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Controlling mappers in MapReduce

Spark/Big Dataeasy0.4 min readPremium

**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)...

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452
questions in Spark/Big Data
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88E|81M|283H
in this category
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1,863
across 7 categories
Asked at these companies
JP Morgan

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Expert Answer
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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. Cost implications: Too many mappers = scheduling overhead; too few = stragglers. Best practice: Aim for 100–200MB splits; use CombineFileInputFormat for small files.

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