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You are given 10 worker machines with 100 GB RAM and 25 CPU cores. How would you determine the number of executors and the size of each executor?

Spark/Big Dataeasy0.7 min readPremium

**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:...

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Meesho
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Expert Answer
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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.

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