[SPARK-37829][SQL] Dataframe.joinWith outer-join should return a null…

… value for unmatched row

### What changes were proposed in this pull request?

When doing an outer join with joinWith on DataFrames, unmatched rows return Row objects with null fields instead of a single null value. This is not a expected behavior, and it's a regression introduced in [this commit](cd92f25).
This pull request aims to fix the regression, note this is not a full rollback of the commit, do not add back "schema" variable.

```
case class ClassData(a: String, b: Int)
val left = Seq(ClassData("a", 1), ClassData("b", 2)).toDF
val right = Seq(ClassData("x", 2), ClassData("y", 3)).toDF

left.joinWith(right, left("b") === right("b"), "left_outer").collect
```

```
Wrong results (current behavior):    Array(([a,1],[null,null]), ([b,2],[x,2]))
Correct results:                     Array(([a,1],null), ([b,2],[x,2]))
```

### Why are the changes needed?

We need to address the regression mentioned above. It results in unexpected behavior changes in the Dataframe joinWith API between versions 2.4.8 and 3.0.0+. This could potentially cause data correctness issues for users who expect the old behavior when using Spark 3.0.0+.

### Does this PR introduce _any_ user-facing change?

No

### How was this patch tested?

Added unit test (use the same test in previous [closed pull request](#35140), credit to Clément de Groc)
Run sql-core and sql-catalyst submodules locally with ./build/mvn clean package -pl sql/core,sql/catalyst

Closes #40755 from kings129/encoder_bug_fix.

Authored-by: --global <[email protected]>
Signed-off-by: Wenchen Fan <[email protected]>

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/encoders/ExpressionEncoder.scala

@@ -110,22 +110,29 @@ object ExpressionEncoder {
     }
     val newSerializer = CreateStruct(serializers)
 
+    def nullSafe(input: Expression, result: Expression): Expression = {
+      If(IsNull(input), Literal.create(null, result.dataType), result)
+    }
+
     val newDeserializerInput = GetColumnByOrdinal(0, newSerializer.dataType)
-    val deserializers = encoders.zipWithIndex.map { case (enc, index) =>
+    val childrenDeserializers = encoders.zipWithIndex.map { case (enc, index) =>
       val getColExprs = enc.objDeserializer.collect { case c: GetColumnByOrdinal => c }.distinct
       assert(getColExprs.size == 1, "object deserializer should have only one " +
         s"`GetColumnByOrdinal`, but there are ${getColExprs.size}")
 
       val input = GetStructField(newDeserializerInput, index)
-      enc.objDeserializer.transformUp {
+      val childDeserializer = enc.objDeserializer.transformUp {
         case GetColumnByOrdinal(0, _) => input
       }
-    }
-    val newDeserializer = NewInstance(cls, deserializers, ObjectType(cls), propagateNull = false)
 
-    def nullSafe(input: Expression, result: Expression): Expression = {
-      If(IsNull(input), Literal.create(null, result.dataType), result)
+      if (enc.objSerializer.nullable) {
+        nullSafe(input, childDeserializer)
+      } else {
+        childDeserializer
+      }
     }
+    val newDeserializer =
+      NewInstance(cls, childrenDeserializers, ObjectType(cls), propagateNull = false)
 
     new ExpressionEncoder[Any](
       nullSafe(newSerializerInput, newSerializer),

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

@@ -30,6 +30,7 @@ import org.apache.spark.TestUtils.withListener
 import org.apache.spark.scheduler.{SparkListener, SparkListenerJobStart}
 import org.apache.spark.sql.catalyst.{FooClassWithEnum, FooEnum, ScroogeLikeExample}
 import org.apache.spark.sql.catalyst.encoders.{OuterScopes, RowEncoder}
+import org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema
 import org.apache.spark.sql.catalyst.plans.{LeftAnti, LeftSemi}
 import org.apache.spark.sql.catalyst.util.sideBySide
 import org.apache.spark.sql.execution.{LogicalRDD, RDDScanExec, SQLExecution}
@@ -2152,6 +2153,50 @@ class DatasetSuite extends QueryTest
       assert(parquetFiles.size === 10)
     }
   }
+
+  test("SPARK-37829: DataFrame outer join") {
+    // Same as "SPARK-15441: Dataset outer join" but using DataFrames instead of Datasets
+    val left = Seq(ClassData("a", 1), ClassData("b", 2)).toDF().as("left")
+    val right = Seq(ClassData("x", 2), ClassData("y", 3)).toDF().as("right")
+    val joined = left.joinWith(right, $"left.b" === $"right.b", "left")
+
+    val leftFieldSchema = StructType(
+      Seq(
+        StructField("a", StringType),
+        StructField("b", IntegerType, nullable = false)
+      )
+    )
+    val rightFieldSchema = StructType(
+      Seq(
+        StructField("a", StringType),
+        StructField("b", IntegerType, nullable = false)
+      )
+    )
+    val expectedSchema = StructType(
+      Seq(
+        StructField(
+          "_1",
+          leftFieldSchema,
+          nullable = false
+        ),
+        // This is a left join, so the right output is nullable:
+        StructField(
+          "_2",
+          rightFieldSchema
+        )
+      )
+    )
+    assert(joined.schema === expectedSchema)
+
+    val result = joined.collect().toSet
+    val expected = Set(
+      new GenericRowWithSchema(Array("a", 1), leftFieldSchema) ->
+        null,
+      new GenericRowWithSchema(Array("b", 2), leftFieldSchema) ->
+        new GenericRowWithSchema(Array("x", 2), rightFieldSchema)
+    )
+    assert(result == expected)
+  }
 }
 
 case class Bar(a: Int)