Github user clockfly commented on a diff in the pull request:
https://github.com/apache/spark/pull/14868#discussion_r77127003
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala
---
@@ -0,0 +1,321 @@
+/*
+ * 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.nio.ByteBuffer
+
+import com.google.common.primitives.{Doubles, Ints, Longs}
+
+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.ApproximatePercentile.{PercentileDigest}
+import org.apache.spark.sql.catalyst.util.{ArrayData, GenericArrayData}
+import org.apache.spark.sql.catalyst.util.QuantileSummaries
+import
org.apache.spark.sql.catalyst.util.QuantileSummaries.{defaultCompressThreshold,
Stats}
+import org.apache.spark.sql.types._
+
+/**
+ * The ApproximatePercentile function returns the approximate
percentile(s) of a column at the given
+ * percentage(s). A percentile is a watermark value below which a given
percentage of the column
+ * values fall. For example, the percentile of column `col` at percentage
50% is the median of
+ * column `col`.
+ *
+ * This function supports partial aggregation.
+ *
+ * @param child child expression that can produce 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 between
+ * 0.0 and 1.0.
+ * @param accuracyExpression Integer literal expression of approximation
accuracy. Higher value
+ * yields better accuracy, the default value is
+ * DEFAULT_PERCENTILE_ACCURACY.
+ */
+@ExpressionDescription(
+ usage =
+ """
+ _FUNC_(col, percentage [, accuracy]) - Returns the approximate
percentile value of numeric
+ column `col` at the given percentage. The value of percentage must
be between 0.0
+ and 1.0. The `accuracy` parameter (default: 10000) is a positive
integer literal which
+ controls approximation accuracy at the cost of memory. Higher value
of `accuracy` yields
+ better accuracy, `1.0/accuracy` is the relative error of the
approximation.
+
+ _FUNC_(col, array(percentage1 [, percentage2]...) [, accuracy]) -
Returns the approximate
+ percentile array of column `col` at the given percentage array. Each
value of the
+ percentage array must be between 0.0 and 1.0. The `accuracy`
parameter (default: 10000) is
+ a positive integer literal which controls approximation accuracy at
the cost of memory.
+ Higher value of `accuracy` yields better accuracy, `1.0/accuracy`
is the relative error of
+ the approximation.
+ """)
+case class ApproximatePercentile(
+ child: Expression,
+ percentageExpression: Expression,
+ accuracyExpression: Expression,
+ override val mutableAggBufferOffset: Int,
+ override val inputAggBufferOffset: Int) extends
TypedImperativeAggregate[PercentileDigest] {
+
+ def this(child: Expression, percentageExpression: Expression,
accuracyExpression: Expression) = {
+ this(child, percentageExpression, accuracyExpression, 0, 0)
+ }
+
+ def this(child: Expression, percentageExpression: Expression) = {
+ this(child, percentageExpression,
Literal(ApproximatePercentile.DEFAULT_PERCENTILE_ACCURACY))
+ }
+
+ // Mark as lazy so that accuracyExpression is not evaluated during tree
transformation.
+ private lazy val accuracy: Int =
accuracyExpression.eval().asInstanceOf[Int]
+
+ override def inputTypes: Seq[AbstractDataType] = {
+ Seq(DoubleType, TypeCollection(DoubleType, ArrayType), IntegerType)
+ }
+
+ // Mark as lazy so that percentageExpression is not evaluated during
tree transformation.
