heady-redis-lattice

You reduce high-latency data access by orchestrating a three-tier cache hierarchy across edge, in-memory, and durable stores, which delivers low-latency reads and consistent state across distributed microservices.

Maintaining cache coherence across edge and database layers is achieved by applying phi-scaled TTLs with write-through policies for hot data and write-back policies for warm data, coordinated via a pub/sub event bus.

You set up the multi-tier cache by wiring your Cloudflare KV, Upstash Redis, and Neon Postgres instances to operate as L1 edge, L2 in-memory, and L3 durable stores, then performing a get or set to observe the tiered behavior.

Yes, you can use an L2 in-memory Redis store for session management and rate limiting, backed by an L3 durable Postgres cache with pgvector embeddings and phi-decay eviction for persistent session state.

Predictive cache warming for hot-path data works by proactively loading embeddings and necessary data into the cache hierarchy before requests arrive, minimizing read latency for high-traffic microservices operations.

Yes, cross-service coordination is supported through a built-in pub/sub event bus that synchronizes state and maintains consistency across edge caches and durable stores in microservices architectures.