TurboHash:用于持久内存上键值存储的哈希表

Xingsheng Zhao, Chen Zhong, Song Jiang
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引用次数: 0

摘要

在非易失字节可寻址内存上设计持久哈希表的主要工作集中在有效地支持使用fence/flush原语的崩溃一致性以及非中断表重哈希操作。当哈希桶中的数据条目不能通过一次原子写来更新时,需要进行位置外更新而不是位置更新,以避免发生故障后的数据损坏。这通常会导致额外的围栏/清除。同时,当采用开放寻址技术,如线性探测时,对于高负载因子,一个键的搜索范围可以很大。过度使用fence/flush和扩展键搜索路径是持久内存中哈希表性能下降的两个主要原因。为了解决这些问题,我们设计了一个名为TurboHash的持久哈希表,用于构建高性能的键值存储。Turbo-Hash在一个设计中具有许多非常理想的功能。(1)它支持位置外更新,其成本与位置内写的成本相当,以提供无锁读。(2)远程线性探测最小化(仅在必要时)。(3)只对分片进行扩容调整,避免了昂贵的目录级重哈希;(4)利用硬件特性实现高I/O和计算效率,包括英特尔Optane DC的性能特性和英特尔AVX指令。我们在Optane持久内存上实现了TurboHash,并进行了广泛的评估。实验结果表明,TurboHash在吞吐量和延迟方面提高了2-8倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TurboHash: A Hash Table for Key-value Store on Persistent Memory
Major efforts on the design of persistent hash table on a non-volatile byte-addressable memory focus on efficient support of crash consistency with fence/flush primitives as well on non-disruptive table rehashing operations. When a data entry in a hash bucket cannot be updated with one atomic write, out-of-place update, instead of in-place update, is required to avoid data corruption after a failure. This often causes extra fences/flushes. Meanwhile, when open addressing techniques, such as linear probing, are adopted for high load factor, the scope of search for a key can be large. Excessive use of fence/flush and extended key search paths are two major sources of performance degradation with hash tables in persistent memory. To address the issues, we design a persistent hash table, named TurboHash, for building high-performance key-value store. Turbo-Hash has a number of much desired features all in one design. (1) It supports out-of-place update with a cost equivalent to that of an in-place write to provide lock-free reads. (2) Long-distance linear probing is minimized (only when necessary). (3) It conducts only shard resizing for expansion and avoids expensive directory-level rehashing; And (4) it exploits hardware features for high I/O and computation efficiency, including Intel's Optane DC's performance characteristics and Intel AVX instructions. We have implemented TurboHash on the Optane persistent memory and conducted extensive evaluations. Experiment results show that TurboHash improves state-of-the-arts by 2-8 times in terms of throughput and latency.
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