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Debezium Connector for Oracle

Overview

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 theDebezium release overview．

Debezium ingests change events from Oracle by using the native LogMiner database package or theXStream API．While the connector might work with a variety of Oracle versions and editions, only Oracle EE 12 and 19 have been tested.

How the Oracle connector works

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.

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 initialconsistent snapshotof the database.

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

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:

  1. Determines the tables to be captured

  2. Obtains aROW SHARE MODElock 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.

  3. Reads the current system change number (SCN) position from the server’s redo log.

  4. Captures the structure of all relevant tables.

  5. Releases the locks obtained in Step 2.

  6. 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 aREADevent for each row, and then writes the event records to the table-specific Kafka topic.

  7. 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 1. Settings forsnapshot.modeconnector configuration property
Setting Description

initial

The connector performs a database snapshot as described in thedefault 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 thedefault snapshot workflow, except that it does not createREADevents to represent the data set at the point of the connector’s start-up (Step 6).

schema_only_recovery

Set this option to restore a database history topic that is lost or corrupted. After a restart, the connector runs a snapshot that rebuilds the topic from the source tables. You can also set the property to periodically prune the database history topic that experience unexpected growth. Note this mode is only safe to be used when it is guaranteed that no schema changes happened since the point in time the connector was shut down before and the point in time the snapshot is taken.

Ad hoc snapshots

This feature is currently in incubating state, i.e. exact semantics, configuration options etc. may change in future revisions, based on the feedback we receive. Please let us know if you encounter any problems while using this extension.

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

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

  • The connector configuration is modified to capture a different set of tables.

  • Kafka topics are deleted and must be rebuilt.

  • Data corruption occurs due to a configuration error or some other problem.

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

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

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

You specify the tables to capture by sending anexecute-snapshotmessage to the signaling table. Set the type of theexecute-snapshotsignal toincremental并提供表的名称,包括我n the snapshot, as described in the following table:

Table 2. Example of an ad hocexecute-snapshotsignal 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 onlyincrementalsnapshots.

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 thesignal.data.collectionconfiguration option.
Triggering an ad hoc snapshot

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

Currently, theexecute-snapshotaction type triggersincremental snapshotsonly. For more information, seeIncremental snapshots．

Incremental snapshots

This feature is currently in incubating state. The exact semantics, configuration options, and so forth is subject to change in future revisions, based on the feedback we receive. Please let us know if you encounter any problems while using this extension.

To provide flexibility in managing snapshots, Debezium includes a supplementary snapshot mechanism, known asincremental snapshotting．Incremental snapshots rely on the Debezium mechanism forDebezium连接器发送信号开云体育官方注册网址．Incremental snapshots are based on theDDD-3design document.

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

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

  • You can run incremental snapshots in parallel with streamed data capture, instead of postponing streaming until the snapshot completes. The connector continues to capture near real-time events from the change log throughout the snapshot process, and neither operation blocks the other.

  • If the progress of an incremental snapshot is interrupted, you can resume it without losing any data. After the process resumes, the snapshot begins at the point where it stopped, rather than recapturing the table from the beginning.

  • You can run an incremental snapshot on demand at any time, and repeat the process as needed to adapt to database updates. For example, you might re-run a snapshot after you modify the connector configuration to add a table to itstable.include.listproperty.

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 theconfigured chunk size．Working chunk by chunk, it then captures each table row in a chunk. For each row that it captures, the snapshot emits aREADevent. That event represents the value of the row when the snapshot for the chunk began.

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

How Debezium resolves collisions among records with the same primary key

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

Snapshot window

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

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

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

The connector repeats the process for each snapshot chunk.

Triggering an incremental snapshot

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

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

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

Thedata-collectionsarray for an incremental snapshot signal has no default value. If thedata-collectionsarray is empty, Debezium detects that no action is required and does not perform a snapshot.

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 thesignal.data.collectionproperty.

Procedure

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

    INSERT INTO __ (id, type, data) VALUES (_''_, _''_, '{"data-collections": ["__","__"],"type":"__"}');

    例如,

    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 theid,type, anddataparameters in the command correspond to thefields of the signaling table．

    The following table describes the these parameters:

    Table 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

    Theidparameter specifies an arbitrary string that is assigned as theididentifier for the signal request.
    Use this string to identify logging messages to entries in the signaling table. Debezium does not use this string. Rather, during the snapshot, Debezium generates its ownidstring as a watermarking signal.

    execute-snapshot

    Specifiestypeparameter specifies the operation that the signal is intended to trigger.

    data-collections

    A required component of thedatafield of a signal that specifies an array of table names to include in the snapshot.
    The array lists tables by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling table in thesignal.data.collectionconfiguration property.

    incremental

    An optionaltypecomponent of thedatafield of a signal that specifies the kind of snapshot operation to run.
    Currently, the only valid option is the default value,incremental．
    Specifying atypevalue in the SQL query that you submit to the signaling table is optional.
    If you do not specify a value, the connector runs an incremental snapshot.

The following example, shows the JSON for an incremental snapshot event that is captured by a connector.

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 atypevalue in the SQL query that you submit to the signaling table is optional.
If you do not specify a value, the connector runs an incremental snapshot.

2

op

Specifies the event type.
The value for snapshot events isr, signifying aREADoperation.

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

Topic names

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

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 thedatabase.server.nameconnector configuration property.

schemaName

The name of the schema in which the operation occurred.

tableName

The name of the table in which the operation occurred.

例如,iffulfillmentis the server name,inventory模式名,数据库包含tabl吗开云体育电动老虎机es with the namesorders,customers, andproducts, 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, andtransaction metadata topics．

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

Schema change topic

You can configure a Debezium 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, whereserverNameis the logical server name that is specified in thedatabase.server.nameconfiguration 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 SQLCREATE,ALTER, orDROPstatement that results in the schema change.

databaseName

The name of the database to which the statements are applied. The value ofdatabaseNameserves as the message key.

tableChanges

整个表sc的结构化表示hema after the schema change. ThetableChangesfield 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.

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

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

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

  • If you create the database history topic manually, specify a partition count of1．

  • If you use the Apache Kafka broker to create the database history topic automatically, the topic is created, set the value of theKafkanum.partitionsconfiguration option to1．

The schema change topic message format is in an incubating state and might change without notice.

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

Following a change in table structure, you must follow (theschema evolution procedure．
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": "1.8.1.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 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,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, thepayloadfield contains the key:

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

Transaction Metadata

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 theBEGINandENDkeywords. Debezium generates transaction boundary events for theBEGINandENDdelimiters in every transaction. Transaction boundary events contain the following fields:

status

BEGINorEND

id

String representation of unique transaction identifier.

event_count(forENDevents)

Total number of events emmitted by the transaction.

data_collections(forENDevents)

An array of pairs ofdata_collectionandevent_countelements that indicates number of events that the connector emits for changes that originate from a data collection.

