On Wed, Aug 27, 2014 at 4:41 PM, Merlin Moncure <mmoncure@gmail.com> wrote:
> On Wed, Aug 27, 2014 at 2:07 PM, Pavel Stehule <pavel.stehule@gmail.com> wrote:
>> Hi
>>
>> one user asked about using a partitioning for faster aggregates queries.
>>
>> I found so there is not any optimization.
>>
>> create table x1(a int, d date);
>> create table x_1 ( check(d = '2014-01-01'::date)) inherits(x1);
>> create table x_2 ( check(d = '2014-01-02'::date)) inherits(x1);
>> create table x_3 ( check(d = '2014-01-03'::date)) inherits(x1);
>>
>> When I have this schema, then optimizer try to do
>>
>> postgres=# explain verbose select max(a) from x1 group by d order by d;
>> QUERY PLAN
>> --------------------------------------------------------------------------------
>> GroupAggregate (cost=684.79..750.99 rows=200 width=8)
>> Output: max(x1.a), x1.d
>> Group Key: x1.d
>> -> Sort (cost=684.79..706.19 rows=8561 width=8)
>> Output: x1.d, x1.a
>> Sort Key: x1.d
>> -> Append (cost=0.00..125.60 rows=8561 width=8)
>> -> Seq Scan on public.x1 (cost=0.00..0.00 rows=1 width=8)
>> Output: x1.d, x1.a
>> -> Seq Scan on public.x_1 (cost=0.00..31.40 rows=2140
>> width=8)
>> Output: x_1.d, x_1.a
>> -> Seq Scan on public.x_2 (cost=0.00..31.40 rows=2140
>> width=8)
>> Output: x_2.d, x_2.a
>> -> Seq Scan on public.x_3 (cost=0.00..31.40 rows=2140
>> width=8)
>> Output: x_3.d, x_3.a
>> -> Seq Scan on public.x_4 (cost=0.00..31.40 rows=2140
>> width=8)
>> Output: x_4.d, x_4.a
>> Planning time: 0.333 ms
>>
>> It can be reduced to:
>>
>> sort by d
>> Append
>> Aggegate (a), d
>> seq scan from x_1
>> Aggregate (a), d
>> seq scan from x_2
>>
>> Are there some plans to use partitioning for aggregation?
>
> Besides min/max, what other aggregates (mean/stddev come to mind)
> would you optimize and how would you determine which ones could be?
> Where is that decision made?
You can't with mean and stddev, only with associative aggregates.
That's min, max, sum, bit_and, bit_or, bool_and, bool_or, count.
