Optimizing BigQuery Query Performance for Complex Joins in GCP Architecture
I've been struggling with this for a few days now and could really use some help. Building an application that relies on Google BigQuery for analytical reporting, I've noticed performance issues with queries that involve complex joins across several large tables. The architecture is designed to pull data from multiple sources, and while the results are accurate, execution times often exceed acceptable limits. I’ve implemented partitioning on some of the larger tables, which has helped reduce query times slightly. However, the joins still perform poorly, especially with the following query: ```sql SELECT a.id, b.name, c.amount FROM `my_project.dataset.table_a` AS a JOIN `my_project.dataset.table_b` AS b ON a.ref_id = b.id JOIN `my_project.dataset.table_c` AS c ON b.id = c.ref_id WHERE a.date BETWEEN '2023-01-01' AND '2023-12-31'; ``` To further enhance performance, I've tried the following approaches: 1. **Materialized Views**: I created a materialized view to pre-aggregate data from `table_b` and `table_c`, but the refresh latency introduced delays when querying fresh data. 2. **Clustering**: Implemented clustering on `ref_id` in the join tables. While this has shown promise, the performance gains aren’t as significant as I had hoped. 3. **Query Execution Plan**: By examining the execution plan, I discovered that the join order was affecting performance. Adjusting the order based on estimated data sizes seemed to yield modest improvements. Despite these efforts, I am still searching for best practices that could lead to more substantial performance increases. Would denormalization be a viable option for my situation? Or are there alternative strategies in managing large datasets effectively in BigQuery? Any insights or recommendations would be greatly appreciated!