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Chapter 6. Debezium Connector for Oracle (Technology Preview)

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 .

Important

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.

Information and procedures for using a Debezium Oracle connector are organized as follows:

6.1. How Debezium Oracle connectors work

To optimally configure and run a Debezium Oracle connector, it is helpful to understand how the connector performs snapshots, streams change events, determines Kafka topic names, and uses metadata.

Details are in the following topics:

6.1.1. How Debezium Oracle connectors perform database snapshots

Typically, the redo logs on an Oracle server are configured to not retain the complete history of the database. As a result, the Debezium Oracle connector cannot retrieve the entire history of the database from the logs. To enable the connector to establish a baseline for the current state of the database, the first time that the connector starts, it performs an initial consistent snapshot of the database.

You can customize the way that the connector creates snapshots by setting the value of the snapshot.mode connector configuration property. By default, the connector’s snapshot mode is set to initial.

Default connector workflow for creating an initial snapshot

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

Determines the tables to be captured
Obtains a ROW SHARE MODE lock on each of the monitored tables to prevent structural changes from occurring during creation of the snapshot. Debezium holds the locks for only a short time.
Reads the current system change number (SCN) position from the server’s redo log.
Captures the structure of all relevant tables.
Releases the locks obtained in Step 2.
Scans all of the relevant database tables and schemas as valid at the SCN position that was read in Step 3 (SELECT * FROM … AS OF SCN 123), generates a READ event for each row, and then writes the event records to the table-specific Kafka topic.
Records the successful completion of the snapshot in the connector offsets.

After the snapshot process begins, if the process is interrupted due to connector failure, rebalancing, or other reasons, the process restarts after the connector restarts. After the connector completes the initial snapshot, it continues streaming from the position that it read in Step 3 so that it does not miss any updates. If the connector stops again for any reason, after it restarts, it resumes streaming changes from where it previously left off.

Table 6.1. Settings for snapshot.mode connector configuration property
Setting	Description
`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.
`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).

6.1.1.1. Ad hoc snapshots

Important

The use of ad hoc snapshots is a Technology Preview feature. Technology Preview features are not supported with Red Hat production service-level agreements (SLAs) and might not be functionally complete; therefore, Red Hat does not recommend implementing any Technology Preview features in production environments. This Technology Preview feature provides early access to upcoming product innovations, enabling you to test functionality and provide feedback during the development process. For more information about support scope, see Technology Preview Features Support Scope.

By default, a connector runs an initial snapshot operation only after it starts for the first time. Following this initial snapshot, under normal circumstances, the connector does not repeat the snapshot process. Any future change event data that the connector captures comes in through the streaming process only.

However, in some situations the data that the connector obtained during the initial snapshot might become stale, lost, or incomplete. To provide a mechanism for recapturing table data, Debezium includes an option to perform ad hoc snapshots. The following changes in a database might be cause for performing an ad hoc snapshot:

The connector configuration is modified to capture a different set of tables.
Kafka topics are deleted and must be rebuilt.
Data corruption occurs due to a configuration error or some other problem.

You can re-run a snapshot for a table for which you previously captured a snapshot by initiating a so-called ad-hoc snapshot. Ad hoc snapshots require the use of signaling tables. You initiate an ad hoc snapshot by sending a signal request to the Debezium signaling table.

When you initiate an ad hoc snapshot of an existing table, the connector appends content to the topic that already exists for the table. If a previously existing topic was removed, Debezium can create a topic automatically if automatic topic creation is enabled.

Ad hoc snapshot signals specify the tables to include in the snapshot. The snapshot can capture the entire contents of the database, or capture only a subset of the tables in the database.

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

Table 6.2. 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 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.

Triggering an ad hoc snapshot

You initiate an ad hoc snapshot by adding an entry with the execute-snapshot signal type to the signaling table. After the connector processes the message, it begins the snapshot operation. The snapshot process reads the first and last primary key values and uses those values as the start and end point for each table. Based on the number of entries in the table, and the configured chunk size, Debezium divides the table into chunks, and proceeds to snapshot each chunk, in succession, one at a time.

Currently, the execute-snapshot action type triggers incremental snapshots only. For more information, see Incremental snapshots.

6.1.1.2. Incremental snapshots

Important

The use of incremental snapshots is a Technology Preview feature. Technology Preview features are not supported with Red Hat production service-level agreements (SLAs) and might not be functionally complete; therefore, Red Hat does not recommend implementing any Technology Preview features in production environments. This Technology Preview feature provides early access to upcoming product innovations, enabling you to test functionality and provide feedback during the development process. For more information about support scope, see Technology Preview Features Support Scope.

To provide flexibility in managing snapshots, Debezium includes a supplementary snapshot mechanism, known as incremental snapshotting. Incremental snapshots rely on the Debezium mechanism for sending signals to a Debezium connector.

In an incremental snapshot, instead of capturing the full state of a database all at once, as in an initial snapshot, Debezium captures each table in phases, in a series of configurable chunks. You can specify the tables that you want the snapshot to capture and the size of each chunk. The chunk size determines the number of rows that the snapshot collects during each fetch operation on the database. The default chunk size for incremental snapshots is 1 KB.

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

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

Incremental snapshot process

When you run an incremental snapshot, Debezium sorts each table by primary key and then splits the table into chunks based on the configured chunk size. Working chunk by chunk, it then captures each table row in a chunk. For each row that it captures, the snapshot emits a READ event. That event represents the value of the row when the snapshot for the chunk began.

As a snapshot proceeds, it’s likely that other processes continue to access the database, potentially modifying table records. To reflect such changes, INSERT, UPDATE, or DELETE operations are committed to the transaction log as per usual. Similarly, the ongoing Debezium streaming process continues to detect these change events and emits corresponding change event records to Kafka.

How Debezium resolves collisions among records with the same primary key

In some cases, the UPDATE or DELETE events that the streaming process emits are received out of sequence. That is, the streaming process might emit an event that modifies a table row before the snapshot captures the chunk that contains the READ event for that row. When the snapshot eventually emits the corresponding READ event for the row, its value is already superseded. To ensure that incremental snapshot events that arrive out of sequence are processed in the correct logical order, Debezium employs a buffering scheme for resolving collisions. Only after collisions between the snapshot events and the streamed events are resolved does Debezium emit an event record to Kafka.

Snapshot window

To assist in resolving collisions between late-arriving READ events and streamed events that modify the same table row, Debezium employs a so-called snapshot window. The snapshot windows demarcates the interval during which an incremental snapshot captures data for a specified table chunk. Before the snapshot window for a chunk opens, Debezium follows its usual behavior and emits events from the transaction log directly downstream to the target Kafka topic. But from the moment that the snapshot for a particular chunk opens, until it closes, Debezium performs a de-duplication step to resolve collisions between events that have the same primary key..

For each data collection, the Debezium emits two types of events, and stores the records for them both in a single destination Kafka topic. The snapshot records that it captures directly from a table are emitted as READ operations. Meanwhile, as users continue to update records in the data collection, and the transaction log is updated to reflect each commit, Debezium emits UPDATE or DELETE operations for each change.

