At Enix, we manage hundreds of Kubernetes clusters for our customers and our own internal use. On cloud, on premises, big and small, from development to production… And there is one particular issue that hits all these clusters at one point or another: the image retrieval process.
Every Kubernetes administrator has faced, or will face, this issue sooner or later: you roll out an update to patch a security issue, fix a bug or rollback to a stable release after a faulty update. And then, surprise: on a few nodes of the cluster, some containers won’t start because the image can’t be pulled. The registry might be down, or having a bad day, or the image might have been removed, or you might be hitting your Docker Hub pull quota. Either way: you’re SOL (sadly out of luck).
After our team ran into that issue one time too many, we decided to try and fix it - hopefully for good. Our solution? A container image caching solution to ensure that any image, pulled at least once into our kubernetes cluster, gets saved to a reliable, trusted cache.
kube-image-keeper was born !
In this article, we’ll tell you everything about it! We’ll start by describing the options that we considered before writing our own, then we’ll describe the architecture of our solution, and we’ll tell you how to get started with it.
Spoiler alert: we’ll talk about proxies, caches, CRD, kubernetes operators…
Before we dive in, here’s a short recap of what we wanted to address:
- Unavailability (outages or reduced performance) of public (SaaS) registries
- Pull limits enforced by some registries (in particular with their free tier)
- Images being removed due to disk space management policies while they’re still in use on some clusters
We acknowledge that these issues can happen in both in-premises and cloud-based clusters, and wanted our solution to fit both scenarios without compromise, which means that we didn’t want to rely on cloud-specific services (like the availability of an object store) or datacenter-specific features (like L2 networks - not that it would have helped in that case anyway).
Adventures, twists & turns before finding our target architecture !
When we started the project in early 2022, we wanted to reuse existing components as much as possible rather than reinvent the wheel.
We found a few promising projects, basically typing “kubernetes images local caching" in our favorite web, either because they seemed to provide a stand-alone solution or because they could be combined with other to provide one:
kubernetes-image-puller: interesting idea, but not for us
kubernetes-image-puller is straight away set aside, after reading the doc and before testing, mainly because the supported images volume is too weak.
💡 Through this component, container images of interest must be retrieved and launched with the sleep command 720h (if this command is not used, images are downloaded BUT the container switches straight away to CrashLoopBackOff mode …) , in order to initialize pods, specifically started to contain the cached images, that will be used in order to serve the upcoming clusters requests.
💡 This design was partly revamped (see here) BUT the result still does not suit our needs.
💡 kubernetes-image-puller deploys a huge number of containers (one container per image and per node / uses a daemonset for the caching mechanism), to fulfill the caching feature.
Let’s take this example : With 5 nodes & 10 images in cache, we already have 50 containers within the cluster dedicated for the caching feature …
An “out of the box” solution with kube-fledged ?
We now start a testing phase with kube-fledged. This solution seems to stick to our needs & mind.
This project allows to set an images list, manually defined through CRDs, into the cache,. This implementation appears to be a solid foundation that we’ll enhance putting in place automated detection for images to be set in cache. At this stage, we target to delegate this task to customized code that will finally update the needed CRDs.
Unfortunately, we then understand that kube-fledged does not optimize the source registry calls (quite the opposite …) and over-consume the nodes local storage.
We feel we’ll never reach our target without embedding a local registry that up-streams as less as possible to source registries !
Tugger in harmony with Harbor ?
This first disappointing experience pushed us to refine our technical needs and think about other solutions. Finally, we decided to use the combination of Harbor (cache/registry) + Tugger (engine)
Below is the architecture diagram, associated to this new round :
We hit the target BUT :
- Managing Harbor, a tool “full” of features and thus heavy during installation & configuration is “overkill” for our needs.
- Tugger comes with a blocking behavior (for us) regarding the users segregation of duties, not compatible with our approach.
💡 Tugger uses a single configuration file, defined through its Helm file values. It does not allow us to segregate “system” configurations (eg : exclude specific images from the caching system) and “users” configurations.
💡 We internally discussed the possibility to work-around our issue including Tugger within our chart rather than including the Tugger chart into the chart dependencies BUT the effort level was too high.
- Finally, we faced what is for us a design issue, with no workaround : the impossibility to exclude specific pods from the pods definition rewriting process (see “Rewrite image url to internal registry” within the diagram above). This functionality is mandatory, mainly for the pod holding the registry/managing its upgrade and for the “system” pods.
💡 With Tugger, the rewrite process is done through a regular expression on the images access URL. In this context, it is quite difficult to segregate the pods that SHOULD BE rewritten VS the ones that SHOULD NOT. As an example, you can not do it via a label.
💡 An option is to filter by namespace (or define a “whitelist”), BUT this is not enough and does not give us the room / granularity we need.
💡 Finally, this regular expression creation process is done through a single configuration file, and needs to be deployed each time a change is made. This way of working complicates the tool use & its integration into a wider context.
All those combined items push us to rollback our plan (complicated installation, extra work, dependencies to other projects, sub optimal configurations).
