Improving database performance by leveraging network-assisted logging

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Hwajung Kim
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引用次数: 0

Abstract

In mission-critical systems like databases and transaction processing systems, write-ahead logging (WAL) is commonly used to ensure fault tolerance against power failures and system malfunctions. However, WAL requires data to be logged before it is permanently stored in persistent storage, causing delays that can slow down the system, even when using advanced technologies like Optane persistent memory. Although seemingly small, such delays can accumulate and affect overall transaction performance. In this paper, we propose an in-transit logging (ITLogging) scheme that performs logging at the network layer by capturing important data upon its arrival at the destination system. Our scheme filters incoming packets and logs the necessary data from the payload before any processing occurs. In case of data loss, our scheme replays packet deliveries to the target system by mimicking the original client actions for recovery. We implement the proposed scheme by allocating a separate core for packet inspection, ensuring that logging operations are handled independently of the application layer’s data processing, thereby avoiding delays in the main processing flow. The experimental results demonstrate that our scheme improves database throughput by 16% for TPC-C and 15% for LinkBench on MySQL, compared with vanilla MySQL.
通过利用网络辅助日志来提高数据库性能
在数据库和事务处理系统等关键任务系统中,预写日志(WAL)通常用于确保对电源故障和系统故障的容错能力。但是,WAL要求在将数据永久存储在持久存储器中之前对其进行日志记录,这会导致延迟,从而降低系统速度,即使在使用Optane持久存储器等先进技术时也是如此。虽然看起来很小,但这种延迟可能会累积并影响整体事务性能。在本文中,我们提出了一种传输中日志(ITLogging)方案,该方案通过捕获到达目标系统的重要数据来在网络层执行日志记录。我们的方案过滤传入的数据包,并在进行任何处理之前记录有效负载中的必要数据。在数据丢失的情况下,我们的方案通过模拟原始客户端操作来重放数据包传递到目标系统以进行恢复。我们通过为数据包检测分配一个单独的核心来实现所提出的方案,确保日志操作的处理独立于应用层的数据处理,从而避免了主处理流的延迟。实验结果表明,与传统的MySQL相比,我们的方案在TPC-C上提高了16%的数据库吞吐量,在LinkBench上提高了15%。
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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