Debezium connector for Db2
Debezium’s Db2 connector can capture row-level changes in the tables of a Db2 database. For information about the Db2 Database versions that are compatible with this connector, see the Debezium release overview.
This connector is strongly inspired by the Debezium implementation of SQL Server, which uses a SQL-based polling model that puts tables into "capture mode". When a table is in capture mode, the Debezium Db2 connector generates and streams a change event for each row-level update to that table.
A table that is in capture mode has an associated change-data table, which Db2 creates. For each change to a table that is in capture mode, Db2 adds data about that change to the table’s associated change-data table. A change-data table contains an entry for each state of a row. It also has special entries for deletions. The Debezium Db2 connector reads change events from change-data tables and emits the events to Kafka topics.
The first time a Debezium Db2 connector connects to a Db2 database, the connector reads a consistent snapshot of the tables for which the connector is configured to capture changes. By default, this is all non-system tables. There are connector configuration properties that let you specify which tables to put into capture mode, or which tables to exclude from capture mode.
When the snapshot is complete the connector begins emitting change events for committed updates to tables that are in capture mode. By default, change events for a particular table go to a Kafka topic that has the same name as the table. Applications and services consume change events from these topics.
The connector requires the use of the abstract syntax notation (ASN) libraries, which are available as a standard part of Db2 for Linux. To use the ASN libraries, you must have a license for IBM InfoSphere Data Replication (IIDR). You do not have to install IIDR to use the ASN libraries. |
The Db2 connector has been tested with Db2 for Linux. It is expected that the connector would also work on other platforms such as Windows, and we’d love to get your feedback if you can confirm this to be the case.
You can also use the Db2 connector with Db2 for z/OS.
This feature is currently incubating and is subject to change in future releases.
For more information, see the db2.platform
configuration option.
You cannot use Debezium with Db2 for z/OS versions that use 6-bytes LSNs. |
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 management user-defined functions (UDFs) in C that you can compile and then use to do the following management tasks:
-
Start, stop, and reinitialize the ASN agent
-
Put tables into capture mode
-
Create the replication (ASN) schemas and change-data tables
-
Remove tables from capture mode
Alternatively, you can use Db2 control commands to accomplish these tasks.
After the tables of interest are in capture mode, the connector reads their corresponding change-data tables to obtain change events for table updates. The connector emits a change event for each row-level insert, update, and delete operation to a Kafka topic that has the same name as the changed table. This is default behavior that you can modify. Client applications read the Kafka topics that correspond to the database tables of interest and can react to each row-level change event.
Typically, the database administrator puts a table into capture mode in the middle of the life of a table. This means that the connector does not have the complete history of all changes that have been made to the table. Therefore, when the Db2 connector first connects to a particular Db2 database, it starts by performing a consistent snapshot of each table that is in capture mode. After the connector completes the snapshot, the connector streams change events from the point at which the snapshot was made. In this way, the connector starts with a consistent view of the tables that are in capture mode, and does not drop any changes that were made while it was performing the snapshot.
Debezium connectors are tolerant of failures. As the connector reads and produces change events, it records the log sequence number (LSN) of the change-data table entry. The LSN is the position of the change event in the database log. If the connector stops for any reason, including communication failures, network problems, or crashes, upon restarting it continues reading the change-data tables where it left off. This includes snapshots. That is, if the snapshot was not complete when the connector stopped, upon restart the connector begins a new snapshot.
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.
As a result, the Debezium Db2 connector cannot retrieve the entire history of the database from the logs.
To enable the connector to establish a baseline for the current state of the database, the first time that the connector starts, it performs an initial consistent snapshot of the tables that are in capture mode.
For each change that the snapshot captures, the connector emits a read
event to the Kafka topic for the captured table.
Default workflow that the Debezium Db2 connector uses to perform an initial snapshot
The following workflow lists the steps that Debezium takes to create a snapshot.
These steps describe the process for a snapshot when the snapshot.mode
configuration property is set to its default value, which is initial
.
You can customize the way that the connector creates snapshots by changing the value of the snapshot.mode
property.
If you configure a different snapshot mode, the connector completes the snapshot by using a modified version of this workflow.
-
Establish a connection to the database.
-
Determine which tables are in capture mode and should be included in the snapshot. By default, the connector captures the data for all non-system tables. After the snapshot completes, the connector continues to stream data for the specified tables. If you want the connector to capture data only from specific tables you can direct the connector to capture the data for only a subset of tables or table elements by setting properties such as
table.include.list
ortable.exclude.list
. -
Obtain a lock on each of the tables in capture mode. This lock ensures that no schema changes can occur in those tables until the snapshot completes. The level of the lock is determined by the
snapshot.isolation.mode
connector configuration property. -
Read the highest (most recent) LSN position in the server’s transaction log.
-
Capture the schema of all tables or all tables that are designated for capture. The connector persists schema information in its internal database schema history topic. The schema history provides information about the structure that is in effect when a change event occurs.
By default, the connector captures the schema of every table in the database that is in capture mode, including tables that are not configured for capture. If tables are not configured for capture, the initial snapshot captures only their structure; it does not capture any table data.
For more information about why snapshots persist schema information for tables that you did not include in the initial snapshot, see Understanding why initial snapshots capture the schema for all tables.
-
Release any locks obtained in Step 3. Other database clients can now write to any previously locked tables.
-
At the LSN position read in Step 4, the connector scans the tables that are designated for capture. During the scan, the connector completes the following tasks:
-
Confirms that the table was created before the snapshot began. If the table was created after the snapshot began, the connector skips the table. After the snapshot is complete, and the connector transitions to streaming, it emits change events for any tables that were created after the snapshot began.
-
Produces a
read
event for each row that is captured from a table. Allread
events contain the same LSN position, which is the LSN position that was obtained in step 4. -
Emits each
read
event to the Kafka topic for the source table. -
Releases data table locks, if applicable.
-
-
Record the successful completion of the snapshot in the connector offsets.
The resulting initial snapshot captures the current state of each row in the captured tables. From this baseline state, the connector captures subsequent changes as they occur.
After the snapshot process begins, if the process is interrupted due to connector failure, rebalancing, or other reasons, the process restarts after the connector restarts.
After the connector completes the initial snapshot, it continues streaming from the position that it read in Step 4 so that it does not miss any updates.
If the connector stops again for any reason, after it restarts, it resumes streaming changes from where it previously left off.
Setting | Description | ||
---|---|---|---|
|
The connector performs a snapshot every time that it starts. After the snapshot completes, the connector begins to stream event records for subsequent database changes. |
||
|
The connector performs a database snapshot as described in the default workflow for creating an initial snapshot. After the snapshot completes, the connector begins to stream event records for subsequent database changes. |
||
|
The connector performs a database snapshot. After the snapshot completes, the connector stops, and does not stream event records for subsequent database changes. |
||
|
Deprecated, see |
||
|
The connector captures the structure of all relevant tables, performing all the steps described in the default snapshot workflow, except that it does not create |
||
|
Set this option to restore a database schema history topic that is lost or corrupted. After a restart, the connector runs a snapshot that rebuilds the topic from the source tables. You can also set the property to periodically prune a database schema history topic that experiences unexpected growth.
|
||
|
After the connector starts, it performs a snapshot only if it detects one of the following circumstances:
|
||
|
Set the snapshot mode to |
||
|
The |
For more information, see snapshot.mode
in the table of connector configuration properties.
Understanding why initial snapshots capture the schema history for all tables
The initial snapshot that a connector runs captures two types of information:
- Table data
-
Information about
INSERT
,UPDATE
, andDELETE
operations in tables that are named in the connector’stable.include.list
property. - Schema data
-
DDL statements that describe the structural changes that are applied to tables. Schema data is persisted to both the internal schema history topic, and to the connector’s schema change topic, if one is configured.
After you run an initial snapshot, you might notice that the snapshot captures schema information for tables that are not designated for capture. By default, initial snapshots are designed to capture schema information for every table that is present in the database, not only from tables that are designated for capture. Connectors require that the table’s schema is present in the schema history topic before they can capture a table. By enabling the initial snapshot to capture schema data for tables that are not part of the original capture set, Debezium prepares the connector to readily capture event data from these tables should that later become necessary. If the initial snapshot does not capture a table’s schema, you must add the schema to the history topic before the connector can capture data from the table.
In some cases, you might want to limit schema capture in the initial snapshot. This can be useful when you want to reduce the time required to complete a snapshot. Or when Debezium connects to the database instance through a user account that has access to multiple logical databases, but you want the connector to capture changes only from tables in a specific logic database.
-
Capturing data from tables not captured by the initial snapshot (no schema change)
-
Capturing data from tables not captured by the initial snapshot (schema change)
-
Setting the
schema.history.internal.store.only.captured.tables.ddl
property to specify the tables from which to capture schema information. -
Setting the
schema.history.internal.store.only.captured.databases.ddl
property to specify the logical databases from which to capture schema changes.
Capturing data from tables not captured by the initial snapshot (no schema change)
In some cases, you might want the connector to capture data from a table whose schema was not captured by the initial snapshot. Depending on the connector configuration, the initial snapshot might capture the table schema only for specific tables in the database. If the table schema is not present in the history topic, the connector fails to capture the table, and reports a missing schema error.
You might still be able to capture data from the table, but you must perform additional steps to add the table schema.
-
You want to capture data from a table with a schema that the connector did not capture during the initial snapshot.
-
No schema changes were applied to the table between the LSNs of the earliest and latest change table entry that the connector reads. For information about capturing data from a new table that has undergone structural changes, see Capturing data from tables not captured by the initial snapshot (schema change).
-
Stop the connector.
-
Remove the internal database schema history topic that is specified by the
schema.history.internal.kafka.topic property
. -
Clear the offsets in the configured Kafka Connect
offset.storage.topic
. For more information about how to remove offsets, see the Debezium community FAQ.Removing offsets should be performed only by advanced users who have experience in manipulating internal Kafka Connect data. This operation is potentially destructive, and should be performed only as a last resort.
-
Apply the following changes to the connector configuration:
-
(Optional) Set the value of
schema.history.internal.captured.tables.ddl
tofalse
. This setting causes the snapshot to capture the schema for all tables, and guarantees that, in the future, the connector can reconstruct the schema history for all tables.Snapshots that capture the schema for all tables require more time to complete.
-
Add the tables that you want the connector to capture to
table.include.list
. -
Set the
snapshot.mode
to one of the following values:initial
-
When you restart the connector, it takes a full snapshot of the database that captures the table data and table structures.
