توضیحات
ABSTRACT
In this paper, we report on an evaluation of four representative Big Data management systems (BDMSs): MongoDB, Hive, AsterixDB, and a commercial parallel sharednothing relational database system. In terms of features, all offer
to store and manage large volumes of data, and all provide some degree of query processing capabilities on top of such data. Our evaluation is based on a micro-benchmark that utilizes a synthetic application that has a social network flavor. We analyze the performance results and discuss the lessons learned from this
effort. We hope that this study will inspire future domain-centric evaluations of BDMSs with a focus on their features
INTRODUCTION
The IT world is excited about the Big Data ”buzz”. Immense volumes of data are generated continuously in different
domains and there is clear merit in analyzing and processing this data. New emerging platforms for this purpose can be largely categorized into two groups: interactive request-serving systems (NoSQL), mainly serving OLTP types of workloads with simple operations, and Big Data analytics systems, which process scan-oriented OLAP types of workloads. The variety of Big Data systems makes it difficult for end users to pick the most appropriate system for a specific use case. In this situation, benchmarking Big Data systems can help provide more insight by offering a better understanding of the systems and also obtaining a set of guidelines to make the correct decision in picking a system for a specific application
Year : 2015
Publisher : IEEE
By : Pouria Pirzadeh , Michael J. Carey ,Till Westmann
File Information : English Language / 8 Page / Size : 431 K
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سال : 2015
ناشر : IEEE
کاری از : Pouria Pirzadeh , Michael J. Carey ,Till Westmann
اطلاعات فایل : زبان انگلیسی /8 صفحه / حجم : 431 K
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