Year: 2025
Type: article
Abstract: Data sharding and partitioning have emerged as essential techniques for managing the exponential growth of information in enterprise systems. As traditional database architectures reach their limits, these distribution strategies enable organizations to effectively scale horizontally across multiple machines while maintaining performance and reliability. This article demystifies the fundamental concepts of sharding and partitioning, distinguishing between horizontal and vertical approaches while examining their respective benefits for scalability, performance, and fault tolerance. Various partitioning strategies—including range-based, hash-based, and directory-based methodologies—are explored in detail, highlighting how each addresses different workload patterns and data characteristics. The discussion extends to critical implementation considerations such as hot spot prevention, partition growth management, cross-partition transactions, and consistency maintenance. Through examination of real-world applications across relational, NoSQL, and NewSQL database paradigms, the article provides practical insights into how partitioning decisions influence system behavior in production environments.
Author: Anish Agarwal
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411219587