PostgreSQL: Unexpected behavior when using CTEs with large datasets
After trying multiple solutions online, I still can't figure this out. I'm trying to debug I'm deploying to production and I'm experiencing an issue with Common Table Expressions (CTEs) in PostgreSQL 13.3 that seems to degrade performance significantly when processing larger datasets. I've created a CTE to aggregate sales data, but when I run the query on a table with over 1 million rows, it takes much longer than expected. Here’s the CTE I’m using: ```sql WITH sales_summary AS ( SELECT store_id, SUM(amount) AS total_sales FROM sales WHERE sale_date BETWEEN '2023-01-01' AND '2023-12-31' GROUP BY store_id ) SELECT ss.store_id, ss.total_sales, s.store_name FROM sales_summary ss JOIN stores s ON ss.store_id = s.id; ``` When I run this query, it takes about 30 seconds to return results. However, if I break it down into two separate queries, one for the aggregation and another for the join, the total execution time drops to about 5 seconds: ```sql SELECT store_id, SUM(amount) AS total_sales FROM sales WHERE sale_date BETWEEN '2023-01-01' AND '2023-12-31' GROUP BY store_id; ``` Then: ```sql SELECT ss.store_id, ss.total_sales, s.store_name FROM (SELECT store_id, SUM(amount) AS total_sales FROM sales WHERE sale_date BETWEEN '2023-01-01' AND '2023-12-31' GROUP BY store_id) ss JOIN stores s ON ss.store_id = s.id; ``` I’ve tried adding indexes on both the `sale_date` and `store_id` fields, but it didn't seem to help. Additionally, I ran an `EXPLAIN ANALYZE` on both versions of the query, and the CTE shows a much higher cost with several nested loops, while the separate queries optimize better with a hash join. Is there a reason why using a CTE causes this performance issue with larger datasets? Are there optimization strategies I might be missing, or is using separate queries the best approach in this case? I recently upgraded to Sql LTS. Thanks in advance! Has anyone dealt with something similar?