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 theDebezium 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.
在捕获模式都有一个关联的表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.
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The connector requires the use of the abstract syntax notation (ASN) libraries, which are available as a standard part of Db2 for Linux. To use the ASN libraries, you must have a license for IBM InfoSphere Data Replication (IIDR). You do not have to install IIDR to use the ASN libraries. |
The Db2 connector has been tested with Db2 for Linux. It is expected that the connector would also work on other platforms such as Windows, and we’d love to get your feedback if you can confirm this to be the case.
Overview
The Debezium Db2 connector is based on theASN Capture/Apply agentsthat 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 areDebezium 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 aconsistent snapshotof each table that is in capture mode. After the connector completes the snapshot, the connector streams change events from the point at which the snapshot was made. In this way, the connector starts with a consistent view of the tables that are in capture mode, and does not drop any changes that were made while it was performing the snapshot.
Debezium connectors are tolerant of failures. As the connector reads and produces change events, it records the log sequence number (LSN) of the change-data table entry. The LSN is the position of the change event in the database log. If the connector stops for any reason, including communication failures, network problems, or crashes, upon restarting it continues reading the change-data tables where it left off. This includes snapshots. That is, if the snapshot was not complete when the connector stopped, upon restart the connector begins a new snapshot.
How the connector works
To optimally configure and run a Debezium Db2 connector, it is helpful to understand how the connector performs snapshots, streams change events, determines Kafka topic names, and handles schema changes.
Snapshots
Db2`s replication feature is not designed to store the complete history of database changes. Consequently, when a Debezium Db2 connector connects to a database for the first time, it takes a consistent snapshot of tables that are in capture mode and streams this state to Kafka. This establishes the baseline for table content.
By default, when a Db2 connector performs a snapshot, it does the following:
Determines which tables are in capture mode, and thus must be included in the snapshot. By default, all non-system tables are in capture mode. Connector configuration properties, such as
table.exclude.listandtable.include.listlet you specify which tables should be in capture mode.Obtains a lock on each of the tables in capture mode. This ensures that no schema changes can occur in those tables during the snapshot. The level of the lock is determined by the
snapshot.isolation.mode连接器配置属性。Reads the highest (most recent) LSN position in the server’s transaction log.
Captures the schema of all tables that are in capture mode. The connector persists this information in its internal database history topic.
Optional, releases the locks obtained in step 2. Typically, these locks are held for only a short time.
At the LSN position read in step 3, the connector scans the capture mode tables as well as their schemas. During the scan, the connector:
Confirms that the table was created before the start of the snapshot. If it was not, the snapshot skips that table. After the snapshot is complete, and the connector starts emitting change events, the connector produces change events for any tables that were created during the snapshot.
Produces areadevent for each row in each table that is in capture mode. Allreadevents contain the same LSN position, which is the LSN position that was obtained in step 3.
Emits eachreadevent to the Kafka topic that has the same name as the table.
Records the successful completion of the snapshot in the connector offsets.
Ad hoc snapshots
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This feature is currently in incubating state, i.e. exact semantics, configuration options etc. may change in future revisions, based on the feedback we receive. Please let us know if you encounter any problems while using this extension. |
By default, a connector runs an initial snapshot operation only after it starts for the first time. Following this initial snapshot, under normal circumstances, the connector does not repeat the snapshot process. Any future change event data that the connector captures comes in through the streaming process only.
However, in some situations the data that the connector obtained during the initial snapshot might become stale, lost, or incomplete. To provide a mechanism for recapturing table data, Debezium includes an option to perform ad hoc snapshots. The following changes in a database might be cause for performing an ad hoc snapshot:
The connector configuration is modified to capture a different set of tables.
Kafka topics are deleted and must be rebuilt.
Data corruption occurs due to a configuration error or some other problem.
You can re-run a snapshot for a table for which you previously captured a snapshot by initiating a so-calledad-hoc snapshot．Ad hoc snapshots require the use ofsignaling 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 ifautomatic topic creationis enabled.
Ad hoc snapshot signals specify the tables to include in the snapshot. The snapshot can capture the entire contents of the database, or capture only a subset of the tables in the database.
You specify the tables to capture by sending anexecute-snapshotmessage to the signaling table. Set the type of theexecute-snapshotsignal toincremental，and provide the names of the tables to include in the snapshot, as described in the following table:
| Field | Default | Value |
|---|---|---|
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指定the type of snapshot that you want to run. |
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N/A |
An array that contains the fully-qualified names of the table to be snapshotted. |
You initiate an ad hoc snapshot by adding an entry with theexecute-snapshotsignal type to the signaling table. After the connector processes the message, it begins the snapshot operation. The snapshot process reads the first and last primary key values and uses those values as the start and end point for each table. Based on the number of entries in the table, and the configured chunk size, Debezium divides the table into chunks, and proceeds to snapshot each chunk, in succession, one at a time.
Currently, theexecute-snapshotaction type triggersincremental snapshotsonly. For more information, seeIncremental snapshots．
Incremental snapshots
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This feature is currently in incubating state. The exact semantics, configuration options, and so forth is subject to change in future revisions, based on the feedback we receive. Please let us know if you encounter any problems while using this extension. |
To provide flexibility in managing snapshots, Debezium includes a supplementary snapshot mechanism, known asincremental snapshotting．Incremental snapshots rely on the Debezium mechanism forsending signals to a Debezium connector．Incremental snapshots are based on theDDD-3design 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 thesize of each chunk．The chunk size determines the number of rows that the snapshot collects during each fetch operation on the database. The default chunk size for incremental snapshots is 1 KB.
