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Chapter 6. Debezium connector for Db2
The Debezium Db2 connector is a Technology Preview feature. Technology Preview features are not supported with Red Hat production service-level agreements (SLAs) and might not be functionally complete; therefore, Red Hat does not recommend implementing any Technology Preview features in production environments. This Technology Preview feature provides early access to upcoming product innovations, enabling you to test functionality and provide feedback during the development process. For more information about support scope, see Technology Preview Features Support Scope.
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 {linux-version}. It is expected that the connector would also work on Windows, AIX and zOS.
Information and procedures for using a Debezium Db2 connector is organized as follows:
- Section 6.1, “Overview of Debezium Db2 connector”
- Section 6.2, “How Debezium Db2 connectors work”
- Section 6.3, “Descriptions of Debezium Db2 connector data change events”
- Section 6.4, “How Debezium Db2 connectors map data types”
- Section 6.5, “Setting up Db2 to run a Debezium connector”
- Section 6.6, “Deploying Debezium Db2 connectors”
- Section 6.7, “Monitoring Debezium Db2 connector performance”
- Section 6.8, “Managing Debezium Db2 connectors”
- Section 6.9, “Updating schemas for Db2 tables in capture mode for Debezium connectors”
6.1. Overview of Debezium Db2 connector
The Debezium Db2 connector is based on the ASN Capture/Apply agents that enable SQL Replication in Db2. A capture agent:
- Generates change-data tables for tables that are in capture mode.
- Monitors tables in capture mode and stores change events for updates to those tables in their corresponding change-data tables.
The Debezium connector uses a SQL interface to query change-data tables for change events.
The database administrator must put the tables for which you want to capture changes into capture mode. For convenience and for automating testing, there are Debezium user-defined functions (UDFs) in C that you can compile and then use to do the following management tasks:
- Start, stop, and reinitialize the ASN agent
- Put tables into capture mode
- Create the replication (ASN) schemas and change-data tables
- Remove tables from capture mode
Alternatively, you can use Db2 control commands to accomplish these tasks.
After the tables of interest are in capture mode, the connector reads their corresponding change-data tables to obtain change events for table updates. The connector emits a change event for each row-level insert, update, and delete operation to a Kafka topic that has the same name as the changed table. This is default behavior that you can modify. Client applications read the Kafka topics that correspond to the database tables of interest and can react to each row-level change event.
Typically, the database administrator puts a table into capture mode in the middle of the life of a table. This means that the connector does not have the complete history of all changes that have been made to the table. Therefore, when the Db2 connector first connects to a particular Db2 database, it starts by performing a consistent snapshot of each table that is in capture mode. After the connector completes the snapshot, the connector streams change events from the point at which the snapshot was made. In this way, the connector starts with a consistent view of the tables that are in capture mode, and does not drop any changes that were made while it was performing the snapshot.
Debezium connectors are tolerant of failures. As the connector reads and produces change events, it records the log sequence number (LSN) of the change-data table entry. The LSN is the position of the change event in the database log. If the connector stops for any reason, including communication failures, network problems, or crashes, upon restarting it continues reading the change-data tables where it left off. This includes snapshots. That is, if the snapshot was not complete when the connector stopped, upon restart the connector begins a new snapshot.
6.2. How Debezium Db2 connectors work
To optimally configure and run a Debezium Db2 connector, it is helpful to understand how the connector performs snapshots, streams change events, determines Kafka topic names, and handles schema changes.
Details are in the following topics:
- Section 6.2.1, “How Debezium Db2 connectors perform database snapshots”
- Section 6.2.2, “How Debezium Db2 connectors read change-data tables”
- Section 6.2.3, “Default names of Kafka topics that receive Debezium Db2 change event records”
- Section 6.2.4, “About the Debezium Db2 connector schema change topic”
- Section 6.2.5, “Debezium Db2 connector-generated events that represent transaction boundaries”
6.2.1. How Debezium Db2 connectors perform database snapshots
Db2`s replication feature is not designed to store the complete history of database changes. Consequently, when a Debezium Db2 connector connects to a database for the first time, it takes a consistent snapshot of tables that are in capture mode and streams this state to Kafka. This establishes the baseline for table content.
