{"title":"ZBTree: A Fast and Scalable B$^+$+-Tree for Persistent Memory","authors":"Wenkui Che;Zhiwen Chen;Daokun Hu;Jianhua Sun;Hao Chen","doi":"10.1109/TKDE.2024.3421232","DOIUrl":null,"url":null,"abstract":"In this paper, we present the design and implementation of ZBTree, a hotness-aware B\n<inline-formula><tex-math>$^+$</tex-math></inline-formula>\n-Tree for persistent memory (PMem). ZBTree leverages the PMem+DRAM architecture, which is featured with a volatile operation layer to accelerate data access and an order-preserving persistent layer to achieve fast recovery and low-overhead consistency and persistence guarantees. The operation layer contains inner nodes for indexing and compacted leaf nodes (DLeaves) that hold metadata. Based on leaf node compaction, we present a data lodging method, which supports to load hot data into fast DRAM dynamically, avoiding PMem accesses for subsequent reads of hot data and achieving improved read performance without incurring extra DRAM usage. In addition, we present a lightweight node splitting mechanism with constant persistence overhead that does not vary with node size. Our extensive evaluations show that ZBTree achieves higher throughput by a factor of 1.4x-6.3x compared to state-of-the-art tree indexes under a wide range of workloads. Meanwhile, ZBTree achieves comparable or faster recovery speed compared to existing designs.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"9547-9563"},"PeriodicalIF":8.9000,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Knowledge and Data Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10638243/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we present the design and implementation of ZBTree, a hotness-aware B
$^+$
-Tree for persistent memory (PMem). ZBTree leverages the PMem+DRAM architecture, which is featured with a volatile operation layer to accelerate data access and an order-preserving persistent layer to achieve fast recovery and low-overhead consistency and persistence guarantees. The operation layer contains inner nodes for indexing and compacted leaf nodes (DLeaves) that hold metadata. Based on leaf node compaction, we present a data lodging method, which supports to load hot data into fast DRAM dynamically, avoiding PMem accesses for subsequent reads of hot data and achieving improved read performance without incurring extra DRAM usage. In addition, we present a lightweight node splitting mechanism with constant persistence overhead that does not vary with node size. Our extensive evaluations show that ZBTree achieves higher throughput by a factor of 1.4x-6.3x compared to state-of-the-art tree indexes under a wide range of workloads. Meanwhile, ZBTree achieves comparable or faster recovery speed compared to existing designs.
期刊介绍:
The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.