As an incremental snapshot proceeds, Debezium uses watermarks to track its progress, maintaining a record of each table row that it captures. This phased approach to capturing data provides the following advantages over the standard initial snapshot process:
You can run incremental snapshots in parallel with streamed data capture, instead of postponing streaming until the snapshot completes. The connector continues to capture near real-time events from the change log throughout the snapshot process, and neither operation blocks the other.
If the progress of an incremental snapshot is interrupted, you can resume it without losing any data. After the process resumes, the snapshot begins at the point where it stopped, rather than recapturing the table from the beginning.
You can run an incremental snapshot on demand at any time, and repeat the process as needed to adapt to database updates. For example, you might re-run a snapshot after you modify the connector configuration to add a table to its
table.include.listproperty.
When you run an incremental snapshot, Debezium sorts each table by primary key and then splits the table into chunks based on theconfigured chunk size．Working chunk by chunk, it then captures each table row in a chunk. For each row that it captures, the snapshot emits aREADevent. 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，orDELETEoperations 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, theUPDATEorDELETEevents 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 theREADevent for that row. When the snapshot eventually emits the correspondingREADevent 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-arrivingREADevents and streamed events that modify the same table row, Debezium employs a so-calledsnapshot window．The snapshot windows demarcates the interval during which an incremental snapshot captures data for a specified table chunk. Before the snapshot window for a chunk opens, Debezium follows its usual behavior and emits events from the transaction log directly downstream to the target Kafka topic. But from the moment that the snapshot for a particular chunk opens, until it closes, Debezium performs a de-duplication step to resolve collisions between events that have the same primary key..
For each data collection, the Debezium emits two types of events, and stores the records for them both in a single destination Kafka topic. The snapshot records that it captures directly from a table are emitted asREADoperations. Meanwhile, as users continue to update records in the data collection, and the transaction log is updated to reflect each commit, Debezium emitsUPDATEorDELETEoperations 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 theREADevents 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 bufferedREADevent, 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 onlyREADevents for which no related transaction log events exist. Debezium emits these remainingREADevents to the table’s Kafka topic.
The connector repeats the process for each snapshot chunk.
Currently, the only way to initiate an incremental snapshot is to send anad hoc snapshot signalto the signaling table on the source database. You submit signals to the table as SQLINSERTqueries. After Debezium detects the change in the signaling table, it reads the signal, and runs the requested snapshot operation.
The query that you submit specifies the tables to include in the snapshot, and, optionally, specifies the kind of snapshot operation. Currently, the only valid option for snapshots operations is the default value,incremental．
To specify the tables to include in the snapshot, provide adata-collectionsarray that lists the tables, for example,{"data-collections": ["public.MyFirstTable", "public.MySecondTable"]}
Thedata-collectionsarray for an incremental snapshot signal has no default value. If thedata-collectionsarray is empty, Debezium detects that no action is required and does not perform a snapshot.
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A signaling data collection exists on the source database and the connector is configured to capture it.
The signaling data collection is specified in the
signal.data.collectionproperty.
Send a SQL query to add the ad hoc incremental snapshot request to the signaling table:
INSERT INTO __ (id, type, data) VALUES (_' '_, _' '_, '{"data-collections": ["_ _","_ _"],"type":"_ _"}'); For example,
INSERT INTO myschema.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["schema1.table1", "schema2.table2"],"type":"incremental"}');The values of the
id，type，anddataparameters in the command correspond to thefields of the signaling table．The following table describes the these parameters:
Table 2. Descriptions of fields in a SQL command for sending an incremental snapshot signal to the signaling table Value Description myschema.debezium_signal指定the fully-qualified name of the signaling table on the source database
ad-hoc-1The
idparameter specifies an arbitrary string that is assigned as theididentifier 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 ownidstring as a watermarking signal.execute-snapshot指定
typeparameter specifies the operation that the signal is intended to trigger.data-collectionsA required component of the
datafield of a signal that specifies an array of table names to include in the snapshot.
The array lists tables by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling table in thesignal.data.collectionconfiguration property.incrementalAn optional
typecomponent of thedata领域的一个信号,表明指定的快照hot operation to run.
Currently, the only valid option is the default value,incremental．
Specifying atypevalue in the SQL query that you submit to the signaling table is optional.
If you do not specify a value, the connector runs an incremental snapshot.
The following example, shows the JSON for an incremental snapshot event that is captured by a connector.
{ "before":null, "after": { "pk":"1", "value":"New data" }, "source": { ... "snapshot":"incremental"(1)}, "op":"r",(2)"ts_ms":"1620393591654", "transaction":null }| Item | Field name | Description |
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1 |
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指定the type of snapshot operation to run. |
2 |
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指定the event type. |
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The Debezium connector for Db2 does not support schema changes while an incremental snapshot is running. |
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 theINSERT，UPDATE，andDELETEoperations that occur in a table to a single Apache Kafka topic that is specific to that table. The connector uses the following convention to name change event topics:
databaseName．schemaName．tableName
The following list provides definitions for the components of the default name:
- databaseName
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The logical name of the connector as specified by the
database.server.name连接器配置属性。 - schemaName
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The name of the schema in which the operation occurred.
- tableName
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The name of the table in which the operation occurred.
