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CPU usage above a configured limit is throttled, which can increase latency without stopping the process. Memory is not throttled in the same way: crossing the container limit can cause the kernel to kill the process, often reported as OOMKilled. Requests affect scheduling and utilization-based HPA calculations, so tune all values from observed workload behavior.

A container can exhaust its CFS quota during a period even if other node CPUs are idle. Inspect container throttling counters and request/limit values alongside latency. Decide whether the limit is needed, whether requests/HPA targets match demand, or whether CPU work should be reduced; do not infer container headroom from node averages.

ForkJoinPool uses worker-local deques and lets idle workers steal tasks from others, which helps recursive, CPU-bound workloads with many small tasks. Long blocking I/O can occupy workers and reduce progress. Use a suitable dedicated executor or managed blocking strategy for blocking operations, and avoid creating huge numbers of tiny tasks with excessive scheduling overhead.

Use async when the code is mostly waiting on IO such as database calls, HTTP requests, or message queues. Use threads or process pools for CPU-intensive work or code that cannot cooperate with the event loop. The key is matching the concurrency model to the bottleneck, not adding async everywhere because it seems modern.

An async route can yield during nonblocking I/O, allowing the event loop to serve other work. A normal def route is typically run in a thread pool. Calling blocking I/O inside async def blocks the event loop; using async syntax alone does not improve CPU-bound work. Choose an execution model based on the library and bottleneck.