typedload VS simdjson

Author of typedload here!

FastAPI relies on (not so fast) pydantic, which is one of the slowest libraries in that category.

Don't expect to find such benchmarks on the pydantic documentation itself, but the competing libraries will have them.

[0] https://ltworf.github.io/typedload/

I wrote typedload, which is significantly faster than pydantic. Just uses normal dataclasses/attrs/NamedTuple, has a better API and is pure Python!

I read all the perftests in the repo. I think they nearly all parse a structure that contains a repetition of the same or similar thing a couple hundred thousand times times and the timing function returns the min and max of 5 attempts. I just picked one example for posting.

Not a Python expert, but could the Pydantic tests be possibly not realistic and/or misleading because they are using kwargs in __init__ [1] to parse the object instead of calling the parse_obj class method [2]? According to some PEPs [3], isn't Python creating a new dictionary for that parameter which would be included in the timing? That would be unfortunate if that accounted for the difference.

Something else I think about is if a performance test doesn't produce a side effect that is checked, a smart compiler or runtime could optimize the whole benchmark away. Or too easy for the CPU to do branch prediction, etc. I think I recall that happening to me in Java in the past, but probably not happened here in Python.

[1] https://github.com/ltworf/typedload/blob/37c72837e0a8fd5f350...

[2] https://docs.pydantic.dev/usage/models/#helper-functions

[3] https://peps.python.org/pep-0692/

It's also supported by simdjson [0] (which has a lot of language bindings [1]):

> Multithreaded processing of gigantic Newline-Delimited JSON (ndjson) and related formats at 3.5 GB/s

[0] https://simdjson.org/

[0] https://github.com/simdjson/simdjson?tab=readme-ov-file#bind...

String parsing is negligible compared to the speed of the DOM which is glacially slow: https://news.ycombinator.com/item?id=38835920

Come on, people, make an effort to learn how insanely fast computers are, and how insanely inefficient our software is.

String parsing can be done at gigabytes per second: https://github.com/simdjson/simdjson If you think that is the slowest operation in the browser, please find some resources that talk about what is actually happening in the browser?

Thanks for all the detailed information! That answers a bunch of my questions and the implementation of strlen is nice.

The instruction I was thinking of is pshufb. An example ‘weird’ use can be found for detecting white space in simdjson: https://github.com/simdjson/simdjson/blob/24b44309fb52c3e2c5...

This works as follows:

1. Observe that each ascii whitespace character ends with a different nibble.

2. Make some vector of 16 bytes which has the white space character whose final nibble is the index of the byte, or some other character with a different final nibble from the byte (eg first element is space =0x20, next could be eg 0xff but not 0xf1 as that ends in the same nibble as index)

3. For each block where you want to find white space, compute pcmpeqb(pshufb(whitespace, input), input). The rules of pshufb mean (a) non-ascii (ie bit 7 set) characters go to 0 so will compare false, (b) other characters are replaced with an element of whitespace according to their last nibble so will compare equal only if they are that whitespace character.

I’m not sure how easy it would be to do such tricks with vgather.vv. In particular, the length of the input doesn’t matter (could be longer) but the length of white space must be 16 bytes. I’m not sure how the whole vlen stuff interacts with tricks like this where you (a) require certain fixed lengths and (b) may have different lengths for tables and input vectors. (and indeed there might just be better ways, eg you could imagine an operation with a 256-bit register where you permute some vector of bytes by sign-extending the nth bit of the 256-bit register into the result where the input byte is n).

Additionally, if you like low level stuff, check out libfmt (https://github.com/fmtlib/fmt) - not a big project, not difficult to understand. Or something like simdjson (https://github.com/simdjson/simdjson).

Everything you said is totally reasonable. I'm a big fan of napkin math and theoretical upper bounds on performance.

simdjson (https://github.com/simdjson/simdjson) claims to fully parse JSON on the order of 3 GB/sec. Which is faster than OP's Go whitespace parsing! These tests are running on different hardware so it's not apples-to-apples.

The phrase "cannot go faster than this" is just begging for a "well ackshully". Which I hate to do. But the fact that there is an existence proof of Problem A running faster in C++ SIMD than OP's Probably B scalar Go is quite interesting and worth calling out imho. But I admit it doesn't change the rest of the post.

I have same question as /u/JDRiverRun : how do you deal with JSON, do you parse json on Rust side or on Emacs side. I see that you are requiring json.el in your lspce.el, but I haven't looked through entire file carefully. If you parse on Rust side, do you use simdjson (there are at least two Rust bindings to it)? If yes, what are your impressions, experiences compared to more "standard" json library?