Re: plan_rows confusion with parallel queries

On 11/02/2016 09:00 PM, Tom Lane wrote:
> Tomas Vondra <tomas.vondra@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=317160width=0)                    (actual time=0.188..170.993 rows=317160 loops=1) Planning time: 0.201 ms
Executiontime: 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..15455.20rows=1 width=8)                     (actual time=107.636..107.636 rows=1 loops=1)   ->  Gather
(cost=15454.87..15455.18rows=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
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