Re: GSOC 2018 Project - A New Sorting Routine

Hi Kefan,

On 07/10/2018 11:02 PM, Kefan Yang wrote:
> Hello, Hackers!
>
> I am working on my project in Google Summer of Code 2018
> <https://wiki.postgresql.org/wiki/GSoC_2018#Sorting_algorithms_benchmark_and_implementation_.282018.29>.
> In this project, I am trying to improve the in-memory sorting routine in
> PostgreSQL. Now I am very excited to share my progress with you guys.
>
> Originally, PostgreSQL is using the QuickSort implemented by J. L.
> Bentley and M. D. McIlroy in "Engineering a sort function" with some
> modifications. This sorting routine is very fast, yet may fall to O(n^2)
> time complexity in the worst case scenario. We are trying to find faster
> sorting algorithms with guaranteed O(nlogn) time complexity.
>

Time complexity is nice, but it merely estimates the number of
comparisons needed by the sort algorithm. It entirely ignores other
factors that are quite important - behavior with caches, for example.
And quicksort works really well in this regard, I think.

The worst-case complexity may be an issue, but we're already dealing
with it by using median-of-three (actually, median-of-nine, IIRC) in
pg_qsort. Hitting the worst-case accidentally is possible, but it should
be quite unlikely. It's still deterministic so an adversary might
construct a data set triggering it and use it for a DDoS, but if that
was a real issue in practice - I assume we'd already hear about it. But
even if it was, I guess the easiest way to deal with it would be to
randomize the selection of pivots.

In other words, replacing quicksort with an algorithm that is slower on
average but has better worst-case behavior is unlikely to be accepted
with joy, when the worst case is unlikely / bordering with impossible.

> In this patch, I
>
> 1. Use IntroSort to implement pg_qsort. IntroSort is a hybrid sorting
> algorithm. It uses Quicksort most of the time, but switch to
> insertion sort when the array is small and heapsort when the
> recursion exceeds depth limit.
> 2. Only check if the array is preordered once on the whole array to get
> better overall performance. Previously the sorting routine checks if
> the array is preordered on every recursion.
>
> After some performance test, I find the new sorting routine
>
> 1. Slightly faster on sorting random arrays.
> 2. Much faster on worst case scenario since it has O(nlogn) worst case
> complexity.
> 3. Has nearly the same performance on mostly sorted arrays.
>
> I use both standalone tests and pgbench to show the result. A more
> detailed report is in the attachment, along with the patch and some
> scripts to reproduce the result.
>

I find those results rather unconvincing.

First of all, testing this on t2.micro is *insane* considering that this
instance type is subject to throttling (depending on CPU credits). I
don't know if this happened to be an issue during your tests, of course.
Furthermore, the instance only has 1 virtual core, so there's likely a
lot of noise due to other tasks (kernel of whatever needs to run).

Secondly, I see the PDF includes results for various data set types
(random, reversed, mostly random, ...) but the archive you provided only
includes the random + killer cases.

And finally, I see the PDF reports "CPU clocks" but I'm not sure what
that actually is? Is that elapsed time in milliseconds or something else?

So I've done a bit of benchmarking by running the battery of tests I've
previously used for sort-related patches, and those results seem much
less optimistic. I've done this on two different x86 machines (one with
an old i5-2500K CPU, the other one with rather new e5-2620v4). Full
results and scripts are available at [1] and [2], a summary of the
results is attached here.

Each spreadsheet has a couple of "comparison N" sheets, where N is the
number of rows on the test. The last set of columns is comparison to
unpatched master, where values below 100% mean "faster than master" and
above 100% "slower than master".

On the (quite old) i5-2500k CPU, there's pretty much no difference
between master with and without the patch.

On the (much newer) e5-2620v4 system, the results seem somewhat more
variable - ~10% regressions on CREATE INDEX cases, ~5% gains on the
other cases, for the smallest data set (10k rows). But as the data set
grows, the regressions pretty clearly prevail. Not great, I guess :-(

I don't want to discourage you from working on sorting, and I'm sure
significant improvements in this area are possible (and needed). But my
guess is that those optimizations will happen at higher level, not by
tweaking the low-level algorithm.

regards

[1] https://bitbucket.org/tvondra/sort-intro-sort-i5/src/master/
[2] https://bitbucket.org/tvondra/sort-intro-sort/src/master/

--
Tomas Vondra http://www.2ndQuadrant.com
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services