Answer: The Stage’s detail screen and the Executor’s log files.

### Primary Indicators of Spark Spilling Spilling occurs when Spark is forced to move data from memory to disk during a shuffle or sort. You can identify this in two main places: 1. **Stage Detail Screen**: Navigating to the **Stages** tab and selecting a specific **Stage ID** will reveal the **Shuffle metrics** section. If spilling occurs, two specific columns appear: - **Shuffle spill (memory)**: The size of the data in memory before it was spilled. - **Shuffle spill (disk)**: The size of the data once serialized and written to disk. 2. **Executor Log Files**: By accessing the **Executors** tab and viewing the `stdout` or `stderr` logs, you can find explicit log entries from the `UnsafeExternalSorter` (e.g., `Spilling data because...`). These entries provide task-level confirmation that memory limits were exceeded. ### Why other options are incorrect: - **Query Detail Screen**: While useful for viewing the physical and logical plans, the SQL tab does not provide the specific low-level shuffle spill counters found in the Stage detail view. - **Driver Log Files**: Spilling is an operation performed by Executors. Consequently, the Driver logs do not record these executor-level memory management events. - **Executor Summary Screen**: Although the main Executor tab shows aggregate metrics, it lacks the granular, per-stage shuffle spill metrics necessary for detailed debugging.

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