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MD5: cc3d1cae2abc7bdcad. The offset for stream s1 is currently between table versions v3 and v4. When the stream is queried or consumed , the records returned include all transactions between table version v4 , the version immediately after the stream offset in the table timeline, and v10 , the most recent committed table version in the timeline, inclusive.
A stream provides the minimal set of changes from its current offset to the current version of the table. Multiple queries can independently consume the same change data from a stream without changing the offset.
A stream advances the offset only when it is used in a DML transaction. This behavior applies to both explicit and autocommit transactions.
By default, when a DML statement is executed, an autocommit transaction is implicitly started and the transaction is committed at the completion of the statement.
Querying a stream alone does not advance its offset, even within an explicit transaction; the stream contents must be consumed in a DML statement.
When a SQL statement queries a stream within an explicit transaction, the stream is queried at the stream advance point i.
A DML statement that selects from a stream consumes all of the change data in the stream as long as the transaction commits successfully.
To ensure multiple statements access the same change records in the stream, surround them with an explicit transaction statement BEGIN..
This locks the stream. DML updates to the source table in parallel transactions are tracked by the change tracking system but do not update the stream until the explicit transaction statement is committed and the existing change data is consumed.
Streams support repeatable read isolation. In repeatable read mode, multiple SQL statements within a transaction see the same set of records in a stream.
This differs from the read committed mode supported for tables, in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.
The delta records returned by streams in a transaction is the range from the current position of the stream until the transaction start time.
The stream position advances to the transaction start time if the transaction commits; otherwise it stays at the same position.
Query stream s1 on table t1. The stream returns the change data capture records. If the stream is used in a DML statement. Update rows in table t1. Query stream s1.
Returns the same state of stream when it was used at Time 2. Commit transaction. If the stream was consumed in DML statements within the transaction, the stream position advances to the transaction start time.
Results include table changes committed by Transaction 1. Within Transaction 1, all queries to stream s1 see the same set of records.
DML changes to table t1 are recorded to the stream only when the transaction is committed. In Transaction 2, queries to the stream see the changes recorded to the table in Transaction 1.
Note that if Transaction 2 had begun before Transaction 1 was committed, queries to the stream would have returned a snapshot of the stream from the position of the stream to the beginning time of Transaction 2 and would not see any changes committed by Transaction 1.
A stream stores data in the same shape as the source table i. Note that streams record the differences between two offsets.
If a row is added and then updated in the current offset, the delta change is a new row. Specifies the unique and immutable ID for the row, which can be used to track changes to specific rows over time.
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.
Stream Consumption Using Tasks. Streams on Shared Tables. Required Access Privileges. When created, a table stream logically takes an initial snapshot of every row in the source table by initializing a point in time called an offset as the current transactional version of the table.
The change tracking system utilized by the stream then records information about the DML changes after this snapshot was taken.
Change records provide the state of a row before and after the change. Change information mirrors the column structure of the tracked source table and includes additional metadata columns that describe each change event.
Note that a stream itself does not contain any table data. A stream only stores the offset for the source table and returns CDC records by leveraging the versioning history for the source table.
When the first stream for a table is created, a pair of hidden columns are added to the source table and begin storing change tracking metadata.
These columns consume a small amount of storage. The CDC records returned when querying a stream rely on a combination of the offset stored in the stream and the change tracking metadata stored in the table.
It may be useful to think of a stream as a bookmark, which indicates a point in time in the pages of a book i. A bookmark may be thrown away and other bookmarks inserted in different places in a book.
So too, a stream may be dropped and other streams created at the same or different points of time either by creating the streams consecutively at different times or by using Time Travel to consume the change records for a table at the same or different offsets.
One example of a consumer of CDC records is a data pipeline , in which only the data in staging tables that has changed since the last extraction is transformed and copied into other tables.
A new table version is created whenever a transaction that includes one or more DML statements is committed to the table. In the transaction history for a table, a stream offset is located between two table versions.
