Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Driver OOM**: (1) `collect()` on large DF. (2) Schema inference on huge file. (3) Large broadcast. Fix: Avoid collect; use limit or write+read. Provide schema. Reduce broadcast threshold. **Executor OOM**: (1) Data skew—one partition huge. (2) Too few partitions; each too...
This medium-level Spark/Big Data 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, spark) will help you answer variations of this question confidently.
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.
Driver OOM: (1) collect() on large DF. (2) Schema inference on huge file. (3) Large broadcast. Fix: Avoid collect; use limit or write+read. Provide schema. Reduce broadcast threshold.
Executor OOM: (1) Data skew—one partition huge. (2) Too few partitions; each too large. (3) Spill disabled or disk full. Fix: Salting; repartition; enable spill; increase partitions.
Why It Happens: Memory < data per partition. Skew puts 80% data in 5% of partitions.
Scalability Trade-offs: Larger executor = more memory but slower GC. Many small executors = more overhead. 4–8 cores, 8–16GB typical.
Cost Implications: OOM causes retries and wasted compute. Right-size from start; monitor Spark UI.
Pro-Move: 'We set spark.driver.maxResultSize; fail fast rather than OOM.' Red Flag: Increasing memory without fixing skew—OOM returns.
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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.