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What are the key performance tuning techniques you apply in Spark jobs to improve performance?

Spark/Big Datamedium0.4 min read

**Partitioning**: repartition by join key before shuffle; coalesce before write. Match partition count to 2–4x cores. **Broadcast**: Small tables < 100MB; no shuffle. **Cache**: Reused DFs; unpersist when done. **Predicate/Column Pruning**: Filter and select early; push to...

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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
Coforge
Key Concepts Tested
joinpartitionspark

Why This Question Matters

This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like Coforge. 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.

How to Approach This

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
88 words

Partitioning: repartition by join key before shuffle; coalesce before write. Match partition count to 2–4x cores.

Broadcast: Small tables < 100MB; no shuffle.

Cache: Reused DFs; unpersist when done.

Predicate/Column Pruning: Filter and select early; push to source.

AQE: Enable; coalesce, skew join, broadcast switch.

Salting: For skewed keys.

Avoid UDFs: Built-in or pandas_udf.

Executor Tuning: 4–8 cores, 8–16GB; avoid huge executors (GC).

Scalability Trade-offs: Each technique has limits; combine for best effect. Profile first.

Cost Implications: 2–5x speedup = 50–80% cost reduction. Order: partitioning, broadcast, then cache.

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