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Chapter 7. 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:

7.1. 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:

7.1.1. 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.

Note

If the time needed to complete the initial snapshot exceeds the UNDO_RETENTION time that is set for the database (fifteen minutes, by default), an ORA-01555 exception can occur. For more information about the error, and about the steps that you can take to recover from it, see the Frequently asked questions.

You can find more information about snapshots in the following sections:

Default workflow that the Oracle connector uses to perform an initial snapshot

The following workflow lists the steps that Debezium takes to create a snapshot. These steps describe the process for a snapshot when the snapshot.mode configuration property is set to its default value, which is initial. You can customize the way that the connector creates snapshots by changig the value of the snapshot.mode property. If you configure a different snapshot mode, the connector completes the snapshot by using a modified version of this workflow.

When the snapshot mode is set to the default, the connector completes the following tasks to create a snapshot:

Establish a connection to the database.
Determine the tables to be captured. By default, the connector captures all tables except those with schemas that exclude them from capture. After the snapshot completes, the connector continues to stream data for the specified tables. If you want the connector to capture data only from specific tables you can direct the connector to capture the data for only a subset of tables or table elements by setting properties such as table.include.list or table.exclude.list.
Obtain a ROW SHARE MODE lock on each of the captured tables to prevent structural changes from occurring during creation of the snapshot. Debezium holds the locks for only a short time.
Read the current system change number (SCN) position from the server’s redo log.
Capture the structure of all database tables, or all tables that are designated for capture. The connector persists schema information in its internal database schema history topic. The schema history provides information about the structure that is in effect when a change event occurs.

Note
By default, the connector captures the schema of every table in the database that is in capture mode, including tables that are not configured for capture. If tables are not configured for capture, the initial snapshot captures only their structure; it does not capture any table data. For more information about why snapshots persist schema information for tables that you did not include in the initial snapshot, see Understanding why initial snapshots capture the schema for all tables.
Release the locks obtained in Step 3. Other database clients can now write to any previously locked tables.
At the SCN position that was read in Step 4, the connector scans the tables that are designated for capture (SELECT * FROM … AS OF SCN 123). During the scan, the connector completes the following tasks:
1. Confirms that the table was created before the snapshot began. If the table was created after the snapshot began, the connector skips the table. After the snapshot is complete, and the connector transitions to streaming, it emits change events for any tables that were created after the snapshot began.
2. Produces a read event for each row that is captured from a table. All read events contain the same SCN position, which is the SCN position that was obtained in step 4.
3. Emits each read event to the Kafka topic for the source table.
4. Releases data table locks, if applicable.
Record the successful completion of the snapshot in the connector offsets.

The resulting initial snapshot captures the current state of each row in the captured tables. From this baseline state, the connector captures subsequent changes as they occur.

After the snapshot process begins, if the process is interrupted due to connector failure, rebalancing, or other reasons, the process restarts after the connector restarts. After the connector completes the initial snapshot, it continues streaming from the position that it read in Step 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.

Table 7.1. Settings for snapshot.mode connector configuration property
Setting	Description
`always`	Perform snapshot on each connector start. After the snapshot completes, the connector begins to stream event records for subsequent database changes.
`initial`	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.
`initial_only`	The connector performs a database snapshot and stops before streaming any change event records, not allowing any subsequent change events to be captured.
`schema_only`	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 `READ` events to represent the data set at the point of the connector’s start-up (Step 6).
`schema_only_recovery`	Set this option to restore a database schema history topic that is lost or corrupted. After a restart, the connector runs a snapshot that rebuilds the topic from the source tables. You can also set the property to periodically prune a database schema history topic that experiences unexpected growth. WARNING: Do not use this mode to perform a snapshot if schema changes were committed to the database after the last connector shutdown.

For more information, see snapshot.mode in the table of connector configuration properties.

7.1.1.1. Description of why initial snapshots capture the schema history for all tables

The initial snapshot that a connector runs captures two types of information:

Table data: Information about INSERT, UPDATE, and DELETE operations in tables that are named in the connector’s table.include.list property.
Schema data: DDL statements that describe the structural changes that are applied to tables. Schema data is persisted to both the internal schema history topic, and to the connector’s schema change topic, if one is configured.

After you run an initial snapshot, you might notice that the snapshot captures schema information for tables that are not designated for capture. By default, initial snapshots are designed to capture schema information for every table that is present in the database, not only from tables that are designated for capture. Connectors require that the table’s schema is present in the schema history topic before they can capture a table. By enabling the initial snapshot to capture schema data for tables that are not part of the original capture set, Debezium prepares the connector to readily capture event data from these tables should that later become necessary. If the initial snapshot does not capture a table’s schema, you must add the schema to the history topic before the connector can capture data from the table.

In some cases, you might want to limit schema capture in the initial snapshot. This can be useful when you want to reduce the time required to complete a snapshot. Or when Debezium connects to the database instance through a user account that has access to multiple logical databases, but you want the connector to capture changes only from tables in a specific logic database.

Additional information

Capturing data from tables not captured by the initial snapshot (no schema change)
Capturing data from tables not captured by the initial snapshot (schema change)
Setting the schema.history.internal.store.only.captured.tables.ddl property to specify the tables from which to capture schema information.
Setting the schema.history.internal.store.only.captured.databases.ddl property to specify the logical databases from which to capture schema changes.

7.1.1.2. Capturing data from tables not captured by the initial snapshot (no schema change)

In some cases, you might want the connector to capture data from a table whose schema was not captured by the initial snapshot. Depending on the connector configuration, the initial snapshot might capture the table schema only for specific tables in the database. If the table schema is not present in the history topic, the connector fails to capture the table, and reports a missing schema error.

You might still be able to capture data from the table, but you must perform additional steps to add the table schema.

Prerequisites

You want to capture data from a table with a schema that the connector did not capture during the initial snapshot.
All entries for the table in the transaction log use the same schema. For information about capturing data from a new table that has undergone structural changes, see Section 7.1.1.3, “Capturing data from tables not captured by the initial snapshot (schema change)”.

Procedure

Stop the connector.
Remove the internal database schema history topic that is specified by the schema.history.internal.kafka.topic property.
In the connector configuration:
1. Set the snapshot.mode to schema_only_recovery.
2. Set the value of schema.history.internal.store.only.captured.tables.ddl to false.
3. Add the tables that you want the connector to capture to table.include.list. This guarantees that in the future, the connector can reconstruct the schema history for all tables.
Restart the connector. The snapshot recovery process rebuilds the schema history based on the current structure of the tables.
(Optional) After the snapshot completes, initiate an incremental snapshot to capture existing data for newly added tables along with changes to other tables that occurred while that connector was off-line.
(Optional) Reset the snapshot.mode back to schema_only to prevent the connector from initiating recovery after a future restart.

7.1.1.3. Capturing data from tables not captured by the initial snapshot (schema change)

If a schema change is applied to a table, records that are committed before the schema change have different structures than those that were committed after the change. When Debezium captures data from a table, it reads the schema history to ensure that it applies the correct schema to each event. If the schema is not present in the schema history topic, the connector is unable to capture the table, and an error results.

If you want to capture data from a table that was not captured by the initial snapshot, and the schema of the table was modified, you must add the schema to the history topic, if it is not already available. You can add the schema by running a new schema snapshot, or by running an initial snapshot for the table.

Prerequisites

You want to capture data from a table with a schema that the connector did not capture during the initial snapshot.
A schema change was applied to the table so that the records to be captured do not have a uniform structure.

Procedure

Initial snapshot captured the schema for all tables (store.only.captured.tables.ddl was set to false)

Edit the table.include.list property to specify the tables that you want to capture.
Restart the connector.
Initiate an incremental snapshot if you want to capture existing data from the newly added tables.

Initial snapshot did not capture the schema for all tables (store.only.captured.tables.ddl was set to true)

If the initial snapshot did not save the schema of the table that you want to capture, complete one of the following procedures:

Procedure 1: Schema snapshot, followed by incremental snapshot

In this procedure, the connector first performs a schema snapshot. You can then initiate an incremental snapshot to enable the connector to synchronize data.

Stop the connector.
Remove the internal database schema history topic that is specified by the schema.history.internal.kafka.topic property.
Clear the offsets in the configured Kafka Connect offset.storage.topic. For more information about how to remove offsets, see the Debezium community FAQ.
Warning
Removing offsets should be performed only by advanced users who have experience in manipulating internal Kafka Connect data. This operation is potentially destructive, and should be performed only as a last resort.
Set values for properties in the connector configuration as described in the following steps:
1. Set the value of the snapshot.mode property to schema_only.
2. Edit the table.include.list to add the tables that you want to capture.
Restart the connector.
Wait for Debezium to capture the schema of the new and existing tables. Data changes that occurred any tables after the connector stopped are not captured.
To ensure that no data is lost, initiate an incremental snapshot.

Procedure 2: Initial snapshot, followed by optional incremental snapshot

In this procedure the connector performs a full initial snapshot of the database. As with any initial snapshot, in a database with many large tables, running an initial snapshot can be a time-consuming operation. After the snapshot completes, you can optionally trigger an incremental snapshot to capture any changes that occur while the connector is off-line.

Stop the connector.
Remove the internal database schema history topic that is specified by the schema.history.internal.kafka.topic property.
Clear the offsets in the configured Kafka Connect offset.storage.topic. For more information about how to remove offsets, see the Debezium community FAQ.
Warning
Removing offsets should be performed only by advanced users who have experience in manipulating internal Kafka Connect data. This operation is potentially destructive, and should be performed only as a last resort.
Edit the table.include.list to add the tables that you want to capture.
Set values for properties in the connector configuration as described in the following steps:
1. Set the value of the snapshot.mode property to initial.
2. (Optional) Set schema.history.internal.store.only.captured.tables.ddl to false.
Restart the connector. The connector takes a full database snapshot. After the snapshot completes, the connector transitions to streaming.
(Optional) To capture any data that changed while the connector was off-line, initiate an incremental snapshot.

7.1.2. 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. Also, the snapshot can capture a subset of the contents of the table(s) in the database.

You specify the tables to capture by sending an execute-snapshot message to the signaling table. Set the type of the execute-snapshot signal to incremental, and provide the names of the tables to include in the snapshot, as described in the following table:

Table 7.2. Example of an ad hoc execute-snapshot signal record
Field	Default	Value
`type`	`incremental`	Specifies the type of snapshot that you want to run. Setting the type is optional. Currently, you can request only `incremental` snapshots.
`data-collections`	N/A	An array that contains regular expressions matching the fully-qualified names of the table to be snapshotted. The format of the names is the same as for the `signal.data.collection` configuration option.
`additional-condition`	N/A	An optional string, which specifies a condition based on the column(s) of the table(s), to capture a subset of the contents of the table(s).
`surrogate-key`	N/A	An optional string that specifies the column name that the connector uses as the primary key of a table during the snapshot process.

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.

7.1.3. 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 1024 rows.

As an incremental snapshot proceeds, Debezium uses watermarks to track its progress, maintaining a record of each table row that it captures. This phased approach to capturing data provides the following advantages over the standard initial snapshot process:

You can run incremental snapshots in parallel with streamed data capture, instead of postponing streaming until the snapshot completes. The connector continues to capture near real-time events from the change log throughout the snapshot process, and neither operation blocks the other.
If the progress of an incremental snapshot is interrupted, you can resume it without losing any data. After the process resumes, the snapshot begins at the point where it stopped, rather than recapturing the table from the beginning.
You can run an incremental snapshot on demand at any time, and repeat the process as needed to adapt to database updates. For example, you might re-run a snapshot after you modify the connector configuration to add a table to its table.include.list property.

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.

Warning

The Debezium connector for Oracle does not support schema changes while an incremental snapshot is running.

7.1.3.1. 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 a signal to the signaling 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 or an array of regular expressions used to match tables, for example,

{"data-collections": ["public.MyFirstTable", "public.MySecondTable"]}

The data-collections array for an incremental snapshot signal has no default value. If the data-collections array is empty, Debezium detects that no action is required and does not perform a snapshot.

Note

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

Signaling is enabled.
- A signaling data collection exists on the source database.
- The signaling data collection is specified in the signal.data.collection property.

Using a source signaling channel to trigger an incremental snapshot

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>","additional-condition":"<additional-condition>"}');

For example,

INSERT INTO myschema.debezium_signal (id, type, data) 1
values ('ad-hoc-1',   2
    'execute-snapshot',  3
    '{"data-collections": ["schema1.table1", "schema2.table2"], 4
    "type":"incremental"}, 5
    "additional-condition":"color=blue"}'); 6

The values of the id,type, and data parameters in the command correspond to the fields of the signaling table.

