SQL Server performance issues are a common pain point for database administrators. One of my most challenging scenarios occurred after deploying a financial analytics database update. Reports that previously ran in less than 3 minutes suddenly ballooned to over 20 minutes. Key stored procedures started underperforming, and CPU usage spiked to critical levels during peak workloads. Through careful investigation, I identified query regressions caused by outdated execution plans and parameter sniffing. Instead of applying temporary fixes, I turned to Query Store and Intelligent Query Processing (IQP) to develop a sustainable, long-term solution. This article provides step-by-step instructions for using these tools, including practical examples, my exact investigation process, configuration changes, benchmark results before and after optimizations, and how these changes improved overall performance and stabilized the production environment.
Performance Issue Investigation: Observing Query Regressions
The performance degradation stemmed from new internal processes introduced into the application, which altered data patterns. Parameter sniffing a common issue where SQL Server cached an execution plan optimized for specific parameters but reused it for parameters with drastically different data distributions caused previously fast queries to slow down. To pinpoint the bottleneck, I queried the sys.dm_exec_requests and sys.dm_exec_query_stats views, which revealed certain stored procedures with much higher CPU and runtime durations than they had before. For example, running the following query helped me confirm which plans were underperforming: