Most notably, the connector does not yet provide full-blown support of changes to the structure of captured tables (e.g. `ALTER TABLE...`) after the initial snapshot has been completed
{prodname} ingests change events from Oracle using the native LogMiner database package or the https://docs.oracle.com/database/121/XSTRM/xstrm_intro.htm#XSTRM72647[XStream API].
To optimally configure and run a {prodname} Oracle connector, it is helpful to understand how the connector performs snapshots, streams change events, determines Kafka topic names, and uses metadata.
2. Obtain an `IN EXCLUSIVE MODE` lock on each of the monitored tables to ensure that no structural changes can occur to any of the tables.
3. Read the current SCN ("system change number") position in the server's redo log.
4. Capture the structure of all relevant tables.
5. Release the locks obtained in step 2, i.e. the locks are held only for a short period of time.
6. Scan all of the relevant database tables and schemas as valid at the SCN position read in step 3 (`SELECT * FROM ... AS OF SCN 123`), and generate a `READ` event for each row and write that event to the appropriate table-specific Kafka topic.
7. Record the successful completion of the snapshot in the connector offsets.
If the connector fails, is rebalanced, or stops after step 1 begins but before step 7 completes,
upon restart the connector will begin a new snapshot.
|The connector captures the structure of all relevant tables, performing all the steps described above, except it does not create any `READ` events representing the dataset at the point of the connector's start-up.
The {prodname} Oracle connector stores the history of schema changes in a database history topic.
This topic reflects an internal connector state and you should not use it directly.
Applications that require notifications about schema changes should obtain the information from the public schema change topic.
the connector writes all of these events to a Kafka topic named `<serverName>`, where `serverName` is the name of the connector that is specified in the `database.server.name` configuration property.
{prodname} emits a new message to this topic whenever a new table is streamed from or when the structure of the table is altered ({link-prefix}:{link-oracle-connector}#oracle-schema-evolution[schema evolution procedure must be followed]).
* `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` that provides number of events emitted by changes originating from given data collection
The transaction events are written to the topic named `<database.server.name>.transaction`.
==== Data events 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
Following is an example of what a message looks like:
[source,json,indent=0,subs="attributes"]
----
{
"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"
}
}
----
[[oracle-events]]
== Data change events
All data change events produced by the Oracle connector have a key and a value, although the structure of the key and value depend on the table from which the change events originated (see {link-prefix}:{link-oracle-connector}#oracle-topic-names[Topic names]).
[WARNING]
====
The {prodname} Oracle connector ensures that all Kafka Connect _schema names_ are http://avro.apache.org/docs/current/spec.html#names[valid Avro schema names].
This means that the logical server name must start with Latin letters or an underscore (e.g., [a-z,A-Z,\_]),
and the remaining characters in the logical server name and all characters in the schema and table names must be Latin letters, digits, or an underscore (e.g., [a-z,A-Z,0-9,\_]).
If not, then all invalid characters will automatically be replaced with an underscore character.
This can lead to unexpected conflicts when the logical server name, schema names, and table names contain other characters, and the only distinguishing characters between table full names are invalid and thus replaced with underscores.
====
{prodname} and Kafka Connect are designed around _continuous streams of event messages_, and the structure of these events may change over time.
This could be difficult for consumers to deal with, so to make it easy Kafka Connect makes each event 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.
[[oracle-change-event-keys]]
=== Change event keys
For a given table, the change event's key will have a structure that contains a field for each column in the primary key (or unique key constraint) of the table at the time the event was created.
Consider a `customers` table defined in the `inventory` database schema:
[source,sql,indent=0]
----
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 `database.server.name` configuration property has the value `server1`,
every change event for the `customers` table while it has this definition will feature the same key structure, which in JSON looks like this:
The `schema` portion of the key contains a Kafka Connect schema describing what is in the key portion, and in our case that means that the `payload` value is not optional, is a structure defined by a schema named `server1.DEBEZIUM.CUSTOMERS.Key`, and has one required field named `id` of type `int32`.
If you look at the value of the key's `payload` field, you can see 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`.
Although the `column.exclude.list` configuration property allows you to remove columns from the event values, all columns in a primary or unique key are always included in the event's key.
If the table does not have a primary or unique key, then the change event's key will be null. This makes sense since the rows in a table without a primary or unique key constraint cannot be uniquely identified.
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` is 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` is an optional field that if present contains the state of the row _before_ the event occurred. The structure will be described by the `server1.INVENTORY.CUSTOMERS.Value` Kafka Connect schema, which the `server1` connector uses for all rows in the `inventory.customers` table.
