Building a Scalable, Observable Recommendation Scheduling Engine from Scratch
This article explains how recommendation systems work, distinguishes online services from offline computation, outlines a typical recommendation flow, and presents a three‑stage evolution (1.0, 2.0, 3.0) with design principles for stability, observability, and efficiency, culminating in a DAG‑based orchestration and traceable execution.
