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What are the key differences between Map and Reduce in Spark?

Spark/Big Datamedium0.4 min read

**Map**: 1:1 transformation. Each input produces one output. Narrow dependency—no shuffle. Examples: `map`, `filter`, `flatMap`. **Reduce**: N:1 aggregation. Combines elements; may require shuffle for global aggregation. Wide dependency. Examples: `reduce`, `reduceByKey`,...

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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
Nielsen
Key Concepts Tested
partitionspark

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Expert Answer
79 words

Map: 1:1 transformation. Each input produces one output. Narrow dependency—no shuffle. Examples: map, filter, flatMap.

Reduce: N:1 aggregation. Combines elements; may require shuffle for global aggregation. Wide dependency. Examples: reduce, reduceByKey, aggregate.

Why reduceByKey > groupByKey: reduceByKey does map-side combine first; less data shuffled. groupByKey shuffles all values.

Scalability Trade-offs: Map scales linearly with partitions. Reduce bottlenecked by shuffle; skew in reduceByKey causes stragglers.

Cost Implications: Reduce is expensive—network and serialization. Minimize reduce scope; use map-side combiner when possible.

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