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A master node ensures that only one copy of redundant input data is processed. Worker nodes now process each group of output data, per key, in parallel. Map-generated data associated with that key value. Similarly, step 3 could sometimes be sped up by assigning Reduce processors that are as close as possible to the Map-generated data they need to process. 3 or an empty return, though one call is allowed to return more than one value. The returns of all calls are collected as the desired result list.
Other options are possible, such as direct streaming from mappers to reducers, or for the mapping processors to serve up their results to reducers that query them. Thus, this function just needs to sum all of its input values to find the total appearances of that word. As another example, imagine that for a database of 1. 1 billion people, one would like to compute the average number of social contacts a person has according to age. K1 between 1 and 1100, representing a batch of 1 million social. The Map step would produce 1. The count info in the record is important if the processing is reduced more than one time.
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