jsonschema VS Fast JSON schema for Python

Compare jsonschema vs Fast JSON schema for Python and see what are their differences.

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jsonschema Fast JSON schema for Python
4 1
474 439
- -
8.0 7.0
30 days ago 4 days ago
Rust Python
MIT License BSD 3-clause "New" or "Revised" License
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jsonschema

Posts with mentions or reviews of jsonschema. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-05-14.
  • Fast Linked Lists
    4 projects | news.ycombinator.com | 14 May 2024
    > This article is disingenuous with its Vec benchmark. Each call to `validate` creates a new Vec, but that means you allocate + free the vec for each validation. Why not store the vec on the validator to reuse the allocation? Why not mention this in the article, i had to dig in the git history to find out whether the vec was getting reallocated.

    The idea comes back to [0] which is similar to one of the steps in the article, and before adding `push` & `pop` I just cloned it to make things work. That's what Rust beginners do.

    > This feels like you had a cool conclusion for your article, 'linked lists faster than vec', but you had to engineer the vec example to be worse. Maybe I'm being cynical.

    Maybe from today's point in time, I'd think the same.

    > It would be interesting to see the performance of a `Vec<&str>` where you reuse the vector, but also a `Vec` where you copy the path bytes directly into the vector and don't bother doing any pointer traversals. The example path sections are all very small - 'inner', 'another', 5 bytes, 7 bytes - less than the length of a pointer! storing a whole `&str` is 16 bytes per element and then you have to rebuild it again anyway in the invalid case.

    Yeah, that makes sense to try!

    > This whole article is kinda bad, it's titled 'blazingly fast linked lists' which gives it some authority but the approach is all wrong. Man, be responsible if you're choosing titles like this. Someone's going to read this and assume it's a reasonable approach, but the entire section with Vec is bonkers.

    > Why are we designing 'blazingly fast' algorithms with rust primitives rather than thinking about where the data needs to go first? Why are we even considering vector clones or other crazy stuff? The thought process behind the naive approach and step 1 is insane to me:

    > 1. i need to track some data that will grow and shrink like a stack, so my solution is to copy around an immutable Vec (???)

    > 2. this is really slow for obvious reasons, how about we: pull in a whole new dependency ('imbl') that attempts to optimize for the general case using complex trees (???????????????)

    That's clickbait-y, though none of the article's ideas aim to be a silver bullet. I mean, there are admittedly dumb ideas in the article, though I won't believe that somebody would come up with a reasonable solution without trying something stupid first. However, I might have used better wording to highlight that and mention that I've come up with some of these ideas when was working on `jsonschema` in the past.

    > I understand you're trying to be complete, but 'some scenarios' is doing a lot of work here. An Arc<[T]> approach is literally just the same as the naive approach but with extra atomic refcounts! Why mention it in this context?

    If you don't need to mutate the data and need to store it in some other struct, it might be useful, i.e. just to have cheap clones. But dang, that indeed is a whole different story.

    > I have no idea why you mention 'code complexity' here (complexity introduced by rust and its lifetimes), but fail to mention how adding a dependency on 'imbl' is a negative.

    Fair. Adding `imbl` wasn't a really good idea for this context at all.

    Overall I think what you say is kind of fair, but I think that our perspectives on the goals of the article are quite different (which does not disregard the criticism).

    Thank you for taking the time and answer!

    - [0] - https://github.com/Stranger6667/jsonschema-rs/commit/1a1c6c3...

  • web service framework and OpenAPI spec
    2 projects | /r/rust | 24 Mar 2023
    Checkout this crate https://crates.io/crates/jsonschema
  • Show HN: Pg_jsonschema – A Postgres extension for JSON validation
    8 projects | news.ycombinator.com | 21 Jul 2022
    The `jsonschema` crate author here.

    First of all, this is an exciting use case, I didn't even anticipate it when started `jsonschema` (it was my excuse to play with Rust). I am extremely pleased to see such a Postgres extension :)

    At the moment it supports Drafts 4, 6, and 7 + partially supports Draft 2019-09 and 2020-12. It would be really cool if we can collaborate on finishing support for these partially supported drafts! What do you think?

    If you'll have any bug reports on the validation part, feel free to report them to our issue tracker - https://github.com/Stranger6667/jsonschema-rs/issues.

    Re: performance - there are a couple of tricks I've been working on, so if anybody is interested in speeding this up, feel free to join here - https://github.com/Stranger6667/jsonschema-rs/pull/373

    P.S. As for the "Prior Art" section, I think that https://github.com/jefbarn/pgx_json_schema should be mentioned there, as it is also based on `pgx` and `jsonschema`.

  • Need help: Faster JSON Schema validation in Rust
    1 project | /r/rust | 14 Jun 2022
    I've been working on rewriting my jsonschema crate for a while and now I want to ask for help as I don't have much bandwidth to work on it. Here is a WIP pull request with things I have so far:

Fast JSON schema for Python

Posts with mentions or reviews of Fast JSON schema for Python. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-31.
  • I wrote okjson - A fast, simple, and pythonic JSON Schema Validator
    8 projects | /r/Python | 31 Mar 2022
    I had a requirement to process and validate large payloads of JSON concurrently for a web service, initially I implemented it using jsonschema and fastjsonschema but I found the whole JSON Schema Specification to be confusing at times and on top of that wanted better performance. Albeit there are ways to compile/cache the schema, I wanted to move away from the schema specification so I wrote a validation library inspired by the design of tiangolo/sqlmodel (type hints) to solve this problem easier.

What are some alternatives?

When comparing jsonschema and Fast JSON schema for Python you can also consider the following projects:

marshmallow - A lightweight library for converting complex objects to and from simple Python datatypes.

lupin is a Python JSON object mapper - Python document object mapper (load python object from JSON and vice-versa)

cattrs - Composable custom class converters for attrs, dataclasses and friends.

serpy - ridiculously fast object serialization

ultrajson - Ultra fast JSON decoder and encoder written in C with Python bindings

cargonauts - A Rust web framework

PyValico - Small python wrapper around https://github.com/rustless/valico

RDFLib plugin providing JSON-LD parsing and serialization - JSON-LD parser and serializer plugins for RDFLib

python-rapidjson - Python wrapper around rapidjson

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