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In many cases, you need to keep data online that spans the last N units of time For example, say you need to keep the last 12 months or the last 5 years online Without partitions, this is generally a massive INSERT followed by a massive DELETE two massive transactions Lots of DML, and lots of redo and undo generated Now with partitions, you can simply do the following: 1 2 3 4 Load a separate table with the new months (or years , or whatever) data Index the table fully (These steps could even be done in another instance and transported to this database) Attach this newly loaded and indexed table onto the end of the partitioned table using a fast DDL command: ALTER TABLE EXCHANGE PARTITION Detach the oldest partition off the other end of the partitioned table..

The _delegatee object of a thread obtained from the pool is changed to the worker context collected by the threadCreation() pointcut This enables the pool to perform the new work requested by the clients The around advice to the session() pointcut puts the captured method, DelegatingThreadrun(), in an infinite loop with the while(true) statement, which ensures that the caller thread never dies (This is because a thread, once dead, cannot be resurrected, and we need to keep all pooled threads alive to hand over to the requesting clients) The proceed() statement causes the captured method to execute, which, in turn, executes the run() method of the collected thread s delegatee After each execution, the thread is put into the thread pool object by the ThreadPoolputThread() method, which will cause that thread to go into a waiting state until it is woken up again by another thread.

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If you upgrade, replication will be supported there as well, probably with some new features Now, consider if you were to develop your own You would have to provide support for all of the versions you wanted to support Interoperability between old and new releases This would be your job If it breaks, you won t be calling support at least not until you can get a test case that is small enough to demonstrate your basic issue When the new release of Oracle comes out, it will be up to you to migrate your replication code to that release Not having a full understanding of what is available to you can come back to haunt you in the long run I was working with some developers with years of experience developing database applications on other databases They built analysis software (trending, reporting, and visualization software).

So, you can now very easily support extremely large objects containing time-sensitive information. The old data can easily be removed from the partitioned table and simply dropped if you do not need it, or it can be archived off elsewhere. New data can be loaded into a separate table, so as to not affect the partitioned table until the loading, indexing, and so on is complete. We will take a look at a complete example of a sliding window later. In short, partitioning can make what would otherwise be daunting, or in some cases unfeasible, operations as easy as they are in a small database.

The last general (potential) benefit of partitioning is in the area of enhanced statement (SELECT, INSERT, UPDATE, DELETE, MERGE) performance. We ll take a look at two classes of statements those that modify information and those that just read information and discuss what benefits we might expect from partitioning in each case.

It was to work on clinical data (healthcare related) They were not aware of SQL syntactical features such as inline views, analytic functions, and scalar subqueries Their major problem was they needed to analyze data from a single parent table to two child tables An entity-relationship diagram (ERD) might look like Figure 1-1..

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