Case Study: Migrating ETCD Volumes in Production

One reason that you stumbled upon this blog post could be that you saw errors like the following in your ETCD instances:

etcd-main-0 etcd 2020-09-03 06:00:07.556157 W | etcdserver: read-only range request "key:\"/registry/deployments/shoot--pwhhcd--devcluster2/kube-apiserver\" " with result "range_response_count:1 size:9566" took too long (13.95374909s) to execute

As it turns out, 14 seconds are way too slow for running Kubernetes API servers. It makes them go into a crash loop (leader election fails). Even worse, this whole thing is self-amplifying: The longer a response takes, the more requests queue up, leading to response times increasing further and further. The system is very unlikely to recover. 😞

On Github, you can easily find the reason for this problem. Most probably your disks are too slow (see etcd-io/etcd#10860). So, when you are (like in our case) on GKE and run your ETCD on their default persistent volumes, consider moving from standard disks to SSDs and the error messages should disappear. A guide on how to use SSD volumes on GKE can be found at Using SSD persistent disks.

Case closed? Well. For some people it might be. But when you are seeing this in your Gardener infrastructure, it’s likely that there is something going wrong. The entire ETCD management is fully managed by Gardener, which makes the problem a bit more interesting to look at. This blog post strives to cover topics such as:

• Gardener operating principles
• Gardener architecture and ETCD management
• Pitfalls with multi-cloud environments
• Migrating GCP volumes to a new storage class

We from metal-stack learned quite a lot about the capabilities of Gardener through this problem. We are happy to share this experience with a broader audience. Gardener adopters and operators read on.

How Gardener Manages ETCDs

In our infrastructure, we use Gardener to provision Kubernetes clusters on bare metal machines in our own data centers using metal-stack. Even if the entire stack could be running on-premise, our initial seed cluster and the metal control plane are hosted on GKE. This way, we do not need to manage a single Kubernetes cluster in our entire landscape manually. As soon as we have Gardener deployed on this initial cluster, we can spin up further Seeds in our own data centers through the concept of ManagedSeeds.

To make this easier to understand, let us give you a simplified picture of how our Gardener production setup looks like:

For every shoot cluster, Gardener deploys an individual, standalone ETCD as a stateful set into a shoot namespace. The deployment of the ETCD stateful set is managed by a controller called etcd-druid, which reconciles a special resource of the kind etcds.druid.gardener.cloud. This Etcd resource is getting deployed during the shoot provisioning flow in the gardenlet.

For failure-safety, the etcd-druid deploys the official ETCD container image along with a sidecar project called etcd-backup-restore. The sidecar automatically takes backups of the ETCD and stores them at a cloud provider, e.g. in S3 Buckets, Google Buckets, or similar. In case the ETCD comes up without or with corrupted data, the sidecar looks into the backup buckets and automatically restores the latest backup before ETCD starts up. This entire approach basically takes away the pain for operators to manually have to restore data in the event of data loss.

As it’s the nature for multi-cloud applications to act upon a variety of cloud providers, with a single installation of Gardener, it is easily possible to spin up new Kubernetes clusters not only on GCP, but on other supported cloud platforms, too.

When the Gardenlet deploys a resource like the Etcd resource into a shoot namespace, a provider-specific extension-controller has the chance to manipulate it through a mutating webhook. This way, a cloud provider can adjust the generic Gardener resource to fit the provider-specific needs. For every cloud that Gardener supports, there is such an extension-controller. For metal-stack, we also maintain one, called gardener-extension-provider-metal.

The Mistake Is in the Deployment

Now that we know how the ETCDs are managed by Gardener, we can come back to the original problem from the beginning of this article. It turned out that the real problem was a misconfiguration in our deployment. Gardener actually does use SSD-backed storage on GCP for ETCDs by default. During reconciliation, the gardener-extension-controller-gcp deploys a storage class called gardener.cloud-fast that enables accessing SSDs on GCP.

But for some reason, in our cluster we did not find such a storage class. And even more interesting, we did not use the gardener-extension-provider-gcp for any shoot reconciliation, only for ETCD backup purposes. And that was the big mistake we made: We reconciled the shoot control plane completely with gardener-extension-provider-metal even though our initial Seed actually runs on GKE and specific parts of the shoot control plane should be reconciled by the GCP extension-controller instead!

This is how the initial Seed resource looked like:

apiVersion: core.gardener.cloud/v1beta1
kind: Seed
metadata:
  name: initial-seed
spec:
  ...
  provider:
    region: gke
    type: metal
  ...
...

Surprisingly, this configuration was working pretty well for a long time. The initial seed properly produced the Kubernetes control planes of our managed seeds that looked like this:

$ kubectl get controlplanes.extensions.gardener.cloud
NAME                 TYPE    PURPOSE    STATUS      AGE
fra-equ01            metal              Succeeded   85d
fra-equ01-exposure   metal   exposure   Succeeded   85d

And this is another interesting observation: There are two ControlPlane resources. One regular resource and one with an exposure purpose. Gardener distinguishes between two types for this exact reason: Environments where the shoot control plane runs on a different cloud provider than the Kubernetes worker nodes. The regular ControlPlane resource gets reconciled by the provider configured in the Shoot resource, and the exposure type ControlPlane by the provider configured in the Seed resource.

