On Sun, 20 Apr 2008 17:15:36 +0200, Francisco Reyes
<[EMAIL PROTECTED]> wrote:
PFC writes:
- If you process up to some percentage of your RAM worth of data,
hashing is going to be a lot faster
Thanks for the excellent breakdown and explanation. I will try and get
sizes of the tables in question and how much memory the machines have.
Actually, the memory used by the hash depends on the number of distinct
values, not the number of rows which are processed...
Consider :
SELECT a GROUP BY a
SELECT a,count(*) GROUP BY a
In both cases the hash only holds discinct values. So if you have 1
million rows to process but only 10 distinct values of "a", the hash will
only contain those 10 values (and the counts), so it will be very small
and fast, it will absorb a huge seq scan without problem. If however, you
have (say) 100 million distinct values for a, using a hash would be a bad
idea. As usual, divide the size of your RAM by the number of concurrent
connections or something.
Note that "a" could be a column, several columns, anything, the size of
the hash will be proportional to the number of distinct values, ie. the
number of rows returned by the query, not the number of rows processed
(read) by the query. Same with hash joins etc, that's why when you join a
very small table to a large one Postgres likes to use seq scan + hash join
on the small table.
- If you need DISTINCT ON, well, you're stuck with the Sort
- So, for the time being, you can replace DISTINCT with GROUP BY...
Have seen a few of those already on some code (new job..) so for those
it is a matter of having a good disk subsystem?
Depends on your RAM, sorting in RAM is always faster than sorting on disk
of course, unless you eat all the RAM and trash the other processes.
Tradeoffs...
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