Requirements

This page expose the different requirements to run Kuma.

Architecture

Kuma supports machines with x86_64 architecture and since 1.7.0 it’s possible to run Kuma on arm64.

Kubernetes

Kuma is validated against Kubernetes 1.19.x, 1.20.x, 1.21.x and 1.22.x.

Envoy

Versions of envoy supported are: ~1.22.0 which means >=1.22.0 and <1.23.0.

Sizing your control-plane

In short, a control-plane with 4vCPU and 2GB of memory will be able to accommodate more than 1000 data planes.

While it’s really hard to give a precise number, a good rule of thumb is to assign about 1MB of memory per data plane. When it comes to CPUs Kuma handles parallelism extremely well (its architecture uses a lot of shared nothing go-routines) so more CPUs usually enable quicker propagation of changes.

That being said we highly recommend you to run your own load tests prior to going to production. There are many ways to run workloads and deploy applications and while we test some of them, you are in the best place to build a realistic benchmark of what you do.

To see if you may need to increase your control-plane’s spec, there are two main metrics to pay attention to:

• propagation time (xds_delivery): this is the time it takes between a change in the mesh and the dataplane receiving its updated configuration. Think about it as the “reactivity of your mesh”.
• configuration generation time (xds_generation): this is the time it takes for the configuration to be generated.

For any large mesh using transparent-proxy it’s highly recommended to use reachable-services.

You can also find tuning configuration in the fine-tuning section of the docs.

Sizing your sidecar container on Kubernetes

When deploying Kuma on Kubernetes, dataplane is deployed as a separate container in Pod. By default, kuma-sidecar container is configured like this:

resources:
    requests:
        cpu: 50m
        memory: 64Mi
    limits:
        cpu: 500m
        memory: 512Mi

This resources configuration should be enough for most use cases. In some cases like when you cannot scale horizontally, or your service handle lots of concurrent traffic, you may need to change those values. You can do it using ContainerPatch resource.

To change thoes resources, you need ContainerPatch with sidecarPatch. You have multiple options. You can modify single parameter of resource section:

apiVersion: kuma.io/v1alpha1
kind: ContainerPatch
metadata:
  name: container-patch-1
  namespace: kuma-system
spec:
  sidecarPatch:
    - op: add
      path: /resources/requests/cpu
      value: '"1"'

You can modify whole limits section:

apiVersion: kuma.io/v1alpha1
kind: ContainerPatch
metadata:
  name: container-patch-1
  namespace: kuma-system
spec:
  sidecarPatch:
    - op: add
      path: /resources/limits
      value: '{
        "cpu": "1",
        "memory": "1G"
      }'

Precise documentation on how to apply those ContainerPatch resources to specific Pod can be found here.