If you select this option, consider setting the value of theschema.history.internal.captured.tables.ddl
property tofalse
to enable the connector to capture the schema of all tables. schema_only
-
When you restart the connector, it takes a snapshot that captures only the table schema. Unlike a full data snapshot, this option does not capture any table data. Use this option if you want to restart the connector more quickly than with a full snapshot.
-
-
Restart the connector. The connector completes the type of snapshot specified by the
snapshot.mode
. -
(Optional) If the connector performed a
schema_only
snapshot, after the snapshot completes, initiate an incremental snapshot to capture data from the tables that you added. The connector runs the snapshot while it continues to stream real-time changes from the tables. Running an incremental snapshot captures the following data changes:-
For tables that the connector previously captured, the incremental snapsot captures changes that occur while the connector was down, that is, in the interval between the time that the connector was stopped, and the current restart.
-
For newly added tables, the incremental snapshot captures all existing table rows.
-
Capturing data from tables not captured by the initial snapshot (schema change)
If a schema change is applied to a table, records that are committed before the schema change have different structures than those that were committed after the change. When Debezium captures data from a table, it reads the schema history to ensure that it applies the correct schema to each event. If the schema is not present in the schema history topic, the connector is unable to capture the table, and an error results.
If you want to capture data from a table that was not captured by the initial snapshot, and the schema of the table was modified, you must add the schema to the history topic, if it is not already available. You can add the schema by running a new schema snapshot, or by running an initial snapshot for the table.
-
You want to capture data from a table with a schema that the connector did not capture during the initial snapshot.
-
A schema change was applied to the table so that the records to be captured do not have a uniform structure.
- Initial snapshot captured the schema for all tables (
store.only.captured.tables.ddl
was set tofalse
) -
-
Edit the
table.include.list
property to specify the tables that you want to capture. -
Restart the connector.
-
Initiate an incremental snapshot if you want to capture existing data from the newly added tables.
-
- Initial snapshot did not capture the schema for all tables (
store.only.captured.tables.ddl
was set totrue
) -
If the initial snapshot did not save the schema of the table that you want to capture, complete one of the following procedures:
- Procedure 1: Schema snapshot, followed by incremental snapshot
-
In this procedure, the connector first performs a schema snapshot. You can then initiate an incremental snapshot to enable the connector to synchronize data.
-
Stop the connector.
-
Remove the internal database schema history topic that is specified by the
schema.history.internal.kafka.topic property
. -
Clear the offsets in the configured Kafka Connect
offset.storage.topic
. For more information about how to remove offsets, see the Debezium community FAQ.Removing offsets should be performed only by advanced users who have experience in manipulating internal Kafka Connect data. This operation is potentially destructive, and should be performed only as a last resort.
-
Set values for properties in the connector configuration as described in the following steps:
-
Set the value of the
snapshot.mode
property toschema_only
. -
Edit the
table.include.list
to add the tables that you want to capture.
-
-
Restart the connector.
-
Wait for Debezium to capture the schema of the new and existing tables. Data changes that occurred any tables after the connector stopped are not captured.
-
To ensure that no data is lost, initiate an incremental snapshot.
-
- Procedure 2: Initial snapshot, followed by optional incremental snapshot
-
In this procedure the connector performs a full initial snapshot of the database. As with any initial snapshot, in a database with many large tables, running an initial snapshot can be a time-consuming operation. After the snapshot completes, you can optionally trigger an incremental snapshot to capture any changes that occur while the connector is off-line.
-
Stop the connector.
-
Remove the internal database schema history topic that is specified by the
schema.history.internal.kafka.topic property
. -
Clear the offsets in the configured Kafka Connect
offset.storage.topic
. For more information about how to remove offsets, see the Debezium community FAQ.Removing offsets should be performed only by advanced users who have experience in manipulating internal Kafka Connect data. This operation is potentially destructive, and should be performed only as a last resort.
-
Edit the
table.include.list
to add the tables that you want to capture. -
Set values for properties in the connector configuration as described in the following steps:
-
Set the value of the
snapshot.mode
property toinitial
. -
(Optional) Set
schema.history.internal.store.only.captured.tables.ddl
tofalse
.
-
-
Restart the connector. The connector takes a full database snapshot. After the snapshot completes, the connector transitions to streaming.
-
(Optional) To capture any data that changed while the connector was off-line, initiate an incremental snapshot.
-
Ad hoc snapshots
By default, a connector runs an initial snapshot operation only after it starts for the first time. Following this initial snapshot, under normal circumstances, the connector does not repeat the snapshot process. Any future change event data that the connector captures comes in through the streaming process only.
However, in some situations the data that the connector obtained during the initial snapshot might become stale, lost, or incomplete. To provide a mechanism for recapturing table data, Debezium includes an option to perform ad hoc snapshots. You might want to perform an ad hoc snapshot after any of the following changes occur in your Debezium environment:
-
The connector configuration is modified to capture a different set of tables.
-
Kafka topics are deleted and must be rebuilt.
-
Data corruption occurs due to a configuration error or some other problem.
You can re-run a snapshot for a table for which you previously captured a snapshot by initiating a so-called ad-hoc snapshot. Ad hoc snapshots require the use of signaling tables. You initiate an ad hoc snapshot by sending a signal request to the Debezium signaling table.
When you initiate an ad hoc snapshot of an existing table, the connector appends content to the topic that already exists for the table. If a previously existing topic was removed, Debezium can create a topic automatically if automatic topic creation is enabled.
Ad hoc snapshot signals specify the tables to include in the snapshot. The snapshot can capture the entire contents of the database, or capture only a subset of the tables in the database. Also, the snapshot can capture a subset of the contents of the table(s) in the database.
You specify the tables to capture by sending an execute-snapshot
message to the signaling table.
Set the type of the execute-snapshot
signal to incremental
or blocking
, and provide the names of the tables to include in the snapshot, as described in the following table:
Field | Default | Value |
---|---|---|
|
|
Specifies the type of snapshot that you want to run. |
|
N/A |
An array that contains regular expressions matching the fully-qualified names of the tables to include in the snapshot. |
|
N/A |
An optional array that specifies a set of additional conditions that the connector evaluates to determine the subset of records to include in a snapshot.
|
|
N/A |
An optional string that specifies the column name that the connector uses as the primary key of a table during the snapshot process. |
You initiate an ad hoc incremental snapshot by adding an entry with the execute-snapshot
signal type to the signaling table, or by sending a signal message to a Kafka signaling topic.
After the connector processes the message, it begins the snapshot operation.
The snapshot process reads the first and last primary key values and uses those values as the start and end point for each table.
Based on the number of entries in the table, and the configured chunk size, Debezium divides the table into chunks, and proceeds to snapshot each chunk, in succession, one at a time.
For more information, see Incremental snapshots.
You initiate an ad hoc blocking snapshot by adding an entry with the execute-snapshot
signal type to the signaling table or signaling topic.
After the connector processes the message, it begins the snapshot operation.
The connector temporarily stops streaming, and then initiates a snapshot of the specified table, following the same process that it uses during an initial snapshot.
After the snapshot completes, the connector resumes streaming.
For more information, see Blocking snapshots.
Incremental snapshots
To provide flexibility in managing snapshots, Debezium includes a supplementary snapshot mechanism, known as incremental snapshotting. Incremental snapshots rely on the Debezium mechanism for sending signals to a Debezium connector. Incremental snapshots are based on the DDD-3 design document.
In an incremental snapshot, instead of capturing the full state of a database all at once, as in an initial snapshot, Debezium captures each table in phases, in a series of configurable chunks. You can specify the tables that you want the snapshot to capture and the size of each chunk. The chunk size determines the number of rows that the snapshot collects during each fetch operation on the database. The default chunk size for incremental snapshots is 1024 rows.
As an incremental snapshot proceeds, Debezium uses watermarks to track its progress, maintaining a record of each table row that it captures. This phased approach to capturing data provides the following advantages over the standard initial snapshot process:
-
You can run incremental snapshots in parallel with streamed data capture, instead of postponing streaming until the snapshot completes. The connector continues to capture near real-time events from the change log throughout the snapshot process, and neither operation blocks the other.
-
If the progress of an incremental snapshot is interrupted, you can resume it without losing any data. After the process resumes, the snapshot begins at the point where it stopped, rather than recapturing the table from the beginning.
-
You can run an incremental snapshot on demand at any time, and repeat the process as needed to adapt to database updates. For example, you might re-run a snapshot after you modify the connector configuration to add a table to its
table.include.list
property.
When you run an incremental snapshot, Debezium sorts each table by primary key and then splits the table into chunks based on the configured chunk size.
Working chunk by chunk, it then captures each table row in a chunk.
For each row that it captures, the snapshot emits a READ
event.
That event represents the value of the row when the snapshot for the chunk began.
As a snapshot proceeds, it’s likely that other processes continue to access the database, potentially modifying table records.
To reflect such changes, INSERT
, UPDATE
, or DELETE
operations are committed to the transaction log as per usual.
Similarly, the ongoing Debezium streaming process continues to detect these change events and emits corresponding change event records to Kafka.
In some cases, the UPDATE
or DELETE
events that the streaming process emits are received out of sequence.
That is, the streaming process might emit an event that modifies a table row before the snapshot captures the chunk that contains the READ
event for that row.
When the snapshot eventually emits the corresponding READ
event for the row, its value is already superseded.
To ensure that incremental snapshot events that arrive out of sequence are processed in the correct logical order, Debezium employs a buffering scheme for resolving collisions.
Only after collisions between the snapshot events and the streamed events are resolved does Debezium emit an event record to Kafka.
To assist in resolving collisions between late-arriving READ
events and streamed events that modify the same table row, Debezium employs a so-called snapshot window.
The snapshot window demarcates the interval during which an incremental snapshot captures data for a specified table chunk.
Before the snapshot window for a chunk opens, Debezium follows its usual behavior and emits events from the transaction log directly downstream to the target Kafka topic.
But from the moment that the snapshot for a particular chunk opens, until it closes, Debezium performs a de-duplication step to resolve collisions between events that have the same primary key..
For each data collection, the Debezium emits two types of events, and stores the records for them both in a single destination Kafka topic.
The snapshot records that it captures directly from a table are emitted as READ
operations.
Meanwhile, as users continue to update records in the data collection, and the transaction log is updated to reflect each commit, Debezium emits UPDATE
or DELETE
operations for each change.
As the snapshot window opens, and Debezium begins processing a snapshot chunk, it delivers snapshot records to a memory buffer.
During the snapshot windows, the primary keys of the READ
events in the buffer are compared to the primary keys of the incoming streamed events.
If no match is found, the streamed event record is sent directly to Kafka.
If Debezium detects a match, it discards the buffered READ
event, and writes the streamed record to the destination topic, because the streamed event logically supersede the static snapshot event.
After the snapshot window for the chunk closes, the buffer contains only READ
events for which no related transaction log events exist.
Debezium emits these remaining READ
events to the table’s Kafka topic.
The connector repeats the process for each snapshot chunk.
Currently, you can use either of the following methods to initiate an incremental snapshot:
The Debezium connector for Db2 does not support schema changes while an incremental snapshot is running. |
Triggering an incremental snapshot
To initiate an incremental snapshot, you can send an ad hoc snapshot signal to the signaling table on the source database.
You submit snapshot signals as SQL INSERT
queries.
After Debezium detects the change in the signaling table, it reads the signal, and runs the requested snapshot operation.
The query that you submit specifies the tables to include in the snapshot, and, optionally, specifies the type of snapshot operation.
Debezium currently supports the incremental
and blocking
snapshot types.
To specify the tables to include in the snapshot, provide a data-collections
array that lists the tables, or an array of regular expressions used to match tables, for example,
{"data-collections": ["public.MyFirstTable", "public.MySecondTable"]}
The data-collections
array for an incremental snapshot signal has no default value.
If the data-collections
array is empty, Debezium interprets the empty array to mean that no action is required, and it does not perform a snapshot.
If the name of a table that you want to include in a snapshot contains a dot ( |
-
-
A signaling data collection exists on the source database.
-
The signaling data collection is specified in the
signal.data.collection
property.
-
-
Send a SQL query to add the ad hoc incremental snapshot request to the signaling table:
INSERT INTO <signalTable> (id, type, data) VALUES ('<id>', '<snapshotType>', '{"data-collections": ["<fullyQualfiedTableName>","<fullyQualfiedTableName>"],"type":"<snapshotType>","additional-conditions":[{"data-collection": "<fullyQualfiedTableName>", "filter": "<additional-condition>"}]}');
For example,
INSERT INTO myschema.debezium_signal (id, type, data) (1) values ('ad-hoc-1', (2) 'execute-snapshot', (3) '{"data-collections": ["schema1.table1", "schema1.table2"], (4) "type":"incremental", (5) "additional-conditions":[{"data-collection": "schema1.table1" ,"filter":"color=\'blue\'"}]}'); (6)
The values of the
id
,type
, anddata
parameters in the command correspond to the fields of the signaling table.
The following table describes the parameters in the example:Table 3. Descriptions of fields in a SQL command for sending an incremental snapshot signal to the signaling table Item Value Description 1
schema.debezium_signal
Specifies the fully-qualified name of the signaling table on the source database.
2
ad-hoc-1
The
id
parameter specifies an arbitrary string that is assigned as theid
identifier for the signal request.
Use this string to identify logging messages to entries in the signaling table. Debezium does not use this string. Rather, during the snapshot, Debezium generates its ownid
string as a watermarking signal.3
execute-snapshot
The
type
parameter specifies the operation that the signal is intended to trigger.4
data-collections
A required component of the
data
field of a signal that specifies an array of table names or regular expressions to match table names to include in the snapshot.
The array lists regular expressions that use the formatschema.table
to match the fully-qualified names of the tables. This format is the same as the one that you use to specify the name of the connector’s signaling table.5
incremental
An optional
type
component of thedata
field of a signal that specifies the type of snapshot operation to run.
Valid values areincremental
andblocking
.
If you do not specify a value, the connector defaults to performing an incremental snapshot.6
additional-conditions
An optional array that specifies a set of additional conditions that the connector evaluates to determine the subset of records to include in a snapshot.
Each additional condition is an object withdata-collection
andfilter
properties. You can specify different filters for each data collection.
* Thedata-collection
property is the fully-qualified name of the data collection that the filter applies to. For more information about theadditional-conditions
parameter, see Running an ad hoc incremental snapshots withadditional-conditions
.
Running an ad hoc incremental snapshots with additional-conditions
If you want a snapshot to include only a subset of the content in a table, you can modify the signal request by appending an additional-conditions
parameter to the snapshot signal.
The SQL query for a typical snapshot takes the following form:
SELECT * FROM <tableName> ....
By adding an additional-conditions
parameter, you append a WHERE
condition to the SQL query, as in the following example:
SELECT * FROM <data-collection> WHERE <filter> ....
The following example shows a SQL query to send an ad hoc incremental snapshot request with an additional condition to the signaling table:
INSERT INTO <signalTable> (id, type, data) VALUES ('<id>', '<snapshotType>', '{"data-collections": ["<fullyQualfiedTableName>","<fullyQualfiedTableName>"],"type":"<snapshotType>","additional-conditions":[{"data-collection": "<fullyQualfiedTableName>", "filter": "<additional-condition>"}]}');
For example, suppose you have a products
table that contains the following columns:
-
id
(primary key) -
color
-
quantity
If you want an incremental snapshot of the products
table to include only the data items where color=blue
, you can use the following SQL statement to trigger the snapshot:
INSERT INTO myschema.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["schema1.products"],"type":"incremental", "additional-conditions":[{"data-collection": "schema1.products", "filter": "color=blue"}]}');
The additional-conditions
parameter also enables you to pass conditions that are based on more than one column.
For example, using the products
table from the previous example, you can submit a query that triggers an incremental snapshot that includes the data of only those items for which color=blue
and quantity>10
:
INSERT INTO myschema.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["schema1.products"],"type":"incremental", "additional-conditions":[{"data-collection": "schema1.products", "filter": "color=blue AND quantity>10"}]}');
The following example, shows the JSON for an incremental snapshot event that is captured by a connector.
{
"before":null,
"after": {
"pk":"1",
"value":"New data"
},
"source": {
...
"snapshot":"incremental" (1)
},
"op":"r", (2)
"ts_ms":"1620393591654",
"ts_us":"1620393591654547",
"ts_ns":"1620393591654547920",
"transaction":null
}
Item | Field name | Description |
---|---|---|
1 |
|
Specifies the type of snapshot operation to run. |
2 |
|
Specifies the event type. |
Using the Kafka signaling channel to trigger an incremental snapshot
You can send a message to the configured Kafka topic to request the connector to run an ad hoc incremental snapshot.
The key of the Kafka message must match the value of the topic.prefix
connector configuration option.
The value of the message is a JSON object with type
and data
fields.
The signal type is execute-snapshot
, and the data
field must have the following fields:
Field | Default | Value |
---|---|---|
|
|
The type of the snapshot to be executed.
Currently Debezium supports the |
|
N/A |
An array of comma-separated regular expressions that match the fully-qualified names of tables to include in the snapshot. |
|
N/A |
An optional array of additional conditions that specifies criteria that the connector evaluates to designate a subset of records to include in a snapshot. |
execute-snapshot
Kafka messageKey = `test_connector` Value = `{"type":"execute-snapshot","data": {"data-collections": ["{collection-container}.table1", "{collection-container}.table2"], "type": "INCREMENTAL"}}`
Debezium uses the additional-conditions
field to select a subset of a table’s content.
Typically, when Debezium runs a snapshot, it runs a SQL query such as:
SELECT * FROM <tableName> ….
When the snapshot request includes an additional-conditions
property, the data-collection
and filter
parameters of the property are appended to the SQL query, for example:
SELECT * FROM <data-collection> WHERE <filter> ….
For example, given a products
table with the columns id
(primary key), color
, and brand
, if you want a snapshot to include only content for which color='blue'
, when you request the snapshot, you could add the additional-conditions
property to filter the content:
:leveloffset: +1
Key = `test_connector`
Value = `{"type":"execute-snapshot","data": {"data-collections": ["schema1.products"], "type": "INCREMENTAL", "additional-conditions": [{"data-collection": "schema1.products" ,"filter":"color='blue'"}]}}`
You can also use the additional-conditions
property to pass conditions based on multiple columns.
For example, using the same products
table as in the previous example, if you want a snapshot to include only the content from the products
table for which color='blue'
, and brand='MyBrand'
, you could send the following request:
:leveloffset: +1
Key = `test_connector`
Value = `{"type":"execute-snapshot","data": {"data-collections": ["schema1.products"], "type": "INCREMENTAL", "additional-conditions": [{"data-collection": "schema1.products" ,"filter":"color='blue' AND brand='MyBrand'"}]}}`
Stopping an incremental snapshot
In some situations, it might be necessary to stop an incremental snapshot. For example, you might realize that snapshot was not configured correctly, or maybe you want to ensure that resources are available for other database operations. You can stop a snapshot that is already running by sending a signal to the signaling table on the source database.
You submit a stop snapshot signal to the signaling table by sending it in a SQL INSERT
query.
The stop-snapshot signal specifies the type
of the snapshot operation as incremental
, and optionally specifies the tables that you want to omit from the currently running snapshot.
After Debezium detects the change in the signaling table, it reads the signal, and stops the incremental snapshot operation if it’s in progress.
You can also stop an incremental snapshot by sending a JSON message to the Kafka signaling topic.
-
-
A signaling data collection exists on the source database.
-
The signaling data collection is specified in the
signal.data.collection
property.
-
-
Send a SQL query to stop the ad hoc incremental snapshot to the signaling table:
INSERT INTO <signalTable> (id, type, data) values ('<id>', 'stop-snapshot', '{"data-collections": ["<fullyQualfiedTableName>","<fullyQualfiedTableName>"],"type":"incremental"}');
For example,
INSERT INTO myschema.debezium_signal (id, type, data) (1) values ('ad-hoc-1', (2) 'stop-snapshot', (3) '{"data-collections": ["schema1.table1", "schema1.table2"], (4) "type":"incremental"}'); (5)
The values of the
id
,type
, anddata
parameters in the signal command correspond to the fields of the signaling table.
The following table describes the parameters in the example:Table 6. Descriptions of fields in a SQL command for sending a stop incremental snapshot signal to the signaling table Item Value Description 1
schema.debezium_signal
Specifies the fully-qualified name of the signaling table on the source database.
2
ad-hoc-1
The
id
parameter specifies an arbitrary string that is assigned as theid
identifier for the signal request.
Use this string to identify logging messages to entries in the signaling table. Debezium does not use this string.3
stop-snapshot
Specifies
type
parameter specifies the operation that the signal is intended to trigger.4
data-collections
An optional component of the
data
field of a signal that specifies an array of table names or regular expressions to match table names to remove from the snapshot.