By default, when a Db2 connector performs a snapshot, it does the following:
-
Determines which tables are in capture mode, and thus must be included in the snapshot. By default, all non-system tables are in capture mode. Connector configuration properties, such as
table.blacklist
andtable.whitelist
let you specify which tables should be in capture mode. -
Obtains a lock on each of the tables in capture mode. This ensures that no schema changes can occur in those tables during the snapshot. The level of the lock is determined by the
snapshot.isolation.mode
connector configuration property. - Reads the highest (most recent) LSN position in the server’s transaction log.
- Captures the schema of all tables that are in capture mode. The connector persists this information in its internal database history topic.
- Optional, releases the locks obtained in step 2. Typically, these locks are held for only a short time.
At the LSN position read in step 3, the connector scans the capture mode tables as well as their schemas. During the scan, the connector:
- 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.
- 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.
- Emits each read event to the Kafka topic that has the same name as the table.
- Records the successful completion of the snapshot in the connector offsets.
6.2.2. How Debezium Db2 connectors read change-data tables
After a complete snapshot, when a Debezium Db2 connector starts for the first time, the connector identifies the change-data table for each source table that is in capture mode. The connector does the following for each change-data table:
- Reads change events that were created between the last stored, highest LSN and the current, highest LSN.
- Orders the change events according to the commit LSN and the change LSN for each event. This ensures that the connector emits the change events in the order in which the table changes occurred.
- Passes commit and change LSNs as offsets to Kafka Connect.
- Stores the highest LSN that the connector passed to Kafka Connect.
After a restart, the connector resumes emitting change events from the offset (commit and change LSNs) where it left off. While the connector is running and emitting change events, if you remove a table from capture mode or add a table to capture mode, the connector detects this and modifies its behavior accordingly.
6.2.3. Default names of Kafka topics that receive Debezium Db2 change event records
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.
6.2.4. About the Debezium Db2 connector 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": "1.2.4.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 } ] } } ] } }
Item | Field name(s) | Description |
---|---|---|
1 |
| Identifies the database and the schema that contain the change. |
2 |
|
Always |
3 |
| An array of one or more items that contain the schema changes generated by a DDL command. |
4 |
| Describes the kind of change. The value is one of the following:
|
5 |
| Full identifier of the table that was created, altered, or dropped. |
6 |
| Represents table metadata after the applied change. |
7 |
| List of columns that compose the table’s primary key. |
8 |
| 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" } }
6.2.5. Debezium Db2 connector-generated events that represent transaction boundaries
Debezium can generate events that represent transaction boundaries and that enrich change data event messages. For every transaction BEGIN
and END
, Debezium generates an event that contains the following fields:
-
status
-BEGIN
orEND
-
id
- string representation of unique transaction identifier -
event_count
(forEND
events) - total number of events emitted by the transaction -
data_collections
(forEND
events) - an array of pairs ofdata_collection
andevent_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" } }
6.3. Descriptions of Debezium Db2 connector data change events
The Debezium Db2 connector generates a data change event for each row-level INSERT
, UPDATE
, and DELETE
operation. Each event contains a key and a value. The structure of the key and the value depends on the table that was changed.
Debezium and Kafka Connect are designed around continuous streams of event messages. However, the structure of these events may change over time, which can be difficult for consumers to handle. To address this, each event contains the schema for its content or, if you are using a schema registry, a schema ID that a consumer can use to obtain the schema from the registry. This makes each event self-contained.
The following skeleton JSON shows the basic four parts of a change event. However, how you configure the Kafka Connect converter that you choose to use in your application determines the representation of these four parts in change events. A schema
field is in a change event only when you configure the converter to produce it. Likewise, the event key and event payload are in a change event only if you configure a converter to produce it. If you use the JSON 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 ... }, }
Item | Field name | Description |
---|---|---|
1 |
|
The first |
2 |
|
The first |
3 |
|
The second |
4 |
|
The second |
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.
