Subsetting data during replication can improve the overall performance of your sync scenario and reduce the size of target databases. This article will explain some of the ways SymmetricDS will allow you to subset your data and how to consider the performance implications with each case. All rights reserved. Here are some issues to highlight: Performance improvements for initial load extract Support for Firebird 3 LookupTableRouter will optimize when initial load select is used Better handling of zero date on MySQL Support service install on MacOS X Upgraded Jetty to latest version for security fixes Upgraded to latest Oracle driver DbSql can run statements from a file There's a lot packed into this release, so see the 3.
Add a comment. Here are some issues to highlight: Support for Java 11 and 12 DbExport option to exclude tables Sync data for table names with special characters Fixed purging of incoming batches immediately for initial load Fixed syncing of data when primary key includes binary type Reduced number of threads used in ack service Support for sticky session with AWS ELB Fixed enums for table creation on MySQL Fixed miscellaneous DbCompare bugs See the 3. Here are some improvements to highlight: Initial Apache HBase support MongoDB clusters support Re-clearing target table if initial load extract is interrupted See the 3.
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Subsetting Data During Replication Written by Josh Hicks Subsetting data during replication can improve the overall performance of your sync scenario and reduce the size of target databases. Find us on Facebook. Parallel propagation uses the same execution mechanism that Oracle uses for parallel query, load, recovery, and other parallel operations. Each server process propagates transactions through a single stream. A parallel coordinator process controls these server processes. The coordinator tracks transaction dependencies, allocates work to the server processes, and tracks their progress.
After a site pushes a deferred transaction to its destination, the transaction remains in the deferred transaction queue until another job purges the applied transaction from the queue. Updatable snapshots in an advanced replication environment can both "push" and "pull" data to and from its master table, respectively. Updates to an updatable snapshot are asynchronously pushed to its master table using Oracle's row-level, asynchronous data propagation mechanisms RPCs, deferred transactions, and job queues.
Identical to basic replication environments, advanced replication systems use Oracle's snapshot refresh mechanism to pull changes asynchronously from a master table to associated updatable and read-only snapshots. An updatable snapshot's push and pull tasks are independent operations that you can configure associatively or separately. For example, an advanced replication environment that consolidates information at a master site might configure updatable snapshots to push changes to the master site every hour but refresh updatable snapshots infrequently, if ever. An Oracle symmetric replication environment requires several unique database user accounts to function properly, including replication administrators, propagators, and receivers.
In most advanced replication configurations, just one account is used for all purposes - as a replication administrator, a replication propagator, and a replication receiver. However, Oracle also supports distinct accounts for unique configurations. Advanced replication systems that support an update-anywhere model of data replicas must address the possibility of replication conflicts. The following sections explain the different types of replication conflicts, when they can occur, and how Oracle can detect and resolve replication conflicts. Three types of conflicts can occur in an advanced replication environment: uniqueness conflicts, update conflicts, and delete conflicts.
For example, consider what happens when two transactions that originate from two diffebent sites each insert a row into a respective table replica with the same primary key value - replication of the transactions will cause a uniqueness conflict. An update conflict happens when the replication of an update to a row conflicts with another update to the same row.
Update conflicts can happen when two different transactions, originating from different sites, update the same row at nearly the same time. A delete conflict happens when two transactions originate from different sites, with one transaction deleting a row that the other transaction updates or deletes. When designing applications that will work on top of a database system that uses advanced replication, you must consider the possibility of replication conflicts.
In doing so, applications must choose to employ one of several different replicated data ownership models that will ensure global database integrity by avoiding or resolving replication conflicts. Primary ownership , also called static ownership , is the replicated data model that basic read-only replication environments support.
Primary ownership prevents all replication conflicts, because only a single server permits update access to a set of replicated data. Rather than control the ownership of data at the table level, applications can employ horizontal and vertical partitioning to establish more granular static ownership of data. For example, applications might have update access to specific columns or rows in a replicated table on a site-by-site basis.
The dynamic ownership replicated data model is less restrictive than primary site ownership. With dynamic ownership, the capability to update a data replica moves from site to site, still ensuring that only one site provides update access to specific data at any given point in time. A workflow system clearly illustrates the concept of a dynamic ownership. For example, related departmental applications can read the status code of a product order to determine when they can and cannot update the order.
Figure illustrates an application that uses a dynamic ownership model. Primary site ownership and dynamic ownership replication data models, which promote conflict avoidance, are often too restrictive or impossible to implement for some database applications. Some applications must operate using a shared ownership replicated data model in which applications can update the data of any table replica at any time.
When a shared data ownership system replicates changes asynchronously store-and-forward replication , corresponding applications must be sure to avoid, or detect and resolve replication conflicts if and when they occur. For example:. When an application uses a shared ownership data model with asynchronous row-level replication, Oracle automatically detects uniqueness, update, and delete conflicts. To detect conflicts during replication, Oracle compares a minimal amount of row data from the originating site with the corresponding row information at the receiving site.
What is Database Replication?
When there are differences, Oracle detects the conflict. To detect replication conflicts accurately, Oracle must be able to uniquely identify and match corresponding rows at different sites during data replication. Typically, Oracle's advanced replication facility uses the primary key of a table to uniquely identify rows in the table. When a table does not have a primary key, you must designate an alternate key - a column or set of columns that Oracle can use to identify rows in the table during data replication. In either case, applications should not be allowed to update the identity columns of a table to ensure that Oracle can identify rows and preserve the integrity of replicated data.
When a receiving site in an advanced replication system is using asynchronous row-level replication and it detects a conflict in a transaction, the default behavior is to log the conflict and the entire transaction, and leave the local version of the data intact. In most cases, you should use Oracle's advanced replication facility to automate the resolution of replication conflicts.
Oracle uses column groups to detect and resolve conflicts during asynchronous, row-level symmetric replication. A column group is a logical grouping of one or more columns in a table.
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Every column in a replicated table is part of a single column group. When configuring replicated tables, you can create column groups and then assign columns and corresponding conflict resolution methods to each group. Each column group in a replicated table can have a list of one or more conflict resolution methods.
Indicating multiple conflict resolution methods for a group allows Oracle to resolve a conflict in different ways should others fail to resolve the conflict. When trying to resolve a conflict for a group, Oracle executes the group's resolution methods in the order that you list for the group. By default, every replicated table has a shadow column group.
A table's shadow column group contains all columns that are not within a specific column group. You cannot assign conflict resolution methods to a table's shadow group. When designing column groups you can choose from among many built-in conflict resolution methods.
Here’s What Can Go Wrong with Database Replication
For example, to resolve update conflicts, you might choose to have Oracle overwrite the column values at the destination site with the column values from the originating site. Oracle offers many other conflict resolution methods. Some applications have special requirements of an advanced replication system. The following sections explain the Oracle unique advanced replication options, including. Batch processing applications can change large amounts of data within a single transaction.
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In such cases, typical row-level replication could saturate a network with a huge quantity of data changes. To avoid such problems, a batch processing application that operates in an advanced replication environment can use Oracle's procedural replication to replicate simple stored procedure calls that will converge data replicas.