GCP BigQuery query performance implementing large datasets and multiple joins
I need some guidance on I'm relatively new to this, so bear with me. I am experiencing important performance optimization when running a query in Google BigQuery that involves multiple joins across large datasets. My query looks something like this: ```sql SELECT a.id, b.name, c.value FROM `my_project.dataset_a` AS a JOIN `my_project.dataset_b` AS b ON a.ref_id = b.id JOIN `my_project.dataset_c` AS c ON b.id = c.ref_id WHERE a.date > '2023-01-01' AND c.category = 'sales'; ``` The datasets involved are quite large, with `dataset_a` containing over 50 million rows and `dataset_b` around 30 million. I am noticing that even with the `WHERE` clause, the query takes several minutes to execute. I've also tried using `EXPLAIN` to analyze the query, and it shows that the join operations are the main bottleneck. To optimize, I've implemented partitioning and clustering on `dataset_a` and `dataset_b`, but the performance hasn't improved significantly. I also experimented with using `WITH` clauses to simplify the query logic, but that didn't help much either. I would appreciate any suggestions on optimizing this query further. Are there specific best practices for handling large joins in BigQuery? Additionally, how can I leverage materialized views or other features to improve performance? Any insights or examples would be greatly appreciated! I'm working on a application that needs to handle this. The project is a web app built with Sql. Any advice would be much appreciated.