On 8/24/14, 6:22 AM, Haribabu Kommi wrote:
Yes, we are mainly targeting CPU-limited sequential scans, Because of
this reason
only I want the worker to handle the predicates also not just reading
the tuples from
disk.
In that case, I would suggest focusing on parallel execution of conditions
regardless of where they show up in the query plan. In my experience, they
often have nothing to do with a seqscan.
Here's a real-world example. We have a view that pivots our applications
accounting journal into a ledger. The expensive part of the view is this:
sum(
CASE
WHEN b.tag::text = 'installment_principal'::text THEN b.type_cd --
type_cd is either 1, 0, or -1
ELSE 0::numeric
END
) * transaction_amount AS installment_principal
The view with this pivot has about 100 of these case statements. Frequently we
only reference a few of them, but anytime we need to refer to 20+ the
evaluation of that expression gets VERY cpu-expensive compared to the rest of
the query.
The other thing I would look at before seqscan filters is join processing and
bitmap index index combining (ie: ANDing together the results of several bitmap
index scans). Those are things that can be very CPU intensive even when doing
simple equality comparisons.
BTW, it's also possible that these cases would be good fits for GPU parallel
execution.
--
Jim C. Nasby, Data Architect [email protected]
512.569.9461 (cell) http://jim.nasby.net
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