Next Generation Cloud-native In-Memory Stores: From Redis to Valkey and Beyond

TL;DR

This paper benchmarks and evaluates the performance, scalability, and sustainability of modern in-memory key-value stores like Redis, Valkey, KeyDB, and Garnet within Kubernetes, highlighting their trade-offs and viability for cloud-native infrastructures.

Contribution

It provides the first comprehensive experimental evaluation of leading in-memory key-value stores, comparing performance, compatibility, and sustainability aspects in realistic cloud-native environments.

Findings

Valkey and KeyDB outperform Redis in throughput and latency.

Trade-offs exist between performance, compatibility, and project maturity.

Garnet shows promising scalability but has less community support.

Abstract

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.