Github user srinathshankar commented on a diff in the pull request:
https://github.com/apache/spark/pull/15637#discussion_r85609917
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/HistogramEndpoints.scala
---
@@ -0,0 +1,465 @@
+/*
+ * 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 scala.collection.immutable.TreeMap
+import scala.collection.mutable
+
+import com.google.common.primitives.{Doubles, Ints, Longs}
+
+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.{Expression,
ExpressionDescription, Literal}
+import
org.apache.spark.sql.catalyst.expressions.aggregate.ApproximatePercentile.{PercentileDigest,
PercentileDigestSerializer}
+import org.apache.spark.sql.catalyst.util.{ArrayBasedMapData,
QuantileSummaries}
+import org.apache.spark.sql.catalyst.util.QuantileSummaries._
+import org.apache.spark.sql.types.{DataType, _}
+import org.apache.spark.unsafe.types.UTF8String
+
+/**
+ * The HistogramEndpoints function for a column returns bins - (distinct
value, frequency) pairs
+ * of equi-width histogram when the number of distinct values is less than
or equal to the
+ * specified maximum number of bins. Otherwise, for column of string type,
it returns an empty
+ * map; for column of numeric type, it returns endpoints of equi-height
histogram - approximate
+ * percentiles at percentages 0.0, 1/numBins, 2/numBins, ...,
(numBins-1)/numBins, 1.0.
+ *
+ * @param child child expression that can produce column value with
`child.eval(inputRow)`
+ * @param numBinsExpression The maximum number of bins.
+ * @param accuracyExpression Accuracy used in computing approximate
percentiles.
+ */
+@ExpressionDescription(
+ usage =
+ """
+ _FUNC_(col, numBins [, accuracy]) - Returns bins - (distinct value,
frequency) pairs
+ of equi-width histogram when the number of distinct values is less
than or equal to the
+ specified maximum number of bins. Otherwise, for column of string
type, it returns an empty
+ map; for column of numeric type, it returns endpoints of equi-height
histogram - approximate
+ percentiles at percentages 0.0, 1/numBins, 2/numBins, ...,
(numBins-1)/numBins, 1.0. The
+ `accuracy` parameter (default: 10000) is a positive integer literal
which controls percentiles
+ 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 HistogramEndpoints(
+ child: Expression,
+ numBinsExpression: Expression,
+ accuracyExpression: Expression,
+ override val mutableAggBufferOffset: Int,
+ override val inputAggBufferOffset: Int) extends
TypedImperativeAggregate[EndpointsDigest] {
+
+ def this(child: Expression, numBinsExpression: Expression,
accuracyExpression: Expression) = {
+ this(child, numBinsExpression, accuracyExpression, 0, 0)
+ }
+
+ def this(child: Expression, numBinsExpression: Expression) = {
+ this(child, numBinsExpression,
Literal(ApproximatePercentile.DEFAULT_PERCENTILE_ACCURACY))
+ }
+
+ // Mark as lazy so that numBinsExpression is not evaluated during tree
transformation.
