Github user hvanhovell commented on the pull request:
https://github.com/apache/spark/pull/9642#issuecomment-156693107
```UnsafeRow``` and ```SpecificRow``` have similar problems. Shouldn't we
fix those as well? For example:
import org.apache.spark.sql.types.IntegerType
import org.apache.spark.sql.catalyst.expressions.SpecificMutableRow
import org.apache.spark.sql.catalyst.expressions.UnsafeRow
val srow = new SpecificMutableRow(IntegerType :: Nil)
srow.isNullAt(0)
srow.getInt(0)
val urow = new UnsafeRow()
urow.pointTo(new Array[Byte](16), 1, 4)
urow.isNullAt(0)
urow.getInt(0)
// Result:
import org.apache.spark.sql.types.IntegerType
import org.apache.spark.sql.catalyst.expressions.SpecificMutableRow
import org.apache.spark.sql.catalyst.expressions.UnsafeRow
srow: org.apache.spark.sql.catalyst.expressions.SpecificMutableRow =
[null]
res129: Boolean = true
res130: Int = 0
urow: org.apache.spark.sql.catalyst.expressions.UnsafeRow = []
res133: Boolean = false
res134: Int = 0
I'd actualy rather not touch this at all. When you are using internal API
you should be more carefull and expect some quirkiness.
I can currently think of only one place in which this causes some problems:
UDFs with primitive parameters. The engine will pass in default values instead
of nulls. Are there any other situations in which this causes problems?
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