The following table describes the parameters in the example:

Table 7.3. Descriptions of fields in a SQL command for sending an incremental snapshot signal to the signaling table
Item	Value	Description
1	`myschema.debezium_signal`	Specifies the fully-qualified name of the signaling table on the source database.
2	`ad-hoc-1`	The `id` parameter specifies an arbitrary string that is assigned as the `id` 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 own `id` string as a watermarking signal.
3	`execute-snapshot`	The `type` parameter specifies the operation that the signal is intended to trigger.
4	`data-collections`	A required component of the `data` field of a signal that specifies an array of table names or regular expressions to match table names to include in the snapshot. The array lists regular expressions which match tables by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling table in the `signal.data.collection` configuration property.
5	`incremental`	An optional `type` component of the `data` field of a signal that specifies the kind of snapshot operation to run. Currently, the only valid option is the default value, `incremental`. If you do not specify a value, the connector runs an incremental snapshot.
6	`additional-condition`	An optional string, which specifies a condition based on the column(s) of the table(s), to capture a subset of the contents of the tables. For more information about the `additional-condition` parameter, see Ad hoc incremental snapshots with `additional-condition`.

Ad hoc incremental snapshots with additional-condition

If you want a snapshot to include only a subset of the content in a table, you can modify the signal request by appending an additional-condition parameter to the snapshot signal.

The SQL query for a typical snapshot takes the following form:

SELECT * FROM <tableName> ....

By adding an additional-condition parameter, you append a WHERE condition to the SQL query, as in the following example:

SELECT * FROM <tableName> WHERE <additional-condition> ....

The following example shows a SQL query to send an ad hoc incremental snapshot request with an additional condition to the signaling table:

INSERT INTO <signalTable> (id, type, data) VALUES ('<id>', '<snapshotType>', '{"data-collections": ["<tableName>","<tableName>"],"type":"<snapshotType>","additional-condition":"<additional-condition>"}');

For example, suppose you have a products table that contains the following columns:

id (primary key)
color
quantity

If you want an incremental snapshot of the products table to include only the data items where color=blue, you can use the following SQL statement to trigger the snapshot:

INSERT INTO myschema.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["schema1.products"],"type":"incremental", "additional-condition":"color=blue"}');

The additional-condition parameter also enables you to pass conditions that are based on more than one column. For example, using the products table from the previous example, you can submit a query that triggers an incremental snapshot that includes the data of only those items for which color=blue and quantity>10:

INSERT INTO myschema.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["schema1.products"],"type":"incremental", "additional-condition":"color=blue AND quantity>10"}');

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	`snapshot`	Specifies the type of snapshot operation to run. Currently, the only valid option is the default value, `incremental`. Specifying a `type` 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.
2	`op`	Specifies the event type. The value for snapshot events is `r`, signifying a `READ` operation.

7.1.3.2. Using the Kafka signaling channel to trigger an incremental snapshot

You can send a message to the configured Kafka topic to request the connector to run an ad hoc incremental snapshot.

The key of the Kafka message must match the value of the topic.prefix connector configuration option.

The value of the message is a JSON object with type and data fields.

The signal type is execute-snapshot, and the data field must have the following fields:

Table 7.4. Execute snapshot data fields
Field	Default	Value
`type`	`incremental`	The type of the snapshot to be executed. Currently Debezium supports only the `incremental` type. See the next section for more details.
`data-collections`	N/A	An array of comma-separated regular expressions that match the fully-qualified names of tables to include in the snapshot. Specify the names by using the same format as is required for the signal.data.collection configuration option.
`additional-condition`	N/A	An optional string that specifies a condition that the connector evaluates to designate a subset of columns to include in a snapshot.

An example of the execute-snapshot Kafka message:

Key = `test_connector`

Value = `{"type":"execute-snapshot","data": {"data-collections": ["schema1.table1", "schema1.table2"], "type": "INCREMENTAL"}}`

Ad hoc incremental snapshots with additional-condition

Debezium uses the additional-condition field to select a subset of a table’s content.

Typically, when Debezium runs a snapshot, it runs a SQL query such as:

SELECT * FROM <tableName> ….

When the snapshot request includes an additional-condition, the additional-condition is appended to the SQL query, for example:

SELECT * FROM <tableName> WHERE <additional-condition> ….

For example, given a products table with the columns id (primary key), color, and brand, if you want a snapshot to include only content for which color='blue', when you request the snapshot, you could append an additional-condition statement to filter the content:

Key = `test_connector`

Value = `{"type":"execute-snapshot","data": {"data-collections": ["schema1.products"], "type": "INCREMENTAL", "additional-condition":"color='blue'"}}`

You can use the additional-condition statement to pass conditions based on multiple columns. For example, using the same products table as in the previous example, if you want a snapshot to include only the content from the products table for which color='blue', and brand='MyBrand', you could send the following request:

Key = `test_connector`

Value = `{"type":"execute-snapshot","data": {"data-collections": ["schema1.products"], "type": "INCREMENTAL", "additional-condition":"color='blue' AND brand='MyBrand'"}}`

7.1.3.3. Stopping an incremental snapshot

You can also stop an incremental snapshot by sending a signal to the table on the source database. You submit a stop snapshot signal to the table by sending a SQL INSERT query.

After Debezium detects the change in the signaling table, it reads the signal, and stops the incremental snapshot operation if it’s in progress.

The query that you submit specifies the snapshot operation of incremental, and, optionally, the tables of the current running snapshot to be removed.

Prerequisites

Signaling is enabled.
- A signaling data collection exists on the source database.
- The signaling data collection is specified in the signal.data.collection property.

Using a source signaling channel to stop an incremental snapshot

Send a SQL query to stop the ad hoc incremental snapshot to the signaling table:

INSERT INTO <signalTable> (id, type, data) values ('<id>', 'stop-snapshot', '{"data-collections": ["<tableName>","<tableName>"],"type":"incremental"}');

For example,

INSERT INTO myschema.debezium_signal (id, type, data) 1
values ('ad-hoc-1',   2
    'stop-snapshot',  3
    '{"data-collections": ["schema1.table1", "schema2.table2"], 4
    "type":"incremental"}'); 5

The values of the id, type, and data parameters in the signal command correspond to the fields of the signaling table.

The following table describes the parameters in the example:

Table 7.5. Descriptions of fields in a SQL command for sending a stop incremental snapshot signal to the signaling table
Item	Value	Description
1	`myschema.debezium_signal`	Specifies the fully-qualified name of the signaling table on the source database.
2	`ad-hoc-1`	The `id` parameter specifies an arbitrary string that is assigned as the `id` identifier for the signal request. Use this string to identify logging messages to entries in the signaling table. Debezium does not use this string.
3	`stop-snapshot`	Specifies `type` parameter specifies the operation that the signal is intended to trigger.
4	`data-collections`	An optional component of the `data` field of a signal that specifies an array of table names or regular expressions to match table names to remove from the snapshot. The array lists regular expressions which match tables by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling table in the `signal.data.collection` configuration property. If this component of the `data` field is omitted, the signal stops the entire incremental snapshot that is in progress.
5	`incremental`	A required component of the `data` field of a signal that specifies the kind of snapshot operation that is to be stopped. Currently, the only valid option is `incremental`. If you do not specify a `type` value, the signal fails to stop the incremental snapshot.

7.1.3.4. Using the Kafka signaling channel to stop an incremental snapshot

You can send a signal message to the configured Kafka signaling topic to stop an ad hoc incremental snapshot.

The key of the Kafka message must match the value of the topic.prefix connector configuration option.

The value of the message is a JSON object with type and data fields.

The signal type is stop-snapshot, and the data field must have the following fields:

Table 7.6. Execute snapshot data fields
Field	Default	Value
`type`	`incremental`	The type of the snapshot to be executed. Currently Debezium supports only the `incremental` type. See the next section for more details.
`data-collections`	N/A	An optional array of comma-separated regular expressions that match the fully-qualified names of the tables to include in the snapshot. Specify the names by using the same format as is required for the signal.data.collection configuration option.

The following example shows a typical stop-snapshot Kafka message:

Key = `test_connector`

Value = `{"type":"stop-snapshot","data": {"data-collections": ["schema1.table1", "schema1.table2"], "type": "INCREMENTAL"}}`

7.1.4. 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:

topicPrefix.schemaName.tableName

The following list provides definitions for the components of the default name:

topicPrefix: The topic prefix as specified by the topic.prefix connector configuration property.
schemaName: The name of the schema in which the operation occurred.
tableName: The name of the table in which the operation occurred.

For example, 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 schema history topics, schema change topics, and transaction metadata topics.

If the default topic name do not meet your requirements, you can configure custom topic names. To configure custom topic names, you specify regular expressions in the logical topic routing SMT. For more information about using the logical topic routing SMT to customize topic naming, see Topic routing.

7.1.5. How Debezium Oracle connectors handle database schema changes

When a database client queries a database, the client uses the database’s current schema. However, the database schema can be changed at any time, which means that the connector must be able to identify what the schema was at the time each insert, update, or delete operation was recorded. Also, a connector cannot necessarily apply the current schema to every event. If an event is relatively old, it’s possible that it was recorded before the current schema was applied.

To ensure correct processing of events that occur after a schema change, Oracle includes in the redo log not only the row-level changes that affect the data, but also the DDL statements that are applied to the database. As the connector encounters these DDL statements in the redo log, it parses them and updates an in-memory representation of each table’s schema. The connector uses this schema representation to identify the structure of the tables at the time of each insert, update, or delete operation and to produce the appropriate change event. In a separate database schema history Kafka topic, the connector records all DDL statements along with the position in the redo log where each DDL statement appeared.

When the connector restarts after either a crash or a graceful stop, it starts reading the redo log from a specific position, that is, from a specific point in time. The connector rebuilds the table structures that existed at this point in time by reading the database schema history Kafka topic and parsing all DDL statements up to the point in the redo log where the connector is starting.

This database schema history topic is internal for internal connector use only. Optionally, the connector can also emit schema change events to a different topic that is intended for consumer applications.

Additional resources

Default names for topics that receive Debezium event records.

7.1.6. 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 tables in the database. The connector writes schema change events to a Kafka topic named <serverName>, where topicName is the namespace that is specified in the topic.prefix configuration property.

Debezium emits a new message to the schema change topic whenever it streams data from a new table, or when the structure of the table is altered.

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, or DROP 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.

Important

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 schema.history.internal.store.only.captured.tables.ddl to true.

Important

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 schema history topic. The internal database schema history topic is for connector use only and it is not intended for direct use by consuming applications. Ensure that applications that require notifications about schema changes consume that information only from the schema change topic.

Important

Never partition the database schema history topic. For the database schema history topic to function correctly, it must maintain a consistent, global order of the event records that the connector emits to it.

To ensure that the topic is not split among partitions, set the partition count for the topic by using one of the following methods:

If you create the database schema history topic manually, specify a partition count of 1.
If you use the Apache Kafka broker to create the database schema history topic automatically, the topic is created, set the value of the Kafka num.partitions configuration option to 1.

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": "2.3.4.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,
      "user_name": "user"
    },
    "ts_ms": 1588252618953, 1
    "databaseName": "ORCLPDB1", 2
    "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\" ", 3
    "tableChanges": [ 4
      {
        "type": "CREATE", 5
        "id": "\"ORCLPDB1\".\"DEBEZIUM\".\"CUSTOMERS\"", 6
        "table": { 7
          "defaultCharsetName": null,
          "primaryKeyColumnNames": [ 8
            "ID"
          ],
          "columns": [ 9
            {
              "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
            }
          ],
          "attributes": [ 10
            {
              "customAttribute": "attributeValue"
            }
          ]
        }
      }
    ]
  }
}

Table 7.7. Descriptions of fields in messages emitted to the schema change topic
Item	Field name	Description
1	`ts_ms`	Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task. In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.
2	`databaseName` `schemaName`	Identifies the database and the schema that contains the change.
3	`ddl`	This field contains the DDL that is responsible for the schema change.
4	`tableChanges`	An array of one or more items that contain the schema changes generated by a DDL command.
5	`type`	Describes the kind of change. The `type` can be set to one of the following values: `CREATE` Table created. `ALTER` Table modified. `DROP` Table deleted.
6	`id`	Full identifier of the table that was created, altered, or dropped. In the case of a table rename, this identifier is a concatenation of `<old>,<new>` table names.
7	`table`	Represents table metadata after the applied change.
8	`primaryKeyColumnNames`	List of columns that compose the table’s primary key.
9	`columns`	Metadata for each column in the changed table.
10	`attributes`	Custom attribute metadata for each table change.

In messages that the connector sends to the schema change topic, the message key is the name of the database that contains the schema change. In the following example, the payload field contains the databaseName key:

{
  "schema": {
    "type": "struct",
    "fields": [
      {
        "type": "string",
        "optional": false,
        "field": "databaseName"
      }
    ],
    "optional": false,
    "name": "io.debezium.connector.oracle.SchemaChangeKey"
  },
  "payload": {
    "databaseName": "ORCLPDB1"
  }
}

7.1.7. 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.

