

  2. Orders the change events according to the commit LSN and the change LSN for each event. This ensures that the connector emits the change events in the order in which the table changes occurred.

  3. Passes commit and change LSNs as offsets to Kafka Connect.

  4. Stores the highest LSN that the connector passed to Kafka Connect.

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

Topic names

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.

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.

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.

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.

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": "1.9.8.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 4. 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"
  }
}

Transaction metadata

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
    }
  ]
}

Unless overridden via the transaction.topic option, 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"
  }
}

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 5. 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.

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.

Change event keys

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

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

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 6. 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.

Change event values

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

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

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": "1.9.8.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 7. 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": "1.9.8.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 8. 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.

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": "1.9.8.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 9. 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.

Data type mappings

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. If the default data type conversions do not meet your needs, you can create a custom converter for the connector.

Basic types

The following table 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 10. 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. Passing the default value helps satisfy compatibility rules when using Avro as the serialization format together with the Confluent schema registry.

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 11. 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 12. 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.

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.

Setting up Db2

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.

Putting tables into capture mode

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

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

    cd $HOME/asncdctools/src
    ./bldrtn asncdc

  2. Start the database if it is not already running. Replace DB_NAME with the name of the database that you want Debezium to connect to.

    db2 start db DB_NAME

  3. Ensure that JDBC can read the Db2 metadata catalog:

    cd $HOME/sqllib/bnd
    db2 bind db2schema.bnd blocking all grant public sqlerror continue

  4. Ensure that the database was recently backed-up. The ASN agents must have a recent starting point to read from. If you need to perform a backup, run the following commands, which prune the data so that only the most recent version is available. If you do not need to retain the older versions of the data, specify dev/null for the backup location.

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

      db2 backup db DB_NAME to BACK_UP_LOCATION

    2. Restart the database:

      db2 restart db DB_NAME

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

    db2 connect to DB_NAME

  6. Copy the Debezium management UDFs and set permissions for them:

    cp $HOME/asncdctools/src/asncdc $HOME/sqllib/function
    chmod 777 $HOME/sqllib/function

  7. Enable the Debezium UDF that starts and stops the ASN capture agent:

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

  8. Create the ASN control tables:

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

  9. Enable the Debezium UDF that adds tables to capture mode and removes tables from capture mode:

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

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

  10. Start the ASN agent:

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

    The preceding statement returns one of the following results:

    • asncap is already running

    • start --> <COMMAND>

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

      /database/config/db2inst1/sqllib/bin/asncap capture_schema=asncdc capture_server=SAMPLE &

  11. Put tables into capture mode. Invoke the following statement for each table that you want to put into capture. Replace MYSCHEMA with the name of the schema that contains the table you want to put into capture mode. Likewise, replace MYTABLE with the name of the table to put into capture mode:

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

  12. Reinitialize the ASN service:

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

Reference table for Debezium Db2 management UDFs

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.

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.

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.

Deployment

To deploy a Debezium Db2 connector, you install the Debezium Db2 connector archive, configure the connector, and start the connector by adding its configuration to Kafka Connect.

Prerequisites
Procedure

  1. Download the Debezium Db2 connector plug-in archive from Maven Central.

  2. Extract the JAR files into your Kafka Connect environment.

  3. Download the JDBC driver for Db2 from Maven Central, and extract the downloaded driver file to the directory that contains the Debezium Db2 connector JAR file (that is, debezium-connector-db2-1.9.8.Final.jar).

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

  4. Add the directory with the JAR files to Kafka Connect’s plugin.path.

  5. Restart your Kafka Connect process to pick up the new JAR files.

If you are working with immutable containers, see Debezium’s container images for Apache ZooKeeper, Apache Kafka and Kafka Connect with the Db2 connector already installed and ready to run.

You can also run Debezium on Kubernetes and OpenShift.

Next steps

Db2 connector configuration example

Following is an example of the configuration for a connector instance that captures data from a Db2 server on port 50000 at 192.168.99.100, which we logically name fullfillment. Typically, you configure the Debezium Db2 connector in a JSON file by setting the configuration properties that are available for the connector.

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

{
  "name": "db2-connector",  (1)
  "config": {
    "connector.class": "io.debezium.connector.db2.Db2Connector", (2)
    "database.hostname": "192.168.99.100", (3)
    "database.port": "50000", (4)
    "database.user": "db2inst1", (5)
    "database.password": "Password!", (6)
    "database.dbname": "mydatabase", (7)
    "database.server.name": "fullfillment", (8)
    "table.include.list": "MYSCHEMA.CUSTOMERS", (9)
    "database.history.kafka.bootstrap.servers": "kafka:9092", (10)
    "database.history.kafka.topic": "dbhistory.fullfillment" (11)
  }
}
1 The name of the connector when registered with a Kafka Connect service.
2 The name of this Db2 connector class.
3 The address of the Db2 instance.
4 The port number of the Db2 instance.
5 The name of the Db2 user.
6 The password for the Db2 user.
7 The name of the database to capture changes from.
8 The logical name of the Db2 instance/cluster, which forms a namespace and is used in all the names of the Kafka topics to which the connector writes, the Kafka Connect schema names, and the namespaces of the corresponding Avro schema when the Avro Connector is used.
9 A list of all tables whose changes Debezium should capture.
10 The list of Kafka brokers that this connector uses to write and recover DDL statements to the database history topic.
11 The name of the database history topic where the connector writes and recovers DDL statements. This topic is for internal use only and should not be used by consumers.

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

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

  • Connects to the Db2 database.

  • Reads change-data tables for tables that are in capture mode.

  • Streams change event records to Kafka topics.

Adding connector configuration

To start running a Db2 connector, create a connector configuration and add the configuration to your Kafka Connect cluster.