For example, consider a Db2 installation with themydatabasedatabase, which contains four tables:PRODUCTS，PRODUCTS_ON_HAND，CUSTOMERS，andORDERSthat are in theMYSCHEMAschema. The connector would emit events to these four Kafka topics:
mydatabase.MYSCHEMA.PRODUCTSmydatabase.MYSCHEMA.PRODUCTS_ON_HANDmydatabase.MYSCHEMA.CUSTOMERSmydatabase.MYSCHEMA.ORDERS
The connector applies similar naming conventions to label its internal database history topics,schema change topics，andtransaction 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, seeTopic routing．
Schema change topic
You can configure a Debezium Db2 connector to produce schema change events that describe schema changes that are applied to captured tables in the database.
Debezium emits a message to the schema change topic when:
A new table goes into capture mode.
A table is removed from capture mode.
在一次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 namedatabase.server.name连接器配置属性。th的消息econnector sends to the schema change topic contain a payload that includes the following elements:
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databaseName -
The name of the database to which the statements are applied. The value of
databaseNameserves as the message key. -
pos -
The position in the binlog where the statements appear.
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tableChanges -
A structured representation of the entire table schema after the schema change. The
tableChangesfield 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.
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For a table that is in capture mode, the connector not only stores the history of schema changes in the schema change topic, but also in an internal database history topic. The internal database history topic is for connector use only and it is not intended for direct use by consuming applications. Ensure that applications that require notifications about schema changes consume that information only from the schema change topic. |
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Never partition the database history topic. For the database history topic to function correctly, it must maintain a consistent, global order of the event records that the connector emits to it. To ensure that the topic is not split among partitions, set the partition count for the topic by using one of the following methods:
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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": "1.8.1.Final", "connector": "db2", "name": "db2", "ts_ms": 1588252618953, "snapshot": "true", "db": "testdb", "schema": "DB2INST1", "table": "CUSTOMERS", "change_lsn": null, "commit_lsn": "00000025:00000d98:00a2", "event_serial_no": null }, "databaseName": "TESTDB",(1)"schemaName": "DB2INST1", "ddl": null,(2)"tableChanges": [(3){ "type": "CREATE",(4)"id": "\"DB2INST1\".\"CUSTOMERS\"",(5)"table": {(6)"defaultCharsetName": null, "primaryKeyColumnNames": [(7)"ID" ], "columns": [(8){ "name": "ID", "jdbcType": 4, "nativeType": null, "typeName": "int identity", "typeExpression": "int identity", "charsetName": null, "length": 10, "scale": 0, "position": 1, "optional": false, "autoIncremented": false, "generated": false }, { "name": "FIRST_NAME", "jdbcType": 12, "nativeType": null, "typeName": "varchar", "typeExpression": "varchar", "charsetName": null, "length": 255, "scale": null, "position": 2, "optional": false, "autoIncremented": false, "generated": false }, { "name": "LAST_NAME", "jdbcType": 12, "nativeType": null, "typeName": "varchar", "typeExpression": "varchar", "charsetName": null, "length": 255, "scale": null, "position": 3, "optional": false, "autoIncremented": false, "generated": false }, { "name": "EMAIL", "jdbcType": 12, "nativeType": null, "typeName": "varchar", "typeExpression": "varchar", "charsetName": null, "length": 255, "scale": null, "position": 4, "optional": false, "autoIncremented": false, "generated": false } ] } } ] } }| Item | Field name | Description |
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1 |
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Identifies the database and the schema that contain the change. |
2 |
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Always |
3 |
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An array of one or more items that contain the schema changes generated by a DDL command. |
4 |
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Describes the kind of change. The value is one of the following:
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5 |
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Full identifier of the table that was created, altered, or dropped. |
6 |
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Represents table metadata after the applied change. |
7 |
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List of columns that compose the table’s primary key. |
8 |
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Metadata for each column in the changed table. |
In messages that the connector sends to the schema change topic, the message key is the name of the database that contains the schema change. In the following example, thepayloadfield contains the key:
{ "schema": { "type": "struct", "fields": [ { "type": "string", "optional": false, "field": "databaseName" } ], "optional": false, "name": "io.debezium.connector.db2.SchemaChangeKey" }, "payload": { "databaseName": "TESTDB" } }Transaction metadata
Debezium can generate events that represent transaction boundaries and that enrich change data event messages.
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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 theBEGINandENDdelimiters in every transaction. Transaction boundary events contain the following fields:
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status -
BEGINorEND． -
id -
String representation of the unique transaction identifier.
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event_count(forENDevents) -
Total number of events emitted by the transaction.
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data_collections(forENDevents) -
An array of pairs of
data_collectionandevent_countelements. that indicates the number of events that the connector emits for changes that originate from a data collection.
{ "status": "BEGIN", "id": "00000025:00000d08:0025", "event_count": null, "data_collections": null } { "status": "END", "id": "00000025:00000d08:0025", "event_count": 2, "data_collections": [ { "data_collection": "testDB.dbo.tablea", "event_count": 1 }, { "data_collection": "testDB.dbo.tableb", "event_count": 1 } ] }Unless overridden via thetransaction.topicoption, the connector emits transaction events to the．transactiontopic.
When transaction metadata is enabled the connector enriches the change eventEnvelopewith a newtransactionfield. This field provides information about every event in the form of a composite of fields:
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id -
String representation of unique transaction identifier.
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total_order -
The absolute position of the event among all events generated by the transaction.
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data_collection_order -
The per-data collection position of the event among all events that were emitted by the transaction.
