Re: using custom scan nodes to prototype parallel sequential scan

> 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.

Thanks,
--
NEC OSS Promotion Center / PG-Strom Project
KaiGai Kohei <kaigai(at)ak(dot)jp(dot)nec(dot)com>