**Why Heap Matters:** Executor runs tasks; each task uses JVM heap. OOM when task + overhead exceeds spark.executor.memory. memoryOverhead (Python, off-heap) is separate—both count toward container limit.
**Tuning:** Increase executor.memory for large shuffles. Rule: (cluster memory / num executors) - overhead. Also: more partitions (smaller per-task), avoid collect(), use broadcast for small tables, cache selectively.
**Cost:** Larger executors = fewer executors for fixed cluster....
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