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，但其演进面临着可扩展性、兼容性和可持续性方面的制约。当前文献缺乏对该领域最新工具的实验性评估。本研究通过基准测试 Redis 替代方案，并在 Kubernetes 部署环境下对 Valkey、KeyDB 和 Garnet 进行实际工作负载下的系统评估，填补了这一及时的研究空白。结果表明，所测试的数据系统之间存在明确的权衡取舍。本研究对新兴的内存键值存储进行了全面的性能与可行性评估，评估指标包括吞吐量、尾部延迟、CPU 与内存效率以及迁移复杂度。我们重点分析了性能、兼容性和长期可行性之间的权衡关系，具体涵盖项目成熟度、社区支持及持续发展等方面。