👀 Views: 0 💬 Answers: 1 📅 Created: 2025-07-24

gcp bigquery performance sql

I've looked through the documentation and I'm still confused about I'm testing a new approach and I've been banging my head against this for hours. I'm performance testing and I've encountered a strange issue with I'm working with a timeout scenario when trying to run a query in BigQuery that involves joining two partitioned tables. The query looks like this: ```sql SELECT a.id, b.value FROM `my_project.my_dataset.partitioned_table_a` AS a JOIN `my_project.my_dataset.partitioned_table_b` AS b ON a.id = b.id WHERE a.partition_date = '2023-10-01' AND b.partition_date = '2023-10-01' ``` When I execute this query, it consistently times out with the following behavior message: ``` behavior: Query job failed. behavior: Query exceeded maximum execution time. ``` I have tried optimizing the query by adding specific partition filters, but it seems like the question continues. The tables are partitioned by the `partition_date` field, and I've made sure that both tables have adequate clustering on the `id` column to speed up the join operation. Additionally, I verified that the total data processed is within limits for my billing plan, yet the timeout occurs after approximately 10 minutes of execution. I've also experimented with breaking down the query into smaller chunks and running them separately, but that approach has increased the complexity of my processing logic. I considered increasing the `maxBytesBilled` setting, but I'm not sure if that will address the timeout scenario without incurring additional costs. Does anyone have suggestions on how to troubleshoot or optimize this query to avoid the timeout? Are there specific best practices for working with partitioned tables in BigQuery that I might be overlooking? I recently upgraded to Sql latest. For context: I'm using Sql on Ubuntu 22.04.