GCP BigQuery Job scenarios with 'Too Large to Transfer' scenarios on Large Dataset
I'm migrating some code and I've tried everything I can think of but I'm learning this framework and I've been banging my head against this for hours....... I'm relatively new to this, so bear with me. I'm running a query in BigQuery that processes a large dataset of about 1.5 TB, but I'm working with a `Job failed because the result was too large to transfer` behavior. I've optimized the query to minimize the amount of data being returned, and I'm using the `SELECT` statement only to fetch the necessary columns. Here’s a snippet of the query I'm running: ```sql SELECT column_a, column_b FROM `my_project.my_dataset.my_table` WHERE condition = 'value' ORDER BY column_a LIMIT 10000; ``` Despite limiting the rows returned, the dataset is still too large. I've tried breaking the query into smaller chunks by adding a `WHERE` clause that segments the data based on date ranges, but I still face the same behavior. My current approach looks something like this: ```sql SELECT column_a, column_b FROM `my_project.my_dataset.my_table` WHERE date_column >= '2023-01-01' AND date_column < '2023-01-02' ORDER BY column_a LIMIT 10000; ``` I’ve also checked the quota limits for my BigQuery project, and it doesn't seem to be an scenario there. I am using the `google-cloud-bigquery` client library (version 2.26.0 in Python) to run this query, and I'm calling `job.result()` to wait for the job to finish. The jobs are being triggered as part of a Cloud Function and seem to be timing out as well. Could anyone suggest a way to handle large datasets effectively in BigQuery, or is there a configuration I might be overlooking that prevents this behavior? Also, is there a practical way to paginate through the results? Any advice or alternate methods would be greatly appreciated. My development environment is macOS. Has anyone else encountered this? Thanks in advance! I'd really appreciate any guidance on this. This issue appeared after updating to Python latest. I'm working in a Windows 11 environment. Thanks for your help in advance!