How to use dataset to groupby

Question

I have a request to use rdd to do so：

val test = Seq(("New York", "Jack"),
    ("Los Angeles", "Tom"),
    ("Chicago", "David"),
    ("Houston", "John"),
    ("Detroit", "Michael"),
    ("Chicago", "Andrew"),
    ("Detroit", "Peter"),
    ("Detroit", "George")
  )
sc.parallelize(test).groupByKey().mapValues(_.toList).foreach(println)

The result is that：

(New York,List(Jack))

(Detroit,List(Michael, Peter, George))

(Los Angeles,List(Tom))

(Houston,List(John))

(Chicago,List(David, Andrew))

How to do it use dataset with spark2.0?

I have a way to use a custom function, but the feeling is so complicated, there is no simple point method？

Answer 1

I would suggest you to start with creating a case class as

case class Monkey(city: String, firstName: String)

This case class should be defined outside the main class. Then you can just use toDS function and use groupBy and aggregation function called collect_list as below

import sqlContext.implicits._
import org.apache.spark.sql.functions._

val test = Seq(("New York", "Jack"),
  ("Los Angeles", "Tom"),
  ("Chicago", "David"),
  ("Houston", "John"),
  ("Detroit", "Michael"),
  ("Chicago", "Andrew"),
  ("Detroit", "Peter"),
  ("Detroit", "George")
)
sc.parallelize(test)
  .map(row => Monkey(row._1, row._2))
  .toDS()
  .groupBy("city")
  .agg(collect_list("firstName") as "list")
  .show(false)

You will have output as

+-----------+------------------------+
|city       |list                    |
+-----------+------------------------+
|Los Angeles|[Tom]                   |
|Detroit    |[Michael, Peter, George]|
|Chicago    |[David, Andrew]         |
|Houston    |[John]                  |
|New York   |[Jack]                  |
+-----------+------------------------+

You can always convert back to RDD by just calling .rdd function