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Can you explain the concept of mappers in Spark, and how are they used in data transformations?

Spark/Big Datamedium0.5 min readPremium
Frequency
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Asked at 1 company
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questions in Spark/Big Data
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88E|81M|283H
in this category
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1,863
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Asked at these companies
Infosys
Key Concepts Tested
partitionspark
Expert AnswerPremium
101 wordsInterview-ready
**Why mapper concept matters**: In MapReduce, mappers = input-split processors. In Spark, the analog is *tasks* in map-side stages. **Spark mapping**: Transformations like `map`, `flatMap`, `filter` produce tasks—one per partition. Each task processes its partition; no shuffle = narrow transformation. Conceptually, 'mappers' = tasks doing map-side work before any shuffle. **Scalability trade-offs**: Partition count = task count; too few = underutilization; too many = overhead....
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