Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/14298#discussion_r73993906 --- 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 -- I think putting buffer object in agg buffer row is better, but that need to be well designed. Can you hold this PR for a while? We are discussing about it internally.
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