Lark VS fastapi

Lark supports, and recommends, writing and storing the grammar in a .lark file. We have syntax highlighting support in all major IDEs, and even in github itself. For example, here is Lark's built-in grammar for Python: https://github.com/lark-parser/lark/blob/master/lark/grammar...

You can also test grammars "live" in our online IDE: https://www.lark-parser.org/ide/

The rationale is that it's more terse and has less visual clutter than a DSL over Python, which makes it easier to read and write.

First, let me introduce myself. My name is Erez. You may know some of the Python libraries I wrote in the past: Lark, Preql and Data-diff.

It's not super maintained but you might enjoy building something with ppci, Pure Python Compiler Infrastructure. It has some front-ends and some back-ends. There's also PeachPy for an assembler. People like using Lark for parsing, I hear.

lark, a parsing library where I am somewhat involved has a really nice solution to this: Rules starting with _ are inlined in a post processing step.

Lark is a good library to assist with this.

If I may ask, did you consider Lark, and if so, why wasn't it fit for your purposes?

A grammar provides the high level constructs you need to define the "shape" of your data, and it largely takes care of the rest. Grammar libraries exist in other language (eg. lark or Parsimonius in Python) and they weren't created just to make XML parsing easier.

I made a solid attempt at an Earley parser framework of my own, but apparently to get the most reliable performance from Earley parsing you need to implement Joop Leo's improvement for right-recursive grammars, which nobody has been able to adequately explain to me. I've read Kegler's open letter to Vaillant, I've tried to read other implementations, I've even tried to beat my head against the original academic paper, but I don't have the background knowledge to make sense of it all.

That’s when I found FastAPI.

FastAPI & Uvicorn

Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696

An API application framework (such as FastAPI)

It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.

This ends up made worse if you’re using sync routes.

If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.

This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759

Fast API.

We will create our API using FastAPI, a modern high-performance web framework for building fast APIs with Python. It is designed to be easy to use, efficient, and highly scalable. Some key features of FastAPI include:

FastAPI is a modern, fast, web framework for building APIs with Python 3.7+ based on standard Python type hints. It is designed to be easy to use, fast to run, and secure. In this blog post, we’ll explore the key features of FastAPI and walk through the process of creating a simple API using this powerful framework.

FastAPI is a modern, fast web framework for building APIs with Python 3.7+ that automatically generates OpenAPI and JSON Schema documentation. While FastAPI simplifies API development, manually creating and updating API documentation can still be a time-consuming task. In this blog post, we’ll explore how to leverage FastAPI’s automatic documentation generation capabilities, specifically focusing on Swagger and ReDoc, and how to streamline the process of documenting your APIs.