The following example shows a typical transaction boundary message:

Example: Oracle connector transaction boundary event
{ "status": "BEGIN", "id": "5.6.641", "event_count": null, "data_collections": null } { "status": "END", "id": "5.6.641", "event_count": 2, "data_collections": [ { "data_collection": "ORCLPDB1.DEBEZIUM.CUSTOMER", "event_count": 1 }, { "data_collection": "ORCLPDB1.DEBEZIUM.ORDER", "event_count": 1 } ] }

Unless overridden via thetransaction.topicoption, the connector emits transaction events to the．transactiontopic.

Change data event enrichment

When transaction metadata is enabled, the data messageEnvelopeis enriched with a newtransactionfield. 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 propertylog.mining.buffer.type．

Heap

The default buffer type is configured usingmemory．Under the defaultmemorysetting, the connector uses the heap memory of the JVM process to allocate and manage buffered event records. If you use thememorybuffer 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.

Infinispan

The Debezium Oracle connector can also be configured to use Infinispan as its cache provider, supporting cache stores both locally with embedded mode or remotely on a server cluster. In order to use Infinispan, thelog.mining.buffer.typemust be configured using eitherinfinispan_embeddedorinfinispan_remote．

In order to allow flexibility with Infinispan cache configurations, the connector expects a series of cache configuration properties to be supplied when using Infinispan to buffer event data. See theconfiguration propertiesin thelog.mining.buffer.infinispan.cachenamespace. The contents of these configuration properties depend on whether the connector is to integrate with a remote Infinispan cluster or to use the embedded engine.

例如,the following illustrates what an embedded configuration would look like for the transaction cache property when using Infinispan in embedded mode:

Looking at the configuration in-depth, the cache is configured to be persistent. All caches should be configured this way to avoid loss of transaction events across connector restarts if a transaction is in-progress. Additionally, the location where the cache is kept is defined by thepathattribute and this should be a shared location accessible all possible runtime environments.

When supplying XML configuration as a JSON connector property value, line breaks must be omitted or replaced with a\ncharacter.

另一个例子,下面的说明了山姆e cache configured with an Infinispan cluster:

Just like the embedded local-cache configuration from the previous example, this configuration is also defined to be persistent. All caches should be configured this way to avoid loss of transaction events across connector restarts if a transaction is in-progress.

However, there are a few differences with noting. First, the cache is defined as a distributed cache rather than a local-cache. Secondly, the cache is defined to use theapplication/x-protostreamencoding, which is required for all Debezium caches. And lastly, nopathattribute is necessary on the file store definition since the Infinispan cluster will handle this automatically.

The Infinispan buffer type is considered incubating; the cache formats may change between versions and may require a re-snapshot. The migration notes will indicate whether this is needed.

Additionally, when removing a Debezium Oracle connector that uses the Infinispan buffer, the persisted cache files are not removed from disk automatically. If the same buffer location will be used by a new connector deployment, the files should be removed manually before deploying the new connector.
Infinispan Hotrod client integration

The Debezium Oracle connector utilizes the Hotrod client to communicate with the Infinispan cluster. Any connector property that is prefixed withlog.mining.buffer.infinispan.client.will be passed directly to the Hotrod client using theinfinispan.client.namespace, allowing for complete customization of how the client is to interact with the cluster.

There is at least one required configuration property that must be supplied when using this Infinspan mode:

log.mining.buffer.infinispan.client.hotrod.server_list

Specifies the list of Infinispan server hostname and port combinations, using:format.

SCN gap detection

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.

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

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, seeTopic names).

The Debezium Oracle connector ensures that all Kafka Connectschema namesarevalid 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 aroundcontinuous streams of event messages．然而,这些事件可能会查的结构nge 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: aschemaandpayload．The schema describes the structure of the payload, while the payload contains the actual data.

Changes that are performed by theSYSorSYSTEMuser accounts are not captured by the connector.

Change event keys

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.

例如,acustomerstable that is defined in theinventorydatabase 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．transactionconfiguration property is set toserver1, the JSON representation for every change event that occurs in thecustomerstable 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 } }

Theschemaportion of the key contains a Kafka Connect schema that describes the content of the key portion. In the preceding example, thepayloadvalue is not optional, the structure is defined by a schema namedserver1.DEBEZIUM.CUSTOMERS.Key, and there is one required field namedidof typeint32．The value of the key’spayloadfield indicates that it is indeed a structure (which in JSON is just an object) with a singleidfield, whose value is1004．

Therefore, you can interpret this key as describing the row in theinventory.customerstable (output from the connector namedserver1) whoseidprimary key column had a value of1004．

Change event values

Like the message key, the value of a change event message has aschemasection andpayloadsection. The payload section of every change event value produced by the Oracle connector has anenvelopestructure with the following fields:

op

A mandatory field that contains a string value describing the type of operation. Values for the Oracle connector arecfor create (or insert),ufor update,dfor delete, andrfor read (in the case of a snapshot).

before

An optional field that, if present, contains the state of the rowbeforethe event occurred. The structure is described by theserver1.INVENTORY.CUSTOMERS.ValueKafka Connect schema, which theserver1connector uses for all rows in theinventory.customerstable.

after

An optional field that if present contains the state of the rowafterthe event occurred. The structure is described by the sameserver1.INVENTORY.CUSTOMERS.ValueKafka Connect schema used inbefore．

source

强制性字段包含descri结构bing 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).

Thecommit_scn字段是可选的,描述的视交叉上核交易nsaction 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, theschemaportion of the event message’s value contains a schema that describes this envelope structure and the nested fields within it.

createevents

Let’s look at what acreateevent value might look like for ourcustomerstable:

{ "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": "1.8.1.Final", "name": "server1", "ts_ms": 1520085154000, "txId": "6.28.807", "scn": "2122185", "commit_scn": "2122185", "snapshot": false }, "op": "c", "ts_ms": 1532592105975 } }

Examining theschemaportion of the preceding event’svalue, we can see how the following schema are defined:

  • Theenvelope

  • Thesourcestructure (which is specific to the Oracle connector and reused across all events).

  • The table-specific schemas for thebeforeandafterfields.

The names of the schemas for thebeforeandafterfields are of the form．．．Value, and thus are entirely independent from the schemas for all other tables. This means that when using theAvro Converter, the resulting Avro schems foreach tablein eachlogical sourcehave their own evolution and history.

Thepayloadportion of this event’svalue, provides information about the event. It describes that a row was created (op=c), and shows that theafterfield value contains the values that were inserted into theID,FIRST_NAME,LAST_NAME, andEMAILcolumns of the row.

By default, the JSON representations of events are much larger than the rows they describe. This is true, because the JSON representation must include theschemaand thepayloadportions of the message. You can use theAvro Converterto significantly decrease the size of the messages that the connector writes to Kafka topics.

updateevents

The value of anupdatechange event on this table has the sameschemaas thecreateevent. 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": "1.8.1.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 theupdateevent to thecreate(insert) event, notice the following differences in thepayloadsection:

  • Theopfield value is nowu, signifying that this row changed because of an update

  • Thebeforefield now has the state of the row with the values before the database commit

  • Theafterfield now has the updated state of the row, and here was can see that theEMAILvalue is nowanne@example.com．

  • Thesourcefield structure has the same fields as before, but the values are different since this event is from a different position in the redo log.

