Effects of Data Skew on Index Selectivity Estimation in Oracle Databases

Abstract

Index selectivity plays a central role in Oracle’s cost-based optimization process, yet it becomes highly unreliable when data distributions exhibit Zipfian or power-law characteristics. In such cases, a small number of high-frequency values distort the optimizer’s cardinality estimates, leading to inefficient index range scans, unstable execution plans, and inconsistent query performance. This study examines how selectivity distortion emerges under non-uniform value frequencies, evaluates the limitations of histogram-based statistical modeling, and analyzes the conditions under which adaptive cursor sharing and parameter-sensitivity detection can stabilize plan behavior. The results show that no single tuning feature is sufficient; performance stability requires coordinated alignment between statistics maintenance, workload predictability, and physical data organization. When data lifecycle monitoring and query structure governance are applied consistently, index performance becomes more robust, even in highly skewed enterprise environments.