Reputation: 507
I am trying to flatten an RDD[(String,Map[String,Int])] to RDD[String,String,Int] and ultimately save it as a dataframe.
val rdd=hashedContent.map(f=>(f._1,f._2.flatMap(x=> (x._1, x._2))))
val rdd=hashedContent.map(f=>(f._1,f._2.flatMap(x=>x)))
All having type mismatch errors. Any help on how to flatten structures like this one? EDIT:
hashedContent -- ("A", Map("acs"->2, "sdv"->2, "sfd"->1)),
("B", Map("ass"->2, "fvv"->2, "ffd"->1)),
("c", Map("dg"->2, "vd"->2, "dgr"->1))
Upvotes: 1
Views: 511
Reputation: 37852
For completeness: an alternative solution (which might be considered more readable) would be to first convert the RDD
into a DataFrame
, and then to transform its structure using explode
:
import org.apache.spark.sql.functions._
import spark.implicits._
rdd.toDF("c1", "map")
.select($"c1", explode($"map"))
.show(false)
// same result:
// +---+---+-----+
// |c1 |key|value|
// +---+---+-----+
// |A |acs|2 |
// |A |sdv|2 |
// |A |sfd|1 |
// |B |ass|2 |
// |B |fvv|2 |
// |B |ffd|1 |
// |c |dg |2 |
// |c |vd |2 |
// |c |dgr|1 |
// +---+---+-----+
Upvotes: 2
Reputation: 24198
You were close:
rdd.flatMap(x => x._2.map(y => (x._1, y._1, y._2)))
.toDF()
.show()
+---+---+---+
| _1| _2| _3|
+---+---+---+
| A|acs| 2|
| A|sdv| 2|
| A|sfd| 1|
| B|ass| 2|
| B|fvv| 2|
| B|ffd| 1|
| c| dg| 2|
| c| vd| 2|
| c|dgr| 1|
+---+---+---+
Data
val data = Seq(("A", Map("acs"->2, "sdv"->2, "sfd"->1)),
("B", Map("ass"->2, "fvv"->2, "ffd"->1)),
("c", Map("dg"->2, "vd"->2, "dgr"->1)))
val rdd = sc.parallelize(data)
Upvotes: 4