基于热点数据的持久性内存索引查询加速

Accelerating Persistent Memory-based Indices Based on Hotspot Data

非易失性内存(Non-Volatile Memory,NVM),也被称为持久性内存(Persistent Memory,PM),具有按位寻址、持久性、存储密度高、低延迟等特点。虽然NVM的延迟远小于闪存,但高于DRAM(Dynamic Random Access Memory)。此外,NVM还有读写不均衡、写次数有限等不足。因此,目前NVM还无法完全代替DRAM。一种更为合理的方法是利用NVM构建基于DRAM+NVM的混合内存架构。文中针对NVM和DRAM构成的混合内存架构,着重研究了基于热点数据的持久性内存索引加速方法。具体而言,以数据访问中的倾斜性特征为基础,利用DRAM的低延迟和NVM的持久性与高存储密度,提出了在持久性内存索引的基础上增加基于DRAM的热点数据缓存,进而提出了可以根据热点数据的变化自动调整缓存的查询自适应索引方法。将所提方法应用到多种持久性内存索引上,包括wBtree,FPTree以及Fast&Fair,并进行了对比实验。结果表明,当热点数据访问达到总访问次数的80%时,所提索引加速方法在3种索引上的查询性能分别取得了52%,33%,37%的提升。

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.