How do you connect Kubernetes clusters located in different data centres?

Published in April 2019

Welcome to Bite-sized Kubernetes learning — a regular column on the most interesting questions that we see online and during our workshops answered by a Kubernetes expert.

Today's answers are curated by Daniele Polencic. Daniele is an instructor and software engineer at Learnk8s.

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How do you connect Kubernetes clusters located in different data centres?

It's relatively common to see infrastructure being replicated and distributed in different geographical regions, particularly in regulated environments.

If one of the regions becomes unavailable, you can always route your traffic to another location and continue serving traffic.

When it comes to Kubernetes, you might want to use a similar strategy and distribute your workloads in different regions.

You may have one or several clusters per team, region, environment, or a combination of them.

Your clusters may be hosted in different cloud providers and on-premise.

But how should you design the infrastructure for such geographical split?

Should you create a single large cluster which spans multiple clouds over a unified network?

Or should you have a lot of smaller clusters and find a way to manage and synchronise them?

One cluster to rule them all

Creating a single cluster that uses a unified network is a challenge.

Consider what happens during a network partition.

If you host a single master node, half of your fleet won't be able to receive new commands because it's unable to reach the master.

And that includes old routing tables ( kube-proxy is unable to download new ones) and no more pods (the kubelet fails to ask for updates).

What's worse is that Kubernetes marks the nodes which it can't see as Lost and reschedules the missing pods on the existing nodes.

So you end up having twice as many pods.

If you decide to have one master node for each region, you will face troubles with the consensus algorithm used in the database — etcd.

etcd uses the raft protocol to agree on a value before it's written to disk.

In other words, the majority of the instances have to reach consensus before any state is written in etcd.

If the latency between the etcd instances spikes, like in the case where you host three etcd instances in different geographical regions, it takes longer to agree and write the value to disk.

The delay is propagated to the Kubernetes controllers.

The controller manager takes longer to be notified of a change and to write the response to the database.

And since you don't have a single controller but a few of them, like a chain reaction, the entire cluster becomes incredibly slow.

You should know that etcd is so sensitive to latency that the official documentation recommend using SSD disks instead of regular hard drives.

At the moment, there are no good examples of a stretched network for a single cluster.

Most of the effort from the community and the SIG-cluster group is focussed on answering another question: cluster federation — finding a way to orchestrate clusters in the same way that Kubernetes orchestrates containers.

Option #1 — Federated clusters with kubefed

The official response from SIG-cluster is kubefed2 — a revised version of the original kube federation client and operator.

The first attempt at managing a collection of clusters as a single entity came from a tool called kube federation.

It was a good start, but kube federation ended up being not so popular because not all resources were supported.

The tool had support for federated Deployments and Services but didn't cover StatefulSets, for example.

Also, the federation configuration was passed as annotations and wasn't flexible.

Can you imagine describing the replicas split for each cluster in a federation using only annotations?

That ended up to be messy.

SIG-cluster has gone a long way since kubefed v1 and decided to tackle the challenge again but from a different angle.

Instead of using annotations, they decided to release a controller which can be installed in your clusters and can be configured using Custom Resource Definitions (CRDs).

You have a Custom Resource Definition (CRD) for each resource that you wish to federate with three sections:

the standard resource definition such as a Deployment

a placement section where you can define how that resource should be distributed in the federation

section where you can define how that resource should be distributed in the federation an override section where you can override weights and settings specified in the placement just for a particular cluster

Here's an example of a federated Deployment with placements and overrides.

federated-deployment.yaml apiVersion : types.federation.k8s.io/v1alpha1 kind : FederatedDeployment metadata : name : test - deployment namespace : test - namespace spec : template : metadata : labels : app : nginx spec : replicas : 3 selector : matchLabels : app : nginx template : metadata : labels : app : nginx spec : containers : - image : nginx name : nginx placement : clusterNames : - cluster2 - cluster1 overrides : - clusterName : cluster2 clusterOverrides : - path : spec.replicas value : 5

As you can imagine, the Deployment is distributed in two clusters: cluster1 and cluster2 .

The first cluster deploys three replicas whereas the second overrides the value to 5.

If you wish to have more control on the number of replicas, kubefed2 exposes a new object called ReplicaSchedulingPreference where you can distribute replicas in weighted proportions:

preference.yaml apiVersion : scheduling.federation.k8s.io/v1alpha1 kind : ReplicaSchedulingPreference metadata : name : test - deployment namespace : test - ns spec : targetKind : FederatedDeployment totalReplicas : 9 clusters : A : weight : 1 B : weight : 2

The CRD structure and the API is not finalised, and there's a lot of activity on the official project repository.

