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Debezium connector for Db2

This connector is in an incubating state. Exact semantics, configuration options and other features may change in future revisions, based on the feedback we receive. Please let us know if you encounter any problems.

Debezium’s Db2 connector can capture row-level changes in the tables of a Db2 database. 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 uses the abstract syntax notation (ASN) libraries that come as a standard part of Db2 LUW (Db2 for Linux, UNIX and Windows) and which you can add to Db2 zOS. To use ASN and hence this connector, you must have a license for the IBM InfoSphere Data Replication (IIDR) product. However, IIDR does not need to be installed.

The Db2 connector has been tested with Db2/Linux 11.5.0.0. It is expected that the connector would also work on Windows, AIX and zOS.

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.

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 this and modifies its behavior accordingly.

Topic names

By default, the Db2 connector writes change events for all insert, update, and delete operations on a single table to a single Kafka topic. The name of the Kafka topic has the following format:

databaseName.schemaName.tableName

databaseName

The logical name of the connector as specified with 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

To configure a Db2 connector to emit change events to differently-named Kafka topics, see the documentation for the topic routing transformation.

Schema change topic

For a table that is in capture mode, the Debezium Db2 connector stores the history of schema changes to that table in a database history topic. This topic reflects an internal connector state and you should not use it. If your application needs to track schema changes, there is a public schema change topic. The name of the schema change topic is the same as the logical server name specified in the connector configuration.

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

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.

A message to the schema change topic contains a logical representation of the table schema, for example:

{
  "schema": {
  ...
  },
  "payload": {
    "source": {
      "version": "1.3.0.CR1",
      "connector": "db2",
      "name": "db2",
      "ts_sec": 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 1. 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 to the schema change topic, the 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. For every transaction BEGIN and END, Debezium generates an event that contains the following fields:

  • status - BEGIN or END

  • id - string representation of unique transaction identifier

  • event_count (for END events) - total number of events emitted by the transaction

  • data_collections (for END events) - an array of pairs of data_collection and event_count that provides the number of events emitted by changes originating from the given data collection

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

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

The connector emits transaction events to the database.server.name.transaction topic.

Data change event enrichment

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

  • id - string representation of unique transaction identifier

  • total_order - 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 converver 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 2. 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 3. 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_sec"
          },
          {
            "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": "1.3.0.CR1",
      "connector": "db2",
      "name": "myconnector",
      "ts_sec": 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 4. 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 connector read the record

  • 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.

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": "1.3.0.CR1",
      "connector": "db2",
      "name": "myconnector",
      "ts_sec": 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 5. 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 connector read the record

  • 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.

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": "1.3.0.CR1",
      "connector": "db2",
      "name": "myconnector",
      "ts_sec": 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 6. 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 connector read the record

  • 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.

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.

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 7. Mappings for Db2 basic data types
Db2 data type Literal type (schema type) Semantic type (schema name) and Notes

BOOLEAN

BOOLEAN

n/a

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 8. 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 9. 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.

Set up

A database administrator must put tables into capture mode before you can run a Debezium Db2 connector to capture changes that are committed to a Db2 database. 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.

This procedure assumes that you are logged in as the db2instl user, which is the default instance and user name when using the Db2 docker container image.

Prerequisites
  • On the machine on which Db2 is running, the content in debezium-connector-db2/src/test/docker/db2-cdc-docker is available in the $HOME/asncdctools/src directory.

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');
  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');

Deployment

With Zookeeper, Kafka, and Kafka Connect installed, the remaining tasks to deploy a Debezium Db2 connector are:

  1. Download the connector’s plug-in archive.

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

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

  4. Obtain the JDBC driver for Db2.

  5. Add the JDBC driver JAR file to the directory with the Debezium Db2 connector JARs.

  6. Configure the connector and add the configuration to your Kafka Connect cluster.

  7. 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 Zookeeper, Kafka and Kafka Connect with the Db2 connector already installed and ready to run. You can also run Debezium on Kubernetes and OpenShift.

Connector configuration example

Following is an example of the configuration for a Db2 connector that connects to a Db2 server on port 50000 at 192.168.99.100, whose logical name is fullfillment. Typically, you configure the Debezium Db2 connector in a .json file using the configuration properties available for the connector.

You can choose to produce events for a subset of the schemas and tables. Optionally, ignore, mask, or truncate columns that are sensitive, too large, or not needed.

{
  "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.

See the complete list of connector properties that you can specify in these configurations.

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 connects to the Db2 database, reads change-data tables for tables in capture mode, and 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
  • The Db2 replication is enabled to expose change-data for tables that are in capture mode

  • The Db2 connector is installed.

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

When 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:

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

Table 10. Required connector configuration properties
Property Default Description

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

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

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.

IP address or hostname of the Db2 database server.

50000

Integer port number of the Db2 database server.

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

Password to use when connecting to the Db2 database server.

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

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 and underscores should 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.

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

A list of host/port pairs that the connector uses to establish an initial connection to the Kafka cluster. This connection is used for retrieving 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 Debezium Kafka Connect process.

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.

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.

