Next Generation Cloud-native In-Memory Stores: From Redis to Valkey and Beyond
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915844524605440 |
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| author | Rosensch"old, Carl-Johan Fauvelle Munck af Awaysheh, Feras M. Awad, Ahmad |
| author_facet | Rosensch"old, Carl-Johan Fauvelle Munck af Awaysheh, Feras M. Awad, Ahmad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19805 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Next Generation Cloud-native In-Memory Stores: From Redis to Valkey and Beyond Rosensch"old, Carl-Johan Fauvelle Munck af Awaysheh, Feras M. Awad, Ahmad Distributed, Parallel, and Cluster Computing Databases 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. |
| title | Next Generation Cloud-native In-Memory Stores: From Redis to Valkey and Beyond |
| topic | Distributed, Parallel, and Cluster Computing Databases |
| url | https://arxiv.org/abs/2510.19805 |