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You can then use tools to organize and visualize the event firing statistics, creating a toplevel profile of the system and identifying which events are most often fired, which event handlers are called, and other details Profiling may identify not only high-frequency events, but also common event sequences that you might need to coalesce into shorter sequences You may need to build different profiles if a system has different modes of operation Once the profile of the system is known, you can apply static optimization techniques Handlers called with the highest frequency can be recoded carefully to improve their speed You can also apply compiler optimizations to inline code or store variables in CPU registers Using a more dynamic technique, you can instrument the system with code that monitors the event firing patterns.

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Note In previous versions, DBAs used the V$SQLAREA view to gather information shown earlier. However, the V$SQL view supplants the V$SQLAREA view by providing all information in that view, plus other important tuningrelated information as well.

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You can query the V$SQL view to find high-resource-using SQL You can determine resource-intensive SQL on the basis of the number of logical reads or buffer gets, or high disk reads, high parse calls, large number of executions, or combinations of these factors It s obvious that a high number of disk reads is inefficient because a high amount of physical I/O slows query performance However, a high number of memory reads (buffer gets) is also expensive because they consume CPU resources You normally have high buffer gets because you re using the wrong index, the wrong driving table in a join, or a similar SQL-related error One of the primary goals of SQL tuning should be to lower the number of unnecessary logical reads If buffer gets and disk reads are at identical levels, it could indicate a missing index.

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The reasoning is this: if you don t have an index, Oracle is forced to do a full table scan However, full table scans can t be kept in the SGA for too long because they might force a lot of other data to be cleared out Consequently, the full table won t get to stay in the SGA for too long unless it s a small table The following simple query shows how the V$SQL view can pinpoint problem SQL statements; both high disk reads and high logical reads are used as the criteria for flagging down poor SQL statements captured by the V$SQL view.

When high-frequency events are detected, the associated code can be optimized on the fly through event-coalescing and other source code changes The changes can then be compiled just-in-time and subsequently used4 A number of experimental dynamic optimization systems have been proposed, including Tempo,5 Dynamo,6 and DynamoRIO7.

The SQL_TEXT column shows the exact SQL statement that s responsible for the high disk reads and logical reads: SQL> 2 3 4 5 SELECT sql_text, executions, buffer_gets, disk_reads, FROM V$SQL WHERE buffer_gets > 100000 OR disk_reads > 100000 ORDER BY buffer_gets + 100*disk_reads DESC; BUFFER_GETS DISK_READS ---------- -------------1615283234 125828 1211625422 3680242 152737737 7186125 96590083 5547319 33272842 6034715.

SQL_TEXT EXECUTIONS ----------- --------------------BEGIN dbms_job.run(1009133); 726216 BEGIN label_sc_pkg.launch_sc; 34665 SELECT COUNT(*) AV_YOUTHS... 70564 SELECT UC.CHART_ID... 37849 SELECT MAX(REC_NUM) FROM... 5163242 SQL>

The following query is a slight variation on the preceding query. It seeks to find out the number of rows processed for each statement:

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).

SQL> SELECT sql_text, rows_processed, 2 buffer_gets, disk_reads, parse_calls 3 FROM V$SQL 4 WHERE buffer_gets > 100000 5 OR disk_reads > 100000 6*ORDER BY buffer_gets + 100*disk_reads DESC; SQL_TEXT ROWS_PROCESSED ---------------------------- ----------BEGIN dbms_job.run(1009133); 9659 BEGIN label_sc_pkg.launch_sc; 3928 SELECT COUNT(*) AV_YOUTHS... 70660 SELECT UC.CHART_ID... 37848 SELECT MAX(REC_NUM) FROM... 5163236 SQL> BUFFER_GETS DISK_READS PARSE_CALLS ----------- --------- -------------1615322749 125830 2078 1214405479 3680515 4 152737737 7186125 3863 96590083 5547319 5476 33272842 6034715 606

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