2016Unpublished venueRequires access

A novel approach for SQL query optimization

Fazal Mithani, Sahista Machchhar, Fernaz Jasdanwala

Open publisher page 9 citations

Abstract

The world's latest scenario suggests that the database size will pilling-up epidemically. Due to this, requirement of larger database is eminent. Furthermore, these data needs to be scalable enough to support future up gradation, keeping in mind that performance is not compromised. The structure of Structured Query Language (SQL) query also plays an enormous role in gauging the performance of execution. In this paper we have suggested a model that allows checking of the query performance with respect to predefined rules, SQL benchmark, strategy of SQL tuning and data freezing. In this paper we systemized existing query formation rules which need to be followed by every query that needs to be optimized. Our approach also suggest the user an optimized query for normalization if required. If filtration is used in given query our approach will also be stating any requirement for indexing on particular column. By using any of the above approach along with avoiding unnecessary usage of data, columns, our proposed model converts the input user SQL query into an optimized SQL query and main aims to give assurance of reduced query execution time.

About this research paper

What this paper is about

The world's latest scenario suggests that the database size will pilling-up epidemically. Due to this, requirement of larger database is eminent. Furthermore, these data needs to be scalable enough to support future up gradation, keeping in mind that performance is not compromised. The structure of Structured Query Language (SQL) query also plays an enormous role in gauging the performance of execution. In this paper we have suggested a model that allows checking of the query performance with respect to predefined rules, SQL benchmark, strategy of SQL tuning and data freezing. In this paper we systemized existing query formation rules which need to be followed by every query that needs to be optimized. Our approach also suggest the user an optimized query for normalization if required. If filtration is used in given query our approach will also be stating any requirement for indexing on particular column. By using any of the above approach along with avoiding unnecessary usage of data, columns, our proposed model converts the input user SQL query into an optimized SQL query and main aims to give assurance of reduced query execution time.

Why it matters

OpenAlex reports 9 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Available abstract

The world's latest scenario suggests that the database size will pilling-up epidemically. Due to this, requirement of larger database is eminent. Furthermore, these data needs to be scalable enough to support future up gradation, keeping in mind that performance is not compromised. The structure of Structured Query Language (SQL) query also plays an enormous role in gauging the performance of execution. In this paper we have suggested a model that allows checking of the query performance with respect to predefined rules, SQL benchmark, strategy of SQL tuning and data freezing. In this paper we systemized existing query formation rules which need to be followed by every query that needs to be optimized. Our approach also suggest the user an optimized query for normalization if required. If filtration is used in given query our approach will also be stating any requirement for indexing on particular column. By using any of the above approach along with avoiding unnecessary usage of data, columns, our proposed model converts the input user SQL query into an optimized SQL query and main aims to give assurance of reduced query execution time.

Key concepts: Computer science, Query by Example, Query optimization, Sargable, SQL, RDF query language, Query language, Query expansion

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