Redis
Core data structures, persistence (RDB/AOF), replication & clustering, caching patterns & eviction, Pub/Sub & Streams, transactions & Lua scripting, and Spring Data Redis.
Redis — Core Data Structures & Commands
beginnerBefore persistence, replication, caching strategy, or Spring Data Redis mean anything, you need fluency with the five core data structures and the single-threaded model that makes every one of them atomic.
Redis — Persistence: RDB & AOF
intermediateRedis lives in RAM by default — this chapter is about the specific, tunable trade-off between how fast it restarts and how much you're willing to lose if it doesn't restart cleanly.
Redis — Replication, Sentinel & Cluster
intermediateHow a single Redis instance becomes a resilient, horizontally-scaled system — leader-follower replication for copies, Sentinel for automatic failover, and Cluster's hash-slot model for sharding.
Redis — Caching Patterns, TTL & Eviction Policies
intermediateCache-aside, stampede prevention, and avalanche prevention aren't Redis-specific ideas — this chapter is where the general patterns from NoSQL Foundations get wired up with real EXPIRE/SET/maxmemory commands.
Redis — Pub/Sub, Streams, Transactions & Lua Scripting
advancedThe four tools Redis gives you for coordinating across clients — from the simplest, least reliable (Pub/Sub) to the strongest, truly atomic one (Lua scripts) — and exactly when each one is the right call.
Spring Boot with Redis — Spring Data Redis & Caching
intermediateEverything from RedisTemplate to @Cacheable to @RedisHash is Spring wiring on top of commands you already know from Chapters 01 and 04 — this chapter is where that wiring gets concrete in a real Spring Boot application.
Redis — The Java Client Ecosystem: Lettuce, Jedis & Redisson
intermediateProEvery code example so far in this category has used one client without naming it. This chapter names the three real options a Spring Boot project actually chooses between — Lettuce, Jedis, and Redisson — and what each one's design actually buys or costs you, so picking one stops being a coin flip.
Redis — Distributed Locking with Redisson's RLock
advancedProA java.util.concurrent lock only coordinates threads inside one JVM. The moment an application runs as more than one instance — which is most real deployments — something else has to coordinate access to a shared resource across all of them, and Redis is a common place to put that coordination.
Redis — Vector Search & Semantic Caching for LLM Applications
advancedProRedis's in-memory speed turns out to matter for a newer workload too: storing and searching embeddings. This chapter covers RediSearch's vector similarity capabilities and the specific pattern of using Redis as a semantic cache in front of an LLM — caching based on what a prompt means rather than its exact text.