Optimizing Query Performance with Adaptive Indexing in Distributed Systems Using Azure Data Explorer

This paper presents a novel adaptive indexing framework designed to optimize query performance in distributed systems using Azure Data Explorer (ADX). As organizations increasingly rely on doud-based analytics for processing large-scale datasets, traditional static indexing approaches often fall short in handling dynamic query patterns and evolving data characteristics. The proposed framework leverages machine learning techniques to analyze historical and real-time query patterns, automatically adjusting indexing strategies to improve query efficiency. The architecture implements query-driven indexing, adaptive materialized views, and incremental indexing mechanisms, integrated seamlessly with ADX’s distributed environment. Experimental results, conducted on datasets ranging from 100GB to 1TB, demonstrate significant improvements in query performance metrics. Compared to traditional indexing methods, the adaptive framework achieved up to 54% reduction in query execution time, 27% decrease in CPU utilization, and 35% reduction in memory consumption. Statistical analysis confirms the significance of these improvements (p < 0.05). The framework’s effectiveness in optimizing resource utilization while maintaining query performance makes it particularly suitable for high-throughput, real-time analytics applications in cloud-based distributed systems.