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.github/ISSUE_TEMPLATE/bug-report.yaml vendored
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- type: markdown
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value: |
Thank you for taking the time to fill ot this bug report! Please read the [FAQ](../../docs/FAQ.md) and check existing issues before submitting.
Thank you for taking the time to fill ot this bug report! Please read the [FAQ](https://spegel.dev/docs/faq/) and check existing issues before submitting a new issue.
- type: input
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label: Spegel version

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CONTRIBUTING.md
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@ -69,7 +69,7 @@ helm upgrade --wait --install --namespace=spegel spegel ./charts/spegel \
kubectl --namespace spegel rollout status daemonset spegel --timeout 60s
```
If all goes well, you will see see something like `daemon set "spegel" successfully rolled out`. See [How do I know that Spegel is working?](./docs/FAQ.md#how-do-i-know-that-spegel-is-working) on how to verify.
If all goes well, you will see see something like `daemon set "spegel" successfully rolled out`. See [How do I know that Spegel is working?](https://spegel.dev/docs/faq/#how-do-i-know-that-spegel-is-working) on how to verify.
## Generating documentation

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README.md
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Spegel, mirror in Swedish, is a stateless cluster local OCI registry mirror.
<p align="center">
<img src="./assets/overview.gif">
<img src="https://spegel.dev/images/overview.gif">
</p>
## Use Cases
## Features
Spegel is for you if you are looking to do any of the following:
Spegel is for you if you are looking to do any of the following.
* Locally cache images from external registries with no explicit configuration.
* Avoid cluster failure during external registry downtime.
@ -17,68 +17,13 @@ Spegel is for you if you are looking to do any of the following:
* Decrease egressing traffic outside of the clusters network.
* Increase image pull efficiency in edge node deployments.
## Background
## Getting Started
Kubernetes does a great job at distributing workloads on multiple nodes. Allowing node failures to occur without affecting uptime. A critical component for this to work is that each node has to be able to pull the workload images before they can start. Each replica running on a node will incur a pull operation. The images may be pulled from geographically close registries within the cloud provider, public registries, or self-hosted registries. This process has a flaw in that each node has to make this round trip separately. Why can't the nodes share the image among themselves?
Read the [getting started](https://spegel.dev/docs/getting-started/) guide to deploy Spegel.
Spegel enables each node in a Kubernetes cluster to act as a local registry mirror, allowing nodes to share images between themselves. Any image already pulled by a node will be available for any other node in the cluster to pull.
## Contributing
This has the benefit of reducing workload startup times and egress traffic as images will be stored locally within the cluster. On top of that it allows the scheduling of new workloads even when external registries are down.
## Installation
Before installing Spegel check the [compatibility guide](./docs/COMPATIBILITY.md) to make sure that it will work with your specific Kubernetes flavor. If everything checks out, the easiest method to deploy Spegel is with Helm.
```shell
helm upgrade --create-namespace --namespace spegel --install --version v0.0.28 spegel oci://ghcr.io/spegel-org/helm-charts/spegel
```
Refer to the [Helm Chart](./charts/spegel) for detailed configuration documentation.
## FAQ
Please consult the [FAQ](./docs/FAQ.md) if you run into any problems.
## Developing
See [contribution guidelines](./CONTRIBUTING.md) for instructions on how to build and test Spegel.
## Architecture
Spegel can run as a stateless application by exploiting the fact that an image pulled by a node is not immediately garbage collected. Spegel is deployed as a Daemonset on each node which acts as both the registry and mirror. Each instance is reachable both locally through a host port and a Service. This enables Containerd to be configured to use the localhost interface as a registry mirror and for Spegel instances to forward requests to each other.
<p align="center">
<img src="./assets/architecture.jpg">
</p>
Images are composed of multiple layers which are stored as individual files on the node disk. Each layer has a digest which is its identifier. Every node advertises the digests which are stored locally on disk. Kademlia is used to enable a distributed advertisement and lookup of digests. An image pull consists of multiple HTTP requests with one request per digest. The request is first sent to Spegel when an image is pulled if it is configured to act as the mirror for the registry. Spegel will lookup the digest within the cluster to see if any node has advertised that they have it. If a node is found the request will be forwarded to that Spegel instance which will serve the file with the specified digest. If a node is not found a 404 response will be returned and Containerd will fallback to using the actual remote registry.
In its core Spegel is a pull only OCI registry which runs locally on every Node in the Kubernetes cluster. Containerd is configured to use the local registry as a mirror, which would serve the image from within the cluster or from the source registry.
## Alternatives
### Private Registry
A common practice, especially for larger enterprises, is to run a private registry like Harbor to replicate images from public registries, storing them within the private network close to the cluster.
This is a great option for those who have the time and budget to invest in running and managing the infrastructure. For others, it may be a good practice but unattainable in reality.