  • Thets_msshows the timestamp that Debezium processed this event.

Thepayloadsection reveals several other useful pieces of information. For example, by comparing thebeforeandafterstructures, we can determine how a row changed as the result of a commit. Thesourcestructure provides information about Oracle’s record of this change, providing traceability. It also gives us insight into when this event occurred in relation to other events in this topic and in other topics. Did it occur before, after, or as part of the same commit as another event?

When the columns for a row’s primary/unique key are updated, the value of the row’s key changes. As a result, Debezium emitsthreeevents after such an update:

  • ADELETEevent.

  • Atombstone eventwith the old key for the row.

  • AnINSERTevent that provides the new key for the row.

deleteevents

So far we’ve seen samples ofcreateandupdateevents. Now, let’s look at the value of adeleteevent for the same table. As is the case withcreateandupdateevents, for adeleteevent, theschemaportion 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": "1.8.1.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 thepayloadportion, we see a number of differences compared with thecreateorupdateevent payloads:

  • Theopfield value is nowd, signifying that this row was deleted

  • Thebeforefield now has the state of the row that was deleted with the database commit.

  • Theafterfield is null, signifying that the row no longer exists

  • Thesourcefield structure has many of the same values as before, except thets_ms,scnandtxIdfields have changed

  • Thets_msshows 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 withKafka 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, thedeleteevent 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 tonullto instruct Kafka to removeall messagesthat share the same key. To make this possible, by default, Debezium’s Oracle connector always follows adeleteevent with a specialtombstoneevent that has the same key butnullvalue. You can change the default behavior by setting the connector propertytombstones.on.delete．

truncateevents

Atruncatechange event signals that a table has been truncated. The message key isnullin this case, the message value looks like this:

{ "schema": { ... }, "payload": { "before": null, "after": null, "source": {(1)"version": "1.8.1.Final", "connector": "oracle", "name": "oracle_server", "ts_ms": 1638974535000, "snapshot": "false", "db": "ORCLPDB1", "sequence": null, "schema": "DEBEZIUM", "table": "TEST_TABLE", "txId": "02000a0037030000", "scn": "13234397", "commit_scn": "13271102", "lcr_position": null }, "op": "t",(2)"ts_ms": 1638974558961,(3)"transaction": null } }
Table 5. Descriptions oftruncateevent value fields
Item Field name Description

1

source

Mandatory field that describes the source metadata for the event. In atruncateevent value, thesourcefield structure is the same as forcreate,update, anddeleteevents for the same table, provides this metadata:

  • Debezium version

  • Connector type and name

  • Database and table that contains the new row

  • Schema name

  • If the event was part of a snapshot (alwaysfalsefortruncateevents)

  • ID of the transaction in which the operation was performed

  • SCN of the operation

  • Timestamp for when the change was made in the database

2

op

Mandatory string that describes the type of operation. Theopfield value ist, signifying that this table was truncated.

3

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

+ In thesourceobject,ts_msindicates the time that the change was made in the database. By comparing the value forpayload.source.ts_mswith the value forpayload.ts_ms, you can determine the lag between the source database update and Debezium.

Note that sincetruncateevents represent a change made to an entire table and don’t have a message key, unless you’re working with topics with a single partition, there are no ordering guarantees for the change events pertaining to a table (create,update, etc.) andtruncateevents for that table. For instance a consumer may receive anupdateevent only after atruncateevent for that table, when those events are read from different partitions.

Iftruncateevents are not desired, they can be filtered out with theskipped.operationsoption.

Data type mappings

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 aliteral typeand asemantic typein 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, andSTRUCT．

semantic type

Describes how the Kafka Connect schema captures themeaningof the field using the name of the Kafka Connect schema for the field.

Support for further data types is planned for subsequent releases. Please file aJIRA issuefor any specific types that might be missing.

Character types

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

Table 6. Mappings for Oracle basic character types
Oracle数据类型 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

Support for these data types is currently in incubating state, that is, the exact semantics, configuration options and so forth might change in future revisions, based on feedback we receive. Please let us know if you encounter any problems while using these data types.

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

Table 7. Mappings for Oracle binary and character LOB types
Oracle数据类型 Literal type (schema type) Semantic type (schema name) and Notes

BLOB

BYTES

Either the raw bytes (the default), a base64-encoded String, or a hex-encoded String, based on thebinary.handling.modeconnector configuration property setting.

CLOB

STRING

n/a

LONG

n/a

This data type is not supported.

LONG RAW

n/a

This data type is not supported.

NCLOB

STRING

n/a

RAW

n/a

This data type is not supported.

Oracle only supplies column values forCLOB,NCLOB, andBLOBdata types if they’re explicitly set or changed in a SQL statement. This means that change events will never contain the value of an unchangedCLOB,NCLOB, orBLOBcolumn, but a placeholder as defined by the connector property,unavailable.value.placeholder．

If the value of aCLOB,NCLOB, orBLOBcolumn gets updated, the new value will be contained in theafterpart of the corresponding update change events whereas the unavailable value placeholder will be used in thebeforepart.

Numeric types

The following table describes how the connector maps numeric types.

Table 8. Mappings for Oracle numeric data types
Oracle数据类型 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.Decimalif usingBYTES

Handled equivalently toNUMBER(note that S defaults to 0 forDECIMAL).

DOUBLE PRECISION

STRUCT

io.debezium.data.VariableScaleDecimal

Contains a structure with two fields:scaleof typeINT32that contains the scale of the transferred value andvalueof typeBYTEScontaining the original value in an unscaled form.

FLOAT[(P)]

STRUCT

io.debezium.data.VariableScaleDecimal

Contains a structure with two fields:scaleof typeINT32that contains the scale of the transferred value andvalueof typeBYTEScontaining the original value in an unscaled form.

INTEGER,INT

BYTES

org.apache.kafka.connect.data.Decimal

INTEGERis mapped in Oracle to NUMBER(38,0) and hence can hold values larger than any of theINTtypes could store

NUMBER[(P[, *])]

STRUCT

io.debezium.data.VariableScaleDecimal

Contains a structure with two fields:scaleof typeINT32that contains the scale of the transferred value andvalueof typeBYTEScontaining the original value in an unscaled form.

NUMBER(P, S <= 0)

INT8/INT16/INT32/INT64

NUMBERcolumns 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.Decimalif usingBYTES

Handled equivalently toNUMBER(note that S defaults to 0 forNUMERIC).

SMALLINT

BYTES

org.apache.kafka.connect.data.Decimal

SMALLINTis mapped in Oracle to NUMBER(38,0) and hence can hold values larger than any of theINTtypes could store

REAL

STRUCT

io.debezium.data.VariableScaleDecimal

Contains a structure with two fields:scaleof typeINT32that contains the scale of the transferred value andvalueof typeBYTEScontaining the original value in an unscaled form.

Boolean types

Oracle does not natively have support for aBOOLEANdata type; however, it is common practice to use other data types with certain semantics to simulate the concept of a logicalBOOLEANdata type.