Prerequisites
Procedure

  1. Create a configuration for the Db2 connector.

  2. Use the Kafka Connect REST API to add that connector configuration to your Kafka Connect cluster.

Results

After the connector starts, it performs a consistent snapshot of the 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.

Connector properties

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

Required Debezium Db2 connector configuration properties

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

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.

+

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

table.include.list

No default

An optional, comma-separated list of regular expressions that match fully-qualified table identifiers for tables whose changes you want the connector to capture. 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.

schema.name.adjustment.mode

avro

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

  • avro replaces the characters that cannot be used in the Avro type name with underscore.

  • none does not apply any adjustment.

Advanced connector configuration properties

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

Property Default Description

converters

No default

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

isbn

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

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

<converterSymbolicName>.type

For example,

isbn.type: io.debezium.test.IsbnConverter

If you want to further control the behavior of a configured converter, you can add one or more configuration parameters to pass values to the converter. To associate any additional configuration parameter with a converter, prefix the parameter names with the symbolic name of the converter.
For example,

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

snapshot.mode

initial

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

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

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

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

snapshot.isolation.mode

repeatable_read

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

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

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

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

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

event.processing.failure.handling.mode

fail

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

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

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

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

poll.interval.ms

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.batch.size

2048

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

max.queue.size

8192

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

max.queue.size.in.bytes

0

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

heartbeat.interval.ms

0

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

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

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

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.include.collection.list

All tables specified in table.include.list

An optional, comma-separated list of regular expressions that match the fully-qualified names (<schemaName>.<tableName>) of the tables to include in a snapshot. The specified items must be named in the connector’s table.include.list property. This property takes effect only if the connector’s snapshot.mode property is set to a value other than never.
This property does not affect the behavior of incremental snapshots.

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.

transaction.topic

${database.server.name}.transaction

Controls the name of the topic to which the connector sends transaction metadata messages. The placeholder ${database.server.name} can be used for referring to the connector’s logical name; defaults to ${database.server.name}.transaction, for example dbserver1.transaction.

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>

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.

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 13. 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.query.timeout.ms

3000

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

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.

Monitoring

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.

Snapshot metrics

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.

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. This metric is available if max.queue.size.in.bytes is set to a positive long value.

CurrentQueueSizeInBytes

long

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

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

Attributes Type Description

ChunkId

string

The identifier of the current snapshot chunk.

ChunkFrom

string

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

ChunkTo

string

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

TableFrom

string

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

TableTo

string

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

Streaming metrics

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 the last start or metrics reset.

TotalNumberOfCreateEventsSeen

long

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

TotalNumberOfUpdateEventsSeen

long

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

TotalNumberOfDeleteEventsSeen

long

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

NumberOfEventsFiltered

long

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

CapturedTables

string[]

The list of tables that are captured by the connector.

QueueTotalCapacity

int

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

QueueRemainingCapacity

int

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

Connected

boolean

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

MilliSecondsBehindSource

long

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

NumberOfCommittedTransactions

long

The number of processed transactions that were committed.

SourceEventPosition

Map<String, String>

The coordinates of the last received event.

LastTransactionId

string

Transaction identifier of the last processed transaction.

MaxQueueSizeInBytes

long

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

CurrentQueueSizeInBytes

long

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

Schema history metrics

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.

MilliSecondsSinceLast​RecoveredChange

long

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

MilliSecondsSinceLast​AppliedChange

long

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

LastRecoveredChange

string

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

LastAppliedChange

string

The string representation of the last applied change.

Management

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 14. 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.

Schema evolution

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.

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.

Offline schema update

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

Prerequisites

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

Procedure

  1. Suspend the application that updates the database.

  2. Wait for the Debezium connector to stream all unstreamed change event records.

  3. Stop the Debezium connector.

  4. Apply all changes to the source table schema.

  5. In the ASN register table, mark the tables with updated schemas as INACTIVE.

  6. Reinitialize the ASN capture service.

  7. Remove the source table with the old schema from capture mode by running the Debezium UDF for removing tables from capture mode.

  8. Add the source table with the new schema to capture mode by running the Debezium UDF for adding tables to capture mode.

  9. In the ASN register table, mark the updated source tables as ACTIVE.

  10. Reinitialize the ASN capture service.

  11. Resume the application that updates the database.

  12. Restart the Debezium connector.

Online schema update

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

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

Prerequisites

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

Procedure when adding a column to the end of a table

  1. Lock the source tables whose schema you want to change.

  2. In the ASN register table, mark the locked tables as INACTIVE.

  3. Reinitialize the ASN capture service.

  4. Apply all changes to the schemas for the source tables.

  5. Apply all changes to the schemas for the corresponding change-data tables.

  6. In the ASN register table, mark the source tables as ACTIVE.

  7. Reinitialize the ASN capture service.

  8. Optional. Restart the connector to see updated column names in change events.

Procedure when adding a column to the middle of a table

  1. Lock the source table(s) to be changed.

  2. In the ASN register table, mark the locked tables as INACTIVE.

  3. Reinitialize the ASN capture service.

  4. For each source table to be changed:

    1. Export the data in the source table.

    2. Truncate the source table.

    3. Alter the source table and add the column.

    4. Load the exported data into the altered source table.

    5. Export the data in the source table’s corresponding change-data table.

    6. Truncate the change-data table.

    7. Alter the change-data table and add the column.

    8. Load the exported data into the altered change-data table.

  5. In the ASN register table, mark the tables as INACTIVE. This marks the old change-data tables as inactive, which allows the data in them to remain but they are no longer updated.

  6. Reinitialize the ASN capture service.

  7. Optional. Restart the connector to see updated column names in change events.