CODEWITHSUNDEEP
apache-sparkapache-spark-sql
ts178
ts178

Reputation: 329

Spark : How to group by distinct values in DataFrame

I have a data in a file in the following format:

1,32    
1,33
1,44
2,21
2,56
1,23

The code I am executing is following:

val sqlContext = new org.apache.spark.sql.SQLContext(sc) 

import spark.implicits._
import sqlContext.implicits._

case class Person(a: Int, b: Int)

val ppl = sc.textFile("newfile.txt").map(_.split(","))
    .map(p=> Person(p(0).trim.toInt, p(1).trim.toInt))
    .toDF()
ppl.registerTempTable("people")

val result = ppl.select("a","b").groupBy('a).agg()
result.show

Expected Output is:

a 32, 33, 44, 23

b 21, 56

Instead of aggregation by sum, count, mean etc. I want every element in the row.

Upvotes: 2

Views: 13097

Answers (1)

Rajat Mishra
Rajat Mishra

Reputation: 3780

Try collect_set function inside agg()

val df = sc.parallelize(Seq(
  (1,3), (1,6), (1,5), (2,1),(2,4)
  (2,1))).toDF("a","b")

+---+---+
|  a|  b|
+---+---+
|  1|  3|
|  1|  6|
|  1|  5|
|  2|  1|
|  2|  4|
|  2|  1|
+---+---+

val df2 = df.groupBy("a").agg(collect_set("b")).show()

+---+--------------+
|  a|collect_set(b)|
+---+--------------+
|  1|     [3, 6, 5]|
|  2|        [1, 4]|
+---+--------------+

And if you want duplicate entries , can use collect_list

val df3 = df.groupBy("a").agg(collect_list("b")).show() 

+---+---------------+
|  a|collect_list(b)|
+---+---------------+
|  1|      [3, 6, 5]|
|  2|      [1, 4, 1]|
+---+---------------+

Upvotes: 11

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