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What role does the executor heap size play in preventing OOM errors?

Python/Codingmedium0.4 min read

**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...

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Frequency
Low
Asked at 1 company
Category
179
questions in Python/Coding
Difficulty Split
127E|24M|28H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
PWC
Key Concepts Tested
partitionpythonspark

Why This Question Matters

This medium-level Python/Coding question appears frequently in data engineering interviews at companies like PWC. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition, python, spark) will help you answer variations of this question confidently.

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Expert Answer
88 words

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. Too few = underutilization; too many = overhead. Sweet spot: 4–8 cores, 8–16GB per executor. Monitor Spark UI for spill and GC.

spark.executor.memory=8g
spark.executor.memoryOverhead=2g

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