To identify performance bottlenecks within a specific operation like a JOIN, you need to see the "Execution Details" or the Execution Graph. This tool provides a visual representation of the query plan, showing each stage of the query (Input, Compute, Output) and where the most time or resources were consumed.
Why C is correct: The Query History (or Job History) in the BigQuery console allows you to select a completed job and view its Execution Graph. This graph explicitly breaks down performance by stage, showing wait times, read times, and compute times. It is specifically designed to identify "bottlenecks" such as Data Skew or Shuffle issues during JOIN operations.
Correcting other options:
A (Audit Logs): Cloud Logging provides high-level metadata (who ran the query, when, and how much it cost), but it does not provide the granular execution plan details required to diagnose a JOIN bottleneck.
B (INFORMATION_SCHEMA): While this view provides great historical data for overall job performance (like total bytes or duration), it is tabular and less efficient than the visual Execution Graph for quickly spotting which specific stage of a complex JOIN is slow.
D (Dry Run): A dry run only estimates the number of bytes that will be read and checks syntax. It does not actually execute the query or generate a performance plan based on real data distribution, so it cannot identify execution bottlenecks.
[Reference: Google Cloud Documentation on BigQuery Query Plan and Execution Graph:, "The execution graph provides a visual representation of the query plan... You can use the execution graph to help you understand how a query is executed and to identify performance issues. For example, you can use it to identify stages that take a long time to complete or that process a large amount of data." (Source: BigQuery query plan and timeline), "In the Google Cloud console, you can see the details of the query plan for a completed query by clicking the Execution graph tab in the Query results section... It displays information about each stage of the query, including the number of records read/written and the time spent." (Source: Monitor query performance), , , ]
Submit