Re: wip: functions median and percentile - Mailing list pgsql-hackers
From | Tom Lane |
---|---|
Subject | Re: wip: functions median and percentile |
Date | |
Msg-id | 17482.1286745419@sss.pgh.pa.us Whole thread Raw |
In response to | Re: wip: functions median and percentile (Dean Rasheed <dean.a.rasheed@gmail.com>) |
Responses |
Re: wip: functions median and percentile
Re: wip: functions median and percentile Re: wip: functions median and percentile Re: wip: functions median and percentile |
List | pgsql-hackers |
Dean Rasheed <dean.a.rasheed@gmail.com> writes: > In the meantime, the attached variation of the patch fixes the temp > file issue and will support all 3 cases. It gives OK performance for > (1) and (2), and poor performance for (3). That could be viewed as a > future development task, which perhaps the window function API would > help with. I think it would be a shame to drop support for (2) just > because we can't do (3) efficiently yet. I started looking at this patch, and noticed that we got so caught up in implementation issues that we forgot the unresolved problem of data types. The original patch defined median(anyelement), which is only well-defined for an odd number of inputs; for an even number of inputs you have to take the left or right item in the central pair. I see that this version definesmedian(float8) returns float8median(float4) returns float8median(numeric) returns numericmedian(int8)returns numericmedian(int4) returns numericmedian(int2) returns numeric which strikes me as possibly being overkill. It was pointed out upthread that while median isn't presently in the standard, Oracle defines it in terms of percentile_cont(0.5) which *is* in the standard. What I read in SQL:2008 is that percentile_cont is defined for all numeric types (returning approximate numeric with implementation-defined precision), and for interval (returning interval), and not for any other input type. So it appears to me that what we ought to support ismedian(float8) returns float8median(interval) returns interval and nothing else --- we can rely on implicit casting to convert any other numeric input type to float8. BTW, as far as the implementation issues go, telling tuplesort that it can use gigabytes of memory no matter what seems quite unacceptable. Put this thing into a hash aggregation and you'll blow out your memory in no time. I don't think it's even a good idea to use work_mem there. I wonder whether it'd be a good idea to augment AggCheckCallContext() so that there's a way for aggregates to find out how much memory they ought to try to use. In a simple aggregation situation it's probably OK to use work_mem, but in a hash aggregation you'd better use less --- perhaps work_mem divided by the number of groups expected. Also, I believe that the lack-of-cleanup problem for tuplesorts spilling to disk should be fixable by using an exprcontext shutdown callback (see RegisterExprContextCallback). Comments? regards, tom lane
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