From: | David Rowley <david(dot)rowley(at)2ndquadrant(dot)com> |
---|---|
To: | Ashutosh Bapat <ashutosh(dot)bapat(at)enterprisedb(dot)com> |
Cc: | Jeevan Chalke <jeevan(dot)chalke(at)enterprisedb(dot)com>, Rajkumar Raghuwanshi <rajkumar(dot)raghuwanshi(at)enterprisedb(dot)com>, PostgreSQL Hackers <pgsql-hackers(at)postgresql(dot)org> |
Subject: | Re: Partition-wise aggregation/grouping |
Date: | 2017-10-10 08:01:44 |
Message-ID: | CAKJS1f9UXdk6ZYyqbJnjFO9a9hyHKGW7B=ZRh-rxy9qxfPA5Gw@mail.gmail.com |
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Lists: | Postg사설 토토SQL |
On 10 October 2017 at 17:57, Ashutosh Bapat
<ashutosh(dot)bapat(at)enterprisedb(dot)com> wrote:
> Append node just returns the result of ExecProcNode(). Charging
> cpu_tuple_cost may make it too expensive. In other places where we
> charge cpu_tuple_cost there's some processing done to the tuple like
> ExecStoreTuple() in SeqNext(). May be we need some other measure for
> Append's processing of the tuple.
I don't think there's any need to invent any new GUC. You could just
divide cpu_tuple_cost by something.
I did a quick benchmark on my laptop to see how much Append really
costs, and with the standard costs the actual cost seems to be about
cpu_tuple_cost / 2.4. So probably cpu_tuple_cost / 2 might be
realistic. create_set_projection_path() does something similar and
brincostestimate() does some similar magic and applies 0.1 *
cpu_operator_cost to the total cost.
# create table p (a int, b int);
# create table p1 () inherits (p);
# insert into p1 select generate_series(1,1000000);
# vacuum analyze p1;
# \q
$ echo "select count(*) from p1;" > p1.sql
$ echo "select count(*) from p;" > p.sql
$ pgbench -T 60 -f p1.sql -n
latency average = 58.567 ms
$ pgbench -T 60 -f p.sql -n
latency average = 72.984 ms
$ psql
psql (11devel)
Type "help" for help.
# -- check the cost of the plan.
# explain select count(*) from p1;
QUERY PLAN
------------------------------------------------------------------
Aggregate (cost=16925.00..16925.01 rows=1 width=8)
-> Seq Scan on p1 (cost=0.00..14425.00 rows=1000000 width=0)
(2 rows)
# -- selecting from the parent is the same due to zero Append cost.
# explain select count(*) from p;
QUERY PLAN
------------------------------------------------------------------------
Aggregate (cost=16925.00..16925.01 rows=1 width=8)
-> Append (cost=0.00..14425.00 rows=1000001 width=0)
-> Seq Scan on p (cost=0.00..0.00 rows=1 width=0)
-> Seq Scan on p1 (cost=0.00..14425.00 rows=1000000 width=0)
(4 rows)
# -- extrapolate the additional time taken for the Append scan and
work out what the planner
# -- should add to the plan's cost, then divide by the number of rows
in p1 to work out the
# -- tuple cost of pulling a row through the append.
# select (16925.01 * (72.984 / 58.567) - 16925.01) / 1000000;
?column?
------------------------
0.00416630302337493743
(1 row)
# show cpu_tuple_cost;
cpu_tuple_cost
----------------
0.01
(1 row)
# -- How does that compare to the cpu_tuple_cost?
# select current_Setting('cpu_tuple_cost')::float8 / 0.00416630302337493743;
?column?
----------------
2.400209476818
(1 row)
Maybe it's worth trying with different row counts to see if the
additional cost is consistent, but it's probably not worth being too
critical here.
--
David Rowley http://www.2ndQuadrant.com/
PostgreSQL Development, 24x7 Support, Training & Services
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