How to Programmatically Get Counts of Running BigQuery Jobs and Their States скачать в хорошем качестве

How to Programmatically Get Counts of Running BigQuery Jobs and Their States

Learn how to retrieve counts of all running queries and their states in Google BigQuery programmatically using Python and the BigQuery client library. --- This video is based on the question https://stackoverflow.com/q/79189667/ asked by the user 'FrustratedWithFormsDesigner' ( https://stackoverflow.com/u/192801/ ) and on the answer https://stackoverflow.com/a/79440565/ provided by the user 'FrustratedWithFormsDesigner' ( https://stackoverflow.com/u/192801/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How to get counts on all queries running in BigQuery Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to drop me a comment under this video. --- Introduction Google BigQuery provides a rich interface to monitor query jobs via its UI, but getting these insights programmatically can be challenging. Many users want to track how many queries are running, their statuses, and details like priority or user email. If you're only seeing jobs from your user or incomplete information when querying INFORMATION_SCHEMA or using BigQuery API, this guide explains how to get a comprehensive and accurate count of running jobs by state. Why Your Initial SQL Query Shows Limited Results The INFORMATION_SCHEMA.JOBS view scoped to your project and region only shows jobs you have permission to see. By default, this means jobs you own. Even if the UI shows jobs from multiple users or service accounts, the SQL query doesn't list those without proper IAM permissions. Therefore, querying the jobs metadata via SQL often won't reflect all activity unless you have admin-level permissions. Best Approach: Using BigQuery Python Client with all_users=True BigQuery's Python client library allows retrieving job metadata programmatically. To capture all jobs across users within your project, pass all_users=True to the list_jobs() call. This requires appropriate IAM permissions (such as bigquery.jobs.listAll or admin roles). Here's an example function that: Lists jobs in the last 5 minutes Counts jobs by their state Tracks counts by job priority and user email [[See Video to Reveal this Text or Code Snippet]] Notes: Use datetime.utcnow() instead of datetime.now() for consistency with BigQuery timestamps. Ensure your service account or user has sufficient IAM permissions to list all jobs. Adjust the time window (timedelta) as needed. Summary Querying INFORMATION_SCHEMA.JOBS SQL views shows only jobs you can access, typically your own. Use the BigQuery API with all_users=True to retrieve jobs submitted by all users in your project. Python's BigQuery client provides an intuitive way to programmatically analyze job states, priorities, and user distribution. Have proper permissions set to access all jobs’ metadata. This approach helps automate monitoring and reporting on BigQuery job activity across your organization.