Limits on when Debezium receives transaction metadata

Debezium registers and receives metadata only for transactions that occur after you deploy the connector. Metadata for transactions that occur before you deploy the connector is not available.

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 or END.
id: String representation of the unique transaction identifier.
ts_ms: The time of a transaction boundary event (BEGIN or END event) at the data source. If the data source does not provide Debezium with the event time, then the field instead represents the time at which Debezium processes the event.
event_count (for END events): Total number of events emmitted by the transaction.
data_collections (for END events): An array of pairs of data_collection and event_count elements that indicates the 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",
  "ts_ms": 1486500577125,
  "event_count": null,
  "data_collections": null
}

{
  "status": "END",
  "id": "5.6.641",
  "ts_ms": 1486500577691,
  "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 topic.transaction option, the connector emits transaction events to the <topic.prefix>.transaction topic.

7.1.7.1. 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"
  }
}

Query Modes

The Debezium Oracle connector integrates with Oracle LogMiner by default. This integration requires a specialized set of steps which includes generating a complex JDBC SQL query to ingest the changes recorded in the transaction logs as change events. The V$LOGMNR_CONTENTS view used by the JDBC SQL query does not have any indices to improve the query’s performance, and so there are different query modes that can be used that control how the SQL query is generated as a way to improve the query’s execution.

The log.mining.query.filter.mode connector property can be configured with one of the following to influence how the JDBC SQL query is generated:

none: (Default) This mode creates a JDBC query that only filters based on the different operation types, such as inserts, updates, or deletes, at the database level. When filtering the data based on the schema, table, or username include/exclude lists, this is done during the processing loop within the connector.

This mode is often useful when capturing a small number of tables from a database that is not heavily saturated with changes. The generated query is quite simple, and focuses primarily on reading as quickly as possible with low database overhead.
in: This mode creates a JDBC query that filters not only operation types at the database level, but also schema, table, and username include/exclude lists. The query’s predicates are generated using a SQL in-clause based on the values specified in the include/exclude list configuration properties.

This mode is often useful when capturing a large number of tables from a database that is heavily saturated with changes. The generated query is much more complex than the none mode, and focuses on reducing network overhead and performing as much filtering at the database level as possible.

Finally, do not specify regular expressions as part of schema and table include/exclude configuration properties. Using regular expressions will cause the connector to not match changes based on these configuration properties, causing changes to be missed.
regex: This mode creates a JDBC query that filters not only operation types at the database level, but also schema, table, and username include/exclude lists. However, unlike the in mode, this mode generates a SQL query using the Oracle REGEXP_LIKE operator using a conjunction or disjunction depending on whether include or excluded values are specified.

This mode is often useful when capturing a variable number of tables that can be identified using a small number of regular expressions. The generated query is much more complex than any other mode, and focuses on reducing network overhead and performing as much filtering at the database level as possible.

7.1.8. 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.

7.1.9. 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.

Warning

SCN gap detection is available only if the large SCN increment occurs while the connector is running and processing near real-time events.

7.1.10. 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.

Note

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.

7.2. 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.

Warning

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.

Warning

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:

7.2.1. 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 <topic.prefix>.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.

7.2.2. 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), or r (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 the server1 connector uses for all rows in the inventory.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 the before 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).
Username who made the change
Tip
The 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": "int64",
                        "optional": true,
                        "field": "ssn"
                    },
                    {
                        "type": "int32",
                        "optional": true,
                        "field": "redo_thread"
                    },
                    {
                        "type": "string",
                        "optional": true,
                        "field": "user_name"
                    },
                    {
                        "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": "2.3.4.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,
            "user_name": "user",
            "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 and after fields.

Tip

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.

Tip

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": "2.3.4.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,
            "user_name": "user",
            "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 is u, 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 the update database commit.
The after field shows the updated state of the row, with the EMAIL value now set to anne@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?

Note

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": "2.3.4.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,
            "user_name": "user",
            "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 is d, 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 is null, 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 the ts_ms, scn, and txId 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": "2.3.4.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,
            "user_name": "user"
        },
        "op": "t", 2
        "ts_ms": 1638974558961, 3
        "transaction": null
    }
}

Table 7.8. Descriptions of truncate event value fields
Item	Field name	Description
1	`source`	Mandatory field that describes the source metadata for the event. In a truncate event value, the `source` field structure is the same as for create, update, and delete events for the same table, provides this metadata: Debezium version Connector type and name Database and table that contains the new row Schema name If the event was part of a snapshot (always `false` for truncate events) ID of the transaction in which the operation was performed SCN of the operation Timestamp for when the change was made in the database Username who performed the change
2	`op`	Mandatory string that describes the type of operation. The `op` field value is `t`, signifying that this table was truncated.
3	`ts_ms`	Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task. In the `source` object, `ts_ms` indicates the time that the change was made in the database. By comparing the value for `payload.source.ts_ms` with the value for `payload.ts_ms`, you can determine the lag between the source database update and Debezium.

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.

7.3. 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, and STRUCT.
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.

Table 7.9. Mappings for Oracle basic character types
Oracle Data Type	Literal type (schema type)	Semantic type (schema name) and Notes
`CHAR[(M)]`	`STRING`	n/a
`NCHAR[(M)]`	`STRING`	n/a
`NVARCHAR2[(M)]`	`STRING`	n/a
`VARCHAR[(M)]`	`STRING`	n/a
`VARCHAR2[(M)]`	`STRING`	n/a

Binary and Character LOB types

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.

Table 7.10. Mappings for Oracle binary and character LOB types
Oracle Data Type	Literal type (schema type)	Semantic type (schema name) and Notes
`BFILE`	n/a	This data type is not supported
`BLOB`	`BYTES`	Either the raw bytes (the default), a base64-encoded String, or a base64-url-safe-encoded String, or a hex-encoded String, based on the `binary.handling.mode` connector configuration property setting.
`CLOB`	`STRING`	n/a
`LONG`	n/a	This data type is not supported.
`LONG RAW`	n/a	This data type is not supported.
`NCLOB`	`STRING`	n/a
`RAW`	n/a	This data type is not supported.

Note

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.

Note

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.

Table 7.11. Mappings for Oracle numeric data types
Oracle Data Type	Literal type (schema type)	Semantic type (schema name) and Notes
`BINARY_FLOAT`	`FLOAT32`	n/a
`BINARY_DOUBLE`	`FLOAT64`	n/a
`DECIMAL[(P, S)]`	`BYTES` / `INT8` / `INT16` / `INT32` / `INT64`	`org.apache.kafka.connect.data.Decimal` if using `BYTES` Handled equivalently to `NUMBER` (note that S defaults to 0 for `DECIMAL`). When the `decimal.handling.mode` property is set to `double`, the connector represents `DECIMAL` values as Java `double` values with schema type `FLOAT64`. When the `decimal.handling.mode` property is set to `string`, the connector represents DECIMAL values as their formatted string representation with schema type `STRING`.
`DOUBLE PRECISION`	`STRUCT`	`io.debezium.data.VariableScaleDecimal` Contains a structure with two fields: `scale` of type `INT32` that contains the scale of the transferred value and `value` of type `BYTES` containing the original value in an unscaled form.
`FLOAT[(P)]`	`STRUCT`	`io.debezium.data.VariableScaleDecimal` Contains a structure with two fields: `scale` of type `INT32` that contains the scale of the transferred value and `value` of type `BYTES` containing the original value in an unscaled form.
`INTEGER`, `INT`	`BYTES`	`org.apache.kafka.connect.data.Decimal` `INTEGER` is mapped in Oracle to NUMBER(38,0) and hence can hold values larger than any of the `INT` types could store
`NUMBER[(P[, *])]`	`STRUCT`	`io.debezium.data.VariableScaleDecimal` Contains a structure with two fields: `scale` of type `INT32` that contains the scale of the transferred value and `value` of type `BYTES` containing the original value in an unscaled form. When the `decimal.handling.mode` property is set to `double`, the connector represents `NUMBER` values as Java `double` values with schema type `FLOAT64`. When the `decimal.handling.mode` property is set to `string`, the connector represents `NUMBER` values as their formatted string representation with schema type `STRING`.
`NUMBER(P, S <= 0)`	`INT8` / `INT16` / `INT32` / `INT64`	`NUMBER` columns with a scale of 0 represent integer numbers. A negative scale indicates rounding in Oracle, for example, a scale of -2 causes rounding to hundreds. Depending on the precision and scale, one of the following matching Kafka Connect integer type is chosen: P - S < 3, `INT8` P - S < 5, `INT16` P - S < 10, `INT32` P - S < 19, `INT64` P - S >= 19, `BYTES` (`org.apache.kafka.connect.data.Decimal`) When the `decimal.handling.mode` property is set to `double`, the connector represents `NUMBER` values as Java `double` values with schema type `FLOAT64`. When the `decimal.handling.mode` property is set to `string`, the connector represents `NUMBER` values as their formatted string representation with schema type `STRING`.
`NUMBER(P, S > 0)`	`BYTES`	`org.apache.kafka.connect.data.Decimal`
`NUMERIC[(P, S)]`	`BYTES` / `INT8` / `INT16` / `INT32` / `INT64`	`org.apache.kafka.connect.data.Decimal` if using `BYTES` Handled equivalently to `NUMBER` (note that S defaults to 0 for `NUMERIC`). When the `decimal.handling.mode` property is set to `double`, the connector represents `NUMERIC` values as Java `double` values with schema type `FLOAT64`. When the `decimal.handling.mode` property is set to `string`, the connector represents `NUMERIC` values as their formatted string representation with schema type `STRING`.
`SMALLINT`	`BYTES`	`org.apache.kafka.connect.data.Decimal` `SMALLINT` is mapped in Oracle to NUMBER(38,0) and hence can hold values larger than any of the `INT` types could store
`REAL`	`STRUCT`	`io.debezium.data.VariableScaleDecimal` Contains a structure with two fields: `scale` of type `INT32` that contains the scale of the transferred value and `value` of type `BYTES` containing the original value in an unscaled form. When the `decimal.handling.mode` property is set to `double`, the connector represents `REAL` values as Java `double` values with schema type `FLOAT64`. When the `decimal.handling.mode` property is set to `string`, the connector represents `REAL` values as their formatted string representation with schema type `STRING`.

As mention above, Oracle allows negative scales in NUMBER type. This can cause an issue during conversion to the Avro format when the number is represented as the Decimal. Decimal type includes scale information, but Avro specification allows only positive values for the scale. Depending on the schema registry used, it may result into Avro serialization failure. To avoid this issue, you can use NumberToZeroScaleConverter, which converts sufficiently high numbers (P - S >= 19) with negative scale into Decimal type with zero scale. It can be configured as follows:

converters=zero_scale
zero_scale.type=io.debezium.connector.oracle.converters.NumberToZeroScaleConverter
zero_scale.decimal.mode=precise

By default, the number is converted to Decimal type (zero_scale.decimal.mode=precise), but for completeness remaining two supported types (double and string) are supported as well.

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 a BOOLEAN type.
Enumerate a subset of columns by using a comma-separated list of regular expressions.
To use this type of conversion, you must set the converters configuration property with the selector 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
`DATE`	`INT64`	`io.debezium.time.Timestamp` Represents the number of milliseconds since the UNIX epoch, and does not include timezone information.
`INTERVAL DAY[(M)] TO SECOND`	`FLOAT64`	`io.debezium.time.MicroDuration` The number of micro seconds for a time interval using the `365.25 / 12.0` formula for days per month average. `io.debezium.time.Interval` (when `interval.handling.mode` is set to `string`) The string representation of the interval value that follows the pattern `P<years>Y<months>M<days>DT<hours>H<minutes>M<seconds>S`, for example, `P1Y2M3DT4H5M6.78S`.
`INTERVAL YEAR[(M)] TO MONTH`	`FLOAT64`	`io.debezium.time.MicroDuration` The number of micro seconds for a time interval using the `365.25 / 12.0` formula for days per month average. `io.debezium.time.Interval` (when `interval.handling.mode` is set to `string`) The string representation of the interval value that follows the pattern `P<years>Y<months>M<days>DT<hours>H<minutes>M<seconds>S`, for example, `P1Y2M3DT4H5M6.78S`.
`TIMESTAMP(0 - 3)`	`INT64`	`io.debezium.time.Timestamp` Represents the number of milliseconds since the UNIX epoch, and does not include timezone information.
`TIMESTAMP, TIMESTAMP(4 - 6)`	`INT64`	`io.debezium.time.MicroTimestamp` Represents the number of microseconds since the UNIX epoch, and does not include timezone information.
`TIMESTAMP(7 - 9)`	`INT64`	`io.debezium.time.NanoTimestamp` Represents the number of nanoseconds since the UNIX epoch, and does not include timezone information.
`TIMESTAMP WITH TIME ZONE`	`STRING`	`io.debezium.time.ZonedTimestamp` A string representation of a timestamp with timezone information.
`TIMESTAMP WITH LOCAL TIME ZONE`	`STRING`	`io.debezium.time.ZonedTimestamp` A string representation of a timestamp in UTC.

