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ScaleStore: A Fast and Cost-Efficient Storage Engine using DRAM, NVMe, and RDMA

Tobias Ziegler; Carsten Binnig; Viktor Leis
In: Zachary G. Ives; Angela Bonifati; Amr El Abbadi (Hrsg.). SIGMOD '22: International Conference on Management of Data. ACM SIGMOD International Conference on Management of Data (SIGMOD-2022), June 12-17, Philadelphia, PA, USA, Pages 685-699, ACM, 2022.


In this paper, we propose ScaleStore, a novel distributed storage engine that exploits DRAM caching, NVMe storage, and RDMA networking to achieve high performance, cost-efficiency, and scalability at the same time. Using low latency RDMA messages, ScaleStore implements a transparent memory abstraction that provides access to the aggregated DRAM memory and NVMe storage of all nodes. In contrast to existing distributed RDMA designs such as NAM-DB or FaRM, ScaleStore stores cold data on NVMe SSDs (flash), lowering the overall hardware cost significantly. The core of ScaleStore is a distributed caching strategy that dynamically decides which data to keep in memory (and which on SSDs) based on the workload. The caching protocol also provides strong consistency in the presence of concurrent data modifications. Our evaluation shows that ScaleStore achieves high performance for various types of workloads (read/write-dominated, uniform/skewed) even when the data size is larger than the aggregated memory of all nodes. We further show that ScaleStore can efficiently handle dynamic workload changes and supports elasticity.

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