starlette
connexion
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starlette | connexion | |
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55 | 23 | |
9,510 | 4,416 | |
3.0% | 0.5% | |
9.2 | 8.2 | |
1 day ago | 10 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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.
starlette
- Ask HN: What is your go-to stack for the web?
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Building Fast APIs with FastAPI: A Comprehensive Guide
Fast Execution: FastAPI is built on top of Starlette and Pydantic, making it one of the fastest Python frameworks for building APIs.
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Embracing Modern Python for Web Development
The framework's efficiency comes from its use of Starlette for building asynchronous web services and Pydantic for robust data validation and serialization, powered by Python's type hints. Pydantic has recently announced the official release of Pydantic V2 (June 2023), which is a ground-up rewrite that offers many new features and performance improvements, so make sure to be using that instead of V1.
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FastHttp for Python (64k requests/s)
Uvicorn + Starlette 8k requests/s
- Microdot "The impossibly small web framework for Python and MicroPython"
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An Introduction to âš¡FastAPI
Starlette documentation
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Writing a chat application in Django 4.2 using async StreamingHttpResponse
Same here, but without these weird utils it doesn't get any better.
I have 7 YoE with Django. Its great at so many things. You see some code, like middlewares, and immediately understand what's going on.
Now, we also have Starlette. The base of all new, fancy asgi libraries. Here's the base middleware class.
https://github.com/encode/starlette/blob/8d7a1cacfb3e1a30cbb...
In the last couple of years I heard 'we're running fastapi on production. Wanna join us?' so many times... but the reality is that it's still not suitable for prod. Who wants to work with a code like that if you have a readable, stable Django? I'm clueless.
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Deploying an ML model to Paperspace and creating an API
Set up Starlette, a tool we'll use to make async requests
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FastAPI middleware doesn't run while making request to websocket endpoint
I never used websockets in FastAPI so I wouldn't know how to guide you more, but Middleware in Websockets are 100% supported by Starlette : https://github.com/encode/starlette/issues/641
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Chat implementation
Websockets are the way but I would not recommend django as it's still not fully async. I would go for other tools.
connexion
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Write OpenAPI with TypeSpec
I like the idea, especially the TS-like syntax around enums and union types. I've always preferred the SDL for GraphQL vs writing OpenAPI for similar reasons.
I echo the sentiment others have brought up, which is the trade-offs of a code-driven schema vs schema-driven code.
At work we use Pydantic and FastAPI to generate the OpenAPI contract, but there's some cruft and care needed around exposing those underlying Pydantic models through the API documentation. It's been easy to create schemas that have compatibility problems when run through other code generators. I know there are projects such as connexction[1] which attempt to inverse this, but I don't have much experience with it. In the GraphQL space it seems that code-first approaches are becoming more favored, though there's a different level of complexity needed to create a "typesafe" GraphQL server (eg. model mismatches between root query resolvers and field resolvers).
[1] https://github.com/spec-first/connexion
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Connexion 3 released!
Connexion is a popular Python web framework (~ 5 million downloads per month) that makes spec-first and api-first development easy. You describe your API in an OpenAPI (or swagger) specification with as much detail as you want and Connexion will guarantee that it works as you specified.
- Connexion 3.0 Released
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Show HN: REST Alternative to GraphQL and tRPC
> While REST APIs don't generally provide the same level of control to clients as GraphQL, many times this could be seen as a benefit especially in scenarios where strict control over data access and operations is crucial.
Rest is more secure, cacheable, and more performant on the server side as field resolution doesn't need to happen like it does with GraphQL. It is not more performant on the client side, and this is a trade-off, but I favor rest applications over GraphQL ones as a DevOps engineer. They are much easier to administer infrastructure-wise, I can cache the requests, etc.
Data at our company suggests that several small queries actually do better performance-wise than one large one. We switched to GraphQL a year and a half ago or so, but this piece of data seems to suggest that we might have been better off just sticking with REST. My suggestion to that effect was not met with optimism either on the client or server side. Apparently there are server-side benefits as well, allowing for more modular development or something like that.
I have used OpenAPI using connexion[1]. It was hard to understand at first, but I really liked that the single source of truth was one schema. It also made it really easy to develop against the API because it came with a UI that showed the documentation for all the rest end points and even had test buttons.
1: https://connexion.readthedocs.io/en/latest/
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Ask HN: Why is there no specification for Command Line Interfaces?
What's the use case? I was thinking about this exact issue because my product ships several CLI tools, but I wasn't convinced it would be worth the effort.
An OpenAPI specification describes an HTTP interface, and I see it as useful because it makes it easier to write code in language-of-choice to generate HTTP requests (by generating client libraries from the OpenAPI spec).
For a CLI, the interface is the command-line. Usually people type these commands, or they end up in bash scripts, or sometimes they get called from programming language of choice by shelling out to the CLI. So I could see a use case for a CLI spec, which would make it easier to generate client libraries (which would shell out to the CLI)... but it seems a little niche.
Or maybe, as input to a documentation tool (like Swagger docs). I would imagine if you're using a CLI library like Python's Click, most of that data is already there. Click Parameters documentation: https://click.palletsprojects.com/en/8.1.x/parameters/
Or maybe, you could start from the spec and then generate code which enforces it. So any changes pass through the spec, which would make it easy to write code (server and client-side) / documentation / changelogs. Some projects like this: Guardrail (Scala) https://github.com/guardrail-dev/guardrail , and Connexion (Python) https://github.com/spec-first/connexion .
But without this ecosystem of tooling, documenting your CLI in a specification didn't really seem worth the effort. Of course, that's a bootstrapping problem.
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Flask is Great!
Connexion is a framework on top of Flask that automagically handles HTTP requests defined using OpenAPI/Swagger.
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What is the best practice for mapping JSON requests to objects and back to JSON?
I recommend you create a OpenAPI Specification and implement a python module that you expose via connexion or on the cli via click(for easy testing).
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Flask-Powered APIs: Fast, Reliable, and Used by the World's Top Companies
I'm here because Swagger-CodeGen created flask-Connexion boilerplate for python.
- Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 1 – Real Python
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Does anybody know any good resources I could use to study ISP architecture?
Personally we just prov them using librouteros and flask-connexion/openapi.
What are some alternatives?
Flask - The Python micro framework for building web applications.
flask-restful - Simple framework for creating REST APIs
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
Flask RestPlus - Fully featured framework for fast, easy and documented API development with Flask
uvicorn - An ASGI web server, for Python. 🦄
flasgger - Easy OpenAPI specs and Swagger UI for your Flask API
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
django-rest-framework - Web APIs for Django. 🎸
starlite - Light, Flexible and Extensible ASGI API framework | Effortlessly Build Performant APIs [Moved to: https://github.com/litestar-org/litestar]
eve - REST API framework designed for human beings
quart - An async Python micro framework for building web applications.
falcon - The no-magic web data plane API and microservices framework for Python developers, with a focus on reliability, correctness, and performance at scale.