As the snapshot window opens, and Debezium begins processing a snapshot chunk, it delivers snapshot records to a memory buffer. During the snapshot windows, the primary keys of the READ events in the buffer are compared to the primary keys of the incoming streamed events. If no match is found, the streamed event record is sent directly to Kafka. If Debezium detects a match, it discards the buffered READ event, and writes the streamed record to the destination topic, because the streamed event logically supersede the static snapshot event. After the snapshot window for the chunk closes, the buffer contains only READ events for which no related transaction log events exist. Debezium emits these remaining READ events to the table’s Kafka topic.

The connector repeats the process for each snapshot chunk.

Triggering an incremental snapshot

Currently, the only way to initiate an incremental snapshot is to send an ad hoc snapshot signal to the signaling table on the source database. You submit signals to the table as SQL INSERT queries. After Debezium detects the change in the signaling table, it reads the signal, and runs the requested snapshot operation.

The query that you submit specifies the tables to include in the snapshot, and, optionally, specifies the kind of snapshot operation. Currently, the only valid option for snapshots operations is the default value, incremental.

To specify the tables to include in the snapshot, provide a data-collections array that lists the tables, for example,
{"data-collections": ["public.MyFirstTable", "public.MySecondTable"]}

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

Prerequisites

Signaling is enabled.
- A signaling data collection exists on the source database and the connector is configured to capture it.
- The signaling data collection is specified in the signal.data.collection property.

Procedure

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

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

For example,

INSERT INTO myschema.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["schema1.table1", "schema2.table2"],"type":"incremental"}');

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

The following table describes the these parameters:

Table 6.3. Descriptions of fields in a SQL command for sending an incremental snapshot signal to the signaling table
Value	Description
`myschema.debezium_signal`	Specifies the fully-qualified name of the signaling table on the source database
`ad-hoc-1`	The `id` parameter specifies an arbitrary string that is assigned as 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.
`execute-snapshot`	Specifies `type` parameter specifies the operation that the signal is intended to trigger.
`data-collections`	A required component of the `data` field of a signal that specifies an array of table names to include in the snapshot. The array lists tables by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling table in the `signal.data.collection` configuration property.
`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`. 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.

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.

Warning

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

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

By default, the Oracle connector writes change events for all INSERT, UPDATE, and DELETE operations that occur in a table to a single Apache Kafka topic that is specific to that table. The connector uses the following convention to name change event topics:

serverName.schemaName.tableName

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

serverName: The logical name of the server as specified by the database.server.name connector configuration property.
schemaName: The name of the schema in which the operation occurred.
tableName: The name of the table in which the operation occurred.

For example, if fulfillment is the server name, inventory is the schema name, and the database contains tables with the names orders, customers, and products, the Debezium Oracle connector emits events to the following Kafka topics, one for each table in the database:

fulfillment.inventory.orders
fulfillment.inventory.customers
fulfillment.inventory.products

The connector applies similar naming conventions to label its internal database history topics, schema change topics, and transaction metadata topics.

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

6.1.3. How Debezium Oracle connectors expose database schema changes

You can configure a Debezium Oracle connector to produce schema change events that describe schema changes that are applied to captured tables in the database. The connector writes schema change events to a Kafka topic named <serverName>, where serverName is the logical server name that is specified in the database.server.name configuration property.

Debezium emits a new message to this topic whenever it streams data from a new table.

Messages that the connector sends to the schema change topic contain a payload, and, optionally, also contain the schema of the change event message. The payload of a schema change event message includes the following elements:

ddl: Provides the SQL CREATE, ALTER, 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

When the connector is configured to capture a table, it stores the history of the table’s schema changes not only in the schema change topic, but also in an internal database history topic. The internal database history topic is for connector use only and it is not intended for direct use by consuming applications. Ensure that applications that require notifications about schema changes consume that information only from the schema change topic.

Important

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

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

If you create the database history topic manually, specify a partition count of 1.
If you use the Apache Kafka broker to create the database history topic automatically, the topic is created, set the value of the Kafka num.partitions configuration option 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": "1.7.2.Final",
      "connector": "oracle",
      "name": "server1",
      "ts_ms": 1588252618953,
      "snapshot": "true",
      "db": "ORCLPDB1",
      "schema": "DEBEZIUM",
      "table": "CUSTOMERS",
      "txId" : null,
      "scn" : "1513734",
      "commit_scn": "1513734",
      "lcr_position" : null
    },
    "databaseName": "ORCLPDB1", 1
    "schemaName": "DEBEZIUM", //
    "ddl": "CREATE TABLE \"DEBEZIUM\".\"CUSTOMERS\" \n   (    \"ID\" NUMBER(9,0) NOT NULL ENABLE, \n    \"FIRST_NAME\" VARCHAR2(255), \n    \"LAST_NAME" VARCHAR2(255), \n    \"EMAIL\" VARCHAR2(255), \n     PRIMARY KEY (\"ID\") ENABLE, \n     SUPPLEMENTAL LOG DATA (ALL) COLUMNS\n   ) SEGMENT CREATION IMMEDIATE \n  PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 \n NOCOMPRESS LOGGING\n  STORAGE(INITIAL 65536 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645\n  PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1\n  BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)\n  TABLESPACE \"USERS\" ", 2
    "tableChanges": [ 3
      {
        "type": "CREATE", 4
        "id": "\"ORCLPDB1\".\"DEBEZIUM\".\"CUSTOMERS\"", 5
        "table": { 6
          "defaultCharsetName": null,
          "primaryKeyColumnNames": [ 7
            "ID"
          ],
          "columns": [ 8
            {
              "name": "ID",
              "jdbcType": 2,
              "nativeType": null,
              "typeName": "NUMBER",
              "typeExpression": "NUMBER",
              "charsetName": null,
              "length": 9,
              "scale": 0,
              "position": 1,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            },
            {
              "name": "FIRST_NAME",
              "jdbcType": 12,
              "nativeType": null,
              "typeName": "VARCHAR2",
              "typeExpression": "VARCHAR2",
              "charsetName": null,
              "length": 255,
              "scale": null,
              "position": 2,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            },
            {
              "name": "LAST_NAME",
              "jdbcType": 12,
              "nativeType": null,
              "typeName": "VARCHAR2",
              "typeExpression": "VARCHAR2",
              "charsetName": null,
              "length": 255,
              "scale": null,
              "position": 3,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            },
            {
              "name": "EMAIL",
              "jdbcType": 12,
              "nativeType": null,
              "typeName": "VARCHAR2",
              "typeExpression": "VARCHAR2",
              "charsetName": null,
              "length": 255,
              "scale": null,
              "position": 4,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            }
          ]
        }
      }
    ]
  }
}

Table 6.4. Descriptions of fields in messages emitted to the schema change topic
Item	Field name	Description
1	`databaseName` `schemaName`	Identifies the database and the schema that contains the change.
2	`ddl`	This field contains the DDL that is responsible for the schema change.
3	`tableChanges`	An array of one or more items that contain the schema changes generated by a DDL command.
4	`type`	Describes the kind of change. The value is one of the following: `CREATE` Table created. `ALTER` Table modified. `DROP` Table deleted.
5	`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.
6	`table`	Represents table metadata after the applied change.
7	`primaryKeyColumnNames`	List of columns that compose the table’s primary key.
8	`columns`	Metadata for each column in the changed table.