Spegel does not aim to replace projects like [Harbor](https://github.com/goharbor/harbor) or [Zot](https://github.com/project-zot/zot) but instead complements them. Having a persistent copy of public images stored geographically close to a cluster is great. Spegel will however enable
nodes to pull from images closer as long as the images are somewhere within the cluster. Additionally, there is no guarantee that a self-managed private registry is always available. In these scenarios
running Spegel is like wearing both belt and suspenders.
### Dragonfly
[Dragonfly](https://github.com/dragonflyoss/Dragonfly2) is a great project that has been around for a while. In some aspects, Spegel takes inspiration from the work done by Dragonfly.
The difference is that Spegel aims to solve a smaller problem set. While it may mean fewer features it also means fewer moving components. Dragonfly requires both Redis and MySQL which
increases the resource consumption and burden on end users to manage additional resources. It also increases the risk of errors occurring during critical moments. The benefit of Spegel
is that it is stateless meaning that any temporary failure of nodes and communication should be easily resolved automatically.
### Kraken
[Kraken](https://github.com/uber/kraken) implements a similar solution to Spegel with its P2P agent component. It is however not heavily maintained, meaning that new features and security updates will not be added.
The problem set that Kraken is attempting to solve is however different from Spegel. It's focused on speeding up image distribution from registries serving thousands of large images. It does this by
having trackers and seeders distribute image layers through a BitTorrent-like method. This means that Kraken requires more moving components to function. Kraken also does not support using it
as a transparent pull-through mirror. Meaning that any image that is supposed to be pulled through Kraken will require changing the registry URL in the image name. This has to be done for all
Pods in the cluster.
Read [contribution guidelines](./CONTRIBUTING.md) for instructions on how to build and test Spegel.
## Acknowledgements

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"stop": "2024-10-13T16:12:53.673+02:00",
"duration": 9673000000
},
{
"start": "2024-10-13T16:08:54+02:00",
"stop": "2024-10-13T16:09:03.663+02:00",
"duration": 9663000000
},
{
"start": "2024-10-13T16:09:27+02:00",
"stop": "2024-10-13T16:09:36.884+02:00",
"duration": 9884000000
},
{
"start": "2024-10-13T16:13:48+02:00",
"stop": "2024-10-13T16:13:57.838+02:00",
"duration": 9838000000
}
]
}
]
}

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Stateless cluster local OCI registry mirror.
## Installation
Make sure that you have read the [compatibility guide](../../docs/COMPATIBILITY.md) before proceeding the with the installation.
### CLI
Delpoy Spegel with the Helm CLI.
```sh
helm upgrade --create-namespace --namespace spegel --install --version v0.0.28 spegel oci://ghcr.io/spegel-org/helm-charts/spegel
```
### Flux
Deploy Spegel with Flux.
```yaml
apiVersion: v1
kind: Namespace
metadata:
name: spegel
---
apiVersion: source.toolkit.fluxcd.io/v1beta2
kind: HelmRepository
metadata:
name: spegel
namespace: spegel
spec:
type: "oci"
interval: 5m0s
url: oci://ghcr.io/spegel-org/helm-charts
---
apiVersion: helm.toolkit.fluxcd.io/v2beta1
kind: HelmRelease
metadata:
name: spegel
namespace: spegel
spec:
interval: 1m
chart:
spec:
chart: spegel
version: "v0.0.28"
interval: 5m
sourceRef:
kind: HelmRepository
name: spegel
```
Read the [getting started](https://spegel.dev/docs/getting-started/) guide to deploy Spegel.
## Values

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{{ template "chart.description" . }}
## Installation
Make sure that you have read the [compatibility guide](../../docs/COMPATIBILITY.md) before proceeding the with the installation.
### CLI
Delpoy Spegel with the Helm CLI.
```sh
helm upgrade --create-namespace --namespace spegel --install --version v0.0.28 spegel oci://ghcr.io/spegel-org/helm-charts/spegel
```
### Flux
Deploy Spegel with Flux.
```yaml
apiVersion: v1
kind: Namespace
metadata:
name: spegel
---
apiVersion: source.toolkit.fluxcd.io/v1beta2
kind: HelmRepository
metadata:
name: spegel
namespace: spegel
spec:
type: "oci"
interval: 5m0s
url: oci://ghcr.io/spegel-org/helm-charts
---
apiVersion: helm.toolkit.fluxcd.io/v2beta1
kind: HelmRelease
metadata:
name: spegel
namespace: spegel
spec:
interval: 1m
chart:
spec:
chart: spegel
version: "v0.0.28"
interval: 5m
sourceRef:
kind: HelmRepository
name: spegel
```
Read the [getting started](https://spegel.dev/docs/getting-started/) guide to deploy Spegel.
{{ template "chart.valuesSection" . }}

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# Benchmark
Spegel performance is measured using the [Benchmark tool](https://github.com/spegel-org/benchmark) to give an idea of the expected performance that Spegel can deliver. The tool enables a generic method of measuring image pull performance using different deployment conditions in Kubernetes.
