Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/11583#discussion_r61763969
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/PivotFirst.scala
---
@@ -0,0 +1,152 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.aggregate
+
+import scala.collection.immutable.HashMap
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.util.GenericArrayData
+import org.apache.spark.sql.types._
+
+object PivotFirst {
+
+ def supportsDataType(dataType: DataType): Boolean =
updateFunction.isDefinedAt(dataType)
+
+ // Currently UnsafeRow does not support the generic update method (throws
+ // UnsupportedOperationException), so we need to explicitly support each
DataType.
+ private val updateFunction: PartialFunction[DataType, (MutableRow, Int,
Any) => Unit] = {
+ case DoubleType =>
+ (row, offset, value) => row.setDouble(offset,
value.asInstanceOf[Double])
+ case IntegerType =>
+ (row, offset, value) => row.setInt(offset, value.asInstanceOf[Int])
+ case LongType =>
+ (row, offset, value) => row.setLong(offset, value.asInstanceOf[Long])
+ case FloatType =>
+ (row, offset, value) => row.setFloat(offset,
value.asInstanceOf[Float])
+ case BooleanType =>
+ (row, offset, value) => row.setBoolean(offset,
value.asInstanceOf[Boolean])
+ case ShortType =>
+ (row, offset, value) => row.setShort(offset,
value.asInstanceOf[Short])
+ case ByteType =>
+ (row, offset, value) => row.setByte(offset, value.asInstanceOf[Byte])
+ case d: DecimalType =>
+ (row, offset, value) => row.setDecimal(offset,
value.asInstanceOf[Decimal], d.precision)
+ }
+}
+
+/**
+ * PivotFirst is a aggregate function used in the second phase of a two
phase pivot to do the
+ * required rearrangement of values into pivoted form.
+ *
+ * For example on an input of
+ * A | B
+ * --+--
+ * x | 1
+ * y | 2
+ * z | 3
+ *
+ * with pivotColumn=A, valueColumn=B, and pivotColumnValues=[z,y] the
output is [3,2].
+ *
+ * @param pivotColumn column that determines which output position to put
valueColumn in.
+ * @param valueColumn the column that is being rearranged.
+ * @param pivotColumnValues the list of pivotColumn values in the order of
desired output. Values
+ * not listed here will be ignored.
+ */
+case class PivotFirst(
+ pivotColumn: Expression,
+ valueColumn: Expression,
+ pivotColumnValues: Seq[Any],
+ mutableAggBufferOffset: Int = 0,
+ inputAggBufferOffset: Int = 0) extends ImperativeAggregate {
+
+ override val children: Seq[Expression] = pivotColumn :: valueColumn ::
Nil
+
+ override lazy val inputTypes: Seq[AbstractDataType] =
children.map(_.dataType)
+
+ override val nullable: Boolean = false
+
+ val valueDataType = valueColumn.dataType
+
+ override val dataType: DataType = ArrayType(valueDataType)
+
+ val pivotIndex = HashMap(pivotColumnValues.zipWithIndex: _*)
+
+ val indexSize = pivotIndex.size
+
+ private val updateRow: (MutableRow, Int, Any) => Unit =
PivotFirst.updateFunction(valueDataType)
+
+ override def update(mutableAggBuffer: MutableRow, inputRow:
InternalRow): Unit = {
+ val pivotColValue = pivotColumn.eval(inputRow)
+ if (pivotColValue != null) {
+ // We ignore rows whose pivot column value is not in the list of
pivot column values.
+ val index = pivotIndex.getOrElse(pivotColValue, -1)
+ if (index >= 0) {
+ val value = valueColumn.eval(inputRow)
+ if (value != null) {
+ updateRow(mutableAggBuffer, mutableAggBufferOffset + index,
value)
+ }
+ }
+ }
+ }
+
+ override def merge(mutableAggBuffer: MutableRow, inputAggBuffer:
InternalRow): Unit = {
+ for (i <- 0 until indexSize) {
+ if (!inputAggBuffer.isNullAt(inputAggBufferOffset + i)) {
+ val value = inputAggBuffer.get(inputAggBufferOffset + i,
valueDataType)
+ updateRow(mutableAggBuffer, mutableAggBufferOffset + i, value)
+ }
+ }
+ }
+
+ override def initialize(mutableAggBuffer: MutableRow): Unit =
valueDataType match {
+ case d: DecimalType =>
+ // Per doc of setDecimal we need to do this instead of setNullAt for
DecimalType.
+ for (i <- 0 until indexSize) {
+ mutableAggBuffer.setDecimal(mutableAggBufferOffset + i, null,
d.precision)
+ }
+ case _ =>
+ for (i <- 0 until indexSize) {
+ mutableAggBuffer.setNullAt(mutableAggBufferOffset + i)
+ }
+ }
+
+ override def eval(input: InternalRow): Any = {
+ val result = new Array[Any](indexSize)
+ for (i <- 0 until indexSize) {
+ result(i) = input.get(mutableAggBufferOffset + i, valueDataType)
+ }
+ new GenericArrayData(result)
+ }
+
+ override def withNewInputAggBufferOffset(newInputAggBufferOffset: Int):
ImperativeAggregate =
+ copy(inputAggBufferOffset = newInputAggBufferOffset)
+
+ override def withNewMutableAggBufferOffset(newMutableAggBufferOffset:
Int): ImperativeAggregate =
+ copy(mutableAggBufferOffset = newMutableAggBufferOffset)
+
+
+ override lazy val aggBufferAttributes: Seq[AttributeReference] =
+ pivotIndex.toList.sortBy(_._2).map(kv =>
AttributeReference(kv._1.toString, valueDataType)())
--- End diff --
How about we avoid of using lazy val for `aggBufferAttributes`,
`aggBufferSchema`, and `inputAggBufferAttributes`?
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