Observability vs logging in production, with a practical guide to when logs, metrics, traces, and correlation IDs answer different debugging questions.
How excessive production logging can bury signal, increase cardinality, distort incident timelines, and make debugging slower even when every service appears well instrumented.
Why some bugs appear only under production load, how concurrency, data shape, queues, retries, and partial failures change behavior, and how to diagnose them without guessing.
A practical debugging workflow for turning vague failures into precise symptoms, testable hypotheses, useful evidence, and fixes that address the cause.