Using the MapReduce Programming Model to Process Large Datasets
You are asked to explain how the MapReduce programming model processes large-scale batch data and to illustrate the end-to-end data flow.
Address the following:
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Roles of the map and reduce functions.
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Data partitioning and how keys are assigned to reducers.
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Combiners: when and how to use them.
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Sorting and shuffling between the map and reduce phases.
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Fault tolerance: how the system handles failures and stragglers.
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A concrete example job: describe the input/output schema and show brief pseudocode for map, combiner (if applicable), and reduce.
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Key performance considerations and tuning levers.