Gregory Stark wrote:

Christian Schröder <cs@deriva.de> writes:

...
  
->  Seq Scan on table2  (cost=0.00..186.64 rows=2 width=4) (actual time=0.052..2.259 rows=42 loops=1)
      Filter: (c ~~ '1131%'::text)
    
...
  
->  Seq Scan on table2  (cost=0.00..200.89 rows=14 width=4) (actual time=0.084..3.419 rows=42 loops=1)
      Filter: ("substring"((c)::text, 1, 4) = 1131'::text)
    
...

  
My question is: Why do I have to optimize my query (use "substring" instead
of "like") instead of having the database do this for me? Or is there a
difference between both queries which I cannot see?
    

The only difference is that the optimizer understands LIKE better than it does
substring and so it tries harder to come up with a good estimate of how many
rows will match. In this case it seems its estimate is actually better (by
pure luck) with the substring() call. But it's still not very good.

Have these tables been analyzed recently? If so try raising the statistics
target on the "c" column. If the number of rows estimated goes up from 2 to
the 14 it's estimating with substring() then you'll get the better plan.
Hopefully it would be even better than that though.

Yes, all tables are "vacuum analyzed" twice per day. (I did not have time to configure the auto-vacuum feature.)

But after increasing the statistics target of the column to 20 and re-analyzing the table the query planner chose the better plan and the query got sped up dramatically. You seem to have found the problem!
I have now increased the default statistics target from 10 to 20 and the statistics target of this column to 500. We have about 190 distinct values in this column, so with a statistics target of 500 the statistics should be as exact as possible. (At least if I have understood well what this parameter means.) Since we have many queries that rely on this column to me it seems to be a good idea to have best statistics about it. I cannot see any disadvantage of this approach, at least if I do it only for one single column. Or do I overlook anything?


  

    
And last question: I do not really understand the first query plan. The actual
time for the outer nested loop is 532673.631 ms. As far as I have understood
the docs this includes the child nodes. But I cannot find the time-consuming
child node. I only see two child nodes: The inner nested loop (which took
31.692 ms) and the index scan (which took 243.643 ms). Or do I have to multiply
the 243.643 ms with 1627 (number of loops)? But even then I get 396407.161 ms,
which is still far away from the 532673.631 ms in the parent node.
    

  

  

The nested loop still has to do some work. Actually it's quite possible that
that extra overhead in nested loop is largely gettimeofday() calls for the
explain analyze. Does the query take less time run without explain analyze
than it does run with it?
  



You seem to be right with your assumption that most of the extra time
is spent in the gettimeofday() calls: Without "explain analyze" the
query took about 6 minutes which is close to 380 seconds that I get
from multiplying the number of loops (1627) with the actual time per
loop (234.643 ms).



Many thanks for your very helpful explanations!



Regards,

    Christian


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