+ private lazy val (returnPercentileArray: Boolean, percentages:
Array[Double]) = {
+ (percentageExpression.dataType, percentageExpression.eval()) match {
+ // Rule ImplicitTypeCasts can cast other numeric types to double
+ case (_, num: Double) => (false, Array(num))
+ case (ArrayType(baseType: NumericType, _), arrayData: ArrayData) =>
+ val numericArray = arrayData.toObjectArray(baseType)
+ (true, numericArray.map { x =>
+ baseType.numeric.toDouble(x.asInstanceOf[baseType.InternalType])
+ })
+ case other =>
+ throw new AnalysisException(s"Invalid data type ${other._1} for
parameter percentage")
+ }
+ }
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ val defaultCheck = super.checkInputDataTypes()
+ if (defaultCheck.isFailure) {
+ defaultCheck
+ } else if (!percentageExpression.foldable ||
!accuracyExpression.foldable) {
+ TypeCheckFailure(s"The accuracy or percentage provided must be a
constant literal")
+ } else if (accuracy <= 0) {
+ TypeCheckFailure(
+ s"The accuracy provided must be a positive integer literal
(current value = $accuracy)")
+ } else if (percentages.exists(percentage => percentage < 0.0D ||
percentage > 1.0D)) {
+ TypeCheckFailure(
+ s"All percentage values must be between 0.0 and 1.0 " +
+ s"(current = ${percentages.mkString(", ")})")
+ } else {
+ TypeCheckSuccess
+ }
+ }
+
+ override def createAggregationBuffer(): PercentileDigest = {
+ val relativeError = 1.0D / accuracy
+ new PercentileDigest(relativeError)
+ }
+
+ override def update(buffer: PercentileDigest, inputRow: InternalRow):
Unit = {
+ val value = child.eval(inputRow)
+ // Ignore empty rows, for example: percentile_approx(null)
+ if (value != null) {
+ buffer.add(value.asInstanceOf[Double])
+ }
+ }
+
+ override def merge(buffer: PercentileDigest, other: PercentileDigest):
Unit = {
+ buffer.merge(other)
+ }
+
+ override def eval(buffer: PercentileDigest): Any = {
+ val result = buffer.getPercentiles(percentages)
+ if (result.length == 0) {
+ null
+ } else if (returnPercentileArray) {
+ new GenericArrayData(result)
+ } else {
+ result(0)
+ }
+ }
+
+ override def withNewMutableAggBufferOffset(newOffset: Int):
ApproximatePercentile =
+ copy(mutableAggBufferOffset = newOffset)
+
+ override def withNewInputAggBufferOffset(newOffset: Int):
ApproximatePercentile =
+ copy(inputAggBufferOffset = newOffset)
+
+ override def children: Seq[Expression] = Seq(child,
percentageExpression, accuracyExpression)
+
+ // Returns null for empty inputs
+ override def nullable: Boolean = true
+
+ override def dataType: DataType = {
+ if (returnPercentileArray) ArrayType(DoubleType) else DoubleType
+ }
+
+ override def prettyName: String = "percentile_approx"
+
+ override def serialize(obj: PercentileDigest): Array[Byte] = {
+ ApproximatePercentile.serializer.serialize(obj)
+ }
+
+ override def deserialize(bytes: Array[Byte]): PercentileDigest = {
+ ApproximatePercentile.serializer.deserialize(bytes)
+ }
+}
+
+object ApproximatePercentile {
+
+ // Default accuracy of Percentile approximation. Larger value means
better accuracy.
+ // The default relative error can be deduced by defaultError = 1.0 /
DEFAULT_PERCENTILE_ACCURACY
+ val DEFAULT_PERCENTILE_ACCURACY: Int = 10000
+
+ /**
+ * PercentileDigest is a probabilistic data structure used for
approximating percentiles
+ * with limited memory. PercentileDigest is backed by
[[QuantileSummaries]].
+ *
+ * @param summaries underlying probabilistic data structure
[[QuantileSummaries]].
+ * @param isCompressed An internal flag from class [[QuantileSummaries]]
to indicate whether the
+ * underlying quantileSummaries is compressed.
+ */
+ class PercentileDigest(
+ private var summaries: QuantileSummaries,
+ private var isCompressed: Boolean) {
+
+ // Trigger compression if the QuantileSummaries's buffer length exceeds
+ // compressThresHoldBufferLength. The buffer length can be get by
+ // quantileSummaries.sampled.length
+ private[this] final val compressThresHoldBufferLength: Int = {
+ // Max buffer length after compression.
+ val maxBufferLengthAfterCompression: Int = (1 /
summaries.relativeError).toInt * 2
+ // A safe upper bound for buffer length before compression
+ maxBufferLengthAfterCompression * 2
+ }
+
+ def this(relativeError: Double) = {
+ this(new QuantileSummaries(defaultCompressThreshold, relativeError),
isCompressed = true)
+ }
+
+ /** Returns compressed object of [[QuantileSummaries]] */
+ def quantileSummaries: QuantileSummaries = {
+ if (!isCompressed) compress()
+ summaries
+ }
+
+ /** Insert an observation value into the PercentileDigest data
structure. */
+ def add(value: Double): Unit = {
+ summaries = summaries.insert(value)
+ // The result of QuantileSummaries.insert is un-compressed
+ isCompressed = false
+
+ // Currently, QuantileSummaries ignores the construction parameter
compressThresHold,
+ // which may cause QuantileSummaries to occupy unbounded memory. We
have to hack around here
+ // to make sure QuantileSummaries doesn't occupy infinite memory.
+ // TODO: Figure out why QuantileSummaries ignores construction
parameter compressThresHold
+ if (summaries.sampled.length >= compressThresHoldBufferLength)
compress()
+ }
+
+ /** In-place merges in another PercentileDigest. */
+ def merge(other: PercentileDigest): Unit = {
+ if (!isCompressed) compress()
+ summaries = summaries.merge(other.quantileSummaries)
--- End diff --
Yes. other.quantileSummaries will get a compression version of
QuantileSummaries.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
