pbacsko commented on code in PR #933:
URL: https://github.com/apache/yunikorn-core/pull/933#discussion_r1707022863
##########
pkg/scheduler/objects/queue.go:
##########
@@ -1017,17 +1016,18 @@ func (sq *Queue) IncAllocatedResource(alloc
*resources.Resource, nodeReported bo
sq.Lock()
defer sq.Unlock()
// all OK update this queue
- sq.allocatedResource = newAllocated
+ sq.allocatedResource = resources.Add(sq.allocatedResource, alloc)
sq.updateAllocatedResourceMetrics()
return nil
}
+// allocatedResFits adds the passed in resource to the allocatedResource of
the queue and checks if it still fits in the
+// queues' maximum. If the resource fits it returns true otherwise false.
// small helper method to access sq.maxResource+sq.allocatedResource and avoid
Clone() call
-func (sq *Queue) allocatedResFits(res *resources.Resource) (bool,
*resources.Resource) {
+func (sq *Queue) allocatedResFits(res *resources.Resource) bool {
sq.RLock()
defer sq.RUnlock()
- newAllocated := resources.Add(sq.allocatedResource, res)
Review Comment:
I'm thinking about this...
This is the error message which Craig reproduced:
```
2024-07-26T14:42:30.364Z DPANIC core.scheduler.application
objects/application.go:1496 queue update failed unexpectedly {"error":
"allocation (map[pods:1]) puts queue 'root' over maximum allocation
(map[[devices.kubevirt.io/kvm:0](http://devices.kubevirt.io/kvm:0)
[devices.kubevirt.io/tun:0](http://devices.kubevirt.io/tun:0)
[devices.kubevirt.io/vhost-net:0](http://devices.kubevirt.io/vhost-net:0)
ephemeral-storage:744815809367 hugepages-1Gi:0 hugepages-2Mi:0
memory:1115240587264 [nvidia.com/gpu:0](http://nvidia.com/gpu:0) pods:660
vcore:320000]), current usage (map[memory:314572800
[nvidia.com/gpu:1](http://nvidia.com/gpu:1) pods:23 vcore:200])"}
```
It says `[nvidia.com/gpu:0]` and this is printed from
`sq.allocatedResource`, which means that this resource type is actually
present. That is, if we want to prevent this bug, this change won't solve it
because both `AddOnlyExisting()` and `FitInMaxUnder()` will consider the gpu
type. Am I missing something here?
I haven't run any code locally, just trying to have a good understanding by
simply looking at it :)
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