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Chapter 6. Debezium Connector for Oracle
Debezium’s Oracle connector captures and records row-level changes that occur in databases on an Oracle server, including tables that are added while the connector is running. You can configure the connector to emit change events for specific subsets of schemas and tables, or to ignore, mask, or truncate values in specific columns.
For information about the Oracle Database versions that are compatible with this connector, see the Debezium Supported Configurations page.
Debezium ingests change events from Oracle by using the native LogMiner database package .
Information and procedures for using a Debezium Oracle connector are organized as follows:
- Section 6.1, “How Debezium Oracle connectors work”
- Section 6.2, “Descriptions of Debezium Oracle connector data change events”
- Section 6.3, “How Debezium Oracle connectors map data types”
- Section 6.4, “Setting up Oracle to work with Debezium”
- Section 6.5, “Deployment of Debezium Oracle connectors”
- Section 6.6, “Descriptions of Debezium Oracle connector configuration properties”
- Section 6.7, “Monitoring Debezium Oracle connector performance”
- Section 6.8, “How Debezium Oracle connectors handle faults and problems”
6.1. How Debezium Oracle connectors work
To optimally configure and run a Debezium Oracle connector, it is helpful to understand how the connector performs snapshots, streams change events, determines Kafka topic names, uses metadata, and implements event buffering.
For more information, see the following topics:
- Section 6.1.1, “How Debezium Oracle connectors perform database snapshots”
- Section 6.1.2, “Default names of Kafka topics that receive Debezium Oracle change event records”
- Section 6.1.3, “How Debezium Oracle connectors expose database schema changes”
- Section 6.1.4, “Debezium Oracle connector-generated events that represent transaction boundaries”
- Section 6.1.5, “How the Debezium Oracle connector uses event buffering”
6.1.1. How Debezium Oracle connectors perform database snapshots
Typically, the redo logs on an Oracle server are configured to not retain the complete history of the database. As a result, the Debezium Oracle 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 database.
You can customize the way that the connector creates snapshots by setting the value of the snapshot.mode
connector configuration property. By default, the connector’s snapshot mode is set to initial
.
Default connector workflow for creating an initial snapshot
When the snapshot mode is set to the default, the connector completes the following tasks to create a snapshot:
- Determines the tables to be captured.
-
Obtains a
ROW SHARE MODE
lock on each of the monitored tables to prevent structural changes from occurring during creation of the snapshot. Debezium holds the locks for only a short time. - Reads the current system change number (SCN) position from the server’s redo log.
- Captures the structure of all relevant tables.
- Releases the locks obtained in Step 2.
-
Scans all of the relevant database tables and schemas as valid at the SCN position that was read in Step 3 (
SELECT * FROM … AS OF SCN 123
), generates aREAD
event for each row, and then writes the event records to the table-specific Kafka topic. - Records the successful completion of the snapshot in the connector offsets.
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 3 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 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 and stops before streaming any change event records, not allowing any subsequent change events to be captured. |
|
The connector captures the structure of all relevant tables, performing all of the steps described in the default snapshot workflow, except that it does not create |
|
Set this option to restore a database 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 history topic that experiences unexpected growth. |
For more information, see snapshot.mode
in the table of connector configuration properties.
6.1.1.1. Ad hoc snapshots
By default, a connector runs an initial snapshot operation only after it starts for the first time. Following this initial snapshot, under normal circumstances, the connector does not repeat the snapshot process. Any future change event data that the connector captures comes in through the streaming process only.
However, in some situations the data that the connector obtained during the initial snapshot might become stale, lost, or incomplete. To provide a mechanism for recapturing table data, Debezium includes an option to perform ad hoc snapshots. The following changes in a database might be cause for performing an ad hoc snapshot:
- The connector configuration is modified to capture a different set of tables.
- Kafka topics are deleted and must be rebuilt.
- Data corruption occurs due to a configuration error or some other problem.
You can re-run a snapshot for a table for which you previously captured a snapshot by initiating a so-called ad-hoc snapshot. Ad hoc snapshots require the use of signaling tables. You initiate an ad hoc snapshot by sending a signal request to the Debezium signaling table.
When you initiate an ad hoc snapshot of an existing table, the connector appends content to the topic that already exists for the table. If a previously existing topic was removed, Debezium can create a topic automatically if automatic topic creation is enabled.
Ad hoc snapshot signals specify the tables to include in the snapshot. The snapshot can capture the entire contents of the database, or capture only a subset of the tables in the database.
You specify the tables to capture by sending an execute-snapshot
message to the signaling table. Set the type of the execute-snapshot
signal to incremental
, and provide the names of the tables to include in the snapshot, as described in the following table:
Field | Default | Value |
---|---|---|
|
|
Specifies the type of snapshot that you want to run. |
| N/A |
An array that contains the fully-qualified names of the table to be snapshotted. |
Triggering an ad hoc snapshot
You initiate an ad hoc snapshot by adding an entry with the execute-snapshot
signal type to the signaling table. After the connector processes the message, it begins the snapshot operation. The snapshot process reads the first and last primary key values and uses those values as the start and end point for each table. Based on the number of entries in the table, and the configured chunk size, Debezium divides the table into chunks, and proceeds to snapshot each chunk, in succession, one at a time.
Currently, the execute-snapshot
action type triggers incremental snapshots only. For more information, see Incremental snapshots.
6.1.1.2. 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.
In an incremental snapshot, instead of capturing the full state of a database all at once, as in an initial snapshot, Debezium captures each table in phases, in a series of configurable chunks. You can specify the tables that you want the snapshot to capture and the size of each chunk. The chunk size determines the number of rows that the snapshot collects during each fetch operation on the database. The default chunk size for incremental snapshots is 1 KB.
As an incremental snapshot proceeds, Debezium uses watermarks to track its progress, maintaining a record of each table row that it captures. This phased approach to capturing data provides the following advantages over the standard initial snapshot process:
- You can run incremental snapshots in parallel with streamed data capture, instead of postponing streaming until the snapshot completes. The connector continues to capture near real-time events from the change log throughout the snapshot process, and neither operation blocks the other.
- If the progress of an incremental snapshot is interrupted, you can resume it without losing any data. After the process resumes, the snapshot begins at the point where it stopped, rather than recapturing the table from the beginning.
-
You can run an incremental snapshot on demand at any time, and repeat the process as needed to adapt to database updates. For example, you might re-run a snapshot after you modify the connector configuration to add a table to its
table.include.list
property.
Incremental snapshot process
When you run an incremental snapshot, Debezium sorts each table by primary key and then splits the table into chunks based on the configured chunk size. Working chunk by chunk, it then captures each table row in a chunk. For each row that it captures, the snapshot emits a READ
event. That event represents the value of the row when the snapshot for the chunk began.
As a snapshot proceeds, it’s likely that other processes continue to access the database, potentially modifying table records. To reflect such changes, INSERT
, UPDATE
, or DELETE
operations are committed to the transaction log as per usual. Similarly, the ongoing Debezium streaming process continues to detect these change events and emits corresponding change event records to Kafka.
How Debezium resolves collisions among records with the same primary key
In some cases, the UPDATE
or DELETE
events that the streaming process emits are received out of sequence. That is, the streaming process might emit an event that modifies a table row before the snapshot captures the chunk that contains the READ
event for that row. When the snapshot eventually emits the corresponding READ
event for the row, its value is already superseded. To ensure that incremental snapshot events that arrive out of sequence are processed in the correct logical order, Debezium employs a buffering scheme for resolving collisions. Only after collisions between the snapshot events and the streamed events are resolved does Debezium emit an event record to Kafka.
Snapshot window
To assist in resolving collisions between late-arriving READ
events and streamed events that modify the same table row, Debezium employs a so-called snapshot window. The snapshot windows demarcates the interval during which an incremental snapshot captures data for a specified table chunk. Before the snapshot window for a chunk opens, Debezium follows its usual behavior and emits events from the transaction log directly downstream to the target Kafka topic. But from the moment that the snapshot for a particular chunk opens, until it closes, Debezium performs a de-duplication step to resolve collisions between events that have the same primary key..
For each data collection, the Debezium emits two types of events, and stores the records for them both in a single destination Kafka topic. The snapshot records that it captures directly from a table are emitted as READ
operations. Meanwhile, as users continue to update records in the data collection, and the transaction log is updated to reflect each commit, Debezium emits UPDATE
or DELETE
operations for each change.
As the snapshot window opens, and Debezium begins processing a snapshot chunk, it delivers snapshot records to a memory buffer. During the snapshot windows, the primary keys of the READ
events in the buffer are compared to the primary keys of the incoming streamed events. If no match is found, the streamed event record is sent directly to Kafka. If Debezium detects a match, it discards the buffered READ
event, and writes the streamed record to the destination topic, because the streamed event logically supersede the static snapshot event. After the snapshot window for the chunk closes, the buffer contains only READ
events for which no related transaction log events exist. Debezium emits these remaining READ
events to the table’s Kafka topic.
The connector repeats the process for each snapshot chunk.
Triggering an incremental snapshot
Currently, the only way to initiate an incremental snapshot is to send an ad hoc snapshot signal to the signaling table on the source database. You submit signals to the table as SQL INSERT
queries. After Debezium detects the change in the signaling table, it reads the signal, and runs the requested snapshot operation.
The query that you submit specifies the tables to include in the snapshot, and, optionally, specifies the kind of snapshot operation. Currently, the only valid option for snapshots operations is the default value, incremental
.
To specify the tables to include in the snapshot, provide a data-collections
array that lists the tables, for example,{"data-collections": ["public.MyFirstTable", "public.MySecondTable"]}
The data-collections
array for an incremental snapshot signal has no default value. If the data-collections
array is empty, Debezium detects that no action is required and does not perform a snapshot.
If the name of a table that you want to include in a snapshot contains a dot (.
) in the name of the database, schema, or table, to add the table to the data-collections
array, you must escape each part of the name in double quotes.
For example, to include a table that exists in the public
schema and that has the name My.Table
, use the following format: "public"."My.Table"
.
Prerequisites
- 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.collection
property.
Procedure
Send a SQL query to add the ad hoc incremental snapshot request to the signaling table:
INSERT INTO _<signalTable>_ (id, type, data) VALUES (_'<id>'_, _'<snapshotType>'_, '{"data-collections": ["_<tableName>_","_<tableName>_"],"type":"_<snapshotType>_"}');
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
, anddata
parameters in the command correspond to the fields of the signaling table.The following table describes the these parameters:
Table 6.3. Descriptions of fields in a SQL command for sending an incremental snapshot signal to the signaling table Value Description myschema.debezium_signal
Specifies the fully-qualified name of the signaling table on the source database
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.execute-snapshot
Specifies
type
parameter specifies the operation that the signal is intended to trigger.
data-collections
A required component of the
data
field 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.collection
configuration property.incremental
An optional
type
component of thedata
field of a signal that specifies the kind of snapshot operation to run.
Currently, the only valid option is the default value,incremental
.
Specifying atype
value in the SQL query that you submit to the signaling table is optional.
If you do not specify a value, the connector runs an incremental snapshot.
The following example, shows the JSON for an incremental snapshot event that is captured by a connector.
Example: Incremental snapshot event message
{ "before":null, "after": { "pk":"1", "value":"New data" }, "source": { ... "snapshot":"incremental" 1 }, "op":"r", 2 "ts_ms":"1620393591654", "transaction":null }
Item | Field name | Description |
---|---|---|
1 |
|
Specifies the type of snapshot operation to run. |
2 |
|
Specifies the event type. |
The Debezium connector for Oracle does not support schema changes while an incremental snapshot is running.
6.1.2. Default names of Kafka topics that receive Debezium Oracle change event records
By default, the Oracle connector writes change events for all 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:
serverName.schemaName.tableName
The following list provides definitions for the components of the default name:
- serverName
-
The logical name of the server as specified by the
database.server.name
connector configuration property. - schemaName
- The name of the schema in which the operation occurred.
- tableName
- The name of the table in which the operation occurred.
For example, if fulfillment
is the server name, inventory
is the schema name, and the database contains tables with the names orders
, customers
, and products
, the Debezium Oracle connector emits events to the following Kafka topics, one for each table in the database:
fulfillment.inventory.orders fulfillment.inventory.customers fulfillment.inventory.products
The connector applies similar naming conventions to label its internal database history topics, schema change topics, and transaction metadata topics.
If the default topic name do not meet your requirements, you can configure custom topic names. To configure custom topic names, you specify regular expressions in the logical topic routing SMT. For more information about using the logical topic routing SMT to customize topic naming, see Topic routing.
6.1.3. How Debezium Oracle connectors expose database schema changes
You can configure a Debezium Oracle connector to produce schema change events that describe structural changes that are applied to captured tables in the database. The connector writes schema change events to a Kafka topic named <serverName>
, where serverName
is the logical server name that is specified in the database.server.name
configuration property.
