Question

Under the MapReduce v1 programming model, what happens in a "Reduce" step?

A. Worker nodes process pieces in parallel.
B. Data is aggregated by worker nodes.
C. Worker nodes store results on their own local file systems.
D. Input is split into pieces.

Answer 1

In the "Reduce" step of MapReduce v1, worker nodes process pieces in parallel to aggregate data from the map phase and generate final results. The outputs are not stored locally but in a shared storage system like HDFS.

Step-by-step explanation:

In the "Reduce" step of the MapReduce v1 programming model, worker nodes process pieces in parallel to aggregate the data. The output produced by the map phase is split into multiple pieces, and each piece is assigned to a different worker node.

For example, consider a word count task where the map phase counts the occurrences of each word in a document. In the reduce phase, worker nodes receive the intermediate counts for different words and combine them to get a final count for each word.

The outputs generated by the reduce phase are not stored on the worker nodes local file systems. Instead, they are written to a shared storage system, such as Hadoop Distributed File System (HDFS), which allows for fault tolerance and easy access by other processes.