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.

n/a

An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be pseudonyms in change event values. A pseudonym is a field value that consists of the hashed value obtained by applying the hashAlgorithm algorithm and the salt salt that you specify in the property name.

Based on the hash algorithm applied, referential integrity is kept while data is masked. Supported hash algorithms are described in the MessageDigest section of the Java Cryptography Architecture Standard Algorithm Name Documentation. The hash value is automatically shortened to the length of the column.

You can specify multiple instances of this property with different algorthims and salts. Fully-qualified names for columns are of the form schemaName.tableName.columnName. For example:

column.mask.hash.SHA-256.with.salt.CzQMA0cB5K = + inventory.orders.customerName, inventory.shipment.customerName

where CzQMA0cB5K is a randomly selected salt.
Depending on the hashAlgorithm used, the salt selected, and the actual data set, the field value may not be completely masked.

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.

true

Controls whether a tombstone event should be generated after a delete event.

true - delete operations are represented by a delete event and a subsequent tombstone event.

false - only a delete event is sent.

After a delete operation, emitting a tombstone event enables Kafka to delete all change event records that have the same key as the deleted row.

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.

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.

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.

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. The following added schema parameters propagate the original type name and also 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.

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.

empty string

A semicolon separated list of tables with regular expressions that match table column names. The connector maps values in matching columns to key fields in change event records that it sends to Kafka topics. This is useful when a table does not have a primary key, or when you want to order change event records in a Kafka topic according to a field that is not a primary key.

Separate entries with semicolons. Insert a colon between the fully-qualified table name and its regular expression. The format is:

schema-name.table-name:_regexp_;…​

For example,

schemaA.table_a:regex_1;schemaB.table_b:regex_2;schemaC.table_c:regex_3

If table_a has a an id column, and regex_1 is ^i (matches any column that starts with i), the connector maps the value in table_a's id column to a key field in change events that the connector sends to Kafka.

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

Table 11. Advanced connector configuration properties
Property Default Description

initial

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

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

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

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.

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.

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.

8192

Positive integer value for the maximum size of the blocking queue. The connector places change events that it reads from the database log into the blocking queue before writing them to Kafka. This queue can provide backpressure for reading change-data tables when, for example, writing records to Kafka is slower than it should be or Kafka is not available. Events that appear in the queue are not included in the offsets that are periodically recorded by the connector. The max.queue.size value should always be larger than the value of the max.batch.size connector configuration property.

2048

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

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.

__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>.

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.

2000

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

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.

Controls which table rows are included in snapshots. This property affects snapshots only. It does not affect events that the connector reads from the log. Specify a comma-separated list of fully-qualified table names in the form schemaName.tableName.

For each table that you specify, also specify another configuration property: snapshot.select.statement.overrides.SCHEMA_NAME.TABLE_NAME. For example: snapshot.select.statement.overrides.customers.orders. Set this property to a SELECT statement that obtains only the rows that you want in the snapshot. When the connector performs a snapshot, it executes this SELECT statement to retrieve data from that table.

A possible use case for setting these properties is large, append-only tables. You can specify a SELECT statement that sets a specific point for where to start a snapshot, or where to resume a snapshot if a previous snapshot was interrupted.

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.

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.

Pass-through connector configuration properties

The connector also supports pass-through configuration properties that it uses when it creates Kafka producers and consumers:

  • All connector configuration properties that begin with the database.history.producer. prefix are used (without the prefix) when creating the Kafka producer that writes to the database history topic.

  • All connector configuration properties that begin with the database.history.consumer. prefix are used (without the prefix) when creating the Kafka consumer that reads the database history when the connector starts.

For example, the following connector configuration properties secure connections to the Kafka broker:

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

Be sure to consult the Kafka documentation for all of the configuration properties for Kafka producers and consumers. Note that the Db2 connector uses the new consumer.

Also, the connector passes configuration properties that start with database. to the JDBC URL, for example, database.applicationName=debezium.

Monitoring

The Debezium Db2 connector provides three types of metrics that are in addition to the built-in support for JMX metrics that Zookeeper, 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=<database.server.name>.

Attributes Type Description

string

The last snapshot event that the connector has read.

long

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

long

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

long

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

string[]

The list of tables that are monitored by the connector.

int

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

int

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

int

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

int

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

boolean

Whether the snapshot was started.

boolean

Whether the snapshot was aborted.

boolean

Whether the snapshot completed.

long

The total number of seconds that the snapshot has taken so far, even if not complete.

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.

Streaming metrics

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

Attributes Type Description

string

The last streaming event that the connector has read.

long

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

long

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

long

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

string[]

The list of tables that are monitored by the connector.

int

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

int

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

boolean

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

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.

long

The number of processed transactions that were committed.

Map<String, String>

The coordinates of the last received event.

string

Transaction identifier of the last processed transaction.

Schema history metrics

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

Attributes Type Description

string

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

long

The time in epoch seconds at what recovery has started.

long

The number of changes that were read during recovery phase.

long

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

long

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

long

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

string

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

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 12. 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. xref:connectors/db2.adoc#debezium-db2-reinitialize-asn-service[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.