soci-snapshotter VS enhancements

So I can give some behind the scenes insight on that. I don't think image caching will be a thing in the way people are explicitly asking, but we are exploring some alternative approaches to speeding up container launch that we think will actually be even more effective than what people are asking for.

First of all we want to leverage some of the learnings from AWS Lambda, in specific some of the research we've done that shows that about 75% of container images only contain 5% unique bytes (https://brooker.co.za/blog/2023/05/23/snapshot-loading.html). This makes deduplication incredibly effective, and allows the deployment of a smart cache that holds the 95% of popular recurring files and file chunks from container images, while letting the unique 5% be loaded over the network. There will be outliers of course, but if you base your image off a well used base image then it will already be in the cache. This is partially implemented. You will notice that if you use certain base images your Fargate container seems to start a bit faster. (Unfortunately we do not really publish this list or commit to what base images are in the cache at this time).

In another step along this path we are working on SOCI Snapshotter (https://github.com/awslabs/soci-snapshotter) forked off of Stargz Snapshotter. This allows a container image to have an attached index file that actually allows it to start up before all the contents are downloaded, and lazy load in remaining chunks of the image as needed. This takes advantage of another aspect of container images which is that many of them don't actually use all of the bytes in the image anyway.

Over time we want to make these two pieces (deduplication and lazy loading) completely behind the scenes so you just upload your image to Elastic Container Registry and AWS Fargate seems to magically start your image dramatically faster than you could locally if downloading the image from scratch.

This behavior allows us to set up snapshots out of band, that is outside the "normal" workflow. One such example is the soci snapshotter which allows image lazy loading. The snapshotter ships with a "rpull" command which performs this out of band prep. During rpull, the command calls the soci snapshotter which creates fuse mounts for each layer that has an index (remote snapshot). For layers that do not have an index, it downloads them as usual (local snapshot). The local snapshot is created with overlay mount. Anyway, this is just a detail, the important bit is that folders created for a local snapshot only contain that layer, which is exactly what overlay does. For example, after rpull we'll see something like the following:

Seekable OCI (SOCI) is a technology open-sourced by AWS that enables containers to launch faster by lazily loading the container image. It’s usually not possible to fetch individual files from gzipped tar files. With SOCI, AWS borrowed some of the design principles from stargz-snapshotter, but took a different approach. A SOCI index is generated separately from the container image and is stored in the registry as an OCI Artifact and linked back to the container image by OCI Reference Types. This means that the container images do not need to be converted, image digests do not change, and image signatures remain valid.

Thanks for these links!

I picked out one at random just to check if my skeptical reaction is fair: https://github.com/kubernetes/enhancements/tree/master/keps/...

- OK, this is actually a really good and useful doc!

- However, it's not an up-front design doc, it has clearly been written after the bulk of the work has been done, to explain and justify rolling out a big change. (See the "implementation history" timeline: https://github.com/kubernetes/enhancements/tree/master/keps/...)

- It looks like the template wasn't very useful; most of the required sections are marked "N/A", and there are comments like The best test for work like this is, more or less, "did it work?"

> was always told early on that although they supported vault on kubernetes via a helm chart, they did not recommend using it on anything but EC2 instances (because of "security" which never really made sense their reasoning).

The reasoning is basically that there are some security and isolation guarantees you don't get in Kubernetes that you do get on bare metal or (to a somewhat lesser extent) in VMs.

In particular for Kubernetes, Vault wants to run as a non-root user and set the IPC_LOCK capability when it starts to prevent its memory from being swapped to disk. While in Docker you can directly enable this by adding capabilities when you launch the container, Kubernetes has an issue because of the way it handles non-root container users specified in a pod manifest, detailed in a (long-dormant) KEP: https://github.com/kubernetes/enhancements/blob/master/keps/... (tl;dr: Kubernetes runs the container process as root, with the specified capabilities added, but then switches it to the non-root UID, which causes the explicitly-added capabilities to be dropped).

You can work around this by rebuilding the container and setting the capability directly on the binary, but the upstream build of the binary and the one in the container image don't come with that set (because the user should set it at runtime if running the container image directly, and the systemd unit sets it via systemd if running as a systemd service, so there's no need to do that except for working around Kubernetes' ambient-capability issue).

> It always surprised me how these conversations went. "Well we don't really recommend kubernetes so we won't support (feature)."

0 is not the request we've defined. And that makes sense. Memory QoS has been in alpha since Kubernetes 1.22 (August 2021) and according to the KEP data was still in alpha as of 1.27.

Note: There's actually a Structured Authentication Config established via KEP-3331. It's in v1.28 as a feature flag gated option and removes the limitation of only having one OIDC provider. I may look into doing an article on it, but for now I'll deal with the issue in a manner that should work even with a bit older versions versions of Kubernetes.

Kubernetes supports a skew policy of n+2 between API server and kubelet. This means if your CP and DP are both on 1.20, you could upgrade your control plane twice (1.20 -> 1.21 -> 1.22) before you need to upgrade your data plane. And when it comes time to upgrade your data plane you can jump from 1.20 to 1.22 to minimize update churn. In the future, this skew will be opened to n+3 https://github.com/kubernetes/enhancements/tree/master/keps/sig-architecture/3935-oldest-node-newest-control-plane

The KEP (Kubernetes Enhancement Proposal) is linked to in the PR [1]. From the summary:

> Sidecar containers are a new type of containers that start among the Init containers, run through the lifecycle of the Pod and don’t block pod termination. Kubelet makes a best effort to keep them alive and running while other containers are running.

[1] https://github.com/kubernetes/enhancements/tree/master/keps/...

This is where the new feature of mutable scheduling directives for jobs comes into play. This feature enables the updating of a job's scheduling directives before it begins. Essentially, it allows custom queue controllers to influence pod placement without needing to directly handle the assignment of pods to nodes themselves. To learn more about this check out the Kubernetes Enhancement Proposal 2926.

What your asking is a (vanilla) Kubernetes non-goal, others have mentioned fluxcd and other add ons that provide primitives for dependency aware deployments. The problem space is so large, that it's unreasonable to to address these concerns in Kubernetes itself, instead, make it extensible... Look at this KEP for example: https://github.com/kubernetes/enhancements/issues/753 Sidecar containers have existed, and been named as such since WAY before that KEP's inception, defining what these things should and shouldn't do is largely arbitrary. Aka: your use-case is niche, if you don't like the behavior, use flux or argo, or write something yourself.

If further interested, I may recommend checking out the KEP. I love how they document the decision making, and all these edge cases :).