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Level Hashing

Level Hashing Non-volatile memory (NVM) technologies as persistent memory are promising candidates to complement or replace DRAM for building future memory systems, due to having the advantages of high density, low power, and non-volatility. In main memory systems, hashing index structures are fundamental building blocks to provide fast query responses. However, hashing index structures originally designed for dynamic random access memory (DRAM) become inefficient for persistent memory due to new challenges including hardware limitations of NVM and the requirement of data consistency. To address these challenges, this article proposes level hashing, a write-optimized and high-performance hashing index scheme with low-overhead consistency guarantee and cost-efficient resizing. Level hashing provides a sharing-based two-level hash table, which achieves constant-scale worst-case time complexity for search, insertion, deletion, and update operations, and rarely incurs extra NVM writes. To guarantee the consistency with low overhead, level hashing leverages log-free consistency schemes for deletion, insertion, and resizing operations, and an opportunistic log-free scheme for update operation. To cost-efficiently resize this hash table, level hashing leverages an in-place resizing scheme that only needs to rehash 1/3 of buckets instead of the entire table to expand a hash table and rehash 2/3 of buckets to shrink a hash table, thus significantly improving the resizing performance and reducing the number of rehashed buckets. Extensive experimental results show that the level hashing speeds up insertions by 1.43.0, updates by 1.22.1, expanding by over 4.3, and shrinking by over 1.4 while maintaining high search and deletion performance compared with start-of-the-art hashing schemes. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Storage (TOS) Association for Computing Machinery

Level Hashing

Zuo, Pengfei; Hua, Yu; Wu, Jie
ACM Transactions on Storage (TOS) , Volume 15 (2): 30 – Jun 21, 2019
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Publisher
Association for Computing Machinery
Copyright
Copyright © 2019 ACM
ISSN
1553-3077
eISSN
1553-3093
DOI
10.1145/3322096
Publisher site
See Article on Publisher Site

Abstract

Non-volatile memory (NVM) technologies as persistent memory are promising candidates to complement or replace DRAM for building future memory systems, due to having the advantages of high density, low power, and non-volatility. In main memory systems, hashing index structures are fundamental building blocks to provide fast query responses. However, hashing index structures originally designed for dynamic random access memory (DRAM) become inefficient for persistent memory due to new challenges including hardware limitations of NVM and the requirement of data consistency. To address these challenges, this article proposes level hashing, a write-optimized and high-performance hashing index scheme with low-overhead consistency guarantee and cost-efficient resizing. Level hashing provides a sharing-based two-level hash table, which achieves constant-scale worst-case time complexity for search, insertion, deletion, and update operations, and rarely incurs extra NVM writes. To guarantee the consistency with low overhead, level hashing leverages log-free consistency schemes for deletion, insertion, and resizing operations, and an opportunistic log-free scheme for update operation. To cost-efficiently resize this hash table, level hashing leverages an in-place resizing scheme that only needs to rehash 1/3 of buckets instead of the entire table to expand a hash table and rehash 2/3 of buckets to shrink a hash table, thus significantly improving the resizing performance and reducing the number of rehashed buckets. Extensive experimental results show that the level hashing speeds up insertions by 1.43.0, updates by 1.22.1, expanding by over 4.3, and shrinking by over 1.4 while maintaining high search and deletion performance compared with start-of-the-art hashing schemes.

Journal

ACM Transactions on Storage (TOS) – Association for Computing Machinery

Published: Jun 21, 2019

Keywords: Persistent memory

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