在并行文件系统上构建轻量级键值存储

Jiaan Zeng, Beth Plale
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引用次数: 2

摘要

随着数据数量和大小的增长,大数据应用开始彻底改变底层存储系统。一方面,键值存储以其无模式的数据模型、高可扩展性等优势,成为大数据应用的后端存储。另一方面,多节点共享的并行文件系统具有大容量、高吞吐量和高带宽的访问能力,广泛应用于高性能计算(HPC)和云计算环境中。在本文中,我们将探讨构建轻量级键值存储的可能性,该存储支持并行文件系统上的并发访问。提出的键值存储依赖于并行文件系统的共享特性来提供分布式访问。我们的键值存储没有组织一个具有长时间运行服务的节点集群来委托访问,而是将自己嵌入到应用程序中,不需要长时间运行的服务,也不需要节点之间的通信。这样的设计不仅简化了分布式键值存储的结构,而且还避免了在文件系统周围运行服务所带来的开销。我们实现了该系统的原型,并将其与Cassandra(最先进的键值存储)进行了比较。初步结果令人鼓舞。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards Building a Lightweight Key-Value Store on Parallel File System
As data grows in number and size, big data applications begin to revolutionize the underlying storage system. On one hand, key-value store has prevailed as the back-end storage for big data applications owning to its schema-less data model, high scalability, and etc. On the other hand, parallel file system shared by multiple nodes offers large-capacity, high-throughput, as well as high-bandwidth access and is used widely in high performance computing (HPC) and cloud computing environments. In this paper, we explore the opportunity of building a lightweight key-value store that supports concurrent access over a parallel file system. The key-value store proposed relies on the sharing nature of parallel file system to provide distributed access. Instead of organizing a cluster of nodes with long running services to delegate the access, our key-value store simply embeds itself into applications and requires no long running services neither communication between nodes. Such a design not only simplifies the structure of a distributed key-value store but also avoids overhead introduced by having running services around the file system. We implemented a prototype of this system and compared it against Cassandra, a state-of-art key-value store. Preliminary results are promising.
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