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
`DATE`	`INT32`	`org.apache.kafka.connect.data.Date` Represents the number of days since the UNIX epoch.
`INTERVAL DAY[(M)] TO SECOND`	`FLOAT64`	`io.debezium.time.MicroDuration` The number of micro seconds for a time interval using the `365.25 / 12.0` formula for days per month average. `io.debezium.time.Interval` (when `interval.handling.mode` is set to `string`) The string representation of the interval value that follows the pattern `P<years>Y<months>M<days>DT<hours>H<minutes>M<seconds>S`, for example, `P1Y2M3DT4H5M6.78S`.
`INTERVAL YEAR[(M)] TO MONTH`	`FLOAT64`	`io.debezium.time.MicroDuration` The number of micro seconds for a time interval using the `365.25 / 12.0` formula for days per month average. `io.debezium.time.Interval` (when `interval.handling.mode` is set to `string`) The string representation of the interval value that follows the pattern `P<years>Y<months>M<days>DT<hours>H<minutes>M<seconds>S`, for example, `P1Y2M3DT4H5M6.78S`.
`TIMESTAMP(0 - 3)`	`INT64`	`org.apache.kafka.connect.data.Timestamp` Represents the number of milliseconds since the UNIX epoch, and does not include timezone information.
`TIMESTAMP(4 - 6)`	`INT64`	`org.apache.kafka.connect.data.Timestamp` Represents the number of milliseconds since the UNIX epoch, and does not include timezone information.
`TIMESTAMP(7 - 9)`	`INT64`	`org.apache.kafka.connect.data.Timestamp` Represents the number of milliseconds since the UNIX epoch, and does not include timezone information.
`TIMESTAMP WITH TIME ZONE`	`STRING`	`io.debezium.time.ZonedTimestamp` A string representation of a timestamp with timezone information.
`TIMESTAMP WITH LOCAL TIME ZONE`	`STRING`	`io.debezium.time.ZonedTimestamp` A string representation of a timestamp in UTC.

ROWID types

The following table describes how the connector maps ROWID (row address) data types.

Table 7.12. Mappings for Oracle ROWID data types
Oracle Data Type	Literal type (schema type)	Semantic type (schema name) and Notes
`ROWID`	`STRING`	n/a
`UROWID`	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.

7.4. 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:

7.4.1. 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.

7.4.2. 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.

7.4.3. Tables that the Debezium Oracle connector excludes when capturing change events

When the Debezium Oracle connector captures tables, it automatically excludes tables that match the following rules:

Compression Advisor tables matching the pattern CMP[3|4]$[0-9]+.
Index-organized tables matching the pattern SYS_IOT_OVER_%.
Spatial tables matching the patterns MDRT_%, MDRS_%, or MDXT_%.
Nested tables

To enable the connector to capture a table with a name that matches any of the preceding rules, you must rename the table.

7.4.4. 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;

Oracle AWS RDS does not allow you to execute the commands above nor does it allow you to log in as sysdba. AWS provides these alternative commands to configure LogMiner. Before executing these commands, ensure that your Oracle AWS RDS instance is enabled for backups.

To confirm that Oracle has backups enabled, execute the command below first. The LOG_MODE should say ARCHIVELOG. If it does not, you may need to reboot your Oracle AWS RDS instance.

Configuration needed for Oracle AWS RDS LogMiner

SQL> SELECT LOG_MODE FROM V$DATABASE;

LOG_MODE
------------
ARCHIVELOG

Once LOG_MODE is set to ARCHIVELOG, execute the commands to complete LogMiner configuration. The first command set the database to archivelogs and the second adds supplemental logging.

Configuration needed for Oracle AWS RDS LogMiner

exec rdsadmin.rdsadmin_util.set_configuration('archivelog retention hours',24);

exec rdsadmin.rdsadmin_util.alter_supplemental_logging('ADD');

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;

7.4.5. 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.

7.4.6. 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.

Warning

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

  GRANT SELECT ON V_$MYSTAT TO c##dbzuser CONTAINER=ALL; 24
  GRANT SELECT ON V_$STATNAME TO c##dbzuser CONTAINER=ALL; 25

  exit;

Table 7.13. Descriptions of permissions / grants
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_$DATABASE	Enables the connector to read the `V$DATABASE` table.
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 `FLASHBACK ANY TABLE` is granted, this should also be granted.
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 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 `DBMS_LOGMNR` package. This is required to interact with Oracle LogMiner. On newer versions of Oracle this is granted via the `LOGMINING` role but on older versions, this must be explicitly granted.
14	EXECUTE ON DBMS_LOGMNR_D	Enables the connector to run methods in the `DBMS_LOGMNR_D` package. This is required to interact with Oracle LogMiner. On newer versions of Oracle this is granted via the `LOGMINING` role but on older versions, this must be explicitly granted.
15 to 25	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.

7.4.7. Support for Oracle standby databases

Important

The ability for the Debezium Oracle connector to ingest changes from a read-only logical standby database is a Developer Preview feature. Developer Preview features are not supported by Red Hat in any way and are not functionally complete or production-ready. Do not use Developer Preview software for production or business-critical workloads. Developer Preview software provides early access to upcoming product software in advance of its possible inclusion in a Red Hat product offering. Customers can use this software to test functionality and provide feedback during the development process. This software might not have any documentation, is subject to change or removal at any time, and has received limited testing. Red Hat might provide ways to submit feedback on Developer Preview software without an associated SLA.

For more information about the support scope of Red Hat Developer Preview software, see Developer Preview Support Scope.

7.5. Deployment of Debezium Oracle connectors

You can use either of the following methods to deploy a Debezium Oracle connector:

Use AMQ Streams to automatically create an image that includes the connector plug-in.
This is the preferred method.
Build a custom Kafka Connect container image from a Dockerfile.

Important

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

Section 7.6, “Descriptions of Debezium Oracle connector configuration properties”

7.5.1. 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 the KafkaConnect custom resource as shown in Section 7.5.3, “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.

7.5.2. 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 the KafkaConnector 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.

Note

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 Managing AMQ Streams on OpenShift.

7.5.3. 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 for storing new container images. You must explicitly create an ImageStream for the cluster. ImageStreams are not available by default. For more information about ImageStreams, see Managing image streams on OpenShift Container Platform.

Procedure

Create a Debezium KafkaConnect custom resource (CR) for the connector, or modify an existing one. For example, create a KafkaConnect CR with the name dbz-connect.yaml that specifies the metadata.annotations and spec.build properties. The following example shows an excerpt from a dbz-connect.yaml file that describes a KafkaConnect custom resource.

Example 7.1. A dbz-connect.yaml file that defines a KafkaConnect custom resource that includes a Debezium connector

In 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: 3.5.0
  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/2.3.4.Final-redhat-00001/debezium-connector-oracle-2.3.4.Final-redhat-00001-plugin.zip  7
          - type: zip
            url: https://maven.repository.redhat.com/ga/io/apicurio/apicurio-registry-distro-connect-converter/2.4.4.Final-redhat-<build-number>/apicurio-registry-distro-connect-converter-2.4.4.Final-redhat-<build-number>.zip  8
          - type: zip
            url: https://maven.repository.redhat.com/ga/io/debezium/debezium-scripting/2.3.4.Final-redhat-00001/debezium-scripting-2.3.4.Final-redhat-00001.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/ojdbc8/21.6.0.0/ojdbc8-21.6.0.0.jar

  bootstrapServers: debezium-kafka-cluster-kafka-bootstrap:9093

  ...

Table 7.14. 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 use `KafkaConnector` 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` are `docker` to push into a container registry such as Docker Hub or Quay, or `imagestream` 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 the `build.output` in the KafkaConnect configuration, see the AMQ Streams Build schema reference in Configuring AMQ Streams on OpenShift.
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-in `name`, 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 the `artifacts.url`. Valid types are `zip`, `tgz`, or `jar`. Debezium connector archives are provided in `.zip` file format. JDBC driver files are in `.jar` format. The `type` value must match the type of the file that is referenced in the `url` 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` and `url` 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` and `url` 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` and `url` for the JAR files of a JSR 223-compliant scripting implementation, which is required by the Debezium scripting SMT. Important If 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 of `artifacts.type` must also be set to `jar`. 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 following KafkaConnector CR, and save it as oracle-inventory-connector.yaml

Example 7.2. oracle-inventory-connector.yaml file that defines the KafkaConnector custom resource for a Debezium connector

apiVersion: 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
    schema.history.internal.kafka.bootstrap.servers: debezium-kafka-cluster-kafka-bootstrap.debezium.svc.cluster.local:9092
    schema.history.internal.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
    topic.prefix: inventory-connector-oracle 10
    table.include.list: PUBLIC.INVENTORY  11

    ...

Table 7.15. 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 account that Debezium uses to connect to the database.
8	The password that Debezium uses to connect to the database user account.
9	The name of the database to capture changes from.
10	The topic prefix for the database instance or cluster. The specified name must be formed only from alphanumeric characters or underscores. Because the topic prefix 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. This 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 the KafkaConnector CR. After the connector pod is ready, Debezium is running.

You are now ready to verify the Debezium Oracle deployment.

7.5.4. 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. The image 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 the KafkaConnect 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 or docker.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/amq-streams-kafka-35-rhel8:2.5.0 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/amq-streams-kafka-35-rhel8:2.5.0
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/2.3.4.Final-redhat-00001/debezium-connector-oracle-2.3.4.Final-redhat-00001-plugin.zip \
&& unzip debezium-connector-oracle-2.3.4.Final-redhat-00001-plugin.zip \
&& rm debezium-connector-oracle-2.3.4.Final-redhat-00001-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 specifies annotations and image properties. The following example shows an excerpt from a dbz-connect.yaml file that describes a KafkaConnect custom resource.

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 that `KafkaConnector` 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 the `STRIMZI_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 named ORCLCDB, and server1 is the server’s logical name.

Oracle inventory-connector.yaml

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnector
metadata:
  name: inventory-connector-oracle 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
    topic.prefix: inventory-connector-oracle 9
    schema.history.internal.kafka.bootstrap.servers: kafka:9092 10
    schema.history.internal.kafka.topic: schema-changes.inventory 11

Table 7.16. 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	Topic prefix 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 schema history topic.
11	The name of the database schema history topic where the connector writes and recovers DDL statements. This topic is for internal use only and should not be used by consumers.

Create your connector instance with Kafka Connect. For example, if you saved your KafkaConnector resource in the inventory-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 the server1 database as defined in the KafkaConnector 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.

7.5.5. 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.

7.5.6. 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:
1. Navigate to Home Search.
2. On the Search page, click Resources to open the Select Resource box, and then type KafkaConnector.
3. From the KafkaConnectors list, click the name of the connector that you want to check, for example inventory-connector-oracle.
4. In the Conditions section, verify that the values in the Type and Status columns are set to Ready and True.

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 7.3. KafkaConnector resource status

Name:         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
    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.
1. Navigate to Home Search.
2. On the Search page, click Resources to open the Select Resource box, and then type KafkaTopic.
3. From the KafkaTopics list, click the name of the topic that you want to check, for example, inventory-connector-oracle.inventory.orders---ac5e98ac6a5d91e04d8ec0dc9078a1ece439081d.
4. In the Conditions section, verify that the values in the Type and Status columns are set to Ready and True.

From a terminal window:

Enter the following command:

oc get kafkatopics

The command returns status information that is similar to the following output:

Example 7.4. KafkaTopic resource status

NAME                                                                    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 7.5. 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":"2.3.4.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 table inventory.products_on_hand. The "before" state of the product_id record is null, indicating that no previous value exists for the record. The "after" state shows a quantity of 3 for the item with product_id 101.

