should have added some of the exception to be clear:

17/09/12 14:14:17 ERROR TaskSetManager: Task 0 in stage 15.0 failed 1 times; aborting job
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 15.0 failed 1 times, most recent failure: Lost task 0.0 in stage 15.0 (TID 15, localhost, executor driver): java.lang.NumberFormatException: For input string: "south carolina"
        at java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)
        at java.lang.Integer.parseInt(Integer.java:580)
        at java.lang.Integer.parseInt(Integer.java:615)
        at scala.collection.immutable.StringLike$class.toInt(StringLike.scala:272)
        at scala.collection.immutable.StringOps.toInt(StringOps.scala:29)
        at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$.castTo(CSVInferSchema.scala:250)


From: jeff saremi <jeffsaremi@hotmail.com>
Sent: Tuesday, September 12, 2017 2:32:03 PM
To: user@spark.apache.org
Subject: Continue reading dataframe from file despite errors
 

I'm using a statement like the following to load my dataframe from some text file

Upon encountering the first error, the whole thing throws an exception and processing stops.

I'd like to continue loading even if that results in zero rows in my dataframe. How can i do that?
thanks


spark.read.schema(SomeSchema).option("sep", "\t").format("csv").load("somepath")