Hi William/Andrew, Thanks for your valuable inputs. I totally agree with you william on the potential impact of outdated low_value and high_value on date columns and columns that are generated using sequences. I have a follow up question on how this data is populated. If I had done an estimate percent say 10%, how does CBO guess the high value?. Best Regards Sriram Kumar On Wed, Oct 1, 2008 at 6:54 AM, William Robertson < william@xxxxxxxxxxxxxxxxxxxx> wrote: > I've found that dates are more likely to present a problem because > sequentially generated keys are usually highly selective anyway. Say a > financial application loads three million trades a day. If the stats are not > updated the optimizer will think a given TRADE_ID has 0 occurrences (since > it's after the column's high endpoint) and round that up to 1, which may > well be correct anyway. However when you run a query for this week's trades > on a Friday and it thinks there are none when in fact there are 15 million > then you have a much bigger problem. That's my experience anyway. > > > 2008/10/1 Niall Litchfield <niall.litchfield@xxxxxxxxx> > >> dates and sequence driven key columns both exhibit this behaviour. >> >> >> On 30/09/2008, William Robertson <william@xxxxxxxxxxxxxxxxxxxx> wrote: >> > I'm not sure how using or not using histograms makes any fundamental >> > difference to your approach. >> > >> > One thing that comes to mind is the use of date predicates. If the >> > optimizer knows the highest sale date you have (for example) is 30 Sept >> > 2008, you are fine today and maybe some of next week, but in a few >> > months' time any queries for "last week's sales" may get unrealistically >> > low cardinality estimates leading to inappropriate index/nested loop >> > access paths, Cartesian joins and so on. >> > >> > If you use partitioning and you have date columns as >> > partition/subpartition keys you could have even more fun in store. >> > >> > >> > -----Original message----- >> > From: Sriram Kumar >> > Date: 30/9/08 22:41 >> >> Hi Folks, >> >> >> >> We had migrated a OLTP/Batch hybrid 9i RBO application to 10g >> >> and after rounds of tuning the application now is working at its best. >> >> We had experimented with various sampling rates for statistics for >> >> various tables and now I feel we are in a optimal point of performance >> >> where CBO is picking up the right Indexes. >> >> >> >> 1) We do not use histograms >> >> 2) From here on the application volumes are bound increase >> proportionately >> >> 3) No new tables would be added in production. >> >> 4) As of now, We have locked the schema stats for the application >> >> schemas. The default scheduler job collects stats for all other schemas >> >> >> >> Since the increase in volume of data is quite proportional to the >> >> existing data, what would be the compelling reason to periodically >> >> recollect statistics for the tables that already have good execution >> >> plans? >> >> >> >> e.g lets say that there is a table with million records and with few >> >> indexes. We have collected the stats for this table and indexes with >> >> million records and all the access paths to this table has been >> >> validated that the CBO is picking up the correct optimal index in >> >> every case. >> >> >> >> Now the volume of the table increases to 2 million and from >> >> application point of view, the access paths that were valid for 1 >> >> million would be the valid for 2 million as well. >> >> >> >> Given this scenario, is it required to collect statistics for 2 >> >> million as well?. I opine that it would not be required but some of my >> >> colleagues feel that we should collect statistics periodically but I >> >> am not able to get a valid reason for periodically recollecting the >> >> stats?. Is this one of the myths? >> >> >> >> I would agree to periodically recollect stats if we are using >> >> histograms but we are not using histograms. Any other reason that >> >> would need a periodic statistic gathering? >> > >