Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/8971#discussion_r41428941
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala ---
@@ -102,6 +103,16 @@ private[sql] sealed abstract class ColumnType[JvmType]
{
override def toString: String = getClass.getSimpleName.stripSuffix("$")
}
+private[sql] object NULL extends ColumnType[Any] {
+
+ override def dataType: DataType = NullType
+ override def defaultSize: Int = 0
+ override def append(v: Any, buffer: ByteBuffer): Unit = {}
+ override def extract(buffer: ByteBuffer): Any = null
+ override def setField(row: MutableRow, ordinal: Int, value: Any): Unit =
row.setNullAt(ordinal)
+ override def getField(row: InternalRow, ordinal: Int): Any = null
+}
+
--- End diff --
Found that:
1. we can create a DataFrame with `NullType` via
`sqlContext.createDataFrame(rdd, schema)` with a schema having a `NullType`
column
2. before, all-null columns are materialized using `GENERIC` column type
But I'm not sure whether such columns are useful, since we can't create
such columns using either our own SQL dialect or HiveQL.
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