Querying a stream returns the changes caused by transactions committed after the offset and at or before the current time.
The following example shows a source table with 10 committed versions in the timeline. The offset for stream s1 is currently between table versions v3 and v4.
When the stream is queried or consumed , the records returned include all transactions between table version v4 , the version immediately after the stream offset in the table timeline, and v10 , the most recent committed table version in the timeline, inclusive.
A stream provides the minimal set of changes from its current offset to the current version of the table. Multiple queries can independently consume the same change data from a stream without changing the offset.
A stream advances the offset only when it is used in a DML transaction. This behavior applies to both explicit and autocommit transactions.
By default, when a DML statement is executed, an autocommit transaction is implicitly started and the transaction is committed at the completion of the statement.
Querying a stream alone does not advance its offset, even within an explicit transaction; the stream contents must be consumed in a DML statement.
When a SQL statement queries a stream within an explicit transaction, the stream is queried at the stream advance point i. A DML statement that selects from a stream consumes all of the change data in the stream as long as the transaction commits successfully.
To ensure multiple statements access the same change records in the stream, surround them with an explicit transaction statement BEGIN..
This locks the stream. DML updates to the source table in parallel transactions are tracked by the change tracking system but do not update the stream until the explicit transaction statement is committed and the existing change data is consumed.
Streams support repeatable read isolation. In repeatable read mode, multiple SQL statements within a transaction see the same set of records in a stream.
This differs from the read committed mode supported for tables, in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.
The delta records returned by streams in a transaction is the range from the current position of the stream until the transaction start time. The stream position advances to the transaction start time if the transaction commits; otherwise it stays at the same position.
Query stream s1 on table t1. The stream returns the change data capture records. Table helps you find conceptual information about Oracle Streams.
Oracle Streams Replication Administrator's Guide for information about apply processes that is specific to replication environments, such as information about applying changes with dependencies and applying DML and DDL changes.
Oracle Streams Replication Administrator's Guide for information about capture processes that is specific to replication environments, such as information about supplemental logging.
Oracle Streams Advanced Queuing User's Guide for detailed information about capturing messages with applications. Oracle Streams Replication Administrator's Guide for detailed information about comparing database objects at two different databases and converging differences in these database objects.
Oracle Streams Replication Administrator's Guide for detailed information about conflicts and conflict resolution.
Oracle Streams Advanced Queuing User's Guide for detailed information about consuming messages with applications.
Oracle Streams Replication Administrator's Guide for detailed information about working with non-Oracle databases. Oracle Database High Availability Overview for information about your high availability options.
Chapter 29, "Information Provisioning Concepts" for information about moving or copying large amounts of information efficiently.
Oracle Streams Replication Administrator's Guide for detailed information about instantiation. Chapter 7, "Rule-Based Transformations" for detailed information about rule-based transformations.
Oracle Streams Replication Administrator's Guide for detailed information about tags. You can set up many different types of Oracle Streams environments, and you have several options for setting them up.
Table helps you find the documentation you need to set up an Oracle Streams environment. Oracle Streams Replication Administrator's Guide for step-by-step instructions to set up an Oracle Streams replication environment by configuring individual components in the correct order.
Oracle Streams Replication Administrator's Guide for the following examples:. An example that provides step-by-step instructions for setting up a simple replication environment that replicates changes to a single table.
An example that provides step-by-step instructions for setting up a heterogeneous replication environment that includes a rule-based transformation.
An example that provides step-by-step instructions for setting up an n-way replication environment with conflict resolution.
Oracle Streams Replication Administrator's Guide for step-by-step instructions to extend an Oracle Streams replication environment by configuring individual components in the correct order.
Oracle Streams Replication Administrator's Guide for an example that provides step-by-step instructions for extending a heterogeneous replication environment.
