We scaled up our container sizes and modified the properties to maximize resource deployment. Oddly, our service latency increased after adding more memory, specifically during peak hours. If resource starvation wasn't the root issue, what else causes this performance degradation?
3 answers
When you increase memory size, you change how frequently the runtime feels compelled to clean house. With a smaller boundary, collection occurs rapidly and frequently, keeping the memory structure compact. Once expanded, the system waits much longer before initiating a cleanup, allowing an enormous pile of unreferenced objects to accumulate. When collection is finally forced, the sheer scale of the workload creates an intense CPU demand spike. This heavy computational burden temporarily starves your regular application threads of CPU cycles, creating noticeable latency spikes that didn't exist before.
Have you verified whether your host platform is experiencing memory swapping issues? Sometimes allocating massive blocks forces the virtual container to read and write directly to slow disk storage rather than physical RAM modules.
You might simply be delaying the inevitable cleanup, causing massive operational spikes instead of small manageable ones.
Cheryl is entirely correct. We observed that smaller, predictable pauses are always preferable for maintaining stable REST API response thresholds compared to infrequent, massive latency spikes that drop active connection pipes.
Raymond, your theory about swapping hit the nail on the head. I checked our infrastructure virtualization metrics and noticed that the operating system was aggressively paging out memory parts to virtual disk swap space because we over-committed the physical host limits, completely destroying our execution efficiency.