Performance Analysis of Algorithmic Sorting on Large Datasets

Abstract

This study analyzes the performance of three standard sorting algorithms—Bubble Sort, Quick Sort, and Merge Sort—on datasets of varying sizes. Sorting efficiency is a critical issue in data-intensive applications, where execution speed and resource utilization directly affect scalability. The research problem addressed is which algorithm performs most effectively across small, medium, and large datasets. The novelty of this paper lies in its empirical comparison of execution time and memory usage, moving beyond theoretical complexity analyses that dominate prior studies. By implementing the algorithms in Python and testing them under controlled conditions on personal hardware, the study provides practical benchmarks relevant to real-world environments. Results demonstrate that Bubble Sort consistently underperforms, particularly as dataset size increases. Quick Sort achieves superior speed, while Merge Sort offers more stable memory consumption. These findings highlight that algorithm choice significantly influences performance outcomes, especially in large-scale data processing. The conclusion emphasizes that Quick Sort is preferable for time-sensitive applications, whereas Merge Sort is advantageous in memory-sensitive contexts. The main contribution is a clear, empirical benchmark that informs algorithm selection in practice, supporting more efficient data handling in startup, academic, and enterprise settings.