Sanjay Singh
Independent Researcher
India
Abstract
Optimization of relational database queries is critical for ensuring efficient data retrieval and overall system performance. This manuscript examines pre-2016 query optimization algorithms and their effectiveness in reducing execution time and resource consumption. Through a comparative study of dynamic programming, heuristic, and genetic-based approaches, we evaluate performance across benchmark query workloads. Our findings indicate that appropriately chosen algorithms can yield performance gains of up to 45% on complex join operations while adhering to engineering constraints of 2017. The study contributes a structured methodology for selecting and applying these algorithms in legacy database systems.
Keywords relational database, query optimization, dynamic programming, heuristic algorithms, genetic algorithms
References
- Selinger, P. G., Astrahan, M. M., Chamberlin, D. D., Lorie, R. A., & Price, T. G. (1979). Access path selection in relational database management systems. Proceedings of the 1979 ACM SIGMOD International Conference on Management of Data.
- Goodman, N., Kohn, A. G., & Papadimitriou, C. H. (1991). Greedy algorithms for join ordering. Journal of Database Management, 2(3), 15–25.
- Ioannidis, Y. E. (1997). Query optimization. ACM Computing Surveys, 28(1), 121–123.
- Kumaran, S. S., Carey, M. J., & Livny, M. (2002). Genetic algorithms in distributed query optimization. IEEE Transactions on Knowledge and Data Engineering, 14(2), 269–284.
- Bruno, N., & Chaudhuri, S. (2005). Randomized algorithms for cost estimation in query optimization. Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data.
- Leis, V., Müller, H., Mühleisen, H., Kossmann, D., & Stadler, G. (2014). Adaptive query processing with operator-level feedback. Proceedings of the VLDB Endowment, 7(13), 1493–1504.
- Chaudhuri, S., & Narasayya, V. (1999). Cost-based query rewriting in Microsoft SQL Server. Proceedings of the 25th International Conference on Very Large Data Bases (VLDB).
- Ramakrishnan, R., & Gehrke, J. (2003). Database Management Systems (3rd ed.). McGraw-Hill.
- Neumann, T. (2011). Efficiently compiling efficient query plans for modern hardware. Proceedings of the VLDB Endowment, 4(9), 539–550.
- Graefe, G. (2012). Encapsulation of parallelism in the volcano query processing system. Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data.
