I'm working on a data processing script that runs fine on small test files but silently fails on the full dataset. I've been reading about python debugging techniques, but just adding print statements everywhere feels messy. How do you systematically track down a bug when there's no error message and the script just stops?
That scenario is super common. Don’t rely on print statements anymore—shift to structured logging. Add a single log file and turn on DEBUG to capture entry, exit, data sizes, and the last operation before it stops. Timestamps help you see the exact spot where it hangs.
First isolate with a smaller dataset to reproduce the bug and confirm the behavior isn’t data-specific. Then use Python’s tracing or a debugger: python -m trace --trace script.py shows every line executed; or drop into pdb/ipdb with breakpoints at key functions to step through what happens before it stops.
Check for swallowed errors. If there are broad try/except blocks, they might hide exceptions. Add targeted except blocks that log the exception or re-raise, or temporarily remove the swallow to see the actual error.
Drop-in cheap 'heartbeat' logs or a progress counter so you know how far the script gets, especially in loops. If it’s a batching job, log batch numbers; if there are file reads, log file names and sizes. That usually reveals where the stop happens.
Monitor resources. A lot of silent terminations come from memory or file handles. Use psutil to log memory usage, open file counts, and CPU spikes during the run. A sudden memory jump right before the stop is a clue.
Create a plan you can follow: set up logging, run on a tiny dataset, verify the log shows progress, then scale up. If the run still stops, try a debugger or trace, capture last known good state, and add a minimal reproducer. If you want, tell me what the script processes and I’ll help sketch a targeted debugging checklist.