dispatch
full-stack-fastapi-template
dispatch | full-stack-fastapi-template | |
---|---|---|
20 | 28 | |
4,602 | 23,069 | |
1.0% | - | |
9.9 | 9.5 | |
4 days ago | 3 days ago | |
Python | TypeScript | |
Apache License 2.0 | MIT License |
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dispatch
- Netflix Dispatch
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Is there any open source project that uses FasAPI?
They use only sync routes in the project and can’t explain why https://github.com/Netflix/dispatch/issues/1073
- Is it really advisable to try to run fastapi with predominantly sync routes in a real world application?
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How to build a scalable project file structure for a beginner.
By far my favorite production FastAPI app to use as a references of how to use these technologies well is NetFlix Dispatch: https://github.com/Netflix/dispatch
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FastAPI Boilerplate using MongoDB, Motor, Docker
Hey, I have a lot of opinions about this template, but these are just my opinions based on my own experiences being burned by these things so take from them what you will: 1. Your version of poetry is outdated, dependency groups don't work that way anymore and this will fail to install on modern poetry 2. You list pyyaml as a dependency but don't use it anywhere 3. The healthcheck endpoint is interesting, but expensive and a security risk. I like the value this provides, but I don't know if exposing it this way or using it as a healthcheck is a good idea 1. You typically don't want to touch external systems (mongo) as part of a healthcheck as this can cause cascading failure chains that get out of hand quickly 2. You typically don't want to touch the underlying system itself 1. which means you can / should get rid of psutil as a dependency 4. You don't need and shouldn't use pytest-asyncio for a FastAPI project. It comes built-in with its own async test handlers that you should be using 5. Having python-dotenv installed in production has burned me many times. I recommend removing this complete, otherwise just moving it to a dev dep 6. Using the src layout prevents a lot of weird import time problems from cropping up in production, I recommend checking it out 7. The entrypoint for the Docker container should be using 1 worker, as containers really prefer if you have only a single root PID chain and nothing else. Deploying this into k8s would cause a lot of issues 8. Native python logging really isn't great for modern production applications. Structlog or Loguru are great alternatives and much easier to use (which should remove your only dependency on pyyaml) 9. The configuration management may not work the way you want since it is weakly typed. Since FastAPI uses Pydantic, you have access to BaseSettings which is a far superior product for configuration management, especially with environment variables 10. The app and API folder structure is an anti-pattern that doesn't scale past projects the size of a tutorial on how to laern FastAPI. I strongly recommend changing this to move of a vertical slice or folder per feature layout such as is used in https://github.com/Netflix/dispatch/tree/master/src/dispatch 11. FastAPI routes don't need `response_model=` anymore in favor of adding the return type to your function signature such as `async def create_thing() -> Thing:` 12. The uuid_masker function is interesting, but exposing UUIDs in logs usually doesn't pose a security risk and only makes debugging more difficult 13. You have some type lies in your code that could burn you such as https://github.com/alexk1919/fastapi-motor-mongo-template/blob/main/app/db/db.py#L10 . This pattern for the global DB handle has also burned me in the past and I had to go back and refactor out all of them to instead to purely use the FastAPI dependency injection chaining 14. datetime.datetime isn't safe to use as it is in sample_resource_common.py, you need a timezone aware implementation 15. Your test suite is stateful, require a running database, leak a lot of implementation details of the underlying models. This is every anti-pattern in the book for unit testing. And if you are going to do integration tests, then you would be better off with tooling designed for it such as playwright. Again, these are all just my opinions and may alone not be enough to warrant changing anything you have here.
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Python projects with best practices on Github?
Two random examples I found from 30 seconds of googling: Here’s Netflix using it in their crisis management tool, and here’s Uber using it in their deep learning framework.
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Open Source Projects based on FastAPI
netflix dispatch
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As a long time programmer what are some important coding styles ?
As someone who uses FastAPI, I find the https://github.com/Netflix/dispatch code to be a great reference.
