PolarDB-SCC: A Cloud-Native Database Ensuring Low Latency for Strongly Consistent Reads

Author:

Yang Xinjun1,Zhang Yingqiang1,Chen Hao1,Sun Chuan1,Li Feifei1,Zhou Wenchao1

Affiliation:

1. Alibaba Group

Abstract

A classic design of cloud-native databases adopts an architecture that consists of one read/write (RW) node and one or more read-only (RO) nodes. In such a design, the propagation of write-ahead logs (WALs) from the RW node to the RO node(s) is typically performed asynchronously. Consequently, system designers either have to accept a loose consistency guarantee, where a read from the RO node may return stale data, or tolerate significant performance degradation in terms of read latency, as it then needs to wait for the log to be propagated and applied. Most commercial cloud-native databases, such as Amazon Aurora, choose performance over strong consistency. As a result, it makes RO nodes useless for many applications requiring read-after-write consistency (a form of strong consistency), and the support for serverless databases (i.e., allowing the RO nodes to be scaled out automatically) is impossible as they require a single endpoint. This paper proposes PolarDB-SCC (PolarDB-Strongly Consistent Cluster), a cloud-native database architecture that guarantees strongly consistent reads with very low latency. The core idea is to eliminate unnecessary waits and reduce the necessary wait time on RO nodes while still supporting strong consistency. To achieve this, it tracks the RW node's modification timestamp at three progressively finer-grained levels. We further design a Linear Lamport timestamp to reduce the RO node's timestamp fetching operations and leverage the RDMA network for all the data transferring ( e.g. , timestamp fetching and log shipment) to minimize network overhead and extra CPU usage. Our evaluation shows that PolarDB-SCC does not incur any noticeable overhead for ensuring strongly consistent reads compared with the eventually consistent (stale) read policy. To the best of our knowledge, PolarDB-SCC is the first "read-write splitting" cloud-native database that supports strongly consistent read with negligible overhead. Compared with a straightforward read-wait design, PolarDB-SCC improves throughput by up to 4.51× and reduces median latency by up to 3.66× in SysBench's read-write workload. PolarDB-SCC is already commercially available at Alibaba Cloud.

Publisher

Association for Computing Machinery (ACM)

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Reference64 articles.

1. Remote memory in the age of fast networks

2. Phillipe Ajoux , Nathan Bronson , Sanjeev Kumar , Wyatt Lloyd , and Kaushik Veeraraghavan . 2015 . Challenges to Adopting Stronger Consistency at Scale . In 15th Workshop on Hot Topics in Operating Systems (HotOS XV). Phillipe Ajoux, Nathan Bronson, Sanjeev Kumar, Wyatt Lloyd, and Kaushik Veeraraghavan. 2015. Challenges to Adopting Stronger Consistency at Scale. In 15th Workshop on Hot Topics in Operating Systems (HotOS XV).

3. Amazon. 2012. Read Consistency of DynamoDB. https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html. "[accessed-April-2022]". Amazon. 2012. Read Consistency of DynamoDB. https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html. "[accessed-April-2022]".

4. Amazon. 2022. Amazon Aurora Serverless. https://aws.amazon.com/rds/aurora/serverless/. Amazon. 2022. Amazon Aurora Serverless. https://aws.amazon.com/rds/aurora/serverless/.

5. Amazon. 2022. Replication with Amazon Aurora. https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.Replication.html. "[accessed-April-2022]". Amazon. 2022. Replication with Amazon Aurora. https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.Replication.html. "[accessed-April-2022]".

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