Cache topology, consistency models, invalidation strategies, and distributed state management for low-latency financial applications. The patterns that keep p99 latency in the millisecond range and survive cache stampedes.
Cache patterns (cache-aside, write-through, write-behind), Redis topology (Cluster vs Sentinel), invalidation strategies, the cache stampede that has felled more banking platforms than any disk failure, and operational patterns for sub-millisecond latency.
Embedded grid topology, partition awareness, near-cache configuration, and the stateful financial workloads where collapsing the network hop between your JVM and its data beats a shared Redis cluster — plus the real costs of embedding state inside application pods.
Driving cache invalidation from a Debezium stream of database changes — the only pattern that survives DBA-level changes the application doesn’t know about.