[WARNING]
====
Whether or not this field and its elements are available is highly dependent on the https://docs.oracle.com/database/121/SUTIL/GUID-D2DDD67C-E1CC-45A6-A2A7-198E4C142FA3.htm#SUTIL1583[Supplemental Logging] configuration applying to the table.
====
* `after` is 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` is a mandatory field that contains a structure describing the source metadata for the event, which in the case of Oracle contains these fields: the {prodname} 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` is optional and 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.
If we look at the `schema` portion of this event's _value_, we can see the schema for the _envelope_, the schema for the `source` structure (which is specific to the Oracle connector and reused across all events), and 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 all other schemas for all other tables.
This means that when using the link:/docs/faq/#avro-converter[Avro Converter], the resulting Avro schems for _each table_ in each _logical source_ have their own evolution and history.
====
If we look at the `payload` portion of this event's _value_, we can see the information in the event, namely that it is describing that the row was created (since `op=c`), and that the `after` field value contains the values of the new inserted row's' `ID`, `FIRST_NAME`, `LAST_NAME`, and `EMAIL` columns.
[TIP]
====
It may appear that the JSON representations of the 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.
It is possible and even recommended to use the link:/docs/faq/#avro-converter[Avro Converter] to dramatically decrease the size of the actual messages written to the Kafka topics.
The value of an _update_ change event on this table will actually have the exact same _schema_, and its payload will be structured the same but will hold different values.
When we compare this to the value in the _insert_ event, we see a couple of 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.
There are several things we can learn by just looking at this `payload` section. We can compare the `before` and `after` structures to determine what actually changed in this row because of the commit.
The `source` structure tells us information about Oracle's record of this change (providing traceability), but more importantly this has information we can compare to other events in this and other topics to know whether this event occurred before, after, or as part of the same Oracle commit as other events.
When the columns for a row's primary/unique key are updated, the value of the row's key has changed so {prodname} will output _three_ events: a `DELETE` event and a {link-prefix}:{link-oracle-connector}#oracle-tombstone-events[tombstone event] with the old key for the row, followed by an `INSERT` event with the new key for the row.
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. Once again, the `schema` portion of the value will be exactly the same as with the _create_ and _update_ events:
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.
But only if the message value is `null` will Kafka know that it can remove _all messages_ with that same key.
To make this possible, {prodname}'s Oracle connector always follows the _delete_ event with a special _tombstone_ event that has the same key but `null` value.
The Oracle conenctor represents changes to rows with events that are structured like the table in which the rows exists. The event contains a field for each column value. How that value is represented in the event depends on the Oracle data type of the column. The following sections describe how the connector maps oracle data types to a _litearl type_ and a _semantic type_ in event fields.
* _litearl 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`.
|`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
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` columns with a scale of 0 represent integer numbers; a negative scale indicates rounding in Oracle, e.g. a scale of -2 will cause rounding to hundreds. +
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.
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.
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.
However, when the `decimal.handling.mode` property is set to `double`, the connector will represent the values as Java double values with schema type `FLOAT64`.
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 will determine 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:
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 will use the predefined Kafka Connect logical types.
This may be useful when consumers only know about the built-in Kafka Connect logical types and are unable to handle variable-precision time values.
Since Oracle supports precision that exceeds what Kafka Connect's logical types support, using `connect` time precision will *result in a loss of precision* when the database column has a _fractional second precision_ value that is greater than 3:
[cols="25%a,20%a,55%a",options="header"]
|===
|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.
You can find a template for setting up Oracle in a virtual machine (via Vagrant) in the https://github.com/debezium/oracle-vagrant-box/[oracle-vagrant-box/] repository.
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.
[source,indent=0]
----
ALTER TABLE inventory.customers ADD SUPPLEMENTAL LOG DATA (ALL) COLUMNS;
----
=== Creating Users for the connector
The {prodname} Oracle connector requires that users accounts be set up with specific permissions so that the connector can capture change events.
The following briefly describes these user configurations using a multi-tenant database model.
