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From cloud-fan <...@git.apache.org>
Subject [GitHub] spark pull request #14298: [SPARK-16283][SQL] Implement `percentile_approx` ...
Date Mon, 08 Aug 2016 07:29:33 GMT
Github user cloud-fan commented on a diff in the pull request:

    https://github.com/apache/spark/pull/14298#discussion_r73832267
  
    --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/PercentileApprox.scala
---
    @@ -0,0 +1,462 @@
    +/*
    + * 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.mutable.ArrayBuffer
    +
    +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.expressions._
    +import org.apache.spark.sql.catalyst.expressions.aggregate.QuantileSummaries.Stats
    +import org.apache.spark.sql.catalyst.util._
    +import org.apache.spark.sql.types._
    +
    +/**
    + * Computes an approximate percentile (quantile) using the G-K algorithm (see below),
for very
    + * large numbers of rows where the regular percentile() UDAF might run out of memory.
    + *
    + * The input is a single double value or an array of double values representing the percentiles
    + * requested. The output, corresponding to the input, is either a single double value
or an
    + * array of doubles that are the percentile values.
    + */
    +@ExpressionDescription(
    +  usage = """_FUNC_(col, p [, B]) - Returns an approximate pth percentile of a numeric
column in the
    +     group. The B parameter, which defaults to 1000, controls approximation accuracy
at the cost of
    +     memory; higher values yield better approximations.
    +    _FUNC_(col, array(p1 [, p2]...) [, B]) - Same as above, but accepts and returns an
array of
    +     percentile values instead of a single one.
    +    """)
    +case class PercentileApprox(
    +    child: Expression,
    +    percentilesExpr: Expression,
    +    bExpr: Option[Expression],
    +    percentiles: Seq[Double],  // the extracted percentiles
    +    B: Int,                    // the extracted B
    +    resultAsArray: Boolean,    // whether to return the result as an array
    +    mutableAggBufferOffset: Int = 0,
    +    inputAggBufferOffset: Int = 0) extends ImperativeAggregate {
    +
    +  private def this(child: Expression, percentilesExpr: Expression, bExpr: Option[Expression])
= {
    +    this(
    +      child = child,
    +      percentilesExpr = percentilesExpr,
    +      bExpr = bExpr,
    +      // validate and extract percentiles
    +      percentiles = PercentileApprox.validatePercentilesLiteral(percentilesExpr)._1,
    +      // validate and extract B
    +      B = bExpr.map(PercentileApprox.validateBLiteral(_)).getOrElse(PercentileApprox.B_DEFAULT),
    +      // validate and mark whether we should return results as array of double or not
    +      resultAsArray = PercentileApprox.validatePercentilesLiteral(percentilesExpr)._2)
    +  }
    +
    +  // Constructor for the "_FUNC_(col, p) / _FUNC_(col, array(p1, ...))" form
    +  def this(child: Expression, percentilesExpr: Expression) = {
    +    this(child, percentilesExpr, None)
    +  }
    +
    +  // Constructor for the "_FUNC_(col, p, B) / _FUNC_(col, array(p1, ...), B)" form
    +  def this(child: Expression, percentilesExpr: Expression, bExpr: Expression) = {
    +    this(child, percentilesExpr, Some(bExpr))
    +  }
    +
    +  override def prettyName: String = "percentile_approx"
    +
    +  override def withNewMutableAggBufferOffset(newMutableAggBufferOffset: Int): ImperativeAggregate
=
    +    copy(mutableAggBufferOffset = newMutableAggBufferOffset)
    +
    +  override def withNewInputAggBufferOffset(newInputAggBufferOffset: Int): ImperativeAggregate
=
    +    copy(inputAggBufferOffset = newInputAggBufferOffset)
    +
    +  override def children: Seq[Expression] =
    +    bExpr.map(child :: percentilesExpr :: _ :: Nil).getOrElse(child :: percentilesExpr
:: Nil)
    +
    +  // we would return null for empty inputs
    +  override def nullable: Boolean = true
    +
    +  override def dataType: DataType = if (resultAsArray) ArrayType(DoubleType) else DoubleType
    +
    +  override def inputTypes: Seq[AbstractDataType] = Seq(NumericType, AnyDataType, IntegralType)
    +
    +  override def checkInputDataTypes(): TypeCheckResult =
    +    TypeUtils.checkForNumericExpr(child.dataType, "function percentile_approx")
    +
    +  // The number of intermediate outputs is highly relative to the actual data-set (an
upper bound is
    +  // (11/2e)log(2en), where e is the relativeError parameter, n is the number of items
in the
    +  // dataset) -- thus it's hard to allocate agg buffer in advance without knowing the
size of
    +  // inputs. Due to this reason, currently we don't support partial mode.
    --- End diff --
    
    can you explain a bit more about this? AFAIK, hive supports partial aggregate for `percentile_approx`,
and it looks to me that your implementation keeps the buffer data(`QuantileSummaries`) in
this aggregate function object, instead of letting aggregate operator manage it, that's the
main reason why we can't support partial aggregate for `percentile_approx` I think.


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