Fast data access without the consistency traps
Day 1
Redis Data Structures, Spring Integration & Caching Patterns
- Redis data structures: String, List, Set, Sorted Set, Hash, HyperLogLog, Stream
- TTL and expiration: EXPIRE, EXPIREAT, key eviction policies (LRU, LFU, volatile-*)
- Spring Data Redis: RedisTemplate, StringRedisTemplate, Jackson2JsonRedisSerializer
- Repository abstraction: @RedisHash, @Indexed, secondary index support
- Spring Cache: @Cacheable, @CachePut, @CacheEvict, @Caching
- Cache-aside vs read-through vs write-through — choosing the right strategy
- Cache stampede: probabilistic early expiration, request coalescing
- Distributed locking: Redisson RLOCK, expiry and watchdog
- Rate limiting with Redis: token bucket via Lua scripts
- Redis Pub/Sub and Streams: lightweight messaging patterns
- Redis Cluster: sharding model, hash slots, cluster client configuration
- Cache warming strategies: preloading on startup
What your team walks away with
Developers who use Redis deliberately — choosing the right data structure, the right eviction policy, and the right caching strategy for each use case — without creating invisible consistency problems.
- Use Redis data structures correctly beyond simple key-value caching
- Configure Spring Cache with proper key generation, TTL, and eviction policies
- Implement distributed locking and rate limiting with Redis
- Design caching strategies that avoid cache stampede and stale data problems
Book the Redis training
A focused one-day course covering Redis fundamentals through production caching patterns.
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