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A standard i. This stream type performs a join on inserted and deleted rows in the change set to provide the row level delta. As a net effect, for example, a row that is inserted and then deleted between two transactional points of time in a table is removed in the delta i.
An append-only table stream tracks row inserts only. Update and delete operations including table truncates are not recorded.
For example, if 10 rows are inserted into a table and then 5 of those rows are deleted before the offset for an append-only stream is advanced, the stream records 10 rows.
An append-only stream returns the appended rows only and therefore can be much more performant than a standard stream for extract, load, transform ELT and similar scenarios that depend exclusively on row inserts.
For example, the source table can be truncated immediately after the rows in an append-only stream are consumed, and the record deletions do not contribute to the overhead the next time the stream is queried or consumed.
Supported on external tables only. An insert-only stream tracks row inserts only; they do not record delete operations that remove rows from an inserted set i.
For example, in-between any two offsets, if File1 is removed from the cloud storage location referenced by the external table, and File2 is added, the stream returns records for the rows in File2 only.
Unlike when tracking CDC data for standard tables, Snowflake cannot access the historical records for files in cloud storage. Overwritten files are essentially handled as new files: The old version of the file is removed from cloud storage, but the insert-only stream does not record the delete operation.
The new version of the file is added to cloud storage, and the insert-only stream records the rows as inserts. The stream does not record the diff of the old and new file versions.
Support for insert-only table streams is provided as a preview feature. The following diagram shows how the contents of a standard table stream change as rows in the source table are updated.
Whenever a DML statement consumes the stream contents, the stream position advances to track the next set of DML changes to the table i. A stream becomes stale when its offset is outside of the data retention period for its source table.
When a stream becomes stale, the historical data for the source table is no longer accessible, including any unconsumed change records.
To prevent a stream from becoming stale, consume the stream records within a transaction during the retention period for the table. If the data retention period for a source table is less than 14 days , and a stream has not been consumed, Snowflake temporarily extends this period to prevent it from going stale.
When the stream is consumed, the extended data retention period is reduced to the default period for the table.
Currently, when a database or schema that contains a source table and stream is cloned, any unconsumed records in the stream in the clone are inaccessible.
This behavior is consistent with Time Travel for tables. Multiple tasks that consume change data from a single table stream retrieve different deltas.
When a task consumes the change data in a stream using a DML statement, the stream advances the offset. The change data is no longer available for the next task to consume.
Currently, we recommend that only a single task consumes the change data from a stream. Multiple streams can be created for the same table and consumed by different tasks.
The CHANGES clause enables querying change tracking metadata between two points in time without having to create a table stream with an explicit transactional offset.
Multiple queries can retrieve the change tracking metadata between different transactional start and endpoints. A stream stores the current transactional version of a table and is the appropriate source of CDC records in most scenarios.
Currently, either of the following must be true before change tracking metadata is recorded for a table:. Either option adds a pair of hidden columns to the table and begins storing change tracking metadata.
The columns consume a small amount of storage. No change tracking metadata for the table is available for the period before one of these conditions is satisfied.
Creating streams on shared tables enables data consumers to track data manipulation language DML changes made in those tables.
This topic describes the steps for data providers to configure shared tables for stream creation and for consumers to create the streams. Working with Shares.
You can use all of the capabilities of Oracle Streams at the same time. If your needs change, then you can implement a new capability of Oracle Streams without sacrificing existing capabilities.
Using Oracle Streams, you control what information is put into a stream, how the stream flows or is routed from database to database, what happens to messages in the stream as they flow into each database, and how the stream terminates.
By configuring specific capabilities of Oracle Streams, you can address specific requirements. Based on your specifications, Oracle Streams can capture, stage, and manage messages in the database automatically, including, but not limited to, data manipulation language DML changes and data definition language DDL changes.
You can also put user-defined messages into a stream, and Oracle Streams can propagate the information to other databases or applications automatically.
