performance-concurrency-advisor

To identify the bottleneck causing latency spikes in a Kotlin Spring service, correlate latency and throughput metrics with traces and resource signals. This reveals whether thread-pool contention, slow endpoints, or blocking in reactive paths is the true limiting factor.

Thread-pool contention happens when concurrent requests exhaust available workers, causing queue waits. Diagnose it by analyzing traces and resource signals to correlate rising latency with pool saturation, confirming the bottleneck rather than relying on generic tips.

The best way to fix blocking in reactive paths is to isolate the blocking operation from the reactive thread pool. Use evidence-driven analysis of traces and metrics to locate the synchronous block, then offload it to a dedicated scheduler to restore throughput.

Yes, you can use SQL execution plans to guide performance optimization of slow endpoints. Correlating SQL plans with trace data helps pinpoint database-level bottlenecks and validates whether query tuning will improve end-to-end service latency.

When optimizing throughput in a Spring service, consider tradeoffs between increased resource utilization and system stability. Evidence-driven analysis helps evaluate recommended changes, ensuring that improvements to throughput and latency preserve correctness.

To verify a performance optimization change improved throughput, re-evaluate the same metrics, traces, and resource signals used during diagnosis. This structured validation confirms that the bottleneck is resolved without introducing new contention.