My intention is to carry out 2), the initial investigation will focus on the top wait events and from here drill into the area of the report that might indicate what is causing these waits. So, my question is, of the 16 hours of remaining data what is the best approach to investigating high level performance issues? Should I ġ) Generate a SNAP for the whole 16 hour range and use this for all investigations?Ģ) Start with a SNAP for the 16 hour range and then drill into other sections of this range as a sanity check (binary search the range to check for unusual behaviour)?ģ) Pick the last 30 minute interval and use this for investigations? My approach (after gathering all the user info about the problem) is to discard the first 8 or so hours of data as the re-start will skew my analysis (lots of PIO due to warming up the cache and lots of CPU usage due to parsing). My plan is to gather STATSPACK snapshot data every 30 minutes for one of these re-start cycles and then investigate the performance stats. This obviously impact DB performance due to the loss of library and buffer cache contents and will be something that I will recommend changing (but is does not address the upgrade performance issue). The disks are then cloned and the clones backed up to tape. This site employs a backup strategy that requires them to shutdown the database nightly. The performance hit has not been quantified yet but is 'felt throughout the day'. The feeling is that there has been some performance degradation since the upgrade. I've been asked to look at a site who have just carried out a DB upgrade. This is just an sanity check on my appraoch.
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