Re: WIP: BRIN multi-range indexes

On 1/19/21 9:44 PM, John Naylor wrote:
> On Tue, Jan 12, 2021 at 1:42 PM Tomas Vondra
> <tomas(dot)vondra(at)enterprisedb(dot)com <mailto:tomas(dot)vondra(at)enterprisedb(dot)com>>
> wrote:
> > I suspect it'd due to minmax having to decide which "ranges" to merge,
> > which requires repeated sorting, etc. I certainly don't dare to claim
> > the current algorithm is perfect. I wouldn't have expected such big
> > difference, though - so definitely worth investigating.
>
> It seems that monotonically increasing (or decreasing) values in a table
> are a worst case scenario for multi-minmax indexes, or basically, unique
> values within a range. I'm guessing it's because it requires many passes
> to fit all the values into a limited number of ranges. I tried using
> smaller pages_per_range numbers, 32 and 8, and that didn't help.
>
> Now, with a different data distribution, using only 10 values that
> repeat over and over, the results are muchs more sympathetic to multi-minmax:
>
> insert into iot (num, create_dt)
> select random(), '2020-01-01 0:00'::timestamptz + (x % 10 || '
> seconds')::interval
> from generate_series(1,5*365*24*60*60) x;
>
> create index cd_single on iot using brin(create_dt);
> 27.2s
>
> create index cd_multi on iot using brin(create_dt
> timestamptz_minmax_multi_ops);
> 30.4s
>
> create index cd_bt on iot using btree(create_dt);
> 61.8s
>
> Circling back to the monotonic case, I tried running a simple perf
> record on a backend creating a multi-minmax index on a timestamptz
> column and these were the highest non-kernel calls:
> +   21.98%    21.91%  postgres         postgres            [.]
> FunctionCall2Coll
> +    9.31%     9.29%  postgres         postgres            [.]
> compare_combine_ranges
> +    8.60%     8.58%  postgres         postgres            [.] qsort_arg
> +    5.68%     5.66%  postgres         postgres            [.]
> brin_minmax_multi_add_value
> +    5.63%     5.60%  postgres         postgres            [.] timestamp_lt
> +    4.73%     4.71%  postgres         postgres            [.]
> reduce_combine_ranges
> +    3.80%     0.00%  postgres         [unknown]           [.]
> 0x0320016800040000
> +    3.51%     3.50%  postgres         postgres            [.] timestamp_eq
>
> There's no one place that's pathological enough to explain the 4x
> slowness over traditional BRIN and nearly 3x slowness over btree when
> using a large number of unique values per range, so making progress here
> would have to involve a more holistic approach.
>

Yeah. This very much seems like the primary problem is in how we build
the ranges incrementally - with monotonic sequences, we end up having to
merge the ranges over and over again. I don't know what was the
structure of the table, but I guess it was kinda narrow (very few
columns), which exacerbates the problem further, because the number of
rows per range will be way higher than in real-world.

I do think the solution to this might be to allow more values during
batch index creation, and only "compress" to the requested number at the
very end (when serializing to on-disk format).

There are a couple additional comments about possibly replacing
sequential scan with a binary search, that could help a bit too.

regards

--
Tomas Vondra
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