On Fri, Dec 20, 2019 at 1:58 PM Tom Lane <tgl@xxxxxxxxxxxxx> wrote:
Jeff Janes <jeff.janes@xxxxxxxxx> writes:
> The docs for parallel_tuple_cost are quite terse, as the reference section
> of the docs usually are:
> "Sets the planner's estimate of the cost of transferring one tuple from a
> parallel worker process to another process. The default is 0.1."
> If you were take the doc description literally, then the default value
> seems much too high, as it doesn't take 10x the (default) cpu_tuple_cost to
> transfer a tuple up from a parallel worker.
Really? If anything, I'd have thought it might be worse than 10x.
Cross-process communication isn't cheap, at least not according to
my instincts.
I was a bit surprised. I set it up so that there was a fine-tunable filter which can be applied in the parallel workers, and then only the surviving tuples get passed up to the leader. The use of a parallel seq scan didn't become slower than the non-parallel version until over 95% of the tuples were surviving the filter. If I wanted to make the estimated cost cross-over point match the measured time cross-over point, I had to mark the parallel_tuple_cost down to about 0.011. This was an 8 CPU machine, an AWS m5.4xlarge, with max_parallel_workers_per_gather=7. (On my crummy 2-CPU Windows 10 laptop running ubuntu via VirtualBox, the cross-over point was closer to 40% of the tuples surviving, and the parallel_tuple_cost to match cross-over point would be about 0.016, but I don't have enough RAM to make a large enough all-in-shared-buffer table to really get a good assessments).
My method was to make shared_buffers be a large fraction of RAM (55GB, out of 64GB), then make a table slightly smaller than that and forced it into shared_buffers with pg_prewarm. I set seq_page_cost = random_age_cost = 0, to accurately reflect the fact that no IO is occuring.
create table para_seq as select floor(random()*10000)::int as id, random() as x, md5(random()::text)||md5(random()::text) t from generate_series(1,8000000*55);
vacuum ANALYZE para_seq ;
select pg_prewarm('para_seq');
vacuum ANALYZE para_seq ;
select pg_prewarm('para_seq');
explain (analyze, buffers, settings, timing off) select * from para_seq where id<9500;
Where you can change the 9500 to tune the selectivity of the filter. Is this the correct way to try to isolate just the overhead of transferring of a tuple away from other considerations so it can be measured?
I don't think the fact that EXPLAIN ANALYZE throws away the result set without reading it should change anything. Reading it should add the same fixed overhead to both parallel and non-parallel, so would dilute out percentage difference without change absolute differences.
I tried it with wider tuples as well, but not so wide they would activate TOAST, and didn't really see a difference in the conclusion.
> On the other hand, you
> probably don't want a query which consumes 8x the CPU resources just to
> finish only 5% faster (on an otherwise idle server with 8 CPUs). Maybe
> this Amdahl factor is what inspired the high default value?
I think the large value of parallel_setup_cost is what's meant to
discourage that scenario.
I think that can only account for overhead like forking and setting up memory segments. The overhead of moving around tuples (more than single-threaded execution already moves them around) would need to scale with the number of tuples moved around.
Cheers,
Jeff
