Published Paper PDF: https://ijrmeet.org/wp-content/uploads/2025/06/IJRMEET0625130027_Sharded%20Database%20Architectures%20for%20Real-Time%20Analytical%20Workloads.pdf
DOI: https://doi.org/10.63345/ijrmeet.org.v13.i6.2
Niharika Singh
ABES Engineering College
Crossing Republic, Ghaziabad, India 201009
Abstract
Sharded database architectures have emerged as a pivotal strategy to manage the growing demand for real-time analytical workloads in large-scale distributed systems. By partitioning data across multiple nodes (shards), systems can achieve horizontal scalability, enhanced throughput, and reduced query latencies. This manuscript investigates the design principles, implementation strategies, and performance implications of sharded database systems tailored to real-time analytical use cases. We begin by outlining the motivations for sharding in analytical contexts, followed by a comprehensive literature review addressing existing sharding methodologies, consistency models, and query-routing mechanisms. Our methodology details a combined statistical and simulation-based approach to evaluate sharded architectures under varying load conditions and partitioning schemes. A statistical analysis section presents a comparative table showcasing key performance metrics—throughput, latency, and resource utilization—across different shard configurations. The simulation research section describes our experimental testbed, workload generation process, and the metrics captured. Results demonstrate how optimal shard key selection, balanced data distribution, and adaptive query routing can significantly improve performance in real-time analytical environments. Finally, the conclusion synthesizes insights on best practices for designing sharded databases, highlights trade-offs between consistency and availability, and suggests avenues for future research.
Keywords
Sharded database architectures, real-time analytics, horizontal scalability, distributed systems, partitioning schemes
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