How to validate date format in a dataframe column in spark scala

Question

I have a dataframe with one DateTime column and many other columns.

All I wanted to do is parse this DateTime column value and check if the format is \"yyyy-MM

Answer 1

Here we define a function for checking whether a String is compatible with your format requirements, and we partition the list into compatible/non pieces. The types are shown with full package names, but you should use import statements, of course.

val fmt = "yyyy-MM-dd HH:mm:ss"
val df = java.time.format.DateTimeFormatter.ofPattern(fmt)
def isCompatible(s: String) = try {
  java.time.LocalDateTime.parse(s, df)
  true
} catch {
  case e: java.time.format.DateTimeParseException => false
}
val dts = Seq("2016-11-07 15:16:17", "2016-11-07 24:25:26")
val yesNo = dts.partition { s => isCompatible(s) }
println(yesNo)

Answer 2

You can use filter() to get the valid/invalid records in dataframe. This code can be improvable with scala point of view.

  val DATE_TIME_FORMAT = "yyyy-MM-dd HH:mm:ss"

  def validateDf(row: Row): Boolean = try {
    //assume row.getString(1) with give Datetime string
    java.time.LocalDateTime.parse(row.getString(1), java.time.format.DateTimeFormatter.ofPattern(DATE_TIME_FORMAT))
    true
  } catch {
    case ex: java.time.format.DateTimeParseException => {
      // Handle exception if you want
      false
    }
  }



val session = SparkSession.builder
  .appName("Validate Dataframe")
  .getOrCreate

val df = session. .... //Read from any datasource

import session.implicits._ //implicits provide except() on df  

val validDf = df.filter(validateDf(_))
val inValidDf = df.except(validDf)

Answer 3

Use option("dateFormat", "MM/dd/yyyy") to validate date field in dataframe.It will discard the invalid rows.

 val df=spark.read.format("csv").option("header", "false").
            option("dateFormat", "MM/dd/yyyy").
            schema(schema).load("D:/cca175/data/emp.csv")