The array lists regular expressions which match tables by their fully-qualified names in the formatschema.table
If you omit this component from the
data
field, the signal stops the entire incremental snapshot that is in progress.5
incremental
A required component of the
data
field of a signal that specifies the type of snapshot operation that is to be stopped.
Currently, the only valid option isincremental
.
If you do not specify atype
value, the signal fails to stop the incremental snapshot.
Using the Kafka signaling channel to stop an incremental snapshot
You can send a signal message to the configured Kafka signaling topic to stop an ad hoc incremental snapshot.
The key of the Kafka message must match the value of the topic.prefix
connector configuration option.
The value of the message is a JSON object with type
and data
fields.
The signal type is stop-snapshot
, and the data
field must have the following fields:
Field | Default | Value |
---|---|---|
|
|
The type of the snapshot to be executed.
Currently Debezium supports only the |
|
N/A |
An optional array of comma-separated regular expressions that match the fully-qualified names of the tables an array of table names or regular expressions to match table names to remove from the snapshot. |
The following example shows a typical stop-snapshot
Kafka message:
:leveloffset: +1
Key = `test_connector`
Value = `{"type":"stop-snapshot","data": {"data-collections": ["schema1.table1", "schema1.table2"], "type": "INCREMENTAL"}}`
Custom snapshotter SPI
For more advanced uses, you can fine-tune control of the snapshot by implementing one of the following interfaces:
io.debezium.snapshot.spi.Snapshotter
-
Controls whether the connector takes a snapshot.
io.debezium.snapshot.spi.SnapshotQuery
-
Controls how data is queried during a snapshot.
io.debezium.snapshot.spi.SnapshotLock
-
Controls whether the connector locks tables when taking a snapshot.
/**
* {@link Snapshotter} is used to determine the following details about the snapshot process:
* <p>
* - Whether a snapshot occurs. <br>
* - Whether streaming continues during the snapshot. <br>
* - Whether the snapshot includes schema (if supported). <br>
* - Whether to snapshot data or schema following an error.
* <p>
* Although Debezium provides many default snapshot modes,
* to provide more advanced functionality, such as partial snapshots,
* you can customize implementation of the interface.
* For more information, see the documentation.
*
*
*
*/
@Incubating
public interface Snapshotter extends Configurable {
/**
* @return the name of the snapshotter.
*
*
*/
String name();
/**
* @param offsetExists is {@code true} when the connector has an offset context (i.e. restarted)
* @param snapshotInProgress is {@code true} when the connector is started, but a snapshot is already in progress
*
* @return {@code true} if the snapshotter should take a data snapshot
*/
boolean shouldSnapshotData(boolean offsetExists, boolean snapshotInProgress);
/**
* @param offsetExists is {@code true} when the connector has an offset context (i.e. restarted)
* @param snapshotInProgress is {@code true} when the connector is started, but a snapshot is already in progress
*
* @return {@code true} if the snapshotter should take a schema snapshot
*/
boolean shouldSnapshotSchema(boolean offsetExists, boolean snapshotInProgress);
/**
* @return {@code true} if the snapshotter should stream after taking a snapshot
*/
boolean shouldStream();
/**
* @return {@code true} whether the schema can be recovered if database schema history is corrupted.
*/
boolean shouldSnapshotOnSchemaError();
/**
* @return {@code true} whether the snapshot should be re-executed when there is a gap in data stream.
*/
boolean shouldSnapshotOnDataError();
/**
*
* @return {@code true} if streaming should resume from the start of the snapshot
* transaction, or {@code false} for when a connector resumes and takes a snapshot,
* streaming should resume from where streaming previously left off.
*/
default boolean shouldStreamEventsStartingFromSnapshot() {
return true;
}
/**
* Lifecycle hook called after the snapshot phase is successful.
*/
default void snapshotCompleted() {
// no operation
}
/**
* Lifecycle hook called after the snapshot phase is aborted.
*/
default void snapshotAborted() {
// no operation
}
}
/**
* {@link SnapshotQuery} is used to determine the query used during a data snapshot
*
*
*/
public interface SnapshotQuery extends Configurable, Service {
/**
* @return the name of the snapshot lock.
*
*
*/
String name();
/**
* Generate a valid query string for the specified table, or an empty {@link Optional}
* to skip snapshotting this table (but that table will still be streamed from)
*
* @param tableId the table to generate a query for
* @param snapshotSelectColumns the columns to be used in the snapshot select based on the column
* include/exclude filters
* @return a valid query string, or none to skip snapshotting this table
*/
Optional<String> snapshotQuery(String tableId, List<String> snapshotSelectColumns);
}
/**
* {@link SnapshotLock} is used to determine the table lock mode used during schema snapshot
*
*
*/
public interface SnapshotLock extends Configurable, Service {
/**
* @return the name of the snapshot lock.
*
*
*/
String name();
/**
* Returns a SQL statement for locking the given table during snapshotting, if required by the specific snapshotter
* implementation.
*/
Optional<String> tableLockingStatement(Duration lockTimeout, String tableId);
}
Blocking snapshots
To provide more flexibility in managing snapshots, Debezium includes a supplementary ad hoc snapshot mechanism, known as a blocking snapshot. Blocking snapshots rely on the Debezium mechanism for sending signals to a Debezium connector.
A blocking snapshot behaves just like an initial snapshot, except that you can trigger it at run time.
You might want to run a blocking snapshot rather than use the standard initial snapshot process in the following situations:
-
You add a new table and you want to complete the snapshot while the connector is running.
-
You add a large table, and you want the snapshot to complete in less time than is possible with an incremental snapshot.
When you run a blocking snapshot, Debezium stops streaming, and then initiates a snapshot of the specified table, following the same process that it uses during an initial snapshot. After the snapshot completes, the streaming is resumed.
You can set the following properties in the data
component of a signal:
-
data-collections: to specify which tables must be snapshot.
-
data-collections: Specifies the tables that you want the snapshot to include.
This property accepts a comma-separated list of regular expressions that match fully-qualified table names. The behavior of the property is similar to the behavior of thetable.include.list
property, which specifies the tables to capture in a blocking snapshot. -
additional-conditions: You can specify different filters for different table.
-
The
data-collection
property is the fully-qualified name of the table for which the filter will be applied, and can be case-sensitive or case-insensitive depending on the database. -
The
filter
property will have the same value used in thesnapshot.select.statement.overrides
, the fully-qualified name of the table that should match by case.
-
For example:
{"type": "blocking", "data-collections": ["schema1.table1", "schema1.table2"], "additional-conditions": [{"data-collection": "schema1.table1", "filter": "SELECT * FROM [schema1].[table1] WHERE column1 = 0 ORDER BY column2 DESC"}, {"data-collection": "schema1.table2", "filter": "SELECT * FROM [schema1].[table2] WHERE column2 > 0"}]}
A delay might exist between the time that you send the signal to trigger the snapshot, and the time when streaming stops and the snapshot starts. As a result of this delay, after the snapshot completes, the connector might emit some event records that duplicate records captured by the snapshot.
Change-data tables
After a complete snapshot, when a Debezium Db2 connector starts for the first time, the connector identifies the change-data table for each source table that is in capture mode. The connector does the following for each change-data table:
-
Reads change events that were created between the last stored, highest LSN and the current, highest LSN.
-
Orders the change events according to the commit LSN and the change LSN for each event. This ensures that the connector emits the change events in the order in which the table changes occurred.
-
Passes commit and change LSNs as offsets to Kafka Connect.
-
Stores the highest LSN that the connector passed to Kafka Connect.
After a restart, the connector resumes emitting change events from the offset (commit and change LSNs) where it left off. While the connector is running and emitting change events, if you remove a table from capture mode or add a table to capture mode, the connector detects the change, and modifies its behavior accordingly.
Topic names
By default, the Db2 connector writes change events for all of the INSERT
, UPDATE
, and DELETE
operations that occur in a table to a single Apache Kafka topic that is specific to that table.
The connector uses the following convention to name change event topics:
topicPrefix.schemaName.tableName
The following list provides definitions for the components of the default name:
- topicPrefix
-
The topic prefix as specified by the
topic.prefix
connector configuration property. - schemaName
-
The name of the schema in which the operation occurred.
- tableName
-
The name of the table in which the operation occurred.
For example, consider a Db2 installation with the mydatabase
database, which contains four tables: PRODUCTS
, PRODUCTS_ON_HAND
, CUSTOMERS
, and ORDERS
that are in the MYSCHEMA
schema. The connector would emit events to these four Kafka topics:
-
mydatabase.MYSCHEMA.PRODUCTS
-
mydatabase.MYSCHEMA.PRODUCTS_ON_HAND
-
mydatabase.MYSCHEMA.CUSTOMERS
-
mydatabase.MYSCHEMA.ORDERS
The connector applies similar naming conventions to label its internal database schema history topics, schema change topics, and transaction metadata topics.
If the default topic name do not meet your requirements, you can configure custom topic names. To configure custom topic names, you specify regular expressions in the logical topic routing SMT. For more information about using the logical topic routing SMT to customize topic naming, see Topic routing.
Schema history topic
When a database client queries a database, the client uses the database’s current schema. However, the database schema can be changed at any time, which means that the connector must be able to identify what the schema was at the time each insert, update, or delete operation was recorded. Also, a connector cannot necessarily apply the current schema to every event. If an event is relatively old, it’s possible that it was recorded before the current schema was applied.
To ensure correct processing of events that occur after a schema change, the Debezium Db2 connector stores a snapshot of the new schema based on the structures of the Db2 change data tables, which mirror the structures of their associated data tables. The connector stores the table schema information, together with the LSN of operations the result in schema changes, in the database schema history Kafka topic. The connector uses the stored schema representation to produce change events that correctly mirror the structure of tables at the time of each insert, update, or delete operation.
When the connector restarts after either a crash or a graceful stop, it resumes reading entries in the Db2 change data tables from the last position that it read. Based on the schema information that the connector reads from the database schema history topic, the connector applies the table structures that existed at the position where the connector restarts.
If you update the schema of a Db2 table that is in capture mode, it’s important that you also update the schema of the corresponding change table. You must be a Db2 database administrator with elevated privileges to update database schema. For more information about how to update Db2 database schema in Debezium environments, see Schema history eveolution.
The database schema history topic is for internal connector use only. Optionally, the connector can also emit schema change events to a different topic that is intended for consumer applications.
-
Default names for topics that receive Debezium event records.
Schema change topic
You can configure a Debezium Db2 connector to produce schema change events that describe schema changes that are applied to tables in the database.
