Real-time state has a cost your memory bill won’t show

The article argues that many real-time systems duplicate state because one platform processes data while another serves it to applications. Each copy adds memory, serialization, network traffic and monitoring work, and the author describes a fraud-scoring example where a velocity count is computed in Flink and then published to Redis. The piece separates several kinds of staleness: replica lag inside a serving store, export lag between processor and store, and the check-then-act race that can cause a duplicate refund. It notes that moving computation to where the data lives can reduce update steps, but that neither approach removes the need for idempotent requests or careful coordination. It also cautions that cited studies compare caching against no caching, not one product against another, and that a memory-only calculation is not a total-cost estimate.