## Method
The benchmarks were run on AKS v1.29 with 50 `Standard_D4ds_v5` nodes. The environment was setup using the provided [Terraform configuration](https://github.com/spegel-org/benchmark/tree/main/terraform). Spegel v0.0.27 is installed in the cluster using the default configuration.
The measurements are done using the generated [benchmark images](https://github.com/spegel-org/benchmark/pkgs/container/benchmark). These images are provided as a v1 and v2 to simulate a rolling upgrade.
* 10 MB 1 layer
* 10 MB 4 layers
* 100 MB 1 layer
* 100 MB 4 layers
* 1 GB 1 layer
* 1 GB 4 layers
The measurements for 10 MB 1 layer is run with the following command. The same measurement is done for each of the image size and layer combinations.
```shell
benchmark measure --result-dir $RESULT_DIR --kubeconfig $KUBECONFIG --namespace spegel-benchmark --images ghcr.io/spegel-org/benchmark:v1-10MB-1 ghcr.io/spegel-org/benchmark:v2-10MB-1
```
Afterwards all of the results are analyzed to generate corresponding graphs.
```shell
benchmark analyze --path $RESULT
```
## Results
The results are compared to the [baseline results](https://github.com/spegel-org/benchmark/tree/main/results) which have been measured with the same setup but without Spegel running in the cluster.
| Image | Baseline | Spegel |
| --- | :---: | :---: |
| 10 MB 1 layer | ![](https://github.com/spegel-org/benchmark/blob/main/results/10MB-1.png) | ![](../benchmark/v0.0.27/10MB-1.png) |
| 10 MB 4 layer | ![](https://github.com/spegel-org/benchmark/blob/main/results/10MB-4.png) | ![](../benchmark/v0.0.27/10MB-4.png) |
| 100 MB 1 layer | ![](https://github.com/spegel-org/benchmark/blob/main/results/100MB-1.png) | ![](../benchmark/v0.0.27/100MB-1.png) |
| 100 MB 4 layer | ![](https://github.com/spegel-org/benchmark/blob/main/results/100MB-4.png) | ![](../benchmark/v0.0.27/100MB-4.png) |
| 1 GB 1 layer | ![](https://github.com/spegel-org/benchmark/blob/main/results/1GB-1.png) | ![](../benchmark/v0.0.27/1GB-1.png) |
| 1 GB 4 layer | ![](https://github.com/spegel-org/benchmark/blob/main/results/1GB-4.png) | ![](../benchmark/v0.0.27/1GB-4.png) |
## Analysis
The image pull duration for v1 and v2 versions differ in shape. This is due to how Kubernetes rolls outs pods for new daemonsets compared to when one is updated. For the v1 images pods are created in batches with no check that the previous batch has started successfully. Images and it's layers are not advertised until the whole image has been pulled. When nodes pull the same image in parallel they will both fetch the image from the original registry. As the new batch of pods are created before the previous batch has pulled the image it means that they will also have to pull the image from the source registry. Batched pod creations is a known weakness of Spegel which does not have a solution currently. Any performance increase seen in the graphs are most likely coincidental due to the time at which the benchmarks were run.
The v2 images however see a greater performance improvement as each pod will wait for the other to complete pulling the image. This means that the first pod will have to pull the image from the source registry. After that the second pod should be able to pull the image from the node which the first pod is deployed to. The table below shows the average pull duration for the baseline and Spegel benchmark. The improvement percentage is calculated with the equation `(baseline - spegel)/baseline * 100`. Images with multiple layers are faster than the the same sized images with singular layers. One explanation is that they benefit from layers being pulled in parallel.
| Image | Baseline (avg) | Spegel (avg) | Improvement |
| --- | --- | --- | --- |
| 10 MB 1 layer | 1220 ms | 210 ms | 81.25% |
| 10 MB 4 layers | 1409 ms | 155 ms | 88.99% |
| 100 MB 1 layer | 1725 ms | 560 ms | 67.53% |
| 100 MB 4 layers | 1573 ms | 500 ms | 68.21% |
| 1 GB 1 layer | 8429 ms | 6682 ms | 20.72% |
| 1 GB 4 layers | 7310 ms | 5125 ms | 29.89% |
While better the performance improvements get less as the images get larger. The best explanation is that the disk bandwidth is getting saturated. Spegel serves image layers from disk and relies on the OS to copy from disk to the TCP socket. Ignoring the overhead in discovering layers the next bottle neck is the network and disk bandwidth available. For the benchmark `Standard_D4ds_v5` VMs with ephemeral disk were used. These VMs have a non-guaranteed throughput of 250 MB/s. With some rough calculations we can see that `1024 MB / 4.392 s = 233 MB/s` which is approaching the max performance of the disk.