* <<oracle-create-users-logminer, `Creating users for Oracle LogMiner`>>
When using a more complex Oracle deployment or needing to use TNS names, then a raw JDBC url can be provided instead of a single hostname-port pair. Here is a similar example but that just passes the raw jdbc url:
"database.url": "jdbc:oracle:thin:@(DESCRIPTION=(ADDRESS_LIST=(LOAD_BALANCE=OFF)(FAILOVER=ON)(ADDRESS=(PROTOCOL=TCP)(HOST=<oracle ip 1>)(PORT=1521))(ADDRESS=(PROTOCOL=TCP)(HOST=<oracle ip 2>)(PORT=1521)))(CONNECT_DATA=SERVICE_NAME=)(SERVER=DEDICATED)))",
The {prodname} Oracle connector supports both deployment practices of pluggable databases (CDB mode) as well as non-pluggable databases (non-CDB mode).
|Unique name for the connector. Attempting to register again with the same name will fail. (This property is required by all Kafka Connect connectors.)
|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.
|Logical name that identifies and provides a namespace for the particular Oracle database server being monitored. The logical name should be unique across all other connectors, since it is used as a prefix for all Kafka topic names emanating from this connector.
|A list of host/port pairs that the connector will use for establishing an initial connection to the Kafka cluster. This connection will be used for retrieving database schema history previously stored by the connector, and for writing each DDL statement read from the source database. This should point to the same Kafka cluster used by the Kafka Connect process.
|A mode for taking an initial snapshot of the structure and optionally data of captured tables. Supported values are _initial_ (will take a snapshot of structure and data of captured tables; useful if topics should be populated with a complete representation of the data from the captured tables) and _schema_only_ (will take a snapshot of the structure of captured tables only; useful if only changes happening from now onwards should be propagated to topics). Once the snapshot is complete, the connector will continue reading change events from the database's redo logs.
|Controls which rows from tables are included in snapshot. +
This property contains a comma-separated list of fully-qualified tables _(SCHEMA_NAME.TABLE_NAME)_. Select statements for the individual tables are specified in further configuration properties, one for each table, identified by the id `snapshot.select.statement.overrides.[SCHEMA_NAME].[TABLE_NAME]`. The value of those properties is the SELECT statement to use when retrieving data from the specific table during snapshotting. _A possible use case for large append-only tables is setting a specific point where to start (resume) snapshotting, in case a previous snapshotting was interrupted._ +
*Note*: This setting has impact on snapshots only. Events captured during log reading are not affected by it.
|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. When using LogMiner, only POSIX regular expressions are supported.
|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. When using LogMiner, only POSIX regular expressions are supported.
|An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be monitored; any table not included in the include list will be excluded from monitoring. Each identifier is of the form _schemaName_._tableName_. By default the connector will monitor every non-system table in each monitored database. May not be used with `table.exclude.list`. When using LogMiner, only POSIX regular expressions are supported.
|An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be excluded from monitoring; any table not included in the exclude list will be monitored. Each identifier is of the form _schemaName_._tableName_. May not be used with `table.include.list`. When using LogMiner, only POSIX regular expressions are supported.
|An optional comma-separated list of regular expressions that match the fully-qualified names of columns that should be included in the change event message values.
Fully-qualified names for columns are of the form _schemaName_._tableName_._columnName_.
Note that primary key columns are always included in the event's key, even if not included in the value.
Do not also set the `column.exclude.list` property.
|An optional comma-separated list of regular expressions that match the fully-qualified names of columns that should be excluded from change event message values.
Fully-qualified names for columns are of the form _schemaName_._tableName_._columnName_.
Note that primary key columns are always included in the event's key, also if excluded from the value.
Do not also set the `column.include.list` property.
|An optional comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be pseudonyms in the change event message values with a field value consisting of the hashed value using the algorithm `_hashAlgorithm_` and salt `_salt_`.
Based on the used hash function referential integrity is kept while data is pseudonymized. Supported hash functions are described in the {link-java7-standard-names}[MessageDigest section] of the Java Cryptography Architecture Standard Algorithm Name Documentation.
The hash is automatically shortened to the length of the column.
|The minimum SCN interval size that this connector will try to read from redo/archive logs. Active batch size will be also increased/decreased by this amount for tuning connector throughput when needed.
|The minimum amount of time that the connector will sleep after reading data from redo/archive logs and before starting reading data again. Value is in milliseconds.
|The maximum amount of time that the connector will sleep after reading data from redo/archive logs and before starting reading data again. Value is in milliseconds.
|The starting amount of time that the connector will sleep after reading data from redo/archive logs and before starting reading data again. Value is in milliseconds.
|The maximum amount of time up or down that the connector will use to tune the optimal sleep time when reading data from logminer. Value is in milliseconds.
Multiple properties with different lengths can be used in a single configuration, although in each the length must be a positive integer or zero. Fully-qualified names for columns are of the form _pdbName_._schemaName_._tableName_._columnName_.
