Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Why It Matters (Architectural Logic)**: Executor sizing directly impacts job runtime and cost. Over-provision = waste; under-provision = OOM and retries. Balance parallelism vs. overhead. Reserve ~1 executor per worker for OS/daemons. Per node: ~100GB RAM, 25 cores. Typical:...
This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Meesho. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (spark) will help you answer variations of this question confidently.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.
Why It Matters (Architectural Logic): Executor sizing directly impacts job runtime and cost. Over-provision = waste; under-provision = OOM and retries. Balance parallelism vs. overhead.
Reserve ~1 executor per worker for OS/daemons. Per node: ~100GB RAM, 25 cores. Typical: 1 core = 1 executor. Executors per node: 4-5 (each ~4-5 cores, ~16-20GB). Leave 1 executor for driver/YARN. Example: 10 workers × 4 executors = 40 executors. Each executor: 4 cores, 16GB (4GB overhead). Formula: executor_cores = 4, executor_memory = 16g, num_executors = 10 * 4. Consider: executor memory overhead (~10%); shuffle/networking; avoid too many small executors (overhead). Tune via spark.executor.cores, spark.executor.memory, spark.executor.instances.
Scalability Trade-offs: 4-5 executors per node typical; leave 1 for driver. Executor memory overhead ~10%. Too many small executors = scheduling overhead.
Cost Implications: Right-sizing can cut cluster cost 20-40%. Dynamic allocation scales to zero when idle.
Pro-Move: 4-5 executors per node; leave overhead for shuffle. Red Flag: 1 executor per node—underutilization.
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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.