Skip to main content

Distroless image internals

We used a Google "distroless" image as the base image on a go application deployment in a project a while ago. I never looked into what distroless really means, but I had a guess. I'm also curious to look under the hood of a docker image if they really are just merged archives.

Recently there was a "Docker without docker" blog post in Hacker news about how simple docker (or rather, OCI) image format is. Spoiler: an image is tar archives on top of tar archives. The post has also a detailed explanation of how to pull images from a container registry. I shamelessly took the image pull script they shared and modified it a bit to pull the static distroless image from the Google container registry.

The image is only one layer, and here is how it looks like after I extracted it and removed all the root level empty directories with all directories with at least one file expanded.

It looks pretty empty to me, and I suppose that's the origin of the distroless term! :thinksmart:

I compared it to an alpine image with the GNU/Linux diff tool, and the output is enormous.

Comments

Popular posts from this blog

I'm not a passionate developer

A family friend of mine is an airlane pilot. A dream job for most, right? As a child, I certainly thought so. Now that I can have grown-up talks with him, I have discovered a more accurate description of his profession. He says that the truth about the job is that it is boring. To me, that is not that surprising. Airplanes are cool and all, but when you are in the middle of the Atlantic sitting next to the colleague you have been talking to past five years, how stimulating can that be? When he says the job is boring, it is not a bad kind of boring. It is a very specific boring. The "boring" you would want as a passenger. Uneventful.  Yet, he loves his job. According to him, an experienced pilot is most pleased when each and every tiny thing in the flight plan - goes according to plan. Passengers in the cabin of an expert pilot sit in the comfort of not even noticing who is flying. As someone employed in a field where being boring is not exactly in high demand, this sounds pro...

PydanticAI + evals + LiteLLM pipeline

I gave a tech talk at a Python meetup titled "Overengineering an LLM pipeline". It's based on my experiences of building production-grade stuff with LLMs I'm not sure how overengineered it actually turned out. Experimental would be a better term as it is using PydanticAI graphs library, which is in its very early stages as of writing this, although arguably already better than some of the pipeline libraries. Anyway, here is a link to it. It is a CLI poker app where you play one hand against an LLM. The LLM (theoretically) gets better with a self-correcting mechanism based on the evaluation score from another LLM. It uses the annotated past games as an additional context to potentially improve its decision-making. https://github.com/juho-y/archipylago-poker

"You are a friendly breadwinner"

A recent blog post by Pete Koomen about how we still lack truly "AI-native" software got me thinking about the kinds of applications I’d like to see. As the blog post says, AI should handle the boring stuff and leave the interesting parts for me. I listed down a few tasks I've dealt with recently and wrote some system prompts for potential agentic AIs: Check that the GDPR subprocessor list is up to date. Also, ensure we have a signed data processing agreement in place with the necessary vendors. Write a summary of what you did and highlight any oddities or potentially outdated vendors. Review our product’s public-facing API. Ensure the domain objects are named consistently. Here's a link to our documentation describing the domain. Conduct a SOC 2 audit of our system and write a report with your findings. Send the report to Slack. Once you get approval, start implementing the necessary changes. These could include HR-related updates, changes to cloud infras...