System observability is essential for software engineers to understand what's happening within a system during various processes, like database backups or payment attempts. The article emphasizes two levels of observability: whole-system and per-process. Whole-system metrics provide a broad overview, useful for capacity planning and fault detection, but they fall short in identifying specific issues within individual processes. For example, if a friend planning a surprise party sleeps 30% of the time, whole-system observations reveal time spent but don't pinpoint how that affects the party planning. To effectively optimize systems, focusing on the slowest processes first offers the highest return on investment. This is grounded in Amdahl’s law, which states that eliminating bottlenecks in processing yields the most significant speedup. Per-process measurements are vital for assessing where to apply optimization efforts. While simulating single-process conditions can mimic per-process observations, actual per-process data is more effective. The article categorizes observability techniques by detail and cost: metrics are simple and cheap, system sampling provides a momentary glimpse of activity, while process tracing records every event, offering a complete timeline of operations. Process tracing is highlighted as the most informative but also the most resource-intensive method. It allows engineers to reconstruct the sequence of events and understand the impact of optimizations on specific processes. The author references Tristan Hume's article for exploring practical techniques in tracing, noting that while tracing may seem costly, it can be feasible and beneficial when implemented thoughtfully. An example of clever job smarts in observability is provided, showcasing how Hume correlated packets with user-space events in a Python program, illustrating a unique approach to troubleshooting.