7.6. 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 schema history connector configuration properties that control how Debezium processes events that it reads from the database schema history topic.
- Pass-through database schema history properties
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
`name`	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.)
`connector.class`	No default	The name of the Java class for the connector. Always use a value of `io.debezium.connector.oracle.OracleConnector` for the Oracle connector.
`converters`	No default	Enumerates a comma-separated list of the symbolic names of the custom converter instances that the connector can use. For example, `boolean`. This property is required to enable the connector to use a custom converter. For each converter that you configure for a connector, you must also add a `.type` property, which specifies the fully-qualifed name of the class that implements the converter interface. The `.type` property uses the following format: `<converterSymbolicName>.type` 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. For example, to define a `selector` parameter that specifies the subset of columns that the `boolean` converter processes, add the following property: boolean.selector: .MYTABLE.FLAG,..IS_ARCHIVED
`tasks.max`	`1`	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.
`database.hostname`	No default	IP address or hostname of the Oracle database server.
`database.port`	No default	Integer port number of the Oracle database server.
`database.user`	No default	Name of the Oracle user account that the connector uses to connect to the Oracle database server.
`database.password`	No default	Password to use when connecting to the Oracle database server.
`database.dbname`	No default	Name of the database to connect to. In a container database environment, specify the name of the root container database (CDB), not the name of an included pluggable database (PDB).
`database.url`	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.
`database.pdb.name`	No default	Name of the Oracle pluggable database to connect to. Use this property with container database (CDB) installations only.
`topic.prefix`	No default	Topic prefix that 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 topic prefix 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 schema history topic.
`database.connection.adapter`	`logminer`	The adapter implementation that the connector uses when it streams database changes. You can set the following values: `logminer`(default):: The connector uses the native Oracle LogMiner API.
`snapshot.mode`	initial	Specifies the mode that the connector uses to take snapshots of a captured table. You can set the following values: `always` The snapshot includes the structure and data of the captured tables. Specify this value to populate topics with a complete representation of the data from the captured tables on each connector start. `initial` The snapshot includes the structure and data of the captured tables. Specify this value to populate topics with a complete representation of the data from the captured tables. If the snapshot completes successfully, upon next connector start snapshot is not executed again. `initial_only` The snapshot includes the structure and data of the captured tables. The connector performs an initial snapshot and then stops, without processing any subsequent changes. `schema_only` The snapshot includes only the structure of captured tables. Specify this value if you want the connector to capture data only for changes that occur after the snapshot. `schema_only_recovery` This is a recovery setting for a connector that has already been capturing changes. When you restart the connector, this setting enables recovery of a corrupted or lost database schema history topic. You might set it periodically to "clean up" a database schema history topic that has been growing unexpectedly. Database schema history topics require infinite retention. Note this mode is only safe to be used when it is guaranteed that no schema changes happened since the point in time the connector was shut down before and the point in time the snapshot is taken. After the snapshot is complete, the connector continues to read change events from the database’s redo logs except when `snapshot.mode` is configured as `initial_only`. For more information, see the table of `snapshot.mode` options.
`snapshot.locking.mode`	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: `shared` Enables concurrent access to the table, but prevents any session from acquiring an exclusive table lock. The connector acquires a `ROW SHARE` level lock while it captures table schema. `none` Prevents the connector from acquiring any table locks during the snapshot. Use this setting only if no schema changes might occur during the creation of the snapshot.
`snapshot.include.collection.list`	All tables specified in the connector’s `table.include.list` property.	An optional, comma-separated list of regular expressions that match the fully-qualified names (`<databaseName>.<schemaName>.<tableName>`) of the tables to include in a snapshot. In a multitenant container database (CDB) environment, the regular expression must include the pluggable database (PDB) name, using the format `<pdbName>.<schemaName>.<tableName>`. To match the name of a table, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the table; it does not match substrings that might be present in a table name. Only POSIX regular expressions are valid. A snapshot can only include tables that are named in the connector’s `table.include.list` property. This property takes effect only if the connector’s `snapshot.mode` property is set to a value other than `never`. This property does not affect the behavior of incremental snapshots.
`snapshot.select.statement.overrides`	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 `<schemaName>.<tableName>`. For example, `"snapshot.select.statement.overrides": "inventory.products,customers.orders"` For each table in the list, add a further configuration property that specifies the `SELECT` statement for the connector to run on the table when it takes a snapshot. The specified `SELECT` statement determines the subset of table rows to include in the snapshot. Use the following format to specify the name of this `SELECT` statement property: `snapshot.select.statement.overrides.<schemaName>.<tableName>` For example, `snapshot.select.statement.overrides.customers.orders` Example: From a `customers.orders` table that includes the soft-delete column, `delete_flag`, add the following properties if you want a snapshot to include only those records that are not soft-deleted: "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 `delete_flag = 0`.
`schema.include.list`	No default	An optional, comma-separated list of regular expressions that match names of schemas for which you want to capture changes. Only POSIX regular expressions are valid. Any schema name not included in `schema.include.list` is excluded from having its changes captured. By default, all non-system schemas have their changes captured. To match the name of a schema, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the schema; it does not match substrings that might be present in a schema name. If you include this property in the configuration, do not also set the `schema.exclude.list` property.
`include.schema.comments`	`false`	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.
`schema.exclude.list`	No default	An optional, comma-separated list of regular expressions that match names of schemas for which you do not want to capture changes. Only POSIX regular expressions are valid. Any schema whose name is not included in `schema.exclude.list` has its changes captured, with the exception of system schemas. To match the name of a schema, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the schema; it does not match substrings that might be present in a schema name. If you include this property in the configuration, do not set the`schema.include.list` property.
`table.include.list`	No default	An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be captured. Only POSIX regular expressions are valid. When this property is set, the connector captures changes only from the specified tables. Each table identifier uses the following format: `<schema_name>.<table_name>` By default, the connector monitors every non-system table in each captured database. To match the name of a table, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the table; it does not match substrings that might be present in a table name. If you include this property in the configuration, do not also set the `table.exclude.list` property.
`table.exclude.list`	No default	An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be excluded from monitoring. Only POSIX regular expressions are valid. 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: `<schemaName>.<tableName>`. To match the name of a table, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the table; it does not match substrings that might be present in a table name. If you include this property in the configuration, do not also set the `table.include.list` property.
`column.include.list`	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. Only POSIX regular expressions are valid. Fully-qualified names for columns use the following format: `<Schema_name>.<table_name>.<column_name>` The primary key column is always included in an event’s key, even if you do not use this property to explicitly include its value. To match the name of a column, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the column it does not match substrings that might be present in a column name. If you include this property in the configuration, do not also set the `column.exclude.list` property.
`column.exclude.list`	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. Only POSIX regular expressions are valid. Fully-qualified column names use the following format: `<schema_name>.<table_name>.<column_name>` The primary key column is always included in an event’s key, even if you use this property to explicitly exclude its value. To match the name of a column, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the column it does not match substrings that might be present in a column name. If you include this property in the configuration, do not set the `column.include.list` property.
`skip.messages.without.change`	`false`	Specifies whether to skip publishing messages when there is no change in included columns. This would essentially filter messages if there is no change in columns included as per `column.include.list` or `column.exclude.list` properties.
`column.mask.hash.hashAlgorithm.with.salt.salt`; `column.mask.hash.v2.hashAlgorithm.with.salt.salt`	n/a	An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns. Fully-qualified names for columns are of the form `<schemaName>.<tableName>.<columnName>`. To match the name of a column Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the column; the expression does not match substrings that might be present in a column name. In the resulting change event record, the values for the specified columns are replaced with pseudonyms. A pseudonym consists of the hashed value that results from applying the 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. In the following example, `CzQMA0cB5K` is a randomly selected salt. 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. Depending on the hashAlgorithm used, the salt selected, and the actual data set, the resulting data set might not be completely masked. Hashing strategy version 2 should be used to ensure fidelity if the value is being hashed in different places or systems.
`binary.handling.mode`	bytes	Specifies how binary (`blob`) columns should be represented in change events, including: `bytes` represents binary data as byte array (default), `base64` represents binary data as base64-encoded String, `base64-url-safe` represents binary data as base64-url-safe-encoded String, `hex` represents binary data as hex-encoded (base16) String
`schema.name.adjustment.mode`	none	Specifies how schema names should be adjusted for compatibility with the message converter used by the connector. Possible settings: `none` does not apply any adjustment. `avro` replaces the characters that cannot be used in the Avro type name with underscore. `avro_unicode` replaces the underscore or characters that cannot be used in the Avro type name with corresponding unicode like _uxxxx. Note: _ is an escape sequence like backslash in Java
`field.name.adjustment.mode`	none	Specifies how field names should be adjusted for compatibility with the message converter used by the connector. Possible settings: `none` does not apply any adjustment. `avro` replaces the characters that cannot be used in the Avro type name with underscore. `avro_unicode` replaces the underscore or characters that cannot be used in the Avro type name with corresponding unicode like _uxxxx. Note: _ is an escape sequence like backslash in Java See Avro naming for more details.
`decimal.handling.mode`	`precise`	Specifies how the connector should handle floating point values for `NUMBER`, `DECIMAL` and `NUMERIC` columns. You can set one of the following options: `precise` (default) Represents values precisely by using `java.math.BigDecimal` values represented in change events in a binary form. `double` Represents values by using `double` values. Using `double` values is easier, but can result in a loss of precision. `string` Encodes values as formatted strings. Using the `string` option is easier to consume, but results in a loss of semantic information about the real type. For more information, see Numeric types.
`interval.handling.mode`	`numeric`	Specifies how the connector should handle values for `interval` columns: `numeric` represents intervals using approximate number of microseconds. `string` represents intervals exactly by using the string pattern representation `P<years>Y<months>M<days>DT<hours>H<minutes>M<seconds>S`. For example: `P1Y2M3DT4H5M6.78S`.
`event.processing.failure.handling.mode`	`fail`	Specifies how the connector should react to exceptions during processing of events. You can set one of the following options: `fail` Propagates the exception (indicating the offset of the problematic event), causing the connector to stop. `warn` Causes the problematic event to be skipped. The offset of the problematic event is then logged. `skip` Causes the problematic event to be skipped.
`max.batch.size`	`2048`	A positive integer value that specifies the maximum size of each batch of events to process during each iteration of this connector.
`max.queue.size`	`8192`	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 `max.queue.size` to be larger than the value of `max.batch.size`.
`max.queue.size.in.bytes`	`0` (disabled)	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. If `max.queue.size` is also set, writing to the queue is blocked when the size of the queue reaches the limit specified by either property. For example, if you set `max.queue.size=1000`, and `max.queue.size.in.bytes=5000`, writing to the queue is blocked after the queue contains 1000 records, or after the volume of the records in the queue reaches 5000 bytes.
`poll.interval.ms`	`500` (0.5 second)	Positive integer value that specifies the number of milliseconds the connector should wait during each iteration for new change events to appear.
`tombstones.on.delete`	`true`	Controls whether a delete event is followed by a tombstone event. The following values are possible: `true` For each delete operation, the connector emits a delete event and a subsequent tombstone event. `false` For each delete operation, the connector emits only a delete event. 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.
`message.key.columns`	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. To establish a custom message key for a table, list the table, followed by the columns to use as the message key. Each list entry takes the following format: `<fullyQualifiedTableName>`:`<keyColumn>`,`<keyColumn>` To base a table key on multiple column names, insert commas between the column names. Each fully-qualified table name is a regular expression in the following format: `<schemaName>.<tableName>` The property can include entries for multiple tables. Use a semicolon to separate table entries in the list. The following example sets the message key for the tables `inventory.customers` and `purchase.orders`: `inventory.customers:pk1,pk2;(.*).purchaseorders:pk3,pk4` For the table `inventory.customer`, the columns `pk1` and `pk2` are specified as the message key. For the `purchaseorders` tables in any schema, the columns `pk3` and `pk4` server as the message key. There is no limit to the number of columns that you use to create custom message keys. However, it’s best to use the minimum number that are required to specify a unique key.