Debezium emits a new message to this topic whenever it streams data from a new table.
Messages that the connector sends to the schema change topic contain a payload, and, optionally, also contain the schema of the change event message. The payload of a schema change event message includes the following elements:
ddl
-
Provides the SQL
CREATE
,ALTER
, orDROP
statement that results in the schema change. databaseName
-
The name of the database to which the statements are applied. The value of
databaseName
serves as the message key. 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.
By default, the connector uses the ALL_TABLES
database view to identify the table names to store in the schema history topic. Within that view, the connector can access data only from tables that are available to the user account through which it connects to the database. You can modify settings so that the schema history topic stores a different subset of tables. Use one of the following methods to alter the set of tables that the topic stores: * Change the permissions of the account that Debezium uses to access the database so that a different set of tables are visible in the ALL_TABLES
view. * Set the connector property database.history.store.only.captured.tables.ddl
to true
.
When the connector is configured to capture a table, it stores the history of the table’s schema changes not only in the schema change topic, but also in an internal database history topic. The internal database history topic is for connector use only and it is not intended for direct use by consuming applications. Ensure that applications that require notifications about schema changes consume that information only from the schema change topic.
Never partition the database history topic. For the database history topic to function correctly, it must maintain a consistent, global order of the event records that the connector emits to it.
To ensure that the topic is not split among partitions, set the partition count for the topic by using one of the following methods:
-
If you create the database history topic manually, specify a partition count of
1
. -
If you use the Apache Kafka broker to create the database history topic automatically, the topic is created, set the value of the Kafka
num.partitions
configuration option to1
.
Example: Message emitted to the Oracle connector schema change topic
The following example shows a typical schema change message in JSON format. The message contains a logical representation of the table schema.
{ "schema": { ... }, "payload": { "source": { "version": "1.9.7.Final", "connector": "oracle", "name": "server1", "ts_ms": 1588252618953, "snapshot": "true", "db": "ORCLPDB1", "schema": "DEBEZIUM", "table": "CUSTOMERS", "txId" : null, "scn" : "1513734", "commit_scn": "1513754", "lcr_position" : null, "rs_id": "001234.00012345.0124", "ssn": 1, "redo_thread": 1 }, "databaseName": "ORCLPDB1", 1 "schemaName": "DEBEZIUM", // "ddl": "CREATE TABLE \"DEBEZIUM\".\"CUSTOMERS\" \n ( \"ID\" NUMBER(9,0) NOT NULL ENABLE, \n \"FIRST_NAME\" VARCHAR2(255), \n \"LAST_NAME" VARCHAR2(255), \n \"EMAIL\" VARCHAR2(255), \n PRIMARY KEY (\"ID\") ENABLE, \n SUPPLEMENTAL LOG DATA (ALL) COLUMNS\n ) SEGMENT CREATION IMMEDIATE \n PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 \n NOCOMPRESS LOGGING\n STORAGE(INITIAL 65536 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645\n PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1\n BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)\n TABLESPACE \"USERS\" ", 2 "tableChanges": [ 3 { "type": "CREATE", 4 "id": "\"ORCLPDB1\".\"DEBEZIUM\".\"CUSTOMERS\"", 5 "table": { 6 "defaultCharsetName": null, "primaryKeyColumnNames": [ 7 "ID" ], "columns": [ 8 { "name": "ID", "jdbcType": 2, "nativeType": null, "typeName": "NUMBER", "typeExpression": "NUMBER", "charsetName": null, "length": 9, "scale": 0, "position": 1, "optional": false, "autoIncremented": false, "generated": false }, { "name": "FIRST_NAME", "jdbcType": 12, "nativeType": null, "typeName": "VARCHAR2", "typeExpression": "VARCHAR2", "charsetName": null, "length": 255, "scale": null, "position": 2, "optional": false, "autoIncremented": false, "generated": false }, { "name": "LAST_NAME", "jdbcType": 12, "nativeType": null, "typeName": "VARCHAR2", "typeExpression": "VARCHAR2", "charsetName": null, "length": 255, "scale": null, "position": 3, "optional": false, "autoIncremented": false, "generated": false }, { "name": "EMAIL", "jdbcType": 12, "nativeType": null, "typeName": "VARCHAR2", "typeExpression": "VARCHAR2", "charsetName": null, "length": 255, "scale": null, "position": 4, "optional": false, "autoIncremented": false, "generated": false } ] } } ] } }
Item | Field name | Description |
---|---|---|
1 |
| Identifies the database and the schema that contains the change. |
2 |
| This field contains the DDL that is responsible for the schema change. |
3 |
| An array of one or more items that contain the schema changes generated by a DDL command. |
4 |
|
Describes the kind of change. The
|
5 |
|
Full identifier of the table that was created, altered, or dropped. In the case of a table rename, this identifier is a concatenation of |
6 |
| Represents table metadata after the applied change. |
7 |
| List of columns that compose the table’s primary key. |
8 |
| Metadata for each column in the changed table. |
In messages 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 databaseName
key:
{ "schema": { "type": "struct", "fields": [ { "type": "string", "optional": false, "field": "databaseName" } ], "optional": false, "name": "io.debezium.connector.oracle.SchemaChangeKey" }, "payload": { "databaseName": "ORCLPDB1" } }
6.1.4. Debezium Oracle connector-generated events that represent transaction boundaries
Debezium can generate events that represent transaction metadata boundaries and that enrich data change event messages.
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.
Database transactions are represented by a statement block that is enclosed between the BEGIN
and END
keywords. 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 unique transaction identifier.
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 number of events that the connector emits for changes that originate from a data collection.
The following example shows a typical transaction boundary message:
Example: Oracle connector transaction boundary event
{ "status": "BEGIN", "id": "5.6.641", "event_count": null, "data_collections": null } { "status": "END", "id": "5.6.641", "event_count": 2, "data_collections": [ { "data_collection": "ORCLPDB1.DEBEZIUM.CUSTOMER", "event_count": 1 }, { "data_collection": "ORCLPDB1.DEBEZIUM.ORDER", "event_count": 1 } ] }
Unless overridden via the transaction.topic
option, the connector emits transaction events to the <database.server.name>
.transaction
topic.
6.1.4.1. How the Debezium Oracle connector enriches change event messages with transaction metadata
When transaction metadata is enabled, the data message Envelope
is enriched 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.
The following example shows a typical transaction event message:
{ "before": null, "after": { "pk": "2", "aa": "1" }, "source": { ... }, "op": "c", "ts_ms": "1580390884335", "transaction": { "id": "5.6.641", "total_order": "1", "data_collection_order": "1" } }
6.1.5. How the Debezium Oracle connector uses event buffering
Oracle writes all changes to the redo logs in the order in which they occur, including changes that are later discarded by a rollback. As a result, concurrent changes from separate transactions are intertwined. When the connector first reads the stream of changes, because it cannot immediately determine which changes are committed or rolled back, it temporarily stores the change events in an internal buffer. After a change is committed, the connector writes the change event from the buffer to Kafka. The connector drops change events that are discarded by a rollback.
You can configure the buffering mechanism that the connector uses by setting the property log.mining.buffer.type
.
Heap
The default buffer type is configured using memory
. Under the default memory
setting, the connector uses the heap memory of the JVM process to allocate and manage buffered event records. If you use the memory
buffer setting, be sure that the amount of memory that you allocate to the Java process can accommodate long-running and large transactions in your environment.
6.1.6. How the Debezium Oracle connector detects gaps in SCN values
When the Debezium Oracle connector is configured to use LogMiner, it collects change events from Oracle by using a start and end range that is based on system change numbers (SCNs). The connector manages this range automatically, increasing or decreasing the range depending on whether the connector is able to stream changes in near real-time, or must process a backlog of changes due to the volume of large or bulk transactions in the database.
Under certain circumstances, the Oracle database advances the SCN by an unusually high amount, rather than increasing the SCN value at a constant rate. Such a jump in the SCN value can occur because of the way that a particular integration interacts with the database, or as a result of events such as hot backups.
The Debezium Oracle connector relies on the following configuration properties to detect the SCN gap and adjust the mining range.
log.mining.scn.gap.detection.gap.size.min
- Specifies the minimum gap size.
log.mining.scn.gap.detection.time.interval.max.ms
- Specifies the maximum time interval.
The connector first compares the difference in the number of changes between the current SCN and the highest SCN in the current mining range. If the difference between the current SCN value and the highest SCN value is greater than the minimum gap size, then the connector has potentially detected a SCN gap. To confirm whether a gap exists, the connector next compares the timestamps of the current SCN and the SCN at the end of the previous mining range. If the difference between the timestamps is less than the maximum time interval, then the existence of an SCN gap is confirmed.
When an SCN gap occurs, the Debezium connector automatically uses the current SCN as the end point for the range of the current mining session. This allows the connector to quickly catch up to the real-time events without mining smaller ranges in between that return no changes because the SCN value was increased by an unexpectedly large number. When the connector performs the preceding steps in response to an SCN gap, it ignores the value that is specified by the log.mining.batch.size.max property. After the connector finishes the mining session and catches back up to real-time events, it resumes enforcement of the maximum log mining batch size.
SCN gap detection is available only if the large SCN increment occurs while the connector is running and processing near real-time events.
6.1.7. How Debezium manages offsets in databases that change infrequently
The Debezium Oracle connector tracks system change numbers in the connector offsets so that when the connector is restarted, it can begin where it left off. These offsets are part of each emitted change event; however, when the frequency of database changes are low (every few hours or days), the offsets can become stale and prevent the connector from successfully restarting if the system change number is no longer available in the transaction logs.
For connectors that use non-CDB mode to connect to Oracle, you can enable heartbeat.interval.ms
to force the connector to emit a heartbeat event at regular intervals so that offsets remain synchronized.
For connectors that use CDB mode to connect to Oracle, maintaining synchronization is more complicated. Not only must you set heartbeat.interval.ms
, but it’s also necessary to set heartbeat.action.query
. Specifying both properties is required, because in CDB mode, the connector specifically tracks changes inside the PDB only. A supplementary mechanism is needed to trigger change events from within the pluggable database. At regular intervals, the heartbeat action query causes the connector to insert a new table row, or update an existing row in the pluggable database. Debezium detects the table changes and emits change events for them, ensuring that offsets remain synchronized, even in pluggable databases that process changes infrequently.
For the connector to use the heartbeat.action.query
with tables that are not owned by the connector user account, you must grant the connector user permission to run the necessary INSERT
or UPDATE
queries on those tables.
6.2. Descriptions of Debezium Oracle connector data change events
Every data change event that the Oracle connector emits has a key and a value. The structures of the key and value depend on the table from which the change events originate. For information about how Debezium constructs topic names, see Topic names.
The Debezium Oracle connector ensures that all Kafka Connect schema names are valid Avro schema names. This means that the logical server name must start with alphabetic characters or an underscore ([a-z,A-Z,_]), and the remaining characters in the logical server name and all characters in the schema and table names must be alphanumeric characters or an underscore ([a-z,A-Z,0-9,\_]). The connector automatically replaces invalid characters with an underscore character.
Unexpected naming conflicts can result when the only distinguishing characters between multiple logical server names, schema names, or table names are not valid characters, and those characters are replaced with underscores.
Debezium and Kafka Connect are designed around continuous streams of event messages. However, the structure of these events might change over time, which can be difficult for topic consumers to handle. To facilitate the processing of mutable event structures, each event in Kafka Connect is self-contained. Every message key and value has two parts: a schema and payload. The schema describes the structure of the payload, while the payload contains the actual data.
Changes that are performed by the SYS
or SYSTEM
user accounts are not captured by the connector.
The following topics contain more details about data change events:
6.2.1. About keys in Debezium Oracle connector change events
For each changed table, the change event key is structured such that a field exists for each column in the primary key (or unique key constraint) of the table at the time when the event is created.
For example, a customers
table that is defined in the inventory
database schema, might have the following change event key:
CREATE TABLE customers ( id NUMBER(9) GENERATED BY DEFAULT ON NULL AS IDENTITY (START WITH 1001) NOT NULL PRIMARY KEY, first_name VARCHAR2(255) NOT NULL, last_name VARCHAR2(255) NOT NULL, email VARCHAR2(255) NOT NULL UNIQUE );
If the value of the <database.server.name>
.transaction
configuration property is set to server1
, the JSON representation for every change event that occurs in the customers
table in the database features the following key structure:
{ "schema": { "type": "struct", "fields": [ { "type": "int32", "optional": false, "field": "ID" } ], "optional": false, "name": "server1.INVENTORY.CUSTOMERS.Key" }, "payload": { "ID": 1004 } }
The schema
portion of the key contains a Kafka Connect schema that describes the content of the key portion. In the preceding example, the payload
value is not optional, the structure is defined by a schema named server1.DEBEZIUM.CUSTOMERS.Key
, and there is one required field named id
of type int32
. The value of the key’s payload
field indicates that it is indeed a structure (which in JSON is just an object) with a single id
field, whose value is 1004
.
