Comparative Performance Analysis of AVL, Red-Black, and B-Tree Data Structures in Insertion and Search Operations

The selection and efficient use of data structures are fundamental to the performance of modern computational systems, particularly in applications involving large-scale data processing. Although balanced tree structures such as AVL Trees, Red-Black Trees, and B-Trees are well established in the literature, their practical performance can vary significantly depending on implementation details, programming language characteristics, and execution environments. While theoretical analyses provide asymptotic guarantees, they often overlook constant factors, memory behavior, and language-specific overheads that directly influence real-world performance. In this context, experimental evaluation becomes essential to bridge the gap between theoretical predictions and practical behavior. Furthermore, Python is widely used in academic and prototyping scenarios, yet its interpreted nature and reliance on high-level abstractions may impact the efficiency of data structure implementations. Therefore, analyzing how classical structures behave when implemented or simulated in Python contributes to a more realistic understanding of their applicability. This study is justified by the need to empirically assess these differences, providing insights that support more informed decisions in the selection of data structures for specific computational contexts, especially in environments where rapid development and ease of implementation are prioritized.