Details are in the following topics:
6.3.1. About keys in Debezium db2 change events
A change event’s key contains the schema for the changed table’s key and the changed row’s actual key. Both the schema and its corresponding payload contain a field for each column in the changed table’s PRIMARY KEY
(or unique constraint) at the time the connector created the event.
Consider the following customers
table, which is followed by an example of a change event key for this table.
Example table
CREATE TABLE customers ( ID INTEGER IDENTITY(1001,1) NOT NULL PRIMARY KEY, FIRST_NAME VARCHAR(255) NOT NULL, LAST_NAME VARCHAR(255) NOT NULL, EMAIL VARCHAR(255) NOT NULL UNIQUE );
Example change event key
Every change event that captures a change to the customers
table has the same event key schema. For as long as the customers
table has the previous definition, every change event that captures a change to the customers
table has the following key structure. In JSON, it looks like this:
{ "schema": { 1 "type": "struct", "fields": [ 2 { "type": "int32", "optional": false, "field": "ID" } ], "optional": false, 3 "name": "mydatabase.MYSCHEMA.CUSTOMERS.Key" 4 }, "payload": { 5 "ID": 1004 } }
Item | Field name | Description |
---|---|---|
1 |
|
The schema portion of the key specifies a Kafka Connect schema that describes what is in the key’s |
2 |
|
Specifies each field that is expected in the |
3 |
|
Indicates whether the event key must contain a value in its |
4 |
|
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.
|
5 |
|
Contains the key for the row for which this change event was generated. In this example, the key, contains a single |
6.3.2. About values in Debezium Db2 change events
The value in a change event is a bit more complicated than the key. Like the key, the value has a schema
section and a payload
section. The schema
section contains the schema that describes the Envelope
structure of the payload
section, including its nested fields. Change events for operations that create, update or delete data all have a value payload with an envelope structure.
Consider the same sample table that was used to show an example of a change event key:
Example table
CREATE TABLE customers ( ID INTEGER IDENTITY(1001,1) NOT NULL PRIMARY KEY, FIRST_NAME VARCHAR(255) NOT NULL, LAST_NAME VARCHAR(255) NOT NULL, EMAIL VARCHAR(255) NOT NULL UNIQUE );
The event value portion of every change event for the customers
table specifies the same schema. The event value’s payload varies according to the event type:
create events
The following example shows the value portion of a change event that the connector generates for an operation that creates data in the customers
table:
{ "schema": { 1 "type": "struct", "fields": [ { "type": "struct", "fields": [ { "type": "int32", "optional": false, "field": "ID" }, { "type": "string", "optional": false, "field": "FIRST_NAME" }, { "type": "string", "optional": false, "field": "LAST_NAME" }, { "type": "string", "optional": false, "field": "EMAIL" } ], "optional": true, "name": "mydatabase.MYSCHEMA.CUSTOMERS.Value", 2 "field": "before" }, { "type": "struct", "fields": [ { "type": "int32", "optional": false, "field": "ID" }, { "type": "string", "optional": false, "field": "FIRST_NAME" }, { "type": "string", "optional": false, "field": "LAST_NAME" }, { "type": "string", "optional": false, "field": "EMAIL" } ], "optional": true, "name": "mydatabase.MYSCHEMA.CUSTOMERS.Value", "field": "after" }, { "type": "struct", "fields": [ { "type": "string", "optional": false, "field": "version" }, { "type": "string", "optional": false, "field": "connector" }, { "type": "string", "optional": false, "field": "name" }, { "type": "int64", "optional": false, "field": "ts_ms" }, { "type": "boolean", "optional": true, "default": false, "field": "snapshot" }, { "type": "string", "optional": false, "field": "db" }, { "type": "string", "optional": false, "field": "schema" }, { "type": "string", "optional": false, "field": "table" }, { "type": "string", "optional": true, "field": "change_lsn" }, { "type": "string", "optional": true, "field": "commit_lsn" }, ], "optional": false, "name": "io.debezium.connector.db2.Source", 