We finally moved to our own solution : kuik !
kuik, our image caching solution : Docker registry + dev !
We finally decided to replace the previous combination by the Docker registry “[distribution](https://github.com/distribution/distribution)”, easier to install than Harbor, and a k8s operator, developed on our own.
The operator is broken down into 3 main components :
- A mutating webhook, rewriting on-the-fly the pods definitions, updating the registry it uses.
- A first controller, examining pods and creating “CachedImages” custom resources accordingly.
- Another controller, watching the “CachedImages” & taking care of pulling to the cache the relevant images.
For the sake of completeness :
On top of the registry & the operator, we also put in place a proxy in front of the registry. Thus, when kubelet asks the CRI to pull pods images, the proxy will transfer the request to the upstream registry, including the authentication, in conditions in which the images are not yet available in the cache.
Final project architecture :
The architecture design for our kubernetes k8s image local caching/proxy solution !
A couple of details :
- At the moment, there is no way to scale the registry component. A single pod plays this role.
- The proxy can be launched on the control-plane.
- This setup is a “standard one” for “infrastructure components”, such as logging agents or metrology. It was thus a MUST HAVE for “our” proxy.
- The proxy is deployed through a daemonset. The CRI does not have access to the kubernetes service discovery and is thus not “services aware”. 2 solutions in order to get it access the proxy :
- Through an ingress
- Through localhost, with a daemonset.
- With the 1st solution, the registry is publicly available. This is something we wanted to avoid, at least in the very beginning. We thus used the 2nd solution.
Typical scenario for container image retrieval :
Once the missing component was developed & integrated, we finally got what we were waiting for : kube-image-keeper “production/OPS ready” !
As a matter of fact :
- We now control the pods on which the cache mechanism is used, and on top of if the ones in which this mechanism MUSTN’T apply (registry, control-plane components)
- We manage the cache lifecycle, and can interact with it (cache images follow-up, partial/full removal, garbage-collecting …)
- The tool is compatible with the vast majority of images (versioning, formalism, registries …). We did not want to put in place a docker image cache.
- This component is an enabler for Day 0, Day 1 and Day 2 operations and does not add extra risks in production environment :
- A kubernetes cluster using it remains “OPS compatible”, even though the tool goes down
- The tool becomes “transparent” in case the used images can not be set in cache for some reason.
- It can be easily updated, and the cached images are kept in the context of an upgrade.
- Finally, the tool is not hardly linked to a specific source registry, and is compatible with all the ecosystem registries (given by the Cloud Providers, CI/CD solutions, Quay, Docker …)
Last info to facilitate you use :
- The tool was tested with :
- k8s releases from 1.21 to 1.24
- k8s.io/client-go, k8s.io/apimachinery and k8s.io/api (v0.20.6) libraries
- If you plan to use kube-image-keeper in production, we strongly recommend that you put in place a persistent storage solution !
💡 The “force pull” (imagePullPolicy: Always) does not force the container image retrieval with the source registry, BUT with kube-image-keeper Some cases you can face :
● The proxy, deployed through a DaemonSet, can be reached by the CRI through localhost, using the hostPort configuration within the proxy containers. This feature allows to “publish” a port from the host and requires, when used with kube-router, the use for the plugin portmap.
● As of today, there is no way to manage container images based on a digest rather than a tag. The rational behind this limitation is that a digest is an image manifest hash, containing itself the registry URL associated with the image. Thus, caching the image changes its digest and as a consequence, once the image is set in cache, it is not anymore referenced by its original digest. We would need to rewrite the container image. This is not feasible with the current design.
What about the future ?
We come today with a stable release we share to you, the open source community ! kuik sources are available here : enix/kube-image-keeper.
We plan to keep on working on this project and already identified a couple of topics to add into our ‘23 roadmap :
- Having the capability to “easily” scale the local registry composent, to have something fully “hitless”
- Adding new blocks to the tool (security, events notifications …), and optimizing the architecture, switching to a “pull-through” cache. This approach is already implemented by a couple of projects, but with limitations, preventing us to plan an integration within our tool :
- Gitlab dependency proxy : A prerequisite here is to use images ONLY coming from the Docker Hub.
- Harbor proxy cache : If the cached image is not available on the origin registry, the image is not given back. Moreover, each source registry MUST BE explicitly configured.
- Docker distribution : Implies to deploy a project instance per used registry.
- For each single example given here, the registry credentials MUST BE filled into the tool configuration on top of the imagePullSecrets field.
- Increasing the test code coverage with automation. The coverage today is approximately 50%. The integration tests are the main ones missing as of today !
- Improving the behavior around container images versioning management / set into the cache (e.g smoothly allowing to update a “latest” version, in the case it was updated in the source registry).
“Last but not least” : This blogpost is just the beginning ! We plan to supplement it with a kube-image-keeper installation “deep dive” & another one that will explain a specific use case
Feel free to contact us for any question related to kube-image-keeper ! And … do not forget to try the tool and let us know your thoughts !
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