FlashKey:一个高性能的Flash友好的键值存储

Madhurima Ray, K. Kant, Peng Li, S. Trika
{"title":"FlashKey:一个高性能的Flash友好的键值存储","authors":"Madhurima Ray, K. Kant, Peng Li, S. Trika","doi":"10.1109/IPDPS47924.2020.00104","DOIUrl":null,"url":null,"abstract":"Key-value stores (KVS) provide an efficient storage for increasing amounts of semi-structured or unstructured data generated by many applications. Most KVS in existence have been designed for hard-disk based storage where avoiding random accesses is crucial for good performance. Unfortunately, the resulting storage structures result in high read, write, and space amplifications when used on modern SSDs. In this paper, we introduce a KV store especially designed for SSDs, called FlashKey, and demonstrate that even as an initial implementation, it substantially outperforms the two most popular commercial KVS in existence, namely, Google’s LevelDB and Facebook’s RocksDB. In particular, we show that FlashKey achieves up to 85% improvement in average access latency, 2x improvement in tail latencies, and 12x improvement in write amplification, at comparable or better space-amplification. Furthermore, FlashKey can easily trade off space and write amplifications, thereby providing a new tuning knob that is difficult to implement in LevelDB and RocksDB.","PeriodicalId":6805,"journal":{"name":"2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"76 1","pages":"976-985"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"FlashKey:A High-Performance Flash Friendly Key-Value Store\",\"authors\":\"Madhurima Ray, K. Kant, Peng Li, S. Trika\",\"doi\":\"10.1109/IPDPS47924.2020.00104\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Key-value stores (KVS) provide an efficient storage for increasing amounts of semi-structured or unstructured data generated by many applications. Most KVS in existence have been designed for hard-disk based storage where avoiding random accesses is crucial for good performance. Unfortunately, the resulting storage structures result in high read, write, and space amplifications when used on modern SSDs. In this paper, we introduce a KV store especially designed for SSDs, called FlashKey, and demonstrate that even as an initial implementation, it substantially outperforms the two most popular commercial KVS in existence, namely, Google’s LevelDB and Facebook’s RocksDB. In particular, we show that FlashKey achieves up to 85% improvement in average access latency, 2x improvement in tail latencies, and 12x improvement in write amplification, at comparable or better space-amplification. Furthermore, FlashKey can easily trade off space and write amplifications, thereby providing a new tuning knob that is difficult to implement in LevelDB and RocksDB.\",\"PeriodicalId\":6805,\"journal\":{\"name\":\"2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)\",\"volume\":\"76 1\",\"pages\":\"976-985\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPDPS47924.2020.00104\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPDPS47924.2020.00104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

键值存储(KVS)为许多应用程序生成的越来越多的半结构化或非结构化数据提供了一种高效的存储方式。现有的大多数KVS都是为基于硬盘的存储而设计的,避免随机访问对于良好的性能至关重要。不幸的是,当在现代ssd上使用时,由此产生的存储结构会导致很高的读、写和空间放大。在本文中,我们介绍了一个专门为ssd设计的KV存储,称为FlashKey,并证明即使作为初始实现,它也大大优于现有的两种最流行的商业KVS,即谷歌的LevelDB和Facebook的RocksDB。特别是,我们表明FlashKey在同等或更好的空间放大下,平均访问延迟提高了85%,尾部延迟提高了2倍,写放大提高了12倍。此外,FlashKey可以很容易地权衡空间和写入放大,从而提供了一个难以在LevelDB和RocksDB中实现的新调谐旋钮。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FlashKey:A High-Performance Flash Friendly Key-Value Store
Key-value stores (KVS) provide an efficient storage for increasing amounts of semi-structured or unstructured data generated by many applications. Most KVS in existence have been designed for hard-disk based storage where avoiding random accesses is crucial for good performance. Unfortunately, the resulting storage structures result in high read, write, and space amplifications when used on modern SSDs. In this paper, we introduce a KV store especially designed for SSDs, called FlashKey, and demonstrate that even as an initial implementation, it substantially outperforms the two most popular commercial KVS in existence, namely, Google’s LevelDB and Facebook’s RocksDB. In particular, we show that FlashKey achieves up to 85% improvement in average access latency, 2x improvement in tail latencies, and 12x improvement in write amplification, at comparable or better space-amplification. Furthermore, FlashKey can easily trade off space and write amplifications, thereby providing a new tuning knob that is difficult to implement in LevelDB and RocksDB.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信