AI in Dynamic Data Sharding for Optimized Performance in Large Databases

Abstract

Dynamic data sharding, the process of distributing a database across multiple servers to enhance performance and scalability, has gained significant attention with the increasing volume of data in large databases. Traditional sharding techniques often suffer from inefficiencies related to static partitioning, which can lead to load imbalances and bottlenecks. This paper presents an innovative approach that integrates artificial intelligence (AI) techniques to automate and optimize the dynamic sharding process. By leveraging machine learning algorithms, the proposed method dynamically adjusts the data distribution based on real-time workload patterns and usage trends. The experimental results demonstrate that AI-enhanced dynamic sharding significantly improves query response times, reduces latency, and increases overall system throughput compared to conventional methods. Additionally, the proposed approach minimizes the operational overhead associated with manual sharding configurations, enabling organizations to focus on core business objectives while ensuring optimal database performance. This study highlights the potential of AI to transform data management strategies in large-scale databases, providing a foundation for future research in automated database optimization.