Re: Speed up COPY FROM text/CSV parsing using SIMD

Hello.

I tried Ayoub Kazar's test files again, using Nazir Bilal Yavuz's v3
patches, but with one difference since my last attempt: this time, I used 5
million lines per file. For each 5 million line file, I ran the import 5
times and averaged the results.

(I found that even using 1 million lines could sometimes produce surprising
speedups where the newer algorithm should be at least a tiny bit slower
than the non-simd version.)

The text file with no special characters is 30% faster. The CSV file with
no special characters is 39% faster. The text file with roughly 1/3rd
special characters is 0.5% slower. The CSV file with roughly 1/3rd special
characters is 2.7% slower.

I also tried files that alternated lines with no special characters and
lines with 1/3rd special characters, thinking I could force the algorithm
to continually check whether or not it should use simd and therefore force
more overhead in the try-simd/don't-try-simd housekeeping code. The text
file was still 50% faster. The CSV file was still 13% faster.

On Mon, Nov 24, 2025 at 3:59 PM Nathan Bossart <nathandbossart(at)gmail(dot)com>
wrote:

> On Thu, Nov 20, 2025 at 03:55:43PM +0300, Nazir Bilal Yavuz wrote:
> > On Thu, 20 Nov 2025 at 00:01, Nathan Bossart <nathandbossart(at)gmail(dot)com>
> wrote:
> >> + /* Load a chunk of data into a vector register */
> >> + vector8_load(&chunk, (const uint8 *)
> &copy_input_buf[input_buf_ptr]);
> >>
> >> In other places, processing 2 or 4 vectors of data at a time has proven
> >> faster. Have you tried that here?
> >
> > Sorry, I could not find the related code piece. I only saw the
> > vector8_load() inside of hex_decode_safe() function and its comment
> > says:
> >
> > /*
> > * We must process 2 vectors at a time since the output will be half the
> > * length of the input.
> > */
> >
> > But this does not mention any speedup from using 2 vectors at a time.
> > Could you please show the related code?
>
> See pg_lfind32().
>
> --
> nathan
>

--
-- Manni Wood EDB: https://www.enterprisedb.com