GTAO_Booster_PoC VS pysimdjson

Everyone should keep in mind that this is the same game whose client-update-handshake-dance largely consisted of an enormous JSON of all the updates that had ever been done to the gamestate, which then got parsed naively. This situation was recognized and patched [0] by an interested third party [1].

Not a slight against the code monkey that implemented it that way, because they surely didn't bother to think about complexity consequences when the gamestate was new and the JSON wasn't 10MB yet, but a slight on Rockstar, because the cash cow that GTA:O turned out to be deserves some fuckin' maintenance, doesn't it? Then again, the eye-popping prevalence of hax0rs in Rockstar games probably ought to signify priorities to even the most casual observer. As others in these comments observe, with what those hackers can achieve, it's not surprising to learn there's RCEs too.

[0] https://github.com/tostercx/GTAO_Booster_PoC

[1] https://nee.lv/2021/02/28/How-I-cut-GTA-Online-loading-times...

The update was in line with the work of a guy named t0st, who found out why the loading times were so slow in the start and he made a [proof of concept fix](https://github.com/tostercx/GTAO_Booster_PoC) which Rockstar has awarded him $10,000 irl for.

This is an article that reports load times for GTA V PC online were reduced by at least 70% using a mod. Files and instructions to build the mod are found here.

How likely is one to get suspended if one uses the proof of concept for playing the game

The proof of concept source code is on Github.

Its magic function wrapping comes at a cost, trading ease of use for runtime performance. When you have a single C++ function to call that will run for a "long" time, pybind all the way. But pysimdjson tends to call a single function very quickly, and the overhead of a single function call is orders of magnitude slower than with cython when being explit with types and signatures. Wrap a class in pybind11 and cython and compare the stack trace between the two, and the difference is startling.

Ex: https://github.com/TkTech/pysimdjson/issues/73

simdjson

You are aware of simdjson being available in python if you really need some json crunching, albeit json module in Python is implemented in C itself, so I don't think understand why do you think Python is slow there?

json: 113.79130696877837 ms

While `orjson`, is faster than `ujson`/`json` here, it's only ~6% faster (in this benchmark). `simdjson` and `msgspec` (my library, see https://jcristharif.com/msgspec/) are much faster due to them avoiding creating PyObjects for fields that are never used.

If spyql's query engine can determine the fields it will access statically before processing, you might find using `msgspec` for JSON gives a nice speedup (it'll also type check the JSON if you know the type of each field). If this information isn't known though, you may find using `pysimdjson` (https://pysimdjson.tkte.ch/) gives an easy speed boost, as it should be more of a drop-in for `orjson`.

I don't think JSON is really the problem - parsing 10MB of JSON is not so slow. For example, using Python's json.load takes about 800ms for a 47MB file on my system, using something like simdjson cuts that down to ~70ms.