Address comments.

sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala

@@ -85,17 +85,28 @@ case class Exchange(
       keySchema: Array[DataType],
       valueSchema: Array[DataType],
       numPartitions: Int): Serializer = {
+    // In ExternalSorter's spillToMergeableFile function, key-value pairs are written out
+    // through write(key) and then write(value) instead of write((key, value)). Because
+    // SparkSqlSerializer2 assumes that objects passed in are Product2, we cannot safely use
+    // it when spillToMergeableFile in ExternalSorter will be used.
+    // So, we will not use SparkSqlSerializer2 when
+    //  - Sort-based shuffle is enabled and the number of reducers (numPartitions) is greater
+    //     then the bypassMergeThreshold; or
+    //  - newOrdering is defined.
+    val cannotUseSqlSerializer2 =
+      (sortBasedShuffleOn && numPartitions > bypassMergeThreshold) || newOrdering.nonEmpty
+
     val useSqlSerializer2 =
-      !(sortBasedShuffleOn && numPartitions > bypassMergeThreshold) &&
-        child.sqlContext.conf.useSqlSerializer2 &&
-        SparkSqlSerializer2.support(keySchema) &&
-        SparkSqlSerializer2.support(valueSchema)
+        child.sqlContext.conf.useSqlSerializer2 &&  // SparkSqlSerializer2 is enabled.
+        !cannotUseSqlSerializer2 &&                 // Safe to use Serializer2.
+        SparkSqlSerializer2.support(keySchema) &&   // The schema of key is supported.
+        SparkSqlSerializer2.support(valueSchema)    // The schema of value is supported.
 
     val serializer = if (useSqlSerializer2) {
-      logInfo("Use SparkSqlSerializer2.")
+      logInfo("Using SparkSqlSerializer2.")
       new SparkSqlSerializer2(keySchema, valueSchema)
     } else {
-      logInfo("Use SparkSqlSerializer.")
+      logInfo("Using SparkSqlSerializer.")
       new SparkSqlSerializer(sparkConf)
     }
 
@@ -160,7 +171,7 @@ case class Exchange(
           } else {
             new ShuffledRDD[Row, Null, Null](rdd, part)
           }
-        val keySchema = sortingExpressions.map(_.dataType).toArray
+        val keySchema = child.output.map(_.dataType).toArray
         shuffled.setSerializer(serializer(keySchema, null, numPartitions))
 
         shuffled.map(_._1)

sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala

@@ -31,7 +31,17 @@ import org.apache.spark.sql.catalyst.expressions.SpecificMutableRow
 import org.apache.spark.sql.types._
 
 /**
- * The serialization stream for SparkSqlSerializer2.
+ * The serialization stream for [[SparkSqlSerializer2]]. It assumes that the object passed in
+ * its `writeObject` are [[Product2]]. The serialization functions for the key and value of the
+ * [[Product2]] are constructed based on their schemata.
+ * The benefit of this serialization stream is that compared with general-purpose serializers like
+ * Kryo and Java serializer, it can significantly reduce the size of serialized and has a lower
+ * allocation cost, which can benefit the shuffle operation. Right now, its main limitations are:
+ *  1. It does not support complex types, i.e. Map, Array, and Struct.
+ *  2. It assumes that the objects passed in are [[Product2]]. So, it cannot be used when
+ *     [[org.apache.spark.util.collection.ExternalSorter]]'s merge sort operation is used because
+ *     the objects passed in the serializer are not in the type of [[Product2]]. Also also see
+ *     the comment of the `serializer` method in [[Exchange]] for more information on it.
  */
 private[sql] class Serializer2SerializationStream(
     keySchema: Array[DataType],
@@ -61,7 +71,7 @@ private[sql] class Serializer2SerializationStream(
 }
 
 /**
- * The deserialization stream for SparkSqlSerializer2.
+ * The corresponding deserialization stream for [[Serializer2SerializationStream]].
  */
 private[sql] class Serializer2DeserializationStream(
     keySchema: Array[DataType],