peps VS typedload

Compare peps vs typedload and see what are their differences.

typedload

Python library to load dynamically typed data into statically typed data structures (by ltworf)
Our great sponsors
  • WorkOS - The modern identity platform for B2B SaaS
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • SaaSHub - Software Alternatives and Reviews
peps typedload
36 5
4,133 252
1.5% -
9.8 8.0
4 days ago 5 days ago
reStructuredText Python
- GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

peps

Posts with mentions or reviews of peps. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-07-22.

typedload

Posts with mentions or reviews of typedload. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-06.
  • Show HN: Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19
    20 projects | news.ycombinator.com | 6 Mar 2023
    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/

  • Pydantic vs Protobuf vs Namedtuples vs Dataclasses
    4 projects | /r/Python | 25 Feb 2023
    I wrote typedload, which is significantly faster than pydantic. Just uses normal dataclasses/attrs/NamedTuple, has a better API and is pure Python!
  • Informatica serve a qualcosa?
    1 project | /r/Universitaly | 4 Feb 2023
  • Show HN: Python framework is faster than Golang Fiber
    19 projects | news.ycombinator.com | 10 Jan 2023
    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/

What are some alternatives?

When comparing peps and typedload you can also consider the following projects:

materials - Bonus materials, exercises, and example projects for our Python tutorials

codon - A high-performance, zero-overhead, extensible Python compiler using LLVM

pyenv-virtualenv - a pyenv plugin to manage virtualenv (a.k.a. python-virtualenv)

ustore - Multi-Modal Database replacing MongoDB, Neo4J, and Elastic with 1 faster ACID solution, with NetworkX and Pandas interfaces, and bindings for C 99, C++ 17, Python 3, Java, GoLang 🗄️

gcc

pydantic-core - Core validation logic for pydantic written in rust

DIPs - D Improvement Proposals

msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML

faster-cpython - How to make CPython faster.

koda-validate - Typesafe, Composable Validation

MLStyle.jl - Julia functional programming infrastructures and metaprogramming facilities

socketify.py - Bringing Http/Https and WebSockets High Performance servers for PyPy3 and Python3