On 11/02/2016 09:00 PM, Tom Lane wrote:
Tomas Vondra <tomas.von...@2ndquadrant.com> writes:
while eye-balling some explain plans for parallel queries, I got a bit
confused by the row count estimates. I wonder whether I'm alone.
I got confused by that a minute ago, so no you're not alone. The problem
is even worse in join cases. For example:
Gather (cost=34332.00..53265.35 rows=100 width=8)
Workers Planned: 2
-> Hash Join (cost=33332.00..52255.35 rows=100 width=8)
Hash Cond: ((pp.f1 = cc.f1) AND (pp.f2 = cc.f2))
-> Append (cost=0.00..8614.96 rows=417996 width=8)
-> Parallel Seq Scan on pp (cost=0.00..8591.67 rows=416667 widt
h=8)
-> Parallel Seq Scan on pp1 (cost=0.00..23.29 rows=1329 width=8
)
-> Hash (cost=14425.00..14425.00 rows=1000000 width=8)
-> Seq Scan on cc (cost=0.00..14425.00 rows=1000000 width=8)
There are actually 1000000 rows in pp, and none in pp1. I'm not bothered
particularly by the nonzero estimate for pp1, because I know where that
came from, but I'm not very happy that nowhere here does it look like
it's estimating a million-plus rows going into the join.
Yeah. I wonder how tools visualizing explain plans are going to compute
time spent in a given node (i.e. excluding the time spent in child
nodes), or expected cost of that node.
So far we could do something like
self_time = total_time - child_node_time * nloops
and example in this plan it's pretty clear we spend ~130ms in Aggregate:
QUERY PLAN
----------------------------------------------------------------------------
Aggregate (cost=17140.50..17140.51 rows=1 width=8)
(actual time=306.675..306.675 rows=1 loops=1)
-> Seq Scan on tables (cost=0.00..16347.60 rows=317160 width=0)
(actual time=0.188..170.993 rows=317160 loops=1)
Planning time: 0.201 ms
Execution time: 306.860 ms
(4 rows)
But in parallel plans it can easily happen that
child_node_time * nloops > total_time
Consider for example this parallel plan:
QUERY PLAN
----------------------------------------------------------------------------
Finalize Aggregate (cost=15455.19..15455.20 rows=1 width=8)
(actual time=107.636..107.636 rows=1 loops=1)
-> Gather (cost=15454.87..15455.18 rows=3 width=8)
(actual time=107.579..107.629 rows=4 loops=1)
Workers Planned: 3
Workers Launched: 3
-> Partial Aggregate (cost=14454.87..14454.88 rows=1 ...)
(actual time=103.895..103.895 rows=1 loops=4)
-> Parallel Seq Scan on tables
(cost=0.00..14199.10 rows=102310 width=0)
(actual time=0.059..59.217 rows=79290 loops=4)
Planning time: 0.052 ms
Execution time: 109.250 ms
(8 rows)
Reading explains for parallel plans will always be complicated, but
perhaps overloading the nloops like this makes it more complicated?
regards
--
Tomas Vondra http://www.2ndQuadrant.com
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services
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