An example that provides step-by-step instructions for setting up a messaging environment that sends messages between databases. An example that provides step-by-step instructions for setting up message notifications that inform applications when new messages are in a queue.
Oracle Streams Advanced Queuing User's Guide for detailed instructions about setting up messaging environments. Oracle Streams Replication Administrator's Guide for information about the best practices to follow when setting up an Oracle Streams environment.
Table helps you find the documentation you need to manage an Oracle Streams environment. Oracle Enterprise Manager online help for information about managing apply handlers and apply tags using Oracle Enterprise Manager, and about dropping apply processes using Oracle Enterprise Manager.
Chapter 16, "Managing Oracle Streams Information Consumption" for information about managing apply processes using Oracle-supplied packages.
Oracle Streams Replication Administrator's Guide for information about managing apply processes in a replication environment using Oracle-supplied packages.
Oracle Streams Replication Administrator's Guide for information about managing supplemental logging.
Oracle Streams Replication Administrator's Guide for information about avoiding conflicts and configuring conflict resolution.
Oracle Streams Replication Administrator's Guide for information about performing instantiations. Oracle Streams Replication Administrator's Guide for information about the best practices to follow when managing an Oracle Streams environment.
Oracle Enterprise Manager online help for information about scheduling, unscheduling, and dropping propagations using Oracle Enterprise Manager.
Oracle Streams Advanced Queuing User's Guide for information about managing propagations using Oracle-supplied packages and other administrative interfaces.
Oracle Enterprise Manager online help for information about managing queues, queue tables, and Advanced Queuing transformations using Oracle Enterprise Manager.
Oracle Streams Advanced Queuing User's Guide for information about managing queues using Oracle-supplied packages and other administrative interfaces.
Oracle Enterprise Manager online help for information about managing rules using Oracle Enterprise Manager. Chapter 17, "Managing Rules" for information about managing rules using Oracle-supplied packages.
Oracle Enterprise Manager online help for information about managing rule-based transformations using Oracle Enterprise Manager.
Chapter 18, "Managing Rule-Based Transformations" for information about managing rule-based transformations using Oracle-supplied packages. Oracle Enterprise Manager online help for information about troubleshooting an Oracle Streams environment using Oracle Enterprise Manager.
Chapter 20, "Troubleshooting an Oracle Streams Environment" for general information about troubleshooting an Oracle Streams environment. Oracle Streams Replication Administrator's Guide for information about troubleshooting an Oracle Streams replication environment.
Oracle Streams Replication Administrator's Guide for information about splitting off an unavailable destination database from a replication environment and merging the database back into the replication environment when it becomes available again.
Oracle Enterprise Manager online help for information about monitoring apply process parameters, apply handler s, and apply errors using Oracle Enterprise Manager.
Chapter 25, "Monitoring Oracle Streams Apply Processes" for information about monitoring apply processes using data dictionary views. Oracle Streams Replication Administrator's Guide for information about monitoring apply processes in a replication environment using data dictionary views.
Oracle Enterprise Manager online help for information about monitoring capture process parameters using Oracle Enterprise Manager.
Oracle Streams Replication Administrator's Guide for information about monitoring supplemental logging using data dictionary views.
Oracle Streams Replication Administrator's Guide for information about monitoring conflict detection and update conflict handlers using data dictionary views.
Oracle Database Reference. Oracle Streams Advanced Queuing User's Guide for information about monitoring messaging environments using data dictionary views.
Oracle Streams topology and performance statistics. Oracle Enterprise Manager online help for information about monitoring rules using Oracle Enterprise Manager.
Chapter 26, "Monitoring Rules" for information about monitoring rules using data dictionary views.
Chapter 27, "Monitoring Rule-Based Transformations" for information about monitoring rule-based transformations using data dictionary views.
Note: Oracle Enterprise Manager currently does not support monitoring synchronous captures. Oracle Streams Replication Administrator's Guide for information about monitoring tags using data dictionary views.
