Github user aa8y commented on a diff in the pull request:
https://github.com/apache/spark/pull/12708#discussion_r61793288
--- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala ---
@@ -335,6 +358,19 @@ trait Row extends Serializable {
def getAs[T](fieldName: String): T = getAs[T](fieldIndex(fieldName))
/**
+ * Returns the value of a given fieldName as a scala.util.Try object.
+ */
+ def attempt[T](fieldName: String): Try[T] =
Try(getAs[T](fieldIndex(fieldName)))
--- End diff --
I am not sure what you mean by an _external_ row. But I don't understand
why it's not possible that when a file is being read, a few bad rows might
creep in. My understanding is data interpretation is done lazily in Spark. So I
can totally do this.
```
scala> :paste
// Entering paste mode (ctrl-D to finish)
import org.apache.spark.sql.types._
import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.expressions.{GenericRow,
GenericRowWithSchema}
val schema = StructType(
StructField("col1", StringType) ::
StructField("col2", DoubleType) ::
StructField("col3", IntegerType) ::
Nil)
val values = Array(1.0, "value2", 1)
val sampleRow: Row = new GenericRowWithSchema(values, schema)
// Exiting paste mode, now interpreting.
import org.apache.spark.sql.types._
import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.expressions.{GenericRow,
GenericRowWithSchema}
schema: org.apache.spark.sql.types.StructType =
StructType(StructField(col1,StringType,true),
StructField(col2,DoubleType,true), StructField(col3,IntegerType,true))
values: Array[Any] = Array(1.0, value2, 1)
sampleRow: org.apache.spark.sql.Row = [1.0,value2,1]
scala> sampleRow.getAs[String](0)
java.lang.ClassCastException: java.lang.Double cannot be cast to
java.lang.String
...
```
And when the value accessed it will fail with due to a wrong cast.
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