Following is an example of a message:
{ "before": null, "after": { "pk": "2", "aa": "1" }, "source": { ... }, "op": "c", "ts_ms": "1580390884335", "transaction": { "id": "00000025:00000d08:0025", "total_order": "1", "data_collection_order": "1" } }Data change events
The Debezium Db2 connector generates a data change event for each row-levelINSERT，UPDATE，andDELETEoperation. 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 aroundcontinuous 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. Aschemafield 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 |
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1 |
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The first |
2 |
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The first |
3 |
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The second |
4 |
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The second |
By default, the connector streams change event records to topics with names that are the same as the event’s originating table. Seetopic names．
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The Debezium Db2 connector ensures that all Kafka Connect schema names adhere to theAvro 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’sPRIMARY KEY(or unique constraint) at the time the connector created the event.
Consider the followingcustomerstable, 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 );每一个变化事件,抓住了改变customerstable has the same event key schema. For as long as thecustomerstable has the previous definition, every change event that captures a change to thecustomerstable 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 |
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1 |
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The schema portion of the key specifies a Kafka Connect schema that describes what is in the key’s |
2 |
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指定each field that is expected in the |
3 |
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Indicates whether the event key must contain a value in its |
4 |
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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 formatconnector-name．database-name．table-name．
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5 |
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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 aschemasection and apayloadsection. Theschemasection contains the schema that describes theEnvelopestructure of thepayloadsection, 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 thecustomerstable specifies the same schema. The event value’s payload varies according to the event type:
createevents
The following example shows the value portion of a change event that the connector generates for an operation that creates data in thecustomerstable:
{ "schema": {(1)"type": "struct", "fields": [ { "type": "struct", "fields": [ { "type": "int32", "optional": false, "field": "ID" }, { "type": "string", "optional": false, "field": "FIRST_NAME" }, { "type": "string", "optional": false, "field": "LAST_NAME" }, { "type": "string", "optional": false, "field": "EMAIL" } ], "optional": true, "name": "mydatabase.MYSCHEMA.CUSTOMERS.Value",(2)"field": "before" }, { "type": "struct", "fields": [ { "type": "int32", "optional": false, "field": "ID" }, { "type": "string", "optional": false, "field": "FIRST_NAME" }, { "type": "string", "optional": false, "field": "LAST_NAME" }, { "type": "string", "optional": false, "field": "EMAIL" } ], "optional": true, "name": "mydatabase.MYSCHEMA.CUSTOMERS.Value", "field": "after" }, { "type": "struct", "fields": [ { "type": "string", "optional": false, "field": "version" }, { "type": "string", "optional": false, "field": "connector" }, { "type": "string", "optional": false, "field": "name" }, { "type": "int64", "optional": false, "field": "ts_ms" }, { "type": "boolean", "optional": true, "default": false, "field": "snapshot" }, { "type": "string", "optional": false, "field": "db" }, { "type": "string", "optional": false, "field": "schema" }, { "type": "string", "optional": false, "field": "table" }, { "type": "string", "optional": true, "field": "change_lsn" }, { "type": "string", "optional": true, "field": "commit_lsn" }, ], "optional": false, "name": "io.debezium.connector.db2.Source",(3)"field": "source" }, { "type": "string", "optional": false, "field": "op" }, { "type": "int64", "optional": true, "field": "ts_ms" } ], "optional": false, "name": "mydatabase.MYSCHEMA.CUSTOMERS.Envelope"(4)}, "payload": {(5)"before": null,(6)"after": {(7)“ID”:1005年,“FIRST_NAME”:“约翰”,“LAST_NAME”:“做的e", "EMAIL": "john.doe@example.org" }, "source": {(8)"version": "1.8.1.Final", "connector": "db2", "name": "myconnector", "ts_ms": 1559729468470, "snapshot": false, "db": "mydatabase", "schema": "MYSCHEMA", "table": "CUSTOMERS", "change_lsn": "00000027:00000758:0003", "commit_lsn": "00000027:00000758:0005", }, "op": "c",(9)"ts_ms": 1559729471739(10)} }| Item | Field name | Description |
|---|---|---|
1 |
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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 |
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In the |
3 |
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4 |
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5 |
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The value’s actual data. This is the information that the change event is providing. |
6 |
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An optional field that specifies the state of the row before the event occurred. When the |
7 |
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An optional field that specifies the state of the row after the event occurred. In this example, the |
8 |
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强制性字段描述元数据来源for the event. The
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9 |
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Mandatory string that describes the type of operation that caused the connector to generate the event. In this example,
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10 |
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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. |
updateevents
The value of a change event for an update in the samplecustomerstable has the same schema as acreateevent for that table. Likewise, theupdateevent value’s payload has the same structure. However, the event value payload contains different values in anupdateevent. Here is an example of a change event value in an event that the connector generates for an update in thecustomerstable:
{ "schema": { ... }, "payload": { "before": {(1)“ID”:1005年,“FIRST_NAME”:“约翰”,“LAST_NAME”:“做的e", "EMAIL": "john.doe@example.org" }, "after": {(2)“ID”:1005年,“FIRST_NAME”:“约翰”,“LAST_NAME”:“做的e", "EMAIL": "noreply@example.org" }, "source": {(3)"version": "1.8.1.Final", "connector": "db2", "name": "myconnector", "ts_ms": 1559729995937, "snapshot": false, "db": "mydatabase", "schema": "MYSCHEMA", "table": "CUSTOMERS", "change_lsn": "00000027:00000ac0:0002", "commit_lsn": "00000027:00000ac0:0007", }, "op": "u",(4)"ts_ms": 1559729998706(5)} }| Item | Field name | Description |
|---|---|---|
1 |
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An optional field that specifies the state of the row before the event occurred. In anupdateevent value, the |
2 |
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An optional field that specifies the state of the row after the event occurred. You can compare the |
3 |
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强制性字段描述元数据来源for the event. The
