Abstract: In the context of Relational Database Management Systems (RDBMSs), we investigate how data size and index strategies affect query response time. With the explosive increase of data, efficient data processing and user satisfaction are highly dependent on the performance of database. Response Time is the major indicator of the performance being experienced. The study is conducted with an analytical method through performance evaluation experiments in which query execution times are measured for several size datasets with indexed and non-indexed configurations (none, B-Tree, Hash). Anyway, for large dataset, we would take Cost-Based Optimization (CBO) into consideration the optimize execution plans (i.e., adaptive or learned indexing). The response time for the above query is a strong function of data size (positive correlation) and degradation is large in case of no indexing. Correct indexing can hugely reduce response time; we estimate at a figure of around 35– 60%. ((Note that B-Tree indexing works comparably or better on various workloads: response times for heavy workloads are 40–45% baseline on B-Tree as opposed to 55–60% in the case of Hash.) This process highlights the importance of proper indexing strategies for both performance and scalability, as well has having useful implications on database design principles as well in data-intensive applications.

Keywords: Relational Databases, Indexing, Query Optimization, Data Size, Performance Analysis

Pages: 33-37

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