Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/8132#discussion_r36958593
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
---
@@ -270,6 +271,18 @@ private[sql] object PartitioningUtils {
private val upCastingOrder: Seq[DataType] =
Seq(NullType, IntegerType, LongType, FloatType, DoubleType, StringType)
+ def checkPartitionColumnOfValidDataType(
+ schema: StructType,
+ partitionColumns: Array[String]): Unit = {
+
+ ResolvedDataSource.partitionColumnsSchema(schema,
partitionColumns).foreach { field =>
+ field.dataType match {
+ case _: AtomicType | NullType => // OK
--- End diff --
I see. Just tried this snippet in `spark-shell`:
```
scala> import sqlContext.implicits._
import sqlContext.implicits._
scala> (0 until 10).map(_ => (null, null)).toDF("a", "b")
scala.MatchError: Null (of class
scala.reflect.internal.Types$TypeRef$$anon$6)
at
org.apache.spark.sql.catalyst.ScalaReflection$class.schemaFor(ScalaReflection.scala:92)
at
org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:29)
at
org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$schemaFor$1.apply(ScalaReflection.scala:128)
at
org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$schemaFor$1.apply(ScalaReflection.scala:126)
```
Seems that we simply don't match `Null` when inferring the schema. Also,
the partition discovery process always promotes partition columns of `NullType`
to `StringType` (e.g., you may have a partition column whose values are all
`null`). This is similar to the JSON data source.
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