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In the context of monitoring a Spark application within a Databricks environment, you are tasked with identifying and resolving executor-related issues to optimize performance. The application is critical for processing large datasets in real-time, and any delay could impact downstream analytics. Given the constraints of cost efficiency and the need for minimal downtime, which of the following steps would you take to accurately identify executor-related issues using the Spark UI, and what would be your immediate action to mitigate these issues? Choose the best option.
A
Navigate to the 'Storage' tab to analyze the data distribution across executors, assuming uneven data distribution is the root cause of performance issues.
B
Inspect the 'Executors' tab to identify executors with high memory usage or low task execution rates, which may indicate performance bottlenecks, and consider reallocating resources or adjusting the executor configuration.
C
Review the 'Environment' tab to check for any misconfigurations in executor settings that could be causing the issues, and then restart the affected executors.
D
Examine the 'Jobs' tab to look for executors processing an unusually high number of tasks, suggesting an imbalance, and then manually redistribute the tasks among executors.