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

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

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

Debezium can generate events that represent transaction metadata boundaries and that enrich data change event messages.

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 unique transaction identifier.
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 number of events that the connector emits for changes that originate from a data collection.

The following example shows a typical transaction boundary message:

Example: Oracle connector transaction boundary event

{
  "status": "BEGIN",
  "id": "5.6.641",
  "event_count": null,
  "data_collections": null
}

{
  "status": "END",
  "id": "5.6.641",
  "event_count": 2,
  "data_collections": [
    {
      "data_collection": "ORCLPDB1.DEBEZIUM.CUSTOMER",
      "event_count": 1
    },
    {
      "data_collection": "ORCLPDB1.DEBEZIUM.ORDER",
      "event_count": 1
    }
  ]
}

The connector emits transaction events to the <database.server.name>.transaction topic.

6.1.4.1. Change data event enrichment

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"
  }
}

Event buffering

Oracle writes all changes to the redo logs in the order in which they occur, including changes that are later discarded by a rollback. As a result, concurrent changes from separate transactions are intertwined. When the connector first reads the stream of changes, because it cannot immediately determine which changes are committed or rolled back, it temporarily stores the change events in an internal buffer. After a change is committed, the connector writes the change event from the buffer to Kafka. The connector drops change events that are discarded by a rollback.

You can configure the buffering mechanism that the connector uses by setting the property log.mining.buffer.type.

Heap

The default buffer type is configured using memory. Under the default memory setting, the connector uses the heap memory of the JVM process to allocate and manage buffered event records. If you use the memory buffer setting, be sure that the amount of memory that you allocate to the Java process can accommodate long-running and large transactions in your environment.

6.1.5. Gaps between Oracle 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 because of large or bulk transactions in the database.

Under certain circumstances, the Oracle database advances the system change number by an unusually high amount, rather than increasing it 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 this difference 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. Additionally, the connector will ignore the mining maximum batch size for this iteration only when this occurs.

Warning

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

6.2. Descriptions of Debezium Oracle connector data change events

Every data change event that the Oracle connector emits has a key and a value. The structures of the key and value depend on the table from which the change events originate. For information about how Debezium constructs topic names, see Topic names).

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:

6.2.1. About keys in Debezium Oracle connector change events

For each changed table, the change event key is structured such that a field exists for each column in the primary key (or unique key constraint) of the table at the time when the event is created.

For example, a customers table that is defined in the inventory database schema, might have the following change event key:

CREATE TABLE customers (
  id NUMBER(9) GENERATED BY DEFAULT ON NULL AS IDENTITY (START WITH 1001) NOT NULL PRIMARY KEY,
  first_name VARCHAR2(255) NOT NULL,
  last_name VARCHAR2(255) NOT NULL,
  email VARCHAR2(255) NOT NULL UNIQUE
);

If the value of the <database.server.name>.transaction configuration property is set to server1, the JSON representation for every change event that occurs in the customers table in the database features the following key structure:

{
    "schema": {
        "type": "struct",
        "fields": [
            {
                "type": "int32",
                "optional": false,
                "field": "ID"
            }
        ],
        "optional": false,
        "name": "server1.INVENTORY.CUSTOMERS.Key"
    },
    "payload": {
        "ID": 1004
    }
}

The schema portion of the key contains a Kafka Connect schema that describes the content of the key portion. In the preceding example, the payload value is not optional, the structure is defined by a schema named server1.DEBEZIUM.CUSTOMERS.Key, and there is one required field named id of type int32. The value of the key’s payload field indicates that it is indeed a structure (which in JSON is just an object) with a single id field, whose value is 1004.

Therefore, you can interpret this key as describing the row in the inventory.customers table (output from the connector named server1) whose id primary key column had a value of 1004.

6.2.2. About values in Debezium Oracle connector change events

Like the message key, the value of a change event message has a schema section and payload section. The payload section of every change event value produced by the Oracle connector has an envelope structure with the following fields:

op: A mandatory field that contains a string value describing the type of operation. Values for the Oracle connector are c for create (or insert), u for update, d for delete, and r for read (in the case of a snapshot).
before: An optional field that, if present, contains 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 the row after the event occurred. The structure is described by the same server1.INVENTORY.CUSTOMERS.Value Kafka Connect schema used in before.
source: A mandatory field that contains a structure describing the source metadata for the event, which in the case of Oracle contains these fields: the Debezium version, the connector name, whether the event is part of an ongoing snapshot or not, the transaction id (not while snapshotting), the SCN of the change, and a timestamp representing the point in time when the record was changed in the source database (during snapshotting, this is the point in time of snapshotting).

Tip

The commit_scn field is optional and describes the SCN of the transaction commit that the change event participates within. This field is only present when using the LogMiner connection adapter.

ts_ms: An optional field that, if present, contains the time (using the system clock in the JVM running the Kafka Connect task) at which the connector processed the event.

And of course, the schema portion of the event message’s value contains a schema that describes this envelope structure and the nested fields within it.

create events

Let’s look at what a create event value might look like for our customers table:

{
    "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": "boolean",
                        "optional": true,
                        "field": "snapshot"
                    }
                ],
                "optional": false,
                "name": "io.debezium.connector.oracle.Source",
                "field": "source"
            },
            {
                "type": "string",
                "optional": false,
                "field": "op"
            },
            {
                "type": "int64",
                "optional": true,
                "field": "ts_ms"
            }
        ],
        "optional": false,
        "name": "server1.DEBEZIUM.CUSTOMERS.Envelope"
    },
    "payload": {
        "before": null,
        "after": {
            "ID": 1004,
            "FIRST_NAME": "Anne",
            "LAST_NAME": "Kretchmar",
            "EMAIL": "annek@noanswer.org"
        },
        "source": {
            "version": "1.7.2.Final",
            "name": "server1",
            "ts_ms": 1520085154000,
            "txId": "6.28.807",
            "scn": "2122185",
            "commit_scn": "2122185",
            "snapshot": false
        },
        "op": "c",
        "ts_ms": 1532592105975
    }
}

Examining the schema portion of the preceding event’s value, we can see how the following schema are defined:

The envelope
The source structure (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. This means that when using the Avro Converter, the resulting Avro schems for each table 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 they describe. This is true, because the JSON representation must include the schema and the payload portions of the message. You can use the Avro Converter to significantly decrease the size of the messages that the connector writes to Kafka topics.

