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What is the difference between partitions and repartitions in Spark, and when do you use each?

Spark/Big Datamedium0.5 min readPremium

**partitionBy (write)**: Organizes output into directories by column (e.g., date). For read pruning. No shuffle during write if data already partitioned. **repartition(n)**: Redistributes into n partitions. Full shuffle. Use to increase parallelism or balance. **coalesce(n)**:...

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Asked at 1 company
Category
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questions in Spark/Big Data
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88E|81M|283H
in this category
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Asked at these companies
Coforge
Key Concepts Tested
partitionspark

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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 (partition, spark) will help you answer variations of this question confidently.

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Expert Answer
95 words

partitionBy (write): Organizes output into directories by column (e.g., date). For read pruning. No shuffle during write if data already partitioned.

repartition(n): Redistributes into n partitions. Full shuffle. Use to increase parallelism or balance.

coalesce(n): Reduces to n partitions. Minimal shuffle. Use before write to reduce files.

When: partitionBy for filtering by partition column. repartition before shuffle-heavy op or to fix skew. coalesce before write to control file count.

Scalability Trade-offs: repartition = expensive. coalesce = cheap. partitionBy = free if data ordered.

Cost Implications: Wrong partition count = small files (storage cost) or underutilization.

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