Indian Journal of Science and Technology
Year: 2015, Volume: 8, Issue: 27, Pages: 1-8
Muthukumar Murugesan1* , K. Karthikeyan2 and K. Sivakumar3
1 Department of Application Delivery, Mphasis Limited, Bangalore - 560048, Karnataka, India; [email protected]
2 Department of Computer Application, Anna University, Madurai - 625007, Tamil Nadu, India; [email protected]
3 Department of Computer Science and Electronics, Hindusthan Institute of Technology, Coimbatore - 641032, Tamil Nadu, India; [email protected]
Objectives: Performance optimization is an ever end process. It requires continuous monitoring and special attention. Widely known fact that 60% of the performance problems are direct result of the inefficient queries. Method of Analysis: SQL Query is one of the most essential parts of application performance. Over the period, inefficient queries pull down the performance in live applications gradually. To achieve better application performance, proper database design and efficient query are needed. Most of the database systems provide expected performance in early stage and drastically will come down, once the data volume is increased and database becomes large size. Findings: Real performances of database systems can be assessed only during the applications are in live Database administrator and query developers cannot make sure 100% performance issues during the database design and query writing. Lot of issues will come mostly in post deployment in particular, when database has heavy transactions per day. There are multiple approaches available in the market to provide the solution for database performance. But, none of the mechanism is available to find out the performance bottleneck proactively. Also, solving performance issues is very painful and time-consuming. Improvement: This paper proposes various novel approaches to identify the performance bottleneck upfront and demonstrates the solution for the same.
Keywords: Database Performance, Performance Bottleneck, Query Performance, Query Tuning, SQL Server Database
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