update events

The value of an update change event on this table has the same schema as the create event. The payload uses the same structure, but it holds different values. Here’s an example:

{
    "schema": { ... },
    "payload": {
        "before": {
            "ID": 1004,
            "FIRST_NAME": "Anne",
            "LAST_NAME": "Kretchmar",
            "EMAIL": "annek@noanswer.org"
        },
        "after": {
            "ID": 1004,
            "FIRST_NAME": "Anne",
            "LAST_NAME": "Kretchmar",
            "EMAIL": "anne@example.com"
        },
        "source": {
            "version": "1.7.2.Final",
            "name": "server1",
            "ts_ms": 1520085811000,
            "txId": "6.9.809",
            "scn": "2125544",
            "commit_scn": "2125544",
            "snapshot": false
        },
        "op": "u",
        "ts_ms": 1532592713485
    }
}

Comparing the value of the update event to the create (insert) event, notice the following differences in the payload section:

The op field value is now u, signifying that this row changed because of an update
The before field now has the state of the row with the values before the database commit
The after field now has the updated state of the row, and here was can see that the EMAIL value is now anne@example.com.
The source field structure has the same fields as before, but the values are different since this event is from a different position in the redo log.
The ts_ms shows the timestamp that Debezium processed this 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

So far we’ve seen samples of create and update events. Now, let’s look at the value of a delete event for the same table. As is the case with create and update events, for a delete event, the schema portion of the value is exactly the same:

{
    "schema": { ... },
    "payload": {
        "before": {
            "ID": 1004,
            "FIRST_NAME": "Anne",
            "LAST_NAME": "Kretchmar",
            "EMAIL": "anne@example.com"
        },
        "after": null,
        "source": {
            "version": "1.7.2.Final",
            "name": "server1",
            "ts_ms": 1520085153000,
            "txId": "6.28.807",
            "scn": "2122184",
            "commit_scn": "2122184",
            "snapshot": false
        },
        "op": "d",
        "ts_ms": 1532592105960
    }
}

If we look at the payload portion, we see a number of differences compared with the create or update event payloads:

The op field value is now d, signifying that this row was deleted
The before field now has the state of the row that was deleted with the database commit.
The after field is null, signifying that the row no longer exists
The source field structure has many of the same values as before, except the ts_ms, scn and txId fields have changed
The ts_ms shows the timestamp that Debezium processed this event.

This event gives a consumer all kinds of information that it can use 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 listed above still works with log compaction, since Kafka can still remove all earlier messages with that 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.

6.3. How Debezium Oracle connectors map data types

To represent changes that occur in a table rows, the Debezium Oracle connector emits change events that are structured like the table in which the rows exists. The event contains a field for each column value. Column values are represented according to the Oracle data type of the column. The following sections describe how the connector maps oracle data types to a literal type and a semantic type in event fields.

literal type: Describes how the value is literally represented using Kafka Connect schema types: INT8, INT16, INT32, INT64, FLOAT32, FLOAT64, BOOLEAN, STRING, BYTES, ARRAY, MAP, and STRUCT.
semantic type: Describes how the Kafka Connect schema captures the meaning of the field using the name of the Kafka Connect schema for the field.

Details are in the following sections:

Character types

The following table describes how the connector maps basic character types.

Table 6.5. 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

The following table describes how the connector maps binary and character large object (LOB) data types.

Table 6.6. Mappings for Oracle binary and character LOB types
Oracle Data Type	Literal type (schema type)	Semantic type (schema name) and Notes
`BLOB`	`BYTES`	The raw bytes.
`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. This means that change events will never contain the value of an unchanged CLOB, NCLOB, or BLOB column, but a placeholder as defined by the connector property, unavailable.value.placeholder.

If the value of a CLOB, NCLOB, or BLOB column gets updated, the new value will be contained in the after part of the corresponding update change events whereas the unavailable value placeholder will be used in the before part.

Numeric types

The following table describes how the connector maps numeric types.

Table 6.7. 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`).
`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.
`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`).
`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`).
`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.

Boolean types

Oracle does not natively have 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.

The operator can configure the out-of-the-box NumberOneToBooleanConverter custom converter that would either map all NUMBER(1) columns to a BOOLEAN or if the selector parameter is set, then a subset of columns could be enumerated using a comma-separated list of regular expressions.

Following is an example configuration:

converters=boolean
boolean.type=io.debezium.connector.oracle.converters.NumberOneToBooleanConverter
boolean.selector=.*MYTABLE.FLAG,.*.IS_ARCHIVED

Decimal types

The setting of the Oracle connector configuration property, decimal.handling.mode determines how the connector maps decimal types.

When the decimal.handling.mode property is set to precise, the connector uses Kafka Connect org.apache.kafka.connect.data.Decimal logical type for all DECIMAL and NUMERIC columns. This is the default mode.

However, when the decimal.handling.mode property is set to double, the connector represents the values as Java double values with schema type FLOAT64.

You can also set the decimal.handling.mode configuration property to use the string option. When the property is set to string, the connector represents DECIMAL and NUMERIC values as their formatted string representation with schema type STRING.

Temporal types

Other than Oracle’s INTERVAL, TIMESTAMP WITH TIME ZONE and TIMESTAMP WITH LOCAL TIME ZONE data types, the other temporal types depend 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 past 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.
`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.
`TIMESTAMP(0 - 3)`	`INT64`	`io.debezium.time.Timestamp` Represents the number of milliseconds past epoch, and does not include timezone information.
`TIMESTAMP, TIMESTAMP(4 - 6)`	`INT64`	`io.debezium.time.MicroTimestamp` Represents the number of microseconds past epoch, and does not include timezone information.
`TIMESTAMP(7 - 9)`	`INT64`	`io.debezium.time.NanoTimestamp` Represents the number of nanoseconds past 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 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.
`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.
`TIMESTAMP(0 - 3)`	`INT64`	`org.apache.kafka.connect.data.Timestamp` Represents the number of milliseconds since epoch, and does not include timezone information.
`TIMESTAMP(4 - 6)`	`INT64`	`org.apache.kafka.connect.data.Timestamp` Represents the number of milliseconds since epoch, and does not include timezone information.
`TIMESTAMP(7 - 9)`	`INT64`	`org.apache.kafka.connect.data.Timestamp` Represents the number of milliseconds since 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.

6.4. Setting up Oracle to work with Debezium

The following steps are necessary to set up Oracle for use with the Debezium Oracle connector. These steps assume the use of the multi-tenancy configuration with a container database and at least one pluggable database. If you do not intend to use a multi-tenant configuration, it might be necessary to adjust the following steps.

For information about using Vagrant to set up Oracle in a virtual machine, see the Debezium Vagrant Box for Oracle database GitHub repository.

For details about setting up Oracle for use with the Debezium connector, see the following sections:

6.4.1. 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;

In addition, supplemental logging must be enabled for captured tables or the database in order for data changes to capture the before state of changed database rows. The following illustrates how to configure this on a specific table, which is the ideal choice to minimize the amount of information captured in the Oracle redo logs.