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4 |
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Mandatory string that describes the type of operation. In anupdateevent value, the |
5 |
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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. |
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Updating the columns for a row’s primary/unique key changes the value of the row’s key. When a key changes, Debezium outputsthreeevents: a |
deleteevents
The value in adeletechange event has the sameschemaportion ascreateandupdateevents for the same table. The event valuepayloadin adeleteevent for the samplecustomerstable looks like this:
{ "schema": { ... }, }, "payload": { "before": {(1)“ID”:1005年,“FIRST_NAME”:“约翰”,“LAST_NAME”:“做的e", "EMAIL": "noreply@example.org" }, "after": null,(2)"source": {(3)"version": "1.8.1.Final", "connector": "db2", "name": "myconnector", "ts_ms": 1559730445243, "snapshot": false, "db": "mydatabase", "schema": "MYSCHEMA", "table": "CUSTOMERS", "change_lsn": "00000027:00000db0:0005", "commit_lsn": "00000027:00000db0:0007" }, "op": "d",(4)"ts_ms": 1559730450205(5)} }| Item | Field name | Description |
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1 |
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可选字段,指定的状态行before the event occurred. In adeleteevent value, the |
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可选字段,指定的状态行after the event occurred. In adeleteevent value, the |
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强制性字段描述元数据来源for the event. In adeleteevent value, the
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4 |
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Mandatory string that describes the type of operation. The |
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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. |
Adeletechange 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 withKafka 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, thedeleteevent 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 benull．To make this possible, after Debezium’s Db2 connector emits adeleteevent, the connector emits a special tombstone event that has the same key but anullvalue.
Data type mappings
Db2’s data types are described inDb2 SQL Data Types．
The Db2 connector represents changes to rows with events that are structured like the table in which the row exists. The event contains a field for each column value. How that value is represented in the event depends on the Db2 data type of the column. This section describes these mappings.
Basic types
The following table describes how the connector maps each of the Db2 data types to aliteral typeand asemantic typein event fields.
literal typedescribes how the value is represented using Kafka Connect schema types:
INT8，INT16，INT32，INT64，FLOAT32，FLOAT64，BOOLEAN，STRING，BYTES，ARRAY，MAP，andSTRUCT．semantic typedescribes how the Kafka Connect schema captures themeaning的使用的名字啊f the Kafka Connect schema for the field.
| Db2 data type | Literal type (schema type) | Semantic type (schema name) and Notes |
|---|---|---|
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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. |
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n/a |
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n/a |
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n/a |
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n/a |
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n/a |
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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 whenusing Avroas the serialization format together with the Confluent schema registry.
Temporal types
Other than Db2’sDATETIMEOFFSETdata type, which contains time zone information, how temporal types are mapped depends on the value of thetime.precision.mode连接器配置属性。The following sections describe these mappings:
time.precision.mode=adaptive
When thetime.precision.modeconfiguration property is set toadaptive，the default, the connector determines the literal type and semantic type based on the column’s data type definition. This ensures that eventsexactlyrepresent the values in the database.
| Db2 data type | Literal type (schema type) | Semantic type (schema name) and Notes |
|---|---|---|
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time.precision.mode=connect
When thetime.precision.modeconfiguration property is set toconnect，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 theconnecttime precisionresults in a loss of precisionwhen the database column has afractional second precisionvalue that is greater than 3.
| Db2 data type | Literal type (schema type) | Semantic type (schema name) and Notes |
|---|---|---|
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Timestamp types
TheDATETIME，SMALLDATETIMEandDATETIME2types represent a timestamp without time zone information. Such columns are converted into an equivalent Kafka Connect value based on UTC. For example, theDATETIME2value "2018-06-20 15:13:16.945104" is represented by anio.debezium.time.MicroTimestampwith the value "1529507596945104".
The timezone of the JVM running Kafka Connect and Debezium does not affect this conversion.
Decimal types
| Db2 data type | Literal type (schema type) | Semantic type (schema name) and Notes |
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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
db2instluser.On the Db2 host, the Debezium management UDFs are available in the $HOME/asncdctools/src directory. UDFs are available from theDebezium examples repository．
Compile the Debezium management UDFs on the Db2 server host by using the
bldrtncommand provided with Db2:cd $HOME/asncdctools/src．/bldrtn asncdcStart the database if it is not already running. Replace
DB_NAMEwith the name of the database that you want Debezium to connect to.db2 start db DB_NAMEEnsure that JDBC can read the Db2 metadata catalog:
cd $HOME/sqllib/bnddb2 bind db2schema.bnd blocking all grant public sqlerror continue确保数据库最近backed-u开云体育电动老虎机p. 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/nullfor the backup location.Back up the database. Replace
DB_NAMEandBACK_UP_LOCATIONwith appropriate values:db2 backup db DB_NAME to BACK_UP_LOCATIONRestart 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
db2instluser so the UDFs should be installed on thedb2inst1user.db2 connect to DB_NAMECopy the Debezium management UDFs and set permissions for them:
cp $HOME/asncdctools/src/asncdc $HOME/sqllib/functionchmod 777 $HOME/sqllib/functionEnable the Debezium UDF that starts and stops the ASN capture agent:
db2 -tvmf $HOME/asncdctools/src/asncdc_UDF.sqlCreate the ASN control tables:
$ db2 -tvmf $HOME/asncdctools/src/asncdctables.sqlEnable the Debezium UDF that adds tables to capture mode and removes tables from capture mode:
$ db2 -tvmf $HOME/asncdctools/src/asncdcaddremove.sqlAfter 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
VALUEstatement to invoke them. For other UDFs, use the SQLCALLstatement.Start the ASN agent:
VALUES ASNCDC.ASNCDCSERVICES('start','asncdc');The preceding statement returns one of the following results:
asncap is already runningstart -->In this case, enter the specified
/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
MYSCHEMAwith the name of the schema that contains the table you want to put into capture mode. Likewise, replaceMYTABLEwith the name of the table to put into capture mode:CALL ASNCDC.ADDTABLE('MYSCHEMA', 'MYTABLE');Reinitialize the ASN service:
值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, theIBMSNAP_CAPPARMStable 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.
