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Intermediate data transformation using a MapReduce job.
Intermediate data transformation using a MapReduce job. 3.
But intermediate data must be written to disk and read again by next application.
A one-dimensional array w temporarily stores the intermediate data during each pass of the lines.
When single error occurs in the intermediate data, multiple bits may upset in the output.
Each reduce task is usually responsible for processing the intermediate data associated with many intermediate keys.
Local registers are used for temporary storage of intermediate data during event processing.
The Reduce function performs the appropriate computations on the intermediate data to produce the final outputs.
Many MapReduce workloads have large and sequential intermediate data sets, SSDs could be a bottleneck.
The Data Footprint corresponds to the size of input and intermediate data used by each application.
The function Map is applied to each data chunk and outputs the intermediate data.
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