ALTER TABLE inventory.customers ADD SUPPLEMENTAL LOG DATA (ALL) COLUMNS;

Minimal supplemental logging must be enabled at the database level and can be configured as follows.

ALTER DATABASE ADD SUPPLEMENTAL LOG DATA;

6.4.2. Redo log sizing

Depending on the database configuration, the size and number of redo logs might not be sufficient to achieve acceptable performance. Before you set up the Debezium Oracle connector, ensure that the capacity of the redo logs is sufficient to support the database.

The capacity of the redo logs for a database must be sufficient to store its data dictionary. In general, the size of the data dictionary increases with the number of tables and columns in the database. If the redo log lacks sufficient capacity, both the database and the Debezium connector might experience performance problems.

Consult with your database administrator to evaluate whether the database might require increased log capacity.

6.4.3. 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;
  GRANT SET CONTAINER TO c##dbzuser CONTAINER=ALL;
  GRANT SELECT ON V_$DATABASE to c##dbzuser CONTAINER=ALL;
  GRANT FLASHBACK ANY TABLE TO c##dbzuser CONTAINER=ALL;
  GRANT SELECT ANY TABLE TO c##dbzuser CONTAINER=ALL;
  GRANT SELECT_CATALOG_ROLE TO c##dbzuser CONTAINER=ALL;
  GRANT EXECUTE_CATALOG_ROLE TO c##dbzuser CONTAINER=ALL;
  GRANT SELECT ANY TRANSACTION TO c##dbzuser CONTAINER=ALL;
  GRANT LOGMINING TO c##dbzuser CONTAINER=ALL;

  GRANT CREATE TABLE TO c##dbzuser CONTAINER=ALL;
  GRANT LOCK ANY TABLE TO c##dbzuser CONTAINER=ALL;
  GRANT CREATE SEQUENCE TO c##dbzuser CONTAINER=ALL;

  GRANT EXECUTE ON DBMS_LOGMNR TO c##dbzuser CONTAINER=ALL;
  GRANT EXECUTE ON DBMS_LOGMNR_D TO c##dbzuser CONTAINER=ALL;

  GRANT SELECT ON V_$LOG TO c##dbzuser CONTAINER=ALL;
  GRANT SELECT ON V_$LOG_HISTORY TO c##dbzuser CONTAINER=ALL;
  GRANT SELECT ON V_$LOGMNR_LOGS TO c##dbzuser CONTAINER=ALL;
  GRANT SELECT ON V_$LOGMNR_CONTENTS TO c##dbzuser CONTAINER=ALL;
  GRANT SELECT ON V_$LOGMNR_PARAMETERS TO c##dbzuser CONTAINER=ALL;
  GRANT SELECT ON V_$LOGFILE TO c##dbzuser CONTAINER=ALL;
  GRANT SELECT ON V_$ARCHIVED_LOG TO c##dbzuser CONTAINER=ALL;
  GRANT SELECT ON V_$ARCHIVE_DEST_STATUS TO c##dbzuser CONTAINER=ALL;

  exit;

6.5. 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.

The Debezium Oracle connector requires the Oracle JDBC driver (ojdbc8.jar) to connect to Oracle databases. For information about how to obtain the driver, see Obtaining the Oracle JDBC driver.

Additional resources

Section 6.6, “Descriptions of Debezium Oracle connector configuration properties”

6.5.1. 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 Upgrading AMQ Streams on OpenShift.

6.5.2. Using AMQ Streams to deploy a Debezium Oracle connector

With earlier versions of AMQ Streams, to deploy Debezium connectors on OpenShift, it was necessary 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 pod. 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.
You have a Red Hat Integration license.
Kafka Connect is deployed on AMQ Streams.
The OpenShift oc CLI client is installed or you have access to the OpenShift Container Platform web console.
Depending on how you intend to store the Kafka Connect build image, you need registry permissions or you must create an ImageStream resource:
To store the build image in an image registry, such as Red Hat Quay.io or Docker Hub
An account and permissions to create and manage images in the registry.
To store the build image as a native OpenShift ImageStream
An ImageStream resource is deployed to the cluster. You must explicitly create an ImageStream for the cluster. ImageStreams are not available by default.

Procedure

Create a Debezium KafkaConnect custom resource (CR) for the connector, or modify an existing one. For example, create a KafkaConnect CR that specifies the metadata.annotations and spec.build properties, as shown in the following example. Save the file with a name such as dbz-connect.yaml.

Example 6.1. A dbz-connect.yaml file that defines a KafkaConnect custom resource that includes a Debezium connector

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: debezium-kafka-connect-cluster
  annotations:
    strimzi.io/use-connector-resources: "true" 1
spec:
  version: 3.00
  build: 2
    output: 3
      type: imagestream  4
      image: debezium-streams-connect:latest
    plugins: 5
      - name: debezium-connector-oracle
        artifacts:
          - type: zip 6
            url: https://maven.repository.redhat.com/ga/io/debezium/debezium-connector-oracle/1.7.2.Final-redhat-<build_number>/debezium-connector-oracle-1.7.2.Final-redhat-<build_number>-plugin.zip  7
          - type: zip
            url: https://maven.repository.redhat.com/ga/io/apicurio/apicurio-registry-distro-connect-converter/2.0-redhat-<build-number>/apicurio-registry-distro-connect-converter-2.0-redhat-<build-number>.zip
          - type: zip
            url: https://maven.repository.redhat.com/ga/io/debezium/debezium-scripting/1.7.2.Final/debezium-scripting-1.7.2.Final.zip

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

Table 6.8. 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 like 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 documentation.
5	The `plugins` configuration lists all of the connectors that you want to include in the Kafka Connect image. For each entry in the list, specify a plug-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. The OpenShift cluster must have access to the specified server.

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 6.2. A 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.MySqlConnector 2
  tasksMax: 1  3
  config:  4
    database.history.kafka.bootstrap.servers: 'debezium-kafka-cluster-kafka-bootstrap.debezium.svc.cluster.local:9092'
    database.history.kafka.topic: schema-changes.inventory
    database.hostname: oracle.debezium-oracle.svc.cluster.local 5
    database.port: 3306   6
    database.user: debezium  7
    database.password: dbz  8
    database.dbname: mydatabase 9
    database.server.name: inventory_connector_oracle 10
    database.include.list: public.inventory  11

Table 6.9. Descriptions of connector configuration settings
Item	Description
1	The name of the connector to register with the Kafka Connect cluster.
2	The name of the connector class.
3	The number of tasks that can operate concurrently.
4	The connector’s configuration.
5	The address of the host database instance.
6	The port number of the database instance.
7	The name of the user account through which Debezium connects to the database.
8	The password for the database user account.
9	The name of the database to capture changes from.
10	The logical name of the database instance or cluster. The specified name must be formed only from alphanumeric characters or underscores. Because the logical name is used as the prefix for any Kafka topics that receive change events from this connector, the name must be unique among the connectors in the cluster. The namespace is also used in the names of related Kafka Connect schemas, and the namespaces of a corresponding Avro schema if you integrate the connector with the Avro connector.
11	The list of tables from which the connector captures change events.