3 "field": "source" }, { "type": "string", "optional": false, "field": "op" }, { "type": "int64", "optional": true, "field": "ts_ms" } ], "optional": false, "name": "mydatabase.MYSCHEMA.CUSTOMERS.Envelope" 4 }, "payload": { 5 "before": null, 6 "after": { 7 "ID": 1005, "FIRST_NAME": "john", "LAST_NAME": "doe", "EMAIL": "john.doe@example.org" }, "source": { 8 "version": "1.2.4.Final", "connector": "db2", "name": "myconnector", "ts_ms": 1559729468470, "snapshot": false, "db": "mydatabase", "schema": "MYSCHEMA", "table": "CUSTOMERS", "change_lsn": "00000027:00000758:0003", "commit_lsn": "00000027:00000758:0005", }, "op": "c", 9 "ts_ms": 1559729471739 10 } }
Item | Field name | Description |
---|---|---|
1 |
| 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 |
|
In the |
3 |
|
|
4 |
|
|
5 |
|
The value’s actual data. This is the information that the change event is providing. |
6 |
|
An optional field that specifies the state of the row before the event occurred. When the |
7 |
|
An optional field that specifies the state of the row after the event occurred. In this example, the |
8 |
|
Mandatory field that describes the source metadata for the event. The
|
9 |
|
Mandatory string that describes the type of operation that caused the connector to generate the event. In this example,
|
10 |
|
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": "1.2.4.Final", "connector": "db2", "name": "myconnector", "ts_ms": 1559729995937, "snapshot": false, "db": "mydatabase", "schema": "MYSCHEMA", "table": "CUSTOMERS", "change_lsn": "00000027:00000ac0:0002", "commit_lsn": "00000027:00000ac0:0007", }, "op": "u", 4 "ts_ms": 1559729998706 5 } }
Item | Field name | Description |
---|---|---|
1 |
|
An optional field that specifies the state of the row before the event occurred. In an update event value, the |
2 |
|
An optional field that specifies the state of the row after the event occurred. You can compare the |
3 |
|
Mandatory field that describes the source metadata for the event. The
|
4 |
|
Mandatory string that describes the type of operation. In an update event value, the |
5 |
|
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": "1.2.4.Final", "connector": "db2", "name": "myconnector", "ts_ms": 1559730445243, "snapshot": false, "db": "mydatabase", "schema": "MYSCHEMA", "table": "CUSTOMERS", "change_lsn": "00000027:00000db0:0005", "commit_lsn": "00000027:00000db0:0007" }, "op": "d", 4 "ts_ms": 1559730450205 5 } }
Item | Field name | Description |
---|---|---|
1 |
|
Optional field that specifies the state of the row before the event occurred. In a delete event value, the |
2 |
|
Optional field that specifies the state of the row after the event occurred. In a delete event value, the |
3 |
|
Mandatory field that describes the source metadata for the event. In a delete event value, the
|
4 |
|
Mandatory string that describes the type of operation. The |
5 |
|
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.
6.4. How Debezium Db2 connectors map data types
Db2’s data types are described in Db2 SQL Data Types.
The Db2 connector represents changes to rows with events that are structured like the table in which the row exists. The event contains a field for each column value. How that value is represented in the event depends on the Db2 data type of the column. This section describes these mappings.
Details are in the following sections:
Basic types
The following table describes how the connector maps each of the Db2 data types to a literal type and a semantic type in event fields.
-
literal type describes how the value is represented using Kafka Connect schema types:
INT8
,INT16
,INT32
,INT64
,FLOAT32
,FLOAT64
,BOOLEAN
,STRING
,BYTES
,ARRAY
,MAP
, andSTRUCT
. - 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.
