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A MapReduce job generally splits the input data set into independent blocks which are processed by the map tasks in a completely parallel manner.
The dynamic data produced by the map tasks are shuffled and sent to the reduce tasks.
Initially, reduce tasks fetch the output data generated by the map tasks and stored on the local disks, using a partitioning function.
So, the buffers used by the map tasks can affect significantly this cost as they can minimize the required I/O operations by keeping more data in memory.
Figure 1 also illustrates that by tuning the buffer size in the map phase (i.e., io.sort.mb parameter), we can decrease the amount of data that is read and written by the map tasks, reducing considerably the performed I/O operations.
Since the intermediate results output by the map tasks are to be handled by the reduce task, these results are stored in each individual machines' local disk instead of HDFS.
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The map tasks then output, in one file per task, a list of hits (sorted by statistical significance) for each of their queries.
To perform fault tolerance, another machine is automatically started by Hadoop to perform the map task again, if one of the machines which runs the map functions fails before it produces the intermediate results.
The stills indicate when both groups fixated the car at a similar rate (a) and when the car first reappears and was highly fixated by the Comprehension group, but not the Map task group, who looked at the storefronts (b).
However, the ECT rule proposed by Dessouky et al. is still applicable in the map task assignment problem.
All the spill files generated by one map task are eventually merged into one single partitioned and sorted intermediate file on the local disk of the map task.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com