HDFSX:支持小文件的大数据分布式文件系统

Passent M. ElKafrawy, Amr M. Sauber, Mohamed M. Hafez
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引用次数: 12

摘要

Hadoop分布式文件系统(HDFS)是一个文件系统,设计用于处理大文件(千兆字节或太字节大小),具有流数据访问模式,在商用硬件上运行集群。然而,大数据可能存在于大量的小文件中,例如:在生物学,天文学或某些应用程序中产生3000万个文件,平均大小为190kb。不幸的是,HDFS无法处理这种破碎的大数据,因为在处理大量小文件时,单个Namenode被认为是瓶颈。在本文中,我们提出了一种新的HDFS (HDFSX)结构,以避免更高的内存使用,网络泛滥,请求开销和单个Namenode的集中故障点(单点故障“SPOF”)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HDFSX: Big data Distributed File System with small files support
Hadoop Distributed File System (HDFS) is a file system designed to handle large files - which are in gigabytes or terabytes size - with streaming data access patterns, running clusters on commodity hardware. However, big data may exist in a huge number of small files such as: in biology, astronomy or some applications generating 30 million files with an average size of 190 Kbytes. Unfortunately, HDFS wouldn't be able to handle such kind of fractured big data because single Namenode is considered a bottleneck when handling large number of small files. In this paper, we present a new structure for HDFS (HDFSX) to avoid higher memory usage, flooding network, requests overhead and centralized point of failure (single point of failure “SPOF”) of the single Namenode.
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