Derive multiple columns from a single column in a Spark DataFrame

Question

I have a DF with a huge parseable metadata as a single string column in a Dataframe, lets call it DFA, with ColmnA.

I would like to break this column, ColmnA into mu

Answer 1

If your resulting columns will be of the same length as the original one, you can create brand new columns with withColumn function and by applying an udf. After this you can drop your original column, eg:

 val newDf = myDf.withColumn("newCol1", myFun(myDf("originalColumn")))
.withColumn("newCol2", myFun2(myDf("originalColumn"))
.drop(myDf("originalColumn"))

where myFun is an udf defined like this:

   def myFun= udf(
    (originalColumnContent : String) =>  {
      // do something with your original column content and return a new one
    }
  )

Answer 2

Assume that after your function there will be a sequence of elements, giving an example as below:

val df = sc.parallelize(List(("Mike,1986,Toronto", 30), ("Andre,1980,Ottawa", 36), ("jill,1989,London", 27))).toDF("infoComb", "age")
df.show
+------------------+---+
|          infoComb|age|
+------------------+---+
|Mike,1986,Toronto| 30|
| Andre,1980,Ottawa| 36|
|  jill,1989,London| 27|
+------------------+---+

now what you can do with this infoComb is that you can start split the string and get more columns with:

df.select(expr("(split(infoComb, ','))[0]").cast("string").as("name"), expr("(split(infoComb, ','))[1]").cast("integer").as("yearOfBorn"), expr("(split(infoComb, ','))[2]").cast("string").as("city"), $"age").show
+-----+----------+-------+---+
| name|yearOfBorn|   city|age|
+-----+----------+-------+---+
|Mike|      1986|Toronto| 30|
|Andre|      1980| Ottawa| 36|
| jill|      1989| London| 27|
+-----+----------+-------+---+

Hope this helps.

Answer 3

This can be easily achieved by using pivot function

df4.groupBy("year").pivot("course").sum("earnings").collect() 

Answer 4

I opted to create a function to flatten one column and then just call it simultaneously with the udf.

First define this:

implicit class DfOperations(df: DataFrame) {

  def flattenColumn(col: String) = {
    def addColumns(df: DataFrame, cols: Array[String]): DataFrame = {
      if (cols.isEmpty) df
      else addColumns(
        df.withColumn(col + "_" + cols.head, df(col + "." + cols.head)),
        cols.tail
      )
    }

    val field = df.select(col).schema.fields(0)
    val newCols = field.dataType.asInstanceOf[StructType].fields.map(x => x.name)

    addColumns(df, newCols).drop(col)
  }

  def withColumnMany(colName: String, col: Column) = {
    df.withColumn(colName, col).flattenColumn(colName)
  }

}

Then usage is very simple:

case class MyClass(a: Int, b: Int)

val df = sc.parallelize(Seq(
  (0),
  (1)
)).toDF("x")

val f = udf((x: Int) => MyClass(x*2,x*3))

df.withColumnMany("test", f($"x")).show()

//  +---+------+------+
//  |  x|test_a|test_b|
//  +---+------+------+
//  |  0|     0|     0|
//  |  1|     2|     3|
//  +---+------+------+

Answer 5

Generally speaking what you want is not directly possible. UDF can return only a single column at the time. There are two different ways you can overcome this limitation:

1. Return a column of complex type. The most general solution is a StructType but you can consider ArrayType or MapType as well.

   import org.apache.spark.sql.functions.udf
   
   val df = Seq(
     (1L, 3.0, "a"), (2L, -1.0, "b"), (3L, 0.0, "c")
   ).toDF("x", "y", "z")
   
   case class Foobar(foo: Double, bar: Double)
   
   val foobarUdf = udf((x: Long, y: Double, z: String) => 
     Foobar(x * y, z.head.toInt * y))
   
   val df1 = df.withColumn("foobar", foobarUdf($"x", $"y", $"z"))
   df1.show
   // +---+----+---+------------+
   // |  x|   y|  z|      foobar|
   // +---+----+---+------------+
   // |  1| 3.0|  a| [3.0,291.0]|
   // |  2|-1.0|  b|[-2.0,-98.0]|
   // |  3| 0.0|  c|   [0.0,0.0]|
   // +---+----+---+------------+
   
   df1.printSchema
   // root
   //  |-- x: long (nullable = false)
   //  |-- y: double (nullable = false)
   //  |-- z: string (nullable = true)
   //  |-- foobar: struct (nullable = true)
   //  |    |-- foo: double (nullable = false)
   //  |    |-- bar: double (nullable = false)
   

   This can be easily flattened later but usually there is no need for that.

2. Switch to RDD, reshape and rebuild DF:

   import org.apache.spark.sql.types._
   import org.apache.spark.sql.Row
   
   def foobarFunc(x: Long, y: Double, z: String): Seq[Any] = 
     Seq(x * y, z.head.toInt * y)
   
   val schema = StructType(df.schema.fields ++
     Array(StructField("foo", DoubleType), StructField("bar", DoubleType)))
   
   val rows = df.rdd.map(r => Row.fromSeq(
     r.toSeq ++
     foobarFunc(r.getAs[Long]("x"), r.getAs[Double]("y"), r.getAs[String]("z"))))
   
   val df2 = sqlContext.createDataFrame(rows, schema)
   
   df2.show
   // +---+----+---+----+-----+
   // |  x|   y|  z| foo|  bar|
   // +---+----+---+----+-----+
   // |  1| 3.0|  a| 3.0|291.0|
   // |  2|-1.0|  b|-2.0|-98.0|
   // |  3| 0.0|  c| 0.0|  0.0|
   // +---+----+---+----+-----+