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apache-sparkapache-spark-sqlspark-streaming

Convert an Spark dataframe columns with an array of JSON objects to multiple rows


I have a streaming JSON data, whose structure can be described with the case class below

case class Hello(A: String, B: Array[Map[String, String]])

Sample data for the same is as below

|  A    | B                                        |
|-------|------------------------------------------|
|  ABC  |  [{C:1, D:1}, {C:2, D:4}]                | 
|  XYZ  |  [{C:3, D :6}, {C:9, D:11}, {C:5, D:12}] |

I want to transform it to

|   A   |  C  |  D   |
|-------|-----|------|
|  ABC  |  1  |  1   |
|  ABC  |  2  |  4   |
|  XYZ  |  3  |  6   |
|  XYZ  |  9  |  11  |
|  XYZ  |  5  |  12  | 

Any help will be appreciated.


Solution

  • As the question went through an evolution I leave the original answer there and this addresses the final question.

    Important point, the input mentioned as follows is now catered for:

    val df0 = Seq (
                ("ABC", List(Map("C" -> "1", "D" -> "2"), Map("C" -> "3", "D" -> "4"))),
                ("XYZ", List(Map("C" -> "44", "D" -> "55"), Map("C" -> "188", "D" -> "199"), Map("C" -> "88", "D" -> "99")))
                  )
                 .toDF("A", "B")
    

    Can also be done like this, but then the script needs to be modified for this, although trivial:

    val df0 = Seq (
               ("ABC", List(Map("C" -> "1",  "D" -> "2"))), 
               ("ABC", List(Map("C" -> "44", "D" -> "55"))),
               ("XYZ", List(Map("C" -> "11", "D" -> "22")))
                  )
                .toDF("A", "B")
    

    Following on from requested format then:

    val df1 = df0.select($"A", explode($"B")).toDF("A", "Bn")
    
    val df2 = df1.withColumn("SeqNum", monotonically_increasing_id()).toDF("A", "Bn", "SeqNum") 
    
    val df3 = df2.select($"A", explode($"Bn"), $"SeqNum").toDF("A", "B", "C", "SeqNum")
    
    val df4 = df3.withColumn("dummy", concat( $"SeqNum", lit("||"), $"A"))
    
    val df5 = df4.select($"dummy", $"B", $"C").groupBy("dummy").pivot("B").agg(first($"C")) 
    
    val df6 = df5.withColumn("A", substring_index(col("dummy"), "||", -1)).drop("dummy")
    
    df6.show(false)
    

    returns:

    +---+---+---+
    |C  |D  |A  |
    +---+---+---+
    |3  |4  |ABC|
    |1  |2  |ABC|
    |88 |99 |XYZ|
    |188|199|XYZ|
    |44 |55 |XYZ|
    +---+---+---+
    

    You may re-sequence columns.