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What are Spark Submit properties?

Spark/Big Datamedium0.4 min read

**Essential Properties**: `--master` (yarn, local, k8s), `--deploy-mode` (client/cluster), `--driver-memory`, `--executor-memory`, `--num-executors`, `--executor-cores`. `--conf` for fine-grained: `spark.sql.shuffle.partitions`, `spark.dynamicAllocation.enabled`. **Why They...

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Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
HCL
Key Concepts Tested
partitionpythonsparksql

Why This Question Matters

This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like HCL. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition, python, 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
89 words

Essential Properties: --master (yarn, local, k8s), --deploy-mode (client/cluster), --driver-memory, --executor-memory, --num-executors, --executor-cores. --conf for fine-grained: spark.sql.shuffle.partitions, spark.dynamicAllocation.enabled.

Why They Matter: Wrong memory = OOM or waste. Too few executors = underutilization. Too many = overhead. shuffle.partitions default (200) often wrong for large data.

Scalability Trade-offs: cluster deploy mode runs driver in cluster—resilient to client disconnect. Dynamic allocation scales executors by backlog; fixed num-executors simpler but less elastic.

Cost Implications: Over-provisioning wastes 20–40%. Start with --executor-memory 4g, --num-executors = (data_partitions / cores_per_executor). Use --packages for dependencies; --py-files for Python libs.

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