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Tackle low-hanging fruits in terms of agent performance improvements and remove major bottlenecks. Also try to reduce GC pressure.
The largest CPU utilization (10%) is RequestToInputWithServices which is creating the input for Rego. We can avoid this for flows that don't have any matching classifiers by running selector matching stage before creating this input.
Classify function that invokes Rego is 6.5%
CheckRequest is 3.6%
Log processing is 10%
Batch processor (for sending cloud metrics) is 5%
Grpc stream handling is 13%
go runtime schedule is 10
gc is about 10%
try to shrink check response (check response is marshaled in Check and unmarshaled in logs processing). (or even cache check responses as they're agent-to-agent?)
We never mutate entities directly in the maps, so copying was not necessary.
Also, added added a type wrapper so it's less likely someone tries mutating the
returned entity.
Drive-by:
* Stop using proto-generated structs for Entities datastructure.
* Fix lock in Clear() (fortunately, used only in tests).
Part of #2170
Tackle low-hanging fruits in terms of agent performance improvements and remove major bottlenecks. Also try to reduce GC pressure.
In italics are marked things that require some bigger changes
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