Debezium emits a message to the schema change topic when:
-
A new table goes into capture mode.
-
A table is removed from capture mode.
-
During a database schema update, there is a change in the schema for a table that is in capture mode.
The connector writes schema change events to a Kafka schema change topic that has the name <topicPrefix>
where <topicPrefix>
is the topic prefix that is specified in the topic.prefix
connector configuration property.
The schema for the schema change event has the following elements:
name
-
The name of the schema change event message.
type
-
The type of the change event message.
version
-
The version of the schema. The version is an integer that is incremented each time the schema is changed.
fields
-
The fields that are included in the change event message.
The following example shows a typical schema in JSON format.
{
"schema": {
"type": "struct",
"fields": [
{
"type": "string",
"optional": false,
"field": "databaseName"
}
],
"optional": false,
"name": "io.debezium.connector.db2.SchemaChangeKey",
"version": 1
},
"payload": {
"databaseName": "inventory"
}
}
Messages that the connector sends to the schema change topic contain a payload that includes the following elements:
databaseName
-
The name of the database to which the statements are applied. The value of
databaseName
serves as the message key. pos
-
The position in the transaction log where the statements appear.
tableChanges
-
A structured representation of the entire table schema after the schema change. The
tableChanges
field contains an array that includes entries for each column of the table. Because the structured representation presents data in JSON or Avro format, consumers can easily read messages without first processing them through a DDL parser.
For a table that is in capture mode, the connector not only stores the history of schema changes in the schema change topic, but also in an internal database schema history topic. The internal database schema history topic is for connector use only and it is not intended for direct use by consuming applications. Ensure that applications that require notifications about schema changes consume that information only from the schema change topic. |
Never partition the database schema history topic. For the database schema history topic to function correctly, it must maintain a consistent, global order of the event records that the connector emits to it. To ensure that the topic is not split among partitions, set the partition count for the topic by using one of the following methods:
|
The format of messages that a connector emits to its schema change topic is in an incubating state and can change without notice. |
The following example shows a message in the schema change topic. The message contains a logical representation of the table schema.
{
"schema": {
...
},
"payload": {
"source": {
"version": "3.0.4.Final",
"connector": "db2",
"name": "db2",
"ts_ms": 0,
"snapshot": "true",
"db": "testdb",
"schema": "DB2INST1",
"table": "CUSTOMERS",
"change_lsn": null,
"commit_lsn": "00000025:00000d98:00a2",
"event_serial_no": null
},
"ts_ms": 1588252618953, (1)
"databaseName": "TESTDB", (2)
"schemaName": "DB2INST1",
"ddl": null, (3)
"tableChanges": [ (4)
{
"type": "CREATE", (5)
"id": "\"DB2INST1\".\"CUSTOMERS\"", (6)
"table": { (7)
"defaultCharsetName": null,
"primaryKeyColumnNames": [ (8)
"ID"
],
"columns": [ (9)
{
"name": "ID",
"jdbcType": 4,
"nativeType": null,
"typeName": "int identity",
"typeExpression": "int identity",
"charsetName": null,
"length": 10,
"scale": 0,
"position": 1,
"optional": false,
"autoIncremented": false,
"generated": false
},
{
"name": "FIRST_NAME",
"jdbcType": 12,
"nativeType": null,
"typeName": "varchar",
"typeExpression": "varchar",
"charsetName": null,
"length": 255,
"scale": null,
"position": 2,
"optional": false,
"autoIncremented": false,
"generated": false
},
{
"name": "LAST_NAME",
"jdbcType": 12,
"nativeType": null,
"typeName": "varchar",
"typeExpression": "varchar",
"charsetName": null,
"length": 255,
"scale": null,
"position": 3,
"optional": false,
"autoIncremented": false,
"generated": false
},
{
"name": "EMAIL",
"jdbcType": 12,
"nativeType": null,
"typeName": "varchar",
"typeExpression": "varchar",
"charsetName": null,
"length": 255,
"scale": null,
"position": 4,
"optional": false,
"autoIncremented": false,
"generated": false
}
],
"attributes": [ (10)
{
"customAttribute": "attributeValue"
}
]
}
}
]
}
}
Item | Field name | Description |
---|---|---|
1 |
|
Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task. In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium. |
2 |
|
Identifies the database and the schema that contain the change. |
3 |
|
Always |
4 |
|
An array of one or more items that contain the schema changes generated by a DDL command. |
5 |
|
Describes the kind of change. The value is one of the following:
|
6 |
|
Full identifier of the table that was created, altered, or dropped. |
7 |
|
Represents table metadata after the applied change. |
8 |
|
List of columns that compose the table’s primary key. |
9 |
|
Metadata for each column in the changed table. |
10 |
|
Custom attribute metadata for each table change. |
In messages that the connector sends to the schema change topic, the message key is the name of the database that contains the schema change.
In the following example, the payload
field contains the key:
{
"schema": {
"type": "struct",
"fields": [
{
"type": "string",
"optional": false,
"field": "databaseName"
}
],
"optional": false,
"name": "io.debezium.connector.db2.SchemaChangeKey",
"version": 1
},
"payload": {
"databaseName": "TESTDB"
}
}
Transaction metadata
Debezium can generate events that represent transaction boundaries and that enrich change data event messages.
Limits on when Debezium receives transaction metadata
Debezium registers and receives metadata only for transactions that occur after you deploy the connector. Metadata for transactions that occur before you deploy the connector is not available. |
Debezium generates transaction boundary events for the BEGIN
and END
delimiters in every transaction.
Transaction boundary events contain the following fields:
status
-
BEGIN
orEND
. id
-
String representation of the unique transaction identifier.
ts_ms
-
The time of a transaction boundary event (
BEGIN
orEND
event) at the data source. If the data source does not provide Debezium with the event time, then the field instead represents the time at which Debezium processes the event. event_count
(forEND
events)-
Total number of events emmitted by the transaction.
data_collections
(forEND
events)-
An array of pairs of
data_collection
andevent_count
elements that indicates the number of events that the connector emits for changes that originate from a data collection.
{
"status": "BEGIN",
"id": "00000025:00000d08:0025",
"ts_ms": 1486500577125,
"event_count": null,
"data_collections": null
}
{
"status": "END",
"id": "00000025:00000d08:0025",
"ts_ms": 1486500577691,
"event_count": 2,
"data_collections": [
{
"data_collection": "testDB.dbo.tablea",
"event_count": 1
},
{
"data_collection": "testDB.dbo.tableb",
"event_count": 1
}
]
}
Unless overridden via the topic.transaction
option,
the connector emits transaction events to the <topic.prefix>
.transaction
topic.
When transaction metadata is enabled the connector enriches the change event Envelope
with a new transaction
field.
This field provides information about every event in the form of a composite of fields:
id
-
String representation of unique transaction identifier.
total_order
-
The absolute position of the event among all events generated by the transaction.
data_collection_order
-
The per-data collection position of the event among all events that were emitted by the transaction.
Following is an example of a message:
{
"before": null,
"after": {
"pk": "2",
"aa": "1"
},
"source": {
...
},
"op": "c",
"ts_ms": "1580390884335",
"ts_us": "1580390884335875",
"ts_ns": "1580390884335875412",
"transaction": {
"id": "00000025:00000d08:0025",
"total_order": "1",
"data_collection_order": "1"
}
}
Data change events
The Debezium Db2 connector generates a data change event for each row-level INSERT
, UPDATE
, and DELETE
operation. Each event contains a key and a value. The structure of the key and the value depends on the table that was changed.
Debezium and Kafka Connect are designed around continuous streams of event messages. However, the structure of these events may change over time, which can be difficult for consumers to handle. To address this, each event contains the schema for its content or, if you are using a schema registry, a schema ID that a consumer can use to obtain the schema from the registry. This makes each event self-contained.
The following skeleton JSON shows the basic four parts of a change event. However, how you configure the Kafka Connect converter that you choose to use in your application determines the representation of these four parts in change events. A schema
field is in a change event only when you configure the converter to produce it. Likewise, the event key and event payload are in a change event only if you configure a converter to produce it. If you use the JSON converter and you configure it to produce all four basic change event parts, change events have this structure:
{
"schema": { (1)
...
},
"payload": { (2)
...
},
"schema": { (3)
...
},
"payload": { (4)
...
},
}
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. For more information, 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.
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
);
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 |
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:
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": "int64",
"optional": false,
"field": "ts_us"
},
{
"type": "int64",
"optional": false,
"field": "ts_ns"
},
{
"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"
},
{
"type": "int64",
"optional": true,
"field": "ts_us"
},
{
"type": "int64",
"optional": true,
"field": "ts_ns"
}
],
"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": "3.0.4.Final",
"connector": "db2",
"name": "myconnector",
"ts_ms": 1559729468470,
"ts_us": 1559729468470476,
"ts_ns": 1559729468470476000,
"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)
"ts_us": 1559729471739762, (10)
"ts_ns": 1559729471739762314 (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": "3.0.4.Final",
"connector": "db2",
"name": "myconnector",
"ts_ms": 1559729995937,
"ts_us": 1559729995937497,
"ts_ns": 1559729995937497000,
"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)
"ts_us": 1559729998706647, (5)
"ts_ns": 1559729998706647825 (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 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": "3.0.4.Final",
"connector": "db2",
"name": "myconnector",
"ts_ms": 1559730445243,
"ts_us": 1559730445243482,
"ts_ns": 1559730445243482000,
"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)
"ts_us": 1559730450205521, (5)
"ts_ns": 1559730450205521475 (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.
Data type mappings
For a complete description of the data types that Db2 supports, see Data Types in the Db2 documentation.
The Db2 connector represents changes to rows with events that are structured like the table in which the row exists. The event contains a field for each column value. How that value is represented in the event depends on the Db2 data type of the column. This section describes these mappings. If the default data type conversions do not meet your needs, you can create a custom converter for the connector.
Basic types
The following table describes how the connector maps each Db2 data type to a literal type and a semantic type in event fields.
-
literal type describes how the value is represented using Kafka Connect schema types:
INT8
,INT16
,INT32
,INT64
,FLOAT32
,FLOAT64
,BOOLEAN
,STRING
,BYTES
,ARRAY
,MAP
, 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) and Notes |
---|---|---|
|
|
Only snapshots can be taken from tables with BOOLEAN type columns. Currently SQL Replication on Db2 does not support BOOLEAN, so Debezium can not perform CDC on those tables. Consider using a different type. |
|
|
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. Passing the default value helps satisfy compatibility rules when using Avro as the serialization format together with the Confluent schema registry.
