http://www.eygle.com/archives/2011/11/simulator_lru_latch.html
KGL=Kernel General Library cache manager, as the name says it deals with library objects such cursors, cached stored object definitions (PL/SQL stored procs, table definitions etc).
KGL simulator is used for estimating the benefit of caching if the cache was larger than currently. The general idea is that when flushing out a library cache object, it's hash value (and few other bits of info) are still kept in the KGL simulator hash table. This stores a history of objects which were in memory but flushed out.
When loading a library cache object (which means that no existing such object is in library cache), Oracle goes and checks the KGL simulator hash table to see whether an object with matching hash value is in there. If a matching object is found, that means that the required object had been in cache in past, but flushed out due space pressure.
Using that information of how many library cache object (re)loads could have been been avoided if cache had been bigger (thanks to KGL simulator history) and knowing how much time the object reloads took, Oracle can predict how much response time would have been saved instancewide if shared pool was bigger. This is seen from v$library_cache_advice.
Anyway, this error was probably raised by a victim session due running out of shared pool space. In other words, someone else may have used up all the memory (or all the large enough chunks) and this allocation for KGL sim failed because of that.
v$sgastat would be the first point for troubleshooting ORA-4031 errors, you need to identify how much free memory you have in shared pool (and who's using up most of the memory).
http://stackoverflow.com/questions/183963/what-is-the-oracle-kgl-simulator
来自 “ ITPUB博客 ” ,链接:http://blog.itpub.net/13750068/viewspace-721974/,如需转载,请注明出处,否则将追究法律责任。
转载于:http://blog.itpub.net/13750068/viewspace-721974/
本文深入探讨了Oracle KGL模拟器的工作原理,包括其在缓存管理、库缓存对象加载过程中的作用以及如何利用KGL模拟器预测内存使用情况以优化性能。通过分析v$library_cache_advice,理解KGL模拟器如何帮助评估缓存大小增加带来的响应时间改善。

1363

被折叠的 条评论
为什么被折叠?



