Debugging Slow SQL Queries Affecting Machine Learning Model Training Time in SQL Server
Does anyone know how to During development of a predictive analytics model using Python and SQL Server 2019, I've encountered a significant slowdown in data retrieval that directly impacts my training cycle..... My SQL queries, designed to pull data from multiple related tables, seem to take excessively long to execute. Here’s a sample SQL statement that I've been working with: ```sql SELECT t1.feature1, t2.feature2, t3.target FROM table1 t1 JOIN table2 t2 ON t1.id = t2.foreign_id JOIN table3 t3 ON t1.id = t3.foreign_id WHERE t1.date >= '2023-01-01' AND t1.date < '2023-10-01'; ``` After profiling the queries, I found that the execution plan suggests a lack of appropriate indexes. To address this, I created indexes on the foreign keys as follows: ```sql CREATE INDEX idx_table1_id ON table1(id); CREATE INDEX idx_table2_foreign_id ON table2(foreign_id); CREATE INDEX idx_table3_foreign_id ON table3(foreign_id); ``` Despite these changes, the performance hasn’t improved as expected. I’ve also tried to analyze the execution plan further, hoping to identify bottlenecks. One point of concern is that the query plan shows a lot of scans rather than seeks. I've even considered breaking the query into smaller parts to load intermediate results into temporary tables, but this adds complexity and doesn’t seem effective either. The challenge lies not only in optimizing the query but also ensuring that data retrieval is efficient enough to keep up with the iterative cycles of model training—where even a few seconds can cascade into hours of wasted time. Any insights into how to better structure these queries or additional SQL Server configurations that might enhance performance would be greatly appreciated. Additionally, if there are best practices around data retrieval for machine learning workloads in SQL Server that could apply here, I’d love to hear about those too. Any feedback is welcome!