Create the connector resource by running the following command:
```
oc create -n <namespace> -f <kafkaConnector>.yaml
```
For example,
```
oc create -n debezium -f {context}-inventory-connector.yaml
```
The connector is registered to the Kafka Connect cluster and starts to run against the database that is specified by spec.config.database.dbname in the KafkaConnector CR. After the connector pod is ready, Debezium is running.

You are now ready to verify the Debezium Oracle deployment.

6.5.3. 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.
You have a copy of the Oracle JDBC driver. Due to licensing requirements, the Debezium Oracle connector does not include the required driver file.
For more information, see Obtaining the Oracle JDBC driver.

Procedure

Create the Debezium Oracle container for Kafka Connect:
1. Download the Debezium Oracle connector archive.
2. Extract the Debezium Oracle connector archive to create a directory structure for the connector plug-in, for example:
```
./my-plugins/
├── debezium-connector-oracle
│   ├── ...
```
3. Create a Dockerfile that uses registry.redhat.io/amq7/amq-streams-kafka-30-rhel8:2.0.0 as the base image. For example, from a terminal window, enter the following, replacing my-plugins with the name of your plug-ins directory:
```
cat <<EOF >debezium-container-for-oracle.yaml 1
FROM registry.redhat.io/amq7/amq-streams-kafka-30-rhel8:2.0.0
USER root:root
COPY ./<my-plugins>/ /opt/kafka/plugins/ 2
USER 1001
EOF
```
  1 1 1 1 1 1
  You can specify any file name that you want.
  2 2 2 2 2 2
  Replace my-plugins with the name of your plug-ins directory.
  The command creates a Dockerfile with the name debezium-container-for-oracle.yaml in the current directory.
4. 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.
5. 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
```
6. 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 as shown in the following example:
```
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect-cluster
  annotations:
    strimzi.io/use-connector-resources: "true" 1
spec:
  #...
  image: debezium-container-for-oracle  2
```
  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
7. 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 1
  labels:
    strimzi.io/cluster: my-connect-cluster
  annotations:
    strimzi.io/use-connector-resources: 'true'
spec:
  class: io.debezium.connector.oracle.OracleConnector 2
  config:
    database.hostname: <oracle_ip_address> 3
    database.port: 1521 4
    database.user: c##dbzuser 5
    database.password: dbz 6
    database.dbname: ORCLCDB 7
    database.pdb.name : ORCLPDB1, 8
    database.server.name: server1 9
    database.history.kafka.bootstrap.servers: kafka:9092 10
    database.history.kafka.topic: schema-changes.inventory 11

Table 6.10. Descriptions of connector configuration settings
Item	Description
1	The name of our connector when we register it with a Kafka Connect service.
2	The name of this Oracle connector class.
3	The address of the Oracle instance.
4	The port number of the Oracle instance.
5	The name of the Oracle user, as specified in Creating users for the connector.
6	The password for the Oracle user, as specified in Creating users for the connector.
7	The name of the database to capture changes from.
8	The name of the Oracle pluggable database that the connector captures changes from. Used in container database (CDB) installations only.
9	Logical name that identifies and provides a namespace for the Oracle database server from which the connector captures changes.
10	The list of Kafka brokers that this connector uses to write and recover DDL statements to the database history topic.
11	The name of the database history topic where the connector writes and recovers DDL statements. This topic is for internal use only and should not be used by consumers.

Create your connector instance with Kafka Connect. For example, if you saved your KafkaConnector resource in 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.

6.5.4. Obtaining the Oracle JDBC driver

Due to licensing requirements, the required driver file is not included in the Debezium Oracle connector archive. Regardless of which deployment method that you use, you have obtain the Oracle JDBC driver to complete the deployment.

There are two methods for obtaining the driver, depending on the deployment method that you use.

If you use AMQ Streams to add the connector to your Kafka Connect image, add an artifact reference to the KafkaConnect custom resource and then add the location of the artifact as the url value.
If you use a Dockerfile to build the connector, download the required driver file directly from Oracle and add it to your Kafka Connect environment.

The following steps describe how to make the driver and available in your environment.

Procedure

Complete one of the following procedures, depending on your deployment type:
- If you use AMQ Streams to deploy the connector:
  1. Navigate to Maven Central and locate the ojdbc8.jar file for your release of Oracle Database.
  2. In the YAML for the KafkaConnector custom resource (CR), add the URL path for the driver to the artifacts.url field for the debezium-connector-oracle artifact.
    For more information about the YAML file for the KafkaConnector CR, see Using AMQ Streams to deploy a Debezium Oracle connector.
- If you use a Dockerfile to deploy the connector:
  1. From a browser, navigate to the Oracle JDBC and UCP Downloads page.
  2. Locate and download the ojdbc8.jar driver file for your version of Oracle Database.
  3. Copy the downloaded file to the directory that stores the Debezium Oracle connector files, for example, <kafka_home>/libs.
    When the connector starts, it is automatically configured to use the specified driver.

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

Oracle Database supports the following deployment types:

Container database (CDB): A database that can contain multiple pluggable databases (PDBs). Database clients connect to each PDB as if it were a standard, non-CDB database.
Non-container database (non-CDB): A standard Oracle database, which does not support the creation of pluggable databases.

For the complete list of the configuration properties that you can set for the Debezium Oracle connector, see Oracle connector properties.

Results

After the connector starts, it performs a consistent snapshot of the Oracle databases that the connector is configured for. The connector then starts generating data change events for row-level operations and streaming the change event records to Kafka topics.

6.5.6. 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 6.3. 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_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 6.4. 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 6.5. Content of a Debezium change event

{"schema":{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"}],"optional":false,"name":"inventory_connector_oracle.inventory.products_on_hand.Key"},"payload":{"product_id":101}}	{"schema":{"type":"struct","fields":[{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"},{"type":"int32","optional":false,"field":"quantity"}],"optional":true,"name":"inventory_connector_oracle.inventory.products_on_hand.Value","field":"before"},{"type":"struct","fields":[{"type":"int32","optional":false,"field":"product_id"},{"type":"int32","optional":false,"field":"quantity"}],"optional":true,"name":"inventory_connector_oracle.inventory.products_on_hand.Value","field":"after"},{"type":"struct","fields":[{"type":"string","optional":false,"field":"version"},{"type":"string","optional":false,"field":"connector"},{"type":"string","optional":false,"field":"name"},{"type":"int64","optional":false,"field":"ts_ms"},{"type":"string","optional":true,"name":"io.debezium.data.Enum","version":1,"parameters":{"allowed":"true,last,false"},"default":"false","field":"snapshot"},{"type":"string","optional":false,"field":"db"},{"type":"string","optional":true,"field":"sequence"},{"type":"string","optional":true,"field":"table"},{"type":"int64","optional":false,"field":"server_id"},{"type":"string","optional":true,"field":"gtid"},{"type":"string","optional":false,"field":"file"},{"type":"int64","optional":false,"field":"pos"},{"type":"int32","optional":false,"field":"row"},{"type":"int64","optional":true,"field":"thread"},{"type":"string","optional":true,"field":"query"}],"optional":false,"name":"io.debezium.connector.oracle.Source","field":"source"},{"type":"string","optional":false,"field":"op"},{"type":"int64","optional":true,"field":"ts_ms"},{"type":"struct","fields":[{"type":"string","optional":false,"field":"id"},{"type":"int64","optional":false,"field":"total_order"},{"type":"int64","optional":false,"field":"data_collection_order"}],"optional":true,"field":"transaction"}],"optional":false,"name":"inventory_connector_oracle.inventory.products_on_hand.Envelope"},"payload":{"before":null,"after":{"product_id":101,"quantity":3},"source":{"version":"1.7.2.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.

6.6. Descriptions of Debezium Oracle connector configuration properties

The Debezium Oracle connector has numerous configuration properties that you can use to achieve the right connector behavior for your application. Many properties have default values. Information about the properties is organized as follows:

Required Debezium Oracle connector configuration properties
Database history connector configuration properties that control how Debezium processes events that it reads from the database history topic.
- Pass-through database 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.
`tasks.max`	`1`	The maximum number of tasks that should be created 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. Must be the CDB name when working with the CDB + PDB model.
`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.
`database.server.name`	No default	Logical name that identifies and provides a namespace for the Oracle database server from which the connector captures changes. The value that you set is used as a prefix for all Kafka topic names that the connector emits. Specify a logical name that is unique among all connectors in your Debezium environment. The following characters are valid: alphanumeric characters, hyphens, dots, and underscores.
`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. `xstream` The connector uses the Oracle XStreams API.
`snapshot.mode`	initial	Specifies the mode that the connector uses to take snapshots of a captured table. You can set the following values: `initial` The snapshot includes the structure and data of captured tables. Specify this value to populate topics with a complete representation of the data from the captured tables. `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. After the snapshot is complete, the connector continues to read change events from the database’s redo logs.
`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 `table.include.list`	An optional, comma-separated list of regular expressions that match the fully-qualified names (`<schemaName>.<tableName>`) of the tables to include in a snapshot. The specified items must be 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. 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. Do not also set the `schema.exclude.list` property. In environments that use the LogMiner implementation, you must use POSIX regular expressions only.
`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. Any schema whose name is not included in `schema.exclude.list` has its changes captured, with the exception of system schemas. Do not also set the `schema.include.list` property. In environments that use the LogMiner implementation, you must use POSIX regular expressions only.
`table.include.list`	No default	An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be monitored. Tables that are not included in the include list are excluded from monitoring. Each table identifier uses the following format: `<schema_name>.<table_name>` By default, the connector monitors every non-system table in each monitored database. Do not use this property in combination with `table.exclude.list`. If you use the LogMiner implementation, use only POSIX regular expressions with this 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. 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>`. Do not use this property in combination with `table.include.list`. If you use the LogMiner implementation, use only POSIX regular expressions with this 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. 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. 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. 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. If you include this property in the configuration, do not set the `column.include.list` property.
`column.mask.hash.<hashAlgorithm>.with.salt.<salt>`	No default	An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns. Fully-qualified names for columns are of the form `<schemaName>.<tableName>.<columnName>`. In 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.
`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 Decimal types.
`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.queue.size`	`8192`	A positive integer value that specifies the maximum size of the blocking queue. Change events read from the database log are placed in the blocking queue before they are written to Kafka. This queue can provide backpressure to the binlog reader when, for example, writes to Kafka are slow, or if Kafka is not available. Events that appear in the queue are not included in the offsets that the connector records periodically. Always specify a value that is larger than the maximum batch size that specified for the `max.batch.size` property.
`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.in.bytes`	`0` (disabled)	Long value for the maximum size in bytes of the blocking queue. To activate the feature, set the value to a positive long data type.
`poll.interval.ms`	`1000` (1 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 to be truncated in change event messages if their length exceeds the specified number of characters. Length is specified as a positive integer. A configuration can include multiple properties that specify different lengths. Specify the fully-qualified name for columns by using the following format: `<schemaName>.<tableName>.<columnName>`.
`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 whose original type and length should be added as a parameter to the corresponding field schemas in the emitted change messages. The schema parameters `__debezium.source.column.type`, `__debezium.source.column.length`, and `__debezium.source.column.scale` are used to propagate the original type name and length (for variable-width types), respectively. Useful to properly size corresponding columns in sink databases. Fully-qualified names for columns are of the form `<tableName>.<columnName>`, or `<schemaName>.<tableName>.<columnName>`.
`datatype.propagate.source.type`	No default	An optional comma-separated list of regular expressions that match the database-specific data type name of columns whose original type and length should be added as a parameter to the corresponding field schemas in the emitted change messages. The schema parameters `__debezium.source.column.type`, `__debezium.source.column.length` and `__debezium.source.column.scale` are used to propagate the original type name and length (for variable-width types), respectively. Useful to properly size corresponding columns in sink databases. Fully-qualified data type names are of the form `<tableName>.<typeName>`, or `<schemaName>.<tableName>.<typeName>`. See the list of Oracle-specific data type names.
`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 the connector no change events occur in captured tables for an extended period. In such a 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.topics.prefix`	`__debezium-heartbeat`	Specifies the string that prefixes the name of the topic to which the connector sends heartbeat messages. The topic is named according to the pattern `<heartbeat.topics.prefix>.<serverName>`.
`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`	`2000`	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.
`sanitize.field.names`	`true` when the connector configuration explicitly specifies the `key.converter` or `value.converter` parameters to use Avro, otherwise defaults to `false`.	Specifies whether field names are normalized to comply with Avro naming requirements. For more information, see Avro naming.
`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.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.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.
`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.view.fetch.size`	`10000`	The number of content records that the connector fetches from the LogMiner content view.
`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.hours`	`0`	Positive integer value that specifies the number of hours to retain long running transactions between redo log switches. When set to `0`, transactions are retained until a commit or rollback is detected. 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.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.
`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`.
`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 Oracle RAC support. 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`	No default	A comma-separated list of the operation types that you want the connector to skip during streaming. You can configure the connector to skip the following types of operations: `c` (insert/create) `u` (update) `d` (delete) By default, no 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. Use the following format to specify the collection name: `<databaseName>.<schemaName>.<tableName>`
`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.

Debezium Oracle connector database history configuration properties

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

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

Table 6.11. Connector database history configuration properties
Property	Default	Description
`database.history.kafka.topic`		The full name of the Kafka topic where the connector stores the database schema history.
`database.history.kafka.bootstrap.servers`		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.
`database.history.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.
`database.history.kafka.recovery.attempts`	`4`	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` x `recovery.poll.interval.ms`.
`database.history.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.
