Splitting row in multiple row in spark-shell

Question

I have imported data in Spark dataframe in spark-shell. Data is filled in it like :

Col1 | Col2 | Col3 | Col4
A1   | 11   | B2   | a|b;1;0xFFFFFF
A1   | 12          


        

Answer 1

Edit after getting this answer about how to make backreference in regexp_replace.

You can use regexp_replace with a backreference, then split twice and explode. It is, imo, cleaner than my original solution

val df = List(
    ("A1"   , "11"   , "B2"   , "a|b;1;0xFFFFFF"),
    ("A1"   , "12"   , "B1"   , "2"),
    ("A2"   , "12"   , "B2"   , "0xFFF45B")
  ).toDF("Col1" , "Col2" , "Col3" , "Col4")

val regExStr = "^([A-z|]+)?;?(\\d+)?;?(0x.*)?$"
val res = df
  .withColumn("backrefReplace",
       split(regexp_replace('Col4,regExStr,"$1;$2;$3"),";"))
  .select('Col1,'Col2,'Col3,
       explode(split('backrefReplace(0),"\\|")).as("letter"),
       'backrefReplace(1)                      .as("digits"),
       'backrefReplace(2)                      .as("hexadecimal")
  )

+----+----+----+------+------+-----------+
|Col1|Col2|Col3|letter|digits|hexadecimal|
+----+----+----+------+------+-----------+
|  A1|  11|  B2|     a|     1|   0xFFFFFF|
|  A1|  11|  B2|     b|     1|   0xFFFFFF|
|  A1|  12|  B1|      |     2|           |
|  A2|  12|  B2|      |      |   0xFFF45B|
+----+----+----+------+------+-----------+

you still need to replace empty strings by nullthough...

---

Previous Answer (somebody might still prefer it):

Here is a solution that sticks to DataFrames but is also quite messy. You can first use regexp_extract three times (possible to do less with backreference?), and finally split on "|" and explode. Note that you need a coalesce for explode to return everything (you still might want to change the empty strings in letter to null in this solution).

val res = df
  .withColumn("alphabets",  regexp_extract('Col4,"(^[A-z|]+)?",1))
  .withColumn("digits",     regexp_extract('Col4,"^([A-z|]+)?;?(\\d+)?;?(0x.*)?$",2))
  .withColumn("hexadecimal",regexp_extract('Col4,"^([A-z|]+)?;?(\\d+)?;?(0x.*)?$",3))
  .withColumn("letter",
     explode(
       split(
         coalesce('alphabets,lit("")),
         "\\|"
       )
     )
   )


res.show    

+----+----+----+--------------+---------+------+-----------+------+
|Col1|Col2|Col3|          Col4|alphabets|digits|hexadecimal|letter|
+----+----+----+--------------+---------+------+-----------+------+
|  A1|  11|  B2|a|b;1;0xFFFFFF|      a|b|     1|   0xFFFFFF|     a|
|  A1|  11|  B2|a|b;1;0xFFFFFF|      a|b|     1|   0xFFFFFF|     b|
|  A1|  12|  B1|             2|     null|     2|       null|      |
|  A2|  12|  B2|      0xFFF45B|     null|  null|   0xFFF45B|      |
+----+----+----+--------------+---------+------+-----------+------+

Note: The regexp part could be so much better with backreference, so if somebody knows how to do it, please comment!

Answer 2

Not sure this is doable while staying 100% with Dataframes, here's a (somewhat messy?) solution using RDDs for the split itself:

import org.apache.spark.sql.functions._
import sqlContext.implicits._

// we switch to RDD to perform the split of Col4 into 3 columns
val rddWithSplitCol4 = input.rdd.map { r =>
  val indexToValue = r.getAs[String]("Col4").split(';').map {
    case s if s.startsWith("0x") => 2 -> s
    case s if s.matches("\\d+") => 1 -> s
    case s => 0 -> s
  }
  val newCols: Array[String] = indexToValue.foldLeft(Array.fill[String](3)("")) {
    case (arr, (index, value)) => arr.updated(index, value)
  }
  (r.getAs[String]("Col1"), r.getAs[Int]("Col2"), r.getAs[String]("Col3"), newCols(0), newCols(1), newCols(2))
}

// switch back to Dataframe and explode alphabets column
val result = rddWithSplitCol4
  .toDF("Col1", "Col2", "Col3", "alphabets", "digits", "hexadecimal")
  .withColumn("alphabets", explode(split(col("alphabets"), "\\|")))

result.show(truncate = false)
// +----+----+----+---------+------+-----------+
// |Col1|Col2|Col3|alphabets|digits|hexadecimal|
// +----+----+----+---------+------+-----------+
// |A1  |11  |B2  |a        |1     |0xFFFFFF   |
// |A1  |11  |B2  |b        |1     |0xFFFFFF   |
// |A1  |12  |B1  |         |2     |           |
// |A2  |12  |B2  |         |      |0xFFF45B   |
// +----+----+----+---------+------+-----------+