Week 3: Distributed Locking — DB & Redis Approaches (Days 11–14)

Every production backend eventually runs work on a timer: nightly reconciliation, report generation, cache warming, payment retries. Spring Boot makes this deceptively easy—annotate a method with @Scheduled, deploy the JAR, and the framework fires your code on a cadence. The problem appears the moment you scale horizontally. Each JVM has its own scheduler thread pool, its own clock, and no awareness that three other instances are about to run the same criticalBusinessTask() at the same instant. You do not have one scheduled job anymore; you have N competing jobs, and only one should win.

Видео Week 3: Distributed Locking — DB & Redis Approaches (Days 11–14) канала Hands On Course Demo