Scalable Query Optimization for Efficient Data Processing Using MapReduce

Document Type

Conference Proceeding

Publication Date

8-17-2015

Abstract

MapReduce is widely acknowledged by both industry and academia as an effective programming model for query processing on big data. It is crucial to design an optimizer which finds the most efficient way to execute an SQL query using MapReduce. However, existing work in parallel query processing either falls short of optimizing an SQL query using MapReduce or the time complexity of the optimizer it uses is exponential. Also, industry solutions such as HIVE, and YSmart do not optimize the join sequence of an SQL query and cannot guarantee an optimal execution plan. In this paper, we propose a scalable optimizer for SQL queries using MapReduce, named SOSQL. Experiments performed on Google Cloud Platform confirmed the scalability and efficiency of SOSQL over existing work.

Identifier

84959543413 (Scopus)

ISBN

[9781467372787]

Publication Title

Proceedings 2015 IEEE International Congress on Big Data Bigdata Congress 2015

External Full Text Location

https://doi.org/10.1109/BigDataCongress.2015.100

First Page

649

Last Page

652

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