Added MIMA excludes for VectorUDT (now public), and added DeveloperApi annotation to it

Author: jkbradley

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

@@ -26,6 +26,7 @@ import scala.collection.JavaConverters._
 import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, Vector => BV}
 
 import org.apache.spark.SparkException
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.mllib.util.NumericParser
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
@@ -110,9 +111,12 @@ sealed trait Vector extends Serializable {
 }
 
 /**
+ * :: DeveloperApi ::
+ *
  * User-defined type for [[Vector]] which allows easy interaction with SQL
  * via [[org.apache.spark.sql.DataFrame]].
  */
+@DeveloperApi
 class VectorUDT extends UserDefinedType[Vector] {
 
   override def sqlType: StructType = {

project/MimaExcludes.scala

@@ -143,6 +143,11 @@ object MimaExcludes {
               "org.apache.spark.graphx.Graph.getCheckpointFiles"),
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.graphx.Graph.isCheckpointed")
+          ) ++ Seq(
+            // SPARK-4789 Standardize ML Prediction APIs
+            ProblemFilters.exclude[MissingTypesProblem]("org.apache.spark.mllib.linalg.VectorUDT"),
+            ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.mllib.linalg.VectorUDT.serialize"),
+            ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.mllib.linalg.VectorUDT.sqlType")
           )
 
         case v if v.startsWith("1.2") =>