When messages reach a destination, Oracle Streams can consume them based on your specifications. Oracle Streams provides two ways to capture database changes implicitly: capture process es and synchronous capture s.
A synchronous capture can capture DML changes made to tables. Rules determine which changes are captured by a capture process or synchronous capture.
Database changes are recorded in the redo log for the database. A capture process captures changes from the redo log and formats each captured change into a message called a logical change record LCR.
The messages captured by a capture process are called captured LCR s. A synchronous capture uses an internal mechanism to capture changes and format each captured change into an LCR.
The messages captured by a synchronous capture are called persistent LCR s. The rule s used by a capture process or a synchronous capture determine which changes it captures.
When changes are captured by a capture process, the database where changes are generated in the redo log is the source database.
When changes are captured by a synchronous capture, the database where the synchronous capture is configured is the source database.
A capture process can capture changes locally at the source database, or it can capture changes remotely at a downstream database.
A synchronous capture can only capture changes locally at the source database. Both a capture process and a synchronous capture enqueue logical change records LCRs into a queue.
When a capture process or a synchronous capture captures changes, it is referred to as implicit capture. Users and applications can also enqueue messages manually.
These messages can be LCRs, or they can be messages of a user-defined type called user message s. When users and applications enqueue messages manually, it is referred to as explicit capture.
Messages are stored or staged in a queue. These message s can be logical change records LCRs or user messages.
A typed queue can stage messages of one specific type only. Oracle Streams propagation s can propagate message s from one queue to another.
These queues can be in the same database or in different databases. Rules determine which messages are propagated by a propagation. Oracle Streams enables you to configure an environment in which changes are shared through directed networks.
In a directed network, propagated message s pass through one or more intermediate databases before arriving at a destination database where they are consumed.
The messages might or might not be consumed at an intermediate database in addition to the destination database. Using Oracle Streams, you can choose which messages are propagated to each destination database, and you can specify the route messages will traverse on their way to a destination database.
A message is consumed when it is dequeued from a queue. An apply process can dequeue messages implicitly. A user, application, or messaging client can dequeue messages explicitly.
The database where messages are consumed is called the destination database. In some configurations, the source database and the destination database can be the same.
Rules determine which messages are dequeued and processed by an apply process. Rules determine which messages are dequeued by a messaging client.
A messaging client dequeues messages when it is invoked by an application or a user. An apply process detects conflicts automatically when directly applying LCRs in a replication environment.
A conflict is a mismatch between the old values in an LCR and the expected data in a table. Typically, a conflict results when the same row in the source database and destination database is changed at approximately the same time.
When a conflict occurs, you need a mechanism to ensure that the conflict is resolved in accordance with your business rules.
Oracle Streams offers a variety of prebuilt conflict handlers. Using these prebuilt handlers, you can define a conflict resolution system for each of your databases that resolves conflicts in accordance with your business rules.
If you have a unique situation that prebuilt conflict resolution handlers cannot resolve, then you can build your own conflict resolution handlers.
If a conflict is not resolved, or if a handler procedure raises an error, then all message s in the transaction that raised the error are saved in the error queue for later analysis and possible reexecution.
Oracle Streams Replication Administrator's Guide. A rule-based transformation is any modification to a message that results when a rule in a positive rule set evaluates to TRUE.
There are two types of rule-based transformations: declarative and custom. Declarative rule-based transformations cover a set of common transformation scenarios for row LCRs, including renaming a schema, renaming a table, adding a column, renaming a column, and deleting a column.
A custom rule-based transformation can modify either LCR s or user message s. For example, a custom rule-based transformation can change the data type of a particular column in an LCR.
During enqueue of a message by a capture process , which can be useful for formatting a message in a manner appropriate for all destination database s.
During propagation of a message, which can be useful for transforming a message before it is sent to a specific remote site. During dequeue of a message by an apply process or messaging client , which can be useful for formatting a message in a manner appropriate for a specific destination database.