Therefore, you can interpret this key as describing the row in the inventory.customers
table (output from the connector named server1
) whose id
primary key column had a value of 1004
.
6.2.2. About values in Debezium Oracle connector change events
The structure of a value in a change event message mirrors the structure of the message key in the change event in the message, and contains both a schema section and a payload section.
Payload of a change event value
An envelope structure in the payload sections of a change event value contains the following fields:
op
-
A mandatory field that contains a string value describing the type of operation. The
op
field in the payload of an Oracle connector change event value contains one of the following values:c
(create or insert),u
(update),d
(delete), orr
(read, which indicates a snapshot). before
-
An optional field that, if present, describes the state of the row before the event occurred. The structure is described by the
server1.INVENTORY.CUSTOMERS.Value
Kafka Connect schema, which theserver1
connector uses for all rows in theinventory.customers
table.
after
-
An optional field that, if present, contains the state of a row after a change occurs. The structure is described by the same
server1.INVENTORY.CUSTOMERS.Value
Kafka Connect schema that is used for thebefore
field. source
A mandatory field that contains a structure that describes the source metadata for the event. In the case of the Oracle connector, the structure includes the following fields:
- The Debezium version.
- The connector name.
- Whether the event is part of an ongoing snapshot or not.
- The transaction id (not includes for snapshots).
- The SCN of the change.
A timestamp that indicates when the record in the source database changed (for snapshots, the timestamp indicates when the snapshot occurred).
TipThe
commit_scn
field is optional and describes the SCN of the transaction commit that the change event participates within.
ts_ms
- An optional field that, if present, contains the time (based on the system clock in the JVM that runs the Kafka Connect task) at which the connector processed the event.
Schema of a change event value
The schema portion of the event message’s value contains a schema that describes the envelope structure of the payload and the nested fields within it.
For more information about change event values, see the following topics:
create events
The following example shows the value of a create event value from the customers
table that is described in the change event keys example:
{ "schema": { "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": "server1.DEBEZIUM.CUSTOMERS.Value", "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": "server1.DEBEZIUM.CUSTOMERS.Value", "field": "after" }, { "type": "struct", "fields": [ { "type": "string", "optional": true, "field": "version" }, { "type": "string", "optional": false, "field": "name" }, { "type": "int64", "optional": true, "field": "ts_ms" }, { "type": "string", "optional": true, "field": "txId" }, { "type": "string", "optional": true, "field": "scn" }, { "type": "string", "optional": true, "field": "commit_scn" }, { "type": "string", "optional": true, "field": "rs_id" }, { "type": "int32", "optional": true, "field": "ssn" }, { "type": "int32", "optional": true, "field": "redo_thread" }, { "type": "boolean", "optional": true, "field": "snapshot" } ], "optional": false, "name": "io.debezium.connector.oracle.Source", "field": "source" }, { "type": "string", "optional": false, "field": "op" }, { "type": "int64", "optional": true, "field": "ts_ms" } ], "optional": false, "name": "server1.DEBEZIUM.CUSTOMERS.Envelope" }, "payload": { "before": null, "after": { "ID": 1004, "FIRST_NAME": "Anne", "LAST_NAME": "Kretchmar", "EMAIL": "annek@noanswer.org" }, "source": { "version": "1.9.7.Final", "name": "server1", "ts_ms": 1520085154000, "txId": "6.28.807", "scn": "2122185", "commit_scn": "2122185", "rs_id": "001234.00012345.0124", "ssn": 1, "redo_thread": 1, "snapshot": false }, "op": "c", "ts_ms": 1532592105975 } }
In the preceding example, notice how the event defines the following schema:
-
The envelope (
server1.DEBEZIUM.CUSTOMERS.Envelope
). -
The
source
structure (io.debezium.connector.oracle.Source
, which is specific to the Oracle connector and reused across all events). -
The table-specific schemas for the
before
andafter
fields.
The names of the schemas for the before
and after
fields are of the form <logicalName>.<schemaName>.<tableName>.Value
, and thus are entirely independent from the schemas for all other tables. As a result, when you use the Avro converter, the Avro schemas for tables in each logical source have their own evolution and history.
The payload
portion of this event’s value, provides information about the event. It describes that a row was created (op=c
), and shows that the after
field value contains the values that were inserted into the ID
, FIRST_NAME
, LAST_NAME
, and EMAIL
columns of the row.
By default, the JSON representations of events are much larger than the rows that they describe. The larger size is due to the JSON representation including both the schema and payload portions of a message. You can use the Avro Converter to decrease the size of messages that the connector writes to Kafka topics.
update events
The following example shows an update change event that the connector captures from the same table as the preceding create event.
{ "schema": { ... }, "payload": { "before": { "ID": 1004, "FIRST_NAME": "Anne", "LAST_NAME": "Kretchmar", "EMAIL": "annek@noanswer.org" }, "after": { "ID": 1004, "FIRST_NAME": "Anne", "LAST_NAME": "Kretchmar", "EMAIL": "anne@example.com" }, "source": { "version": "1.9.7.Final", "name": "server1", "ts_ms": 1520085811000, "txId": "6.9.809", "scn": "2125544", "commit_scn": "2125544", "rs_id": "001234.00012345.0124", "ssn": 1, "redo_thread": 1, "snapshot": false }, "op": "u", "ts_ms": 1532592713485 } }
The payload has the same structure as the payload of a create (insert) event, but the following values are different:
-
The value of the
op
field isu
, signifying that this row changed because of an update. -
The
before
field shows the former state of the row with the values that were present before theupdate
database commit. -
The
after
field shows the updated state of the row, with theEMAIL
value now set toanne@example.com
. -
The structure of the
source
field includes the same fields as before, but the values are different, because the connector captured the event from a different position in the redo log. -
The
ts_ms
field shows the timestamp that indicates when Debezium processed the event.
The payload
section reveals several other useful pieces of information. For example, by comparing the before
and after
structures, we can determine how a row changed as the result of a commit. The source
structure provides information about Oracle’s record of this change, providing traceability. It also gives us insight into when this event occurred in relation to other events in this topic and in other topics. Did it occur before, after, or as part of the same commit as another event?
When the columns for a row’s primary/unique key are updated, the value of the row’s key changes. As a result, Debezium emits three events after such an update:
-
A
DELETE
event. - A tombstone event with the old key for the row.
-
An
INSERT
event that provides the new key for the row.
delete events
The following example shows a delete event for the table that is shown in the preceding create and update event examples. The schema
portion of the delete event is identical to the schema
portion for those events.
{ "schema": { ... }, "payload": { "before": { "ID": 1004, "FIRST_NAME": "Anne", "LAST_NAME": "Kretchmar", "EMAIL": "anne@example.com" }, "after": null, "source": { "version": "1.9.7.Final", "name": "server1", "ts_ms": 1520085153000, "txId": "6.28.807", "scn": "2122184", "commit_scn": "2122184", "rs_id": "001234.00012345.0124", "ssn": 1, "redo_thread": 1, "snapshot": false }, "op": "d", "ts_ms": 1532592105960 } }
The payload
portion of the event reveals several differences when compared to the payload of a create or update event:
-
The value of the
op
field isd
, signifying that the row was deleted. -
The
before
field shows the former state of the row that was deleted with the database commit. -
The value of the
after
field isnull
, signifying that the row no longer exists. -
The structure of the
source
field includes many of the keys that exist in create or update events, but the values in thets_ms
,scn
, andtxId
fields are different. -
The
ts_ms
shows a timestamp that indicates when Debezium processed this event.
The delete event provides consumers with the information that they require to process the removal of this row.
The Oracle connector’s events are designed to work with Kafka log compaction, which allows for the removal of some older messages as long as at least the most recent message for every key is kept. This allows Kafka to reclaim storage space while ensuring the topic contains a complete dataset and can be used for reloading key-based state.
When a row is deleted, the delete event value shown in the preceding example still works with log compaction, because Kafka is able to remove all earlier messages that use the same key. The message value must be set to null
to instruct Kafka to remove all messages that share the same key. To make this possible, by default, Debezium’s Oracle connector always follows a delete event with a special tombstone event that has the same key but null
value. You can change the default behavior by setting the connector property tombstones.on.delete
.
truncate events
A truncate change event signals that a table has been truncated. The message key is null
in this case, the message value looks like this:
{ "schema": { ... }, "payload": { "before": null, "after": null, "source": { 1 "version": "1.9.7.Final", "connector": "oracle", "name": "oracle_server", "ts_ms": 1638974535000, "snapshot": "false", "db": "ORCLPDB1", "sequence": null, "schema": "DEBEZIUM", "table": "TEST_TABLE", "txId": "02000a0037030000", "scn": "13234397", "commit_scn": "13271102", "lcr_position": null, "rs_id": "001234.00012345.0124", "ssn": 1, "redo_thread": 1 }, "op": "t", 2 "ts_ms": 1638974558961, 3 "transaction": null } }
Item | Field name | Description |
---|---|---|
1 |
|
Mandatory field that describes the source metadata for the event. In a truncate event value, the
|
2 |
|
Mandatory string that describes the type of operation. The |
3 |
|
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 |
Because truncate events represent changes made to an entire table, and have no message key, in topics with multiple partitions, there is no guarantee that consumers receive truncate events and change events (create, update, etc.) for to a table in order. For example, when a consumer reads events from different partitions, it might receive an update event for a table after it receives a truncate event for the same table. Ordering can be guaranteed only if a topic uses a single partition.
If you do not want to capture truncate events, use the skipped.operations
option to filter them out.
6.3. How Debezium Oracle connectors map data types
When the Debezium Oracle connector detects a change in the value of a table row, it emits a change event that represents the change. Each change event record is structured in the same way as the original table, with the event record containing a field for each column value. The data type of a table column determines how the connector represents the column’s values in change event fields, as shown in the tables in the following sections.
For each column in a table, Debezium maps the source data type to a literal type and, and in some cases, a semantic type, in the corresponding event field.
- Literal types
-
Describe how the value is literally represented, using one of the following Kafka Connect schema types:
INT8
,INT16
,INT32
,INT64
,FLOAT32
,FLOAT64
,BOOLEAN
,STRING
,BYTES
,ARRAY
,MAP
, andSTRUCT
. - Semantic types
- Describe how the Kafka Connect schema captures the meaning of the field, by using the name of the Kafka Connect schema for the field.
If the default data type conversions do not meet your needs, you can create a custom converter for the connector.
For some Oracle large object (CLOB, NCLOB, and BLOB) and numeric data types, you can manipulate the way that the connector performs the type mapping by changing default configuration property settings. For more information about how Debezium properties control mappings for these data types, see Binary and Character LOB types and Numeric types.
For more information about how the Debezium connector maps Oracle data types, see the following topics:
Character types
The following table describes how the connector maps basic character types.
Oracle Data Type | Literal type (schema type) | Semantic type (schema name) and Notes |
---|---|---|
|
| n/a |
|
| n/a |
|
| n/a |
|
| n/a |
|
| n/a |
Use of the BLOB
, CLOB
, and NCLOB
with the Debezium Oracle connector is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process. For more information about the support scope of Red Hat Technology Preview features, see https://access.redhat.com/support/offerings/techpreview.
The following table describes how the connector maps binary and character large object (LOB) data types.
Oracle Data Type | Literal type (schema type) | Semantic type (schema name) and Notes |
---|---|---|
| n/a | This data type is not supported |
|
|
Either the raw bytes (the default), a base64-encoded String, or a hex-encoded String, based on the |
|
| n/a |
| n/a | This data type is not supported. |
| n/a | This data type is not supported. |
|
| n/a |
| n/a | This data type is not supported. |
Oracle only supplies column values for CLOB
, NCLOB
, and BLOB
data types if they’re explicitly set or changed in a SQL statement. As a result, change events never contain the value of an unchanged CLOB
, NCLOB
, or BLOB
column. Instead, they contain placeholders as defined by the connector property, unavailable.value.placeholder
.
If the value of a CLOB
, NCLOB
, or BLOB
column is updated, the new value is placed in the after
element of the corresponding update change event. The before
element contains the unavailable value placeholder.
Numeric types
The following table describes how the Debezium Oracle connector maps numeric types.
You can modify the way that the connector maps the Oracle DECIMAL
, NUMBER
, NUMERIC
, and REAL
data types by changing the value of the connector’s decimal.handling.mode
configuration property. When the property is set to its default value of precise
, the connector maps these Oracle data types to the Kafka Connect org.apache.kafka.connect.data.Decimal
logical type, as indicated in the table. When the value of the property is set to double
or string
, the connector uses alternate mappings for some Oracle data types. For more information, see the Semantic type and Notes column in the following table.
