Hi there, We have a large-scale recommendation system serving millions of users which is built using Golang. It has worked well until recently when we are trying to enlarge our index or candidate pool by 10X in which case the number of candidate objects created to serve each user request can also increase by 5~10X. Those huge number of objects created on heap cause a big jump of the CPU used for GC itself and thus significantly reduces the system throughput.
We have tried different ways to reduce GC cost, like using soft memory limit <https://weaviate.io/blog/gomemlimit-a-game-changer-for-high-memory-applications> and dynamically tuning the value of GOGC similar to what is described here <https://www.uber.com/blog/how-we-saved-70k-cores-across-30-mission-critical-services/>. Those indeed helped, but they won't reduce the intrinsic cost of GC because the huge number of objects in heap have to be recycled anyway. I'm wondering if you have any suggestions about how to reduce object allocations during request serving? Thanks! Best -- You received this message because you are subscribed to the Google Groups "golang-nuts" group. To unsubscribe from this group and stop receiving emails from it, send an email to golang-nuts+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/golang-nuts/a6b8f58f-9452-43e6-9e63-92d944dd0caan%40googlegroups.com.
