You can sort of hack this by reading it as an RDD[String] and trying to
implement a custom parser i.e.
Val rddRows = rdd.map parseMyCols
Def parseMyCols(rawVal: String) : Row = {
parse(rawVal) match {
case Success(parsedRowValues) = > Row(parsedRowValues :+ “”: _*)
case Failure(exception) => Row( nullList :+ exception.getMessage )
}
}
Hope this helps
On 17 Jun 2019, at 06:31, Ajay Thompson
<ajay.thomp...@thedatateam.in<mailto:ajay.thomp...@thedatateam.in>> wrote:
There's a column which captures the corrupted record. However, the exception
isn't captured. If the exception is captured in another column it'll be very
useful.
On Mon, 17 Jun, 2019, 10:56 AM Gourav Sengupta,
<gourav.sengu...@gmail.com<mailto:gourav.sengu...@gmail.com>> wrote:
Hi,
it already does, I think, you just have to add the column in the schema that
you are using to read.
Regards,
Gourav
On Sun, Jun 16, 2019 at 2:48 PM
<ajay.thomp...@thedatateam.in<mailto:ajay.thomp...@thedatateam.in>> wrote:
Hi Team,
Can we have another column which gives the corrupted record reason in
permissive mode while reading csv.
Thanks,
Ajay
