Update date format in spark dataframe for multiple spark columns - scala

Question

I have a Spark dataframe where few columns having a different type of date format.
To handle this I have written below code to keep a consistent type of format for all the date columns.
As the date column date format may get change every time hence I have defined a set of date formats in dt_formats.
def to_timestamp_multiple(s: Column, formats: Seq[String]): Column = {
coalesce(formats.map(fmt => to_timestamp(s, fmt)):_*)
}
val dt_formats= Seq("dd-MMM-yyyy", "MMM-dd-yyyy", "yyyy-MM-dd","MM/dd/yy","dd-MM-yy","dd-MM-yyyy","yyyy/MM/dd","dd/MM/yyyy")
val newDF = df.withColumn("ETD1", date_format(to_timestamp_multiple($"ETD",Seq("dd-MMM-yyyy", dt_formats)).cast("date"), "yyyy-MM-dd")).drop("ETD").withColumnRenamed("ETD1","ETD")
But here I have to create a new column then I have to drop older column then rename the new column.
that make the code unnecessary very clumsy hence I want to get override from this code.
I am trying to implement similar functionality by writing a Scala below function but it is throwing the exception org.apache.spark.sql.catalyst.parser.ParseException:, but I am unable to identify the what change I should made to make it work..
val CleansedData= rawDF.selectExpr(rawDF.columns.map(
x => { x match {
case "ETA" => s"""date_format(to_timestamp_multiple($x, dt_formats).cast("date"), "yyyy-MM-dd") as ETA"""
case _ => x
} } ) : _*)
Hence seeking help.
Thanks in advance.

Answer 1

Create a UDF in order to use with select. The select method takes columns and produces another DataFrame.
Also, instead of using coalesce, it might be more straightforward simply to build a parser that handles all of the formats. You can use DateTimeFormatterBuilder for this.
import java.time.format.DateTimeFormatter
import java.time.format.DateTimeFormatterBuilder
import org.apache.spark.sql.functions.udf
import java.time.LocalDate
import scala.util.Try
import java.sql.Date
val dtFormatStrings:Seq[String] = Seq("dd-MMM-yyyy", "MMM-dd-yyyy", "yyyy-MM-dd","MM/dd/yy","dd-MM-yy","dd-MM-yyyy","yyyy/MM/dd","dd/MM/yyyy")
// use foldLeft with appendOptional method, which for each format,
// returns a new builder with that additional possible format
val initBuilder = new DateTimeFormatterBuilder()
val builder: DateTimeFormatterBuilder = dtFormatStrings.foldLeft(initBuilder)(
(b: DateTimeFormatterBuilder, s:String) => b.appendOptional(DateTimeFormatter.ofPattern(s)))
val formatter = builder.toFormatter()
// Create the UDF, which just takes
// any function returning a sql-compatible type (java.sql.Date, here)
def toTimeStamp2(dateString:String): Date = {
val dateTry: Try[Date] = Try(java.sql.Date.valueOf(LocalDate.parse(dateString, formatter)))
dateTry.toOption.getOrElse(null)
}
val timeConversionUdf = udf(toTimeStamp2 _)
// example DF and new DF
val df = Seq(("05/08/20"), ("2020-04-03"), ("unparseable")).toDF("ETD")
df.select(timeConversionUdf(col("ETD"))).toDF("ETD2").show
Output:
+----------+
| ETD2|
+----------+
|2020-05-08|
|2020-04-03|
| null|
+----------+
Note that unparseable values end up null, as shown.

Answer 2

try withColumn(...) with same name and coalesce as below-
val dt_formats= Seq("dd-MMM-yyyy", "MMM-dd-yyyy", "yyyy-MM-dd","MM/dd/yy","dd-MM-yy","dd-MM-yyyy","yyyy/MM/dd","dd/MM/yyyy")
val newDF = df.withColumn("ETD", coalesce(dt_formats.map(fmt => to_date($"ETD", fmt)):_*))