Re: effect of JIT tuple deform?

2018-06-24 22:32 GMT+02:00 Dmitry Dolgov <9erthalion6(at)gmail(dot)com>:

> > On 23 June 2018 at 08:47, Pavel Stehule <pavel(dot)stehule(at)gmail(dot)com> wrote:
> >
> >
> > 2018-06-23 8:35 GMT+02:00 Pavel Stehule <pavel(dot)stehule(at)gmail(dot)com>:
> >>
> >> Hi
> >>
> >> I try to measure effect of JIT tuple deform and I don't see any possible
> >> effect.
> >>
> >> Is it this feature active in master branch?
> >>
> >> Is possible to see this feature in EXPLAIN ANALYZE?
> >
> >
> > Unfortunately I got slowdown
> >
> > 0. shared buffers = 1GB
> > 1. create table with 50 int columns
> > 2. insert into this table 4M rows
>
> Hi,
>
> Looks like I can reproduce the situation you're talking about (with some
> minor
> differences)
>
> =# explain analyze select sum(data45) from test_deform;
> QUERY PLAN
> ------------------------------------------------------------
> -------------------
> Finalize Aggregate
> (cost=127097.71..127097.72 rows=1 width=8)
> (actual time=813.957..813.957 rows=1 loops=1)
> -> Gather
> (cost=127097.50..127097.71 rows=2 width=8)
> (actual time=813.946..813.950 rows=3 loops=1)
> Workers Planned: 2
> Workers Launched: 2
> -> Partial Aggregate
> (cost=126097.50..126097.51 rows=1 width=8)
> (actual time=802.585..802.585 rows=1 loops=3)
> -> Parallel Seq Scan on test_deform
> (cost=0.00..121930.80 rows=1666680 width=4)
> (actual time=0.040..207.326 rows=1333333 loops=3)
> Planning Time: 0.212 ms
> JIT:
> Functions: 6
> Generation Time: 3.076 ms
> Inlining: false
> Inlining Time: 0.000 ms
> Optimization: false
> Optimization Time: 1.328 ms
> Emission Time: 8.601 ms
> Execution Time: 820.127 ms
> (16 rows)
>
> =# set jit_tuple_deforming to off;
>
> =# explain analyze select sum(data45) from test_deform;
> QUERY PLAN
> ------------------------------------------------------------
> -------------------
> Finalize Aggregate
> (cost=127097.71..127097.72 rows=1 width=8)
> (actual time=742.578..742.578 rows=1 loops=1)
> -> Gather
> (cost=127097.50..127097.71 rows=2 width=8)
> (actual time=742.529..742.569 rows=3 loops=1)
> Workers Planned: 2
> Workers Launched: 2
> -> Partial Aggregate
> (cost=126097.50..126097.51 rows=1 width=8)
> (actual time=727.876..727.876 rows=1 loops=3)
> -> Parallel Seq Scan on test_deform
> (cost=0.00..121930.80 rows=1666680 width=4)
> (actual time=0.044..204.097 rows=1333333 loops=3)
> Planning Time: 0.361 ms
> JIT:
> Functions: 4
> Generation Time: 2.840 ms
> Inlining: false
> Inlining Time: 0.000 ms
> Optimization: false
> Optimization Time: 0.751 ms
> Emission Time: 6.436 ms
> Execution Time: 749.827 ms
> (16 rows)
>
> But at the same time on the bigger dataset (20M rows instead of 4M) the
> very
> same query gets better:
>
> =# explain analyze select sum(data45) from test_deform;
> QUERY PLAN
> ------------------------------------------------------------
> -------------------
> Finalize Aggregate
> (cost=631482.92..631482.93 rows=1 width=8)
> (actual time=2350.288..2350.288 rows=1 loops=1)
> -> Gather
> (cost=631482.71..631482.92 rows=2 width=8)
> (actual time=2350.276..2350.279 rows=3 loops=1)
> Workers Planned: 2
> Workers Launched: 2
> -> Partial Aggregate
> (cost=630482.71..630482.72 rows=1 width=8)
> (actual time=2328.378..2328.379 rows=1 loops=3)
> -> Parallel Seq Scan on test_deform
> (cost=0.00..609649.37 rows=8333337 width=4)
> (actual time=0.027..1175.960 rows=6666667 loops=3)
> Planning Time: 0.600 ms
> JIT:
> Functions: 6
> Generation Time: 3.661 ms
> Inlining: true
> Inlining Time: 65.373 ms
> Optimization: true
> Optimization Time: 120.885 ms
> Emission Time: 58.836 ms
> Execution Time: 2543.280 ms
> (16 rows)
>
> =# set jit_tuple_deforming to off;
>
> =# explain analyze select sum(data45) from test_deform;
> QUERY PLAN
> ------------------------------------------------------------
> -------------------
> Finalize Aggregate
> (cost=631482.92..631482.93 rows=1 width=8)
> (actual time=3616.977..3616.977 rows=1 loops=1)
> -> Gather
> (cost=631482.71..631482.92 rows=2 width=8)
> (actual time=3616.959..3616.971 rows=3 loops=1)
> Workers Planned: 2
> Workers Launched: 2
> -> Partial Aggregate
> (cost=630482.71..630482.72 rows=1 width=8)
> (actual time=3593.220..3593.220 rows=1 loops=3)
> -> Parallel Seq Scan on test_deform
> (cost=0.00..609649.37 rows=8333337 width=4)
> (actual time=0.049..1027.353 rows=6666667 loops=3)
> Planning Time: 0.149 ms
> JIT:
> Functions: 4
> Generation Time: 1.803 ms
> Inlining: true
> Inlining Time: 23.529 ms
> Optimization: true
> Optimization Time: 18.843 ms
> Emission Time: 9.307 ms
> Execution Time: 3625.674 ms
> (16 rows)
>
> `perf diff` indeed shows that in the first case (with the 4M rows dataset)
> the
> jitted version has some noticeable delta for one call, and unfortunately
> so far
> I couldn't figure out which one exactly because of JIT (btw, who can
> explain
> how to see a correct full `perf report` in this case? Somehow `perf
> inject --jit -o
> perf.data.jitted` and jit_profiling_support didn't help).
>
> But since on the bigger dataset I've got expected results, maybe it's just
> a
> sign that JIT kicks in too early in this case and what's necessary is to
> adjust
> jit_above_cost/jit_optimize_above_cost/jit_inline_above_cost?
>

maybe llvm does real compilation too late. It is too strange, because I
though about JIT cost like initial (fixed) costs. Now, it looks so this
cost is related to row numbers, and then the situation is much more complex.

Regards

Pavel