Spark Scala: Need to get records with NULL dates coming on top after ordering by a different date using spark

Question

I have the following data:

+-----------+-----------+-----------+-----+-----------+
| Env1_date | Env2_date | Env3_date | Pid | orderDate |
+-----------+-----------+-----------+-----+-----------+
| Null      | Null      | 1/9/2020  | abc | 10/6/2020 |
| Null      | 1/9/2020  | 1/8/2020  | pqr | 10/4/2020 |
| 1/9/2020  | Null      | Null      | xyz | 10/2/2020 |
| 1/8/2020  | 1/7/2020  | Null      | uvw | 10/1/2020 |
+-----------+-----------+-----------+-----+-----------+

I am trying to create 3 new columns which basically tells if Pid is valid for env1, env2 and env3. For that, i first sort the records on orderDate column in descending order(already sorted in the above table).

1. If for Env1_date, Env2_date, Env3_date, the top records are Null, they are considered valid. After the Null records, if the date is less than a particular date(in this example 1/9/2020), its considered valid. Any other records are flagged as invalid.

2. If top records are not NULL, need to check if dates are equal to 1/9/2020. If so, they are also flagged as valid

My output should look like below:

+-----------+-----------+-----------+-----+-----------+-----------+-----------+-----------+
| Env1_date | Env2_date | Env3_date | Pid | orderDate | Env1_Flag | Env2_Flag | Env3_Flag |
+-----------+-----------+-----------+-----+-----------+-----------+-----------+-----------+
| Null      | Null      | 1/9/2020  | abc | 10/6/2020 | Valid     | Valid     | Valid     |
| Null      | 1/9/2020  | 1/8/2020  | pqr | 10/4/2020 | Valid     | Valid     | Invalid   |
| 1/9/2020  | Null      | Null      | xyz | 10/2/2020 | Valid     | Invalid   | Invalid   |
| 1/8/2020  | 1/7/2020  | Null      | uvw | 10/1/2020 | Invalid   | Invalid   | Invalid   |
+-----------+-----------+-----------+-----+-----------+-----------+-----------+-----------+

I am trying to achieve this using Spark 1.5 and scala.

I tried using lag function. But not able to include all the scenarios. Not sure how to approach this problem.

Can someone please help me here.

Note: Windows function, toDf(), createDataFrame() functions don't work in the spark i am using. Its a custom spark environment and there are few restrictions in place

Answer 1

One way you can do this is by collecting all your data to the driver and processing it as a regular array and then turning it to DF again. Beware though, data should fit into the driver.

I wrote the code that can work with data you provided. If you tune it a bit(especially the data comparison part), you should get what you expected.

  // This is how your data is going to look like when you collect it with df.collect
  val arrayData = Array(
    Array("null", "null", "1/9/2020", "abc", "10/6/2020"),
    Array("null", "1/9/2020", "1/8/2020", "pqr", "10/4/2020"),
    Array("1/9/2020", "null", "null", "xyz", "10/2/2020"),
    Array("1/8/2020", "1/7/2020", "null", "uvw", "10/1/2020"),
  )

  // just printing
  arrayData.foreach(arr => println(arr.mkString(" \t| ")))
  println("-".repeat(30))

  // rotates the array, so column become rows and vice verse
  def shiftArray(arr: Array[Array[String]])
    = for(i <- arr(0).indices.toArray) yield arr.map(arr => arr(i))

  // the function that does the validation part
  val someDate = "1/9/2020"
  def processColumn(arr: Array[String]) = {
    val (startingNulls, rest) = arr.span(_ == "null")
    val startingNullsValidated: Array[String] = startingNulls.map(_ => "Valid")
    val restValidated: Array[String] = rest.map(date => if (date == someDate) "Valid" else "Invalid") // implement custom date comparison
    startingNullsValidated ++ restValidated
  }

  val shiftedArray: Array[Array[String]] = shiftArray(arrayData)

  // you need to validate only first 3 columns, so i used take/slice
  val validatedArray = {
    val columnsToProcess = shiftedArray.take(3)
    val otherColumns = shiftedArray.slice(3, shiftedArray.length)
    val processedColumns = for (arr <- columnsToProcess) yield processColumn(arr)
    processedColumns ++ otherColumns
  }

