-1

I have this input DataFrame

input_df:

|C1|C2|C3 |
|-------------|
|A| 1 | 12/06/2012 |
|A| 2 | 13/06/2012 |
|B| 3 | 12/06/2012 |
|B| 4 | 17/06/2012 |
|C| 5 | 14/06/2012 |
|----------|

and after transformations, i want to get this kind of DataFrame grouping by C1 and creating C4 column wich is form by a list of couple from C2 and C3

output_df:

|C1 | C4 |
|---------------------------------------------|
|A| (1, 12/06/2012), (2, 12/06/2012) |
|B| (3, 12/06/2012), (4, 12/06/2012) |
|C| (5, 12/06/2012) |
|---------------------------------------------|

I appoach the result when I try this:

val output_df = input_df.map(x => (x(0), (x(1), x(2))) ).groupByKey()

I obtain this result

(A,CompactBuffer((1, 12/06/2012), (2, 13/06/2012)))    
(B,CompactBuffer((3, 12/06/2012), (4, 17/06/2012)))   
(C,CompactBuffer((5, 14/06/2012)))

But I don't know how to convert this into DataFrame and if this is the good way to do it.
Any advise is welcome even with another approach

a.moussa
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1 Answers1

1

//please, try this

val conf = new SparkConf().setAppName("groupBy").setMaster("local[*]")
val sc = new SparkContext(conf)
sc.setLogLevel("WARN")
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext.implicits._

val rdd = sc.parallelize(
  Seq(("A",1,"12/06/2012"),("A",2,"13/06/2012"),("B",3,"12/06/2012"),("B",4,"17/06/2012"),("C",5,"14/06/2012")) )

val v1 = rdd.map(x => (x._1, x ))
val v2 = v1.groupByKey()
val v3 = v2.mapValues(v => v.toArray)

val df2 = v3.toDF("aKey","theValues")
df2.printSchema()

val first = df2.first
println (first)

println (first.getString(0))

val values = first.getSeq[Row](1)

val firstArray = values(0)

println (firstArray.getString(0)) //B
println (firstArray.getInt(1)) //3
println (firstArray.getString(2)) //12/06/2012
Hocine BOUZELAT
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