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Specific guidance about how to configure Db2 capture agent parameters is beyond the scope of this documentation. |
In theIBMSNAP_CAPPARMStable, the following parameters have the greatest effect on reducing CPU load:
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COMMIT_INTERVAL -
指定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．
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SLEEP_INTERVAL -
指定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，andKafka Connectare installed.
Db2 is installed andcapture mode is enabled for tablesto prepare the database to be used with the Debezium connector.
Download theconnector’s plug-in archive．
Extract the JAR files into your Kafka Connect environment.
Add the directory with the JAR files toKafka Connect’s
plugin.path．Obtain theJDBC driver for Db2．
Add the JDBC driver JAR file to the directory with the Debezium Db2 connector JARs.
Configure the connector and add the configuration to your Kafka Connect cluster.
重新启动卡夫卡连接过程加快new JAR files.
If you are working with immutable containers, seeDebezium’s container imagesfor Apache ZooKeeper, Apache Kafka and Kafka Connect with the Db2 connector already installed and ready to run.
You can alsorun 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 namefullfillment．Typically, you configure the Debezium Db2 connector in a JSON file by setting the configuration properties that are available for the connector.
You can choose to produce events for a subset of the schemas and tables in a database. Optionally, you can ignore, mask, or truncate columns that contain sensitive data, that are larger than a specified size, or that you do not need.
{ "name": "db2-connector",(1)"config": { "connector.class": "io.debezium.connector.db2.Db2Connector",(2)"database.hostname": "192.168.99.100",(3)"database.port": "50000",(4)"database.user": "db2inst1",(5)"database.password": "Password!",(6)"database.dbname": "mydatabase",(7)"database.server.name": "fullfillment",(8)"table.include.list": "MYSCHEMA.CUSTOMERS",(9)"database.history.kafka.bootstrap.servers": "kafka:9092",(10)"database.history.kafka.topic": "dbhistory.fullfillment"(11)} }| 1 | The name of the connector when registered with a Kafka Connect service. |
| 2 | The name of this Db2 connector class. |
| 3 | The address of the Db2 instance. |
| 4 | The port number of the Db2 instance. |
| 5 | The name of the Db2 user. |
| 6 | The password for the Db2 user. |
| 7 | 数据库的名称来捕获变化。开云体育电动老虎机 |
| 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 theAvro Connectoris used. |
| 9 | A list of all tables whose changes Debezium should capture. |
| 10 | The list of Kafka brokers that this connector uses to write and recover DDL statements to the database history topic. |
| 11 | The name of the database history topic where the connector writes and recovers DDL statements. This topic is for internal use only and should not be used by consumers. |
For the complete list of the configuration properties that you can set for the Debezium Db2 connector, seeDb2 connector properties．
You can send this configuration with aPOSTcommand 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 enabledto expose change data for tables that are in capture mode.
The Db2 connector is installed.
Create a configuration for the Db2 connector.
Use theKafka Connect REST APIto add that connector configuration to your Kafka Connect cluster.
After the connector starts, itperforms a consistent snapshotof 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 history connector configuration propertiesthat control how Debezium processes events that it reads from the database history topic.
Pass-through database driver propertiesthat control the behavior of the database driver.
Required Debezium Db2 connector configuration properties
The following configuration properties arerequiredunless a default value is available.
| Property | Default | Description |
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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. |
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No default |
The name of the Java class for the connector. Always use a value of |
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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. |
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No default |
IP address or hostname of the Db2 database server. |
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Integer port number of the Db2 database server. |
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No default |
Name of the Db2 database user for connecting to the Db2 database server. |
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No default |
Password to use when connecting to the Db2 database server. |
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No default |
The name of the Db2 database from which to stream the changes |
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No default |
Logical name that identifies and provides a namespace for the particular Db2 database server that hosts the database for which Debezium is capturing changes. Only alphanumeric characters, hyphens, dots and underscores must be used in the database server logical name. The logical name should be unique across all other connectors, since it is used as a topic name prefix for all Kafka topics that receive records from this connector. |
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No default |
An optional, comma-separated list of regular expressions that match fully-qualified table identifiers for tables whose changes you want the connector to capture. Any table not included in the include list does not have its changes captured. Each identifier is of the formschemaName．tableName．By default, the connector captures changes in every non-system table. Do not also set the |
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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 formschemaName．tableName．Do not also set the |
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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 formschemaName．tableName．columnName．Primary key columns are always included in the event’s key, even if they are excluded from the value. |
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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 formschemaName．tableName．columnName．In the resulting change event record, the values for the specified columns are replaced with pseudonyms. A pseudonym consists of the hashed value that results from applying the specifiedhashAlgorithmandsalt．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 theMessageDigest sectionof 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. |
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Time, date, and timestamps can be represented with different kinds of precision: |
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Controls whether adeleteevent is followed by a tombstone event. |
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Boolean value that specifies whether the connector should publish changes in the database schema to a Kafka topic with the same name as the database server ID. Each schema change is recorded with a key that contains the database name and a value that is a JSON structure that describes the schema update. This is independent of how the connector internally records database history. |
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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 formschemaName．tableName．columnName．In change event records, values in these columns are truncated if they are longer than the number of characters specified bylengthin the property name. You can specify multiple properties with different lengths in a single configuration. Length must be a positive integer, for example, |
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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 formschemaName．tableName．columnName．In change event values, the values in the specified table columns are replaced withlengthnumber of asterisk ( |
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n/a |
An optional, comma-separated list of regular expressions that match the fully-qualified names of columns. Fully-qualified names for columns are of the formdatabaseName．tableName．columnName，ordatabaseName．schemaName．tableName．columnName． |
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n/a |
An optional, comma-separated list of regular expressions that match the database-specific data type name for some columns. Fully-qualified data type names are of the formdatabaseName．tableName．typeName，ordatabaseName．schemaName．tableName．typeName． |
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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. |
Advanced connector configuration properties
The followingadvancedconfiguration properties have defaults that work in most situations and therefore rarely need to be specified in the connector’s configuration.