Temporal types
Except for the DATETIMEOFFSET
data type, which contains time zone information, Db2 maps temporal types based on the value of the time.precision.mode
connector configuration property.
The following sections describe these mappings:
time.precision.mode=adaptive
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) and Notes |
---|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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) and Notes |
---|---|---|
|
|
|
|
|
|
|
|
|
Timestamp types
The DATETIME
type represents a timestamp without time zone information.
Such columns are converted into an equivalent Kafka Connect value based on UTC.
For example, the DATETIME
value "2018-06-20 15:13:16.945104" is represented by an io.debezium.time.Timestamp
with the value "1529507596000".
The timezone of the JVM running Kafka Connect and Debezium does not affect this conversion.
Decimal types
Db2 data type | Literal type (schema type) | Semantic type (schema name) and Notes |
---|---|---|
|
|
|
|
|
|
Setting up Db2
For Debezium to capture change events that are committed to Db2 tables, a Db2 database administrator with the necessary privileges must configure tables in the database for change data capture. After you begin to run Debezium you can adjust the configuration of the capture agent to optimize performance.
Putting tables into capture mode
To put tables into capture mode, Debezium provides a set of user-defined functions (UDFs) for your convenience. The procedure here shows how to install and run these management UDFs. Alternatively, you can run Db2 control commands to put tables into capture mode. The administrator must then enable CDC for each table that you want Debezium to capture.
-
You are logged in to Db2 as the
db2instl
user. -
On the Db2 host, the Debezium management UDFs are available in the $HOME/asncdctools/src directory. UDFs are available from the Debezium examples repository.
-
The Db2 command
bldrtn
is on PATH, e.g. by runningexport PATH=$PATH:/opt/ibm/db2/V11.5.0.0/samples/c/
with Db2 11.5
-
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 connect to DB_NAME db2 bind db2schema.bnd blocking all grant public sqlerror continue
-
Ensure that the database was recently backed-up. The ASN agents must have a recent starting point to read from. If you need to perform a backup, run the following commands, which prune the data so that only the most recent version is available. If you do not need to retain the older versions of the data, specify
dev/null
for the backup location.-
Back up the database. Replace
DB_NAME
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 from an SQL client:
VALUES ASNCDC.ASNCDCSERVICES('start','asncdc');
or from the shell:
db2 "VALUES ASNCDC.ASNCDCSERVICES('start','asncdc');"
The preceding statement returns one of the following results:
-
asncap is already running
-
start -->
<COMMAND>
In this case, enter the specified
<COMMAND>
in the terminal window as shown in the following example:/database/config/db2inst1/sqllib/bin/asncap capture_schema=asncdc capture_server=SAMPLE &
-
-
Put tables into capture mode. Invoke the following statement for each table that you want to put into capture. Replace
MYSCHEMA
with the name of the schema that contains the table you want to put into capture mode. Likewise, 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');
Effect of Db2 capture agent configuration on server load and latency
When a database administrator enables change data capture for a source table, the capture agent begins to run. The agent reads new change event records from the transaction log and replicates the event records to a capture table. Between the time that a change is committed in the source table, and the time that the change appears in the corresponding change table, there is always a small latency interval. This latency interval represents a gap between when changes occur in the source table and when they become available for Debezium to stream to Apache Kafka.
Ideally, for applications that must respond quickly to changes in data, you want to maintain close synchronization between the source and capture tables. You might imagine that running the capture agent to continuously process change events as rapidly as possible might result in increased throughput and reduced latency — populating change tables with new event records as soon as possible after the events occur, in near real time. However, this is not necessarily the case. There is a performance penalty to pay in the pursuit of more immediate synchronization. Each time that the change agent queries the database for new event records, it increases the CPU load on the database host. The additional load on the server can have a negative effect on overall database performance, and potentially reduce transaction efficiency, especially during times of peak database use.
It’s important to monitor database metrics so that you know if the database reaches the point where the server can no longer support the capture agent’s level of activity. If you experience performance issues while running the capture agent, adjust capture agent settings to reduce CPU load.
Db2 capture agent configuration parameters
On Db2, the IBMSNAP_CAPPARMS
table contains parameters that control the behavior of the capture agent.
You can adjust the values for these parameters to balance the configuration of the capture process to reduce CPU load and still maintain acceptable levels of latency.
Specific guidance about how to configure Db2 capture agent parameters is beyond the scope of this documentation. |
In the IBMSNAP_CAPPARMS
table, the following parameters have the greatest effect on reducing CPU load:
COMMIT_INTERVAL
-
-
Specifies the number of seconds that the capture agent waits to commit data to the change data tables.
-
A higher value reduces the load on the database host and increases latency.
-
The default value is
30
.
-
SLEEP_INTERVAL
-
-
Specifies the number of seconds that the capture agent waits to start a new commit cycle after it reaches the end of the active transaction log.
-
A higher value reduces the load on the server, and increases latency.
-
The default value is
5
.
-
-
For more information about capture agent parameters, see the Db2 documentation.
Deployment
To deploy a Debezium Db2 connector, you install the Debezium Db2 connector archive, configure the connector, and start the connector by adding its configuration to Kafka Connect.
-
Apache ZooKeeper, Apache Kafka, and Kafka Connect are installed.
-
Db2 is installed and capture mode is enabled for tables to prepare the database to be used with the Debezium connector.
-
Download the Debezium Db2 connector plug-in archive from Maven Central.
-
Extract the JAR files into your Kafka Connect environment.
-
Download the JDBC driver for Db2 from Maven Central, and extract the downloaded driver file to the directory that contains the Debezium Db2 connector JAR file (that is,
debezium-connector-db2-3.0.4.Final.jar
).Due to licensing requirements, the Debezium Db2 connector archive does not include the Db2 JDBC driver that Debezium requires to connect to a Db2 database. To enable the connector to access the database, you must add the driver to your connector environment.
-
Add the directory with the JAR files to Kafka Connect’s
plugin.path
. -
Restart your Kafka Connect process to pick up the new JAR files.
If you are working with immutable containers, see Debezium’s container images for Apache ZooKeeper, Apache Kafka and Kafka Connect with the Db2 connector already installed and ready to run.
You can also run Debezium on Kubernetes and OpenShift.
Db2 connector configuration example
Following is an example of the configuration for a connector instance that captures data from a Db2 server on port 50000 at 192.168.99.100, which we logically name fullfillment
.
Typically, you configure the Debezium Db2 connector in a JSON file by setting the configuration properties that are available for the connector.
You can choose to produce events for a subset of the schemas and tables in a database. Optionally, you can ignore, mask, or truncate columns that contain sensitive data, that are larger than a specified size, or that you do not need.
{
"name": "db2-connector", (1)
"config": {
"connector.class": "io.debezium.connector.db2.Db2Connector", (2)
"database.hostname": "192.168.99.100", (3)
"database.port": "50000", (4)
"database.user": "db2inst1", (5)
"database.password": "Password!", (6)
"database.dbname": "mydatabase", (7)
"topic.prefix": "fullfillment", (8)
"table.include.list": "MYSCHEMA.CUSTOMERS", (9)
"schema.history.internal.kafka.bootstrap.servers": "kafka:9092", (10)
"schema.history.internal.kafka.topic": "schemahistory.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 schema history topic. |
11 | The name of the database schema history topic where the connector writes and recovers DDL statements. This topic is for internal use only and should not be used by consumers. |
For the complete list of the configuration properties that you can set for the Debezium Db2 connector, see Db2 connector properties.
You can send this configuration with a POST
command to a running Kafka Connect service.
The service records the configuration and starts one connector task that performs the following actions:
-
Connects to the Db2 database.
-
Reads change-data tables for tables that are in capture mode.
-
Streams change event records to Kafka topics.
Adding connector configuration
To start running a Db2 connector, create a connector configuration and add the configuration to your Kafka Connect cluster.
-
Db2 replication is enabled to expose change data for tables that are in capture mode.
-
The Db2 connector is installed.
-
Create a configuration for the Db2 connector.
-
Use the Kafka Connect REST API to add that connector configuration to your Kafka Connect cluster.
After the connector starts, it performs a consistent snapshot of the Db2 database tables that the connector is configured to capture changes for. The connector then starts generating data change events for row-level operations and streaming change event records to Kafka topics.
Connector properties
The Debezium Db2 connector has numerous configuration properties that you can use to achieve the right connector behavior for your application. Many properties have default values. Information about the properties is organized as follows:
-
Database schema history connector configuration properties that control how Debezium processes events that it reads from the database schema history topic.
Required Debezium Db2 connector configuration properties
The following configuration properties are required unless a default value is available.
Property | Default | Description | ||||||
---|---|---|---|---|---|---|---|---|
No default |
Unique name for the connector. Attempting to register again with the same name will fail. This property is required by all Kafka Connect connectors. |
|||||||
No default |
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. |
|||||||
No default |
IP address or hostname of the Db2 database server. |
|||||||
|
Integer port number of the Db2 database server. |
|||||||
No default |
Name of the Db2 database user for connecting to the Db2 database server. |
|||||||
No default |
Password to use when connecting to the Db2 database server. |
|||||||
No default |
The name of the Db2 database from which to stream the changes |
|||||||
|
The Db2 server platform. You can specify one of the following options:
|
|||||||
No default |
Topic prefix which provides a namespace for the particular Db2 database server that hosts the database for which Debezium is capturing changes.
Only alphanumeric characters, hyphens, dots and underscores must be used in the topic prefix name.
The topic prefix should be unique across all other connectors, since this topic prefix is used for all Kafka topics that receive records from this connector.
|
|||||||
No default |
An optional, comma-separated list of regular expressions that match fully-qualified table identifiers for tables whose changes you want the connector to capture.
When this property is set, the connector captures changes only from the specified tables.
Each identifier is of the form schemaName.tableName. By default, the connector captures changes in every non-system table. To match the name of a table, Debezium applies the regular expression that you specify as an anchored regular expression.
That is, the specified expression is matched against the entire name string of the table it does not match substrings that might be present in a table name. |
|||||||
No default |
An optional, comma-separated list of regular expressions that match fully-qualified table identifiers for tables whose changes you do not want the connector to capture.
The connector captures changes in each non-system table that is not included in the exclude list.
Each identifier is of the form schemaName.tableName. To match the name of a table, Debezium applies the regular expression that you specify as an anchored regular expression.
That is, the specified expression is matched against the entire name string of the table it does not match substrings that might be present in a table name. |
|||||||
empty string |
An optional, comma-separated list of regular expressions that match the fully-qualified names of columns to include in change event record values.