`column.truncate.to.length.chars`	No default	An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns. Set this property if you want the connector to mask the values for a set of columns, for example, if they contain sensitive data. Set `length` to a positive integer to replace data in the specified columns with the number of asterisk (``) characters specified by the length* in the property name. Set length to `0` (zero) to replace data in the specified columns with an empty string. The fully-qualified name of a column observes the following format: `<schemaName>.<tableName>.<columnName>`. To match the name of a column, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the column; the expression does not match substrings that might be present in a column name. You can specify multiple properties with different lengths in a single configuration.
`column.mask.with.length.chars`	No default	An optional comma-separated list of regular expressions for masking column names in change event messages by replacing characters with asterisks (`*`). Specify the number of characters to replace in the name of the property, for example, `column.mask.with.8.chars`. Specify length as a positive integer or zero. Then add regular expressions to the list for each character-based column name where you want to apply a mask. Use the following format to specify fully-qualified column names: `<schemaName>.<tableName>.<columnName>`. The connector configuration can include multiple properties that specify different lengths.
`column.propagate.source.type`	No default	An optional, comma-separated list of regular expressions that match the fully-qualified names of columns for which you want the connector to emit extra parameters that represent column metadata. When this property is set, the connector adds the following fields to the schema of event records: `__debezium.source.column.type` `__debezium.source.column.length` `__debezium.source.column.scale` These parameters propagate a column’s original type name and length (for variable-width types), respectively. Enabling the connector to emit this extra data can assist in properly sizing specific numeric or character-based columns in sink databases. The fully-qualified name of a column observes one of the following formats: `<tableName>.<columnName>`, or `<schemaName>.<tableName>.<columnName>`. To match the name of a column, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the column; the expression does not match substrings that might be present in a column name.
`datatype.propagate.source.type`	No default	An optional, comma-separated list of regular expressions that specify the fully-qualified names of data types that are defined for columns in a database. When this property is set, for columns with matching data types, the connector emits event records that include the following extra fields in their schema: `__debezium.source.column.type` `__debezium.source.column.length` `__debezium.source.column.scale` These parameters propagate a column’s original type name and length (for variable-width types), respectively. Enabling the connector to emit this extra data can assist in properly sizing specific numeric or character-based columns in sink databases. The fully-qualified name of a column observes one of the following formats: `<tableName>.<typeName>`, or `<schemaName>.<tableName>.<typeName>`. To match the name of a data type, Debezium applies the regular expression that you specify as an anchored regular expression. That is, the specified expression is matched against the entire name string of the data type; the expression does not match substrings that might be present in a type name. For the list of Oracle-specific data type names, see the Oracle data type mappings.
`heartbeat.interval.ms`	`0`	Specifies, in milliseconds, how frequently the connector sends messages to a heartbeat topic. Use this property to determine whether the connector continues to receive change events from the source database. It can also be useful to set the property in situations where no change events occur in captured tables for an extended period. In such a case, although the connector continues to read the redo log, it emits no change event messages, so that the offset in the Kafka topic remains unchanged. Because the connector does not flush the latest system change number (SCN) that it read from the database, the database might retain the redo log files for longer than necessary. If the connector restarts, the extended retention period could result in the connector redundantly sending some change events. The default value of `0` prevents the connector from sending any heartbeat messages.
`heartbeat.action.query`	No default	Specifies a query that the connector executes on the source database when the connector sends a heartbeat message. For example: `INSERT INTO test_heartbeat_table (text) VALUES ('test_heartbeat')` The connector runs the query after it emits 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.
`snapshot.delay.ms`	No default	Specifies an interval in milliseconds that the connector waits after it starts before it takes a snapshot. Use this property to prevent snapshot interruptions when you start multiple connectors in a cluster, which might cause re-balancing of connectors.
`snapshot.fetch.size`	`10000`	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.
`query.fetch.size`	`10000`	Specifies the number of rows that will be fetched for each database round-trip of a given query. Using a value of `0` will use the JDBC driver’s default fetch size.
`provide.transaction.metadata`	`false`	Set the property to `true` if you want Debezium to generate events with transaction boundaries and enriches data events envelope with transaction metadata. See Transaction Metadata for additional details.
`log.mining.strategy`	`redo_log_catalog`	Specifies the mining strategy that controls how Oracle LogMiner builds and uses a given data dictionary for resolving table and column ids to names. `redo_log_catalog`:: Writes the data dictionary to the online redo logs causing more archive logs to be generated over time. This also enables tracking DDL changes against captured tables, so if the schema changes frequently this is the ideal choice. `online_catalog`:: Uses the database’s current data dictionary to resolve object ids and does not write any extra information to the online redo logs. This allows LogMiner to mine substantially faster but at the expense that DDL changes cannot be tracked. If the captured table(s) schema changes infrequently or never, this is the ideal choice.
`log.mining.query.filter.mode`	`none`	Specifies the mining query mode that controls how the Oracle LogMiner query is built. `none`:: The query is generated without doing any schema, table, or username filtering in the query. `in`:: The query is generated using a standard SQL in-clause to filter schema, table, and usernames on the database side. The schema, table, and username configuration include/exclude lists should not specify any regular expressions as the query is built using the values directly. `regex`:: The query is generated using Oracle’s `REGEXP_LIKE` operator to filter schema and table names on the database side, along with usernames using a SQL in-clause. The schema and table configuration include/exclude lists can safely specify regular expressions.
`log.mining.buffer.type`	`memory`	The buffer type controls how the connector manages buffering transaction data. `memory` - Uses the JVM process' heap to buffer all transaction data. Choose this option if you don’t expect the connector to process a high number of long-running or large transactions. When this option is active, the buffer state is not persisted across restarts. Following a restart, recreate the buffer from the SCN value of the current offset.
`log.mining.session.max.ms`	`0`	The maximum number of milliseconds that a LogMiner session can be active before a new session is used. For low volume systems, a LogMiner session may consume too much PGA memory when the same session is used for a long period of time. The default behavior is to only use a new LogMiner session when a log switch is detected. By setting this value to something greater than `0`, this specifies the maximum number of milliseconds a LogMiner session can be active before it gets stopped and started to deallocate and reallocate PGA memory.
`log.mining.restart.connection`	`false`	Specifies whether the JDBC connection will be closed and re-opened on log switches or when mining session has reached maximum lifetime threshold. By default, the JDBC connection is not closed across log switches or maximum session lifetimes. This should be enabled if you experience excessive Oracle SGA growth with LogMiner.
`log.mining.batch.size.min`	`1000`	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.
`log.mining.batch.size.max`	`100000`	The maximum SCN interval size that this connector uses when reading from redo/archive logs.
`log.mining.batch.size.default`	`20000`	The starting SCN interval size that the connector uses for reading data from redo/archive logs. This also servers as a measure for adjusting batch size - when the difference between current SCN and beginning/end SCN of the batch is bigger than this value, batch size is increased/decreased.
`log.mining.sleep.time.min.ms`	`0`	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.
`log.mining.sleep.time.max.ms`	`3000`	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.
`log.mining.sleep.time.default.ms`	`1000`	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.
`log.mining.sleep.time.increment.ms`	`200`	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.
`log.mining.archive.log.hours`	`0`	The number of hours in the past from SYSDATE to mine archive logs. When the default setting (`0`) is used, the connector mines all archive logs.
`log.mining.archive.log.only.mode`	`false`	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). Redo logs use a circular buffer that can be archived at any point. In environments where online redo logs are archived frequently, this can lead to LogMiner session failures. In contrast to redo logs, archive logs are guaranteed to be reliable. Set this option to `true` to force the connector to mine archive logs only. After you set the connector to mine only the archive logs, the latency between an operation being committed and the connector emitting an associated change event might increase. The degree of latency depends on how frequently the database is configured to archive online redo logs.
`log.mining.archive.log.only.scn.poll.interval.ms`	`10000`	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 `log.mining.archive.log.only.mode` is not enabled, this setting is not used.
`log.mining.transaction.retention.ms`	`0`	Positive integer value that specifies the number of milliseconds to retain long running transactions between redo log switches. When set to `0`, transactions are retained until a commit or rollback is detected. By default, 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.
`log.mining.archive.destination.name`	No default	Specifies the configured Oracle archive destination to use when mining archive logs with LogMiner. The default behavior automatically selects the first valid, local configured destination. However, you can use a specific destination can be used by providing the destination name, for example, `LOG_ARCHIVE_DEST_5`.
`log.mining.username.include.list`	No default	List of database users to include from the LogMiner query. It can be useful to set this property if you want the capturing process to include changes from the specified users.
`log.mining.username.exclude.list`	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.
`log.mining.scn.gap.detection.gap.size.min`	`1000000`	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 `log.mining.scn.gap.detection.time.interval.max.ms` then an SCN gap is detected, and the connector uses a mining window larger than the configured maximum batch.
`log.mining.scn.gap.detection.time.interval.max.ms`	`20000`	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 `log.mining.scn.gap.detection.gap.size.min`, then an SCN gap is detected and the connector uses a mining window larger than the configured maximum batch.
`log.mining.flush.table.name`	`LOG_MINING_FLUSH`	Specifies the name of the flush table that coordinates flushing the Oracle LogWriter Buffer (LGWR) to the redo logs. Typically, multiple connectors can use the same flush table. However, if connectors encounter table lock contention errors, use this property to specify a dedicated table for each connector deployment.
`lob.enabled`	`false`	Controls whether or not large object (CLOB or BLOB) column values are emitted in change events. By default, change events have large object columns, but the columns contain no values. There is a certain amount of overhead in processing and managing large object column types and payloads. To capture large object values and serialized them in change events, set this option to `true`. Note Use of large object data types is a Technology Preview feature.
`unavailable.value.placeholder`	`__debezium_unavailable_value`	Specifies the constant that the connector provides to indicate that the original value is unchanged and not provided by the database.
`rac.nodes`	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: Specify a value for `database.port`, and use the specified port value for each address in the `rac.nodes` list. For example: database.port=1521 rac.nodes=192.168.1.100,192.168.1.101 Specify a value for `database.port`, and override the default port for one or more entries in the list. The list can include entries that use the default `database.port` value, and entries that define their own unique port values. For example: database.port=1521 rac.nodes=192.168.1.100,192.168.1.101:1522 If you supply a raw JDBC URL for the database by using the `database.url` property, instead of defining a value for `database.port`, each RAC node entry must explicitly specify a port value.
`skipped.operations`	`t`	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: `c` (insert/create) `u` (update) `d` (delete) `t` (truncate) By default, only truncate operations are skipped.
`signal.data.collection`	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. Use the following format to specify the collection name: `<databaseName>.<schemaName>.<tableName>`
`signal.enabled.channels`	source	List of the signaling channel names that are enabled for the connector. By default, the following channels are available: `source` `kafka` `file` `jmx`
`notification.enabled.channels`	No default	List of notification channel names that are enabled for the connector. By default, the following channels are available: `sink` `log` `jmx`
`incremental.snapshot.chunk.size`	`1024`	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.
`topic.naming.strategy`	`io.debezium.schema.SchemaTopicNamingStrategy`	The name of the TopicNamingStrategy class that should be used to determine the topic name for data change, schema change, transaction, heartbeat event etc., defaults to `SchemaTopicNamingStrategy`.
`topic.delimiter`	`.`	Specify the delimiter for topic name, defaults to `.`.
`topic.cache.size`	`10000`	The size used for holding the topic names in bounded concurrent hash map. This cache will help to determine the topic name corresponding to a given data collection.
`topic.heartbeat.prefix`	`__debezium-heartbeat`	Controls the name of the topic to which the connector sends heartbeat messages. The topic name has this pattern: topic.heartbeat.prefix.topic.prefix For example, if the topic prefix is `fulfillment`, the default topic name is `__debezium-heartbeat.fulfillment`.
`topic.transaction`	`transaction`	Controls the name of the topic to which the connector sends transaction metadata messages. The topic name has this pattern: topic.prefix.topic.transaction For example, if the topic prefix is `fulfillment`, the default topic name is `fulfillment.transaction`.
`snapshot.max.threads`	`1`	Specifies the number of threads that the connector uses when performing an initial snapshot. To enable parallel initial snapshots, set the property to a value greater than 1. In a parallel initial snapshot, the connector processes multiple tables concurrently. Important Parallel initial snapshots 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 Technology Preview Features Support Scope.
`errors.max.retries`	`-1`	The maximum number of retries on retriable errors (e.g. connection errors) before failing (-1 = no limit, 0 = disabled, > 0 = num of retries).

Debezium Oracle connector database schema history configuration properties

Debezium provides a set of schema.history.internal.* properties that control how the connector interacts with the schema history topic.

The following table describes the schema.history.internal properties for configuring the Debezium connector.

Table 7.17. Connector database schema history configuration properties
Property	Default	Description
`schema.history.internal.kafka.topic`	No default	The full name of the Kafka topic where the connector stores the database schema history.
`schema.history.internal.kafka.bootstrap.servers`	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.
`schema.history.internal.kafka.recovery.poll.interval.ms`	`100`	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.
`schema.history.internal.kafka.query.timeout.ms`	`3000`	An integer value that specifies the maximum number of milliseconds the connector should wait while fetching cluster information using Kafka admin client.
`schema.history.internal.kafka.create.timeout.ms`	`30000`	An integer value that specifies the maximum number of milliseconds the connector should wait while create kafka history topic using Kafka admin client.
`schema.history.internal.kafka.recovery.attempts`	`100`	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 `recovery.attempts` × `recovery.poll.interval.ms`.
`schema.history.internal.skip.unparseable.ddl`	`false`	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 `false`. Skipping should be used only with care as it can lead to data loss or mangling when the binlog is being processed.
`schema.history.internal.store.only.captured.tables.ddl`	`false`	A Boolean value that specifies whether the connector records schema structures from all tables in a schema or database, or only from tables that are designated for capture. Specify one of the following values: `false` (default) During a database snapshot, the connector records the schema data for all non-system tables in the database, including tables that are not designated for capture. It’s best to retain the default setting. If you later decide to capture changes from tables that you did not originally designate for capture, the connector can easily begin to capture data from those tables, because their schema structure is already stored in the schema history topic. Debezium requires the schema history of a table so that it can identify the structure that was present at the time that a change event occurred. `true` During a database snapshot, the connector records the table schemas only for the tables from which Debezium captures change events. If you change the default value, and you later configure the connector to capture data from other tables in the database, the connector lacks the schema information that it requires to capture change events from the tables.
`schema.history.internal.store.only.captured.databases.ddl`	`false`	A Boolean value that specifies whether the connector records schema structures from all logical databases in the database instance. Specify one of the following values: `true` The connector records schema structures only for tables in the logical database and schema from which Debezium captures change events. `false` The connector records schema structures for all logical databases. Note The default value is `true` for MySQL Connector

Pass-through database schema history properties for configuring producer and consumer clients

Debezium relies on a Kafka producer to write schema changes to database schema history topics. Similarly, it relies on a Kafka consumer to read from database schema history topics when a connector starts. You define the configuration for the Kafka producer and consumer clients by assigning values to a set of pass-through configuration properties that begin with the schema.history.internal.producer.* and schema.history.internal.consumer.* prefixes. The pass-through producer and consumer database schema history properties control a range of behaviors, such as how these clients secure connections with the Kafka broker, as shown in the following example:

schema.history.internal.producer.security.protocol=SSL
schema.history.internal.producer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks
schema.history.internal.producer.ssl.keystore.password=test1234
schema.history.internal.producer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks
schema.history.internal.producer.ssl.truststore.password=test1234
schema.history.internal.producer.ssl.key.password=test1234

schema.history.internal.consumer.security.protocol=SSL
schema.history.internal.consumer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks
schema.history.internal.consumer.ssl.keystore.password=test1234
schema.history.internal.consumer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks
schema.history.internal.consumer.ssl.truststore.password=test1234
schema.history.internal.consumer.ssl.key.password=test1234

Debezium strips the prefix from the property name before it passes the property to the Kafka client.

See the Kafka documentation for more details about Kafka producer configuration properties and Kafka consumer configuration properties.

Debezium connector Kafka signals configuration properties

Debezium provides a set of signal.* properties that control how the connector interacts with the Kafka signals topic.

The following table describes the Kafka signal properties.

Table 7.18. Kafka signals configuration properties
Property	Default	Description
`signal.kafka.topic`	<topic.prefix>-signal	The name of the Kafka topic that the connector monitors for ad hoc signals. Note If automatic topic creation is disabled, you must manually create the required signaling topic. A signaling topic is required to preserve signal ordering. The signaling topic must have a single partition.
`signal.kafka.groupId`	kafka-signal	The name of the group ID that is used by Kafka consumers.
`signal.kafka.bootstrap.servers`	No default	A list of host/port pairs that the connector uses for establishing an initial connection to the Kafka cluster. Each pair references the Kafka cluster that is used by the Debezium Kafka Connect process.
`signal.kafka.poll.timeout.ms`	`100`	An integer value that specifies the maximum number of milliseconds that the connector waits when polling signals.

Debezium connector pass-through signals Kafka consumer client configuration properties

The Debezium connector provides for pass-through configuration of the signals Kafka consumer. Pass-through signals properties begin with the prefix signals.consumer.*. For example, the connector passes properties such as signal.consumer.security.protocol=SSL to the Kafka consumer.

Debezium strips the prefixes from the properties before it passes the properties to the Kafka signals consumer.

Debezium connector sink notifications configuration properties

The following table describes the notification properties.

Table 7.19. Sink notification configuration properties
Property	Default	Description
`notification.sink.topic.name`	No default	The name of the topic that receives notifications from Debezium. This property is required when you configure the `notification.enabled.channels` property to include `sink` as one of the enabled notification channels.

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 driver.*. For example, the connector passes properties such as driver.foobar=false to the JDBC URL.

As is the case with the pass-through properties for database schema history clients, Debezium strips the prefixes from the properties before it passes them to the database driver.

7.7. 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.

7.7.1. Debezium Oracle connector snapshot metrics

The MBean is debezium.oracle:type=connector-metrics,context=snapshot,server=<topic.prefix>.

Snapshot metrics are not exposed unless a snapshot operation is active, or if a snapshot has occurred since the last connector start.

The following table lists the shapshot metrics that are available.

Attributes	Type	Description
`LastEvent`	`string`	The last snapshot event that the connector has read.
`MilliSecondsSinceLastEvent`	`long`	The number of milliseconds since the connector has read and processed the most recent event.
`TotalNumberOfEventsSeen`	`long`	The total number of events that this connector has seen since last started or reset.
`NumberOfEventsFiltered`	`long`	The number of events that have been filtered by include/exclude list filtering rules configured on the connector.
`CapturedTables`	`string[]`	The list of tables that are captured by the connector.
`QueueTotalCapacity`	`int`	The length the queue used to pass events between the snapshotter and the main Kafka Connect loop.
`QueueRemainingCapacity`	`int`	The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop.
`TotalTableCount`	`int`	The total number of tables that are being included in the snapshot.
`RemainingTableCount`	`int`	The number of tables that the snapshot has yet to copy.
`SnapshotRunning`	`boolean`	Whether the snapshot was started.
`SnapshotPaused`	`boolean`	Whether the snapshot was paused.
`SnapshotAborted`	`boolean`	Whether the snapshot was aborted.
`SnapshotCompleted`	`boolean`	Whether the snapshot completed.
`SnapshotDurationInSeconds`	`long`	The total number of seconds that the snapshot has taken so far, even if not complete. Includes also time when snapshot was paused.
`SnapshotPausedDurationInSeconds`	`long`	The total number of seconds that the snapshot was paused. If the snapshot was paused several times, the paused time adds up.
`RowsScanned`	`Map<String, Long>`	Map containing the number of rows scanned for each table in the snapshot. Tables are incrementally added to the Map during processing. Updates every 10,000 rows scanned and upon completing a table.
`MaxQueueSizeInBytes`	`long`	The maximum buffer of the queue in bytes. This metric is available if `max.queue.size.in.bytes` is set to a positive long value.
`CurrentQueueSizeInBytes`	`long`	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
`ChunkId`	`string`	The identifier of the current snapshot chunk.
`ChunkFrom`	`string`	The lower bound of the primary key set defining the current chunk.
`ChunkTo`	`string`	The upper bound of the primary key set defining the current chunk.
`TableFrom`	`string`	The lower bound of the primary key set of the currently snapshotted table.
`TableTo`	`string`	The upper bound of the primary key set of the currently snapshotted table.

7.7.2. Debezium Oracle connector streaming metrics

The MBean is debezium.oracle:type=connector-metrics,context=streaming,server=<topic.prefix>.

The following table lists the streaming metrics that are available.

Attributes	Type	Description
`LastEvent`	`string`	The last streaming event that the connector has read.
`MilliSecondsSinceLastEvent`	`long`	The number of milliseconds since the connector has read and processed the most recent event.
`TotalNumberOfEventsSeen`	`long`	The total number of events that this connector has seen since the last start or metrics reset.
`TotalNumberOfCreateEventsSeen`	`long`	The total number of create events that this connector has seen since the last start or metrics reset.
`TotalNumberOfUpdateEventsSeen`	`long`	The total number of update events that this connector has seen since the last start or metrics reset.
`TotalNumberOfDeleteEventsSeen`	`long`	The total number of delete events that this connector has seen since the last start or metrics reset.
`NumberOfEventsFiltered`	`long`	The number of events that have been filtered by include/exclude list filtering rules configured on the connector.
`CapturedTables`	`string[]`	The list of tables that are captured by the connector.
`QueueTotalCapacity`	`int`	The length the queue used to pass events between the streamer and the main Kafka Connect loop.
`QueueRemainingCapacity`	`int`	The free capacity of the queue used to pass events between the streamer and the main Kafka Connect loop.
`Connected`	`boolean`	Flag that denotes whether the connector is currently connected to the database server.
`MilliSecondsBehindSource`	`long`	The number of milliseconds between the last change event’s timestamp and the connector processing it. The values will incoporate any differences between the clocks on the machines where the database server and the connector are running.
`NumberOfCommittedTransactions`	`long`	The number of processed transactions that were committed.
`SourceEventPosition`	`Map<String, String>`	The coordinates of the last received event.
`LastTransactionId`	`string`	Transaction identifier of the last processed transaction.
`MaxQueueSizeInBytes`	`long`	The maximum buffer of the queue in bytes. This metric is available if `max.queue.size.in.bytes` is set to a positive long value.
`CurrentQueueSizeInBytes`	`long`	The current volume, in bytes, of records in the queue.

The Debezium Oracle connector also provides the following additional streaming metrics:

Table 7.20. Descriptions of additional streaming metrics
Attributes	Type	Description
`CurrentScn`	`BigInteger`	The most recent system change number that has been processed.
`OldestScn`	`BigInteger`	The oldest system change number in the transaction buffer.
`CommittedScn`	`BigInteger`	The last committed system change number from the transaction buffer.
`OffsetScn`	`BigInteger`	The system change number currently written to the connector’s offsets.
`CurrentRedoLogFileName`	`string[]`	Array of the log files that are currently mined.
`MinimumMinedLogCount`	`long`	The minimum number of logs specified for any LogMiner session.
`MaximumMinedLogCount`	`long`	The maximum number of logs specified for any LogMiner session.
`RedoLogStatus`	`string[]`	Array of the current state for each mined logfile with the format `filename\|status`.
`SwitchCounter`	`int`	The number of times the database has performed a log switch for the last day.
`LastCapturedDmlCount`	`long`	The number of DML operations observed in the last LogMiner session query.
`MaxCapturedDmlInBatch`	`long`	The maximum number of DML operations observed while processing a single LogMiner session query.
`TotalCapturedDmlCount`	`long`	The total number of DML operations observed.
`FetchingQueryCount`	`long`	The total number of LogMiner session query (aka batches) performed.
`LastDurationOfFetchQueryInMilliseconds`	`long`	The duration of the last LogMiner session query’s fetch in milliseconds.
`MaxDurationOfFetchQueryInMilliseconds`	`long`	The maximum duration of any LogMiner session query’s fetch in milliseconds.
`LastBatchProcessingTimeInMilliseconds`	`long`	The duration for processing the last LogMiner query batch results in milliseconds.
`TotalParseTimeInMilliseconds`	`long`	The time in milliseconds spent parsing DML event SQL statements.
`LastMiningSessionStartTimeInMilliseconds`	`long`	The duration in milliseconds to start the last LogMiner session.
`MaxMiningSessionStartTimeInMilliseconds`	`long`	The longest duration in milliseconds to start a LogMiner session.
`TotalMiningSessionStartTimeInMilliseconds`	`long`	The total duration in milliseconds spent by the connector starting LogMiner sessions.
`MinBatchProcessingTimeInMilliseconds`	`long`	The minimum duration in milliseconds spent processing results from a single LogMiner session.
`MaxBatchProcessingTimeInMilliseconds`	`long`	The maximum duration in milliseconds spent processing results from a single LogMiner session.
`TotalProcessingTimeInMilliseconds`	`long`	The total duration in milliseconds spent processing results from LogMiner sessions.
`TotalResultSetNextTimeInMilliseconds`	`long`	The total duration in milliseconds spent by the JDBC driver fetching the next row to be processed from the log mining view.
`TotalProcessedRows`	`long`	The total number of rows processed from the log mining view across all sessions.
`BatchSize`	`int`	The number of entries fetched by the log mining query per database round-trip.
`MillisecondToSleepBetweenMiningQuery`	`long`	The number of milliseconds the connector sleeps before fetching another batch of results from the log mining view.
`MaxBatchProcessingThroughput`	`long`	The maximum number of rows/second processed from the log mining view.
`AverageBatchProcessingThroughput`	`long`	The average number of rows/second processed from the log mining.
`LastBatchProcessingThroughput`	`long`	The average number of rows/second processed from the log mining view for the last batch.
`NetworkConnectionProblemsCounter`	`long`	The number of connection problems detected.
`HoursToKeepTransactionInBuffer`	`int`	The number of hours that transactions are retained by the connector’s in-memory buffer without being committed or rolled back before being discarded. For more information, see `log.mining.transaction.retention.ms`.
`NumberOfActiveTransactions`	`long`	The number of current active transactions in the transaction buffer.
`NumberOfCommittedTransactions`	`long`	The number of committed transactions in the transaction buffer.
`NumberOfRolledBackTransactions`	`long`	The number of rolled back transactions in the transaction buffer.
`CommitThroughput`	`long`	The average number of committed transactions per second in the transaction buffer.
`RegisteredDmlCount`	`long`	The number of registered DML operations in the transaction buffer.
`LagFromSourceInMilliseconds`	`long`	The time difference in milliseconds between when a change occurred in the transaction logs and when its added to the transaction buffer.
`MaxLagFromSourceInMilliseconds`	`long`	The maximum time difference in milliseconds between when a change occurred in the transaction logs and when its added to the transaction buffer.
`MinLagFromSourceInMilliseconds`	`long`	The minimum time difference in milliseconds between when a change occurred in the transaction logs and when its added to the transaction buffer.
`AbandonedTransactionIds`	`string[]`	An array of the most recent abandoned transaction identifiers removed from the transaction buffer due to their age. See `log.mining.transaction.retention.ms` for details.
`RolledBackTransactionIds`	`string[]`	An array of the most recent transaction identifiers that have been mined and rolled back in the transaction buffer.
`LastCommitDurationInMilliseconds`	`long`	The duration of the last transaction buffer commit operation in milliseconds.
`MaxCommitDurationInMilliseconds`	`long`	The duration of the longest transaction buffer commit operation in milliseconds.
`ErrorCount`	`int`	The number of errors detected.
`WarningCount`	`int`	The number of warnings detected.
`ScnFreezeCount`	`int`	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 `0`.
`UnparsableDdlCount`	`int`	The number of DDL records that have been detected but could not be parsed by the DDL parser. This should always be `0`; however when allowing unparsable DDL to be skipped, this metric can be used to determine if any warnings have been written to the connector logs.
`MiningSessionUserGlobalAreaMemoryInBytes`	`long`	The current mining session’s user global area (UGA) memory consumption in bytes.
`MiningSessionUserGlobalAreaMaxMemoryInBytes`	`long`	The maximum mining session’s user global area (UGA) memory consumption in bytes across all mining sessions.
`MiningSessionProcessGlobalAreaMemoryInBytes`	`long`	The current mining session’s process global area (PGA) memory consumption in bytes.
`MiningSessionProcessGlobalAreaMaxMemoryInBytes`	`long`	The maximum mining session’s process global area (PGA) memory consumption in bytes across all mining sessions.

