This year at work I’ve been building out a platform on Google’s managed Kubernetes offering GKE. While GKE makes it easier to automate the provisioning and management of clusters - managing updates of anything more than a handful of clusters can quickly becomes painful without the right tools.

This post outlines some features I’ve either come to value or will prioritize in future when faced with cluster sprawl. I limit these features to the practical management of clusters updates so as to keep the scope of the post small. Monitoring & alerting would be another interesting list but you won’t find that here…

Where did all these clusters come from anyway?

It’s easy to underestimate the number of clusters you’ll end up managing. Sure you’ll maybe have one for each environment. Usually that’d be three at a minimum, for me this number was five.

This isn’t a post about managing five clusters though. There are lots of dimensions that have a multiplicative effect. These will be different for each of us. My other dimensions were separate hardware for data processing reasons; long term migrations to and from different cloud accounts and deployments in different regions.

The obvious one is different teams or groups. However, I didn’t find myself in this situation.

All in all, in the end I found myself managing a fleet of around 30 clusters.

I’m of the view that the best way to create and update GKE clusters right now is using Terraform. Where I work at Jetstack we have a Terraform module for this job if you’ve not started yet. This post will make some references to Terraform use.

Multi-dimensional cluster deployments

With my environments multiplied by my other dimensions I found myself with N development, N stage and N production (etc. etc.) clusters. This quickly grew to a number that made it impossible to complete a master+node pool upgrade within a working day when deploying updates in series.

I want a means of controlling which clusters can be updated in parallel and which can’t. For example, I might want to roll out all my dev clusters at the same time.

Many continuous deployment tooling is not set up in this way and as more cluster configs land it can quickly get out of hand if they’re run in series. I suppose cluster upgrades are likely to be the longest deployments in the whole business (and perhaps by some margin too).

This raises the question - are synchronous pipelines really the best way to roll out cluster updates? In the long term, with things like Cluster API perhaps not…

One plan to rule them all

When you’re sitting in front of a multi-hour pipeline run, it’s nice to know what’s coming up next. With Terraform version prefixes or GKE alias versions it’s possible to find that you’re actually about to roll out a minor update to all your clusters.

I guess this comes down in part to one’s use of Auto-upgrade. I was not using this feature in this case.

Terraform plan you might say. Perhaps, but I think there’s a benefit to keeping the Terraform stacks small to reduce the blast radius and enable the above feature of concurrent cluster deployments.

In the end this was a script for me. It parsed our cluster manifests and made calls against the GKE APIs to check the master and node versions against the values we had in config. This allowed me to warn of inconsistencies from missed runs and any unexpected updates.

I suppose that ideally these might be better implemented as a Prometheus exporter that alerts when config falls behind the available GKE version for that prefix in that location.

Terraform version_prefix and timeouts

There’s a data resource in Terraform called google_container_engine_versions. This enables the selection of cluster versions from the available versions in that location in a predictable manner.

This can be used to give a similar behaviour to the GKE alias version feature - however that can lead to version mismatches when deploying in different regions due to differences in availability. With this feature it’s possible to use a prefix (read GKE alias) but also drop down to a fixed version if required. There’s an example of this in my personal infrastructure repo.

timeouts are probably part of your config already but they deserve a mention. Set these allowing for 30 mins for a regional master upgrade and 12 mins for a node upgrade if you want to be safe. Those are around the maximums I’ve seen this year anyway.

Pesky PDBs

PodDisruptionBudgets can easily be misconfigured by tenants and can delay node upgrades to their maximum deadlines. Something I’d to play with is an OPA policy to block PDB resources where minAvailable is set to an inappropriate value for the desired replicas of that deployment.


That’s my half wish list, half brain dump of GKE cluster upgrade tools.

Once feature that would make a lot of this less painful would be be some dials to tune on the rate at which GKE cycles nodes. Something like a deployment with maxSurge and maxUnavailable would be nice but on a basic level a feature comparable to AWS CodeDeploy’s AllAtOnce would be good to have for some pre production environments. GKE Product team if you’re listening…

I was working with relatively small clusters of big nodes, thoughts and prayers for those with more nodes… hope you found an entertaining book to read…