Considering the Map-Reduce paradigm and the Hadoop platform, jobs often run on inexpensive hardware where faults may be frequent.

How is Hadoop able to carry out Map-Reduce jobs to completion even in the presence of problems?

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Question

Considering the Map-Reduce paradigm and the Hadoop platform, jobs often run on inexpensive hardware where faults may be frequent. 
   
 How is Hadoop able to carry out Map-Reduce jobs to completion even in the presence of problems?

(select the option that best fits the question).

Answer

Repartitioning mode is activated to avoiding reprocessing corrupted data.

Answer

No response (no penalty if only option answered).

Answer

Map tasks can be restarted in other nodes.

Answer

Intermediate output of mappers is reliably stored to be retrieved during recovery of reducers.

Answer

When the jobtracker fails, it is re-executed up to 4 times.

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