You can use Oracle Streams to achieve little or no down time for one-time operations, such as upgrading a database. Table helps you find the documentation you need to perform one-time operations with Oracle Streams.
Appendix D, "Online Database Upgrade with Oracle Streams" for information about using Oracle Streams to perform a database upgrade with little or no down time.
Appendix E, "Online Database Maintenance with Oracle Streams" for information about using Oracle Streams to perform a database maintenance operations with little or no down time.
These database 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 Database software patch or patch set.
What Are the Uses of Oracle Streams? Figure shows the Oracle Streams information flow. What Can Oracle Streams Do? Stage Messages in a Queue Messages are stored or staged in a queue.
Propagate Messages from One Queue to Another Oracle Streams propagation s can propagate message s from one queue to another.
Consume Messages A message is consumed when it is dequeued from a queue. Detect and Resolve Conflicts An apply process detects conflicts automatically when directly applying LCRs in a replication environment.
Transform Messages A rule-based transformation is any modification to a message that results when a rule in a positive rule set evaluates to TRUE.
Either type of rule-based transformation can occur at the following times: 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.
Note: A rule must be in a positive rule set for its rule-based transformation to be invoked. Share Information With Non-Oracle Databases In addition to information sharing between Oracle databases, Oracle Streams supports heterogeneous information sharing between Oracle databases and non-Oracle databases.
Data Replication 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.
Data Warehouse Loading Data warehouse loading is a special case of data replication. Database Availability During Upgrade and Maintenance Operations You can use the features of Oracle Streams to achieve little or no database down time during database upgrade and maintenance operations.
Message Queuing 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.
Event Management and Notification Business events are valuable communications between applications or organizations.
Data Protection One solution for data protection is to create a local or remote copy of a production database. Sample Replication Configuration With Downstream Capture Figure shows a sample replication configuration that uses a downstream capture process.
Sample Replication Configuration That Uses Synchronous Captures Figure shows a sample replication configuration that uses synchronous captures to capture changes instead of capture processes.
Sample Configuration That Performs Capture and Apply in a Single Database Figure shows a sample configuration that captures database changes with a capture process and applies these changes with an apply process in a single database.
Sample Messaging Configuration Figure shows a sample messaging configuration. Administration Tools for an Oracle Streams Environment Several tools are available for configuring, administering, and monitoring your Oracle Streams environment.
Oracle Streams Documentation Roadmap Oracle Streams provides many options for setting up, managing, and monitoring information-sharing environments.
Documentation About Setting Up or Extending an Oracle Streams Environment You can set up many different types of Oracle Streams environments, and you have several options for setting them up.
Documentation About Database Using Oracle Streams for Upgrade and Maintenance You can use Oracle Streams to achieve little or no down time for one-time operations, such as upgrading a database.
Table Documentation About Data Upgrade and Maintenance With Oracle Streams For instructions about See upgrading a database and using Oracle Streams to achieve little or no down time Appendix D, "Online Database Upgrade with Oracle Streams" for information about using Oracle Streams to perform a database upgrade with little or no down time performing database maintenance operations and using Oracle Streams to achieve little or no down time Appendix E, "Online Database Maintenance with Oracle Streams" for information about using Oracle Streams to perform a database maintenance operations with little or no down time.
Oracle Streams best practices. Oracle Streams interoperability with other Oracle Database components.
Appendix B, "Oracle Streams Restrictions". Chapter 30, "Using Information Provisioning". Oracle-supplied packages related to Oracle Streams. Oracle Enterprise Manager online help for information about managing rules using Oracle Enterprise Manager Chapter 17, "Managing Rules" for information about managing rules using Oracle-supplied packages.
Oracle Enterprise Manager online help for information about managing rule-based transformations using Oracle Enterprise Manager Chapter 18, "Managing Rule-Based Transformations" for information about managing rule-based transformations using Oracle-supplied packages.
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