Sharding in Distributed Databases: Comparative Analysis of Horizontal, Vertical, and Range-Based Sharding Techniques

Authors

Surbhi Kanthed

Abstract

The surge in data volume and velocity has propelled the adoption of distributed databases as critical infrastructures for scalable, fault-tolerant, and high-performance data management.
Among the key strategies enabling these architectures is sharding—the process of partitioning large datasets into smaller, more manageable units (shards). This white paper provides an in-depth examination of three principal sharding techniques—horizontal, vertical, and range-based sharding—delving into their underlying mechanisms, comparative benefits, trade-offs, and real-world applicability. We weave together foundational theories, contemporary implementations, and novel research insights. Furthermore, we propose an advanced framework that addresses pervasive sharding challenges such as dynamic load balancing, multi-tenant management, and automated re-sharding. By discussing practical implementation details, performance considerations, and compliance requirements, this paper aspires to offer a comprehensive resource for database practitioners and researchers aiming to design or refine sharding strategies in modern distributed systems. Finally, we chart possible future research directions, underscoring the enduring importance of sharding in shaping next-generation data platforms.

Keywords

Sharding in Distributed Databases: Comparative Analysis of Horizontal, Vertical, and Range-Based Sharding Techniques. Surbhi Kanthed. 2023. IJIRCT, Volume 9, Issue 2. Pages 1-17. https://www.ijirct.org/viewPaper.php?paperId=2503032