With the existing configuration the gardener-extension-provider-gcp does not kick in and hence, it neither deploys the gardener.cloud-fast storage class nor does it mutate the Etcd resource to point to it. And in the end, we are left with ETCD volumes using the default storage class (which is what we do for ETCD stateful sets in the metal-stack seeds, because our default storage class uses csi-lvm that writes into logical volumes on the SSD disks in our physical servers).

The correction we had to make was a one-liner: Setting the provider type of the initial Seed resource to gcp.

$ kubectl get seed initial-seed -o yaml
apiVersion: core.gardener.cloud/v1beta1
kind: Seed
metadata:
  name: initial-seed
spec:
  ...
  provider:
    region: gke
    type: gcp # <-- here
  ...
...

This change moved over the control plane exposure reconciliation to the gardener-extension-provider-gcp:

$ kubectl get -n <shoot-namespace> controlplanes.extensions.gardener.cloud
NAME                 TYPE    PURPOSE    STATUS      AGE
fra-equ01            metal              Succeeded   85d
fra-equ01-exposure   gcp     exposure   Succeeded   85d

And boom, after some time of waiting for all sorts of magic reconciliations taking place in the background, the missing storage class suddenly appeared:

$ kubectl get sc
NAME                  PROVISIONER            
gardener.cloud-fast   kubernetes.io/gce-pd
standard (default)    kubernetes.io/gce-pd

Also, the Etcd resource was now configured properly to point to the new storage class:

$ kubectl get -n <shoot-namespace> etcd etcd-main -o yaml
apiVersion: druid.gardener.cloud/v1alpha1
kind: Etcd
metadata:
  ...
  name: etcd-main
spec:
  ...
  storageClass: gardener.cloud-fast # <-- was pointing to default storage class before!
  volumeClaimTemplate: main-etcd
...

The Migration

Now that the deployment was in place such that this mistake would not repeat in the future, we still had the ETCDs running on the default storage class. The reconciliation does not delete the existing persistent volumes (PVs) on its own.

To bring production back up quickly, we temporarily moved the ETCD pods to other nodes in the GKE cluster. These were nodes which were less occupied, such that the disk throughput was a little higher than before. But surely that was not a final solution.

For a proper solution we had to move the ETCD data out of the standard disk PV into a SSD-based PV.

Even though we had the etcd-backup-restore sidecar, we did not want to fully rely on the restore mechanism to do the migration. The backup should only be there for emergency situations when something goes wrong. Thus, we came up with another approach to introduce the SSD volume: GCP disk snapshots. This is how we did the migration:

1. Scale down etcd-druid to zero in order to prevent it from disturbing your migration
2. Scale down the kube-apiservers deployment to zero, then wait for the ETCD stateful to take another clean snapshot
3. Scale down the ETCD stateful set to zero as well
4. (in order to prevent Gardener from trying to bring up the downscaled resources, we used small shell constructs like while true; do kubectl scale deploy etcd-druid --replicas 0 -n garden; sleep 1; done)
5. Take a drive snapshot in GCP from the volume that is referenced by the ETCD PVC
6. Create a new disk in GCP from the snapshot on a SSD disk
7. Delete the existing PVC and PV of the ETCD (oops, data is now gone!)
8. Manually deploy a PV into your Kubernetes cluster that references this new SSD disk
9. Manually deploy a PVC with the name of the original PVC and let it reference the PV that you have just created
10. Scale up the ETCD stateful set and check that ETCD is running properly
11. (if something went terribly wrong, you still have the backup from the etcd-backup-restore sidecar, delete the PVC and PV again and let the sidecar bring up ETCD instead)
12. Scale up the kube-apiserver deployment again
13. Scale up etcd-druid again
14. (stop your shell hacks ;D)

This approach worked very well for us and we were able to fix our production deployment issue. And what happened: We have never seen any crashing kube-apiservers again. 🎉

Conclusion

As bad as problems in production are, they are the best way for learning from your mistakes. For new users of Gardener it can be pretty overwhelming to understand the rich configuration possibilities that Gardener brings. However, once you get a hang of how Gardener works, the application offers an exceptional versatility that makes it very much suitable for production use-cases like ours.

This example has shown how Gardener:

• Can handle arbitrary layers of infrastructure hosted by different cloud providers.
• Allows provider-specific tweaks to gain ideal performance for every cloud you want to support.
• Leverages Kubernetes core principles across the entire project architecture, making it vastly extensible and resilient.
• Brings useful disaster recovery mechanisms to your infrastructure (e.g. with etcd-backup-restore).

We hope that you could take away something new through this blog post. With this article we also want to thank the SAP Gardener team for helping us to integrate Gardener with metal-stack. It’s been a great experience so far. 😄 😍

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