Elevating Data Throughput in Distributed Key-Value Systems with Data Distribution

Abstract

A distributed system is a collection of independent computers that appears to its users as a single coherent system. These systems are designed to improve performance, reliability, availability, and scalability by distributing workloads across multiple nodes and are widely used in modern applications such as databases, search engines, cloud services, and web platforms. One key architectural strategy in distributed systems is sharding, or data partitioning, which involves splitting data into smaller pieces and distributing them across multiple nodes. This allows systems to scale horizontally, improving performance as more nodes are added. Without sharding, several issues emerge. Scalability becomes a major bottleneck as all data resides in a single logical unit, making it difficult to manage increasing traffic or data volume. Hotspots and load imbalances occur when a few nodes handle most of the requests, leading to resource strain and inefficiencies. A non-sharded system also introduces a single point of failure—if the central node fails, the entire system may be disrupted. Additionally, performance deteriorates due to increased latency caused by larger data indexes and more complex queries. Maintenance tasks such as backups or schema migrations also become more difficult and time-consuming in monolithic datasets. Furthermore, such systems lack the ability to leverage parallelism across nodes, reducing throughput and responsiveness under concurrent load. In summary, not using sharding in distributed systems results in degraded performance, poor scalability, and higher operational risks, whereas sharding enables better fault isolation, load distribution, and elastic growth. A distributed system connects multiple computers to function as a single, unified system, enabling scalability and high availability. Without sharding—dividing data across nodes—such systems face significant challenges. A non-sharded setup can lead to scalability limits, performance bottlenecks, and increased latency as data volume grows. It may also create hotspots, where a few nodes handle most of the load, and introduce a single point of failure. Maintenance becomes complex, and parallelism is underutilized. Sharding addresses these issues by distributing data and load evenly, improving throughput, fault tolerance, and operational efficiency, making it essential for modern, large-scale distributed architectures.