Github user jiangxb1987 commented on a diff in the pull request:

    https://github.com/apache/spark/pull/14136#discussion_r70244013
  
    --- Diff: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/Percentile.scala
 ---
    @@ -0,0 +1,148 @@
    +/*
    + * 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 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._
    +import org.apache.spark.util.collection.OpenHashMap
    +
    +/**
    + * The Percentile aggregate function computes the exact percentile(s) of 
expr at pc with range in
    + * [0, 1].
    + * The parameter pc can be a DoubleType or DoubleType array.
    + */
    +@ExpressionDescription(
    +  usage = """_FUNC_(epxr, pc) - Returns the percentile(s) of expr at pc 
(range: [0,1]). pc can be
    +  a double or double array.""")
    +case class Percentile(
    +                     child: Expression,
    +                     pc: Seq[Double],
    +                     mutableAggBufferOffset: Int = 0,
    +                     inputAggBufferOffset: Int = 0)
    +  extends ImperativeAggregate {
    +
    +  def this(child: Expression, pc: Double) = {
    +    this(child = child, pc = Seq(pc), mutableAggBufferOffset = 0, 
inputAggBufferOffset = 0)
    +  }
    +
    +  override def prettyName: String = "percentile"
    +
    +  override def withNewMutableAggBufferOffset(newMutableAggBufferOffset: 
Int): ImperativeAggregate =
    +    copy(mutableAggBufferOffset = newMutableAggBufferOffset)
    +
    +  override def withNewInputAggBufferOffset(newInputAggBufferOffset: Int): 
ImperativeAggregate =
    +    copy(inputAggBufferOffset = newInputAggBufferOffset)
    +
    +  var counts = new OpenHashMap[Long, Long]()
    +
    +  override def children: Seq[Expression] = Seq(child)
    +
    +  override def nullable: Boolean = false
    +
    +  override def dataType: DataType = ArrayType(DoubleType)
    +
    +  override def inputTypes: Seq[AbstractDataType] = Seq(AnyDataType)
    +
    +  override def supportsPartial: Boolean = false
    +
    +  override def aggBufferSchema: StructType = 
StructType.fromAttributes(aggBufferAttributes)
    +
    +  override val aggBufferAttributes: Seq[AttributeReference] = 
pc.map(percentile =>
    +    AttributeReference(percentile.toString, DoubleType)())
    +
    +  override val inputAggBufferAttributes: Seq[AttributeReference] =
    +    aggBufferAttributes.map(_.newInstance())
    +
    +  override def initialize(buffer: MutableRow): Unit = {
    +    for (i <- 0 until pc.size) {
    +      buffer.setNullAt(mutableAggBufferOffset + i)
    +    }
    +  }
    +
    +  override def update(buffer: MutableRow, input: InternalRow): Unit = {
    +    val v = child.eval(input)
    +
    +    v match {
    +      case o: Int => counts.changeValue(o.toLong, 1L, _ + 1L)
    +      case o: Long => counts.changeValue(o, 1L, _ + 1L)
    +      case _ => return false
    +    }
    +  }
    +
    +  override def merge(buffer: MutableRow, inputBuffer: InternalRow): Unit = 
{
    +    sys.error("Percentile cannot be used in partial aggregations.")
    +  }
    +
    +  override def eval(buffer: InternalRow): Any = {
    +    if (counts.size == 0) {
    +      return new GenericArrayData(Seq.empty)
    +    }
    +
    +    // Sort all items and generate a sequence, then accumulate the counts
    +    val sortedCounts = counts.toSeq.sortBy(_._1)
    +    val aggreCounts = sortedCounts.scanLeft(0L, 0L) { (k1: (Long, Long), 
k2: (Long, Long)) =>
    +      (k2._1, k1._2 + k2._2)
    +    }.drop(1)
    +    val maxPosition = aggreCounts.last._2 - 1
    +
    +    new GenericArrayData(pc.map { percentile =>
    +      if (percentile < 0.0 || percentile > 1.0) {
    --- End diff --
    
    Yep, you are right.


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