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What do you understand by data shuffling in Spark? Why is it important?

Spark/Big Datamedium0.5 min readPremium

**Shuffle**: Redistribution of data across partitions so that rows with same key land on same partition. Required for groupBy, join, distinct. **Why Important**: Enables global aggregations and joins. But **expensive**—network I/O, serialization, disk (if spill). Often the...

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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
Freecharge
Key Concepts Tested
joinpartitionspark

Why This Question Matters

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

Shuffle: Redistribution of data across partitions so that rows with same key land on same partition. Required for groupBy, join, distinct.

Why Important: Enables global aggregations and joins. But expensive—network I/O, serialization, disk (if spill). Often the bottleneck.

Minimize: (1) Broadcast small tables. (2) Column pruning before shuffle. (3) Partition by key when writing. (4) Filter before shuffle. (5) Coalesce after filter. (6) Salting for skew.

Scalability Trade-offs: Shuffle scales with data volume and partition count. Network bandwidth limits throughput.

Cost Implications: Shuffle-heavy jobs = long runtime, high cost. Reducing shuffle bytes = proportional savings.

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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 a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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