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uvicorn | starlette | |
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50 | 49 | |
7,022 | 8,674 | |
2.9% | 2.1% | |
8.7 | 0.0 | |
6 days ago | 7 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
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uvicorn
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Unresolved Memory Management Issues in FastAPI/Starlette/Uvicorn/Python During High-Load Scenarios
There's an open discussion under the Uvicorn repository and we prepared a repository for Reproduction GitHub Repo
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How to Dockerize and Deploy a Fast API Application to Kubernetes Cluster
FastAPI is a popular Python Web framework that developers use to create RESTful APIs. It is based on Pydantic and Python-type hints that assist in the serialization, deserialization, and validation of data. In this tutorial, we will use FastAPI to create a simple "Hello World" application. We test and run the application locally. FastAPI requires a ASGI server to run the application production such as Uvicorn.
- FastAPI 0.100.0:Release Notes
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Unlocking Performance: A Guide to Async Support in Django
Uvicorn and Daphne are both ASGI server implementations that can be used with Django to serve your application using the ASGI protocol. Uvicorn is built on top of the uvloop library, which is a fast implementation of the event loop based on libuv, while Daphne is maintained as part of the Django Channels project and was designed to handle the unique requirements of Django applications that utilize asynchronous features, such as real-time updates, bidirectional communication, and long-lived connections.
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Deploy a dockerized FastAPI application to AWS
So now we test if the application is working by running the application with uvicorn. Command has this format: uvicorn .:
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Cloud Run cold starts much slower than Cloud Functions?
Using a simple uvicorn hello world application, I was unable to replicate your case. Using the Dockerfile from your tutorial, and the example.py code from the uvicorn front page, along with your CMD, I got:
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Containerizing Shiny for Python and Shinylive Applications
If you look at the printout after launching the app, you'll notice that Shiny is using Uvicorn under the hood. This is a common way of containerizing apps using FastAPI deployments.
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I've built a self-hosted file storage
You can scale with application server, by adding more processes to server requests. I use Gunicorn with Uvicorn workers.
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A proposal for mascot
There is also uvicorn
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Understanding python async with FastAPI
When writing a web app with FastAPI, using async/await and a library like uvicorn, the GIL is less of an issue as we are primarily IO bound.
starlette
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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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Is it really advisable to try to run fastapi with predominantly sync routes in a real world application?
Correct. Yeah I’ve been trying to find stuff. It often circles back to the issue I linked (https://github.com/encode/starlette/issues/1724)
In this thread, starlette people admitting they don’t have anywhere near the tuning features necessary for more heavily sync oriented people https://github.com/encode/starlette/issues/1724
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Starlite updates March '22 | 2.0 is coming
Perhaps you are thinking about Starlette, the toolkit Starlite was originally based on? But Starlette as well won't have an influence on FastAPI's Pydantic dependencies, since Starlette never dependent on Pydantic in the first place.
- Where did I mess up ? Any ideas ?
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How to use FastAPI for microservices in Python
The framework's official website mentions a number of pros of FastAPI. In my opinion, the most useful features from a microservice perspective are: the simplicity of code (easy to use and avoid boilerplate), high operational capacity thanks to Starlette and Pydantic and compatibility with industry standards - OpenAPI and JSON Schema.
- Ask HN: What are the best open source TypeScript projects I can learn from?
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What happened with the community?
I'm under the impression if you want a python async solution starlete or starlite is a better option then fastapi due to performance and also have a lot open issues and merge requests.
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Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 1 – Real Python
Check out starlette if you haven’t yet. Those guys are a good time on their discord. I think this is the right link - I’m on mobile. https://github.com/encode/starlette
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What stack are you all using to get going with your projects? and has anyone tried RedwoodJS?
I'm primarily a back-end dev and Django/Flask used to be my goto frameworks for building websites. At my last company we deployed some FastAPI code in production, and since then I've been enjoying the Starlette framework quite a lot (which FastAPI builds on top of). And in my free time these days, I've been building a boilerplate codebase on top of Starlette so it's quicker to build user-facing applications using the framework.
What are some alternatives?
hypercorn
daphne - Django Channels HTTP/WebSocket server
hypercorn - Hypercorn is an ASGI and WSGI Server based on Hyper libraries and inspired by Gunicorn.
Flask - The Python micro framework for building web applications.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
dash - Data Apps & Dashboards for Python. No JavaScript Required.
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
uvloop - Ultra fast asyncio event loop.
mkcert - A simple zero-config tool to make locally trusted development certificates with any names you'd like.
sentry-asgi - Sentry integration for ASGI frameworks.