## Conclusion
The benchmarks has shown that Spegel works best during updates of existing deployments and daemonsets rather than when new ones are created. Future work needs to be done in allowing advertisement of image pulls in progress to increase performance. It also shows that disk performance eventually matters when pulling large images. For this reason it could be beneficial to deploy larger VMs with higher disk throughput rather than more smaller VMs.

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# Compatibility
Currently, Spegel only works with Containerd, in the future other container runtime interfaces may be supported. Spegel relies on [Containerd registry mirroring](https://github.com/containerd/containerd/blob/main/docs/hosts.md#cri) to route requests to the correct destination.
This requires Containerd to be properly configured, if it is not Spegel will exit. First of all the registry config path needs to be set, this is not done by default in Containerd. Second of all discarding unpacked layers cannot be enabled.
Some Kubernetes flavors come with this setting out of the box, while others do not. Spegel is not able to write this configuration for you as it requires a restart of Containerd to take effect.
```toml
version = 2
[plugins."io.containerd.grpc.v1.cri".registry]
config_path = "/etc/containerd/certs.d"
[plugins."io.containerd.grpc.v1.cri".containerd]
discard_unpacked_layers = false
```
# Kubernetes
Spegel has been tested on the following Kubernetes distributions for compatibility. Green status means Spegel will work out of the box, yellow will require additional configuration, and red means that Spegel will not work.
| Status | Distribution |
| --- | --- |
| :green_circle: | AKS |
| :green_circle: | Minikube |
| :yellow_circle: | EKS |
| :yellow_circle: | K3S and RKE2 |
| :yellow_circle: | Kind |
| :yellow_circle: | Talos |
| :red_circle: | GKE |
| :red_circle: | DigitalOcean |
## EKS
Discard unpacked layers is enabled by default, meaning that layers that are not required for the container runtime will be removed after consumed.
This needs to be disabled as otherwise all of the required layers of an image would not be present on the node.
### Amazon Linux 2
If your EKS AMI is based on AL2, the included containerd config [imports overrides](https://github.com/awslabs/amazon-eks-ami/blob/main/templates/al2/runtime/containerd-config.toml)
from `/etc/containerd/config.d/*.toml` by default. The best way to change containerd settings is to add a file to the import directory using a custom node bootstrap script in your launch template.
```shell
#!/bin/bash
set -ex
mkdir -p /etc/containerd/config.d
cat > /etc/containerd/config.d/spegel.toml << EOL
[plugins."io.containerd.grpc.v1.cri".registry]
config_path = "/etc/containerd/certs.d"
[plugins."io.containerd.grpc.v1.cri".containerd]
discard_unpacked_layers = false
EOL
/etc/eks/bootstrap.sh
```
### Amazon Linux 2023
If you are using an AL2023-based EKS AMI, bootstrap involves [nodeadm configuration](https://awslabs.github.io/amazon-eks-ami/nodeadm/). To change containerd settings, you should add a
nodeadm configuration section.
```yaml
...
--MIMEBOUNDARY
Content-Transfer-Encoding: 7bit
Content-Type: application/node.eks.aws
Mime-Version: 1.0
---
apiVersion: node.eks.aws/v1alpha1
kind: NodeConfig
spec:
containerd:
config: |
[plugins."io.containerd.grpc.v1.cri".containerd]
discard_unpacked_layers = false
--MIMEBOUNDARY
...
```
## Kind
Spegel uses Kind for its end-to-end tests.
Discard unpacked layers is enabled by default, meaning that layers that are not required for the container runtime will be removed after consumed.
This needs to be disabled as otherwise all of the required layers of an image would not be present on the node.
In order to be more like a "real" Kubernetes cluster, you may want to set [Containerd's metadata sharing policy](https://github.com/containerd/containerd/blob/main/docs/ops.md#bolt-metadata-plugin) to `isolated`.
```yaml
apiVersion: kind.x-k8s.io/v1alpha4
kind: Cluster
containerdConfigPatches:
- |-
[plugins."io.containerd.grpc.v1.cri".registry]
config_path = "/etc/containerd/certs.d"
[plugins."io.containerd.grpc.v1.cri".containerd]
discard_unpacked_layers = false
[plugins."io.containerd.metadata.v1.bolt"]
content_sharing_policy = "isolated"
```
For a full example, see the end-to-end tests' [Kind configuration](../test/e2e/kind-config-iptables.yaml) for a full example.
## K3S and RKE2
K3S and RKE2 embeds Spegel, refer to their [documentation](https://docs.k3s.io/installation/registry-mirror?_highlight=spegel) for deployment information.
## Talos
Talos will by default discard unpacked layers, which has to be disabled with a machine configuration.
```yaml
machine:
files:
- path: /etc/cri/conf.d/20-customization.part
op: create
content: |
[plugins."io.containerd.cri.v1.images"]
discard_unpacked_layers = false
```
Talos also uses a different path as its Containerd registry config path.
```yaml
spegel:
containerdRegistryConfigPath: /etc/cri/conf.d/hosts
```
Talos comes with Pod Security Admission [pre-configured](https://www.talos.dev/latest/kubernetes-guides/configuration/pod-security/). The default profile is too restrictive and needs to be changed to privileged.
```shell
kubectl label namespace spegel pod-security.kubernetes.io/enforce=privileged
```
## GKE
GKE does not set the registry config path in its Containerd configuration. On top of that it uses the old mirror configuration for the internal mirroring service.
## DigitalOcean
DigitalOcean does not set the registry config path in its Containerd configuration.

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# FAQ
Frequently asked questions, please read these before creating a new issue.
## Can I use Spegel in production?
Spegel is being used by multiple users in production for over a year without any major issues. The great thing is that pulling images would not stop working if you for some reason would find an issue with Spegel.
A fallback to the original registry will always occur if Spegel can't be reached or serve the requested image.
## What performance increase can I expect with Spegel?
Read the [benchmark documentation](./BENCHMARK.md) for information of expected gains.
## How do I know that Spegel is working?
Spegel is meant to be a painless experience to install, meaning that it may be difficult initially to know if things are working or not. Simply put a good indicator that things are working is if all Spegel pods have started and are in a ready state.
Spegel does a couple of checks on startup to verify that any required configuration is correct, if it is not it will exit with an error. While it runs it will log all received requests, both those it mirrors and it serves.
An incoming request to Spegel that is mirrored will receive the following log.
```
{"level":"info","ts":1692304805.9038486,"caller":"gin@v0.0.9/logger.go:53","msg":"","path":"/v2/library/nginx/blobs/sha256:1cb127bd932119089b5ffb612ffa84537ddd1318e6784f2fce80916bbb8bd166","status":200,"method":"GET","latency":0.005075836,"ip":"172.18.0.5","handler":"mirror"}
```
While the Spegel instance on the other end will log.
```
{"level":"info","ts":1692304805.9035861,"caller":"gin@v0.0.9/logger.go:53","msg":"","path":"/v2/library/nginx/blobs/sha256:1cb127bd932119089b5ffb612ffa84537ddd1318e6784f2fce80916bbb8bd166","status":200,"method":"GET","latency":0.003644997,"ip":"172.18.0.5","handler":"blob"}
```
## Will image pulls break or be delayed if a spegel instance fails or is removed?
Spegel acts as a best-effort cache and the worst-case scenario is always that images are pulled from the upstream registry (e.g. Docker Hub).
However, should a spegel instance fail (perhaps because the node died), there will be a time interval when its images remain advertised. Currently, spegel advertises images with a TTL of 10 minutes. Other spegel peers may try to forward requests to the failed instance, delaying the response to the pulling client. In benign scenarios, this delay is the length of an intra-cluster round trip (the HTTP request and an ICMP unreachable response), likely <1ms. Of course, there are less benign scenarios (e.g. inter-node packet loss) where no replies will come back and spegel's forwarder will eventually time out before moving on to the next available peer. Spegel uses the standard library's httputil.ReverseProxy to forward requests, which in turn depends on DefaultTransport to decide how long to wait before giving up.
Please note that a client is likely to request several layers in parallel and in many cases the advertising instances will have a similar routing distance, so spegel will spread its forwards across those instances. Thus, the benign scenario is unlikely to impact pod startup time. Only when the routing distance is different (e.g. edge locations) or when an image dominated by one large layer is affected is pod startup time materially increased.
## Why am I not able to pull the new version of my tagged image?
Reusing the same tag multiple times for different versions of an image is generally a bad idea. The most common scenario is the use of the `latest` tag. This makes it difficult to determine which version of the image is being used. On top of that, the image will not be updated if it is already cached on the node.
Some people have chosen to power forward with reusing tags and chosen to instead set the image pull policy to `AlwaysPull`, forcing the image to be updated every time a pod is started. This will however not work with Spegel as the tag could be resolved by another node in the cluster resulting in the same "old" image being pulled.
There are two solutions to work around this problem, allowing users to continue with their way of working before using Spegel.
The best and preferable solution is to deploy [k8s-digester](https://github.com/google/k8s-digester) alongside Spegel. This will allow you to enjoy all the benefits of Spegel will continuously updating image tag versions. The way it works is that k8s-digester will, for each pod created, resolve tags to image digests and add them to the image reference.
This means that all pods that originally reference images by tag will instead do so with digest. This means that k8s-digester will resolve the new digest for a tag if a new version is pushed to the registry. Using k8s-digester means that tags will be updated while using Spegel to distribute the layers between nodes. It also means that Spegel will be able
to continue distributing images if the external registry became unavailable. The reason this works is that the mutating webhook is configured to ignore errors, and instead, Spegel will be used to resolve the tag to a digest.
