I noticed that when setting up a scheduled query in BigQuery, two types of jobs are executed from a single scheduled query.
One is scheduled_query_ and the other is script_job_.
When I checked the total_bytes_billed in INFORMATION_SCHEMA.JOBS_BY_PROJECT, both scheduled_query_ and script_job_ had total_bytes_billed values of 1000 (for example).
I’m curious if this means that every time this scheduled query runs, a total of 2000 bytes are billed.
Yes, that is correct. When analyzing the total_bytes_billed in the INFORMATION_SCHEMA.JOBS_BY_PROJECT table within Google Cloud BigQuery, it represents the bytes processed for each individual job. When setting up a scheduled query, two types of jobs are typically executed: scheduled_query_ and script_job_. If both of these jobs show total_bytes_billed values of 1000 bytes each, it signifies that for each execution of the scheduled query, you are billed for a total of 2000 bytes.
The scheduled_query_ job is responsible for managing and orchestrating the scheduled execution of your query. This job ensures that the query runs at the predefined intervals, handling the scheduling and any related administrative tasks. On the other hand, the script_job_ job corresponds to the actual execution of the script or query itself. This is the stage where the main data processing occurs, and hence, it also incurs billing costs based on the data it processes.
Understanding how billing works in BigQuery is important for managing and optimizing costs. BigQuery’s pricing model is primarily based on the volume of data processed by your queries. The total_bytes_billed metric reflects the cost associated with this data processing. Therefore, each job contributes to your overall BigQuery bill according to the number of bytes it processes. In your specific case, since both the scheduled_query_ and script_job_ jobs process 1000 bytes each, you end up being billed for a total of 2000 bytes per run of the scheduled query.
If you are concerned about the costs associated with your queries, it is essential to explore optimization strategies. Effective query optimization can significantly reduce the amount of data processed, leading to lower costs. One approach is to refine your SQL queries to be more efficient, possibly by minimizing the use of complex joins or aggregates. Additionally, focusing on data filtering is crucial; ensure that your queries are designed to process only the data you actually need, thereby avoiding unnecessary data scans. Another valuable strategy is caching; if your queries are repetitive, consider caching the results to prevent reprocessing the same data multiple times.
By implementing these optimization techniques, you can reduce the data processed by your queries and, consequently, lower your BigQuery costs. Understanding the billing implications of each job and proactively optimizing your queries can lead to significant cost savings and more efficient data processing.
Thanks for the above answer @ms4446
What is the formula to calculate the cost if total bytes billed is given