Db2 data type | Literal type (schema type) | Semantic type (schema name) |
---|---|---|
|
| n/a |
|
| n/a |
|
| n/a |
|
| n/a |
|
| n/a |
|
| n/a |
|
|
|
|
|
|
|
|
|
|
| n/a |
|
| n/a |
|
| n/a |
|
| n/a |
|
| n/a |
|
|
|
|
|
|
|
| n/a |
|
| n/a |
|
| n/a |
|
|
|
If present, a column’s default value is propagated to the corresponding field’s Kafka Connect schema. Change events contain the field’s default value unless an explicit column value had been given. Consequently, there is rarely a need to obtain the default value from the schema.
Temporal types
Other than Db2’s DATETIMEOFFSET
data type, which contains time zone information, how temporal types are mapped depends on the value of the time.precision.mode
connector configuration property. The following sections describe these mappings:
time.precision.mode=adaptive
When the time.precision.mode
configuration property is set to adaptive
, the default, the connector determines the literal type and semantic type based on the column’s data type definition. This ensures that events exactly represent the values in the database.
Db2 data type | Literal type (schema type) | Semantic type (schema name) |
---|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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.
Db2 data type | Literal type (schema type) | Semantic type (schema name) |
---|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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.
Db2 data type | Literal type (schema type) | Semantic type (schema name) |
---|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
6.5. Setting up Db2 to run a Debezium connector
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
Compile the Debezium management UDFs on the Db2 server host by using the
bldrtn
command provided with Db2:cd $HOME/asncdctools/src
./bldrtn asncdc
Start the database if it is not already running. Replace
DB_NAME
with the name of the database that you want Debezium to connect to.db2 start db DB_NAME
Ensure that JDBC can read the Db2 metadata catalog:
cd $HOME/sqllib/bnd
db2 bind db2schema.bnd blocking all grant public sqlerror continue
Ensure that the database was recently backed-up. The ASN agents must have a recent starting point to read from. If you need to perform a backup, run the following commands, which prune the data so that only the most recent version is available. If you do not need to retain the older versions of the data, specify
dev/null
for the backup location.Back up the database. Replace
DB_NAME
andBACK_UP_LOCATION
with appropriate values:db2 backup db DB_NAME to BACK_UP_LOCATION
Restart the database:
db2 restart db DB_NAME
Connect to the database to install the Debezium management UDFs. It is assumed that you are logged in as the
db2instl
user so the UDFs should be installed on thedb2inst1
user.db2 connect to DB_NAME
Copy the Debezium management UDFs and set permissions for them:
cp $HOME/asncdctools/src/asncdc $HOME/sqllib/function
chmod 777 $HOME/sqllib/function
Enable the Debezium UDF that starts and stops the ASN capture agent:
db2 -tvmf $HOME/asncdctools/src/asncdc_UDF.sql
Create the ASN control tables:
$ db2 -tvmf $HOME/asncdctools/src/asncdctables.sql
Enable the Debezium UDF that adds tables to capture mode and removes tables from capture mode:
$ db2 -tvmf $HOME/asncdctools/src/asncdcaddremove.sql
After you set up the Db2 server, use the UDFs to control Db2 replication (ASN) with SQL commands. Some of the UDFs expect a return value in which case you use the SQL
VALUE
statement to invoke them. For other UDFs, use the SQLCALL
statement.Start the ASN agent:
VALUES ASNCDC.ASNCDCSERVICES('start','asncdc');
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, replaceMYTABLE
with the name of the table to put into capture mode:CALL ASNCDC.ADDTABLE('MYSCHEMA', 'MYTABLE');
Reinitialize the ASN service:
VALUES ASNCDC.ASNCDCSERVICES('reinit','asncdc');
Additional resource
6.6. Deploying Debezium Db2 connectors
To deploy a Debezium Db2 connector, install the Debezium Db2 connector archive, configure the connector, and start the connector by adding its configuration to Kafka Connect. Details are in the following topics:
6.6.1. Steps for installing Debezium Db2 connectors
To install the Db2 connector, follow the procedures in Installing Debezium on OpenShift. The main steps are:
- Set up Db2 to run a Debezium connector. This enables Db2 replication to expose change-data for tables that are in capture mode.