`database.history.store.only.monitored.tables.ddl` Deprecated and scheduled for removal in a future release; use `database.history.store.only.captured.tables.ddl` instead.	`false`	A Boolean value that specifies whether the connector should record all DDL statements `true` records only those DDL statements that are relevant to tables whose changes are being captured by Debezium. Set to `true` with care because missing data might become necessary if you change which tables have their changes captured. The safe default is `false`.
`database.history.store.only.captured.tables.ddl`	`false`	A Boolean value that specifies whether the connector should record all DDL statements `true` records only those DDL statements that are relevant to tables whose changes are being captured by Debezium. Set to `true` with care because missing data might become necessary if you change which tables have their changes captured. The safe default is `false`.

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

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

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

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

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

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

Debezium Oracle connector pass-through database driver configuration properties

The Debezium connector provides for pass-through configuration of the database driver. Pass-through database properties begin with the prefix database.*. For example, the connector passes properties such as database.foobar=false to the JDBC URL.

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

6.7. Monitoring Debezium Oracle connector performance

The Debezium Oracle connector provides three metric types in addition to the built-in support for JMX metrics that Apache Zookeeper, Apache Kafka, and Kafka Connect have.

snapshot metrics; for monitoring the connector when performing snapshots
streaming metrics; for monitoring the connector when processing change events
schema history metrics; for monitoring the status of the connector’s schema history

Please refer to the monitoring documentation for details of how to expose these metrics via JMX.

6.7.1. Debezium Oracle connector snapshot metrics

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

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

The following table lists the shapshot metrics that are available.

Attributes	Type	Description
`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.
`MonitoredTables` Deprecated and scheduled for removal in a future release; use the `CapturedTables` metric instead.	`string[]`	The list of tables that are monitored by 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.
`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.
`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. It will be enabled if `max.queue.size.in.bytes` is passed with a positive long value.
`CurrentQueueSizeInBytes`	`long`	The current data of records in the queue in bytes.

The connector also provides the following additional snapshot metrics when an incremental snapshot is executed:

Attributes	Type	Description
`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.

Important

Incremental 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 https://access.redhat.com/support/offerings/techpreview.

6.7.2. Debezium Oracle connector streaming metrics

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

The following table lists the streaming metrics that are available.

Attributes	Type	Description
`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 last started or reset.
`NumberOfEventsFiltered`	`long`	The number of events that have been filtered by include/exclude list filtering rules configured on the connector.
`MonitoredTables` Deprecated and scheduled for removal in a future release; use the 'CapturedTables' metric instead	`string[]`	The list of tables that are monitored by 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.
`CurrentQueueSizeInBytes`	`long`	The current data of records in the queue in bytes.

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

Table 6.12. Descriptions of additional streaming metrics
Attributes	Type	Description
`CurrentScn`	`string`	The most recent system change number that has been processed.
`OldestScn`	`string`	The oldest system change number in the transaction buffer.
`ComittedScn`	`string`	The last committed system change number from the transaction buffer.
`OffsetScn`	`string`	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. See `log.mining.transaction.retention` for more details.
`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 abandoned transaction identifiers removed from the transaction buffer due to their age. See `log.mining.transaction.retention.hours` for details.
`RolledBackTransactionIds`	`string[]`	An array of 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 the system change number has been checked for advancement and remains unchanged. This is an indicator that long-running transaction(s) are ongoing and preventing the connector from flushing the latest processed system change number to the connector’s offsets. Under optimal operations, this should always be or remain close 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.

6.7.3. Debezium Oracle connector schema history metrics

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

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

Attributes	Type	Description
`Status`	`string`	One of `STOPPED`, `RECOVERING` (recovering history from the storage), `RUNNING` describing the state of the database 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.

6.8. How Debezium Oracle connectors handle faults and problems

Debezium is a distributed system that captures all changes in multiple upstream databases; it never misses or loses an event. When the system is operating normally or being managed carefully then Debezium provides exactly once delivery of every change event record.

If a fault occurs, Debezium does not lose any events. However, while it is recovering from the fault, it might repeat some change events. In these abnormal situations, Debezium, like Kafka, provides at least once delivery of change events.

The rest of this section describes how Debezium handles various kinds of faults and problems.

ORA-25191 - Cannot reference overflow table of an index-organized table

Oracle might issue this error during the snapshot phase when encountering an index-organized table (IOT). This error means that the connector has attempted to execute an operation that must be executed against the parent index-organized table that contains the specified overflow table.

To resolve this, the IOT name used in the SQL operation should be replaced with the parent index-organized table name. To determine the parent index-organized table name, use the following SQL:

SELECT IOT_NAME
  FROM DBA_TABLES
 WHERE OWNER='<tablespace-owner>'
   AND TABLE_NAME='<iot-table-name-that-failed>'

The connector’s table.include.list or table.exclude.list configuration options should then be adjusted to explicitly include or exclude the appropriate tables to avoid the connector from attempting to capture changes from the child index-organized table.

LogMiner adapter does not capture changes made by SYS or SYSTEM

Oracle uses the SYS and SYSTEM accounts for lots of internal changes and therefore the connector automatically filters changes made by these users when fetching changes from LogMiner. Never use the SYS or SYSTEM user accounts for changes to be emitted by the Debezium Oracle connector.

이 콘텐츠는 선택한 언어로 제공되지 않습니다.

Chapter 6. Debezium Connector for Oracle (Technology Preview)

6.1. How Debezium Oracle connectors work

6.1.1. How Debezium Oracle connectors perform database snapshots

6.1.1.1. Ad hoc snapshots

6.1.1.2. Incremental snapshots

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

6.1.3. How Debezium Oracle connectors expose database schema changes

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

6.1.4.1. Change data event enrichment

6.1.5. Gaps between Oracle SCN values

6.2. Descriptions of Debezium Oracle connector data change events

6.2.1. About keys in Debezium Oracle connector change events

6.2.2. About values in Debezium Oracle connector change events

6.3. How Debezium Oracle connectors map data types

6.4. Setting up Oracle to work with Debezium

6.4.1. Preparing Oracle databases for use with Debezium

6.4.2. Redo log sizing

6.4.3. Creating an Oracle user for the Debezium Oracle connector

6.5. Deployment of Debezium Oracle connectors

6.5.1. Debezium Oracle connector deployment using AMQ Streams

6.5.2. Using AMQ Streams to deploy a Debezium Oracle connector

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

6.5.4. Obtaining the Oracle JDBC driver

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

6.5.6. Verifying that the Debezium Oracle connector is running

6.6. Descriptions of Debezium Oracle connector configuration properties

6.7. Monitoring Debezium Oracle connector performance

6.7.1. Debezium Oracle connector snapshot metrics

6.7.2. Debezium Oracle connector streaming metrics

6.7.3. Debezium Oracle connector schema history metrics

6.8. How Debezium Oracle connectors handle faults and problems

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