Efficient Indexing and Retrieval Mechanisms for Large-Scale Databases

Abstract

Efficient indexing and retrieval are pivotal in managing large-scale databases, enabling fast response times amid exponential data growth. This paper examines traditional and advanced indexing mechanisms—such as B-trees, B+-trees, ISAM, bitmap indexes, block range indexes (BRIN), and emerging learned indexes—emphasizing their roles in high-performance data access for expansive datasets. We outline their structural features, operational complexities, and retrieval efficiencies within the context of large-scale database systems.

Our research methodology involves a literature-based comparative analysis, using theoretical evaluation of computational complexity, storage overhead, and performance implications drawn from seminal works pre-2019. We also review practical observations from database implementations and benchmark reports.

Key findings indicate that B-trees and B+-trees provide logarithmic lookup performance and strong support for range queries, serving as foundational structures in many systems. ISAM offers simpler indexed sequential access but suffers from maintenance issues due to overflow chaining . Bitmap indexes optimize retrieval for low-cardinality and complex queries using compact bitwise operations . BRIN indexes are lightweight yet highly efficient for very large, ordered datasets by summarizing block-level data . Pre-2019 frameworks also laid conceptual groundwork for learned indexes, which use model-based structures to outperform traditional B-trees in speed and memory efficiency .

Advantages include fast lookup performance, support for range scanning, and optimized space utilization. Conversely, disadvantages encompass overhead in index maintenance, inefficiencies under specific workloads, or limitations in adaptability. Results and discussion compare trade-offs across index types given query patterns and data distributions. The paper concludes by highlighting that no single structure suffices for all workloads; a hybrid or adaptive indexing strategy is often optimal. Future work could explore integrating learned models into indexing ecosystems, adapting indexing dynamically based on workload profiles, and extending indexing to high-dimensional or metric spaces.