Oracle Data Type | Literal type (schema type) | Semantic type (schema name) and Notes |
---|---|---|
|
| n/a |
|
| n/a |
|
|
When the
When the |
|
|
|
|
|
|
|
|
|
|
|
When the
When the |
|
|
When the
When the |
|
|
|
|
|
When the
When the |
|
|
|
|
|
When the
When the |
Boolean types
Oracle does not provide native support for a BOOLEAN
data type. However, it is common practice to use other data types with certain semantics to simulate the concept of a logical BOOLEAN
data type.
To enable you to convert source columns to Boolean data types, Debezium provides a NumberOneToBooleanConverter
custom converter that you can use in one of the following ways:
-
Map all
NUMBER(1)
columns to aBOOLEAN
type. Enumerate a subset of columns by using a comma-separated list of regular expressions.
To use this type of conversion, you must set theconverters
configuration property with theselector
parameter, as shown in the following example:converters=boolean boolean.type=io.debezium.connector.oracle.converters.NumberOneToBooleanConverter boolean.selector=.*MYTABLE.FLAG,.*.IS_ARCHIVED
Temporal types
Other than the Oracle INTERVAL
, TIMESTAMP WITH TIME ZONE
, and TIMESTAMP WITH LOCAL TIME ZONE
data types, the way that the connector converts temporal types depends on the value of the time.precision.mode
configuration property.
When the time.precision.mode
configuration property is set to adaptive
(the default), then the connector determines the literal and semantic type for the temporal types based on the column’s data type definition so that events exactly represent the values in the database:
Oracle data type | Literal type (schema type) | Semantic type (schema name) and Notes |
---|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
When the time.precision.mode
configuration property is set to connect
, then the connector uses the predefined Kafka Connect logical types. This can be useful when consumers only know about the built-in Kafka Connect logical types and are unable to handle variable-precision time values. Because the level of precision that Oracle supports exceeds the level that the logical types in Kafka Connect support, if you set time.precision.mode
to connect
, a loss of precision results when the fractional second precision value of a database column is greater than 3:
Oracle data type | Literal type (schema type) | Semantic type (schema name) and Notes |
---|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
ROWID types
The following table describes how the connector maps ROWID (row address) data types.
Oracle Data Type | Literal type (schema type) | Semantic type (schema name) and Notes |
---|---|---|
|
| n/a |
| n/a | This data type is not supported. |
User-defined types
Oracle enables you to define custom data types to provide flexibility when the built-in data types do not satisfy your requirements. There are a several user-defined types such as Object types, REF data types, Varrays, and Nested Tables. At this time, you cannot use the Debezium Oracle connector with any of these user-defined types.
Oracle-supplied types
Oracle provides SQL-based interfaces that you can use to define new types when the built-in or ANSI-supported types are insufficient. Oracle offers several commonly used data types to serve a broad array of purposes such as Any, XML, or Spatial types. At this time, you cannot use the Debezium Oracle connector with any of these data types.
Default Values
If a default value is specified for a column in the database schema, the Oracle connector will attempt to propagate this value to the schema of the corresponding Kafka record field. Most common data types are supported, including:
-
Character types (
CHAR
,NCHAR
,VARCHAR
,VARCHAR2
,NVARCHAR
,NVARCHAR2
) -
Numeric types (
INTEGER
,NUMERIC
, etc.) -
Temporal types (
DATE
,TIMESTAMP
,INTERVAL
, etc.)
If a temporal type uses a function call such as TO_TIMESTAMP
or TO_DATE
to represent the default value, the connector will resolve the default value by making an additional database call to evaluate the function. For example, if a DATE
column is defined with the default value of TO_DATE('2021-01-02', 'YYYY-MM-DD')
, the column’s default value will be the number of days since the UNIX epoch for that date or 18629
in this case.
If a temporal type uses the SYSDATE
constant to represent the default value, the connector will resolve this based on whether the column is defined as NOT NULL
or NULL
. If the column is nullable, no default value will be set; however, if the column isn’t nullable then the default value will be resolved as either 0
(for DATE
or TIMESTAMP(n)
data types) or 1970-01-01T00:00:00Z
(for TIMESTAMP WITH TIME ZONE
or TIMESTAMP WITH LOCAL TIME ZONE
data types). The default value type will be numeric except if the column is a TIMESTAMP WITH TIME ZONE
or TIMESTAMP WITH LOCAL TIME ZONE
in which case its emitted as a string.
6.4. Setting up Oracle to work with Debezium
The following steps are necessary to set up Oracle for use with the Debezium Oracle connector. These steps assume the use of the multi-tenancy configuration with a container database and at least one pluggable database. If you do not intend to use a multi-tenant configuration, it might be necessary to adjust the following steps.
For details about setting up Oracle for use with the Debezium connector, see the following sections:
- Section 6.4.1, “Compatibility of the Debezium Oracle connector with Oracle installation types”
- Section 6.4.2, “Schemas that the Debezium Oracle connector excludes when capturing change events”
- Section 6.4.3, “Preparing Oracle databases for use with Debezium”
- Section 6.4.4, “Resizing Oracle redo logs to accommodate the data dictionary”
- Section 6.4.5, “Creating an Oracle user for the Debezium Oracle connector”
- Section 6.4.6, “Support for Oracle standby databases”
6.4.1. Compatibility of the Debezium Oracle connector with Oracle installation types
An Oracle database can be installed either as a standalone instance or using Oracle Real Application Cluster (RAC). The Debezium Oracle connector is compatible with both types of installation.
6.4.2. Schemas that the Debezium Oracle connector excludes when capturing change events
When the Debezium Oracle connector captures tables, it automatically excludes tables from the following schemas:
-
appqossys
-
audsys
-
ctxsys
-
dvsys
-
dbsfwuser
-
dbsnmp
-
qsmadmin_internal
-
lbacsys
-
mdsys
-
ojvmsys
-
olapsys
-
orddata
-
ordsys
-
outln
-
sys
-
system
-
wmsys
-
xdb
To enable the connector to capture changes from a table, the table must use a schema that is not named in the preceding list.
6.4.3. Preparing Oracle databases for use with Debezium
Configuration needed for Oracle LogMiner
ORACLE_SID=ORACLCDB dbz_oracle sqlplus /nolog CONNECT sys/top_secret AS SYSDBA alter system set db_recovery_file_dest_size = 10G; alter system set db_recovery_file_dest = '/opt/oracle/oradata/recovery_area' scope=spfile; shutdown immediate startup mount alter database archivelog; alter database open; -- Should now "Database log mode: Archive Mode" archive log list exit;
To enable Debezium to capture the before state of changed database rows, you must also enable supplemental logging for captured tables or for the entire database. The following example illustrates how to configure supplemental logging for all columns in a single inventory.customers
table.
ALTER TABLE inventory.customers ADD SUPPLEMENTAL LOG DATA (ALL) COLUMNS;
Enabling supplemental logging for all table columns increases the volume of the Oracle redo logs. To prevent excessive growth in the size of the logs, apply the preceding configuration selectively.
Minimal supplemental logging must be enabled at the database level and can be configured as follows.
ALTER DATABASE ADD SUPPLEMENTAL LOG DATA;
6.4.4. Resizing Oracle redo logs to accommodate the data dictionary
Depending on the database configuration, the size and number of redo logs might not be sufficient to achieve acceptable performance. Before you set up the Debezium Oracle connector, ensure that the capacity of the redo logs is sufficient to support the database.
The capacity of the redo logs for a database must be sufficient to store its data dictionary. In general, the size of the data dictionary increases with the number of tables and columns in the database. If the redo log lacks sufficient capacity, both the database and the Debezium connector might experience performance problems.
Consult with your database administrator to evaluate whether the database might require increased log capacity.
6.4.5. Creating an Oracle user for the Debezium Oracle connector
For the Debezium Oracle connector to capture change events, it must run as an Oracle LogMiner user that has specific permissions. The following example shows the SQL for creating an Oracle user account for the connector in a multi-tenant database model.
The connector captures database changes that are made by its own Oracle user account. However, it does not capture changes that are made by the SYS
or SYSTEM
user accounts.
Creating the connector’s LogMiner user
sqlplus sys/top_secret@//localhost:1521/ORCLCDB as sysdba CREATE TABLESPACE logminer_tbs DATAFILE '/opt/oracle/oradata/ORCLCDB/logminer_tbs.dbf' SIZE 25M REUSE AUTOEXTEND ON MAXSIZE UNLIMITED; exit; sqlplus sys/top_secret@//localhost:1521/ORCLPDB1 as sysdba CREATE TABLESPACE logminer_tbs DATAFILE '/opt/oracle/oradata/ORCLCDB/ORCLPDB1/logminer_tbs.dbf' SIZE 25M REUSE AUTOEXTEND ON MAXSIZE UNLIMITED; exit; sqlplus sys/top_secret@//localhost:1521/ORCLCDB as sysdba CREATE USER c##dbzuser IDENTIFIED BY dbz DEFAULT TABLESPACE logminer_tbs QUOTA UNLIMITED ON logminer_tbs CONTAINER=ALL; GRANT CREATE SESSION TO c##dbzuser CONTAINER=ALL; 1 GRANT SET CONTAINER TO c##dbzuser CONTAINER=ALL; 2 GRANT SELECT ON V_$DATABASE to c##dbzuser CONTAINER=ALL; 3 GRANT FLASHBACK ANY TABLE TO c##dbzuser CONTAINER=ALL; 4 GRANT SELECT ANY TABLE TO c##dbzuser CONTAINER=ALL; 5 GRANT SELECT_CATALOG_ROLE TO c##dbzuser CONTAINER=ALL; 6 GRANT EXECUTE_CATALOG_ROLE TO c##dbzuser CONTAINER=ALL; 7 GRANT SELECT ANY TRANSACTION TO c##dbzuser CONTAINER=ALL; 8 GRANT LOGMINING TO c##dbzuser CONTAINER=ALL; 9 GRANT CREATE TABLE TO c##dbzuser CONTAINER=ALL; 10 GRANT LOCK ANY TABLE TO c##dbzuser CONTAINER=ALL; 11 GRANT CREATE SEQUENCE TO c##dbzuser CONTAINER=ALL; 12 GRANT EXECUTE ON DBMS_LOGMNR TO c##dbzuser CONTAINER=ALL; 13 GRANT EXECUTE ON DBMS_LOGMNR_D TO c##dbzuser CONTAINER=ALL; 14 GRANT SELECT ON V_$LOG TO c##dbzuser CONTAINER=ALL; 15 GRANT SELECT ON V_$LOG_HISTORY TO c##dbzuser CONTAINER=ALL; 16 GRANT SELECT ON V_$LOGMNR_LOGS TO c##dbzuser CONTAINER=ALL; 17 GRANT SELECT ON V_$LOGMNR_CONTENTS TO c##dbzuser CONTAINER=ALL; 18 GRANT SELECT ON V_$LOGMNR_PARAMETERS TO c##dbzuser CONTAINER=ALL; 19 GRANT SELECT ON V_$LOGFILE TO c##dbzuser CONTAINER=ALL; 20 GRANT SELECT ON V_$ARCHIVED_LOG TO c##dbzuser CONTAINER=ALL; 21 GRANT SELECT ON V_$ARCHIVE_DEST_STATUS TO c##dbzuser CONTAINER=ALL; 22 GRANT SELECT ON V_$TRANSACTION TO c##dbzuser CONTAINER=ALL; 23 exit;
Item | Role name | Description |
---|---|---|
1 | CREATE SESSION | Enables the connector to connect to Oracle. |
2 | SET CONTAINER | Enables the connector to switch between pluggable databases. This is only required when the Oracle installation has container database support (CDB) enabled. |
3 | SELECT ON V_$DATABASE |
Enables the connector to read the |
4 | FLASHBACK ANY TABLE | Enables the connector to perform Flashback queries, which is how the connector performs the initial snapshot of data. |
5 | SELECT ANY TABLE | Enables the connector to read any table. |
6 | SELECT_CATALOG_ROLE | Enables the connector to read the data dictionary, which is needed by Oracle LogMiner sessions. |
7 | EXECUTE_CATALOG_ROLE | Enables the connector to write the data dictionary into the Oracle redo logs, which is needed to track schema changes. |
8 | SELECT ANY TRANSACTION |
Enables the snapshot process to perform a Flashback snapshot query against any transaction. When |
9 | LOGMINING | This role was added in newer versions of Oracle as a way to grant full access to Oracle LogMiner and its packages. On older versions of Oracle that don’t have this role, you can ignore this grant. |
10 | CREATE TABLE | Enables the connector to create its flush table in its default tablespace. The flush table allows the connector to explicitly control flushing of the LGWR internal buffers to disk. |
11 | LOCK ANY TABLE | Enables the connector to lock tables during schema snapshot. If snapshot locks are explicitly disabled via configuration, this grant can be safely ignored. |
12 | CREATE ANY SEQUENCE | Enables the connector to create a sequence in its default tablespace. |
13 | EXECUTE ON DBMS_LOGMNR |
Enables the connector to run methods in the |
14 | EXECUTE ON DBMS_LOGMNR_D |
Enables the connector to run methods in the |
15 to 23 | SELECT ON V_$…. | Enables the connector to read these tables. The connector must be able to read information about the Oracle redo and archive logs, and the current transaction state, to prepare the Oracle LogMiner session. Without these grants, the connector cannot operate. |
6.4.6. Support for Oracle standby databases
The Debezium Oracle connector cannot be used with Oracle physical or logical standby databases.
