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Understanding Issue Correlations: A Case Study of the Hadoop System

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Over the last decade, Hadoop has evolved into a widely used platform for Big Data applications. Acknowledging its wide-spread use, we present a comprehensive analysis of the solved issues with applied patches in the Hadoop ecosystem

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ABSTRACT
Over the last decade, Hadoop has evolved into a widely used platform for Big Data applications. Acknowledging its
wide-spread use, we present a comprehensive analysis of the solved issues with applied patches in the Hadoop ecosystem. The analysis is conducted with a focus on Hadoop’s two essential components: HDFS (storage) and MapReduce (computation), it involves a total of 4218 solved issues over the last six years, covering 2180 issues from HDFS and 2038 issues from MapReduce. Insights derived from the study concern system design and development, particularly with respect to correlated issues and correlations between root causes of issues and characteristics of the Hadoop subsystems. These findings shed light on the future development of Big Data systems, on their testing, and on bug-finding tools

INTRODUCTION

Recent extensive work on data-intensive applications and on the systems supporting them are mirrored by substantial efforts to improve and enhance well-established frameworks like Hadoop [3] Hadoop is an open-source project which has a considerable development and deployment history dating back to the year of 2002,

Year : 2015

By : Jian Huang , Xuechen Zhang , Karsten Schwan

File Information : English Language /14 Page /Size : 324 K

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سال : 2015

کاری از : Jian Huang , Xuechen Zhang , Karsten Schwan

اطلاعات فایل : زبان انگلیسی /14 صفحه / حجم : 324 K

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