distroless VS kubernetes

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.

> 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.

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.

# 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"]

Lots of examples without the entire OS as other comments mention, an example would be Googles distroless[0]

[0]: https://github.com/GoogleContainerTools/distroless

Docker doesn't do this all the time. Distroless Docker containers are relatively common. https://github.com/GoogleContainerTools/distroless

Deployment: https://github.com/GoogleContainerTools/distroless

Or use distroless image as it includes one, among others. https://github.com/GoogleContainerTools/distroless/blob/main/base/README.md

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With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

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In this project, you will understand and get hands on experience around the entire concept around CI/CD from applications perspective. To fully gain real expertise around this idea, it is best to see it in action across different programming languages and from the platform perspective too. From the application perspective, we will be focusing on PHP here; there are more projects ahead that are based on Java, Node.js, .Net and Python. By the time you start working on Terraform, Docker and Kubernetes projects, you will get to see the platform perspective of CI/CD in action.