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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