Phoenix: A MapReduce implementation with new enhancements

A. Al-Badarneh, Hassan M. Najadat, Majd Al-Soud, Rasha Mosaid
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引用次数: 1

Abstract

Lately, the large increasing in data amount results in compound and large data-sets that caused the appearance of “Big Data” concept which gained the attention of industrial organizations as well as academic communities. Big data APIs that need large memory can benefit from Phoenix MapReduce implementation for shared-memory machines, instead of large, distributed clusters of computers. This paper evaluates the design and the prototype of Phoenix, Phoenix performance, as well as Phoenix limitations. This paper also suggests some new approaches to get over of some Phoenix limitation and enhance its performance on large-scale shared memory. The major contribution of this work is finding new approaches that get over the <;key, value> pairs limitation in phoenix framework using hash tables with B+Trees and get over the collisions problem of hash tables.
Phoenix:一个带有新增强的MapReduce实现
近年来,由于数据量的大幅增长,导致数据集的复合和大,“大数据”概念的出现,引起了行业组织和学术界的关注。需要大内存的大数据api可以从Phoenix MapReduce对共享内存机器的实现中受益,而不是大型分布式计算机集群。本文评价了凤凰号的设计和原型,凤凰号的性能,以及凤凰号的局限性。本文还提出了一些新的方法来克服Phoenix的一些限制,提高其在大规模共享内存上的性能。这项工作的主要贡献是找到了新的方法,通过使用带有B+树的哈希表来克服phoenix框架中的对限制,并解决哈希表的冲突问题。
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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