On Wed, Dec 3, 2014 at 3:23 PM, Kouhei Kaigai <kaigai(at)ak(dot)jp(dot)nec(dot)com> wrote:
>> On Fri, Nov 14, 2014 at 02:51:32PM +1300, David Rowley wrote:
>> > Likely for most aggregates, like count, sum, max, min, bit_and and
>> > bit_or the merge function would be the same as the transition
>> > function, as the state type is just the same as the input type. It
>> > would only be aggregates like avg(), stddev*(), bool_and() and
>> > bool_or() that would need a new merge function made... These would be
>> > no more complex than the transition functions... Which are just a few
>> lines of code anyway.
>> >
>> > We'd simply just not run parallel query if any aggregates used in the
>> > query didn't have a merge function.
>> >
>> > When I mentioned this, I didn't mean to appear to be placing a road
>> > block.I was just bringing to the table the information that COUNT(*) +
>> > COUNT(*) works ok for merging COUNT(*)'s "sub totals", but AVG(n) + AVG(n)
>> does not.
>>
>> Sorry, late reply, but, FYI, I don't think our percentile functions can't
>> be parallelized in the same way:
>>
>> test=> \daS *percent*
>> List of
>> aggregate functions
>> Schema | Name | Result data type |
>> Argument data types | Description
>> ------------+-----------------+--------------------+----------
>> ------------------------------------+---------------------------------
>> ----
>> pg_catalog | percent_rank | double precision | VARIADIC
>> "any" ORDER BY VARIADIC "any" | fractional rank of hypothetical row
>> pg_catalog | percentile_cont | double precision | double
>> precision ORDER BY double precision | continuous distribution percentile
>> pg_catalog | percentile_cont | double precision[] | double
>> precision[] ORDER BY double precision | multiple continuous percentiles
>> pg_catalog | percentile_cont | interval | double
>> precision ORDER BY interval | continuous distribution
>> percentile
>> pg_catalog | percentile_cont | interval[] | double
>> precision[] ORDER BY interval | multiple continuous percentiles
>> pg_catalog | percentile_disc | anyelement | double
>> precision ORDER BY anyelement | discrete percentile
>> pg_catalog | percentile_disc | anyarray | double
>> precision[] ORDER BY anyelement | multiple discrete percentiles
>>
> Yep, it seems to me the type of aggregate function that is not obvious
> to split into multiple partitions.
> I think, it is valuable even if we can push-down a part of aggregate
> functions which is well known by the core planner.
> For example, we know count(*) = sum(nrows), we also know avg(X) can
> be rewritten to enhanced avg() that takes both of nrows and partial
> sum of X. We can utilize these knowledge to break-down aggregate
> functions.
Postgres-XC (Postgres-XL) has implemented such parallel aggregate
logic some time ago using a set of sub functions and a finalization
function to do the work.
My 2c.
--
Michael