Note: Depending on the `_hashAlgorithm_` used, the `_salt_` selected and the actual data set, the resulting masked data set may not be completely anonymized.
| Specifies how the connector should handle floating point values for `NUMBER`, `DECIMAL` and `NUMERIC` columns: `precise` (the default) represents them precisely using `java.math.BigDecimal` values represented in change events in a binary form; or `double` represents them using `double` values, which may result in a loss of precision but will be far easier to use. `string` option encodes values as formatted string which is easy to consume but a semantic information about the real type is lost. See <<decimal-values>>.
|Positive integer value that specifies the maximum size of the blocking queue into which change events read from the database log are placed before they are written to Kafka. This queue can provide backpressure to the binlog reader when, for example, writes to Kafka are slower or if Kafka is not available. Events that appear in the queue are not included in the offsets periodically recorded by this connector. Defaults to 8192, and should always be larger than the maximum batch size specified in the `max.batch.size` property.
|Positive integer value that specifies the maximum size of each batch of events that should be processed during each iteration of this connector. Defaults to 2048.
|Long value for the maximum size in bytes of the blocking queue. The feature is disabled by default, it will be active if it's set with a positive long value.
|Positive integer value that specifies the number of milliseconds the connector should wait during each iteration for new change events to appear. Defaults to 1000 milliseconds, or 1 second.
| Controls whether a tombstone event should be generated after a delete event. +
When `true` the delete operations are represented by a delete event and a subsequent tombstone event. When `false` only a delete event is sent. +
Emitting the tombstone event (the default behavior) allows Kafka to completely delete all events pertaining to the given key once the source record got deleted.
|An optional comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be truncated in the change event message values if the field values are longer than the specified number of characters. Multiple properties with different lengths can be used in a single configuration, although in each the length must be a positive integer. Fully-qualified names for columns are of the form _pdbName_._schemaName_._tableName_._columnName_.
|An optional comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be replaced in the change event message values with a field value consisting of the specified number of asterisk (`*`) characters. Multiple properties with different lengths can be used in a single configuration, although in each the length must be a positive integer or zero. Fully-qualified names for columns are of the form _pdbName_._schemaName_._tableName_._columnName_.
|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 `pass:[_]pass:[_]debezium.source.column.type`, `pass:[_]pass:[_]debezium.source.column.length` and `pass:[_]pass:[_]debezium.source.column.scale` will be used to propagate the original type name and length (for variable-width types), respectively.
Useful to properly size corresponding columns in sink databases.
|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 `pass:[_]pass:[_]debezium.source.column.type`, `pass:[_]pass:[_]debezium.source.column.length` and `pass:[_]pass:[_]debezium.source.column.scale` will be used to propagate the original type name and length (for variable-width types), respectively.
Useful to properly size corresponding columns in sink databases.
|`true` when connector configuration explicitly specifies the `key.converter` or `value.converter` parameters to use Avro, otherwise defaults to `false`.
Any transaction that exceeds this configured value will be discarded entirely and no messages emitted for the operations that were part of the transaction.
While this option allows the behavior to be configured on a case-by-case basis,
we have plans to enhance this behavior in a future release by means of adding a scalable transaction buffer, (see {link-prefix}:{jira-url}/browse/DBZ-3123[DBZ-3123]).
The {prodname} Oracle connector has three metric types in addition to the built-in support for JMX metrics that Zookeeper, Kafka, and Kafka Connect have.
* <<oracle-snapshot-metrics, snapshot metrics>>; for monitoring the connector when performing snapshots
* <<oracle-streaming-metrics, streaming metrics>>; for monitoring the connector when processing change events
* <<oracle-monitoring-streaming-logminer, logminer metrics>>; additional metrics captured when using the LogMiner adpter to process change events
* <<oracle-schema-history-metrics, schema history metrics>>; for monitoring the status of the connector's schema history
Please refer to the {link-prefix}:{link-debezium-monitoring}#monitoring-debezium[monitoring documentation] for details of how to expose these metrics via JMX.
[[oracle-monitoring-snapshots]]
[[oracle-snapshot-metrics]]
=== Snapshot Metrics
The *MBean* is `debezium.oracle:type=connector-metrics,context=snapshot,server=_<database.server.name>_`.
|The number of hours that transactions will be retained by the connector's in-memory buffer without being committed or rolled back before being discarded.
See <<oracle-property-log-mining-transaction-retention-hours, `log.mining.transaction.retention`>> for more details.
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.
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.