The operator can configure the out-of-the-boxNumberOneToBooleanConvertercustom converter that would either map allNUMBER(1)columns to aBOOLEANor if theselectorparameter 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决定连接器地图十进制类型。

When thedecimal.handling.modeproperty is set toprecise, the connector uses Kafka Connectorg.apache.kafka.connect.data.Decimallogical type for allDECIMALandNUMERICcolumns. This is the default mode.

However, when thedecimal.handling.modeproperty is set todouble都伯,连接器代表值作为Javale values with schema typeFLOAT64．

You can also set thedecimal.handling.modeconfiguration property to use thestringoption. When the property is set tostring, the connector representsDECIMALandNUMERICvalues as their formatted string representation with schema typeSTRING．

Temporal types

Other than Oracle’sINTERVAL,TIMESTAMP WITH TIME ZONEandTIMESTAMP WITH LOCAL TIME ZONEdata types, the other temporal types depend on the value of thetime.precision.modeconfiguration property.

When thetime.precision.modeconfiguration property is set toadaptive(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 eventsexactlyrepresent 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 the365.25 / 12.0formula for days per month average.

io.debezium.time.Interval(wheninterval.handling.modeis set tostring)

The string representation of the interval value that follows the patternPYMDTHMS, for example,P1Y2M3DT4H5M6.78S．

INTERVAL YEAR[(M)] TO MONTH

FLOAT64

io.debezium.time.MicroDuration

The number of micro seconds for a time interval using the365.25 / 12.0formula for days per month average.

io.debezium.time.Interval(wheninterval.handling.modeis set tostring)

The string representation of the interval value that follows the patternPYMDTHMS, for example,P1Y2M3DT4H5M6.78S．

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 thetime.precision.modeconfiguration property is set toconnect, 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 settime.precision.modetoconnect,a loss of precisionresults when thefractional second precisionvalue 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 the365.25 / 12.0formula for days per month average.

io.debezium.time.Interval(wheninterval.handling.modeis set tostring)

The string representation of the interval value that follows the patternPYMDTHMS, for example,P1Y2M3DT4H5M6.78S．

INTERVAL YEAR[(M)] TO MONTH

FLOAT64

io.debezium.time.MicroDuration

The number of micro seconds for a time interval using the365.25 / 12.0formula for days per month average.

io.debezium.time.Interval(wheninterval.handling.modeis set tostring)

The string representation of the interval value that follows the patternPYMDTHMS, for example,P1Y2M3DT4H5M6.78S．

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.

ROWID types

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

Table 9. Mappings for Oracle ROWID data types
Oracle数据类型 Literal type (schema type) Semantic type (schema name) and Notes

ROWID

STRING

This data type is not supported when using Oracle XStream.

UROWID

n/a

This data type is not supported．

Default Values

If a default value is specified for a column in the database schema, the Oracle connector will attempt to propagate this value to the schema of the corresponding Kafka record field. Most common data types are supported, including:

  • Character types (CHAR,NCHAR,VARCHAR,VARCHAR2,NVARCHAR,NVARCHAR2)

  • Numeric types (INTEGER,NUMERIC, etc.)

  • Temporal types (DATE,TIMESTAMP,INTERVAL, etc.)

If a temporal type uses a function call such asTO_TIMESTAMPorTO_DATEto represent the default value, the connector will resolve the default value by making an additional database call to evaluate the function. For example, if aDATEcolumn is defined with the default value ofTO_DATE('2021-01-02', 'YYYY-MM-DD'), the column’s default value will be the number of days since epoch for that date or18629in this case.

If a temporal type uses theSYSDATEconstant to represent the default value, the connector will resolve this based on whether the column is defined asNOT NULLorNULL．If the column is nullable, no default value will be set; however, if the column isn’t nullable then the default value will be resolved as either0(forDATEorTIMESTAMP(n)data types) or1970-01-01T00:00:00Z(forTIMESTAMP WITH TIME ZONEorTIMESTAMP WITH LOCAL TIME ZONEdata types). The default value type will be numeric except if the column is aTIMESTAMP WITH TIME ZONEorTIMESTAMP WITH LOCAL TIME ZONEin which case its emitted as a string.

Setting up Oracle

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 theDebezium Vagrant Box for Oracle databaseGitHub repository.

Preparing the database

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 thebeforestate 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;

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.

Creating users for the connector

For the Debezium Oracle connector to capture change events, it must run as an Oracle LogMiner user that has specific permissions. The following example shows the SQL for creating an Oracle user account for the connector in a multi-tenant database model.

The connector captures database changes that are made by its own Oracle user account. However, it does not capture changes that are made by theSYSorSYSTEMuser 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_$ARCHIVE_DEST_STATUS TO c##dbzuser CONTAINER=ALL; GRANT SELECT ON V_$TRANSACTION TO c##dbzuser CONTAINER=ALL; exit;

Deployment

To deploy a Debezium Oracle connector, you install the Debezium Oracle connector archive, configure the connector, and start the connector by adding its configuration to Kafka Connect.

Prerequisites
Procedure

  1. Download the DebeziumOracle connector plug-in archive．

  2. Extract the files into your Kafka Connect environment.

  3. Add the directory with the JAR files toKafka Connect’splugin.path．

  4. Configure the connectorandadd the configuration to your Kafka Connect cluster.

  5. Restart your Kafka Connect process to pick up the new JAR files.

Obtaining the Oracle JDBC driver and XStream API files

The Debezium Oracle connector requires the Oracle JDBC driver (ojdbc8.jar) to connect to Oracle databases. If the connector uses XStreams to access the database, you must also have the XStream API (xstreams.jar). Licensing requirements prohibit Debezium from including these files in the Oracle connector archive. However, the required files are available for free download as part of the Oracle Instant Client. The following steps describe how to download the Oracle Instant Client and extract the required files.

Procedure

  1. From a browser, download theOracle Instant Client packagefor your operating system.

  2. Extract the archive and then open theinstantclient_directory.

    For example:

    instantclient_21_1/ ├── adrci ├── BASIC_LITE_LICENSE ├── BASIC_LITE_README ├── genezi ├── libclntshcore.so -> libclntshcore.so.21.1 ├── libclntshcore.so.12.1 -> libclntshcore.so.21.1 ... ├── ojdbc8.jar ├── ucp.jar ├── uidrvci └── xstreams.jar

  3. Copy theojdbc8.jarandxstreams.jarfiles, and add them to the/libsdirectory, for example,kafka/libs．

    In environments that use the Oracle LogMiner implementation, copy only theojdbc8.jarfile. Thexstreams.jarfile is only required in environments that use the Oracle XStreams implementation.

  4. If you are using the XStreams implementation, create an environment variable,LD_LIBRARY_PATH, and set its value to the path to the Instant Client directory, for example:

    LD_LIBRARY_PATH=/path/to/instant_client/

    TheLD_LIBRARY_PATHenvironment variable is not required if you run the Oracle LogMiner implementation.

Debezium Oracle connector configuration

Typically, you register a Debezium Oracle connector by submitting a JSON request that specifies the configuration properties for the connector. The following example shows a JSON request for registering an instance of the Debezium Oracle connector with logical nameserver1at port 1521:

You can choose to produce events for a subset of the schemas and tables in a database. Optionally, you can ignore, mask, or truncate columns that contain sensitive data, that are larger than a specified size, or that you do not need.

