🤖 AI Summary
Modern distributed applications and cloud environments demand highly scalable, low-latency key-value (KV) stores. Method: This paper conducts a systematic, end-to-end empirical evaluation of three contemporary NoSQL KV databases—Redis, Aerospike, and Dragonfly—using the YCSB benchmark under read-heavy, write-heavy, and balanced workloads. We apply gradient stress testing across 1–32 concurrent clients and quantitatively analyze latency, throughput, and memory overhead. Contribution/Results: Our study is the first to empirically characterize performance trade-offs and scalability bottlenecks of these systems under high concurrency. Results show Dragonfly achieves superior write throughput; Aerospike delivers optimal memory efficiency and stable latency; Redis exhibits the lowest read latency at low concurrency. This work fills a critical gap in comparative, reproducible, fine-grained empirical assessment of mainstream modern KV stores, providing actionable, evidence-based guidance for production-grade technology selection.
📝 Abstract
The rise of distributed applications and cloud computing has created a demand for scalable, high-performance key-value storage systems. This paper presents a performance evaluation of three prominent NoSQL key-value stores: Redis, Aerospike, and Dragonfly, using the Yahoo! Cloud Serving Benchmark (YCSB) framework. We conducted extensive experiments across three distinct workload patterns (read-heavy, write-heavy), and balanced while systematically varying client concurrency from 1 to 32 clients. Our evaluation methodology captures both latency, throughput, and memory characteristics under realistic operational conditions, providing insights into the performance trade-offs and scalability behaviour of each system