| Property | Default | Description |
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指定the criteria for performing a snapshot when the connector starts: |
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在一次snapshot, controls the transaction isolation level and how long the connector locks the tables that are in capture mode. The possible values are: |
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指定how the connector handles exceptions during processing of events. The possible values are: |
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Positive integer value that specifies the number of milliseconds the connector should wait for new change events to appear before it starts processing a batch of events. Defaults to 1000 milliseconds, or 1 second. |
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Positive integer value for the maximum size of the blocking queue. The connector places change events that it reads from the database log into the blocking queue before writing them to Kafka. This queue can provide backpressure for reading change-data tables when, for example, writing records to Kafka is slower than it should be or Kafka is not available. Events that appear in the queue are not included in the offsets that are periodically recorded by the connector. The |
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正整数的值指定的最大值size of each batch of events that the connector processes. |
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Long value for the maximum size in bytes of the blocking queue. The feature is disabled by default, it will be active if it’s set with a positive long value. |
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Controls how frequently the connector sends heartbeat messages to a Kafka topic. The default behavior is that the connector does not send heartbeat messages. |
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指定the prefix for the name of the topic to which the connector sends heartbeat messages. The format for this topic name is |
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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. |
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在一次snapshot, the connector reads table content in batches of rows. This property specifies the maximum number of rows in a batch. |
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正整数的值指定的最大值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 snapshotsprovides details. Other possible settings are: |
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No default |
指定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 |
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Indicates whether field names are sanitized to adhere toAvro naming requirements． |
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Determines whether the connector generates events with transaction boundaries and enriches change event envelopes with transaction metadata. Specify |
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控制connec主题的名称tor sends transaction metadata messages. The placeholder |
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No default |
comma-separated list of operation types that will be skipped during streaming. The operations include: |
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No default |
Fully-qualified name of the data collection that is used to sendsignalsto the connector. Use the following format to specify the collection name: |
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The maximum number of rows that the connector fetches and reads into memory during an incremental snapshot chunk. Increasing the chunk size provides greater efficiency, because the snapshot runs fewer snapshot queries of a greater size. However, larger chunk sizes also require more memory to buffer the snapshot data. Adjust the chunk size to a value that provides the best performance in your environment. |
Debezium connector database history configuration properties
Debezium provides a set ofdatabase.history.*properties that control how the connector interacts with the schema history topic.
The following table describes thedatabase.historyproperties for configuring the Debezium connector.
| Property | Default | Description |
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The full name of the Kafka topic where the connector stores the database schema history. |
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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. |
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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. |
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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 |
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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 |
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Deprecated and scheduled for removal in a future release; use |
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A Boolean value that specifies whether the connector should record all DDL statements
The safe default is |
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A Boolean value that specifies whether the connector should record all DDL statements
The safe default is |
Debezium relies on a Kafka producer to write schema changes to database history topics. Similarly, it relies on a Kafka consumer to read from database history topics when a connector starts. You define the configuration for the Kafka producer and consumer clients by assigning values to a set of pass-through configuration properties that begin with thedatabase.history.producer.*anddatabase.history.consumer.*prefixes. The pass-through producer and consumer database history properties control a range of behaviors, such as how these clients secure connections with the Kafka broker, as shown in the following example:
database.history.producer.security.protocol=SSL database.history.producer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks database.history.producer.ssl.keystore.password=test1234 database.history.producer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks database.history.producer.ssl.truststore.password=test1234 database.history.producer.ssl.key.password=test1234 database.history.consumer.security.protocol=SSL database.history.consumer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks database.history.consumer.ssl.keystore.password=test1234 database.history.consumer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks database.history.consumer.ssl.truststore.password=test1234 database.history.consumer.ssl.key.password=test1234Debezium strips the prefix from the property name before it passes the property to the Kafka client.
See the Kafka documentation for more details aboutKafka producer configuration propertiesandKafka consumer configuration properties．
Debezium connector pass-through database driver configuration properties
The Debezium connector provides for pass-through configuration of the database driver. Pass-through database properties begin with the prefixdatabase.*．For example, the connector passes properties such asdatabase.foobar=falseto the JDBC URL.
As is the case with thepass-through properties for database history clients，Debezium strips the prefixes from the properties before it passes them to the database driver.