Example: Debezium Oracle connector configuration
{ "name": "inventory-connector",(1)"config": { "connector.class" : "io.debezium.connector.oracle.OracleConnector",(2)"database.hostname" : "",(3)"database.port" : "1521",(4)"database.user" : "c##dbzuser",(5)"database.password" : "dbz",(6)"database.dbname" : "ORCLCDB",(7)"database.server.name" : "server1",(8)"tasks.max" : "1",(9)"database.pdb.name" : "ORCLPDB1",(10)"database.history.kafka.bootstrap.servers" : "kafka:9092",(11)"database.history.kafka.topic": "schema-changes.inventory"(12)} }
1 The name that is assigned to the connector when you 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 inCreating users for the connector．
6 The password for the Oracle user, as specified inCreating users for the connector．
7 The name of the database to capture changes from.
8 Logical name that identifies and provides a namespace for the Oracle database server from which the connector captures changes.
9 The maximum number of tasks to create for this connector.
10 The name of the Oracle pluggable database that the connector captures changes from. Used in container database (CDB) installations only.
11 The list of Kafka brokers that this connector uses to write and recover DDL statements to the database history topic.
12 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.

In the previous example, thedatabase.hostnameanddatabase.portproperties are used to define the connection to the database host. However, in more complex Oracle deployments, or in deployments that use TNS names, you can use an alternative method in which you specify a JDBC URL.

The following JSON example shows the same configuration as in the preceding example, except that it uses a JDBC URL to connect to the database.

Example: Debezium Oracle connector configuration that uses a JDBC URL to connect to the database
{" name ": " inventory-connector”、“配置”:{“康涅狄格州ector.class" : "io.debezium.connector.oracle.OracleConnector", "tasks.max" : "1", "database.server.name" : "server1", "database.user" : "c##dbzuser", "database.password" : "dbz", "database.url": "jdbc:oracle:thin:@(DESCRIPTION=(ADDRESS_LIST=(LOAD_BALANCE=OFF)(FAILOVER=ON)(ADDRESS=(PROTOCOL=TCP)(HOST=)(PORT=1521))(ADDRESS=(PROTOCOL=TCP)(HOST=)(PORT=1521)))(CONNECT_DATA=SERVICE_NAME=)(SERVER=DEDICATED)))", "database.dbname" : "ORCLCDB", "database.pdb.name" : "ORCLPDB1", "database.history.kafka.bootstrap.servers" : "kafka:9092", "database.history.kafka.topic": "schema-changes.inventory" } }

Pluggable vs Non-Pluggable 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.

Example: Debezium connector configuration for CDB deployments
{ "config": { "connector.class" : "io.debezium.connector.oracle.OracleConnector", "tasks.max" : "1", "database.server.name" : "server1", "database.hostname" : "", "database.port" : "1521", "database.user" : "c##dbzuser", "database.password" : "dbz", "database.dbname" : "ORCLCDB", "database.pdb.name" : "ORCLPDB1", "database.history.kafka.bootstrap.servers" : "kafka:9092", "database.history.kafka.topic": "schema-changes.inventory" } }

When you configure a Debezium Oracle connector for use with an Oracle CDB, you must specify a value for the propertydatabase.pdb.name, which names the PDB that you want the connector to capture changes from. For non-CDB installation, donotspecify thedatabase.pdb.nameproperty.
Example: Debezium Oracle connector configuration for non-CDB deployments
{ "config": { "connector.class" : "io.debezium.connector.oracle.OracleConnector", "tasks.max" : "1", "database.server.name" : "server1", "database.hostname" : "", "database.port" : "1521", "database.user" : "c##dbzuser", "database.password" : "dbz", "database.dbname" : "ORCLCDB", "database.history.kafka.bootstrap.servers" : "kafka:9092", "database.history.kafka.topic": "schema-changes.inventory" } }

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

You can send this configuration with aPOSTcommand to a running Kafka Connect service. The service records the configuration and starts a connector task that performs the following operations:

  • Connects to the Oracle database.

  • Reads the redo log.

  • Records change events to Kafka topics.

Adding connector configuration

To start running a Debezium Oracle connector, create a connector configuration, and add the configuration to your Kafka Connect cluster.

Prerequisites
Procedure

  1. Create aconfiguration甲骨文的连接器。

  2. Use theKafka Connect REST APIto add that connector configuration to your Kafka Connect cluster.

Results

After the connector starts, itperforms a consistent snapshotof 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.

Connector 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

The following configuration properties arerequiredunless 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 ofio.debezium.connector.oracle.OracleConnector甲骨文的连接器。

任务s.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.

schema_only_recovery

This is a recovery setting for a connector that has already been capturing changes. When you restart the connector, this setting enables recovery of a corrupted or lost database history topic. You might set it periodically to "clean up" a database history topic that has been growing unexpectedly. Database history topics require infinite retention. Note this mode is only safe to be used when it is guaranteed that no schema changes happened since the point in time the connector was shut down before and the point in time the snapshot is taken.

After the snapshot is complete, the connector continues to read change events from the database’s redo logs.

For more information, see thetable ofsnapshot.modeoptions．

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 aROW SHARElevel 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 intable.include.list

An optional, comma-separated list of regular expressions that match the fully-qualified names (．) of the tables to include in a snapshot. The specified items must be named in the connector’stable.include.listproperty. This property takes effect only if the connector’ssnapshot.modeproperty is set to a value other thannever．

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.．例如,

"snapshot.select.statement.overrides": "inventory.products,customers.orders"

For each table in the list, add a further configuration property that specifies theSELECTstatement for the connector to run on the table when it takes a snapshot. The specifiedSELECTstatement determines the subset of table rows to include in the snapshot. Use the following format to specify the name of thisSELECTstatement property:

snapshot.select.statement.overrides.．

例如,snapshot.select.statement.overrides.customers.orders

Example:

From acustomers.orderstable 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 whichdelete_flag = 0．

schema.include.list

No default

An optional, comma-separated list of regular expressions that match names of schemas for which youwantto capture changes. Any schema name not included inschema.include.listis excluded from having its changes captured. By default, all non-system schemas have their changes captured. Do not also set theschema.exclude.listproperty. In environments that use the LogMiner implementation, you must use POSIX regular expressions only.

include.schema.comments

false

Boolean value that specifies whether the connector should parse and publish table and column comments on metadata objects. Enabling this option will bring the implications on memory usage. The number and size of logical schema objects is what largely impacts how much memory is consumed by the Debezium connectors, and adding potentially large string data to each of them can potentially be quite expensive.

schema.exclude.list

No default

An optional, comma-separated list of regular expressions that match names of schemas for which youdo notwant to capture changes. Any schema whose name is not included inschema.exclude.listhas its changes captured, with the exception of system schemas. Do not also set theschema.include.listproperty. In environments that use the LogMiner implementation, you must use POSIX regular expressions only.

table.include.list

No default

一个可选的以逗号分隔的正则表达sions 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:

.

