Github user datumbox commented on the issue:
https://github.com/apache/spark/pull/17059
Yeah, Scala Long matches. Here is the "stand-alone" script that I used to
confirm that everything works ok (tested on Spark 2.1):
```scala
import org.apache.spark.sql.types._
import org.apache.spark.sql.functions._
val u = udf { (n: Any) =>
n match {
case v: Int => v
case v: Number =>
val intV = v.intValue
if (v.doubleValue == intV) {
intV
}
else {
throw new IllegalArgumentException("out of range")
}
case _ => throw new IllegalArgumentException("invalid type")
}
}
val df = sqlContext.range(10)
.withColumn("int_success", lit(123))
.withColumn("long_success", lit(1231L))
.withColumn("long_fail", lit(1231000000000L))
.withColumn("decimal_success", lit(123).cast(DecimalType(5, 2)))
.withColumn("decimal_fail", lit(123.1).cast(DecimalType(5, 2)))
.withColumn("double_success", lit(123.0))
.withColumn("double_fail", lit(123.1))
.withColumn("double_fail2", lit(1231000000000.0))
.withColumn("string_fail", lit("123.1"))
// these work fine
df.select(u(df.col("int_success"))).show
df.select(u(df.col("long_success"))).show
df.select(u(df.col("decimal_success"))).show
df.select(u(df.col("double_success"))).show
// these fail with out of int range exception
df.select(u(df.col("long_fail"))).show
df.select(u(df.col("decimal_fail"))).show
df.select(u(df.col("double_fail"))).show
df.select(u(df.col("double_fail2"))).show
// this fails with invalid type exception
df.select(u(df.col("string_fail"))).show
```
Cool, I'll commit the changes tonight so that @mlnick can check the final
code.
@srowen I really appreciate your input; you did raise good points and
especially for handling the SQL datatypes. Thank you.
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