msamirkhan commented on a change in pull request #29354:
URL: https://github.com/apache/spark/pull/29354#discussion_r465982616



##########
File path: 
external/avro/src/main/scala/org/apache/spark/sql/avro/AvroDeserializer.scala
##########
@@ -367,15 +372,45 @@ class AvroDeserializer(
     }
   }
 
-  private def createArrayData(elementType: DataType, length: Int): ArrayData = 
elementType match {
-    case BooleanType => UnsafeArrayData.fromPrimitiveArray(new 
Array[Boolean](length))
-    case ByteType => UnsafeArrayData.fromPrimitiveArray(new 
Array[Byte](length))
-    case ShortType => UnsafeArrayData.fromPrimitiveArray(new 
Array[Short](length))
-    case IntegerType => UnsafeArrayData.fromPrimitiveArray(new 
Array[Int](length))
-    case LongType => UnsafeArrayData.fromPrimitiveArray(new 
Array[Long](length))
-    case FloatType => UnsafeArrayData.fromPrimitiveArray(new 
Array[Float](length))
-    case DoubleType => UnsafeArrayData.fromPrimitiveArray(new 
Array[Double](length))
-    case _ => new GenericArrayData(new Array[Any](length))
+  private def getArrayDataCreator(elementType: DataType): Int => ArrayData = 
elementType match {
+    case BooleanType => length => UnsafeArrayData.createFreshArray(length, 1)
+    case ByteType => length => UnsafeArrayData.createFreshArray(length, 1)
+    case ShortType => length => UnsafeArrayData.createFreshArray(length, 2)
+    case IntegerType => length => UnsafeArrayData.createFreshArray(length, 4)
+    case LongType => length => UnsafeArrayData.createFreshArray(length, 8)
+    case FloatType => length => UnsafeArrayData.createFreshArray(length, 4)
+    case DoubleType => length => UnsafeArrayData.createFreshArray(length, 8)
+    case _ => length => new GenericArrayData(new Array[Any](length))
+  }
+
+  private def getRowCreator(st: StructType): () => InternalRow = {
+    val constructorsArray = new Array[Unit => MutableValue](st.fields.length)
+    var i = 0
+    while (i < st.fields.length) {
+      st.fields(i).dataType match {
+        case BooleanType => constructorsArray(i) = _ => new MutableBoolean
+        case ByteType => constructorsArray(i) = _ => new MutableByte
+        case ShortType => constructorsArray(i) = _ => new MutableShort
+        // We use INT for DATE internally
+        case IntegerType | DateType => constructorsArray(i) = _ => new 
MutableInt
+        // We use Long for Timestamp internally
+        case LongType | TimestampType => constructorsArray(i) = _ => new 
MutableLong
+        case FloatType => constructorsArray(i) = _ => new MutableFloat
+        case DoubleType => constructorsArray(i) = _ => new MutableDouble
+        case _ => constructorsArray(i) = _ => new MutableAny
+      }
+      i += 1
+    }
+
+    () => {
+      val array = new Array[MutableValue](constructorsArray.length)
+      var i = 0
+      while (i < constructorsArray.length) {
+        array(i) = constructorsArray(i)(Unit)
+        i += 1
+      }
+      new SpecificInternalRow(array)
+    }

Review comment:
       The profiler showed some time being spent in SpecificInternalRow 
constructor, and we saw improvements when moving to this model where based on 
the schema we can fill in a constructors array and for each data point, call 
these constructors one by one. In retrospect, changes can instead be made to 
the SpecificInternalRow constructor which will benefit 
https://github.com/apache/spark/pull/29353 as well. So this has been reverted 
in a latter commit. Read time improvements can be found in the pdf attached to 
PR in pg 2 under column B.
   
   The changes to SpecificInternalRow constructor can be found here: 
https://github.com/apache/spark/pull/29353#issuecomment-669459288




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