Hello.

I have some questions about memory autotuning in the Operator.

1. Does the autotuner try to upgrade the job with more memory allocated if it 
intercepts OutOfMemoryError? Say I initially provided too little memory for TM 
`resource` - will the job fail and stop on initializing or will the operator 
try to increase memory after it catches OutOfMemoryError?
2. When autoscaler scales a TM from, say, from 1 to 2 instances with higher 
parallelism, does it also double memory and CPU requested in each iteration of 
upscaling, or it would have already measured RAM requirements when it was 
collecting stats and request just what it thinks is needed? I see there’s a 
config option where it increases memory when scaling down so that the job 
doesn’t fail because fewer TMs are doing the job of what more TMs used to be 
doing, so I was wondering if the opposite is true when scaling up.
3. Will autotuner ever increase requested memory beyond what was initially 
requested in TM’s `resource` block in the Deployment CRD? Same for CPU?..
4. Does the operator care about available resources in k8s, or it just makes an 
“optimistic” request and hopes it will be granted? What happens if it requests 
more than available? Keep retrying? Stay in pending state waiting for 
resources? Time out? Exit? Is there a rollback and retry with smaller amount of 
resources requested if a request with larger demands fails? I see there’s an 
option that, when enabled, can refresh information about available resources 
periodically, which should prevent or reduce inadvertent greedy requests. But 
what’s the strategy used by the operator if the request is already too large to 
handle?

Thanks a lot!
/Maxim

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