Monitoring
The Debezium Db2 connector provides three types of metrics that are in addition to the built-in support for JMX metrics that Apache ZooKeeper, Apache Kafka, and Kafka Connect provide.
Snapshot metricsprovide information about connector operation while performing a snapshot.
Streaming metricsprovide information about connector operation when the connector is capturing changes and streaming change event records.
Schema history metricsprovide information about the status of the connector’s schema history.
Debezium monitoring documentationprovides details for how to expose these metrics by using JMX.
Snapshot metrics
TheMBeanisdebezium.db2:type=connector-metrics,context=snapshot,server=．
Snapshot metrics are not exposed unless a snapshot operation is active, or if a snapshot has occurred since the last connector start.
The following table lists the shapshot metrics that are available.
| Attributes | Type | Description |
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The last snapshot event that the connector has read. |
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The number of milliseconds since the connector has read and processed the most recent event. |
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The total number of events that this connector has seen since last started or reset. |
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The number of events that have been filtered by include/exclude list filtering rules configured on the connector. |
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The list of tables that are captured by the connector. |
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The length the queue used to pass events between the snapshotter and the main Kafka Connect loop. |
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The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop. |
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The total number of tables that are being included in the snapshot. |
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The number of tables that the snapshot has yet to copy. |
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Whether the snapshot was started. |
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Whether the snapshot was aborted. |
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Whether the snapshot completed. |
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The total number of seconds that the snapshot has taken so far, even if not complete. |
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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. |
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The maximum buffer of the queue in bytes. It will be enabled if |
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The current data of records in the queue in bytes. |
The connector also provides the following additional snapshot metrics when an incremental snapshot is executed:
| Attributes | Type | Description |
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The identifier of the current snapshot chunk. |
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The lower bound of the primary key set defining the current chunk. |
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The upper bound of the primary key set defining the current chunk. |
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The lower bound of the primary key set of the currently snapshotted table. |
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The upper bound of the primary key set of the currently snapshotted table. |
Streaming metrics
TheMBeanisdebezium.db2:type=connector-metrics,context=streaming,server=．
The following table lists the streaming metrics that are available.
| Attributes | Type | Description |
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The last streaming event that the connector has read. |
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The number of milliseconds since the connector has read and processed the most recent event. |
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The total number of events that this connector has seen since last started or reset. |
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The number of events that have been filtered by include/exclude list filtering rules configured on the connector. |
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The list of tables that are captured by the connector. |
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The length the queue used to pass events between the streamer and the main Kafka Connect loop. |
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The free capacity of the queue used to pass events between the streamer and the main Kafka Connect loop. |
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Flag that denotes whether the connector is currently connected to the database server. |
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The number of milliseconds between the last change event’s timestamp and the connector processing it. The values will incoporate any differences between the clocks on the machines where the database server and the connector are running. |
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The number of processed transactions that were committed. |
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The coordinates of the last received event. |
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Transaction identifier of the last processed transaction. |
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The maximum buffer of the queue in bytes. |
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The current data of records in the queue in bytes. |
Schema history metrics
TheMBeanisdebezium.db2:type=connector-metrics,context=schema-history,server=．
The following table lists the schema history metrics that are available.
| Attributes | Type | Description |
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One of |
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The time in epoch seconds at what recovery has started. |
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The number of changes that were read during recovery phase. |
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the total number of schema changes applied during recovery and runtime. |
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The number of milliseconds that elapsed since the last change was recovered from the history store. |
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The number of milliseconds that elapsed since the last change was applied. |
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The string representation of the last change recovered from the history store. |
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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 SQLVALUEstatement to invoke them. For other UDFs, use the SQLCALLstatement.
| Task | Command and notes |
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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, Db2’s replication feature creates a change-data table that contains all changes to that source table. However, change-data table schemas are static. If you update the schema for a table in capture mode then you must also update the schema of its corresponding change-data table. A Debezium Db2 connector cannot do this. A database administrator with elevated privileges must update schemas for tables that are in capture mode.
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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.
One or more tables that are in capture mode require schema updates.
Suspend the application that updates the database.
Wait for the Debezium connector to stream all unstreamed change event records.
Stop the Debezium connector.
Apply all changes to the source table schema.
In the ASN register table, mark the tables with updated schemas as
INACTIVE．Remove the source table with the old schema from capture mode byrunning the Debezium UDF for removing tables from capture mode．
Add the source table with the new schema to capture mode byrunning the Debezium UDF for adding tables to capture mode．
In the ASN register table, mark the updated source tables as
ACTIVE．Resume the application that updates the database.
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.
One or more tables that are in capture mode require schema updates.
Lock the source tables whose schema you want to change.
In the ASN register table, mark the locked tables as
INACTIVE．Apply all changes to the schemas for the source tables.
对一的模式应用的所有更改ding change-data tables.
In the ASN register table, mark the source tables as
ACTIVE．Optional. Restart the connector to see updated column names in change events.
Lock the source table(s) to be changed.
In the ASN register table, mark the locked tables as
INACTIVE．For each source table to be changed:
Export the data in the source table.
Truncate the source table.
Alter the source table and add the column.
Load the exported data into the altered source table.
Export the data in the source table’s corresponding change-data table.
Truncate the change-data table.
Alter the change-data table and add the column.
Load the exported data into the altered change-data table.
In the ASN register table, mark the tables as
INACTIVE．This marks the old change-data tables as inactive, which allows the data in them to remain but they are no longer updated.Optional. Restart the connector to see updated column names in change events.