Scalable Caching with Amazon ElastiCache Redis Cluster Mode: A Quantitative Performance Study
Abstract
Enterprise applications increasingly depend on distributed caching to sustain sub-millisecond response times at scale. Amazon ElastiCache Redis, operating in cluster mode, provides horizontal partitioning across configurable shard topologies, enabling throughput and memory capacity to grow in proportion to demand. While many organizations have adopted cluster configurations, empirical guidance on topology selection, key distribution optimization, and the measurable performance impact of individual tuning techniques remains sparse. This paper addresses that gap through systematic benchmarking across multiple cluster topologies (3 to 90 shards), three Graviton-based instance families (m6g, r6g, r7g), three workload profiles, and five optimization techniques, augmented by client library analysis, memory optimization guidance, and production cost validation. Production case studies from financial services, e-commerce, and real-time analytics platforms validate laboratory findings. Results offer empirical guidance for cloud architects designing caching architectures that balance latency requirements, horizontal scalability objectives, and infrastructure cost efficiency.