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How do you use pdb's post-mortem debugging mode to inspect a program's state exactly at the point where an unhandled exception occurred? How would you profile memory allocations specifically (not just CPU time) to find which objects are consuming the most memory in a running process? How do you use conditional breakpoints in pdb to pause execution only when a specific condition is met, avoiding manually stepping through many unrelated iterations? How would you use line_profiler (a third-party tool) to get LINE-BY-LINE timing information within a specific function, more granular than cProfile's function-level data?
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How would you use py-spy (a sampling profiler) to profile a running Python process in PRODUCTION without restarting it or adding any code changes?

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py-spy attaches to an ALREADY-RUNNING Python process from OUTSIDE it (using OS-level process introspection, no code changes or restarts needed) and samples the call stack periodically, producing either a live top-style view (`py-spy top --pid <pid>`) or a flame graph (`py-spy record`) — ideal for diagnosing performance issues in a live production service.
# Attach to a running process by PID, no code modification needed
py-spy top --pid 12345

# Record a flame graph over 30 seconds
py-spy record -o profile.svg --pid 12345 --duration 30
Real-world example Diagnosing why a production web server is suddenly consuming excessive CPU, without restarting the service or deploying profiling code.

Common follow-ups: Why is a SAMPLING profiler like py-spy generally considered safer for production use than a deterministic profiler like cProfile?

Debugging & Profiling
All 15 Debugging & Profiling questions