[SPARK-13410][SQL] Support unionAll for DataFrames with UDT columns.

## What changes were proposed in this pull request?

This PR adds equality operators to UDT classes so that they can be correctly tested for dataType equality during union operations.

This was previously causing `"AnalysisException: u"unresolved operator 'Union;""` when trying to unionAll two dataframes with UDT columns as below.

```
from pyspark.sql.tests import PythonOnlyPoint, PythonOnlyUDT
from pyspark.sql import types

schema = types.StructType([types.StructField("point", PythonOnlyUDT(), True)])

a = sqlCtx.createDataFrame([[PythonOnlyPoint(1.0, 2.0)]], schema)
b = sqlCtx.createDataFrame([[PythonOnlyPoint(3.0, 4.0)]], schema)

c = a.unionAll(b)
```

## How was the this patch tested?

Tested using two unit tests in sql/test.py and the DataFrameSuite.

Additional information here : https://issues.apache.org/jira/browse/SPARK-13410

Author: Franklyn D'souza <franklynd@gmail.com>

Closes #11279 from damnMeddlingKid/udt-union-all.

python/pyspark/sql/tests.py

@@ -601,6 +601,24 @@ class SQLTests(ReusedPySparkTestCase):
         point = df1.head().point
         self.assertEqual(point, PythonOnlyPoint(1.0, 2.0))
 
+    def test_unionAll_with_udt(self):
+        from pyspark.sql.tests import ExamplePoint, ExamplePointUDT
+        row1 = (1.0, ExamplePoint(1.0, 2.0))
+        row2 = (2.0, ExamplePoint(3.0, 4.0))
+        schema = StructType([StructField("label", DoubleType(), False),
+                             StructField("point", ExamplePointUDT(), False)])
+        df1 = self.sqlCtx.createDataFrame([row1], schema)
+        df2 = self.sqlCtx.createDataFrame([row2], schema)
+
+        result = df1.unionAll(df2).orderBy("label").collect()
+        self.assertEqual(
+            result,
+            [
+                Row(label=1.0, point=ExamplePoint(1.0, 2.0)),
+                Row(label=2.0, point=ExamplePoint(3.0, 4.0))
+            ]
+        )
+
     def test_column_operators(self):
         ci = self.df.key
         cs = self.df.value

sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala

@@ -86,6 +86,11 @@ abstract class UserDefinedType[UserType] extends DataType with Serializable {
     this.getClass == dataType.getClass
 
   override def sql: String = sqlType.sql
+
+  override def equals(other: Any): Boolean = other match {
+    case that: UserDefinedType[_] => this.acceptsType(that)
+    case _ => false
+  }
 }
 
 /**
@@ -112,4 +117,9 @@ private[sql] class PythonUserDefinedType(
       ("serializedClass" -> serializedPyClass) ~
       ("sqlType" -> sqlType.jsonValue)
   }
+
+  override def equals(other: Any): Boolean = other match {
+    case that: PythonUserDefinedType => this.pyUDT.equals(that.pyUDT)
+    case _ => false
+  }
 }

sql/core/src/main/scala/org/apache/spark/sql/test/ExamplePointUDT.scala

@@ -26,7 +26,12 @@ import org.apache.spark.sql.types._
  * @param y y coordinate
  */
 @SQLUserDefinedType(udt = classOf[ExamplePointUDT])
-private[sql] class ExamplePoint(val x: Double, val y: Double) extends Serializable
+private[sql] class ExamplePoint(val x: Double, val y: Double) extends Serializable {
+  override def equals(other: Any): Boolean = other match {
+    case that: ExamplePoint => this.x == that.x && this.y == that.y
+    case _ => false
+  }
+}
 
 /**
  * User-defined type for [[ExamplePoint]].

sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala

@@ -112,6 +112,22 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
     )
   }
 
+  test("unionAll should union DataFrames with UDTs (SPARK-13410)") {
+    val rowRDD1 = sparkContext.parallelize(Seq(Row(1, new ExamplePoint(1.0, 2.0))))
+    val schema1 = StructType(Array(StructField("label", IntegerType, false),
+                    StructField("point", new ExamplePointUDT(), false)))
+    val rowRDD2 = sparkContext.parallelize(Seq(Row(2, new ExamplePoint(3.0, 4.0))))
+    val schema2 = StructType(Array(StructField("label", IntegerType, false),
+                    StructField("point", new ExamplePointUDT(), false)))
+    val df1 = sqlContext.createDataFrame(rowRDD1, schema1)
+    val df2 = sqlContext.createDataFrame(rowRDD2, schema2)
+
+    checkAnswer(
+      df1.unionAll(df2).orderBy("label"),
+      Seq(Row(1, new ExamplePoint(1.0, 2.0)), Row(2, new ExamplePoint(3.0, 4.0)))
+    )
+  }
+
   test("empty data frame") {
     assert(sqlContext.emptyDataFrame.columns.toSeq === Seq.empty[String])
     assert(sqlContext.emptyDataFrame.count() === 0)