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Add advice about control plane resilience for large clusters

pull/24424/head
Tim Bannister 2020-10-08 02:40:40 +01:00
parent 0600eaedde
commit f80591272c
1 changed files with 25 additions and 10 deletions
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35
content/en/docs/setup/best-practices/cluster-large.md
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@ -45,6 +45,17 @@ Typically you would run one or two control plane instances per failure zone,
scaling those instances vertically first and then scaling horizontally after reaching
the point of falling returns to (vertical) scale.
You should run at least one instance per failure zone to provide fault-tolerance. Kubernetes
nodes do not automatically steer traffic towards control-plane endpoints that are in the
same failure zone; however, your cloud provider might have its own mechanisms to do this.
For example, using a managed load balancer, you configure the load balancer to send traffic
that originates from the kubelet and Pods in failure zone _A_, and direct that traffic only
to the control plane hosts that are also in zone _A_. If a single control-plane host or
endpoint failure zone _A_ goes offline, that means that all the control-plane traffic for
nodes in zone _A_ is now being sent between zones. Running multiple control plane hosts in
each zone makes that outcome less likely.
### etcd storage
To improve performance of large clusters, you can store Event objects in a separate
@ -57,15 +68,16 @@ When creating a cluster, you can (using custom tooling):
## Addon resources
To prevent memory leaks or other resource issues in cluster
{{< glossary_tooltip text="addon" term_id="addons" >}} from consuming all the resources
available on a node, Kubernetes sets
[resource limits](/docs/concepts/configuration/manage-resources-containers/) on addon
Pods to limit the amount of CPU and memory that they can consume.
Kubernetes [resource limits](/docs/concepts/configuration/manage-resources-containers/)
help to minimise the impact of memory leaks and other ways that pods and containers can
impact on other components. These resource limits can and should apply to
{{< glossary_tooltip text="addon" term_id="addons" >}} just as they apply to application
workloads.
For example:
For example, you can set CPU and memory limits for a logging component:
```yaml
...
containers:
- name: fluentd-cloud-logging
image: fluent/fluentd-kubernetes-daemonset:v1
@ -75,10 +87,13 @@ For example:
memory: 200Mi
```
These limits are static and are based on data collected from addons running on
small clusters. Most addons consume a lot more resources when running on large
clusters. So, if a large cluster is deployed without adjusting these values, the
addon(s) may continuously get killed because they keep hitting the limits.
Addons' default limits are typically based on data collected from experience running
each addon on small or medium Kubernetes clusters. When running on large
clusters, addons often consume more of some resources than their default limits.
If a large cluster is deployed without adjusting these values, the addon(s)
may continuously get killed because they keep hitting the memory limit.
Alternatively, the addon may run but with poor performance due to CPU time
slice restrictions.
To avoid running into cluster addon resource issues, when creating a cluster with
many nodes, consider the following: