performance-concurrency-advisor

Diagnose latency spikes in Spring services by correlating thread pool saturation with database query performance using evidence from metrics and thread dumps. This determines whether bottlenecks are I/O-bound or CPU-bound for targeted optimization.

Identify coroutine blocking in Kotlin by analyzing thread dumps and system signals to detect reactive flow blocking and lock contention. This evidence-driven approach pinpoints exact concurrency constraints causing throughput degradation.

Analyze database contention and slow endpoints by evaluating SQL explain plans, distributed traces, and latency metrics together. This systematic evaluation reveals whether transactional scopes or query performance drive the bottleneck.

Yes, you can tune thread pools and event loops for high-throughput Kotlin services by systematically evaluating concurrency behavior against real system signals. This enables recommending high-leverage architectural changes for pool saturation.

Avoid evidence-based concurrency analysis when you lack accessible metrics, traces, or thread dumps, as this method requires real system signals to pinpoint constraints and recommend architectural changes.