CloudyBench: A Testbed for A Comprehensive Evaluation of Cloud-Native Databases
Chao Zhang, Guoliang Li, Leyao Liu, Tao Lv, Ju Fan · 2025
As more and more on-premise databases are moving towards the cloud service, it is crucial to have a benchmark to holistically evaluate the performance of their core features including elasticity, multi-tenancy, and cost-efficiency. However, existing benchmarks lack specific workload patterns and metrics for evaluating cloud-native databases, and the real workload is often unavailable due to privacy requirements. In this paper, we propose a new testbed for cloud-native databases, named CloudyBench. Its core contribution is to provide tailored workloads and metrics to evaluate the service quality of cloud-native databases in various dimensions. First, we design cloud-native workload patterns with peaks and valleys for elasticity evaluation. Second, we devise new multi-tenancy patterns by posing varied resource contention to evaluate the resource scheduling among tenants. Third, we propose a unified metric that considers performance, cost, elasticity, multitenancy, replication lag time, and fail-over. Fourth, we provide an evaluation testbed for evaluating cloud-native databases. To verify the effectiveness of CloudyBench, extensive experiments have been conducted over five commercial representatives from multiple cloud providers. We also obtain a number of insights for the performance implications of cloud-native databases from the architectural perspective.