Diya Kapoor
Independent Researcher
India
Abstract
Efficient data retrieval in distributed databases remains a critical challenge due to data distribution across multiple nodes, network latency, and potential load imbalance. This paper explores the application of hashing techniques to enhance retrieval speed and balance query loads in distributed database systems. We analyze different hashing methods, including consistent hashing and static hashing, for their impact on lookup times and system scalability. A comparative statistical study of retrieval latency and load distribution is presented. Experimental results indicate that consistent hashing significantly improves data retrieval efficiency by minimizing rehashing overhead and balancing query loads, especially in dynamic distributed environments. The study concludes that hashing-based data partitioning strategies offer scalable solutions for improving distributed database performance.
Keywords
Distributed databases, data retrieval efficiency, hashing techniques, consistent hashing, data partitioning, load balancing.
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