Fully-qualified names for columns are of the form schemaName.tableName.columnName. To match the name of a column, Debezium applies the regular expression that you specify as an anchored regular expression.
That is, the specified expression is matched against the entire name string of the column; it does not match substrings that might be present in a column name.
If you include this property in the configuration, do not also set the |
|||||||
empty string |
An optional, comma-separated list of regular expressions that match the fully-qualified names of columns to exclude from change event values.
Fully-qualified names for columns are of the form schemaName.tableName.columnName. To match the name of a column, Debezium applies the regular expression that you specify as an anchored regular expression.
That is, the specified expression is matched against the entire name string of the column; it does not match substrings that might be present in a column name.
Primary key columns are always included in the event’s key, even if they are excluded from the value.
If you include this property in the configuration, do not set the |
|||||||
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. A pseudonym consists of the hashed value that results from applying the specified hashAlgorithm and salt.
Based on the hash function that is used, referential integrity is maintained, while column values are replaced with pseudonyms.
Supported hash functions are described in the MessageDigest section of the Java Cryptography Architecture Standard Algorithm Name Documentation. column.mask.hash.SHA-256.with.salt.CzQMA0cB5K = inventory.orders.customerName, inventory.shipment.customerName If necessary, the pseudonym is automatically shortened to the length of the column.
The connector configuration can include multiple properties that specify different hash algorithms and salts. |
|||||||
|
Time, date, and timestamps can be represented with different kinds of precision: |
|||||||
|
Controls whether a delete event is followed by a tombstone 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 schema history. |
|||||||
n/a |
An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns.
Set this property if you want to truncate the data in a set of columns when it exceeds the number of characters specified by the length in the property name.
Set The fully-qualified name of a column observes the following format: schemaName.tableName.columnName. To match the name of a column, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the column; the expression does not match substrings that might be present in a column name. You can specify multiple properties with different lengths in a single configuration. |
|||||||
n/a |
An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns.
Set this property if you want the connector to mask the values for a set of columns, for example, if they contain sensitive data.
Set The fully-qualified name of a column observes the following format: schemaName.tableName.columnName. You can specify multiple properties with different lengths in a single configuration. |
|||||||
n/a |
An optional, comma-separated list of regular expressions that match the fully-qualified names of columns for which you want the connector to emit extra parameters that represent column metadata. When this property is set, the connector adds the following fields to the schema of event records:
These parameters propagate a column’s original type name and length (for variable-width types), respectively. The fully-qualified name of a column observes one of the following formats: databaseName.tableName.columnName, or databaseName.schemaName.tableName.columnName. |
|||||||
n/a |
An optional, comma-separated list of regular expressions that specify the fully-qualified names of data types that are defined for columns in a database. When this property is set, for columns with matching data types, the connector emits event records that include the following extra fields in their schema:
These parameters propagate a column’s original type name and length (for variable-width types), respectively. The fully-qualified name of a column observes one of the following formats: databaseName.tableName.typeName, or databaseName.schemaName.tableName.typeName. For the list of Db2-specific data type names, see the Db2 data type mappings . |
|||||||
empty string |
A list of expressions that specify the columns that the connector uses to form custom message keys for change event records that it publishes to the Kafka topics for specified tables. By default, Debezium uses the primary key column of a table as the message key for records that it emits.
In place of the default, or to specify a key for tables that lack a primary key, you can configure custom message keys based on one or more columns.
The property can list entries for multiple tables.
Use a semicolon to separate entries for different tables in the list. |
|||||||
none |
Specifies how schema names should be adjusted for compatibility with the message converter used by the connector. Possible settings:
|
|||||||
none |
Specifies how field names should be adjusted for compatibility with the message converter used by the connector. Possible settings:
See Avro naming for more details. |
Advanced connector configuration properties
The following advanced configuration properties have defaults that work in most situations and therefore rarely need to be specified in the connector’s configuration.
Property | Default | Description | ||
---|---|---|---|---|
No default |
Enumerates a comma-separated list of the symbolic names of the custom converter instances that the connector can use.
For example,
You must set the For each converter that you configure for a connector, you must also add a
For example, isbn.type: io.debezium.test.IsbnConverter If you want to further control the behavior of a configured converter, you can add one or more configuration parameters to pass values to the converter.
To associate any additional configuration parameter with a converter, prefix the parameter names with the symbolic name of the converter. isbn.schema.name: io.debezium.db2.type.Isbn |
|||
initial |
Specifies the criteria for performing a snapshot when the connector starts:
For more information, see custom snapshotter SPI. |
|||
false |
If the |
|||
false |
If the |
|||
false |
If the |
|||
false |
If the |
|||
false |
If the |
|||
No default |
If |
|||
exclusive |
Controls whether and for how long the connector holds a table lock. Table locks prevent other database clients from performing certain table operations during a snapshot. You can set the following values:
|
|||
No default |
When |
|||
|
Specifies how the connector queries data while performing a snapshot.
This setting enables you to manage snapshot content in a more flexible manner compared to using the |
|||
No default |
When |
|||
|
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 500 milliseconds, or 0.5 second. |
|||
|
Positive integer value that specifies the maximum size of each batch of events that the connector processes. |
|||
|
Positive integer value that specifies the maximum number of records that the blocking queue can hold.
When Debezium reads events streamed from the database, it places the events in the blocking queue before it writes them to Kafka.
The blocking queue can provide backpressure for reading change events from the database
in cases where the connector ingests messages faster than it can write them to Kafka, or when Kafka becomes unavailable.
Events that are held in the queue are disregarded when the connector periodically records offsets.
Always set the value of |
|||
|
A long integer value that specifies the maximum volume of the blocking queue in bytes.
By default, volume limits are not specified for the blocking queue.
To specify the number of bytes that the queue can consume, set this property to a positive long value. |
|||
|
Controls how frequently the connector sends heartbeat messages to a Kafka topic. The default behavior is that the connector does not send heartbeat messages. |
|||
No default |
An interval in milliseconds that the connector should wait before performing a snapshot when the connector starts. If you are starting multiple connectors in a cluster, this property is useful for avoiding snapshot interruptions, which might cause re-balancing of connectors. |
|||
0 |
Specifies the time, in milliseconds, that the connector delays the start of the streaming process after it completes a snapshot.
Setting a delay interval helps to prevent the connector from restarting snapshots in the event that a failure occurs immediately after the snapshot completes, but before the streaming process begins.
Set a delay value that is higher than the value of the |
|||
All tables specified in |
An optional, comma-separated list of regular expressions that match the fully-qualified names ( To match the name of a table, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the table; it does not match substrings that might be present in a table name. |
|||
|
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: |
|||
No default |
Specifies the table rows to include in a snapshot. Use the property if you want a snapshot to include only a subset of the rows in a table. This property affects snapshots only. It does not apply to events that the connector reads from the log. The property contains a comma-separated list of fully-qualified table names in the form From a "snapshot.select.statement.overrides": "customer.orders", "snapshot.select.statement.overrides.customer.orders": "SELECT * FROM customers.orders WHERE delete_flag = 0 ORDER BY id DESC" In the resulting snapshot, the connector includes only the records for which |
|||
|
Determines whether the connector generates events with transaction boundaries and enriches change event envelopes with transaction metadata. Specify |
|||
|
A comma-separated list of the operation types that you want the connector to skip during streaming. You can configure the connector to skip the following types of operations:
Set the value to |
|||
No default |
Fully-qualified name of the data collection that is used to send signals to the connector.
Use the following format to specify the collection name: |
|||
source |
List of the signaling channel names that are enabled for the connector. By default, the following channels are available:
|
|||
No default |
List of the notification channel names that are enabled for the connector. By default, the following channels are available:
|
|||
|
The maximum number of rows that the connector fetches and reads into memory during an incremental snapshot chunk. Increasing the chunk size provides greater efficiency, because the snapshot runs fewer snapshot queries of a greater size. However, larger chunk sizes also require more memory to buffer the snapshot data. Adjust the chunk size to a value that provides the best performance in your environment. |
|||
|
Specifies the watermarking mechanism that the connector uses during an incremental snapshot to deduplicate events that might be captured by an incremental snapshot and then recaptured after streaming resumes.
|
|||
|
The name of the TopicNamingStrategy class that should be used to determine the topic name for data change, schema change, transaction, heartbeat event etc., defaults to |
|||
|
Specify the delimiter for topic name, defaults to |
|||
|
The size used for holding the topic names in bounded concurrent hash map. This cache will help to determine the topic name corresponding to a given data collection. |
|||
|
Controls the name of the topic to which the connector sends heartbeat messages. The topic name has this pattern: |
|||
|
Controls the name of the topic to which the connector sends transaction metadata messages. The topic name has this pattern: |
|||
|
Specifies the number of threads that the connector uses when performing an initial snapshot. To enable parallel initial snapshots, set the property to a value greater than 1. In a parallel initial snapshot, the connector processes multiple tables concurrently. This feature is incubating. |
|||
|
Defines tags that customize MBean object names by adding metadata that provides contextual information.
Specify a comma-separated list of key-value pairs.
Each key represents a tag for the MBean object name, and the corresponding value represents a value for the key, for example, The connector appends the specified tags to the base MBean object name. Tags can help you to organize and categorize metrics data. You can define tags to identify particular application instances, environments, regions, versions, and so forth. For more information, see Customized MBean names. |
|||
|
Specifies how the connector responds after an operation that results in a retriable error, such as a connection error.
|
|||
|
Specifies the time, in milliseconds, that the connector waits for a query to complete.
Set the value to |
|||
|
Specifies the name of the database schema that stores CDC control structures. |
|||
|
Specifies the name of the database schema that stores CDC control structures. |
Debezium Db2 connector database schema history configuration properties
Debezium provides a set of schema.history.internal.*
properties that control how the connector interacts with the schema history topic.
The following table describes the schema.history.internal
properties for configuring the Debezium connector.
Property | Default | Description |
---|---|---|
No default |
The full name of the Kafka topic where the connector stores the database schema history. |
|
No default |
A list of host/port pairs that the connector uses for establishing an initial connection to the Kafka cluster. This connection is used for retrieving the database schema history previously stored by the connector, and for writing each DDL statement read from the source database. Each pair should point to the same Kafka cluster used by the Kafka Connect process. |
|
|
An integer value that specifies the maximum number of milliseconds the connector should wait during startup/recovery while polling for persisted data. The default is 100ms. |
|
|
An integer value that specifies the maximum number of milliseconds the connector should wait while fetching cluster information using Kafka admin client. |
|
|
An integer value that specifies the maximum number of milliseconds the connector should wait while create kafka history topic using Kafka admin client. |
|
|
The maximum number of times that the connector should try to read persisted history data before the connector recovery fails with an error. The maximum amount of time to wait after receiving no data is |
|
|
A Boolean value that specifies whether the connector should ignore malformed or unknown database statements or stop processing so a human can fix the issue.