6.5. Deployment of Debezium Oracle connectors
You can use either of the following methods to deploy a Debezium Oracle connector:
Due to licensing requirements, the Debezium Oracle connector archive does not include the Oracle JDBC driver that the connector requires to connect to an Oracle database. To enable the connector to access the database, you must add the driver to your connector environment. For more information, see Obtaining the Oracle JDBC driver.
Additional resources
6.5.1. Obtaining the Oracle JDBC driver
Due to licensing requirements, the Oracle JDBC driver file that Debezium requires to connect to an Oracle database is not included in the Debezium Oracle connector archive. The driver is available for download from Maven Central. Depending on the deployment method that you use, you retrieve the driver by adding a command to the Kafka Connect custom resource or to the Dockerfile that you use to build the connector image.
-
If you use AMQ Streams to add the connector to your Kafka Connect image, add the Maven Central location for the driver to
builds.plugins.artifact.url
in theKafkaConnect
custom resource as shown in Section 6.5.3, “Using AMQ Streams to deploy a Debezium Oracle connector”. -
If you use a Dockerfile to build a container image for the connector, insert a
curl
command in the Dockerfile to specify the URL for downloading the required driver file from Maven Central. For more information, see Deploying a Debezium Oracle connector by building a custom Kafka Connect container image from a Dockerfile.
6.5.2. Debezium Oracle connector deployment using AMQ Streams
Beginning with Debezium 1.7, the preferred method for deploying a Debezium connector is to use AMQ Streams to build a Kafka Connect container image that includes the connector plug-in.
During the deployment process, you create and use the following custom resources (CRs):
-
A
KafkaConnect
CR that defines your Kafka Connect instance and includes information about the connector artifacts needs to include in the image. -
A
KafkaConnector
CR that provides details that include information the connector uses to access the source database. After AMQ Streams starts the Kafka Connect pod, you start the connector by applying theKafkaConnector
CR.
In the build specification for the Kafka Connect image, you can specify the connectors that are available to deploy. For each connector plug-in, you can also specify other components that you want to make available for deployment. For example, you can add Service Registry artifacts, or the Debezium scripting component. When AMQ Streams builds the Kafka Connect image, it downloads the specified artifacts, and incorporates them into the image.
The spec.build.output
parameter in the KafkaConnect
CR specifies where to store the resulting Kafka Connect container image. Container images can be stored in a Docker registry, or in an OpenShift ImageStream. To store images in an ImageStream, you must create the ImageStream before you deploy Kafka Connect. ImageStreams are not created automatically.
If you use a KafkaConnect
resource to create a cluster, afterwards you cannot use the Kafka Connect REST API to create or update connectors. You can still use the REST API to retrieve information.
Additional resources
- Configuring Kafka Connect in Using AMQ Streams on OpenShift.
- Creating a new container image automatically using AMQ Streams in Deploying and Upgrading AMQ Streams on OpenShift.
6.5.3. Using AMQ Streams to deploy a Debezium Oracle connector
With earlier versions of AMQ Streams, to deploy Debezium connectors on OpenShift, you were required to first build a Kafka Connect image for the connector. The current preferred method for deploying connectors on OpenShift is to use a build configuration in AMQ Streams to automatically build a Kafka Connect container image that includes the Debezium connector plug-ins that you want to use.
During the build process, the AMQ Streams Operator transforms input parameters in a KafkaConnect
custom resource, including Debezium connector definitions, into a Kafka Connect container image. The build downloads the necessary artifacts from the Red Hat Maven repository or another configured HTTP server.
The newly created container is pushed to the container registry that is specified in .spec.build.output
, and is used to deploy a Kafka Connect cluster. After AMQ Streams builds the Kafka Connect image, you create KafkaConnector
custom resources to start the connectors that are included in the build.
Prerequisites
- You have access to an OpenShift cluster on which the cluster Operator is installed.
- The AMQ Streams Operator is running.
- An Apache Kafka cluster is deployed as documented in Deploying and Upgrading AMQ Streams on OpenShift.
- Kafka Connect is deployed on AMQ Streams
- You have a Red Hat Integration license.
-
The OpenShift
oc
CLI client is installed or you have access to the OpenShift Container Platform web console. Depending on how you intend to store the Kafka Connect build image, you need registry permissions or you must create an ImageStream resource:
- To store the build image in an image registry, such as Red Hat Quay.io or Docker Hub
- An account and permissions to create and manage images in the registry.
- To store the build image as a native OpenShift ImageStream
- An ImageStream resource is deployed to the cluster. You must explicitly create an ImageStream for the cluster. ImageStreams are not available by default.
Procedure
- Log in to the OpenShift cluster.
Create a Debezium
KafkaConnect
custom resource (CR) for the connector, or modify an existing one. For example, create aKafkaConnect
CR that specifies themetadata.annotations
andspec.build
properties, as shown in the following example. Save the file with a name such asdbz-connect.yaml
.Example 6.1. A
dbz-connect.yaml
file that defines aKafkaConnect
custom resource that includes a Debezium connectorIn the example that follows, the custom resource is configured to download the following artifacts:
- The Debezium Oracle connector archive.
- The Service Registry archive. The Service Registry is an optional component. Add the Service Registry component only if you intend to use Avro serialization with the connector.
- The Debezium scripting SMT archive and the associated language dependencies that you want to use with the Debezium connector. The SMT archive and language dependencies are optional components. Add these components only if you intend to use the Debezium content-based routing SMT or filter SMT.
- The Oracle JDBC driver, which is required to connect to Oracle databases, but is not included in the connector archive.
apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect metadata: name: debezium-kafka-connect-cluster annotations: strimzi.io/use-connector-resources: "true" 1 spec: version: 3.00 build: 2 output: 3 type: imagestream 4 image: debezium-streams-connect:latest plugins: 5 - name: debezium-connector-oracle artifacts: - type: zip 6 url: https://maven.repository.redhat.com/ga/io/debezium/debezium-connector-oracle/1.9.7.Final-redhat-00003/debezium-connector-oracle-1.9.7.Final-redhat-00003-plugin.zip 7 - type: zip url: https://maven.repository.redhat.com/ga/io/apicurio/apicurio-registry-distro-connect-converter/2.3.0.Final-redhat-<build-number>/apicurio-registry-distro-connect-converter-2.3.0.Final-redhat-<build-number>.zip 8 - type: zip url: https://maven.repository.redhat.com/ga/io/debezium/debezium-scripting/1.9.7.Final-redhat-00003/debezium-scripting-1.9.7.Final-redhat-00003.zip 9 - type: jar url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy/3.0.11/groovy-3.0.11.jar 10 - type: jar url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy-jsr223/3.0.11/groovy-jsr223-3.0.11.jar - type: jar url: https://repo1.maven.org/maven2/org/codehaus/groovy/groovy-json3.0.11/groovy-json-3.0.11.jar - type: jar 11 url: https://repo1.maven.org/maven2/com/oracle/database/jdbc/ojdbc8/21.1.0.0/ojdbc8-21.1.0.0.jar bootstrapServers: debezium-kafka-cluster-kafka-bootstrap:9093
Table 6.11. Descriptions of Kafka Connect configuration settings Item Description 1
Sets the
strimzi.io/use-connector-resources
annotation to"true"
to enable the Cluster Operator to useKafkaConnector
resources to configure connectors in this Kafka Connect cluster.2
The
spec.build
configuration specifies where to store the build image and lists the plug-ins to include in the image, along with the location of the plug-in artifacts.3
The
build.output
specifies the registry in which the newly built image is stored.4
Specifies the name and image name for the image output. Valid values for
output.type
aredocker
to push into a container registry like Docker Hub or Quay, orimagestream
to push the image to an internal OpenShift ImageStream. To use an ImageStream, an ImageStream resource must be deployed to the cluster. For more information about specifying thebuild.output
in the KafkaConnect configuration, see the AMQ Streams Build schema reference documentation.5
The
plugins
configuration lists all of the connectors that you want to include in the Kafka Connect image. For each entry in the list, specify a plug-inname
, and information for about the artifacts that are required to build the connector. Optionally, for each connector plug-in, you can include other components that you want to be available for use with the connector. For example, you can add Service Registry artifacts, or the Debezium scripting component.6
The value of
artifacts.type
specifies the file type of the artifact specified in theartifacts.url
. Valid types arezip
,tgz
, orjar
. Debezium connector archives are provided in.zip
file format. JDBC driver files are in.jar
format. Thetype
value must match the type of the file that is referenced in theurl
field.7
The value of
artifacts.url
specifies the address of an HTTP server, such as a Maven repository, that stores the file for the connector artifact. Debezium connector artifacts are available in the Red Hat Maven repository. The OpenShift cluster must have access to the specified server.8
(Optional) Specifies the artifact
type
andurl
for downloading the Service Registry component. Include the Service Registry artifact, only if you want the connector to use Apache Avro to serialize event keys and values with the Service Registry, instead of using the default JSON converter.9
(Optional) Specifies the artifact
type
andurl
for the Debezium scripting SMT archive to use with the Debezium connector. Include the scripting SMT only if you intend to use the Debezium content-based routing SMT or filter SMT To use the scripting SMT, you must also deploy a JSR 223-compliant scripting implementation, such as groovy.10
(Optional) Specifies the artifact
type
andurl
for the JAR files of a JSR 223-compliant scripting implementation, which is required by the Debezium scripting SMT.ImportantIf you use AMQ Streams to incorporate the connector plug-in into your Kafka Connect image, for each of the required scripting language components
artifacts.url
must specify the location of a JAR file, and the value ofartifacts.type
must also be set tojar
. Invalid values cause the connector fails at runtime.To enable use of the Apache Groovy language with the scripting SMT, the custom resource in the example retrieves JAR files for the following libraries:
-
groovy
-
groovy-jsr223
(scripting agent) -
groovy-json
(module for parsing JSON strings)
The Debezium scripting SMT also supports the use of the JSR 223 implementation of GraalVM JavaScript.
11
Specifies the location of the Oracle JDBC driver in Maven Central. The required driver is not included in the Debezium Oracle connector archive.
Apply the
KafkaConnect
build specification to the OpenShift cluster by entering the following command:oc create -f dbz-connect.yaml
Based on the configuration specified in the custom resource, the Streams Operator prepares a Kafka Connect image to deploy.
After the build completes, the Operator pushes the image to the specified registry or ImageStream, and starts the Kafka Connect cluster. The connector artifacts that you listed in the configuration are available in the cluster.Create a
KafkaConnector
resource to define an instance of each connector that you want to deploy.
For example, create the followingKafkaConnector
CR, and save it asoracle-inventory-connector.yaml
Example 6.2.
oracle-inventory-connector.yaml
file that defines theKafkaConnector
custom resource for a Debezium connectorapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnector metadata: labels: strimzi.io/cluster: debezium-kafka-connect-cluster name: inventory-connector-oracle 1 spec: class: io.debezium.connector.oracle.OracleConnector 2 tasksMax: 1 3 config: 4 database.history.kafka.bootstrap.servers: debezium-kafka-cluster-kafka-bootstrap.debezium.svc.cluster.local:9092 database.history.kafka.topic: schema-changes.inventory database.hostname: oracle.debezium-oracle.svc.cluster.local 5 database.port: 1521 6 database.user: debezium 7 database.password: dbz 8 database.dbname: mydatabase 9 database.server.name: inventory-connector-oracle 10 table.include.list: PUBLIC.INVENTORY 11
Table 6.12. Descriptions of connector configuration settings Item Description 1
The name of the connector to register with the Kafka Connect cluster.
2
The name of the connector class.
3
The number of tasks that can operate concurrently.
4
The connector’s configuration.
5
The address of the host database instance.
6
The port number of the database instance.
7
The name of the user account through which Debezium connects to the database.
8
The password for the database user account.
9
The name of the database to capture changes from.
10
The logical name of the database instance or cluster.
