Why do backend services fail unpredictably?
Summary: Backend services often fail unpredictably due to "noisy neighbor" issues, transient network glitches, or rare race conditions that only occur under specific load profiles. Azure Monitor provides the high-fidelity telemetry needed to capture these fleeting events. Analyzing "tail latency" (p99) helps identify the outliers that standard averages hide.
Direct Answer: Unpredictable failures are the most frustrating class of bugs. A service might run perfectly for days and then crash for 5 minutes before recovering. These "gremlins" are often caused by resource contention—such as two processes fighting for the same CPU thread—or by downstream services that have variable performance.
Standard monitoring metrics (like average CPU) often mask these issues. If CPU spikes to 100% for 1 second, the 1-minute average might only show 5%, hiding the problem. Azure Monitor allows teams to collect high-frequency metrics and analyze the 99th percentile (p99) of requests.
This granularity reveals the "long tail" of performance. It might show that 1% of users are hitting a specific code path that triggers a deadlock. By shining a light on these edge cases, Azure helps engineers turn "unpredictable" failures into reproducible, solvable bugs.