[GitHub] [couchdb-helm] gpothier edited a comment on issue #40: Cluster auto-scaling best practices

[GitBox <gi...@apache.org>]

gpothier edited a comment on issue #40:
URL: https://github.com/apache/couchdb-helm/issues/40#issuecomment-933572542


   Hi, mostly thinking out loud here, but would the following be a valid scaling strategy?
   1. Initially deploy the CouchDB cluster with a large number of CouchDB nodes (ie. k8s pods), configuring their resource allocations quite low so that many pods can run on a single k8s node.
   2. When usage increases (as measured by global CPU utilization), increase the resource allocations of the pods so that they get spread out to more (and possibly more powerful) k8s nodes. 
   3. Conversely, when usage decreases, reduce the resource allocations so that pods can regroup to fewer k8s nodes
   
   This way, there is no need for resharding. However (and please note I am a k8s beginner), I don't think this "migration" of pods to other nodes when their resource allocations change would be automatic, so it would probably require killing the pods to force them being recreated elsewhere.
   
   EDIT: just realized that changing the resource requests of pods according to actual usage and migrating them to other k8s nodes is the Vertical Pod Autoscaler's job, so it seems scaling could be achieved by implementing point 1 above and properly configuring a Vertical Pod Autoscaler (and a Cluster Autoscaler).


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