hpy VS fastify

There is a merge request up to add autogen rust bindings to hpy

https://github.com/hpyproject/hpy/pull/457

Are you referencing https://github.com/hpyproject/hpy?

I do hope it takes off.

The HPy project [0] seems like a promising way out of this.

[0] https://hpyproject.org/

socketify.py breaks the record for Python no other Python WebFramework/Server as able to reach 6.2 mi requests per second before in TechEmPower Benchmarks, this puts Python at the same level of performance that Golang, Rust and C++ for web development, in fact Golang got 5.2 mi req/s in this same round. Almost every server or web framework tries to use JIT to boost the performance, but only socketify.py deliveries this level of performance, and even without JIT socketify.py is twice as fast any other web framework/server in active development, and still can be much more optimized using HPy (https://hpyproject.org/). Python will get even faster and faster in future!

I think Propose a better messaging for Python is the option and a lot of languages will learn it from Rust, because rust erros are the best described errors I see in my life lol. Cargo is amazing and I think we will need a better poetry/pip for sure, HPy project will modernize extensions and packages 📦 too https://hpyproject.org/

It also show that PyPy3 will not magically boost your performance, you need to integrate in a manner that PyPy3 can optimize and delivery CPU performance, with a more complex example maybe it can help more. But why socketify is so much faster using PyPy3? The answer is CFFI, socketify did not use Cython for integration and cannot delivery the full performance on Python3, this will be solved with HPy.

HPy integration to better support CPython, PyPy and GraalPython

Absolutely everything in CPython is a PyObject, and that can’t be changed without breaking the C API. A PyObject contains (among other things) a type pointer, a reference count, and a data field; none of these things can be changed without (again) breaking the C API.

There have definitely been attempts to modernize; the HPy project (https://hpyproject.org/), for instance, moves towards a handle-oriented API that keeps implementation details private and thus enables certain optimizations.

For example, Fastify removed the instanceof operatorfrom its codebase because it was causing problems for those developers that rely on jest as a testing framework.

Both are frameworks but NextJs is for Forntend (web app in browser that use ReactJs under the hood) and NestJs is for Backend (server app running on a server witch use Express or Fastify). The only thing similar between them is the typed language in which they were written.

Restify & Fastify Hapi

Let's create a sample Node API project we can work with throughout to build a new docker image. We will leverage Fastify to create an API that we configure via the fastify-cli.

Fastify

In setting out to build this service, we wanted to use gRPC for its APIs. We’ve been reaching for REST when building APIs so far, primarily out of necessity, i.e., our public APIs needed auto-generated client SDKs and docs for developers working with them. We built those APIs with Fastify and Typebox but felt burned by a code-first approach to generating an OpenAPI spec. I’ll spare you the details and save that experience/learning for another article. Suffice it to say we love gRPC’s schema-first approach. This blog post summarizes our feelings well

Fastify – Fast and Low overhead web framework

Custom Functions are powered by Fastify (a light-weight Node.js framework that claims to be faster than Express.js), so they’re extremely flexible.