GENMR: Generalized Query Processing through Map Reduce In Cloud Database Management System

TL;DR

This paper introduces GENMR, a generalized model that converts various RDBMS queries into MapReduce codes for efficient big data processing in cloud databases, outperforming existing tools like HIVE and PIG.

Contribution

The paper presents a novel generalized model GENMR that translates multiple RDBMS queries into MapReduce, enhancing big data processing performance in cloud database systems.

Findings

GENMR outperforms HIVE and PIG in processing speed.

Optimization of mapper placement improves parallelism and performance.

GENMR effectively handles queries from various RDBMS systems.

Abstract

Big Data, Cloud computing, Cloud Database Management techniques, Data Science and many more are the fantasizing words which are the future of IT industry. For all the new techniques one common thing is that they deal with Data, not just Data but the Big Data. Users store their various kinds of data on cloud repositories. Cloud Database Management System deals with such large sets of data. For processing such gigantic amount of data, traditional approaches are not suitable because these approaches are not able to handle such size of data. To handle these, various solutions have been developed such as Hadoop, Map Reduce Programming codes, HIVE, PIG etc. Map Reduce codes provides both scalability and reliability. But till date, users are habitual of SQL, Oracle kind of codes for dealing with data and they are not aware of Map Reduce codes. In this paper, a generalized model GENMR has been implemented, which takes queries written in various RDBMS forms like SQL, ORACLE, DB2, MYSQL and convert into Map Reduce codes. A comparison has been done to evaluate the performance of GENMR with latest techniques like HIVE and PIG and it has been concluded that GENMR shows much better performance as compare to both the techniques. We also introduce an optimization technique for mapper placement problems to enhance the effect of parallelism which improves the performance of such Amalgam approach.