In this scenario we are measuring the indexing speed (how many documents a given index is able to process per second) on a Map-Reduce index.
The index performs an aggregation on the Tags and the CreationDate fields from the Questions collection of the Stackoverflow dataset. Querying this index will return information about the occurrence of Tags (Count) for a particular Month.
This is a common scenario and typical usage of a Map-Reduce index to perform an aggregation operation. The advantage of doing this in the index, instead of a Query, is that the results are pre-computed and almost no additional operations are needed to satisfy the query, giving you instant results.
The difference of this scenario from the Indexing Map-Reduce scenario above is that the grouping is done on two fields, so we have double work to do, but the performance difference is only ~14% slower.
You can read more about the Map-Reduce indexes here.
The graph displays the number of documents mapped per second, we can see that this scenario is disk bounded, and faster disk lets us index more documents.