What are Redis scalability patterns for large applications?

Redis scalability patterns enable handling massive workloads through horizontal scaling, read replicas, intelligent caching, and architectural optimizations.\n\n• **Why Scalability Matters:**\nHandle growing user bases, manage increasing data volumes, maintain performance under load, and support global applications.\n\n• **Horizontal Scaling with Clustering:**\nRedis Cluster automatically shards data across nodes, provides linear scalability, and handles failover transparently.\n\n16384 hash slots distributed across masters\nAutomatic data partitioning\nClient-side routing for optimal performance\n\n• **Read Scaling with Replicas:**\nMultiple read replicas per master, geographic distribution, load balancing, and eventual consistency trade-offs.\n\nMaster handles writes\nReplicas serve read traffic\nLoad balancer distributes read requests\n\n• **Application-Level Sharding:**\nCustom partitioning logic, consistent hashing, business rule-based distribution, and cross-shard operation handling.\n\ndef get_shard(key):\n return hash(key) % num_shards\n\nwrite_redis = shards[get_shard(user_id)]\nwrite_redis.set(f"user:{user_id}", data)\n\n• **Caching Layer Architecture:**\nMulti-tier caching (L1: local, L2: Redis), cache-aside pattern, write-through/write-behind strategies, and intelligent cache warming.\n\n• **Performance Optimization:**\nPipelining for bulk operations, Lua scripting for atomic operations, connection multiplexing, and asynchronous processing.\n\n• **Auto-Scaling Implementation:**\nMonitor key metrics (memory, CPU, ops/sec), automatic node provisioning, data rebalancing, and predictive scaling.\n\n• **Best Practices:**\nDesign for horizontal scaling from start, implement proper monitoring, test scaling scenarios regularly, optimize for access patterns, and plan for graceful degradation.