What would be your first action to take when you encounter an out-of-memory issue in Spark?

When an out-of-memory (OOM) error occurs in Spark, the first step is to identify where the memory pressure is coming from. Check the executor logs for the exact exception and look at the executor UI to see memory usage. Common culprits are large shuffle files, excessive caching, or a skewed dataset that forces a single executor to hold too much data. Once you pinpoint the source, you can take corrective actions: reduce the size of the shuffle by using broadcast joins or partitioning, persist only necessary RDDs, or adjust the memory fraction (spark.memory.fraction) and storage fraction (spark.memory.storageFraction). If the issue is due to insufficient executor memory, increase spark.executor.memory or enable dynamic allocation. Finally, consider using off‑heap memory (spark.memory.offHeap.enabled) for large data structures. The key is to monitor, isolate the root cause, and then adjust configuration or code accordingly.