Database Sharding and Partitioning in Spring Boot Applications: Strategies for Scalability and Performance

As applications grow in scale, managing large databases becomes increasingly challenging.Database sharding and partitioning are key strategies employed to distribute data across multiple nodes, thus improving scalability and performance.This paper investigates the implementation of database sharding and partitioning in Spring Boot applications.It explores the trade-offs between vertical and horizontal sharding, evaluates their impact on system design, and presents a comparative analysis of different sharding strategies.The study also examines the effects of sharding on query performance, data consistency, and system complexity, providing mathematical models where applicable.The findings offer valuable insights for developers and architects aiming to optimize large-scale Spring Boot applications.