Latency Profiling in Python: From Code Bottlenecks to Observability
9 days ago
- #Python
- #Performance
- #Observability
- Latency profiling in Python focuses on identifying and quantifying delays in request processing stages.
- Averages can be misleading; percentiles (95th, 99th, 99.9th) are crucial for understanding latency outliers.
- Performance profiling differs from latency profiling; the latter targets rare, high-impact delays.
- Latency is multi-dimensional, involving wall clock time (user experience) and CPU time (actual computation).
- Micro-latencies (function calls, cache misses) and macro-latencies (network jitter, I/O stalls) both impact performance.
- Latency distributions, not fixed numbers, reveal variance and jitter, critical in low-latency systems.
- Python profiling tools include cProfile, py-spy, line_profiler, and snakeviz, each suited for different scenarios.
- Instrumentation embeds measurement points to collect metrics, traces, and logs for real-time system behavior analysis.
- Prometheus and OpenTelemetry help in tracking and visualizing latency metrics and traces.
- Continuous profiling in production helps detect latency shifts under load and over time, preventing issues before they escalate.