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Describe Spark's memory management model. How do you handle heap memory overhead issues?

Python/Codingmedium0.2 min readPremium

**Model:** Executor split—execution (shuffle, compute) + storage (cache). Off-heap for large. **OOM fixes:** More executor memory; fewer partitions; avoid collect; broadcast small joins; repartition. **Tune:** spark.memory.fraction, storageFraction. **Why:** GC and spill hurt...

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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
American Express
Key Concepts Tested
joinpartitionspark

Why This Question Matters

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

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Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.

Expert Answer
38 words

Model: Executor split—execution (shuffle, compute) + storage (cache). Off-heap for large. OOM fixes: More executor memory; fewer partitions; avoid collect; broadcast small joins; repartition. Tune: spark.memory.fraction, storageFraction. Why: GC and spill hurt performance. Monitor: GC logs; Spark UI.

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According to DataEngPrep.tech, this is one of the most frequently asked Python/Coding interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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