7.7.3. Debezium Oracle connector schema history metrics

The MBean is debezium.oracle:type=connector-metrics,context=schema-history,server=<topic.prefix>.

The following table lists the schema history metrics that are available.

Attributes	Type	Description
`Status`	`string`	One of `STOPPED`, `RECOVERING` (recovering history from the storage), `RUNNING` describing the state of the database schema history.
`RecoveryStartTime`	`long`	The time in epoch seconds at what recovery has started.
`ChangesRecovered`	`long`	The number of changes that were read during recovery phase.
`ChangesApplied`	`long`	the total number of schema changes applied during recovery and runtime.
`MilliSecondsSinceLastRecoveredChange`	`long`	The number of milliseconds that elapsed since the last change was recovered from the history store.
`MilliSecondsSinceLastAppliedChange`	`long`	The number of milliseconds that elapsed since the last change was applied.
`LastRecoveredChange`	`string`	The string representation of the last change recovered from the history store.
`LastAppliedChange`	`string`	The string representation of the last applied change.

7.8. Oracle connector frequently asked questions

Is Oracle 11g supported?

Oracle 11g is not supported; however, we do aim to be backward compatible with Oracle 11g on a best-effort basis. We rely on the community to communicate compatibility concerns with Oracle 11g as well as provide bug fixes when a regression is identified.

Isn’t Oracle LogMiner deprecated?

No, Oracle only deprecated the continuous mining option with Oracle LogMiner in Oracle 12c and removed that option starting with Oracle 19c. The Debezium Oracle connector does not rely on this option to function, and therefore can safely be used with newer versions of Oracle without any impact.

How do I change the position in the offsets?

The Debezium Oracle connector maintains two critical values in the offsets, a field named scn and another named commit_scn. The scn field is a string that represents the low-watermark starting position the connector used when capturing changes.

Find out the name of the topic that contains the connector offsets. This is configured based on the value set as the offset.storage.topic configuration property.
Find out the last offset for the connector, the key under which it is stored and identify the partition used to store the offset. This can be done using the kafkacat utility script provided by the Kafka broker installation. An example might look like this:
```
kafkacat -b localhost -C -t my_connect_offsets -f 'Partition(%p) %k %s\n'
Partition(11) ["inventory-connector",{"server":"server1"}] {"scn":"324567897", "commit_scn":"324567897: 0x2832343233323:1"}
```
The key for inventory-connector is ["inventory-connector",{"server":"server1"}], the partition is 11 and the last offset is the contents that follows the key.
To move back to a previous offset the connector should be stopped and the following command has to be issued:
```
echo '["inventory-connector",{"server":"server1"}]|{"scn":"3245675000","commit_scn":"324567500"}' | \
kafkacat -P -b localhost -t my_connect_offsets -K \| -p 11
```
This writes to partition 11 of the my_connect_offsets topic the given key and offset value. In this example, we are reversing the connector back to SCN 3245675000 rather than 324567897.

What happens if the connector cannot find logs with a given offset SCN?

The Debezium connector maintains a low and high -watermark SCN value in the connector offsets. The low-watermark SCN represents the starting position and must exist in the available online redo or archive logs in order for the connector to start successfully. When the connector reports it cannot find this offset SCN, this indicates that the logs that are still available do not contain the SCN and therefore the connector cannot mine changes from where it left off.

When this happens, there are two options. The first is to remove the history topic and offsets for the connector and restart the connector, taking a new snapshot as suggested. This will guarantee that no data loss will occur for any topic consumers. The second is to manually manipulate the offsets, advancing the SCN to a position that is available in the redo or archive logs. This will cause changes that occurred between the old SCN value and the newly provided SCN value to be lost and not written to the topics. This is not recommended.

What’s the difference between the various mining strategies?

The Debezium Oracle connector provides two options for log.mining.strategy.

The default is redo_in_catalog, and this instructs the connector to write the Oracle data dictionary to the redo logs everytime a log switch is detected. This data dictionary is necessary for Oracle LogMiner to track schema changes effectively when parsing the redo and archive logs. This option will generate more than usual numbers of archive logs but allows tables being captured to be manipulated in real-time without any impact on capturing data changes. This option generally requires more Oracle database memory and will cause the Oracle LogMiner session and process to take slightly longer to start after each log switch.

The alternative option, online_catalog, does not write the data dictionary to the redo logs. Instead, Oracle LogMiner will always use the online data dictionary that contains the current state of the table’s structure. This also means that if a table’s structure changes and no longer matches the online data dictionary, Oracle LogMiner will be unable to resolve table or column names if the table’s structure is changed. This mining strategy option should not be used if the tables being captured are subject to frequent schema changes. It’s important that all data changes be lock-stepped with the schema change such that all changes have been captured from the logs for the table, stop the connector, apply the schema change, and restart the connector and resume data changes on the table. This option requires less Oracle database memory and Oracle LogMiner sessions generally start substantially faster since the data dictionary does not need to be loaded or primed by the LogMiner process.

Why does the connector appear to stop capturing changes 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 than database.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.

What’s the cause for ORA-01555 and how to handle it?

The Debezium Oracle connector uses flashback queries when the initial snapshot phase executes. A flashback query is a special type of query that relies on the flashback area, maintained by the database’s UNDO_RETENTION database parameter, to return the results of a query based on what the contents of the table had at a given time, or in our case at a given SCN. By default, Oracle generally only maintains an undo or flashback area for approximately 15 minutes unless this has been increased or decreased by your database administrator. For configurations that capture large tables, it may take longer than 15 minutes or your configured UNDO_RETENTION to perform the initial snapshot and this will eventually lead to this exception:

ORA-01555: snapshot too old: rollback segment number 12345 with name "_SYSSMU11_1234567890$" too small

The first way to deal with this exception is to work with your database administrator and see whether they can increase the UNDO_RETENTION database parameter temporarily. This does not require a restart of the Oracle database, so this can be done online without impacting database availability. However, changing this may still lead to the above exception or a "snapshot too old" exception if the tablespace has inadequate space to store the necessary undo data.

The second way to deal with this exception is to not rely on the initial snapshot at all, setting the snapshot.mode to schema_only and then instead relying on incremental snapshots. An incremental snapshot does not rely on a flashback query and therefore isn’t subject to ORA-01555 exceptions.

What’s the cause for ORA-04036 and how to handle it?

The Debezium Oracle connector may report an ORA-04036 exception when the database changes occur infrequently. An Oracle LogMiner session is started and re-used until a log switch is detected. The session is re-used as it provides the optimal performance utilization with Oracle LogMiner, but should a long-running mining session occur, this can lead to excessive PGA memory usage, eventually causing an exception like this:

ORA-04036: PGA memory used by the instance exceeds PGA_AGGREGATE_LIMIT

This exception can be avoided by specifying how frequent Oracle switches redo logs or how long the Debezium Oracle connector is allowed to re-use the mining session. The Debezium Oracle connector provides a configuration option, log.mining.session.max.ms, which controls how long the current Oracle LogMiner session can be re-used for before being closed and a new session started. This allows the database resources to be kept in-check without exceeding the PGA memory allowed by the database.

What’s the cause for ORA-01882 and how to handle it?

The Debezium Oracle connector may report the following exception when connecting to an Oracle database:

ORA-01882: timezone region not found

This happens when the timezone information cannot be correctly resolved by the JDBC driver. In order to solve this driver related problem, the driver needs to be told to not resolve the timezone details using regions. This can be done by specifying a driver pass through property using driver.oracle.jdbc.timezoneAsRegion=false.

What’s the cause for ORA-25191 and how to handle it?

The Debezium Oracle connector automatically ignores index-organized tables (IOT) as they are not supported by Oracle LogMiner. However, if an ORA-25191 exception is thrown, this could be due to a unique corner case for such a mapping and the additional rules may be necessary to exclude these automatically. An example of an ORA-25191 exception might look like this:

ORA-25191: cannot reference overflow table of an index-organized table

If an ORA-25191 exception is thrown, please raise a Jira issue with the details about the table and it’s mappings, related to other parent tables, etc. As a workaround, the include/exclude configuration options can be adjusted to prevent the connector from accessing such tables.

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Chapter 7. Debezium Connector for Oracle

7.1. How Debezium Oracle connectors work

7.1.1. How Debezium Oracle connectors perform database snapshots

7.1.1.1. Description of why initial snapshots capture the schema history for all tables

7.1.1.2. Capturing data from tables not captured by the initial snapshot (no schema change)

7.1.1.3. Capturing data from tables not captured by the initial snapshot (schema change)

7.1.2. Ad hoc snapshots

7.1.3. Incremental snapshots

7.1.3.1. Triggering an incremental snapshot

7.1.3.2. Using the Kafka signaling channel to trigger an incremental snapshot

7.1.3.3. Stopping an incremental snapshot

7.1.3.4. Using the Kafka signaling channel to stop an incremental snapshot

7.1.4. Default names of Kafka topics that receive Debezium Oracle change event records

7.1.5. How Debezium Oracle connectors handle database schema changes

7.1.6. How Debezium Oracle connectors expose database schema changes

7.1.7. Debezium Oracle connector-generated events that represent transaction boundaries

7.1.7.1. How the Debezium Oracle connector enriches change event messages with transaction metadata

7.1.8. How the Debezium Oracle connector uses event buffering

7.1.9. How the Debezium Oracle connector detects gaps in SCN values

7.1.10. How Debezium manages offsets in databases that change infrequently

7.2. Descriptions of Debezium Oracle connector data change events

7.2.1. About keys in Debezium Oracle connector change events

7.2.2. About values in Debezium Oracle connector change events

7.3. How Debezium Oracle connectors map data types

7.4. Setting up Oracle to work with Debezium

7.4.1. Compatibility of the Debezium Oracle connector with Oracle installation types

7.4.2. Schemas that the Debezium Oracle connector excludes when capturing change events

7.4.3. Tables that the Debezium Oracle connector excludes when capturing change events

7.4.4. Preparing Oracle databases for use with Debezium

7.4.5. Resizing Oracle redo logs to accommodate the data dictionary

7.4.6. Creating an Oracle user for the Debezium Oracle connector

7.4.7. Support for Oracle standby databases

7.5. Deployment of Debezium Oracle connectors

7.5.1. Obtaining the Oracle JDBC driver

7.5.2. Debezium Oracle connector deployment using AMQ Streams

7.5.3. Using AMQ Streams to deploy a Debezium Oracle connector

7.5.4. Deploying a Debezium Oracle connector by building a custom Kafka Connect container image from a Dockerfile

7.5.5. Configuration of container databases and non-container-databases

7.5.6. Verifying that the Debezium Oracle connector is running

7.6. Descriptions of Debezium Oracle connector configuration properties

7.7. Monitoring Debezium Oracle connector performance

7.7.1. Debezium Oracle connector snapshot metrics

7.7.2. Debezium Oracle connector streaming metrics

7.7.3. Debezium Oracle connector schema history metrics

7.8. Oracle connector frequently asked questions

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