What is the primary advantage of using Spark event logs?

Using Spark event logs primarily provides a valuable tool for troubleshooting issues by recording event details throughout the execution of Spark applications. These logs capture a wide array of events, including job start and end times, task failures, and executor memory usage among others, which can be instrumental in diagnosing problems that arise during data processing.

When an issue occurs, developers and data engineers can consult these logs to get a comprehensive view of the execution flow. This information helps to identify where the process may have deviated from expected performance or encountered errors, making it easier to resolve the underlying issues. By analyzing the logged events, it becomes possible to pinpoint bottlenecks or failures, leading to quicker resolution and improved application reliability.

Other options, while potentially beneficial in specific contexts, do not capture the primary utility of Spark event logs as effectively. For instance, while real-time job execution insight is important, the event logs themselves record completed events rather than offering real-time feedback. The other choices focus on aspects like configuration details or data lineage, which, while valuable, do not reflect the primary function of Spark event logs in the problem-solving context.