Dynamic conversion of Array of double columns into multiple columns in nested spark dataframe - scala

Question

My current DataFrame looks like as below:
{"id":"1","inputs":{"values":{"0.2":[1,1],"0.4":[1,1],"0.6":[1,1]}},"id1":[1,2]}
I want to transform this dataframe into the below dataFrame:
{"id":"1", "v20":[1,1],"v40":[1,1],"v60":[1,1],"id1":[1,2]}
This means that, each 'values' array's items (0.2, 0.4 and 0.6) will be multiplied by 100, prepended with the letter 'v', and extracted into separate columns.
How does the code would look like in order to achieve this. I have tried withColumn but couldn't achieve this.

Answer 1

Try the below code and please find the inline comments for the code explanation
import org.apache.spark.sql.SaveMode
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.StructType
object DynamicCol {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder().master("local[*]").getOrCreate()
val df = spark.read.json("src/main/resources/dyamicCol.json") /// Load the JSON file
val dfTemp = df.select(col("inputs.values").as("values")) // Temp Dataframe for fetching the nest values
val index = dfTemp
.schema.fieldIndex("values")
val propSchema = dfTemp.schema(index).dataType.asInstanceOf[StructType]
val dfFinal = propSchema.fields.foldLeft(df)( (df,field) => { // Join Dataframe with the list of nested columns
val colNameInt = (field.name.toDouble * 100).toInt
val colName = s"v$colNameInt"
df.withColumn(colName,col("inputs.values.`" + field.name + "`")) // Add the nested column mappings
} ).drop("inputs") // Drop the extra column
dfFinal.write.mode(SaveMode.Overwrite).json("src/main/resources/dyamicColOut.json") // Output the JSON file
}
}

Answer 2

I would make the logic for the change of column name splitter into 2 parts, the one that is a numeric value, and the one that doesn't change.
def stringDecimalToVNumber(colName:String): String =
"v" + (colName.toFloat * 100).toInt.toString
and form a single function that transforms according to the case
val floatRegex = """(\d+\.?\d*)""".r
def transformColumnName(colName:String): String = colName match {
case floatRegex(v) => stringDecimalToVNumber(v) //it's a float, transform it
case x => x // keep it
now we have the function to transform the end of the columns, let's pick the schema dynamicly.
val flattenDF = df.select("id","inputs.values.*")
val finalDF = flattenDF
.schema.names
.foldLeft(flattenDF)((dfacum,x) => {
val newName = transformColumnName(x)
if (newName == x)
dfacum // the name didn't need to be changed
else
dfacum.withColumnRenamed(x, transformColumnName(x))
})
This will dynamically transform all the columns inside inputs.values to the new name, and put them in next to id.