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Availability and multi-cluster documentation.
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# Availability
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This document collects advice on reasoning about and provisioning for high-availability when using Kubernetes clusters.
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## Failure modes
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This is an incomplete list of things that could go wrong, and how to deal with them.
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Root causes:
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- VM(s) shutdown
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- network partition within cluster, or between cluster and users.
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- crashes in Kubernetes software
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- data loss or unavailability of persistent storage (e.g. GCE PD or AWS EBS volume).
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- operator error misconfigures kubernetes software or application software.
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Specific scenarios:
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- Apiserver VM shutdown or apiserver crashing
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- Results
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- unable to stop, update, or start new pods, services, replication controller
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- existing pods and services should continue to work normally, unless they depend on the Kubernetes API
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- Apiserver backing storage lost
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- Results
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- apiserver should fail to come up.
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- kubelets will not be able to reach it but will continute to run the same pods and provide the same service proxying.
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- manual recovery or recreation of apiserver state necessary before apiserver is restarted.
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- Supporting services (node controller, replication controller manager, scheduler, etc) VM shutdown or crashes
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- currently those are colocated with the apiserver, and their unavailability has similar consequences as apiserver
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- in future, these will be replicated as well and may not be co-located
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- they do not have own persistent state
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- Node (thing that runs kubelet and kube-proxy and pods) shutdown
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- Results
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- pods on that Node stop running
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- Kubelet software fault
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- Results
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- crashing kubelet cannot start new pods on the node
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- kubelet might delete the pods or not
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- node marked unhealthy
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- replication controllers start new pods elsewhere
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- Cluster operator error
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- Results:
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- loss of pods, services, etc
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- lost of apiserver backing store
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- users unable to read API
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- etc
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Mitigations:
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- Action: Use IaaS providers automatic VM restarting feature for IaaS VMs.
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- Mitigates: Apiserver VM shutdown or apiserver crashing
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- Mitigates: Supporting services VM shutdown or crashes
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- Action use IaaS providers reliable storage (e.g GCE PD or AWS EBS volume) for VMs with apiserver+etcd.
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- Mitigates: Apiserver backing storage lost
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- Action: Use Replicated APIserver feature (when complete: feature is planned but not implemented)
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- Mitigates: Apiserver VM shutdown or apiserver crashing
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- Will tolerate one or more similtaneous apiserver failures.
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- Mitigates: Apiserver backing storage lost
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- Each apiserver has independent storage. Etcd will recover from loss of one member. Risk of total data loss greatly reduced.
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- Action: Snapshot apiserver PDs/EBS-volumes periodically
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- Mitigates: Apiserver backing storage lost
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- Mitigates: Some cases of operator error
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- Mitigates: Some cases of kubernetes software fault
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- Action: use replication controller and services in front of pods
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- Mitigates: Node shutdown
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- Mitigates: Kubelet software fault
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- Action: applications (containers) designed to tolerate unexpected restarts
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- Mitigates: Node shutdown
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- Mitigates: Kubelet software fault
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- Action: Multiple independent clusters (and avoid making risky changes to all clusters at once)
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- Mitigates: Everything listed above.
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## Chosing Multiple Kubernetes Clusters
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You may want to set up multiple kubernetes clusters, both to
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have clusters in different regions to be nearer to your users; and to tolerate failures and/or invasive maintenance.
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### Scope of a single cluster
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On IaaS providers such as Google Compute Engine or Amazon Web Services, a VM exists in a
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[zone](https://cloud.google.com/compute/docs/zones) or [availability
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zone](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-regions-availability-zones.html).
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We suggest that all the VMs in a Kubernetes cluster should be in the same availability zone, because:
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- compared to having a single global Kubernetes cluster, there are fewer single-points of failure
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- compared to a cluster that spans availability zones, it is easier to reason about the availability properties of a
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single-zone cluster.
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- when the Kubernetes developers are designing the system (e.g. making assumptions about latency, bandwidth, or
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correlated failures) they are assuming all the machines are in a single data center, or otherwise closely connected.
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It is okay to have multiple clusters per availability zone, though on balance we think fewer is better.
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Reasons to prefer fewer clusters are:
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- improved bin packing of Pods in some cases with more nodes in one cluster.
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- reduced operational overhead (though the advantage is diminished as ops tooling and processes matures).
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- reduced costs for per-cluster fixed resource costs, e.g. apiserver VMs (but small as a percentage
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of overall cluster cost for medium to large clusters).
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Reasons to have multiple clusters include:
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- strict security policies requiring isolation of one class of work from another (but, see Partitioning Clusters
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below).
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- test clusters to canary new Kubernetes releases or other cluster software.
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### Selecting the right number of clusters
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The selection of the number of kubernetes clusters may be a relatively static choice, only revisted occasionally.
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By contrast, the number of nodes in a cluster and the number of pods in a service may be change frequently according to
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load and growth.
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To pick the number of clusters, first, decide which regions you need to be in to have adequete latency to all your end users, for services that will run
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on Kubernetes (if you use a Content Distribution Network, the latency requirements for the CDN-hosted content need not
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be considered). Legal issues might influence this as well. For example, a company with a global customer base might decide to have clusters in US, EU, AP, and SA regions.
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Call the number of regions to be in `R`.
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Second, decide how many clusters should be able to be unavailable at the same time, while still being available. Call
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the number that can be unavailable `U`. If you are not sure, then 1 is a fine choice.
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If it is allowable for load-balancing to direct traffic to any region in the event of a cluster failure, then
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then you need `R + U` clusters. If it is not (e.g you want to ensure low latency for all users in the event of a
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cluster failure), then you need to have `R * U` clusters (`U` in each of `R` regions). In any case, try to put each cluster in a different zone.
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Finally, if any of your clusters would need more than the maximum recommended number of nodes for a Kubernetes cluster, then
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you may need even more clusters. Our roadmap (
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https://github.com/GoogleCloudPlatform/kubernetes/blob/24e59de06e4da61f5dafd4cd84c9340a2c0d112f/docs/roadmap.md)
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calls for maximum 100 node clusters at v1.0 and maximum 1000 node clusters in the middle of 2015.
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## Working with multiple clusters
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When you have multiple clusters, you would typically create services with the same config in each cluster and put each of those
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service instances behind a load balancer (AWS Elastic Load Balancer, GCE Forwarding Rule or HTTP Load Balancer), so that
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failures of a single cluster are not visible to end users.
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