Reviewed by Aditya Kumar · Last reviewed 2026-03-24
Executor memory holds: **(1)** Cached RDD/DataFrame partitions (storage fraction), **(2)** Shuffle output written by map tasks for reduce tasks, **(3)** Working memory for task execution (joins, aggregations, sorting), **(4)** Off-heap (e.g., for native operations, Tungsten)....
This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like Altimetrik, Infosys. 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.
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.
Executor memory holds: (1) Cached RDD/DataFrame partitions (storage fraction), (2) Shuffle output written by map tasks for reduce tasks, (3) Working memory for task execution (joins, aggregations, sorting), (4) Off-heap (e.g., for native operations, Tungsten). Why it matters: Execution memory competes with storage; undersized executors cause spills and OOM. Scalability trade-off: More memory per executor → fewer executors for same cluster RAM; trade-off between parallelism and per-task memory. Small executors (e.g., 2GB) limit shuffle buffer; large executors (e.g., 32GB) may waste memory and hurt scheduling. Cost implication: OOM causes job retries and wasted compute; spills add disk I/O. Tune spark.executor.memory, spark.memory.fraction, spark.memory.storageFraction, and spark.executor.memoryOverhead (for off-heap). Best practice: Leave headroom for OS; monitor executor memory in Spark UI; align partition count with parallelism.
Red Flag: 'We use 64GB executors everywhere.' Pro-Move: 'I profile with Spark UI, set memoryOverhead for off-heap, and size executors so shuffle and cache fit without spill; we A/B test executor size vs count for cost.'
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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 2 companies. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.