go-containerregistry VS image-spec

I also explored another module, go-containerregistry, in order to build images without root privileges. The approach is completely different, and we can manipulate each component of the container image separately. This can present an advantage, if you're looking for a way to fine tune things.

Use crane ls in a different job to check the tags in the registry. Create an artifact from its output that you evaluate in your kaniko job to check if the build should run or not.

Multi-arch builds are easy to "transfer" IMHO

crane cp docker.io/openfaas/gateway:0.10.0 ghcr.io/openfaas/gateway:0.10.0

If you've not used it yet - do take a look. Crane doesn't pull the images into a local Docker library for re-tagging and re-pushing.

https://github.com/google/go-containerregistry/blob/main/cmd...

crane - tool to copy images from one repo to another - https://github.com/google/go-containerregistry/blob/main/cmd/crane/doc/crane.md

pretty sure the crane being referred by alex is this one: https://github.com/google/go-containerregistry/tree/main/cmd/crane

https://github.com/google/go-containerregistry/tree/main/cmd...

It was recommended in this article:

This is one of my absolute favorite topics. Pardon me while I rant and self-promote :D

Dockerfiles are great for flexibility, and have been a critical contributor to the adoption of Docker containers. It's very easy to take a base image, add a thing to it, and publish your version.

Unfortunately Dockerfiles are also full of gotchas and opaque cargo-culted best practices to avoid them. Being an open-ended execution environment, it's basically impossible to tell even during the build what's being added to the image, which has downstream implications for anybody trying to get an SBOM from the image for example.

Instead, I contribute to a number of tools to build and manage images without Dockerfiles. Each of them are less featureful than Dockerfiles, but being more constrained in what they can do, you can get a lot more visibility into what they're doing, since they're not able to do "whatever the user wants".

1. https://github.com/google/go-containerregistry is a Go module to interact with images in the registry and in tarballs and layouts, in the local docker daemon. You can append layers, squash layers, modify metadata, etc.

2. crane is a CLI that uses the above (in the same repo) to make many of the same modifications from the commandline. `crane append` for instance adds a layer containing some contents to an image, entirely in the registry, without even pulling the base image.

3. ko (https://ko.build) is a tool to build Go applications into images without Dockerfiles or Docker at all. It runs `go build`, appends that binary on top of a base image, and pushes it directly to the registry. It generates an SBOM declaring what Go modules went into the app it put into the image, since that's all it can do.

4. apko (https://apko.dev) is a tool to assemble an image from pre-built apks, without Docker. It's capable of producing "distroless" images easily with config in YAML. It generates an SBOM declaring exactly what apks it put in the image, since that's all it can do.

Bazel's rules_docker is another contender in the space, and GCP's distroless images use it to place Debian .debs into an image. Apko is its spiritual successor, and uses YAML instead of Bazel's own config language, which makes it a lot easier to adopt and use (IMO), with all of the same benefits.

I'm excited to see more folks realizing that Dockerfiles aren't always necessary, and can sometimes make your life harder. I'm extra excited to see more tools and tutorials digging into the details of how container images work, and preaching the gospel that they can be built and modified using existing tooling and relatively simple libraries. Excellent article!

Look at using tools like skopeo or crane

Pretty much any tool works: docker, podman, kaniko, crane(if you're brave), ko... list goes on.

A buildpack is a software, designed to transform application source code into executable (OCI) images that can run on a variety of cloud platforms. At its core, a buildpack is a directory that includes a specific file named buildpack.toml. This file contains metadata and configuration details that dictate how the buildpack should behave. Buildpacks in simple terms, is a set of standards defining how the different steps that are required to build a compliant container image can be automated. Using those standards, there are projects that have been built round enabling that using an CLI or an API. The most common way of doing that is through the Cloud Native Buildpacks' Pack project. Pack is a CLI command that can run in the same system the developers are using to actually go through creating a Dockerfile.

Eventually, once zstd support gets fully supported, and tiny gzip compression windows are not a limitation, then compressing a full layer would almost certainly have a better ratio over several smaller layers

https://github.com/opencontainers/image-spec/issues/803

Please note that label-schema has been superseded by https://github.com/opencontainers/image-spec/blob/main/annotations.md<^

GitHub Container Registry stores container images within your organization or personal account, and allows you to associate an image with a repository. It currently supports both the Docker Image Manifest V2, Schema 2 and Open Container Initiative (OCI) specifications.

We build all services as containerized workloads, i.e., OCI images - sometimes called Docker images. We deploy these to the Kubernetes product offered by the cloud vendor. Whenever we need some capability, containers are the answer. This insulates our applications from the vendor. In principle, we could switch providers as long as Kubernetes is available.

Build images with anything that makes OCI compliant images, push, and profit.

it's funny that you mention this because it is actually the thing that is next on my agenda for the image, as you can probably see already I bake in OCI image annotations in our image, which is great for including some core pieces of meta data. In addition to this though I will soon be including custom labels for Base64 encoded YAMLs for Kubernetes deployments using this image. I will look at including helm configuration as well. Then it should be just as easy as: $ docker pull registry.gitlab.com/crafty-controller/crafty-4:latest $ docker image inspect registry.gitlab.com/crafty-controller/crafty-4:latest | jq -r ".[].Config.Labels.\"org.arcadiatech.crafty.k8s.deployment\"" | base64 -d | kubectl apply -f -

They still don't do anything really of substance, they're just gateways to their vendor's world - booking systems, payment systems, etc. You learn those as you go along. Yes, as a potential employee, you need to be able to tick those boxes on your CV, but if you understand the underlying technology, it's mostly a matter of booking your own AWS or Azure server for $5-10 a month for a few weeks, and fooling around. (Docker is a bit different in the sense that they were the first to popularize today's de-facto container image standard, the "Docker container", which has since been accepted as a proper standard and renamed to "OCI image format"; but at the end of the day, at this point in time, Docker in itself is still just a company out for the money, and the multi-GB installation of their product can, for the essential functionality part, be replaced by a few hundred lines of Bash code. The cool boys today don't use Docker, they use [Podman(https://podman.io/), which is essentially a much more lightweight drop-in replacement ;-) )