A practical example

Let's assume that we want to conduct sentiment analysis on a video. Sentiment analysis is where we try to flag different portions of a video with human emotions of sadness, happiness, fear, joy, frustration, and ecstasy. It is a compute-intensive job where lots of computing power is needed. As you will see in the following figure, to design the compute dimension, we have pided the processing into five tasks, consisting of two stages. All the data transformation and preparation is implemented in three mappers. For that, we pide the video into three different partitions, called splits. After the mappers are executed, the resulting processed video is inputted to the two aggregators, called reducers. To conduct the required sentiment analysis, the reducers group the video according to the emotions. Finally, the results are combined in the output:

Note that the number of mappers directly translates to the runtime parallelism of the algorithm. The optimal number of mappers and reducers is dependent on the characteristics of the data, the type of algorithm that is needed to be used, and the number of resources available.