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3. Mohan Rajagopalan, Saumya K. Debray, Matti A. Hiltunen, and Richard D. Schlichting, Profile-Directed Optimization of Event-Based Programs (proceedings of the ACM Special Interest Group on Programming Languages [SIGPLAN], Berlin, Germany, June 2002). 4. Andreas Krall, Efficient JavaVM Just-in-Time Compilation (proceedings of the International Conference on Parallel Architectures and Compilation Techniques, Paris, France, October 1998). Matthew Arnold, Michael Hind, and Barbara Ryder, An Empirical Study of Adaptive Optimization (proceedings of the 13th International Workshop on Languages and Compilers for Parallel Computing, Yorktown Heights, New York, August 2000). 5. Charles Consel, Luke Hornof, Julia L. Lawall, Renaud Marlet, Gilles Muller, Jacques Noye, Scott Thibault, and Eugen-Nicolae Volanschi, Tempo: Specializing Systems Applications and Beyond, ACM Computing Surveys, Symposium on Partial Evaluation, September 1998. 6. Vasanth Bala, Evelyn Duesterwald, and Sanjeev Banerjia, Dynamo: A Transparent Dynamic Optimization System (proceedings of the SIGPLAN 2000 Conference on Programming Language Design and Implementation, Vancouver, British Columbia, Canada, June 2000). 7. Derek Bruening, Timothy Garnett, and Saman Amarasinghe, An Infrastructure for Adaptive Dynamic Optimization (proceedings of the 1st International Symposium on Code Generation and Optimization, San Francisco, CA, March 2003).

They were using a 1GB Shared pool inside of a 11GB SGA This is true: 01GB dedicated to block buffer cache and other elements and 1GB dedicated to caching unique queries that would never be executed again The reason for the cold start was that if they left the system running for more than a day, they would run out of free memory in the Shared pool At that point, the overhead of aging structures out (especially from a structure so large) was such that it overwhelmed the system and performance was massively degraded (not that performance was that great anyway, since they were managing a 1GB Shared pool) Additionally, the people working on this system constantly wanted to add more and more CPUs to the machine, due to the fact that hard-parsing SQL is so CPU intensive.

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If none of the previous steps indicated any problems, chances are that your system is suffering from a serious contention for some resource such as library cache latches. Check to see if there s contention for critical database resources such as locks and latches. For example, parsing similar SQL statements leads to an excessive use of CPU resources and affects instance performance by increasing the contention for the library cache or the shared pool. Contention for resources manifests itself in the form of wait events. The wait event analysis earlier in this chapter gave you a detailed explanation of various critical wait events. You can use AWR and ASH reports to examine the top wait events in your database. The V$SESS_TIME_MODEL (and the V$SYS_TIME_MODEL) view is useful in finding out accumulated time for various database operations at the individual session level. This view helps you understand precisely where most of the CPU time is being spent. As explained in 17, the V$SESS_TIME_MODEL view shows the following things, among others: DB time, which is the elapsed time spent in performing database user level calls. DB CPU is the amount of CPU time spent on database user-level calls. Background CPU time is the amount of CPU time used by the background processes. Hard parse elapsed time is the time spent hard parsing SQL statements. PL/SQL execution elapsed time is the amount of time spent running the PL/SQL interpreter. Connection management call elapsed time is the amount of time spent making session connect and disconnect calls. You can use segment data in the V$SEGMENT_STATISTICS view to find out the hot table and index segments causing a particular type of wait, and focus on eliminating (or reducing, anyway) that wait event.

Let s say you encounter a situation where one of your key nightly batch jobs is running past its time window and continuing on into the daytime, where it s going to hurt the online OLTP performance. You know the batch job used to finish within the stipulated time, but now it s tending to take a much longer time. In Oracle Database 10g Release 2, you can use the Database Control s Compare

Summary

By correcting the application and allowing it to use bind variables, not only did the physical machine requirements drop (they then had many times more CPU power than they needed), but also the allocation of memory to the various pools was reversed Instead of a 1GB Shared pool, they had less then 100MB allocated and they never used it all over many weeks of continuous uptime One last comment about the Shared pool and the parameter SHARED_POOL_SIZE In Oracle9i and before, there is no direct relationship between the outcome of the query ops$tkyte@ORA9IR2> select sum(bytes) from v$sgastat where pool = 'shared pool'; SUM(BYTES) ---------100663296 and the SHARED_POOL_SIZE parameter ops$tkyte@ORA9IR2> show parameter shared_pool_size NAME TYPE VALUE ------------------------------------ ----------- -----------------------------shared_pool_size big integer 83886080 other than the fact that the SUM(BYTES) FROM V$SGASTAT will always be larger than the SHARED_ POOL_SIZE.

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