A county open-data lake stores permit archives as many HDFS blocks. What generally increases how many map tasks can run in parallel for a MapReduce job?
Select an answer to reveal the explanation.
Short Explanation
More slices of bread mean more toasters working at once. In MapReduce, input splits—often tied to HDFS blocks—drive how many mappers can run side by side. One giant unreplicated blob or killing YARN does the opposite.
Full Explanation
MapReduce parallelism on the map side is largely driven by the number of input splits the framework creates from the input paths. Those splits commonly align with HDFS block boundaries, so more blocks/splits generally enable more concurrent mappers. Collapsing data onto one disk, disabling resource management, or removing observability does not increase useful parallel map capacity.