The safe default is |
|
|
A Boolean value that specifies whether the connector records schema structures from all tables in a schema or database, or only from tables that are designated for capture.
|
|
|
A Boolean value that specifies whether the connector records schema structures from all logical databases in the database instance.
|
Pass-through Db2 connector configuration properties
The connector supports pass-through properties that enable Debezium to specify custom configuration options for fine-tuning the behavior of the Apache Kafka producer and consumer. For information about the full range of configuration properties for Kafka producers and consumers, see the Kafka documentation.
Pass-through properties for configuring how producer and consumer clients interact with schema history topics
Debezium relies on an Apache Kafka producer to write schema changes to database schema history topics.
Similarly, it relies on a Kafka consumer to read from database schema history topics when a connector starts.
You define the configuration for the Kafka producer and consumer clients by assigning values to a set of pass-through configuration properties that begin with the schema.history.internal.producer.*
and schema.history.internal.consumer.*
prefixes.
The pass-through producer and consumer database schema history properties control a range of behaviors, such as how these clients secure connections with the Kafka broker, as shown in the following example:
schema.history.internal.producer.security.protocol=SSL
schema.history.internal.producer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks
schema.history.internal.producer.ssl.keystore.password=test1234
schema.history.internal.producer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks
schema.history.internal.producer.ssl.truststore.password=test1234
schema.history.internal.producer.ssl.key.password=test1234
schema.history.internal.consumer.security.protocol=SSL
schema.history.internal.consumer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks
schema.history.internal.consumer.ssl.keystore.password=test1234
schema.history.internal.consumer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks
schema.history.internal.consumer.ssl.truststore.password=test1234
schema.history.internal.consumer.ssl.key.password=test1234
Debezium strips the prefix from the property name before it passes the property to the Kafka client.
For more information about Kafka producer configuration properties and Kafka consumer configuration properties, see the Apache Kafka documentation .
Pass-through properties for configuring how the Db2 connector interacts with the Kafka signaling topic
Debezium provides a set of signal.*
properties that control how the connector interacts with the Kafka signals topic.
The following table describes the Kafka signal
properties.
Property | Default | Description | ||
---|---|---|---|---|
<topic.prefix>-signal |
The name of the Kafka topic that the connector monitors for ad hoc signals.
|
|||
kafka-signal |
The name of the group ID that is used by Kafka consumers. |
|||
No default |
A list of the host and port pairs that the connector uses to establish its initial connection to the Kafka cluster. Each pair references the Kafka cluster that is used by the Debezium Kafka Connect process. |
|||
|
An integer value that specifies the maximum number of milliseconds that the connector waits when polling signals. |
Pass-through properties for configuring the Kafka consumer client for the signaling channel
The Debezium connector provides for pass-through configuration of the signals Kafka consumer.
Pass-through signals properties begin with the prefix signals.consumer.*
.
For example, the connector passes properties such as signal.consumer.security.protocol=SSL
to the Kafka consumer.
Debezium strips the prefixes from the properties before it passes the properties to the Kafka signals consumer.
Pass-through properties for configuring the Db2 connector sink notification channel
The following table describes properties that you can use to configure the Debezium sink notification
channel.
Property | Default | Description |
---|---|---|
No default |
The name of the topic that receives notifications from Debezium.
This property is required when you configure the |
Debezium connector pass-through database driver configuration properties
The Debezium connector provides for pass-through configuration of the database driver.
Pass-through database properties begin with the prefix driver.*
.
For example, the connector passes properties such as driver.foobar=false
to the JDBC URL.
Debezium strips the prefixes from the properties before it passes the properties to the database driver.
Monitoring
The Debezium Db2 connector provides three types of metrics that are in addition to the built-in support for JMX metrics that Apache ZooKeeper, Apache Kafka, and Kafka Connect provide.
-
Snapshot metrics provide information about connector operation while performing a snapshot.
-
Streaming metrics provide information about connector operation when the connector is capturing changes and streaming change event records.
-
Schema history metrics provide information about the status of the connector’s schema history.
Debezium monitoring documentation provides details for how to expose these metrics by using JMX.
Customized MBean names
Debezium connectors expose metrics via the MBean name for the connector. These metrics, which are specific to each connector instance, provide data about the behavior of the connector’s snapshot, streaming, and schema history processes.
By default, when you deploy a correctly configured connector, Debezium generates a unique MBean name for each of the different connector metrics. To view the metrics for a connector process, you configure your observability stack to monitor its MBean. But these default MBean names depend on the connector configuration; configuration changes can result in changes to the MBean names. A change to the MBean name breaks the linkage between the connector instance and the MBean, disrupting monitoring activity. In this scenario, you must reconfigure the observability stack to use the new MBean name if you want to resume monitoring.
To prevent monitoring disruptions that result from MBean name changes, you can configure custom metrics tags.
You configure custom metrics by adding the custom.metric.tags
property to the connector configuration.
The property accepts key-value pairs in which each key represents a tag for the MBean object name, and the corresponding value represents the value of that tag.
For example: k1=v1,k2=v2
.
Debezium appends the specified tags to the MBean name of the connector.
After you configure the custom.metric.tags
property for a connector, you can configure the observability stack to retrieve metrics associated with the specified tags.
The observability stack then uses the specified tags, rather than the mutable MBean names to uniquely identify connectors.
Later, if Debezium redefines how it constructs MBean names, or if the topic.prefix
in the connector configuration changes, metrics collection is uninterrupted,
because the metrics scrape task uses the specified tag patterns to identify the connector.
A further benefit of using custom tags, is that you can use tags that reflect the architecture of your data pipeline, so that metrics are organized in a way that suits you operational needs.
For example, you might specify tags with values that declare the type of connector activity, the application context, or the data source, for example, db1-streaming-for-application-abc
.
If you specify multiple key-value pairs, all of the specified pairs are appended to the connector’s MBean name.
The following example illustrates how tags modify the default MBean name.
By default, the Db2 connector uses the following MBean name for streaming metrics:
debezium.db2:type=connector-metrics,context=streaming,server=<topic.prefix>
If you set the value of custom.metric.tags
to database=salesdb-streaming,table=inventory
, Debezium generates the following custom MBean name:
debezium.db2:type=connector-metrics,context=streaming,server=<topic.prefix>,database=salesdb-streaming,table=inventory
Snapshot metrics
The MBean is debezium.db2:type=connector-metrics,context=snapshot,server=<topic.prefix>
.
Snapshot metrics are not exposed unless a snapshot operation is active, or if a snapshot has occurred since the last connector start.
The following table lists the snapshot metrics that are available.
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 include/exclude list filtering rules configured on the connector. |
|
|
The list of tables that are captured 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 paused. |
|
|
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. Includes also time when snapshot was paused. |
|
|
The total number of seconds that the snapshot was paused. If the snapshot was paused several times, the paused time adds up. |
|
|
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. |
|
|
The maximum buffer of the queue in bytes. This metric is available if |
|
|
The current volume, in bytes, of records in the queue. |
The connector also provides the following additional snapshot metrics when an incremental snapshot is executed:
Attributes | Type | Description |
---|---|---|
|
The identifier of the current snapshot chunk. |
|
|
The lower bound of the primary key set defining the current chunk. |
|
|
The upper bound of the primary key set defining the current chunk. |
|
|
The lower bound of the primary key set of the currently snapshotted table. |
|
|
The upper bound of the primary key set of the currently snapshotted table. |
Streaming metrics
The MBean is debezium.db2:type=connector-metrics,context=streaming,server=<topic.prefix>
.
The following table lists the streaming metrics that are available.
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 data change events reported by the source database since the last connector start, or since a metrics reset. Represents the data change workload for Debezium to process. |
|
|
The total number of create events processed by the connector since its last start or metrics reset. |
|
|
The total number of update events processed by the connector since its last start or metrics reset. |
|
|
The total number of delete events processed by the connector since its last start or metrics reset. |
|
|
The number of events that have been filtered by include/exclude list filtering rules configured on the connector. |
|
|
The list of tables that are captured 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 incorporate 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. |
|
|
The maximum buffer of the queue in bytes. This metric is available if |
|
|
The current volume, in bytes, of records in the queue. |
Schema history metrics
The MBean is debezium.db2:type=connector-metrics,context=schema-history,server=<topic.prefix>
.
The following table lists the schema history metrics that are available.
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. |
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.
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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, the replication feature in Db2 creates a change-data table that contains all changes to that source table. However, change-data table schemas are static. If you update the schema for a table in capture mode then you must also update the schema of its corresponding change-data table. A Debezium Db2 connector cannot do this. A database administrator with elevated privileges must update schemas for tables that are in capture mode.
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.
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One or more tables that are in capture mode require schema updates.
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Suspend the application that updates the database.
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Wait for the Debezium connector to stream all unstreamed change event records.
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Stop the Debezium connector.
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Apply all changes to the source table schema.
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In the ASN register table, mark the tables with updated schemas as
INACTIVE
. -
Remove the source table with the old schema from capture mode by running the Debezium UDF for removing tables from capture mode.
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Add the source table with the new schema to capture mode by running the Debezium UDF for adding tables to capture mode.
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In the ASN register table, mark the updated source tables as
ACTIVE
. -
Resume the application that updates the database.
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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.
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One or more tables that are in capture mode require schema updates.
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Lock the source tables whose schema you want to change.
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In the ASN register table, mark the locked tables as
INACTIVE
. -
Apply all changes to the schemas for the source tables.
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Apply all changes to the schemas for the corresponding change-data tables.
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In the ASN register table, mark the source tables as
ACTIVE
. -
Optional. Restart the connector to see updated column names in change events.
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Lock the source table(s) to be changed.
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In the ASN register table, mark the locked tables as
INACTIVE
. -
For each source table to be changed:
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Export the data in the source table.
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Truncate the source table.
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Alter the source table and add the column.
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Load the exported data into the altered source table.
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Export the data in the source table’s corresponding change-data table.
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Truncate the change-data table.
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Alter the change-data table and add the column.
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Load the exported data into the altered change-data table.
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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. -
Optional. Restart the connector to see updated column names in change events.