trace_fastapi_request

FastAPI services with defined SLOs (success rate and latency objectives) can detect reliability degradation before total failure by monitoring error budget burn rate. A burn rate exceeding 1.0 indicates the service is consuming its error budget faster than sustainable.

FastAPI applications grouping multiple endpoints into a single latency SLO may violate targets when one slow endpoint drags down the aggregate percentile. The 99th percentile latency objective (e.g., P99 < 250ms) can fail even when most endpoints perform well.

Each middleware layer in FastAPI creates coroutine boundaries and adds latency overhead. Production stacks with authentication, logging, CORS, and monitoring middleware can reduce throughput by 80% compared to baseline.

P95 and P99 latencies diverge significantly from median/P50 latencies, indicating tail latency problems that affect user experience despite healthy average metrics.

Distributed tracing context fails to propagate correctly across async boundaries (asyncio.gather, background tasks), breaking trace continuity and hiding performance bottlenecks in concurrent operations.

Deep dependency trees in FastAPI dependency injection cause redundant validation and initialization overhead on every request, visible as pre-handler latency in traces.