Adaptive Query Optimization in Distributed SQL Databases

Abstract

Query optimization is a critical component of modern database systems, particularly in distributed environments where latency, data replication, and heterogeneous resources introduce complexity. This paper examines adaptive query optimization techniques that respond to runtime conditions and feedback, emphasizing cost based analysis, real time execution statistics, and plan revisions. The study investigates how distributed SQL engines, including CockroachDB and Google Spanner, utilize feedback driven optimization to handle performance degradation caused by dynamic workloads, node failures, and data skew. Experimental evaluations show notable improvements in query response times and throughput when adaptive strategies are employed. This work contributes to the broader field of distributed database research by demonstrating how adaptive optimization enhances consistency, scalability, and efficiency in production environments.