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Scaling Coder

Coder's control plane (coderd) and workspaces are deployed in a Kubernetes namespace. This document outlines vertical and horizontal scaling techniques to ensure the coderd pods can accommodate user and workspace load on a Coder deployment.

Vertical scaling is preferred over horizontal scaling!

Vertical Scaling

Vertical scaling or scaling up the Coder control plane (which consists of the coderd pod and any additional replicas) is done by adding additional computing resources to the coderd pods in the Helm chart's values.yaml file.

Download the values file for a deployed Coder release with the following command:

helm get values coder > values.yaml -n coder

Experiment with increasing CPU and memory requests and limits as the number of workspaces in your Coder deployment increase. Pay particular attention to whether users have their workspaces configured to auto-start at the same time each day, which produces spike loads on the coderd pods. To best prevent Out of Memory conditions aka OOM Kills, configure the memory requests and limits to be the same Gi values. e.g., 8Gi

Increasing coderd CPU and memory resources requires sufficient Kubernetes node machine types to accomodate coderd, Coder workspaces and additional system and 3rd party pods on the same cluster namespace.

These are example values.yaml resources for coderd's CPU and memory for a larger deployment with hundreds of workspaces autostarting at the same time each day:

      cpu: "4"
      memory: "8Gi"
      cpu: "8"
      memory: "8Gi"

Leading indicators of undersized coderd pods include users experiencing disconnects in the web terminal, a web IDE like code-server or slowness within the Coder UI dashboard. One condition that may be occuring is an OOM Kill where one or more coderd pod fails, restarts or fails to restart and enteres a CrashLoopBackOff status. If coderd restarts and there are active workspaces and user sessions, they will be reconnected to a new coderd pod causing a disconnect situation. As a Kubernetes administrator, you can also notice restarts by noticing frequently changing and low AGE column when getting the pods:

kubectl get pods -n coder | grep coderd

Horizontal Scaling

Another way to distribute user and workspace load on a Coder deployment is to add additional coderd pods.

  replicas: 3

Coder load balances user and workspace requests across the coderd replicas ensuring sufficient resources and response time.

There is not a linear relationship between nodes and coderd replicas so experiment with incrementing replicas as you increase nodes. e.g., 8 nodes and 3 coderd replicas.

Horizontal Pod Autoscaling

Horizontal Pod Autoscaling (HPA) is another Kubernetes technique to automatically add, and remove, additional coderd pods when the existing pods exceed sustained CPU and memory thresholds. Consult Kubernetes HPA documentation for the various API version implementations of HPA.

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