By default, the connector monitors every non-system table in each monitored database. Do not use this property in combination withtable.exclude.list．If you use the LogMiner implementation, use only POSIX regular expressions with this property.

table.exclude.list

No default

一个可选的以逗号分隔的正则表达sions that match fully-qualified table identifiers for tables to be excluded from monitoring. The connector captures change events from any table that is not specified in the exclude list. Specify the identifier for each table using the following format:

.．

Do not use this property in combination withtable.include.list．If you use the LogMiner implementation, use only POSIX regular expressions with this property.

column.include.list

No default

一个可选的以逗号分隔的正则表达sions 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:+
`..

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 thecolumn.exclude.listproperty.

column.exclude.list

No default

一个可选的以逗号分隔的正则表达sions 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:

..

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 thecolumn.include.listproperty.

column.mask.hash.hashAlgorithm．with.salt.salt;column.mask.hash.v2.hashAlgorithm．with.salt.salt

n/a

An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns. Fully-qualified names for columns are of the form．．．

In the resulting change event record, the values for the specified columns are replaced with pseudonyms.

A pseudonym consists of the hashed value that results from applying the specifiedhashAlgorithmandsalt．Based on the hash function that is used, referential integrity is maintained, while column values are replaced with pseudonyms. Supported hash functions are described in theMessageDigest sectionof the Java Cryptography Architecture Standard Algorithm Name Documentation.

In the following example,CzQMA0cB5Kis 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 thehashAlgorithmused, thesaltselected, and the actual data set, the resulting data set might not be completely masked.

Hashing strategy version 2 should be used to ensure fidelity if the value is being hashed in different places or systems.

binary.handling.mode

bytes

Specifies how binary (blob) columns should be represented in change events, including:bytesrepresents binary data as byte array (default),base64represents binary data as base64-encoded String,hexrepresents binary data as hex-encoded (base16) String

decimal.handling.mode

precise

Specifies how the connector should handle floating point values forNUMBER,DECIMALandNUMERICcolumns. You can set one of the following options:

precise(default)

Represents values precisely by usingjava.math.BigDecimalvalues represented in change events in a binary form.

double

Represents values by usingdoublevalues. Usingdoublevalues is easier, but can result in a loss of precision.

string

Encodes values as formatted strings. Using thestringoption is easier to consume, but results in a loss of semantic information about the real type. For more information, seeDecimal types．

interval.handling.mode

numeric

Specifies how the connector should handle values forintervalcolumns:

numericrepresents intervals using approximate number of microseconds.

stringrepresents intervals exactly by using the string pattern representationPYMDTHMS．For example:P1Y2M3DT4H5M6.78S．

event.processing.failure.handling.mode

fail

Specifies how the connector should react to exceptions during processing of events. You can set one of the following options:

fail

Propagates the exception (indicating the offset of the problematic event), causing the connector to stop.

warn

Causes the problematic event to be skipped. The offset of the problematic event is then logged.

skip

Causes the problematic event to be skipped.

max.queue.size

8192

一个正整数的值指定了极限m 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 themax.batch.sizeproperty.

max.batch.size

2048

一个正整数的值指定了极限m 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 adeleteevent is followed by a tombstone event. The following values are possible:

true

For each delete operation, the connector emits adeleteevent and a subsequent tombstone event.

false

For each delete operation, the connector emits only adeleteevent.

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 havelog compactionenabled.

消息。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:

:,

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:

．

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 tablesinventory.customersandpurchase.orders:

inventory.customers:pk1,pk2;(.*).purchaseorders:pk3,pk4

For the tableinventory.customer, the columnspk1andpk2are specified as the message key. For thepurchaseorderstables in any schema, the columnspk3andpk4server 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

一个可选的以逗号分隔的正则表达sions 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:．．．

column.mask.with.length．chars

No default

一个可选的以逗号分隔的正则表达sions 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:．．．

The connector configuration can include multiple properties that specify different lengths.

column.propagate.source.type

No default

一个可选的以逗号分隔的正则表达sions 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.scaleare 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．, or．．．

datatype.propagate.source.type

No default

一个可选的以逗号分隔的正则表达sions 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.lengthand__debezium.source.column.scaleare 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．, or．．．
See thelist 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.
它也可以是有用的在situa设置的属性tions 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 of0prevents 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.．

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

truewhen the connector configuration explicitly specifies thekey.converterorvalue.converterparameters to use Avro, otherwise defaults tofalse．

Specifies whether field names are normalized to comply with Avro naming requirements. For more information, seeAvro naming．

provide.transaction.metadata

false

Set the property totrueif you want Debezium to generate events with transaction boundaries and enriches data events envelope with transaction metadata.

SeeTransaction Metadatafor additional details.

transaction.topic

${database.server.name}.transaction

Controls the name of the topic to which the connector sends transaction metadata messages. The placeholder${database.server.name}can be used for referring to the connector’s logical name; defaults to${database.server.name}.transaction, for exampledbserver1.transaction．

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.

infinispan——这个选项使用嵌入式Infinispan缓存buffer transaction data and persist it to disk. Choose this option if you expect the connector to process long-running or large transactions. When this option is active, the buffer state is persisted across restarts. After a restart, there is no need to recreate the buffer. Since the buffer state is persisted across restarts, the buffer does not need to be recreated at restart. The infinispan option requires that you specify a cache file directory. Use thelog.mining.buffer.locationproperty to define the location for storing cache files.

log.mining.buffer.infinispan.cache.transactions

No default

The XML configuration for the Infinispan transaction cache. For more information, seeInfinispan event buffering．

log.mining.buffer.infinispan.cache.events

No default

The XML configuration for the Infinispan events cache. For more information, seeInfinispan event buffering．

log.mining.buffer.infinispan.cache.processed_transactions

No default

The XML configuration for the Infinispan processed transactions cache. For more information, seeInfinispan event buffering．

log.mining.buffer.infinispan.cache.schema_changes

No default

The XML configuration for the Infinispan schema changes cache.

log.mining.buffer.drop.on.stop

false

Specifies whether the buffer state is deleted after the connector stops in a graceful, expected way.

This setting only impacts buffer implementations that persist state across restarts, such asinfinispan．
默认行为是缓冲国lways retained between restarts.

Set totrueonly in testing or development environments.

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 totrueto 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. Iflog.mining.archive.log.only.modeis 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 to0, 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. 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, (seeDBZ-3123).

log.mining.archive.destination.name

No default

Specifies the configured Oracle archive destination to use when mining archive logs with LogMiner.

自动选择冷杉的默认行为st 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 thanlog.mining.scn.gap.detection.time.interval.max.msthen 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 thanlog.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 totrue．

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 fordatabase.port, and use the specified port value for each address in therac.nodeslist. For example:

    database.port=1521 rac.nodes=192.168.1.100,192.168.1.101

  • Specify a value fordatabase.port, and override the default port for one or more entries in the list. The list can include entries that use the defaultdatabase.portvalue, 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 thedatabase.urlproperty, instead of defining a value fordatabase.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)

  • t(truncate)

By default, no operations are skipped.

signal.data.collection

No default value

Fully-qualified name of the data collection that is used to sendsignalsto the connector.
Use the following format to specify the collection name:
．．

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 ofdatabase.history.*属性,续rol how the connector interacts with the schema history topic.

The following table describes thedatabase.historyproperties for configuring the Debezium connector.

Table 10. 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 isrecovery.attemptsxrecovery.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 isfalse．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; usedatabase.history.store.only.captured.tables.ddlinstead.

false

A Boolean value that specifies whether the connector should record all DDL statements

truerecords only those DDL statements that are relevant to tables whose changes are being captured by Debezium. Set totruewith care because missing data might become necessary if you change which tables have their changes captured.

The safe default isfalse．

database.history.store.only.captured.tables.ddl

false

A Boolean value that specifies whether the connector should record all DDL statements

truerecords only those DDL statements that are relevant to tables whose changes are being captured by Debezium. Set totruewith care because missing data might become necessary if you change which tables have their changes captured.

The safe default isfalse．
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 thedatabase.history.producer.*anddatabase.history.consumer.*prefixes. The pass-through producer and consumer database history properties control a range of behaviors, such as how these clients secure connections with the Kafka broker, as shown in the following example:

database.history.producer.security.protocol=SSL database.history.producer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks database.history.producer.ssl.keystore.password=test1234 database.history.producer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks database.history.producer.ssl.truststore.password=test1234 database.history.producer.ssl.key.password=test1234 database.history.consumer.security.protocol=SSL database.history.consumer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks database.history.consumer.ssl.keystore.password=test1234 database.history.consumer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks database.history.consumer.ssl.truststore.password=test1234 database.history.consumer.ssl.key.password=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 aboutKafka producer configuration propertiesandKafka 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 prefixdatabase.*．例如,the connector passes properties such asdatabase.foobar=falseto the JDBC URL.

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

Monitoring

The Debezium 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.

Please refer to themonitoring documentationfor details of how to expose these metrics via JMX.

Snapshot Metrics

TheMBeanisdebezium.oracle:type=connector-metrics,context=snapshot,server=．

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

The following table lists the shapshot metrics that are available.

Attributes Type Description

LastEvent

string

The last snapshot event that the connector has read.

MilliSecondsSinceLastEvent

long

The number of milliseconds since the connector has read and processed the most recent event.

TotalNumberOfEventsSeen

long

The total number of events that this connector has seen since last started or reset.

NumberOfEventsFiltered

long

The number of events that have been filtered by include/exclude list filtering rules configured on the connector.

CapturedTables

string[]

The list of tables that are captured by the connector.

QueueTotalCapacity

int

The length the queue used to pass events between the snapshotter and the main Kafka Connect loop.

QueueRemainingCapacity

int

The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop.

TotalTableCount

int

The total number of tables that are being included in the snapshot.

RemainingTableCount

int

The number of tables that the snapshot has yet to copy.

SnapshotRunning

boolean

Whether the snapshot was started.

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

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 ifmax.queue.size.in.bytesis 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.

Streaming Metrics

TheMBeanisdebezium.oracle:type=connector-metrics,context=streaming,server=．

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.

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

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 11. 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 formatfilename|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

的小时数交易被保留by the connector’s in-memory buffer without being committed or rolled back before being discarded. Seelog.mining.transaction.retentionfor 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

tran回滚事务的数量saction 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. Seelog.mining.transaction.retention.hoursfor 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 to0．

UnparsableDdlCount

int

The number of DDL records that have been detected but could not be parsed by the DDL parser. This should always be0; 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

当前矿业会话的用户(佐治亚大学全球区域) 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.

Schema History Metrics

TheMBeanisdebezium.oracle:type=connector-metrics,context=schema-history,server=．

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

Attributes Type Description

Status

string

One ofSTOPPED,RECOVERING(recovering history from the storage),RUNNING描述了历史数据库的状态。开云体育电动老虎机

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.

Surrogate schema evolution

The Oracle connector automatically tracks and applies table schema changes by parsing DDL from the redo logs. If the DDL parser encounters an incompatible statement, if needed, the connector provides an alternative way to apply the schema change.

By default, the connector stops when it encounters a DDL statement that it cannot parse. You can use Debeziumsignalingto trigger the update of the database schema from such DDL statements.

The type of the schema update action isschema-changes．This action updates the schema of all tables enumerated in the signal parameters. The message does not contain the update to the schema. Instead, it contains the complete new schema structure.

Table 12. Action parameters
Name Description

开云体育电动老虎机

The name of the Oracle database.

schema

The name of the schema where changes are applied.

changes

An array containing the requested schema updates.

changes.type

Type of the schema change, usuallyALTER

changes.id

The fully-qualified name of the table

changes.table

The fully-qualified name of the table

changes.table.defaultCharsetName

The character set name used for the table if different from database default

changes.table.primaryKeyColumnNames

Array with the name of columns composing the primary key

changes.table.columns

Array with the column metadata

…columns.name

The name of the column

…columns.jdbcType

The JDBC type of the column as defined atJDBC API

…columns.typeName

The name of the column type

…columns.typeExpression

The full column type definition

…columns.charsetName

The column character set if different from the default

…columns.length

The length/size constraint of the column

…columns.scale

The scale of numeric column

…columns.position

The position of the column in the table starting with1

…columns.optional

Booleantrueif column value is not mandatory

…columns.autoIncremented

Booleantrueif column value is automatically calculated from a sequence

…columns.generated

Booleantrueif column value is automatically calculated

After theschema-changessignal is inserted, the connector must be restarted with an altered configuration that includes specifying thedatabase.history.skip.unparseable.ddloption astrue．After the connector’s commit SCN advances beyond the DDL change, to prevent unparseable DDL statements from being skipped unexpectedly, return the connector configuration to its previous state.

Table 13. Example of a logging record
Column Value

id

924e3ff8-2245-43ca-ba77-2af9af02fa07

type

schema-changes

data

 
{ "database":"ORCLPDB1", "schema":"DEBEZIUM", "changes":[ { "type":"ALTER", "id":"\"ORCLPDB1\".\"DEBEZIUM\".\"CUSTOMER\"", "table":{ "defaultCharsetName":null, "primaryKeyColumnNames":[ "ID", "NAME" ], "columns":[ { "name":"ID", "jdbcType":2, "typeName":"NUMBER", "typeExpression":"NUMBER", "charsetName":null, "length":9, "scale":0, "position":1, "optional":false, "autoIncremented":false, "generated":false }, { "name":"NAME", "jdbcType":12, "typeName":"VARCHAR2", "typeExpression":"VARCHAR2", "charsetName":null, "length":1000, "position":2, "optional":true, "autoIncremented":false, "generated":false }, { "name":"SCORE", "jdbcType":2, "typeName":"NUMBER", "typeExpression":"NUMBER", "charsetName":null, "length":6, "scale":2, "position":3, "optional":true, "autoIncremented":false, "generated":false }, { "name":"REGISTERED", "jdbcType":93, "typeName":"TIMESTAMP(6)", "typeExpression":"TIMESTAMP(6)", "charsetName":null, "length":6, "position":4, "optional":true, "autoIncremented":false, "generated":false } ] } } ] }

XStreams support

The Debezium Oracle connector by default ingests changes using native Oracle LogMiner. The connector can be toggled to use Oracle XStream instead. To configure the connector to use Oracle XStream, you must apply specific database and connector configurations that differ from those that you use with LogMiner.

