Computer Science ›› 2022, Vol. 49 ›› Issue (8): 26-32.doi: 10.11896/jsjkx.210700176

• Database & Big Data & Data Science • Previous Articles     Next Articles

Accelerating Persistent Memory-based Indices Based on Hotspot Data

LIU Gao-cong, LUO Yong-ping, JIN Pei-quan   

  1. School of Computer Science and Technology,University of Science and Technology of China,Hefei 230027,China
  • Received:2021-07-19 Revised:2022-02-27 Online:2022-08-15 Published:2022-08-02
  • About author:LIU Gao-cong,born in 1999,postgra-duate.His main research interests include database technologies for NVM and so on.
    JIN Pei-quan,born in 1975,Ph.D,associate professor,is a senior member of China Computer Federation.His main research interests include databases and big data.
  • Supported by:
    National Natural Science Foundation of China(62072419).

Abstract: Non-volatile memory(NVM),also known as persistent memory(PM),has the characteristics of bit-based addressing,durability,high storage density and low latency.Although the latency of NVM is much smaller than that of solid-state drives,it is greater than that of DRAM.In addition,NVM has shortcomings such as unbalanced reading and writing as well as short writing life.Therefore,currently NVM cannot completely replace DRAM.A more reasonable method is using NVM to build a hybrid memory architecture based on DRAM+NVM.Based on the observation that many data accesses in database applications are skewed,this paper focuses on the hybrid memory architecture composed of NVM and DRAM and proposes a hotspot data-based speedup method for persistent memory indices.Particularly,we utilize the low latency of DRAM and the durability and high sto-rage density of NVM,and propose to add a DRAM-based hotspot-data cache for persistent memory indices.Then,we present a query-adaptive indexing method that can automatically adjust the cache according to the change of hotspot data.We apply the proposed method to several persistent memory indices,including wBtree,FPTree and Fast&Fair,and conduct comparative experiments.The results show that when the number of hotspot data visits accounts for 80% of the total visits,the proposed method can accelerate the query performance of the three indices by 52%,33% and 37%,respectively.

Key words: Non-volatile memory, Hybrid memory architecture, Hotspot data, Adaptive index

CLC Number: 

  • TP311
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