Wild guess - something is looking at underlying OS or hardware info (e.g.
number of processors) and scalng memory or threads based on that.

On Mar 28, 2017 9:39 AM, "bg" <griffin...@gmail.com> wrote:

> I have an image that is basic Java application. I'm trying to minimize the
> amount of memory I submit the Job with in Kubernetes.
>
> Using Java's -Xmx flag I limit the program to using 25MB of memory
> (-Xmx25m) and using docker I run the container with 30M of memory: docker
> run -m 30m <image_id>
>
> That works fine so I go and deploy my image (including the -Xmx25m flag)
> to Kubernetes as a Job with resource request of 30MB of memory and the pods
> keep terminating with Reason: OOMKilled and Exit Code: 137.
>
> So I start doubling the amount of memory requested: 64M, 128, 256, 512
> until finally the pods are successful at 512MB of memory!
>
> So my question is why would a program that can run in docker with 30MB of
> memory require 512MB of memory when submitted to a kubernetes cluster?
>
>
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