You should keep a close eye on kubefed2, but you should also be aware that this is not production ready yet.

You can learn more about kubefed2 from the official kubefed2 article published on the Kubernetes Blog and from the official repository of the kubefed project.

Option #2 — Cluster federation the Booking.com way

Booking.com's engineering team wasn't involved with the design of kubefed v2, so they developed Shipper — an operator for multi-cluster, multi-region, multi-cloud deployment.

Shipper has a few similarities with kubefed2.

Both tools let you customise the rollout strategy to multiple clusters — which clusters are involved in the deployment and how many replicas for each of them.

However, Shipper is designed to minimise the risk of a deployment going wrong.

In Shipper, you define a series of steps that describe the replicas split between previous and current deployment and how much traffic should they receive.

When you submit the resource to the cluster, the Shipper controller takes care of rolling out the change in all federated clusters, one step at the time.

Also, Shipper is very opinionated.

As an example, Shipper is designed to take Helm charts as input, and you can't use vanilla resources.

Here's a high-level overview of how Shipper works.

Instead of creating a standard Deployment, you should create an Application resource that wraps a Helm chart like this:

application.yaml apiVersion : shipper.booking.com/v1alpha1 kind : Application metadata : name : super - server spec : revisionHistoryLimit : 3 template : chart : name : nginx repoUrl : https : //storage.googleapis.com/shipper - demo version : 0.0.1 clusterRequirements : regions : - name : local strategy : steps : - capacity : contender : 1 incumbent : 100 name : staging traffic : contender : 0 incumbent : 100 - capacity : contender : 100 incumbent : 0 name : full on traffic : contender : 100 incumbent : 0 values : replicaCount : 3

Shipper is an interesting contender in the multi-cluster deployment space, but its tight integration with Helm is also a significant concern.

What if the community shifts its focus from Helm to kustomize or kapitan?

You can learn more about Shipper and its philosophy by reading the official press release.

If you prefer to dive into the code, you should head over to the official project repository.

Option #3 — Magic ✨ cluster federation

Kubefed v2 and Shipper solve cluster federation by exposing new resources to the cluster using Custom Resource Definitions.

But what if you don't want to rewrite all of your Deployments, StatefulSets, DaemonSets etc. to be federated?

Can you still federate your existing cluster without changing the YAML?

multi-cluster-scheduler is a project from Admirality that aims at solving scheduling workloads across clusters.

But instead of creating a new way to interact with the cluster and wrapping resources in Custom Resource Definitions, the multi-cluster-scheduler injects itself in the standard Kubernetes lifecycle and intercept all the calls that create pods.

When a Pod is created, it is immediately replaced by a dummy Pod.

The multi-cluster-scheduler uses mutating pod admission webhooks to intercept the call and create a dummy pod that sleeps.

The original Pod goes through another round of scheduling where the placement decision is taken after interrogating the entire federation.

Finally, the Pod is deployed in the target cluster.

In other news, you end up having one extra Pod which doesn't do much — it's just a placeholder.

But the benefit is that you didn't have to write any new resource to make your Deployments federated.

Every resource that creates a pod is automatically federation-ready.

It's a neat idea because you can suddenly have deployments that span multiple regions without even noticing it, but it also feels quite risky as there's a lot of magic going on.

But if Shipper was mostly focussed at minimising the impact of rolling out deployments, multi-cluster-scheduler is a more general tool which is perhaps more appropriate for batch jobs.

It doesn't feature any advanced mechanism to roll out deployments in an incremental fashion.

If you wish to dive more into the multi-cluster-scheduler, you should check out the official repository page.

If you prefer to read about the multi-cluster-scheduler in action, Admiralty has an interesting use case applied to Argo — the open source Kubernetes native workflows, events, CI and CD.

Connecting and managing multiple clusters together is a complex topic, and there isn't a solution that fits all.

If you're interested in diving more into the topic, you should have a look at the following resources:

That's all folks

Thanks for reading until the end!

If you know of a better way to connect multiple clusters, please get in touch and let us know.

We will add it to the links above.

A special thank you goes to Chris Nesbitt-Smith and Vincent De Smet (SRE at swatmobile.io) that reviewed the content of this article and offered some insightful feedback on how federation works.