In-memory key-value datastores have become indispensable building blocks of modern cloud-native infrastructures, yet their evolution faces scalability, compatibility, and sustainability constraints. The current literature lacks an experimental evaluation of state-of-the-art tools in the domain. This study addressed this timely gap by benchmarking Redis alternatives and systematically evaluating Valkey, KeyDB, and Garnet under realistic workloads within Kubernetes deployments. The results demonstrate clear trade-offs among the benchmarked data systems. Our study presents a comprehensive performance and viability assessment of the emerging in-memory key-value stores. Metrics include throughput, tail latency, CPU and memory efficiency, and migration complexity. We highlight trade-offs between performance, compatibility, and long-term viability, including project maturity, community support, and sustained development.

翻译：内存键值数据库已成为现代云原生基础设施不可或缺的构建模块，但其演进面临可扩展性、兼容性与可持续性等多重约束。当前学术界缺乏对该领域前沿工具的实验性评估。本研究通过基准测试Redis替代方案，并系统评估Valkey、KeyDB与Garnet在Kubernetes部署环境下真实工作负载中的表现，填补了这一时效性研究空白。实验结果表明，被测数据系统间存在明确的性能权衡。本研究对新兴内存键值存储进行了全面的性能与可行性评估，指标涵盖吞吐量、尾部延迟、CPU与内存效率及迁移复杂度。我们重点揭示了性能、兼容性与长期可行性之间的权衡关系，包括项目成熟度、社区支持度与持续发展能力等维度。