When a transformation is performed during apply, an apply process can apply the transformed message directly or send the transformed message to an apply handler for processing.
A rule must be in a positive rule set for its rule-based transformation to be invoked. A rule-based transformation specified for a rule in a negative rule set is ignored by capture process es, propagation s, apply process es, and messaging client s.
Throughout this document, "rule-based transformation" is used when the text applies to both declarative and custom rule-based transformations.
This document distinguishes between the two types of rule-based transformations when necessary. Every redo entry in the redo log has a tag associated with it.
The data type of the tag is RAW. By default, when a user or application generates redo entries, the value of the tag is NULL for each redo entry, and a NULL tag consumes no space in the redo entry.
The size limit for a tag value is bytes. In Oracle Streams, rule s can have conditions relating to tag values to control the behavior of Oracle Streams client s.
For example, a tag can be used to determine whether an LCR contains a change that originated in the local database or at a different database, so that you can avoid change cycling sending an LCR back to the database where it originated.
Also, a tag can be used to specify the set of destination database s for each LCR. Tags can be used for other LCR tracking purposes as well.
You can specify Oracle Streams tags for redo entries generated by a certain session or by an apply process. These tags then become part of the LCRs captured by a capture process or synchronous capture.
Typically, tags are used in Oracle Streams replication environments, but you can use them whenever it is necessary to track database changes and LCRs.
In addition to information sharing between Oracle databases, Oracle Streams supports heterogeneous information sharing between Oracle databases and non-Oracle databases.
Oracle Streams can capture data manipulation language DML and data definition language DDL changes made to database objects and replicate those changes to one or more other databases.
An Oracle Streams capture process or synchronous capture captures changes made to source database objects and formats them into LCRs, which can be propagated to destination database s and then applied by Oracle Streams apply process es.
The destination databases can allow DML and DDL changes to the same database objects, and these changes might or might not be propagated to the other databases in the environment.
In other words, you can configure an Oracle Streams environment with one database that propagates changes, or you can configure an environment where changes are propagated between databases bidirectionally.
Also, the tables for which data is shared do not need to be identical copies at all databases. Both the structure and the contents of these tables can differ at different databases, and the information in these tables can be shared between these databases.
Data warehouse loading is a special case of data replication. Some of the most critical tasks in creating and maintaining a data warehouse include refreshing existing data, and adding new data from the operational databases.
Oracle Streams components can capture changes made to a production system and send those changes to a staging database or directly to a data warehouse or operational data store.
Oracle Streams capture of redo data with a capture process avoids unnecessary overhead on the production systems. Support for data transformations and user-defined apply procedures enables the necessary flexibility to reformat data or update warehouse-specific data fields as data is loaded.
In addition, Change Data Capture uses some of the components of Oracle Streams to identify data that has changed so that this data can be loaded into a data warehouse.
Oracle Database Data Warehousing Guide for more information about data warehouses. You can use the features of Oracle Streams to achieve little or no database down time during database upgrade and maintenance operations.
Maintenance operations include migrating a database to a different platform, migrating a database to a different character set, modifying database schema objects to support upgrades to user-created applications, and applying an Oracle software patch.
Oracle Streams Advanced Queuing AQ enables user applications to enqueue message s into a queue , propagate messages to subscribing queues, notify user applications that messages are ready for consumption , and dequeue messages at the destination.
Oracle Streams AQ supports all the standard features of message queuing systems, including multiconsumer queues, publish and subscribe, content-based routing, Internet propagation, transformations, and gateways to other messaging subsystems.
You can create a queue at a database, and applications can enqueue messages into the queue explicitly. Subscribing applications or messaging client s can dequeue messages directly from this queue.
If an application is remote, then a queue can be created in a remote database that subscribes to messages published in the source queue.
The destination application can dequeue messages from the remote queue. MD5: b3abeeb62fdc SHA edf3e6ea8d0efbdc MD5: bb9b7fa2a0acefd5fcdff. SHA aafcd93d11f2acb4c73b8.
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