One caveat when deploying k8s-digester is that it will by default modify both pods but also any other parent resource that creates pods. This in turn has the side effect of only setting the
digest once when the parent resource is created, and never again. For that reason it is a good idea to modify the mutating webhook to only include pods, that way the digest will be
updated every time a new pod is created.
```yaml
apiVersion: admissionregistration.k8s.io/v1
kind: MutatingWebhookConfiguration
metadata:
name: digester-mutating-webhook-configuration
labels:
control-plane: controller-manager
digester/operation: webhook
digester/system: "yes"
webhooks:
- name: digester-webhook-service.digester-system.svc
admissionReviewVersions:
- v1
- v1beta1
clientConfig:
service:
name: digester-webhook-service
namespace: digester-system
path: /v1/mutate
caBundle: Cg==
failurePolicy: Ignore # kpt-set: ${failure-policy}
namespaceSelector:
matchLabels:
digest-resolution: enabled
reinvocationPolicy: IfNeeded
rules:
- resources:
- pods
apiGroups:
- ''
apiVersions:
- v1
operations:
- CREATE
- UPDATE
scope: Namespaced
sideEffects: None
timeoutSeconds: 15
```
The second option, which should be used only if using k8s-digester is not possible, is to disable tag resolving altogether in Spegel. There are two options when doing this. It can either be disabled only for `latest` tags or for all tags.
This can be done by changing the Helm charts values from their defaults.
```yaml
spegel:
resolveTags: false
resolveLatestTag: false
```
Please note that this does however remove Spegel's ability to protect against registry outages for any images referenced by tags.
## Why am I able to pull private images without image pull secrets?
An image pulled by a Kubernetes node is cached locally on disk. Meaning that other pods running on the same node that require the same image do not have to pull the same image again. Spegel relies on this mechanism to be able to distribute images.
This may however not be a desirable feature when running a multi-tenant cluster where private images are pulled using credentials. In this scenario, only those pods with the correct credentials would be able to use the image.
Ownership of private images has been an issue for a long time in Kubernetes as indicated by the unresolved issue https://github.com/kubernetes/kubernetes/issues/18787 created back in 2015. The short answer is that a good solution does not exist, with or without Spegel.
The current [suggested solution](https://kubernetes.io/docs/reference/access-authn-authz/admission-controllers/#alwayspullimages) is to enforce an `AlwaysPull` image policy for private images that require authentication. Doing so will force a request to the registry to
validate the digest or resolve the tag. This request will only succeed with the proper authentication. This is a mediocre solution at best as it creates a hard dependency on the external registry, meaning the pod will not be able to start even if the image is cached on the node.
This solution does however not work when using Spegel, instead, Spegel may make the problem worse. Without Spegel an image that would want to use a private image, it does not have access to would have to be scheduled on a node that has already pulled the image.
With Spegel that image will be available to all nodes in the cluster. Currently, a good solution for Spegel does not exist. There are two reasons for this. The first is that credentials are not included when pulling an image from a registry mirror, a good choice as doing so would mean sharing credentials with third parties.
Additionally, Spegel would have no method of validating the credentials even if they were included in the requests. So for the time being if you have these types of requirements Spegel may not be the choice for you.
## How do I use Spegel in conjunction with another registry cache?
Spegel can be used with other registry caches in cases where the best effort caching offered by Spegel is not enough. In these situations, if the image is not cached within the cluster the image should be pulled from the secondary cache.
This is configured by adding the domain of the registry to the `additionalMirrorRegistries` list in the Helm values. Registries added to this list will be included in the mirror configuration created by Spegel.
```yaml
spegel:
additionalMirrorRegistries:
- https://zot.example.com
```
## Why is my node running out of disk space?
By default the kubelet on every node is configured to [garbage collect](https://kubernetes.io/docs/concepts/architecture/garbage-collection/#containers-images) unused images when the disk space starts to run out. Some Kubernetes clusters come with image garbage collection disabled by default. This can cause a nodes disk to fill up quickly, especially on nodes with small disks to begin with. Spegel does not have a built in garbage collection instead it depends completely on the kubelt garbage collection beign properly configured.
## What should I do if other pods are scheduled on new nodes before Spegel?
The Kubernetes scheduler will assign pods to nodes as soon as the node reports as ready. This causes a race to schedule and start Spegel on any new node, before other pods are scheduled. If this does not happen the mirror configuration will not be written to the node before the node starts pulling images for other new pods scheduled on it. Defeating the purpose of Spegel in this scenario. This problem is not unique to Spegel, but is a wider problem in Kubernetes for critical daemonsets. There are two closed KEPs [#1003](https://github.com/kubernetes/enhancements/pull/1003) and [#75890](https://github.com/kubernetes/kubernetes/issues/75890) which attempted to solve this without being accepted.
The best solution to address this problem currently is to use [nidhogg](https://github.com/pelotech/nidhogg) to taint nodes which are not running pods from specific daemonsets. It implements for the most part the features suggested in both KEPs. Ensuring that all image pulls go through Spegel, even on new nodes.
## Can I deploy multiple Spegel clusters?
In certain situations deploying multiple separate Spegel clusters is beneficial. For example when a Kubernetes cluster spans multiple regions, it may be beneficial to limit nodes too only pull images from within the same region. Spegel can be deployed multiple times by simply using different names for each Helm deployment and setting a unique node selector. It is important to note that the node port service needs a unique port per deployment. Spegel will then only deploy on nodes with the matching labels and elect a unique leader for each deployment of Spegel. As the two Spegel clusters will never communicate they will not be able to discover layers outside of their own region, limiting requests to their specific region.
```bash
helm upgrade --create-namespace --namespace spegel --install --version ${SPEGEL_VERSION} spegel-one oci://ghcr.io/spegel-org/helm-charts/spegel --set "nodeSelector.group=one" --set "service.registry.nodePort=30021"
helm upgrade --create-namespace --namespace spegel --install --version ${SPEGEL_VERSION} spegel-two oci://ghcr.io/spegel-org/helm-charts/spegel --set "nodeSelector.group=two" --set "service.registry.nodePort=30022"
```

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# Spegel: Visual Architecture Guide
This document provides a comprehensive set of diagrams explaining Spegel's architecture, flows, and operations.
## 1. High-Level Cluster Architecture
Shows how Spegel pods form a P2P network within the cluster. Containerd interacts with Spegel for image pulls and handles fallback to external registry when needed.
```mermaid
graph TB
subgraph "External"
ER["External Registry"]
end
subgraph "Kubernetes Cluster"
subgraph "Node 1"
SP1["Spegel Pod"]
CD1["Containerd"]
SP1 <-->|interacts| CD1
CD1 -->|fallback| ER
end
subgraph "Node 2"
SP2["Spegel Pod"]
CD2["Containerd"]
SP2 <-->|interacts| CD2
CD2 -->|fallback| ER
end
subgraph "Node 3"
SP3["Spegel Pod"]
CD3["Containerd"]
SP3 <-->|interacts| CD3
CD3 -->|fallback| ER
end
SP1 <-->|P2P Network| SP2
SP2 <-->|P2P Network| SP3
SP3 <-->|P2P Network| SP1
end
```
## 2. Pod Component Architecture
Details the internal components of a Spegel pod and their relationships, showing how the registry service, P2P components, and state management interact with each other and with containerd.
```mermaid
graph TB
subgraph "Spegel Pod"
subgraph "Registry Service"
RS[HTTP Server /v2/]
RH[Request Handler]
RS --> RH
end
subgraph "P2P Components"
P2P[P2P Router]
DHT[DHT Provider]
BS[Bootstrapper]
P2P --> DHT
BS --> P2P
end
subgraph "State Management"
ST[State Tracker]
MT[Metrics]
ST --> MT
end
CD[Containerd Client]
RH --> P2P
ST --> P2P
CD --> ST
end
subgraph "Node Components"
CDD[Containerd Daemon]
CS[Content Store]
CDD --> CS
end
CD --> CDD
```
## 3. Image Pull Flow
Shows the sequence of operations during an image pull request, demonstrating both successful peer pulls and fallback to external registry.
```mermaid
sequenceDiagram
participant CD as Containerd
participant SR as Spegel Registry
participant P2P as P2P Router
participant PR as Peer Registry
participant ER as External Registry
Note over SR,P2P: 20ms default resolve timeout
Note over SR,P2P: 3 default resolve retries
CD->>SR: GET /v2/{name}/manifests/{ref}
SR->>P2P: Resolve(key, allowSelf, retries)
alt Peer Found
P2P-->>SR: Return Peer Address
SR->>PR: Request Content
PR-->>SR: Stream Content
SR-->>CD: Return Content
CD->>CS: Store Content
else No Peers Available (within 20ms)
SR-->>CD: 404 Not Found
CD->>ER: Request from External
ER-->>CD: Return Content
CD->>CS: Store Content
end
```
## 4. P2P Network Formation
Shows how nodes discover each other and form the P2P network through leader election and peer sharing.
```mermaid
sequenceDiagram
participant N1 as Node 1
participant N2 as Node 2
participant N3 as Node 3
participant LE as Leader Election
Note over N1,LE: 10s lease duration
Note over N1,LE: 5s renew deadline
Note over N1,LE: 2s retry period
N1->>LE: Participate in Election
N2->>LE: Participate in Election
N3->>LE: Participate in Election
LE->>N1: Elected Leader
N2->>N1: Discover Leader
N3->>N1: Discover Leader
N1->>N2: Share Peer List
N1->>N3: Share Peer List
N2->>N3: Establish P2P Connection
Note over N1,N3: P2P Network Formed
```
## 5. State Management and Content Advertisement
Shows how content availability is maintained and advertised in the P2P network, including periodic refresh cycles and event-driven updates.
