Using aggregateByKey instead of groupBy()

In this section, we will explore the reason why we use aggregateByKey instead of groupBy.

We will cover the following topics:

• Why we should avoid the use of groupByKey
• What aggregateByKey gives us
• Implementing logic using aggregateByKey

First, we will create our array of user transactions, as shown in the following example:

 val keysWithValuesList =
 Array(
 UserTransaction("A", 100),
 UserTransaction("B", 4),
 UserTransaction("A", 100001),
 UserTransaction("B", 10),
 UserTransaction("C", 10)
 )

We will then use parallelize to create an RDD, as we want our data to be key-wise. This is shown in the following example:

 val data = spark.parallelize(keysWithValuesList)
 val keyed = data.keyBy(_.userId)

In the preceding code, we invoked keyBy for userId to have the data of payers, key, and user...