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Persistent Memory Disaggregation for Cloud-Native Relational Databases

Chaoyi Ruan, Yingqiang Zhang, Chao Bi, Xiaosong Ma, Hao Chen, Feifei Li, Xinjun Yang, Cheng Li, Ashraf Aboulnaga, Yinlong Xu

2023Year
24Citations
7Top-tier citations

Abstract

The recent emergence of commodity persistent memory (PM) hardware has altered the landscape of the storage hierarchy. It brings multi-fold benefits to database systems, with its large capacity, low latency, byte addressability, and persistence. However, PM has not been incorporated into the popular disaggregated architecture of cloud-native databases.

In this paper, we present PilotDB, a cloud-native relational database designed to fully utilize disaggregated PM resources. PilotDB possesses a new disaggregated DB architecture that allows compute nodes to be computation-heavy yet data-light, as enabled by large buffer pools and fast data persistence offered by remote PMs. We then propose a suite of novel mechanisms to facilitate RDMAfriendly remote PM accesses and minimize operations involving CPUs on the computation-light PM nodes. In particular, PilotDB adopts a novel compute-node-driven log organization that reduces network/PM bandwidth consumption and a log-pull design that enables fast, optimistic remote PM reads aggressively bypassing the remote PM node CPUs. Evaluation with both standard SQL benchmarks and a real-world production workload demonstrates that PilotDB (1) achieves excellent performance as compared to the best-performing baseline using local, high-end resources, (2) significantly outperforms a state-of-the-art DRAM-disaggregation

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