```mermaid
sequenceDiagram
participant ST as State Tracker
participant CD as Containerd
participant P2P as P2P Router
participant DHT as DHT Network
participant MT as Metrics
Note over ST,DHT: Content TTL: 10 minutes
Note over ST,DHT: Refresh: Every 9 minutes
loop Every 9 minutes
ST->>CD: List Images
CD-->>ST: Image List
loop For each image
ST->>P2P: Advertise(image_keys)
P2P->>DHT: Provide(keys)
end
ST->>MT: Update Metrics
end
CD-->>ST: Image Event (Create/Update/Delete)
ST->>P2P: Update Advertisement
ST->>MT: Update Metrics
```
## 6. Content Resolution Process
Shows how content is located and retrieved from peers in the network, including peer selection and retry mechanisms.
```mermaid
sequenceDiagram
participant SR as Spegel Registry
participant P2P as P2P Router
participant DHT as DHT Network
participant PR1 as Peer 1
participant PR2 as Peer 2
SR->>P2P: Resolve(content_key)
P2P->>DHT: FindProviders(key)
par Parallel Resolution
DHT-->>P2P: Found Peer 1
DHT-->>P2P: Found Peer 2
end
P2P->>SR: Return First Available Peer
Note over SR,PR2: Default 20ms timeout
Note over SR,PR2: 3 retry attempts
alt Try Peer 1
SR->>PR1: Request Content
PR1-->>SR: Stream Content
else Peer 1 Fails
SR->>PR2: Request Content
PR2-->>SR: Stream Content
end
```
## 7. Data Flow Paths
Shows the content paths and system control flows, including peer transfers and fallback mechanisms.
```mermaid
graph LR
subgraph "Content Paths"
CD[Containerd]
SP[Spegel]
P[Peers]
ER[External Registry]
CS[Content Store]
CD -->|Request| SP
SP -->|Check| P
P -->|Content| SP
SP -->|Return| CD
CD -->|Store| CS
SP -->|404| CD
CD -->|Fallback| ER
end
subgraph "P2P Operations"
P2P[P2P Network]
DHT[DHT]
ST[State Tracker]
P2P -->|Advertise| DHT
DHT -->|Discover| P2P
ST -->|Update| P2P
end
```
## 8. Failure Handling
Shows how different types of failures are handled in the system.
```mermaid
sequenceDiagram
participant CD as Containerd
participant SR as Spegel Registry
participant P2P as P2P Router
participant PR as Peer
participant ER as External Registry
Note over SR,ER: Failure Scenarios
alt Peer Not Found
CD->>SR: Request Content
SR->>P2P: Resolve(key)
P2P--xSR: No Peers Available
SR-->>CD: 404 Not Found
CD->>ER: Fallback Request
end
alt Peer Connection Failed
SR->>PR: Request Content
PR--xSR: Connection Failed
SR->>P2P: Resolve(key) Retry
P2P-->>SR: Alternative Peer
end
alt Content Corrupted
SR->>PR: Request Content
PR-->>SR: Stream Content
SR--xCD: Verification Failed
CD->>ER: Fallback Request
end
```
## 9. Metrics Collection
Shows how metrics are collected and organized across the system components.
```mermaid
graph TB
subgraph "Metrics Sources"
RQ[Registry Requests]
P2P[P2P Operations]
ST[State Changes]
end
subgraph "Metric Types"
CT[Counters]
HT[Histograms]
GT[Gauges]
end
subgraph "Prometheus Metrics"
MR[mirror_requests_total]
RD[resolve_duration_seconds]
AI[advertised_images]
AK[advertised_keys]
RL[request_latency]
IF[requests_inflight]
end
RQ --> CT
RQ --> HT
P2P --> HT
P2P --> GT
ST --> GT
CT --> MR
HT --> RD
HT --> RL
GT --> AI
GT --> AK
GT --> IF
```

13
docs/METRICS.md
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@ -1,13 +0,0 @@
# Metrics
| Name| Type | Labels |
| ---------- | ----------- | ----------- |
| spegel_advertised_images | Gauge | `registry` |
| spegel_resolve_duration_seconds | Histogram | `router` |
| spegel_advertised_keys | Gauge | `registry` |
| spegel_advertised_image_tags | Gauge | `registry` |
| spegel_advertised_image_digests | Gauge | `registry` |
| spegel_mirror_requests_total | Counter | `registry` <br/> `cache=hit\|miss` <br/> `source=internal\|external` |
| http_request_duration_seconds | Histogram | `handler` <br/> `method` <br/> `code` |
| http_response_size_bytes | Histogram | `handler` <br/> `method` <br/> `code` |
| http_requests_inflight | Gauge | `handler` |