Sentence examples for map tasks and from inspiring English sources

Each job contains two kinds of tasks: map tasks and reduce tasks.

Initially, reduce tasks fetch the output data generated by the map tasks and stored on the local disks, using a partitioning function.

For example, assume that we have a job that consists of 10 map tasks and 3 available slots and the average task execution time is 5 s.

In the next iteration, ({Updelta }) FR is now the delta input of Map tasks and is processed in the same way as the first iteration.

As each reduce task will have the inputs from multiple map tasks, this data flow between map tasks and reduce tasks is called "the shuffle".

In this case, the JobTracker will rerun all those completed map tasks, and reschedule any tasks in progress on other TaskTrackers.

Since several map tasks can read entities in parallel and process them, the reduce adds one more load balancing level between the map task and the reduce task for the purpose of decreasing the overload on these tasks.

In addition, iMapReduce assigns every map task and its corresponding reduce task to the same node to avoid network communication overhead.

A mapper opens a TCP socket to listen for incoming connections whenever it has finished a map task and its output is available.

However, in block 3, the Map task and the shuffled baseline were not significantly different (F (1, 427.758) = 2.375, p > 0.05), while all other task comparisons in block 3 were significantly different (p values < 0.001, F values > 34.187).

After taking this file as an input, it will create the map task and compute the result for each query in single virtual partition and pass this result to the reduce task with subject sequence id, query sequence id and calculated score.