A Multi-tenant Key-value SSD with Secondary Index for Search Query Processing and Analysis

Author:

Min Donghyun1ORCID,Kim Kihyun1ORCID,Moon Chaewon1ORCID,Khan Awais2ORCID,Lee Seungjin1ORCID,Yun Changhwan3ORCID,Chung Woosuk3ORCID,Kim Youngjae1ORCID

Affiliation:

1. Dept. of Computer Science and Engineering, Sogang University, Republic of Korea

2. Oak Ridge National Laboratory, USA

3. Memory System R&D, SK Hynix

Abstract

Key-value SSDs (KVSSDs) introduced so far are limited in their use as an alternative to the key-value store running on the host due to the following technical limitations. First, they were designed only for a single tenant, limiting the use of multiple tenants. Second, they mainly focused on designing indexes for primary key-based searches, without supporting various queries using a combination of primary key and non-primary attribute-based searches. This article proposes Cerberus , a Log Structured Merged (LSM) tree-based KVSSD armed with (1) namespace and performance isolation for multiple tenants in a multi-tenant environment and (2) capability for processing non-primary attribute-based search queries. Specifically, Cerberus identifies the tenant’s namespace and splits a single large LSM-tree into namespace-specific LSM-tree indexes for tenants. Cerberus also manages secondary LSM-tree indexes to enable non-primary attribute-based data access and fast search query processing. With the SSD-internal CPU/DRAM resources, Cerberus supports non-primary attribute-based search queries and handles complex queries that are combined with search and computing operations. We prototyped Cerberus on the Cosmos+ OpenSSD platform. When there are multiple tenants, Cerberus exhibits up to 2.9× higher read throughput and negligible write overhead compared to existing KVSSD. Cerberus also shows lower latency by up to 9.31× for non-primary attribute-based queries.

Funder

National Research Foundation of Korea

Korean government

Publisher

Association for Computing Machinery (ACM)

Subject

Hardware and Architecture,Software

Reference52 articles.

1. 2013. Cassandra. http://cassandra.apache.org.

2. 2017. Cosmos+ OpenSSD Platform. http://www.openssd-project.org/.

3. 2018. EleasticSearch. https://www.elastic.co/.

4. 2020. MongoDB Manual.https://docs.mongodb.com/manual/.

5. Stefan Aulbach, Torsten Grust, Dean Jacobs, Alfons Kemper, and Jan Rittinger. 2008. Multi-tenant databases for software as a service: Schema-mapping techniques. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. 1195–1206.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3