- Use Red Hat AMQ Streams to set up Apache Kafka and Kafka Connect on OpenShift. AMQ Streams offers operators and images that bring Kafka to OpenShift.
- Download the Debezium Db2 connector.
- Extract the files into your Kafka Connect environment.
Add the plug-in’s parent directory to your Kafka Connect
plugin.path
, for example:plugin.path=/kafka/connect
The above example assumes that you extracted the Debezium Db2 connector to the
/kafka/connect/Debezium-connector-db2
path.- Restart your Kafka Connect process to ensure that the new JAR files are picked up.
6.6.2. Debezium db2 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 a Debezium Db2 connector in a .yaml
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.
apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnector metadata: name: inventory-connector 1 labels: strimzi.io/cluster: my-connect-cluster spec: class: io.debezium.connector.db2.Db2Connector tasksMax: 1 2 config: 3 database.hostname: 192.168.99.100 4 database.port: 50000 database.user: db2inst1 database.password: Password! database.dbname: mydatabase database.server.name: fullfillment 5 database.whitelist: public.inventory 6
Item | Description |
---|---|
1 | The name of the connector. |
2 | Only one task should operate at any one time. |
3 | The connector’s configuration. |
4 | The database host, which is the address of the Db2 instance. |
5 | The logical name of the Db2 instance/cluster, which forms a namespace and is used in the names of the Kafka topics to which the connector writes, the names of Kafka Connect schemas, and the namespaces of the corresponding Avro schema when the Avro Connector is used. |
6 |
Changes in only the |
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.
6.6.3. Adding Debezium Db2 connector configuration to Kafka Connect
You can use a provided Debezium container to deploy a Debezium Db2 connector. In this procedure, you build a custom Kafka Connect container image for Debezium, configure the Debezium connector as needed, and then add your connector configuration to your Kafka Connect environment.
Prerequisites
- Podman or Docker is installed and you have sufficient rights to create and manage containers.
- You installed the Debezium Db2 connector archive.
Procedure
Extract the Debezium Db2 connector archive to create a directory structure for the connector plug-in, for example:
tree ./my-plugins/ ./my-plugins/ ├── debezium-connector-db2 │ ├── ...
Create and publish a custom image for running your Debezium connector:
Create a new
Dockerfile
by usingregistry.redhat.io/amq7/amq-streams-kafka-25-rhel7:1.5.0
as the base image. In the following example, you would replace my-plugins with the name of your plug-ins directory:FROM registry.redhat.io/amq7/amq-streams-kafka-25-rhel7:1.5.0 USER root:root COPY ./my-plugins/ /opt/kafka/plugins/ USER 1001
Before Kafka Connect starts running the connector, Kafka Connect loads any third-party plug-ins that are in the
/opt/kafka/plugins
directory.Build the container image. For example, if you saved the
Dockerfile
that you created in the previous step asdebezium-container-for-db2
, and if theDockerfile
is in the current directory, then you would run the following command:podman build -t debezium-container-for-db2:latest .
Push your custom image to your container registry, for example:
podman push debezium-container-for-db2:latest
Point to the new container image. Do one of the following:
Edit the
spec.image
property of theKafkaConnector
custom resource. If set, this property overrides theSTRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE
variable in the Cluster Operator. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnector metadata: name: my-connect-cluster spec: #... image: debezium-container-for-db2
-
In the
install/cluster-operator/050-Deployment-strimzi-cluster-operator.yaml
file, edit theSTRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE
variable to point to the new container image and reinstall the Cluster Operator. If you edit this file you must apply it to your OpenShift cluster.