The specified name must be formed only from alphanumeric characters or underscores.
Because the logical name is used as the prefix for any Kafka topics that receive change events from this connector, the name must be unique among the connectors in the cluster.
The namespace is also used in the names of related Kafka Connect schemas, and the namespaces of a corresponding Avro schema if you integrate the connector with the Avro connector.11
The list of tables from which the connector captures change events.
Create the connector resource by running the following command:
oc create -n <namespace> -f <kafkaConnector>.yaml
For example,
oc create -n debezium -f {context}-inventory-connector.yaml
The connector is registered to the Kafka Connect cluster and starts to run against the database that is specified by
spec.config.database.dbname
in theKafkaConnector
CR. After the connector pod is ready, Debezium is running.
You are now ready to verify the Debezium Oracle deployment.
6.5.4. Deploying a Debezium Oracle connector by building a custom Kafka Connect container image from a Dockerfile
To deploy a Debezium Oracle connector, you must build a custom Kafka Connect container image that contains the Debezium connector archive, and then push this container image to a container registry. You then need to create the following custom resources (CRs):
-
A
KafkaConnect
CR that defines your Kafka Connect instance. Theimage
property in the CR specifies the name of the container image that you create to run your Debezium connector. You apply this CR to the OpenShift instance where Red Hat AMQ Streams is deployed. AMQ Streams offers operators and images that bring Apache Kafka to OpenShift. -
A
KafkaConnector
CR that defines your Debezium Oracle connector. Apply this CR to the same OpenShift instance where you apply theKafkaConnect
CR.
Prerequisites
- Oracle Database is running and you completed the steps to set up Oracle to work with a Debezium connector.
- AMQ Streams is deployed on OpenShift and is running Apache Kafka and Kafka Connect. For more information, see Deploying and Upgrading AMQ Streams on OpenShift
- Podman or Docker is installed.
-
You have an account and permissions to create and manage containers in the container registry (such as
quay.io
ordocker.io
) to which you plan to add the container that will run your Debezium connector. The Kafka Connect server has access to Maven Central to download the required JDBC driver for Oracle. You can also use a local copy of the driver, or one that is available from a local Maven repository or other HTTP server.
For more information, see Obtaining the Oracle JDBC driver.
Procedure
Create the Debezium Oracle container for Kafka Connect:
Create a Dockerfile that uses
registry.redhat.io/amq7/amq-streams-kafka-32-rhel8:2.2.0-12
as the base image. For example, from a terminal window, enter the following command:cat <<EOF >debezium-container-for-oracle.yaml 1 FROM registry.redhat.io/amq7/amq-streams-kafka-32-rhel8:2.2.0-12 USER root:root RUN mkdir -p /opt/kafka/plugins/debezium 2 RUN cd /opt/kafka/plugins/debezium/ \ && curl -O https://maven.repository.redhat.com/ga/io/debezium/debezium-connector-oracle/1.9.7.Final-redhat-<build_number>/debezium-connector-oracle-1.9.7.Final-redhat-<build_number>-plugin.zip \ && unzip debezium-connector-oracle-1.9.7.Final-redhat-<build_number>-plugin.zip \ && rm debezium-connector-oracle-1.9.7.Final-redhat-<build_number>-plugin.zip RUN cd /opt/kafka/plugins/debezium/ \ && curl -O https://repo1.maven.org/maven2/com/oracle/ojdbc/ojdbc8/21.1.0.0/ojdbc8-21.1.0.0.jar USER 1001 EOF
Item Description 1
You can specify any file name that you want.
2
Specifies the path to your Kafka Connect plug-ins directory. If your Kafka Connect plug-ins directory is in a different location, replace this path with the actual path of your directory.
The command creates a Dockerfile with the name
debezium-container-for-oracle.yaml
in the current directory.Build the container image from the
debezium-container-for-oracle.yaml
Docker file that you created in the previous step. From the directory that contains the file, open a terminal window and enter one of the following commands:podman build -t debezium-container-for-oracle:latest .
docker build -t debezium-container-for-oracle:latest .
The preceding commands build a container image with the name
debezium-container-for-oracle
.Push your custom image to a container registry, such as quay.io or an internal container registry. The container registry must be available to the OpenShift instance where you want to deploy the image. Enter one of the following commands:
podman push <myregistry.io>/debezium-container-for-oracle:latest
docker push <myregistry.io>/debezium-container-for-oracle:latest
Create a new Debezium Oracle KafkaConnect custom resource (CR). For example, create a KafkaConnect CR with the name
dbz-connect.yaml
that specifiesannotations
andimage
properties as shown in the following example:apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect metadata: name: my-connect-cluster annotations: strimzi.io/use-connector-resources: "true" 1 spec: image: debezium-container-for-oracle 2
Item Description 1
metadata.annotations
indicates to the Cluster Operator thatKafkaConnector
resources are used to configure connectors in this Kafka Connect cluster.2
spec.image
specifies the name of the image that you created to run your Debezium connector. This property overrides theSTRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE
variable in the Cluster Operator.Apply the
KafkaConnect
CR to the OpenShift Kafka Connect environment by entering the following command:oc create -f dbz-connect.yaml
The command adds a Kafka Connect instance that specifies the name of the image that you created to run your Debezium connector.
Create a
KafkaConnector
custom resource that configures your Debezium Oracle connector instance.You configure a Debezium Oracle connector in a
.yaml
file that specifies the configuration properties for the connector. The connector configuration might instruct Debezium to produce events for a subset of the schemas and tables, or it might set properties so that Debezium ignores, masks, or truncates values in specified columns that are sensitive, too large, or not needed.The following example configures a Debezium connector that connects to an Oracle host IP address, on port
1521
. This host has a database namedORCLCDB
, andserver1
is the server’s logical name.Oracle
inventory-connector.yaml
apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnector metadata: name: inventory-connector 1 labels: strimzi.io/cluster: my-connect-cluster annotations: strimzi.io/use-connector-resources: 'true' spec: class: io.debezium.connector.oracle.OracleConnector 2 config: database.hostname: <oracle_ip_address> 3 database.port: 1521 4 database.user: c##dbzuser 5 database.password: dbz 6 database.dbname: ORCLCDB 7 database.pdb.name : ORCLPDB1, 8 database.server.name: server1 9 database.history.kafka.bootstrap.servers: kafka:9092 10 database.history.kafka.topic: schema-changes.inventory 11
Table 6.13. Descriptions of connector configuration settings Item Description 1
The name of our connector when we register it with a Kafka Connect service.
2
The name of this Oracle connector class.
3
The address of the Oracle instance.
4
The port number of the Oracle instance.
5
The name of the Oracle user, as specified in Creating users for the connector.
6
The password for the Oracle user, as specified in Creating users for the connector.
7
The name of the database to capture changes from.
8
The name of the Oracle pluggable database that the connector captures changes from. Used in container database (CDB) installations only.
9
Logical name that identifies and provides a namespace for the Oracle database server from which the connector captures changes.
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.
Create your connector instance with Kafka Connect. For example, if you saved your
KafkaConnector
resource in theinventory-connector.yaml
file, you would run the following command:oc apply -f inventory-connector.yaml
The preceding command registers
inventory-connector
and the connector starts to run against theserver1
database as defined in theKafkaConnector
CR.
For the complete list of the configuration properties that you can set for the Debezium Oracle connector, see Oracle connector properties.
Results
After the connector starts, it performs a consistent snapshot of the Oracle databases that the connector is configured for. The connector then starts generating data change events for row-level operations and streaming the change event records to Kafka topics.
6.5.5. Configuration of container databases and non-container-databases
Oracle Database supports the following deployment types:
- Container database (CDB)
- A database that can contain multiple pluggable databases (PDBs). Database clients connect to each PDB as if it were a standard, non-CDB database.
- Non-container database (non-CDB)
- A standard Oracle database, which does not support the creation of pluggable databases.
6.5.6. Verifying that the Debezium Oracle connector is running
If the connector starts correctly without errors, it creates a topic for each table that the connector is configured to capture. Downstream applications can subscribe to these topics to retrieve information events that occur in the source database.
To verify that the connector is running, you perform the following operations from the OpenShift Container Platform web console, or through the OpenShift CLI tool (oc):
- Verify the connector status.
- Verify that the connector generates topics.
- Verify that topics are populated with events for read operations ("op":"r") that the connector generates during the initial snapshot of each table.
Prerequisites
- A Debezium connector is deployed to AMQ Streams on OpenShift.
-
The OpenShift
oc
CLI client is installed. - You have access to the OpenShift Container Platform web console.
Procedure
Check the status of the
KafkaConnector
resource by using one of the following methods:From the OpenShift Container Platform web console:
-
Navigate to Home
Search. -
On the Search page, click Resources to open the Select Resource box, and then type
KafkaConnector
. - From the KafkaConnectors list, click the name of the connector that you want to check, for example inventory-connector-oracle.
- In the Conditions section, verify that the values in the Type and Status columns are set to Ready and True.
-
Navigate to Home
From a terminal window:
Enter the following command:
oc describe KafkaConnector <connector-name> -n <project>
For example,
oc describe KafkaConnector inventory-connector-oracle -n debezium
The command returns status information that is similar to the following output:
Example 6.3.
KafkaConnector
resource statusName: inventory-connector-oracle Namespace: debezium Labels: strimzi.io/cluster=debezium-kafka-connect-cluster Annotations: <none> API Version: kafka.strimzi.io/v1beta2 Kind: KafkaConnector ... Status: Conditions: Last Transition Time: 2021-12-08T17:41:34.897153Z Status: True Type: Ready Connector Status: Connector: State: RUNNING worker_id: 10.131.1.124:8083 Name: inventory-connector-oracle Tasks: Id: 0 State: RUNNING worker_id: 10.131.1.124:8083 Type: source Observed Generation: 1 Tasks Max: 1 Topics: inventory_connector_oracle inventory_connector_oracle.inventory.addresses inventory_connector_oracle.inventory.customers inventory_connector_oracle.inventory.geom inventory_connector_oracle.inventory.orders inventory_connector_oracle.inventory.products inventory_connector_oracle.inventory.products_on_hand Events: <none>
Verify that the connector created Kafka topics:
From the OpenShift Container Platform web console.
-
Navigate to Home
Search. -
On the Search page, click Resources to open the Select Resource box, and then type
KafkaTopic
. - From the KafkaTopics list, click the name of the topic that you want to check, for example, inventory-connector-oracle.inventory.orders---ac5e98ac6a5d91e04d8ec0dc9078a1ece439081d.
- In the Conditions section, verify that the values in the Type and Status columns are set to Ready and True.
-
Navigate to Home
From a terminal window:
Enter the following command:
oc get kafkatopics
The command returns status information that is similar to the following output:
Example 6.4.
KafkaTopic
resource statusNAME CLUSTER PARTITIONS REPLICATION FACTOR READY connect-cluster-configs debezium-kafka-cluster 1 1 True connect-cluster-offsets debezium-kafka-cluster 25 1 True connect-cluster-status debezium-kafka-cluster 5 1 True consumer-offsets---84e7a678d08f4bd226872e5cdd4eb527fadc1c6a debezium-kafka-cluster 50 1 True inventory-connector-oracle---a96f69b23d6118ff415f772679da623fbbb99421 debezium-kafka-cluster 1 1 True inventory-connector-oracle.inventory.addresses---1b6beaf7b2eb57d177d92be90ca2b210c9a56480 debezium-kafka-cluster 1 1 True inventory-connector-oracle.inventory.customers---9931e04ec92ecc0924f4406af3fdace7545c483b debezium-kafka-cluster 1 1 True inventory-connector-oracle.inventory.geom---9f7e136091f071bf49ca59bf99e86c713ee58dd5 debezium-kafka-cluster 1 1 True inventory-connector-oracle.inventory.orders---ac5e98ac6a5d91e04d8ec0dc9078a1ece439081d debezium-kafka-cluster 1 1 True inventory-connector-oracle.inventory.products---df0746db116844cee2297fab611c21b56f82dcef debezium-kafka-cluster 1 1 True inventory-connector-oracle.inventory.products-on-hand---8649e0f17ffcc9212e266e31a7aeea4585e5c6b5 debezium-kafka-cluster 1 1 True schema-changes.inventory debezium-kafka-cluster 1 1 True strimzi-store-topic---effb8e3e057afce1ecf67c3f5d8e4e3ff177fc55 debezium-kafka-cluster 1 1 True strimzi-topic-operator-kstreams-topic-store-changelog---b75e702040b99be8a9263134de3507fc0cc4017b debezium-kafka-cluster 1 1 True
Check topic content.
