add a workaround for VectorUDT

Author: mengxr

mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala

@@ -176,27 +176,31 @@ private[spark] class VectorUDT extends UserDefinedType[Vector] {
   }
 
   override def serialize(obj: Any): Row = {
-    val row = new GenericMutableRow(4)
     obj match {
       case SparseVector(size, indices, values) =>
+        val row = new GenericMutableRow(4)
         row.setByte(0, 0)
         row.setInt(1, size)
         row.update(2, indices.toSeq)
         row.update(3, values.toSeq)
+        row
       case DenseVector(values) =>
+        val row = new GenericMutableRow(4)
         row.setByte(0, 1)
         row.setNullAt(1)
         row.setNullAt(2)
         row.update(3, values.toSeq)
+        row
+      // TODO: There are bugs in UDT serialization because we don't have a clear separation between
+      // TODO: internal SQL types and language specific types (including UDT). UDT serialize and
+      // TODO: deserialize may get called twice. See SPARK-7186.
+      case row: Row =>
+        row
     }
-    row
   }
 
   override def deserialize(datum: Any): Vector = {
     datum match {
-      // TODO: something wrong with UDT serialization
-      case v: Vector =>
-        v
       case row: Row =>
         require(row.length == 4,
           s"VectorUDT.deserialize given row with length ${row.length} but requires length == 4")
@@ -211,6 +215,11 @@ private[spark] class VectorUDT extends UserDefinedType[Vector] {
             val values = row.getAs[Iterable[Double]](3).toArray
             new DenseVector(values)
         }
+      // TODO: There are bugs in UDT serialization because we don't have a clear separation between
+      // TODO: internal SQL types and language specific types (including UDT). UDT serialize and
+      // TODO: deserialize may get called twice. See SPARK-7186.
+      case v: Vector =>
+        v
     }
   }