implement-operational-monitoring

Runtime health monitoring identifies thread pool starvation and memory leaks by tracking queue lengths, active counts, and heap usage. It applies safe sampling to long-running services to detect bottlenecks early.

Track API latency percentiles and error rates by collecting throughput metrics including p50, p95, and p99 values. The monitoring imposes strict thresholds and includes timestamps to quantify performance for observability systems.

Low-overhead runtime health monitoring is a safe sampling approach that asynchronously collects memory, thread, throughput, and custom business metrics. You need it to detect memory growth, throughput degradation, and downstream failure patterns early.

Yes, you can export custom business metrics asynchronously to your observability platform. The monitoring collects domain-specific indicators like job queue depths and processing rates to report operational health.

Yes, runtime health monitoring works with back-pressure signals to prevent thread pool starvation. It tracks thread pool queue lengths, active counts, and idle counts to detect when your system approaches capacity limits.

Safe sampling collects metrics asynchronously every 60 seconds, meaning rapid memory spikes between intervals may be missed. Strict thresholds and percentile reporting help detect sustained memory growth rather than transient allocation bursts.