Kubernetes pod autoscaling for beginners

In this episode, were taking a look at how to scale pods on Kubernetes based on CPU or Memory usage. This feature in Kubernetes is called the Horizontal Pod autoscaler.
Before scaling its important to understand your resource usage for the service you wish to scale.
We take a look at resource requests and limits and how they play a key role in autoscaling.

Checkout the source code below 👇🏽 and follow along 🤓

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Source Code 🧐
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If you are new to Kubernetes, check out my getting started playlist on Kubernetes below :)

Kubernetes Guide for Beginners:
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Kubernetes Monitoring Guide:
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Kubernetes Secret Management Guide:
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Music:

[MarcelDempers]

In this episode we learn how to scale pods with the horizontal pod autoscaler.

[ibrahemazad]

the best video I ever watched on the internet explaining HPA

[tiagomedeiros]

I read many articles on many sites and watch many videos to understand pod autoscaler, but all this time, I just needed to watch this video. Thank you.

[yovangrbovich]

Great content as usual, and the production quality is constantly getting better too! Awesome

[emergirie]

Nice discover I like the way you explaining dude thanks for effort.I subscribe and will let other people know you

[happy]

Such a well-done video! Can't believe you haven't gone huge yet. I don't usually comment on YouTube but I felt compelled this time. Looking forward to going through more of your library of content as I get more into Kubernetes and DevOps in general.

[dangvu]

Clearly explained and really useful for beginners, excellent work! May you kindly reply my small question: how can we estimate the resources request and limit for some specific pods?

[DevsLikeUs]

Not having to provision infrastructure is awesome, thank you for the great video.

[nikoladacic]

Good lecture. Good presentation. Interesting fast and to the point. Good job man!!! Keep it coming and thanks. Deserved my SUB definitely. :)

[prabhatnagpal]

Thank you so much for making this concept easy to understand. Actually, I was also struggling setting the values of cpu requests and limits in the deployment, because in my Kubernetes even when the replicas increase, it starts running all pods with same load and didn't distribute evenly among the pods to make it come down and I have faced bad behaviour of scaling in my cluster. I have no clue what is happening

[torbendury]

Again, great content delivered in an easy way and also essy to reproduce. Thanks!

[inf]

Such great work deserves like and comment))

[ankitguhe]

Absolutely useful video, you saved my job 🤣 thanks a ton mate!

[elmeroranchero]

Amazing, thank you very much, loved the edition and the concise way o explaining

[Gandolfof]

Thank you very much! Please make a video on kubernetes e2e testing.

[AmjadW.]

You're awesome! kudos to your efforts

[martinzen]

Absolutely killer video my man, much appreciated. Noob question: does the metrics server require a separate node for a production deployment? Or does it just run in the same k8s service process, the way a plugin would? It would be useful to have a better idea of how this maps to actual cloud infra in terms of VMs/nodes, etc.

[janco]

How do you select a good minimum pod count for the hpa? I see this constant oscillation of it scaling up and down. Should i set my minimum above my normal load?

[vuhaiang]

Congrats on the excellent and well-explained video. However as your example at 7:39, the only resource scaled is CPU, not MEMORY (after scaling up to 4 replicas the memory of each pod remain unchanged). I wonder is this something obvious? And if so how can we actually scale base on memory consumed?

[sachin-sachdeva]

Thanks Marcel. This all is load based - is there a way where I can define it time based e.g. if there is a heavylifting job runs on my cluster between 2-4 AM and I can not afford to miss it?