kubernetes-demo-app
fastapi
kubernetes-demo-app | fastapi | |
---|---|---|
2 | 467 | |
31 | 71,023 | |
- | - | |
4.0 | 9.8 | |
7 months ago | 5 days ago | |
Python | Python | |
- | MIT License |
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.
kubernetes-demo-app
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From local development to Kubernetes — Cluster, Helm, HTTPS, CI/CD, GitOps, Kustomize, ArgoCD — Part[1]
The sample app in the **repo** is a basic FastAPI application that I extracted from official docs, but I added a PostgreSQL database to make the whole tutorial a bit more challenging. The application is very simple, it exposes some endpoints to create Users and Items.
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From local development to Kubernetes — Cluster, Helm, HTTPS, CI/CD, GitOps, Kustomize, ArgoCD — Part[2]
If we go to the **GitHub repo** you can find all the manifests that we worked on so far: Deployment, Service, ClusterIssuer, Ingress, and Secret. This is okay at some point… but look at that secret.yaml **file, which is very unlikely to be there, especially when it’s encoded with **base64 **when we know that is so easy to decrypt. Aside from that, it’s so hard to separate environments: **production, staging, development, etc. There can also be lots of inconsistencies, where I can have some manifest locally and apply it, and another version of that manifest exists in the repo, so it’s very hard to reproduce the same environment if we delete everything.
fastapi
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FastAPI Best Practices: A Condensed Guide with Examples
FastAPI is a modern, high-performance web framework for building APIs with Python, based on standard Python type hints.
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Building an Email Assistant Application with Burr
In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.
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FastAPI Got Me an OpenAPI Spec Really... Fast
That’s when I found FastAPI.
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How to Deploy a Fast API Application to a Kubernetes Cluster using Podman and Minikube
FastAPI & Uvicorn
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Analysing FastAPI Middleware Performance
Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696
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LangChain, Python, and Heroku
An API application framework (such as FastAPI)
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Litestar – powerful, flexible, and highly performant Python ASGI framework
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
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AI-Powered Image Search with CLIP, pgvector, and Fast API
Fast API.
- Ask HN: What is your go-to stack for the web?
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Fun with Avatars: Crafting the core engine | Part. 1
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:
What are some alternatives?
kubernetes-demo-gitops - This is the GitOps repo for project vjanz/kubernetes-demo-app
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
charts - Bitnami Helm Charts
HS-Sanic - Async Python 3.6+ web server/framework | Build fast. Run fast. [Moved to: https://github.com/sanic-org/sanic]
Tornado - Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.
django-ninja - 💨 Fast, Async-ready, Openapi, type hints based framework for building APIs
Flask - The Python micro framework for building web applications.
swagger-ui - Swagger UI is a collection of HTML, JavaScript, and CSS assets that dynamically generate beautiful documentation from a Swagger-compliant API.
Django - The Web framework for perfectionists with deadlines.
starlite - Light, Flexible and Extensible ASGI API framework | Effortlessly Build Performant APIs [Moved to: https://github.com/litestar-org/litestar]
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
django-rest-framework - Web APIs for Django. 🎸