The logic for skipping "admin access" was repeated in three different places. A
single foreachAllocatedDevices with a callback puts it into one function.
The devices which are allocated before starting the allocation always remain
allocated. They can be stored once in a set, then each Filter call for the
different nodes can reuse that set instead of allocating it anew for each node.
goos: linux
goarch: amd64
pkg: k8s.io/kubernetes/test/integration/scheduler_perf
cpu: Intel(R) Core(TM) i9-7980XE CPU @ 2.60GHz
│ before │ after │
│ SchedulingThroughput/Average │ SchedulingThroughput/Average vs base │
PerfScheduling/SchedulingWithResourceClaimTemplateStructured/5000pods_500nodes-36 36.89 ± 2% 54.70 ± 6% +48.26% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/empty_100nodes-36 105.7 ± 5% 106.4 ± 4% ~ (p=0.970 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/empty_500nodes-36 117.8 ± 3% 120.0 ± 4% ~ (p=0.134 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/half_100nodes-36 119.5 ± 4% 112.5 ± 4% -5.86% (p=0.009 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/half_500nodes-36 63.22 ± 2% 87.13 ± 4% +37.82% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/full_100nodes-36 109.5 ± 2% 113.4 ± 2% +3.65% (p=0.006 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/full_500nodes-36 27.56 ± 5% 65.55 ± 3% +137.84% (p=0.002 n=6)
geomean 72.44 90.81 +25.37%
DeviceClasses and different requests are very likely to contain the same
expression string. We don't need to compile that over and over again.
To avoid hanging onto that cache longer than necessary, it's currently tied to
each PreFilter/Filter combination. It might make sense to move this up into the
scheduler plugin and thus reuse compiled expressions for different pods.
goos: linux
goarch: amd64
pkg: k8s.io/kubernetes/test/integration/scheduler_perf
cpu: Intel(R) Core(TM) i9-7980XE CPU @ 2.60GHz
│ before │ after │
│ SchedulingThroughput/Average │ SchedulingThroughput/Average vs base │
PerfScheduling/SchedulingWithResourceClaimTemplateStructured/5000pods_500nodes-36 33.95 ± 4% 36.65 ± 2% +7.95% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/empty_100nodes-36 105.8 ± 2% 106.7 ± 3% ~ (p=0.177 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/empty_500nodes-36 100.7 ± 1% 119.7 ± 3% +18.82% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/half_100nodes-36 90.78 ± 1% 121.10 ± 4% +33.40% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/half_500nodes-36 50.51 ± 7% 63.72 ± 3% +26.17% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/full_100nodes-36 103.7 ± 5% 110.2 ± 2% +6.32% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/full_500nodes-36 28.50 ± 2% 28.16 ± 5% ~ (p=0.102 n=6)
geomean 64.99 73.15 +12.56%
Using unique strings instead of normal strings speeds up allocation with
structured parameters because maps that use those strings as key no longer need
to build hashes of the string content. However, care must be taken to call
unique.Make as little as possible because it is costly.
Pre-allocating the map of allocated devices reduces the need to grow the map
when adding devices.
goos: linux
goarch: amd64
pkg: k8s.io/kubernetes/test/integration/scheduler_perf
cpu: Intel(R) Core(TM) i9-7980XE CPU @ 2.60GHz
│ before │ after │
│ SchedulingThroughput/Average │ SchedulingThroughput/Average vs base │
PerfScheduling/SchedulingWithResourceClaimTemplateStructured/5000pods_500nodes-36 18.06 ± 2% 33.30 ± 2% +84.31% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/empty_100nodes-36 104.7 ± 2% 105.3 ± 2% ~ (p=0.818 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/empty_500nodes-36 96.62 ± 1% 100.75 ± 1% +4.28% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/half_100nodes-36 83.00 ± 2% 90.96 ± 2% +9.59% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/half_500nodes-36 32.45 ± 7% 49.84 ± 4% +53.60% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/full_100nodes-36 95.22 ± 7% 103.80 ± 1% +9.00% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/full_500nodes-36 9.111 ± 10% 27.215 ± 7% +198.69% (p=0.002 n=6)
geomean 45.86 64.26 +40.12%
The Allocate call used to call back into the claim lister for each node. This
was significant work which showed up at the top of the CPU profile. It's
okay to list only once during PreFilter because the Filter call does not change
the claim status between Allocate calls.
goos: linux
goarch: amd64
pkg: k8s.io/kubernetes/test/integration/scheduler_perf
cpu: Intel(R) Core(TM) i9-7980XE CPU @ 2.60GHz
│ before │ after │
│ SchedulingThroughput/Average │ SchedulingThroughput/Average vs base │
PerfScheduling/SchedulingWithResourceClaimTemplateStructured/5000pods_500nodes-36 15.04 ± 0% 18.06 ± 2% +20.07% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/empty_100nodes-36 105.5 ± 1% 104.7 ± 2% ~ (p=0.485 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/empty_500nodes-36 95.83 ± 1% 96.62 ± 1% ~ (p=0.063 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/half_100nodes-36 79.67 ± 3% 83.00 ± 2% +4.18% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/half_500nodes-36 27.11 ± 5% 32.45 ± 7% +19.68% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/full_100nodes-36 84.00 ± 3% 95.22 ± 7% +13.36% (p=0.002 n=6)
PerfScheduling/SteadyStateClusterResourceClaimTemplateStructured/full_500nodes-36 7.110 ± 6% 9.111 ± 10% +28.15% (p=0.002 n=6)
geomean 41.05 45.86 +11.73%
Adds a utility function `ResetLabelValueAllowLists` to reset the allow lists for label values. This facilitates testing by allowing tests to clear the global state between runs and avoid unintended side effects.
Make the following changes:
- When dryrunning if the given kubeconfig does not exist
create a DryRun object without a real client. This means only
a fake client will be used for all actions.
- Skip the preflight check if manifests exist during dryrun.
Print "would ..." instead.
- Add new reactors that handle objects during upgrade.
- Add unit tests for new reactors.
- Print message on "upgrade node" that this is not a CP node
if the apiserver manifest is missing.
- Add a new function GetNodeName() that uses 3 different methods
for fetching the node name. Solves a long standing issue where
we only used the cert in kubelet.conf for determining node name.
- Various other minor fixes.
