Aggregation techniques allow you to combine the elements in the RDD in arbitrary ways to perform some computation. In fact, aggregation is the most important part of big data analytics. Without aggregation, we would not have any way to generate reports and analysis like Top States by Population, which seems to be a logical question asked when given a dataset of all State populations for the past 200 years. Another simpler example is that of a need to just count the number of elements in the RDD, which asks the executors to count the number of elements in each partition and send to the Driver, which then adds the subsets to compute the total number of elements in the RDD.
In this section, our primary focus is on the aggregation functions used to collect and combine data by key. As seen earlier in this chapter, a PairRDD is an RDD of (key - value) pairs where key and...
