distroless
fastapi
distroless | fastapi | |
---|---|---|
122 | 470 | |
17,781 | 71,223 | |
1.4% | - | |
9.4 | 9.8 | |
6 days ago | 5 days ago | |
Starlark | Python | |
Apache License 2.0 | 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.
distroless
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Chainguard Images now available on Docker Hub
lots of questions here regarding what this product is. I guess i can provide some information for the context, from a perspective of an outside contributor.
Chainguard Images is a set of hardened container images.
They were built by the original team that brought you Google's Distroless (https://github.com/GoogleContainerTools/distroless)
However, there were few problems with Distroless:
1. distroless were based on Debian - which in turn, limited to Debian's release cadence for fixing CVE.
2. distroless is using bazelbuild, which is not exactly easy to contrib, customize, etc...
3. distroless images are hard to extend.
Chainguard built a new "undistro" OS for container workload, named Wolfi, using their OSS projects like melange (for packaging pkgs) and apko (for building images).
The idea is (from my understanding) is that
1. You don't have to rely on upstream to cut a release. Chainguard will be doing that, with lots of automation & guardrails in placed. This allow them to fix vulnerabilties extremely fast.
- Language focused Docker images, minus the operating system
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Using Alpine can make Python Docker builds 50Ă— slower
> If you have one image based on Ubuntu in your stack, you may as well base them all on Ubuntu, because you only need to download (and store!) the common base image once
This is only true if your infrastructure is static. If your infrastructure is highly elastic, image size has an impact on your time to scale up.
Of course, there are better choices than Alpine to optimize image size. Distroless (https://github.com/GoogleContainerTools/distroless) is a good example.
- Smaller and Safer Clojure Containers: Minimizing the Software Bill of Materials
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Long Term Ownership of an Event-Driven System
The same as our code dependencies, container updates can include security patches and bug fixes and improvements. However, they can also include breaking changes and it is crucial you test them thoroughly before putting them into production. Wherever possible, I recommend using the distroless base image which will drastically reduce both your image size, your risk vector, and therefore your maintenance version going forward.
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Minimizing Nuxt 3 Docker Images
# Use a large Node.js base image to build the application and name it "build" FROM node:18-alpine as build WORKDIR /app # Copy the package.json and package-lock.json files into the working directory before copying the rest of the files # This will cache the dependencies and speed up subsequent builds if the dependencies don't change COPY package*.json /app # You might want to use yarn or pnpm instead RUN npm install COPY . /app RUN npm run build # Instead of using a node:18-alpine image, we are using a distroless image. These are provided by google: https://github.com/GoogleContainerTools/distroless FROM gcr.io/distroless/nodejs:18 as prod WORKDIR /app # Copy the built application from the "build" image into the "prod" image COPY --from=build /app/.output /app/.output # Since this image only contains node.js, we do not need to specify the node command and simply pass the path to the index.mjs file! CMD ["/app/.output/server/index.mjs"]
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Build Your Own Docker with Linux Namespaces, Cgroups, and Chroot
Lots of examples without the entire OS as other comments mention, an example would be Googles distroless[0]
[0]: https://github.com/GoogleContainerTools/distroless
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Reddit temporarily ban subreddit and user advertising rival self-hosted platform (Lemmy)
Docker doesn't do this all the time. Distroless Docker containers are relatively common. https://github.com/GoogleContainerTools/distroless
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Why elixir over Golang
Deployment: https://github.com/GoogleContainerTools/distroless
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Reviews
Or use distroless image as it includes one, among others. https://github.com/GoogleContainerTools/distroless/blob/main/base/README.md
fastapi
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Github Sponsor Sebastián RamĂrez Python programmer
He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.
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Python: A SQLAlchemy Wrapper Component That Works With Both Flask and FastAPI Frameworks
It has been an interesting exercise developing this wrapper component. The fact that it seamlessly integrates with the FastAPI framework is just a bonus for me; I didn't plan for it since I hadn't learned FastAPI at the time. I hope you find this post useful. Thank you for reading, and stay safe as always.
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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.
What are some alternatives?
iron-alpine - Hardened alpine linux baseimage for Docker.
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
spring-boot-jib - This project is about Containerizing a Spring Boot Application With Jib
HS-Sanic - Async Python 3.6+ web server/framework | Build fast. Run fast. [Moved to: https://github.com/sanic-org/sanic]
jib - 🏗 Build container images for your Java applications.
Tornado - Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.
podman - Podman: A tool for managing OCI containers and pods.
django-ninja - đź’¨ Fast, Async-ready, Openapi, type hints based framework for building APIs
dockerfiles - Various Dockerfiles I use on the desktop and on servers.
Flask - The Python micro framework for building web applications.
docker-alpine - Official Alpine Linux Docker image. Win at minimalism!
swagger-ui - Swagger UI is a collection of HTML, JavaScript, and CSS assets that dynamically generate beautiful documentation from a Swagger-compliant API.