Github user hvanhovell commented on a diff in the pull request:
https://github.com/apache/spark/pull/14136#discussion_r89672415
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/Percentile.scala
---
@@ -0,0 +1,326 @@
+/*
+ * 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 java.io.{ByteArrayInputStream, ByteArrayOutputStream,
DataInputStream, DataOutputStream}
+import java.util
+
+import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import
org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckFailure,
TypeCheckSuccess}
+import org.apache.spark.sql.catalyst.expressions._
+import
org.apache.spark.sql.catalyst.expressions.aggregate.Percentile.Countings
+import org.apache.spark.sql.catalyst.util._
+import org.apache.spark.sql.types._
+import org.apache.spark.util.collection.OpenHashMap
+
+
+/**
+ * The Percentile aggregate function returns the exact percentile(s) of
numeric column `expr` at
+ * the given percentage(s) with value range in [0.0, 1.0].
+ *
+ * The operator is bound to the slower sort based aggregation path because
the number of elements
+ * and their partial order cannot be determined in advance. Therefore we
have to store all the
+ * elements in memory, and that too many elements can cause GC paused and
eventually OutOfMemory
+ * Errors.
+ *
+ * @param child child expression that produce numeric column value with
`child.eval(inputRow)`
+ * @param percentageExpression Expression that represents a single
percentage value or an array of
+ * percentage values. Each percentage value
must be in the range
+ * [0.0, 1.0].
+ */
+@ExpressionDescription(
+ usage =
+ """
+ _FUNC_(col, percentage) - Returns the exact percentile value of
numeric column `col` at the
+ given percentage. The value of percentage must be between 0.0 and
1.0.
+
+ _FUNC_(col, array(percentage1 [, percentage2]...)) - Returns the
exact percentile value array
+ of numeric column `col` at the given percentage(s). Each value of
the percentage array must
+ be between 0.0 and 1.0.
+ """)
+case class Percentile(
+ child: Expression,
+ percentageExpression: Expression,
+ mutableAggBufferOffset: Int = 0,
+ inputAggBufferOffset: Int = 0) extends
TypedImperativeAggregate[Countings] {
+
+ def this(child: Expression, percentageExpression: Expression) = {
+ this(child, percentageExpression, 0, 0)
+ }
+
+ override def prettyName: String = "percentile"
+
+ override def withNewMutableAggBufferOffset(newMutableAggBufferOffset:
Int): Percentile =
+ copy(mutableAggBufferOffset = newMutableAggBufferOffset)
+
+ override def withNewInputAggBufferOffset(newInputAggBufferOffset: Int):
Percentile =
+ copy(inputAggBufferOffset = newInputAggBufferOffset)
+
+ // Mark as lazy so that percentageExpression is not evaluated during
tree transformation.
+ private lazy val returnPercentileArray =
percentageExpression.dataType.isInstanceOf[ArrayType]
+
+ @transient
+ private lazy val percentages = evalPercentages(percentageExpression)
+
+ override def children: Seq[Expression] = child :: percentageExpression
:: Nil
+
+ // Returns null for empty inputs
+ override def nullable: Boolean = true
+
+ override lazy val dataType: DataType = percentageExpression.dataType
match {
+ case _: ArrayType => ArrayType(DoubleType, false)
+ case _ => DoubleType
+ }
+
+ override def inputTypes: Seq[AbstractDataType] =
percentageExpression.dataType match {
+ case _: ArrayType => Seq(NumericType, ArrayType(DoubleType, false))
+ case _ => Seq(NumericType, DoubleType)
+ }
+
+ // Check the inputTypes are valid, and the percentageExpression
satisfies:
+ // 1. percentageExpression must be foldable;
+ // 2. percentages(s) must be in the range [0.0, 1.0].
+ override def checkInputDataTypes(): TypeCheckResult = {
+ // Validate the inputTypes
+ val defaultCheck = super.checkInputDataTypes()
+ if (defaultCheck.isFailure) {
+ defaultCheck
+ } else if (!percentageExpression.foldable) {
+ // percentageExpression must be foldable
+ TypeCheckFailure(s"The percentage(s) must be a constant literal, " +
+ s"but got ${percentageExpression}")
+ } else if (percentages.exists(percentage => percentage < 0.0 ||
percentage > 1.0)) {
+ // percentages(s) must be in the range [0.0, 1.0]
+ TypeCheckFailure(s"Percentage(s) must be between 0.0 and 1.0, " +
+ s"but got ${percentageExpression}")
+ } else {
+ TypeCheckSuccess
+ }
+ }
+
+ override def createAggregationBuffer(): Countings = {
+ // Initialize new Countings instance here.
+ Countings()
+ }
+
+ private def evalPercentages(expr: Expression): Seq[Double] =
(expr.dataType, expr.eval()) match {
--- End diff --
Move this to the definition of percentages. You can also make this much
simpler. The analyzer guarantees that you either get a single double, or an
ArrayData of double:
```scala
@transient
private lazy val percentages = percentageExpression.eval() match {
case p: Double => Seq(p)
case a: ArrayData => a.toDoubleArray().toSeq
}
```
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