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CEO faces backlash after quoting Martin Luther King Jr. in announcing layoffs
Besides that paying $21 to $41 per user for this nuts. Set up a VPS with Dispatch (opensourced by Netflix) and save your company some money.
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Total beginner, use FastAPI?
For production ready code examples I use: https://github.com/Netflix/dispatch
full-stack-fastapi-template
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Building a Secure API with FastAPI, PostgreSQL, and Hanko Authentication
This project is a modification of the authentication flow of the awesome repository made by tiangolo at full-stack-fastapi-postgresql
- Do you know any quality FastAPI starter projects?
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What is a sensible way to go about designing an authentication microservice?
FastAPI with a PostgreSQL database: https://github.com/tiangolo/full-stack-fastapi-postgresql/tree/master
- Faster way to kickstart and develop backend REST apis?
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Is a Framework like Django possible in Rust
Ha! I do write SQL since that's where I cut my teeth many years ago. But I mostly use stored procedures where possible. I prefer not to use ORMs - sorry I don't find that side work... I am not saying Django's was bad or inferior, just do not prefer it. For FastAPI - maybe you missed the various repos with everything you mentioned was missing (there are great ones directly from the maintainer as well as others). No more glue than what you find in all the modules in a large Django project, just maybe in different forms and flavors. Besides, we're here to talk about Rust, making me wonder why we're debating two Python projects. Yes, I fell in love with Django, the romance faded in 2018, and I moved on. Feel free to enjoy using it - I'm not trying to sway you away from it!
- Is there any open source project that uses FasAPI?
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How to build a scalable project file structure for a beginner.
I've just recently switched to a structure that follows Netflix's Dispatch application after starting with https://github.com/tiangolo/full-stack-fastapi-postgresql and it feels way better and organized.
- ORM for FastAPI+PostgreSQL, Tortoise or Sqlalchemy? what would you choose and why?
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Creating a webpage for data entry
Honestly your easiest option for data gathering would be to create google spreadsheets/forms and give each municipality access. For a custom data entry platform I suggest looking for templates like this one and learning how to add custom logic to the boilerplate: https://github.com/tiangolo/full-stack-fastapi-postgresql
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FastAPI Best Practices
I would encourage you to take a look at this repo: https://github.com/tiangolo/full-stack-fastapi-postgresql This is a boilerplate of an application made with fastapi, prepared by the creator of the fastapi himself. You can even set it up yourself locally and have a look how it’s organised. I know it has a lot of different services included, but I find the fastapi part itself to be well thought. Inside the api directory you can notice another folder named api_v1, so you can have multiple versions of your API routes when needed, with the general code in other places that is more generic and can be reused in all your different API versions. The schemas are separated from the models and models itself have different classes depending on what you would actually like to do with the data. The migrations are managed with alembic based on schemas rather than models itself. The settings are a python class that implicitly reads the .env file in your project’s directory. And many, many other interesting patterns to explore. Too much to write in one comment to be honest.
What are some alternatives?
fastapi-best-practices - FastAPI Best Practices and Conventions we used at our startup
fastapi-starter - A FastAPI based low code starter/boilerplate: SQLAlchemy 2.0 (async), Postgres, React-Admin, pytest and cypress
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
fastapi-react - 🚀 Cookiecutter Template for FastAPI + React Projects. Using PostgreSQL, SQLAlchemy, and Docker
fastapi-router-controller - A FastAPI utility to allow Controller Class usage
uvicorn-gunicorn-fastapi-docker - Docker image with Uvicorn managed by Gunicorn for high-performance FastAPI web applications in Python with performance auto-tuning.
opal - Fork of https://github.com/permitio/opal
cookiecutter-djangopackage - A cookiecutter template for creating reusable Django packages quickly.
databases - Async database support for Python. 🗄
fastapi-users - Ready-to-use and customizable users management for FastAPI
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
docker-celery-flower - Minimum docker/fastapi/celery/flower setup