- From a terminal window, enter the following command:
oc exec -n <project> -it <kafka-cluster> -- /opt/kafka/bin/kafka-console-consumer.sh \ > --bootstrap-server localhost:9092 \ > --from-beginning \ > --property print.key=true \ > --topic=<topic-name>
For example,
oc exec -n debezium -it debezium-kafka-cluster-kafka-0 -- /opt/kafka/bin/kafka-console-consumer.sh \ > --bootstrap-server localhost:9092 \ > --from-beginning \ > --property print.key=true \ > --topic=inventory_connector_oracle.inventory.products_on_hand
The format for specifying the topic name is the same as the
oc describe
command returns in Step 1, for example,inventory_connector_oracle.inventory.addresses
.For each event in the topic, the command returns information that is similar to the following output:
Example 6.5. Content of a Debezium change event
{"schema":{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"}],"optional":false,"name":"inventory_connector_oracle.inventory.products_on_hand.Key"},"payload":{"product_id":101}} {"schema":{"type":"struct","fields":[{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"},{"type":"int32","optional":false,"field":"quantity"}],"optional":true,"name":"inventory_connector_oracle.inventory.products_on_hand.Value","field":"before"},{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"},{"type":"int32","optional":false,"field":"quantity"}],"optional":true,"name":"inventory_connector_oracle.inventory.products_on_hand.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":"string","optional":true,"name":"io.debezium.data.Enum","version":1,"parameters":{"allowed":"true,last,false"},"default":"false","field":"snapshot"},{"type":"string","optional":false,"field":"db"},{"type":"string","optional":true,"field":"sequence"},{"type":"string","optional":true,"field":"table"},{"type":"int64","optional":false,"field":"server_id"},{"type":"string","optional":true,"field":"gtid"},{"type":"string","optional":false,"field":"file"},{"type":"int64","optional":false,"field":"pos"},{"type":"int32","optional":false,"field":"row"},{"type":"int64","optional":true,"field":"thread"},{"type":"string","optional":true,"field":"query"}],"optional":false,"name":"io.debezium.connector.oracle.Source","field":"source"},{"type":"string","optional":false,"field":"op"},{"type":"int64","optional":true,"field":"ts_ms"},{"type":"struct","fields":[{"type":"string","optional":false,"field":"id"},{"type":"int64","optional":false,"field":"total_order"},{"type":"int64","optional":false,"field":"data_collection_order"}],"optional":true,"field":"transaction"}],"optional":false,"name":"inventory_connector_oracle.inventory.products_on_hand.Envelope"},"payload":{"before":null,"after":{"product_id":101,"quantity":3},"source":{"version":"1.9.7.Final-redhat-00001","connector":"oracle","name":"inventory_connector_oracle","ts_ms":1638985247805,"snapshot":"true","db":"inventory","sequence":null,"table":"products_on_hand","server_id":0,"gtid":null,"file":"oracle-bin.000003","pos":156,"row":0,"thread":null,"query":null},"op":"r","ts_ms":1638985247805,"transaction":null}}
In the preceding example, the
payload
value shows that the connector snapshot generated a read ("op" ="r"
) event from the tableinventory.products_on_hand
. The"before"
state of theproduct_id
record isnull
, indicating that no previous value exists for the record. The"after"
state shows aquantity
of3
for the item withproduct_id
101
.
6.6. Descriptions of Debezium Oracle connector configuration properties
The Debezium Oracle connector has numerous configuration properties that you can use to achieve the right connector behavior for your application. Many properties have default values. Information about the properties is organized as follows:
- Required Debezium Oracle connector configuration properties
Database history connector configuration properties that control how Debezium processes events that it reads from the database history topic.
- Pass-through database driver properties that control the behavior of the database driver.
Required Debezium Oracle 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 | |
No default |
Enumerates a comma-separated list of the symbolic names of the custom converter instances that the connector can use.
For each converter that you configure for a connector, you must also add a
For example, boolean.type: io.debezium.connector.oracle.converters.NumberOneToBooleanConverter
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 parameters with a converter, prefix the parameter names with the symbolic name of the converter. boolean.selector: .*MYTABLE.FLAG,.*.IS_ARCHIVED | |
| The maximum number of tasks to create for this connector. The Oracle 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 Oracle database server. | |
No default | Integer port number of the Oracle database server. | |
No default | Name of the Oracle user account that the connector uses to connect to the Oracle database server. | |
No default | Password to use when connecting to the Oracle database server. | |
No default | Name of the database to connect to. Must be the CDB name when working with the CDB + PDB model. | |
No default | Specifies the raw database JDBC URL. Use this property to provide flexibility in defining that database connection. Valid values include raw TNS names and RAC connection strings. | |
No default | Name of the Oracle pluggable database to connect to. Use this property with container database (CDB) installations only. | |
No default |
Logical name that identifies and provides a namespace for the Oracle database server from which the connector captures changes. The value that you set is used as a prefix for all Kafka topic names that the connector emits. Specify a logical name that is unique among all connectors in your Debezium environment. The following characters are valid: alphanumeric characters, hyphens, dots, and underscores. Warning Do not change the value of this property. If you change the name value, after a restart, instead of continuing to emit events to the original topics, the connector emits subsequent events to topics whose names are based on the new value. The connector is also unable to recover its database history topic. | |
|
The adapter implementation that the connector uses when it streams database changes. You can set the following values: | |
initial | Specifies the mode that the connector uses to take snapshots of a captured table. You can set the following values:
After the snapshot is complete, the connector continues to read change events from the database’s redo logs except when
For more information, see the table of | |
shared | Controls whether and for how long the connector holds a table lock. Table locks prevent certain types of changes table operations from occurring while the connector performs a snapshot. You can set the following values:
| |
All tables specified in |
An optional, comma-separated list of regular expressions that match the fully-qualified names ( This property does not affect the behavior of incremental snapshots. | |
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 | |
No default |
An optional, comma-separated list of regular expressions that match names of schemas for which you want to capture changes. Any schema name not included in | |
| Boolean value that specifies whether the connector should parse and publish table and column comments on metadata objects. Enabling this option will bring the implications on memory usage. The number and size of logical schema objects is what largely impacts how much memory is consumed by the Debezium connectors, and adding potentially large string data to each of them can potentially be quite expensive. | |
No default |
An optional, comma-separated list of regular expressions that match names of schemas for which you do not want to capture changes. Any schema whose name is not included in | |
No default |
An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be monitored. Tables that are not included in the include list are excluded from monitoring. Each table identifier uses the following format: | |
No default |
An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be excluded from monitoring. The connector captures change events from any table that is not specified in the exclude list. Specify the identifier for each table using the following format:
Do not use this property in combination with | |
No default |
An optional comma-separated list of regular expressions that match the fully-qualified names of columns that want to include in the change event message values. Fully-qualified names for columns use the following format: | |
No default |
An optional comma-separated list of regular expressions that match the fully-qualified names of columns that you want to exclude from change event message values. Fully-qualified column names use the following format: | |
| 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
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. |
bytes |
Specifies how binary ( | |
avro |
Specifies how schema names should be adjusted for compatibility with the message converter used by the connector. Possible settings:
| |
|
Specifies how the connector should handle floating point values for
| |
|
Specifies how the connector should handle values for | |
| Specifies how the connector should react to exceptions during processing of events. You can set one of the following options:
| |
| A positive integer value that specifies the maximum size of each batch of events to process during each iteration of this connector. | |
|
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. | |
| Positive integer value that specifies the number of milliseconds the connector should wait during each iteration for new change events to appear. | |
| Controls whether a delete event is followed by a tombstone event. The following values are possible:
After a source record is deleted, a tombstone event (the default behavior) enables Kafka to completely delete all events that share the key of the deleted row in topics that have log compaction enabled. | |
No default | 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. | |
No default |
An optional comma-separated list of regular expressions that match the fully-qualified names of character-based columns to be truncated in change event messages if their length exceeds the specified number of characters. Length is specified as a positive integer. A configuration can include multiple properties that specify different lengths. Specify the fully-qualified name for columns by using the following format: | |
No default |
An optional comma-separated list of regular expressions for masking column names in change event messages by replacing characters with asterisks ( | |
No default |
An optional comma-separated list of regular expressions that match the fully-qualified names of columns whose original type and length should be added as a parameter to the corresponding field schemas in the emitted change messages. The schema parameters | |
No default |
An optional comma-separated list of regular expressions that match the database-specific data type name of columns whose original type and length should be added as a parameter to the corresponding field schemas in the emitted change messages. The schema parameters | |
|
Specifies, in milliseconds, how frequently the connector sends messages to a heartbeat topic. | |
No default |
Specifies a query that the connector executes on the source database when the connector sends a heartbeat message. Set this property and create a heartbeat table to receive the heartbeat messages to resolve situations in which Debezium fails to synchronize offsets on low-traffic databases that are on the same host as a high-traffic database. After the connector inserts records into the configured table, it is able to receive changes from the low-traffic database and acknowledge SCN changes in the database, so that offsets can be synchronized with the broker. | |
|
Specifies the string that prefixes the name of the topic to which the connector sends heartbeat messages. | |
No default |
Specifies an interval in milliseconds that the connector waits after it starts before it takes a snapshot. | |
| Specifies the maximum number of rows that should be read in one go from each table while taking a snapshot. The connector reads table contents in multiple batches of the specified size. | |
| Specifies whether field names are normalized to comply with Avro naming requirements. For more information, see Avro naming. | |
|
Set the property to See Transaction Metadata for additional details. | |
|
Controls the name of the topic to which the connector sends transaction metadata messages. The placeholder | |
|
Specifies the mining strategy that controls how Oracle LogMiner builds and uses a given data dictionary for resolving table and column ids to names. | |
|
The buffer type controls how the connector manages buffering transaction data. | |
|
The maximum number of milliseconds that a LogMiner session can be active before a new session is used. | |
| The minimum SCN interval size that this connector attempts to read from redo/archive logs. Active batch size is also increased/decreased by this amount for tuning connector throughput when needed. | |
| The maximum SCN interval size that this connector uses when reading from redo/archive logs. | |
| The starting SCN interval size that the connector uses for reading data from redo/archive logs. | |
| The minimum amount of time that the connector sleeps after reading data from redo/archive logs and before starting reading data again. Value is in milliseconds. | |
| The maximum amount of time that the connector ill sleeps after reading data from redo/archive logs and before starting reading data again. Value is in milliseconds. | |
| The starting amount of time that the connector sleeps after reading data from redo/archive logs and before starting reading data again. Value is in milliseconds. | |
| The maximum amount of time up or down that the connector uses to tune the optimal sleep time when reading data from logminer. Value is in milliseconds. | |
| The number of content records that the connector fetches from the LogMiner content view. | |
|
The number of hours in the past from SYSDATE to mine archive logs. When the default setting ( | |
|
Controls whether or not the connector mines changes from just archive logs or a combination of the online redo logs and archive logs (the default). | |
|
The number of milliseconds the connector will sleep in between polling to determine if the starting system change number is in the archive logs. If | |
|
Positive integer value that specifies the number of hours to retain long running transactions between redo log switches. When set to The LogMiner adapter maintains an in-memory buffer of all running transactions. Because all of the DML operations that are part of a transaction are buffered until a commit or rollback is detected, long-running transactions should be avoided in order to not overflow that buffer. Any transaction that exceeds this configured value is discarded entirely, and the connector does not emit any messages for the operations that were part of the transaction. | |
No default |
Specifies the configured Oracle archive destination to use when mining archive logs with LogMiner. | |
No default | List of database users to exclude from the LogMiner query. It can be useful to set this property if you want the capturing process to always exclude the changes that specific users make. | |
|
Specifies a value that the connector compares to the difference between the current and previous SCN values to determine whether an SCN gap exists. If the difference between the SCN values is greater than the specified value, and the time difference is smaller than | |
|
Specifies a value, in milliseconds, that the connector compares to the difference between the current and previous SCN timestamps to determine whether an SCN gap exists. If the difference between the timestamps is less than the specified value, and the SCN delta is greater than | |
|
Controls whether or not large object (CLOB or BLOB) column values are emitted in change events. Note Use of large object data types is a Technology Preview feature. | |
| Specifies the constant that the connector provides to indicate that the original value is unchanged and not provided by the database. | |
No default | A comma-separated list of Oracle Real Application Clusters (RAC) node host names or addresses. This field is required to enable compatibility with an Oracle RAC deployment. Specify the list of RAC nodes by using one of the following methods:
If you supply a raw JDBC URL for the database by using the | |
No default | 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:
By default, no operations are skipped. | |
No default value |
Fully-qualified name of the data collection that is used to send signals to the connector. When you use this property with an Oracle pluggable database (PDB), set its value to the name of the root database. | |
| 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 Oracle connector database history configuration properties
Debezium provides a set of database.history.*
properties that control how the connector interacts with the schema history topic.
The following table describes the database.history
properties for configuring the Debezium connector.
