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Controlling mappers in MapReduce

Spark/Big Dataeasy0.4 min readPremium
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Asked at 1 company
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questions in Spark/Big Data
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in this category
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1,863
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Asked at these companies
JP Morgan
Expert AnswerPremium
82 wordsInterview-ready
**Why control matters**: Mappers = input splits; too many = overhead; too few = underutilization. **Levers**: (1) Split size: `mapreduce.input.fileinputformat.split.maxsize`—larger = fewer mappers. (2) Small files: `CombineFileInputFormat` merges small files into splits. (3) Compression: Splittable formats (LZO, Snappy) enable parallelism; non-splittable (gzip) = one mapper per file. **Scalability trade-offs**: 100–200MB splits = good balance; small files = mapper explosion....
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