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The same effect can be achieved in the other databases using their built-in features or generating Tip

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In order to be self-tuning and self-managing, a database must have the ability to automatically learn how it is being used. To this end, Oracle provides a common manageability infrastructure, which captures workload information and uses it to make sophisticated self-management decisions. The heart of the manageability infrastructure is the new Automatic Workload Repository, or AWR, which serves as a repository for all the other server components that aid automatic management of the database. Oracle has built instrumentation into the various layers of its technology stack to capture the metadata that helps in diagnosing performance. It stores this data in the AWR and utilizes a comprehensive suite of management advisors to provide guidance on optimizing database operations. In the following sections, I briefly explain the various components of the common manageability infrastructure of Oracle Database 10g. You ll fully explore all of these in later chapters.

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The AWR plays the role of the data warehouse of the database, and it is the basis for most of Oracle s self-management functionality. The AWR collects and maintains performance statistics for problemdetection and self-tuning purposes. By default, every 60 minutes the database collects statistical information from the SGA and stores it in the AWR, in the form of snapshots. Several database components, such as the ADDM and other management advisors, use the AWR data to detect problems and for tuning the database. Like the ADDM, the AWR is automatically active upon starting the instance. You ll learn more about the AWR in 17.

Note If you are interested in the gory details of floating-point arithmetic and the subsequent loss of precision,

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Once you understand that each database will implement features in a different way, another example of defensive programming to allow for portability is to layer your access to the database when necessary. For example, say you are programming using JDBC. If all you use is straight SQL SELECTs, INSERTs, UPDATEs, and DELETEs, you probably do not need a layer of abstraction. You may very well be able to code the SQL directly in your application, as long as you limit the constructs you use to those supported by each of the databases you intend to support and that you have verified work exactly the same (remember the NULL=NULL discussion!). Another approach that is both more portable and offers better performance is to use stored procedures to return resultsets. You will discover that every vendor s database can return resultsets from stored procedures, but how they are returned is different. The actual source code you must write is different for different databases. Your two choices here are either to not use stored procedures to return resultsets or to implement different code for different databases. I would definitely follow the different code

The database samples all active sessions every second, and the session information is stored in a circular buffer in SGA. A session that s either waiting for a nonidle event or was on the CPU is considered an active session. Even though the ADDM provides you with detailed instance information by periodically analyzing the AWR data, you are at a loss if you want to know what s happened in the database in a recent time period (such as in the past five minutes). Active Session History and its related historical views provide you with insight into current activity in the database. 19 discusses ASH in detail.

It should be noted that in this case we can sort of have our cake and eat it, too. Using the built-in CAST function, we can perform an on-the-fly conversion of the Oracle NUMBER type to a floating-point type, prior to performing the complex math on it. This results in a CPU usage that is much nearer to that of the native floating-point types: select sum(ln(cast( num_type as binary_double ) )) from t call count ------- -----total 4 cpu elapsed -------- ---------0.08 0.08

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