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. | |
|
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 | |
Deprecated and scheduled for removal in a future release; use |
|
A Boolean value that specifies whether the connector should record all DDL statements
The safe default is |
|
A Boolean value that specifies whether the connector should record all DDL statements
The safe default is |
Pass-through database history properties for configuring producer and consumer clients
Debezium relies on a Kafka producer to write schema changes to database history topics. Similarly, it relies on a Kafka consumer to read from database history topics when a connector starts. You define the configuration for the Kafka producer and consumer clients by assigning values to a set of pass-through configuration properties that begin with the database.history.producer.*
and database.history.consumer.*
prefixes. The pass-through producer and consumer database history properties control a range of behaviors, such as how these clients secure connections with the Kafka broker, as shown in the following example:
database.history.producer.security.protocol=SSL database.history.producer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks database.history.producer.ssl.keystore.password=test1234 database.history.producer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks database.history.producer.ssl.truststore.password=test1234 database.history.producer.ssl.key.password=test1234 database.history.consumer.security.protocol=SSL database.history.consumer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks database.history.consumer.ssl.keystore.password=test1234 database.history.consumer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks database.history.consumer.ssl.truststore.password=test1234 database.history.consumer.ssl.key.password=test1234
Debezium strips the prefix from the property name before it passes the property to the Kafka client.
See the Kafka documentation for more details about Kafka producer configuration properties and Kafka consumer configuration properties.
Debezium Oracle connector pass-through database driver configuration properties
The Debezium connector provides for pass-through configuration of the database driver. Pass-through database properties begin with the prefix database.*
. For example, the connector passes properties such as database.foobar=false
to the JDBC URL.
As is the case with the pass-through properties for database history clients, Debezium strips the prefixes from the properties before it passes them to the database driver.
6.7. Monitoring Debezium Oracle connector performance
The Debezium Oracle connector provides three metric types in addition to the built-in support for JMX metrics that Apache Zookeeper, Apache Kafka, and Kafka Connect have.
- snapshot metrics; for monitoring the connector when performing snapshots
- streaming metrics; for monitoring the connector when processing change events
- schema history metrics; for monitoring the status of the connector’s schema history
Please refer to the monitoring documentation for details of how to expose these metrics via JMX.
6.7.1. Debezium Oracle connector snapshot metrics
The MBean is debezium.oracle:type=connector-metrics,context=snapshot,server=<oracle.server.name>
. Snapshot metrics are not exposed unless a snapshot operation is active, or if a snapshot has occurred since the last connector start.
The following table lists the shapshot metrics that are available.
Attributes | Type | Description |
---|---|---|
| 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 monitored by 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 aborted. | |
| Whether the snapshot completed. | |
| The total number of seconds that the snapshot has taken so far, even if not complete. | |
| Map containing the number of rows scanned for each table in the snapshot. Tables are incrementally added to the Map during processing. Updates every 10,000 rows scanned and upon completing a table. | |
|
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. |
6.7.2. Debezium Oracle connector streaming metrics
The MBean is debezium.oracle:type=connector-metrics,context=streaming,server=<oracle.server.name>
. 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 events that this connector has seen since the last start or metrics reset. | |
| The total number of create events that this connector has seen since the last start or metrics reset. | |
| The total number of update events that this connector has seen since the last start or metrics reset. | |
| The total number of delete events that this connector has seen since the 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 monitored by 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 incoporate any differences between the clocks on the machines where the database server and the connector are running. | |
| The number of processed transactions that were committed. | |
| The coordinates of the last received event. | |
| Transaction identifier of the last processed transaction. | |
|
The maximum buffer of the queue in bytes. This metric is available if | |
| The current volume, in bytes, of records in the queue. |
The Debezium Oracle connector also provides the following additional streaming metrics:
Attributes | Type | Description |
---|---|---|
| The most recent system change number that has been processed. | |
| The oldest system change number in the transaction buffer. | |
| The last committed system change number from the transaction buffer. | |
| The system change number currently written to the connector’s offsets. | |
| Array of the log files that are currently mined. | |
| The minimum number of logs specified for any LogMiner session. | |
| The maximum number of logs specified for any LogMiner session. | |
|
Array of the current state for each mined logfile with the format | |
| The number of times the database has performed a log switch for the last day. | |
| The number of DML operations observed in the last LogMiner session query. | |
| The maximum number of DML operations observed while processing a single LogMiner session query. | |
| The total number of DML operations observed. | |
| The total number of LogMiner session query (aka batches) performed. | |
| The duration of the last LogMiner session query’s fetch in milliseconds. | |
| The maximum duration of any LogMiner session query’s fetch in milliseconds. | |
| The duration for processing the last LogMiner query batch results in milliseconds. | |
| The time in milliseconds spent parsing DML event SQL statements. | |
| The duration in milliseconds to start the last LogMiner session. | |
| The longest duration in milliseconds to start a LogMiner session. | |
| The total duration in milliseconds spent by the connector starting LogMiner sessions. | |
| The minimum duration in milliseconds spent processing results from a single LogMiner session. | |
| The maximum duration in milliseconds spent processing results from a single LogMiner session. | |
| The total duration in milliseconds spent processing results from LogMiner sessions. | |
| The total duration in milliseconds spent by the JDBC driver fetching the next row to be processed from the log mining view. | |
| The total number of rows processed from the log mining view across all sessions. | |
| The number of entries fetched by the log mining query per database round-trip. | |
| The number of milliseconds the connector sleeps before fetching another batch of results from the log mining view. | |
| The maximum number of rows/second processed from the log mining view. | |
| The average number of rows/second processed from the log mining. | |
| The average number of rows/second processed from the log mining view for the last batch. | |
| The number of connection problems detected. | |
|
The number of hours that transactions are retained by the connector’s in-memory buffer without being committed or rolled back before being discarded. See | |
| The number of current active transactions in the transaction buffer. | |
| The number of committed transactions in the transaction buffer. | |
| The number of rolled back transactions in the transaction buffer. | |
| The average number of committed transactions per second in the transaction buffer. | |
| The number of registered DML operations in the transaction buffer. | |
| The time difference in milliseconds between when a change occurred in the transaction logs and when its added to the transaction buffer. | |
| The maximum time difference in milliseconds between when a change occurred in the transaction logs and when its added to the transaction buffer. | |
| The minimum time difference in milliseconds between when a change occurred in the transaction logs and when its added to the transaction buffer. | |
|
An array of the most recent abandoned transaction identifiers removed from the transaction buffer due to their age. See | |
| An array of the most recent transaction identifiers that have been mined and rolled back in the transaction buffer. | |
| The duration of the last transaction buffer commit operation in milliseconds. | |
| The duration of the longest transaction buffer commit operation in milliseconds. | |
| The number of errors detected. | |
| The number of warnings detected. | |
|
The number of times that the system change number was checked for advancement and remains unchanged. A high value can indicate that a long-running transactions is ongoing and is preventing the connector from flushing the most recently processed system change number to the connector’s offsets. When conditions are optimal, the value should be close to or equal to | |
|
The number of DDL records that have been detected but could not be parsed by the DDL parser. This should always be | |
| The current mining session’s user global area (UGA) memory consumption in bytes. | |
| The maximum mining session’s user global area (UGA) memory consumption in bytes across all mining sessions. | |
| The current mining session’s process global area (PGA) memory consumption in bytes. | |
| The maximum mining session’s process global area (PGA) memory consumption in bytes across all mining sessions. |
6.7.3. Debezium Oracle connector schema history metrics
The MBean is debezium.oracle:type=connector-metrics,context=schema-history,server=<oracle.server.name>
.
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. |
6.8. How Debezium Oracle connectors handle faults and problems
Debezium is a distributed system that captures all changes in multiple upstream databases; it never misses or loses an event. When the system is operating normally or being managed carefully then Debezium provides exactly once delivery of every change event record.
If a fault occurs, Debezium does not lose any events. However, while it is recovering from the fault, it might repeat some change events. In these abnormal situations, Debezium, like Kafka, provides at least once delivery of change events.
The rest of this section describes how Debezium handles various kinds of faults and problems.
Logs do not contain offset, perform a new snapshot
In some cases, after the Debezium Oracle connector restarts, it reports the following error:
Online REDO LOG files or archive logs do not contain the offset scn xxxxxxx. Please perform a new snapshot.
After the connector examines the redo and archive logs, if it cannot find the SCN that is recorded in the connector offsets, it returns the preceding error. Because the connector uses the SCN to determine where to resume processing, if the expected SCN if not found, a new snapshot must be completed.
You might find that the V$ARCHIVED_LOG
table contains a record with an SCN that matches the expected range. However, the record might not be available for mining. To be available for mining, a record must include a filename in the NAME
column, a value of NO
in the DELETED
column, and a value of A
(available) in the STATUS
column. If a record does not match any of these criteria, it is considered incomplete and cannot be mined.
At a minimum, archive logs must be retained for as long as the longest downtime window of the connector.
Records that have no value in the NAME
column no longer exist in the file system. In such records, the value of the DELETED
field is set to YES
, and the STATUS
field is set to D
to indicate that the log is deleted.
ORA-25191 - Cannot reference overflow table of an index-organized table
Oracle might issue this error during the snapshot phase when encountering an index-organized table (IOT). This error means that the connector has attempted to execute an operation that must be executed against the parent index-organized table that contains the specified overflow table.
To resolve this, the IOT name used in the SQL operation should be replaced with the parent index-organized table name. To determine the parent index-organized table name, use the following SQL:
SELECT IOT_NAME FROM DBA_TABLES WHERE OWNER='<tablespace-owner>' AND TABLE_NAME='<iot-table-name-that-failed>'
The connector’s table.include.list
or table.exclude.list
configuration options should then be adjusted to explicitly include or exclude the appropriate tables to avoid the connector from attempting to capture changes from the child index-organized table.
ORA-04036: PGA memory used by the instance exceeds PGA_AGGREGATE_LIMIT
Oracle might report this error when Debezium connects to a database in which changes occur infrequently. The Debezium connector starts an Oracle LogMiner session and reuses this session until a log switch is detected. The reuse is both a performance and resource utilization optimization; however, a long-running mining session can cause high Program Global Area (PGA) memory usage.
If your redo log switches infrequently, you can avoid the ORA-04036 error by specifying how frequently Oracle switches logs. A log switch causes the connector to restart the mining session, thereby avoiding high PGA memory usage. The following configuration forces Oracle to switch log files every 20 minutes if a log switch does not occur during that interval:
ALTER SYSTEM SET archive_lag_target=1200 scope=both;
Running the preceding query requires specific administrative privileges. Coordinate with your database administrator to implement the change.
If you are unable to adjust the Oracle database parameter, you can instead use the connector configuration option log.mining.session.max.ms
to control the duration of an Oracle LogMiner session so that it is restarted regularly even if a database log switch has not yet happened.
LogMiner adapter does not capture changes made by SYS or SYSTEM
Oracle uses the SYS
and SYSTEM
accounts to carry out many internal changes. When the Debezium Oracle connector fetches changes from LogMiner, it automatically filters changes that originate from these administrator accounts. To ensure that the connector emit event records when you change a table, never use the SYS
or SYSTEM
user accounts to modify the table.
Connector stops capturing changes from Oracle on AWS
Due to the fixed idle timeout of 350 seconds on the AWS Gateway Load Balancer, JDBC calls that require more than 350 seconds to complete can hang indefinitely.
In situations where calls to the Oracle LogMiner API take more than 350 seconds to complete, a timeout can be triggered, causing the AWS Gateway Load Balancer to hang. For example, such timeouts can occur when a LogMiner session that processes large amounts of data runs concurrently with Oracle’s periodic checkpointing task.
To prevent timeouts from occurring on the AWS Gateway Load Balancer, enable keep-alive packets from the Kafka Connect environment, by performing the following steps as root or a super-user:
From a terminal, run the following command:
sysctl -w net.ipv4.tcp_keepalive_time=60
Edit
/etc/sysctl.conf
and set the value of the following variable as shown:net.ipv4.tcp_keepalive_time=60
Reconfigure the Debezium for Oracle connector to use the
database.url
property rather thandatabase.hostname
and add the(ENABLE=broken)
Oracle connect string descriptor as shown in the following example:database.url=jdbc:oracle:thin:username/password!@(DESCRIPTION=(ENABLE=broken)(ADDRESS_LIST=(ADDRESS=(PROTOCOL=TCP)(Host=hostname)(Port=port)))(CONNECT_DATA=(SERVICE_NAME=serviceName)))
The preceding steps configure the TCP network stack to send keep-alive packets every 60 seconds. As a result, the AWS Gateway Load Balancer does not timeout when JDBC calls to the LogMiner API take more than 350 seconds to complete, enabling the connector to continue to read changes from the database’s transaction logs.