-
Create a
KafkaConnector
custom resource that defines your Debezium Db2 connector instance. See the connector configuration example. Apply the connector instance, for example:
oc apply -f inventory-connector.yaml
This registers
inventory-connector
and the connector starts to run against theinventory
database.Verify that the connector was created and has started to capture changes in the specified database. You can verify the connector instance by watching the Kafka Connect log output as, for example,
inventory-connector
starts.Display the Kafka Connect log output:
oc logs $(oc get pods -o name -l strimzi.io/name=my-connect-cluster-connect)
Review the log output to verify that the initial snapshot has been executed. You should see something like the following lines:
... INFO Starting snapshot for ... ... INFO Snapshot is using user 'debezium' ...
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.
6.6.4. Description of Debezium Db2 connector configuration properties
The Debezium Db2 connector has numerous configuration properties that you can use to achieve the right connector behavior for your application. Many properties have default values. Information about the properties is organized as follows:
The following configuration properties are required unless a default value is available.
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 | ||
| 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. | ||
| 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 | ||
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 | ||
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 | |
|
Time, date, and timestamps can be represented with different kinds of precision: | |
|
Controls whether a tombstone event should be generated after a delete event. | |
| 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, | |
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 ( | |
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. | |
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. | |
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. |
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 |
---|---|---|
|
Specifies the criteria for performing a snapshot when the connector starts: | |
|
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: | |
|
Specifies how the connector handles exceptions during processing of events. The possible values are: | |
| 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. | |
|
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 | |
| Positive integer value that specifies the maximum size of each batch of events that the connector processes. | |
|
Controls how frequently the connector sends heartbeat messages to a Kafka topic. The default behavior is that the connector does not send heartbeat messages. | |
|
Specifies the prefix for the name of the topic to which the connector sends heartbeat messages. The format for this topic name is | |
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. | ||
| During a snapshot, the connector reads table content in batches of rows. This property specifies the maximum number of rows in a batch. | |
|
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: | |
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. | ||
| Indicates whether field names are sanitized to adhere to Avro naming requirements. | |
|
Determines whether the connector generates events with transaction boundaries and enriches change event envelopes with transaction metadata. Specify |
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
.
6.7. Monitoring Debezium Db2 connector performance
The Debezium Db2 connector provides three types of metrics that are in addition to the built-in support for JMX metrics that 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 |
---|---|---|
|
| The last snapshot event that the connector has read. |
|
| The number of milliseconds since the connector has read and processed the most recent event. |
|
| The total number of events that this connector has seen since last started or reset. |
|
| The number of events that have been filtered by whitelist or blacklist filtering rules configured on the connector. |
|
| The list of tables that are monitored by the connector. |
|
| The length the queue used to pass events between the snapshotter and the main Kafka Connect loop. |
|
| The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop. |
|
| The total number of tables that are being included in the snapshot. |
|
| The number of tables that the snapshot has yet to copy. |
|
| Whether the snapshot was started. |
|
| Whether the snapshot was aborted. |
|
| Whether the snapshot completed. |
|
| The total number of seconds that the snapshot has taken so far, even if not complete. |
|
| 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 |
---|---|---|
|
| The last streaming event that the connector has read. |
|
| The number of milliseconds since the connector has read and processed the most recent event. |
|
| The total number of events that this connector has seen since last started or reset. |
|
| The number of events that have been filtered by whitelist or blacklist filtering rules configured on the connector. |
|
| The list of tables that are monitored by the connector. |
|
| The length the queue used to pass events between the streamer and the main Kafka Connect loop. |
|
| The free capacity of the queue used to pass events between the streamer and the main Kafka Connect loop. |
|
| Flag that denotes whether the connector is currently connected to the database server. |
|
| 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. |
|
| The number of processed transactions that were committed. |
|
| The coordinates of the last received event. |
|
| 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 |
---|---|---|
|
|
One of |
|
| The time in epoch seconds at what recovery has started. |
|
| The number of changes that were read during recovery phase. |
|
| the total number of schema changes applied during recovery and runtime. |
|
| The number of milliseconds that elapsed since the last change was recovered from the history store. |
|
| The number of milliseconds that elapsed since the last change was applied. |
|
| The string representation of the last change recovered from the history store. |
|
| The string representation of the last applied change. |
6.8. Managing Debezium Db2 connectors
After you deploy a Debezium Db2 connector, use the Debezium management UDFs to control Db2 replication (ASN) with SQL commands. Some of the UDFs expect a return value in which case you use the SQL VALUE
statement to invoke them. For other UDFs, use the SQL CALL
statement.
