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

2025-10-22

AI Summary

In cloud-native environments, mainstream in-memory key-value databases exhibit limitations in scalability, protocol compatibility, and long-term sustainability, while systematic empirical evaluations of emerging Redis alternatives remain absent. Method: This paper presents the first production-grade Kubernetes evaluation of Valkey, KeyDB, and Garnet, conducting multi-dimensional benchmarking—including throughput, P99 latency, and CPU/memory efficiency—alongside migration complexity analysis across containerized deployment, microservice integration, and data migration pathways. Contribution/Results: The study uncovers fundamental trade-offs among performance, ecosystem maturity, and operational maintainability, precisely delineating the applicability boundaries of each system under diverse workloads. It delivers the first empirically grounded, production-relevant evidence and actionable guidance for selecting in-memory data layer technologies in cloud-native architectures.

Technology Category

• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Machine Learning: Scalability of ML Systems
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

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.

Problem

• Evaluating scalability and compatibility of cloud-native in-memory datastores
• Benchmarking Redis alternatives under realistic Kubernetes workloads
• Assessing performance trade-offs and sustainability of emerging key-value stores

Innovation

• Benchmarked Redis alternatives under realistic workloads
• Evaluated Valkey, KeyDB, and Garnet in Kubernetes
• Assessed performance, compatibility, and sustainability trade-offs