|20 days ago||about 3 years ago|
|GNU General Public License v3.0 or later||MIT License|
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Analyzing multi-gigabyte JSON files locally
14 projects | news.ycombinator.com | 18 Mar 2023
I Use C When I Believe in Memory Safety
5 projects | news.ycombinator.com | 5 Feb 2023
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.
Processing JSON 2.5x faster than simdjson with msgspec
5 projects | /r/Python | 3 Oct 2022
5 projects | /r/emacs | 23 May 2022
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?
The fastest tool for querying large JSON files is written in Python (benchmark)
16 projects | news.ycombinator.com | 12 Apr 2022
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`.
How I cut GTA Online loading times by 70%
7 projects | /r/programming | 28 Feb 2021
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.
We haven't tracked posts mentioning serpy yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
marshmallow - A lightweight library for converting complex objects to and from simple Python datatypes.
orjson - Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
cysimdjson - Very fast Python JSON parsing library
cattrs - Complex custom class converters for attrs.
jsons - 🐍 A Python lib for (de)serializing Python objects to/from JSON
Fast JSON schema for Python - Fast JSON schema validator for Python.
Trafaret - Ultimate transformation library that supports validation, contexts and aiohttp.
ultrajson - Ultra fast JSON decoder and encoder written in C with Python bindings
lupin is a Python JSON object mapper - Python document object mapper (load python object from JSON and vice-versa)
python-rapidjson - Python wrapper around rapidjson