I agree for measuring latency differences you want similar setups. However, by running two versions of the app concurrently on the same machine they both get impacted more or less the same by noisy neighbours. Moreover, by inspecting the flamegraph you can, manually, see these large shifts of time allocation quickly. For automatic comparison you can of course use the raw data.

In addition you can look at total cpu seconds used, memory allocation on kernel level, and specifically for the jvm at the GC metrics and allocation rate. If these numbers change significantly then you know you need to have a look.

We do run this benchmark comparison in most nightly builds and find regressions this way.

https://en.wikipedia.org/wiki/Hardware_performance_counter can help with noisy neighbors. I am still getting into this.

For production systems I use flight recordings (jfrs). To analyze I use java mission control.

For OOME problems I use a heap dump and eclipse memory analysis tool.

For microbenchmarks, I use JMH. But I tend to try and avoid doing those.

I use jmh for micro benchmarks on any code we know is sensitive and to highlight performance differences between different implementations. (Usually keep them around but not run on CI as an archive of what we tried.)

Then we do benchmarking of the whole Java app in the container running async-profiler into pyroscope. We created a test harness for this that spins up and mocks any dependencies based on api subscription data and contracts and simulates performance.

This whole mechanism is generalised and only requires teams that create individual apps to work with contract driven testing for the test harness to function. During and after a benchmark we also verify whether other non functionals still work as required, i.e. whether tracing is still linked to the right requests etc. This works for almost any language that we use.

jmh is what I've always used for small benchmarks.

async-profiler