Remove runtime pattern matching.

Author: yhuai

sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala

@@ -126,6 +126,9 @@ private[sql] case class ParquetTableScan(
         conf)
 
     if (requestedPartitionOrdinals.nonEmpty) {
+      // This check if based on CatalystConverter.createRootConverter.
+      val primitiveRow = output.forall(a => ParquetTypesConverter.isPrimitiveType(a.dataType))
+
       baseRDD.mapPartitionsWithInputSplit { case (split, iter) =>
         val partValue = "([^=]+)=([^=]+)".r
         val partValues =
@@ -143,37 +146,46 @@ private[sql] case class ParquetTableScan(
           relation.partitioningAttributes
             .map(a => Cast(Literal(partValues(a.name)), a.dataType).eval(EmptyRow))
 
-        val mutableRow = new GenericMutableRow(output.size)
-
-        new Iterator[Row] {
-          def hasNext = iter.hasNext
-          def next() = {
-            iter.next() match {
-              case (_, row: SpecificMutableRow) =>
-                // Parquet will leave partitioning columns empty, so we fill them in here.
-                var i = 0
-                while (i < requestedPartitionOrdinals.size) {
-                  row(requestedPartitionOrdinals(i)._2) =
-                    partitionRowValues(requestedPartitionOrdinals(i)._1)
-                  i += 1
-                }
-                row
-
-              case (_, row: Row) =>
-                var i = 0
-                while (i < row.size) {
-                  mutableRow(i) = row(i)
-                  i += 1
-                }
-
-                i = 0
-                while (i < requestedPartitionOrdinals.size) {
-                  mutableRow(requestedPartitionOrdinals(i)._2) =
-                    partitionRowValues(requestedPartitionOrdinals(i)._1)
-                  i += 1
-                }
-
-                mutableRow
+        if (primitiveRow) {
+          new Iterator[Row] {
+            def hasNext = iter.hasNext
+            def next() = {
+              // We are using CatalystPrimitiveRowConverter and it returns a SpecificMutableRow.
+              val row = iter.next()._2.asInstanceOf[SpecificMutableRow]
+
+              // Parquet will leave partitioning columns empty, so we fill them in here.
+              var i = 0
+              while (i < requestedPartitionOrdinals.size) {
+                row(requestedPartitionOrdinals(i)._2) =
+                  partitionRowValues(requestedPartitionOrdinals(i)._1)
+                i += 1
+              }
+              row
+            }
+          }
+        } else {
+          // Create a mutable row since we need to fill in values from partition columns.
+          val mutableRow = new GenericMutableRow(output.size)
+          new Iterator[Row] {
+            def hasNext = iter.hasNext
+            def next() = {
+              // We are using CatalystGroupConverter and it returns a GenericRow.
+              // Since GenericRow is not mutable, we just cast it to a Row.
+              val row = iter.next()._2.asInstanceOf[Row]
+
+              var i = 0
+              while (i < row.size) {
+                mutableRow(i) = row(i)
+                i += 1
+              }
+              // Parquet will leave partitioning columns empty, so we fill them in here.
+              i = 0
+              while (i < requestedPartitionOrdinals.size) {
+                mutableRow(requestedPartitionOrdinals(i)._2) =
+                  partitionRowValues(requestedPartitionOrdinals(i)._1)
+                i += 1
+              }
+              mutableRow
             }
           }
         }

sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala

@@ -476,16 +476,21 @@ private[sql] case class ParquetRelation2(
     // When the data does not include the key and the key is requested then we must fill it in
     // based on information from the input split.
     if (!partitionKeysIncludedInDataSchema && partitionKeyLocations.nonEmpty) {
+      // This check if based on CatalystConverter.createRootConverter.
+      val primitiveRow =
+        requestedSchema.forall(a => ParquetTypesConverter.isPrimitiveType(a.dataType))
+
       baseRDD.mapPartitionsWithInputSplit { case (split: ParquetInputSplit, iterator) =>
         val partValues = selectedPartitions.collectFirst {
           case p if split.getPath.getParent.toString == p.path => p.values
         }.get
 
         val requiredPartOrdinal = partitionKeyLocations.keys.toSeq
-        val mutableRow = new GenericMutableRow(requestedSchema.size)
 
-        iterator.map {
-          case (_, row: SpecificMutableRow) =>
+        if (primitiveRow) {
+          iterator.map { pair =>
+            // We are using CatalystPrimitiveRowConverter and it returns a SpecificMutableRow.
+            val row = pair._2.asInstanceOf[SpecificMutableRow]
             var i = 0
             while (i < requiredPartOrdinal.size) {
               // TODO Avoids boxing cost here!
@@ -494,8 +499,14 @@ private[sql] case class ParquetRelation2(
               i += 1
             }
             row
-
-          case (_, row: Row) =>
+          }
+        } else {
+          // Create a mutable row since we need to fill in values from partition columns.
+          val mutableRow = new GenericMutableRow(requestedSchema.size)
+          iterator.map { pair =>
+            // We are using CatalystGroupConverter and it returns a GenericRow.
+            // Since GenericRow is not mutable, we just cast it to a Row.
+            val row = pair._2.asInstanceOf[Row]
             var i = 0
             while (i < row.size) {
               // TODO Avoids boxing cost here!
@@ -511,6 +522,7 @@ private[sql] case class ParquetRelation2(
               i += 1
             }
             mutableRow
+          }
         }
       }
     } else {