I understand tail latencies -
https://www.youtube.com/watch?v=WdFYY3vEcxo - my prev open source project smf (
https://github.com/smfrpc/smf) uses gill tene's HDR histogram. I think you have a very superficial understanding of seastar. It is not simply marketing, it is a suite of tools and techniques to build low latency software - Specifically for IO intensive apps. Glauber wrote a good into here
https://www.scylladb.com/2018/06/12/scylla-leverages-control....
Seastar is a fundamentally different way of programming from what you mentioned above. Let me give you an example. Seastar takes all the memory up front - never gives it back to the operating system (you can control how much via -m2G, etc). This gives you deterministic allocation latency, is just incrementing a couple of pointers. Memory is split evenly across the number of cores and the way you communicate between cores is message passing - which means you explicitly tell which thread is allowed to read which inbox (similar to actors) - i wrote about it here in 2017 https://www.alexgallego.org/concurrency/smf/2017/12/16/futur...
The point of seastar is to not tune the GC for each application workload So to bring that up means that you missed the whole point of seastar. Instead the programmer explicitly reserves memory units for each subsystem - say 30% for the RPC, 20% for the app specific page-cache (since it's all DMA no kernel page cache), 20% for write-behinds, etc. (obviously in practice most of this is dynamic). It is not one dimension as suggested and not apples to oranges. it is apples to apples. You have a service, you connect your clients - unchanged - and one has better latency. that simple.
It may be your experience that when you download a bin kafka say 2.4.1 you change the GC settings but in a multi-tenant environment that's a moving target. Most enterprises I have talked to, just use the default script to startup kafka w.r.t gc memory settings. (they may change some writers settings, caching, etc)
At the end of the day there is no substitute for testing in your own app with your own firewall settings w/ your own hardware. The result should still give you 10x lower latency.