OPTIMIZING QUERY RESPONSE TIMES IN BIG DATA: A SURVEY OF INDEXING STRATEGIES IN DISTRIBUTED DATABASES

Abstract

Distributed database systems support large-scale data management across geographically distributed clusters, where query performance is significantly influenced by data placement strategies, communication overhead, and workload imbalance. Efficient indexing plays a critical role in mitigating these limitations. This paper presents a systematic survey of indexing techniques for distributed data platforms, encompassing tree-based, hash-based, spatial, and hybrid indexing approaches employed in modern architectures. A structured taxonomy is introduced to categorize existing methods, and comparative performance analyses from representative benchmarks are synthesized, demonstrating substantial reductions in query latency. The survey further examines key challenges, including index maintenance, consistency, and adaptability in dynamic environments. Finally, emerging research directions are discussed, with particular emphasis on intelligent and adaptive indexing mechanisms driven by learning-based techniques.