Optimization

← Previous Chapter: Part 2: Real-Time Inventory | Series Hub | Next Chapter: Part 4: Anticipatory Shipping → Answer-first: Greedy algorithms run in $O(N)$ (<2ms) and work well for simple carts. For complex multi-item baskets across 20+ fulfillment centers, MILP solvers achieve 12–18% lower total shipping costs within a 35ms compute budget.

Go pprof CPU & Memory Profiling: The Production Engineering Guide When a mission-critical Go microservice in Kubernetes suddenly spikes to 95% CPU utilization, latency degrades from 15ms to 800ms, or pods are repeatedly terminated by the Linux kernel OOM (Out-Of-Memory) killer, guessing root causes by inspecting source code is an exercise in futility. In high-concurrency systems, intuition fails. You need empirical, low-overhead runtime telemetry. The Go standard library ships with one of the most sophisticated, low-overhead profiling runtimes in modern software engineering: pprof. ...