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Exact(41)
Third, in the reduce stage, we fix the number of reducers to (N_{node}) (times ) (N^{red}_{core}).
The total number of reducer (N_{red}) used to complete the reduce stage depends on the number of nodes (N_{node}) and the number of core processors (N_{core}^{red}) allocated per node.
In traditional MapReduce, a combiner stage is simply an intermediate or semi-reducer that further processes data prior to the final reduce stage [24].
So the total time (t_{red}) passed in the reduce stage depends on the time (r_{i,j,k}) of the ith reducer assigned to the kth core processor of the jth node.
Finally in the reduce stage, all intermediate paths are aggregated to obtain the final shortest path.
However, in this experiment we can also observe that the reduce stage on VMR is only slightly faster than VCS.
Similar(18)
Output stage: storage of full path The MRA* job submitted from the master node is split into mappers and reducers that run respectively in the map and reduce stages.
To address this issue, we present a Partitioner, SP-Partitioner, that sits between the map and reduce stages to re-balance the workload of the reducers.
While map tasks have only one stage, reduce tasks consist of three: copy, sort and reduce stages.
The proposed approach works in four stages including the map and reduce stages.
MapReduce jobs require communication between map and reduce stages since map outputs are used as input for reduce tasks.
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