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Compare Spark and MapReduce for iterative workloads

Spark/Big Dataeasy0.4 min readPremium
Frequency
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Asked at 1 company
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questions in Spark/Big Data
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Asked at these companies
Microsoft
Key Concepts Tested
spark
Expert AnswerPremium
72 wordsInterview-ready
**Why iteration matters**: ML, graph algorithms are iterative; disk I/O kills performance. **MapReduce**: Each iteration reads/writes disk; no in-memory reuse. **Spark**: Keeps data in memory across iterations; RDD lineage enables lazy + reuse. **Scalability trade-offs**: Spark = 10–100x faster for iterative; MapReduce = simpler, lower memory. **Cost implications**: Spark = less total compute time for iterative; MapReduce = more I/O cost....
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