Task | Command |
---|---|
| |
| |
| |
| |
| |
|
6.9. Updating schemas for Db2 tables in capture mode for Debezium connectors
While a Debezium Db2 connector can capture schema changes, to update a schema, you must collaborate with a database administrator to ensure that the connector continues to produce change events. This is required by the way that Db2 implements replication.
For each table in capture mode, Db2’s replication feature creates a change-data table that contains all changes to that source table. However, change-data table schemas are static. If you update the schema for a table in capture mode then you must also update the schema of its corresponding change-data table. A Debezium Db2 connector cannot do this. A database administrator with elevated privileges must update schemas for tables that are in capture mode.
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.
6.9.1. Performing offline schema updates for Debezium Db2 connectors
You stop the Debezium Db2 connector before you perform an offline schema update. While this is the safer schema update procedure, it might not be feasible for applications with high-availability requirements.
Prerequisites
- One or more tables that are in capture mode require schema updates.
Procedure
- Suspend the application that updates the database.
- Wait for the Debezium connector to stream all unstreamed change event records.
- Stop the Debezium connector.
- Apply all changes to the source table schema.
-
In the ASN register table, mark the tables with updated schemas as
INACTIVE
. - Reinitialize the ASN capture service.
- Remove the source table with the old schema from capture mode by running the Debezium UDF for removing tables from capture mode.
- Add the source table with the new schema to capture mode by running the Debezium UDF for adding tables to capture mode.
-
In the ASN register table, mark the updated source tables as
ACTIVE
. - Reinitialize the ASN capture service.
- Resume the application that updates the database.
- Restart the Debezium connector.
6.9.2. Performing online schema updates for Debezium Db2 connectors
An online schema update does not require application and data processing downtime. That is, you do not stop the Debezium Db2 connector before you perform an online schema update. Also, an online schema update procedure is simpler than the procedure for an offline schema update.
However, when a table is in capture mode, after a change to a column name, the Db2 replication feature continues to use the old column name. The new column name does not appear in Debezium change events. You must restart the connector to see the new column name in change events.
Prerequisites
- One or more tables that are in capture mode require schema updates.
Procedure when adding a column to the end of a table
- Lock the source tables whose schema you want to change.
-
In the ASN register table, mark the locked tables as
INACTIVE
. - Reinitialize the ASN capture service.
- Apply all changes to the schemas for the source tables.
- Apply all changes to the schemas for the corresponding change-data tables.
-
In the ASN register table, mark the source tables as
ACTIVE
. - Reinitialize the ASN capture service.
- Optional. Restart the connector to see updated column names in change events.
Procedure when adding a column to the middle of a table
- Lock the source table(s) to be changed.
-
In the ASN register table, mark the locked tables as
INACTIVE
. - Reinitialize the ASN capture service.
For each source table to be changed:
- Export the data in the source table.
- Truncate the source table.
- Alter the source table and add the column.
- Load the exported data into the altered source table.
- Export the data in the source table’s corresponding change-data table.
- Truncate the change-data table.
- Alter the change-data table and add the column.
- Load the exported data into the altered change-data table.
-
In the ASN register table, mark the tables as
INACTIVE
. This marks the old change-data tables as inactive, which allows the data in them to remain but they are no longer updated. - Reinitialize the ASN capture service.
- Optional. Restart the connector to see updated column names in change events.