SMH - A QUERY OPTIMIZATION TOOL

Omkar Randive, Anuj Bidkar, Yash Mestry, Bajrang Kharat

Abstract: There is tremendous growth in heterogeneous and unstructured data in last few years. Various SQL and NoSQL databases have been developed to store and query this humongous data. MongoDB is prevailing document oriented database among SQL and NoSQL databases. MongoDB has been chosen among other technologies because of its ability to work with variety of latest as well as conventional technologies.On the other hand Hadoop can be contagious. Its’ implementation in one organization can lead to another one elsewhere. The ability to include HIVE in an EMR workflow is yet another splendid point. In this Project we are making a Migration-Tool. Using these three Technologies to Convert or integrate the queries.In consideration of the migration of relational database tables, data along with queries into SMH, there are certain issues like current migration systems being incapable of properly mapping table relationships. It could result in the necessity of multiple query statements, thereby decreasing query performance and data redundancy thus wasting space needlessly. Adding up to those complications are the existing query converters only being capable of converting select queries, the inability of query converters to support table joins and most of the tools requiring SQL, MongoDB command or HIVE knowledge. Hence, the focus of the intended system Sql Mongo Hive -‘SMH’ is to facilitate users to easily transfer from MySQL tables along with proper relationship mapping opted from embedding and referencing, data as well as queries into MongoDB and HIVE or vice-versa without requiring any prior knowledge of MongoDB commands or HIVE.

Keywords: Sql, MongoDB, Hive, Database, Migration, Hadoop, Query Conversion, Map Reduce, Embedding, Relational Database, Database.

DOI: https://doi.org/10.15623/ijret.2017.0610016