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

IF 2.6 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI:10.14778/3611540.3611562
Xinjun Yang, Yingqiang Zhang, Hao Chen, Chuan Sun, Feifei Li, Wenchao Zhou
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引用次数: 1

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.
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PolarDB-SCC:一个云原生数据库,确保低延迟的强一致读取
经典的云原生数据库设计采用一个RW (read/write)节点和一个或多个RO (read/write)节点组成的架构。在这样的设计中,预写日志(write-ahead logs, wal)从RW节点到RO节点的传播通常是异步执行的。因此,系统设计人员要么必须接受松散的一致性保证(从RO节点读取可能返回过时的数据),要么必须容忍读取延迟方面的显著性能下降,因为它需要等待日志被传播和应用。大多数商业云原生数据库(如Amazon Aurora)选择性能而不是强一致性。因此,对于许多需要读写后一致性(强一致性的一种形式)的应用程序来说,它使RO节点变得无用,并且不可能支持无服务器数据库(即允许RO节点自动向外扩展),因为它们需要单个端点。本文提出了PolarDB-SCC (polardb - strong Consistent Cluster),这是一种云原生数据库架构,可以保证读取的强一致性和极低的延迟。其核心思想是消除不必要的等待,减少RO节点上必要的等待时间,同时仍然支持强一致性。为了实现这一点,它在三个逐步细化的级别上跟踪RW节点的修改时间戳。我们进一步设计了Linear Lamport时间戳,以减少RO节点的时间戳获取操作,并利用RDMA网络进行所有数据传输(例如,时间戳获取和日志发送),以最小化网络开销和额外的CPU使用。我们的评估表明,与最终一致的(陈旧的)读取策略相比,PolarDB-SCC在确保强一致性读取方面不会产生任何明显的开销。据我们所知,PolarDB-SCC是第一个“读写分离”的云原生数据库,它支持高一致性读取,开销可以忽略不计。与直接的读取等待设计相比,在SysBench的读写工作负载中,PolarDB-SCC将吞吐量提高了4.51倍,并将中位延迟降低了3.66倍。PolarDB-SCC已经在阿里云上商业化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Proceedings of the Vldb Endowment
Proceedings of the Vldb Endowment Computer Science-General Computer Science
CiteScore
7.70
自引率
0.00%
发文量
95
期刊介绍: The Proceedings of the VLDB (PVLDB) welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission’s topic of the journal’s editorial board. Finally, the submission’s contributions should build on work already published in data management outlets, e.g., PVLDB, VLDBJ, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation.
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