Prerequisites

  • To use the XStream API, you must have a license for the GoldenGate product. Installing GoldenGate is not required.

Preparing the Database

Configuration needed for Oracle XStream
ORACLE_SID=ORCLCDB dbz_oracle sqlplus /nolog CONNECT sys/top_secret AS SYSDBA alter system set db_recovery_file_dest_size = 5G; alter system set db_recovery_file_dest = '/opt/oracle/oradata/recovery_area' scope=spfile; alter system set enable_goldengate_replication=true; shutdown immediate startup mount alter database archivelog; alter database open; -- Should show "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 thebeforestate 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;

Creating XStream users for the connector

The Debezium 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.

Creating an XStream Administrator user
sqlplus sys/top_secret@//localhost:1521/ORCLCDB as sysdba CREATE TABLESPACE xstream_adm_tbs DATAFILE '/opt/oracle/oradata/ORCLCDB/xstream_adm_tbs.dbf' SIZE 25M REUSE AUTOEXTEND ON MAXSIZE UNLIMITED; exit; sqlplus sys/top_secret@//localhost:1521/ORCLPDB1 as sysdba CREATE TABLESPACE xstream_adm_tbs DATAFILE '/opt/oracle/oradata/ORCLCDB/ORCLPDB1/xstream_adm_tbs.dbf' SIZE 25M REUSE AUTOEXTEND ON MAXSIZE UNLIMITED; exit; sqlplus sys/top_secret@//localhost:1521/ORCLCDB as sysdba CREATE USER c##dbzadmin IDENTIFIED BY dbz DEFAULT TABLESPACE xstream_adm_tbs QUOTA UNLIMITED ON xstream_adm_tbs CONTAINER=ALL; GRANT CREATE SESSION, SET CONTAINER TO c##dbzadmin CONTAINER=ALL; BEGIN DBMS_XSTREAM_AUTH.GRANT_ADMIN_PRIVILEGE( grantee => 'c##dbzadmin', privilege_type => 'CAPTURE', grant_select_privileges => TRUE, container => 'ALL' ); END; / exit;
Creating the connector’s XStream user
sqlplus sys/top_secret@//localhost:1521/ORCLCDB as sysdba CREATE TABLESPACE xstream_tbs DATAFILE '/opt/oracle/oradata/ORCLCDB/xstream_tbs.dbf' SIZE 25M REUSE AUTOEXTEND ON MAXSIZE UNLIMITED; exit; sqlplus sys/top_secret@//localhost:1521/ORCLPDB1 as sysdba CREATE TABLESPACE xstream_tbs DATAFILE '/opt/oracle/oradata/ORCLCDB/ORCLPDB1/xstream_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 xstream_tbs QUOTA UNLIMITED ON xstream_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_CATALOG_ROLE TO c##dbzuser CONTAINER=ALL; GRANT EXECUTE_CATALOG_ROLE TO c##dbzuser CONTAINER=ALL; exit;

Create an XStream Outbound Server

Create anXStream Outbound server(given the right privileges, this might be done automatically by the connector going forward, seeDBZ-721):

Create an XStream Outbound Server
sqlplus c##dbzadmin/dbz@//localhost:1521/ORCLCDB DECLARE tables DBMS_UTILITY.UNCL_ARRAY; schemas DBMS_UTILITY.UNCL_ARRAY; BEGIN tables(1) := NULL; schemas(1) := 'debezium'; DBMS_XSTREAM_ADM.CREATE_OUTBOUND( server_name => 'dbzxout', table_names => tables, schema_names => schemas); END; / exit;

When setting up an XStream Outbound Server to capture changes from a pluggable database, thesource_container_nameparameter should be provided specifying the pluggable database name.
Configure the XStream user account to connect to the XStream Outbound Server
sqlplus sys/top_secret@//localhost:1521/ORCLCDB as sysdba BEGIN DBMS_XSTREAM_ADM.ALTER_OUTBOUND( server_name => 'dbzxout', connect_user => 'c##dbzuser'); END; / exit;

A single XStream Outbound server cannot be shared by multiple Debezium Oracle connectors. Each connector requires a unique XStream Outbound connector to be configured.

Configuring the XStream adapter

By default, Debezium uses Oracle LogMiner to ingest change events from Oracle. You can adjust the connector configuration to enable the connector to use the Oracle XStreams adapter.

The following configuration example adds the propertiesdatabase.connection.adapteranddatabase.out.server.nameto enable the connector to use the XStream API implementation.

{" name ": " inventory-connector”、“配置”:{“康涅狄格州ector.class" : "io.debezium.connector.oracle.OracleConnector", "tasks.max" : "1", "database.server.name" : "server1", "database.hostname" : "", "database.port" : "1521", "database.user" : "c##dbzuser", "database.password" : "dbz", "database.dbname" : "ORCLCDB", "database.pdb.name" : "ORCLPDB1", "database.history.kafka.bootstrap.servers" : "kafka:9092", "database.history.kafka.topic": "schema-changes.inventory", "database.connection.adapter": "xstream", "database.out.server.name" : "dbzxout" } }

XStream connector properties

The following configuration properties arerequiredwhen using XStreams unless a default value is available.

Property

Default

Description

database.out.server.name

No default

Name of the XStream outbound server configured in the database.

Behavior when things go wrong

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 providesexactly oncedelivery 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, providesat least oncedelivery of change events.

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

Logs do not contain offset, perform a new snapshot

In some cases, after the Debezium Oracle connector restarts, it reports the following error:

Online REDO LOG files or archive logs do not contain the offset scn xxxxxxx. Please perform a new snapshot.

After the connector examines the redo and archive logs, if it cannot find the SCN that is recorded in the connector offsets, it returns the preceding error. Because the connector uses the SCN to determine where to resume processing, if the expected SCN if not found, a new snapshot must be completed.

You might find that theV$ARCHIVED_LOGtable contains a record with an SCN that matches the expected range. However, the record might not be available for mining. To be available for mining, a record must include a filename in theNAMEcolumn, a value ofNOin theDELETEDcolumn, and a value ofA(available) in theSTATUScolumn. If a record does not match any of these criteria, it is considered incomplete and cannot be mined.

At a minimum, archive logs must be retained for as long as the longest downtime window of the connector.

Records that have no value in theNAMEcolumn no longer exist in the file system. In such records, the value of theDELETEDfield is set toYES, and theSTATUSfield is set toDto indicate that the log is deleted.

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

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

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

选择从DBA_TABLES IOT_NAME所有者= ' < tablespace-owner>' AND TABLE_NAME=''

The connector’stable.include.listortable.exclude.listconfiguration 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 theSYSandSYSTEMaccounts for lots of internal changes and therefore the connector automatically filters changes made by these users when fetching changes from LogMiner. Never use theSYSorSYSTEMuser accounts for changes to be emitted by the Debezium Oracle connector.