How to parse the JSON data using Spark-Scala - scala

Question

I've requirement to parse the JSON data as shown in the expected results below, currently i'm not getting how to include the signals name(ABS, ADA, ADW) in Signal column. Any help would be much appreciated.
I tried something which gives the results as shown below, but i will need to include all the signals in SIGNAL column as well which is shown in the expected results.
jsonDF.select(explode($"ABS") as "element").withColumn("stime", col("element.E")).withColumn("can_value", col("element.V")).drop(col("element")).show()
+-------------+--------- --+
| stime|can_value |
+-------------+--------- +
|value of E |value of V |
+-------------+----------- +
df.printSchema
-- ABS: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- E: long (nullable = true)
| | |-- V: long (nullable = true)
|-- ADA: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- E: long (nullable = true)
| | |-- V: long (nullable = true)
|-- ADW: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- E: long (nullable = true)
| | |-- V: long (nullable = true)
|-- ALT: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- E: long (nullable = true)
| | |-- V: double (nullable = true)
|-- APP: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- E: long (nullable = true)
| | |-- V: double (nullable = true)
I will need output like below:
-----------------+-------------+---------+
|SIGNAL |stime |can_value|
+-----------------+-------------+---------+
|ABS |value of E |value of V |
|ADA |value of E |value of V |
|ADW |value of E |value of V |
+-----------------+-------------+---------+

Answer 1

To get the expected output, and to insert values in Signal column:
jsonDF.select(explode($"ABS") as "element")
.withColumn("stime", col("element.E"))
.withColumn("can_value", col("element.V"))
.drop(col("element"))
.withColumn("SIGNAL",lit("ABS"))
.show()
And the generalized version of the above approach:
(Based on the result of df.printSchema assuming that, you have signal values as column names, and those columns contain array having elements of the form struct(E,V))
val columns:Array[String] = df.columns
var arrayOfDFs:Array[DataFrame] = Array()
for(col_name <- columns){
val temp = df.selectExpr("explode("+col_name+") as element")
.select(
lit(col_name).as("SIGNAL"),
col("element.E").as("stime"),
col("element.V").as("can_value"))
arrayOfDFs = arrayOfDFs :+ temp
}
val jsonDF = arrayOfDFs.reduce(_ union _)
jsonDF.show(false)