  // rotate array back
  val shiftBackValidatedArray = shiftArray(validatedArray)

  // just printing the final result
  shiftBackValidatedArray.foreach(arr => println(arr.mkString(" \t| ")))

Here're the outputs from printlines above

null    | null  | 1/9/2020  | abc   | 10/6/2020
null    | 1/9/2020  | 1/8/2020  | pqr   | 10/4/2020
1/9/2020    | null  | null  | xyz   | 10/2/2020
1/8/2020    | 1/7/2020  | null  | uvw   | 10/1/2020
------------------------------
Valid   | Valid     | Valid     | abc   | 10/6/2020
Valid   | Valid     | Invalid   | pqr   | 10/4/2020
Valid   | Invalid   | Invalid   | xyz   | 10/2/2020
Invalid     | Invalid   | Invalid   | uvw   | 10/1/2020

Answer 2

import spark.implicits._

case class Source(
                 Env1_date: Option[String],
                 Env2_date: Option[String],
                 Env3_date: Option[String],
                 Pid: String,
                 orderDate: String
               )
case class Source1(
                   Env1_date: Option[String],
                   Env2_date: Option[String],
                   Env3_date: Option[String],
                   Pid: String,
                   orderDate: String,
                   Env1_Flag: String,
                    Env2_Flag: String,
                    Env3_Flag: String
                 )

val source = Seq(
  Source(None, None, Some("1/9/2020"), "abc", "10/6/2020"),
  Source(None, Some("1/9/2020"), Some("1/8/2020"), "pqr", "10/4/2020"),
  Source(Some("1/9/2020"), None, None, "xyz", "10/2/2020"),
  Source(Some("1/8/2020"), Some("1/7/2020"), None, "abc", "10/6/2020")
).toDF().as[Source].collect()

var env1NextRowInvalid = false
var env2NextRowInvalid = false
var env3NextRowInvalid = false
val source1 = source.map(i => {
  val env1Flag = if (env1NextRowInvalid == false && (i.Env1_date.getOrElse("") == """1/9/2020""" || i.Env1_date.getOrElse("") == "")) "valid" else "invalid"
  env1NextRowInvalid = if(env1NextRowInvalid == false) (i.Env1_date == "1/9/2020") else true
  val env2Flag = if (env2NextRowInvalid == false && (i.Env2_date.getOrElse("") == """1/9/2020""" || i.Env2_date.getOrElse("") == "")) "valid" else "invalid"
  env2NextRowInvalid = if(env2NextRowInvalid == false) (i.Env2_date.getOrElse("") == "1/9/2020") else true
  val env3Flag = if (env3NextRowInvalid == false && (i.Env3_date.getOrElse("") == """1/9/2020""" || i.Env3_date.getOrElse("") == "")) "valid" else "invalid"
  env3NextRowInvalid = if(env3NextRowInvalid == false) (i.Env3_date.getOrElse("") == "1/9/2020") else true
  Source1(i.Env1_date, i.Env2_date, i.Env3_date, i.Pid, i.orderDate, env1Flag, env2Flag, env3Flag)
})

val resDF = source1.toSeq.toDF()
resDF.show(false)
//  +---------+---------+---------+---+---------+---------+---------+---------+
//  |Env1_date|Env2_date|Env3_date|Pid|orderDate|Env1_Flag|Env2_Flag|Env3_Flag|
//  +---------+---------+---------+---+---------+---------+---------+---------+
//  |null     |null     |1/9/2020 |abc|10/6/2020|valid    |valid    |valid    |
//  |null     |1/9/2020 |1/8/2020 |pqr|10/4/2020|valid    |valid    |invalid  |
//  |1/9/2020 |null     |null     |xyz|10/2/2020|valid    |invalid  |invalid  |
//  |1/8/2020 |1/7/2020 |null     |abc|10/6/2020|invalid  |invalid  |invalid  |
//  +---------+---------+---------+---+---------+---------+---------+---------+