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ASPLOS2023顶会

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

2023年份
24被引次数
7顶会引用

摘要

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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