An Efficient Primary Indexing Method with Sibling Pointers for Large-Scale Database Systems

Efficient data retrieval is critical in modern database systems where data volumes are increasing. Although traditional indexing with B+ tree and bitmap indexes optimizes query performance, it introduces storage overhead, has inefficient mechanisms for handling duplicate keys, and proves challenging to scale. This study presents a new primary index approach that minimizes query execution time and eliminates extraneous lookups by adding sibling pointers, enabling efficient management and retrieval of duplicate keys. Based on a thorough experimental study utilizing a MySQL database of up to 10 million records, the proposed approach achieved significantly faster query execution times (up to 33.5%) and reduced storage overhead (up to 25%) compared to classical techniques. Τhe proposed method provides stable effectiveness regardless of query type and improves scalability over large databases, advancing the field of indexing techniques by providing a low-cost, scalable, and storage-friendly solution applicable to high-traffic workloads and very large datasets. Future directions include its use in distributed and cloud-based environments, with opportunities for improvement through adaptive and AI-driven indexing approaches.