Measuring the Performance of Data Placement Structures for MapReduce-based Data Warehousing Systems

S. Makki, M. R. Hasan
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引用次数: 1

Abstract

The exponential growth of data requires systems that are able to provide a scalable and fault-tolerant infrastructure for storage and processing of vast amount of data efficiently. Hive is a MapReduce-based data warehouse for data aggregation and query analysis. This data warehousing system can arrange millions of rows of data into tables, and its data placement structures play a significant role for increasing the performance of this data warehouse. Hive also provides SQL-like language called HiveQL, which is able to compile MapReduce jobs into queries on Hadoop. In this paper, we measure the efficiency of these data placement structures (Record Columnar File (RCFile) and Optimize Record Columnar File (ORCFile)) in terms of data loading, storage and query processing using MapReduce framework. The experimental results showed the effectiveness of these data placement structures for Hive data warehousing systems. Index Terms Big Data; Hive; MapReduce;
测量基于mapreduce的数据仓库系统的数据放置结构的性能
数据的指数级增长要求系统能够提供可伸缩和容错的基础设施,以便有效地存储和处理大量数据。Hive是一个基于mapreduce的数据仓库,用于数据聚合和查询分析。该数据仓库系统可以将数百万行数据排列到表中,其数据放置结构对于提高该数据仓库的性能起着重要作用。Hive还提供了类似sql的语言HiveQL,它能够将MapReduce任务编译成Hadoop上的查询。在本文中,我们使用MapReduce框架测量了这些数据放置结构(Record Columnar File (RCFile)和Optimize Record Columnar File (ORCFile))在数据加载、存储和查询处理方面的效率。实验结果表明了这些数据放置结构在Hive数据仓库系统中的有效性。大数据;蜂巢;MapReduce;
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