+ private lazy val numBins: Int =
numBinsExpression.eval().asInstanceOf[Int]
+
+ private lazy val percentages: Array[Double] = {
+ val array = new Array[Double](numBins + 1)
+ for (i <- 0 to numBins) {
+ array(i) = i / numBins.toDouble
+ }
+ array
+ }
+
+ private lazy val accuracy: Int =
accuracyExpression.eval().asInstanceOf[Int]
+
+ override def inputTypes: Seq[AbstractDataType] = {
+ Seq(TypeCollection(NumericType, TimestampType, DateType, StringType),
IntegerType, IntegerType)
+ }
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ val defaultCheck = super.checkInputDataTypes()
+ if (defaultCheck.isFailure) {
+ defaultCheck
+ } else if (!numBinsExpression.foldable ||
!accuracyExpression.foldable) {
+ TypeCheckFailure("The maximum number of bins or accuracy provided
must be a constant literal")
+ } else if (numBins < 2) {
+ TypeCheckFailure(
+ "The maximum number of bins provided must be a positive integer
literal >= 2 " +
+ s"(current value = $numBins)")
+ } else if (accuracy <= 0) {
+ TypeCheckFailure(
+ s"The accuracy provided must be a positive integer literal
(current value = $accuracy)")
+ } else {
+ TypeCheckSuccess
+ }
+ }
+
+ override def update(buffer: EndpointsDigest, input: InternalRow): Unit =
{
+ if (buffer.invalid) {
+ return
+ }
+ val evaluated = child.eval(input)
+ if (evaluated != null) {
+ buffer.update(child.dataType, evaluated, numBins)
+ }
+ }
+
+ override def merge(buffer: EndpointsDigest, other: EndpointsDigest):
Unit = {
+ if (buffer.invalid) return
+ if (other.invalid) {
+ buffer.invalid = true
+ buffer.clear()
+ return
+ }
+ buffer.merge(other, numBins)
+ }
+
+ override def eval(buffer: EndpointsDigest): Any = {
+ if (buffer.invalid) {
+ // return empty map
+ ArrayBasedMapData(Map.empty)
+ } else {
+ buffer match {
+ case stringDigest: StringEndpointsDigest =>
+ // sort the result to make it more readable
+ val sorted = TreeMap[UTF8String, Long](stringDigest.bins.toSeq:
_*)
+ ArrayBasedMapData(sorted.keys.toArray, sorted.values.toArray)
+ case numericDigest: NumericEndpointsDigest =>
+ if (!numericDigest.mapInvalid) {
+ val sorted = TreeMap[Double, Long](numericDigest.bins.toSeq:
_*)
+ ArrayBasedMapData(sorted.keys.toArray, sorted.values.toArray)
+ } else {
+ val percentiles =
numericDigest.percentileDigest.getPercentiles(percentages)
+ // we only need percentiles, this is for constructing MapData
+ val padding = new Array[Long](percentiles.length)
+ ArrayBasedMapData(percentiles, padding)
+ }
+ }
+ }
+ }
+
+ override def serialize(buffer: EndpointsDigest): Array[Byte] = {
+ buffer match {
+ case stringDigest: StringEndpointsDigest =>
StringEndpointsDigest.serialize(stringDigest)
+ case numericDigest: NumericEndpointsDigest =>
NumericEndpointsDigest.serialize(numericDigest)
+ }
+ }
+
+ override def deserialize(bytes: Array[Byte]): EndpointsDigest = {
+ child.dataType match {
+ case StringType => StringEndpointsDigest.deserialize(bytes)
+ case _ => NumericEndpointsDigest.deserialize(bytes)
+ }
+ }
+
+ override def createAggregationBuffer(): EndpointsDigest = {
+ child.dataType match {
+ case StringType =>
+ StringEndpointsDigest()
+ case _ =>
+ NumericEndpointsDigest(new PercentileDigest(1.0D / accuracy))
+ }
+ }
+
+ override def withNewMutableAggBufferOffset(newMutableAggBufferOffset:
Int): HistogramEndpoints = {
+ copy(mutableAggBufferOffset = newMutableAggBufferOffset)
+ }
+
+ override def withNewInputAggBufferOffset(newInputAggBufferOffset: Int):
HistogramEndpoints = {
+ copy(inputAggBufferOffset = newInputAggBufferOffset)
+ }
+
+ override def nullable: Boolean = false
+
+ override def dataType: DataType = {
+ child.dataType match {
+ case StringType => MapType(StringType, LongType)
+ case _ => MapType(DoubleType, LongType)
+ }
+ }
+
+ override def children: Seq[Expression] = Seq(child, numBinsExpression,
accuracyExpression)
+
+ override def prettyName: String = "histogram_endpoints"
+}
+
+trait EndpointsDigest {
+
+ // Mark this EndpointsDigest as invalid when:
+ // 1. for string type - the size of the hashmap (ndv of the column)
exceeds numBins;
+ // 2. for numeric type - Some Decimal value out of the range of Double
occurs.
+ var invalid: Boolean = false
+
+ def update(dataType: DataType, value: Any, numBins: Int): Unit
+ def merge(otherDigest: EndpointsDigest, numBins: Int): Unit
+ def clear(): Unit
+
+ // Updates baseMap, and returns success or not.
+ def updateMap[T](baseMap: mutable.HashMap[T, Long], value: T, numBins:
Int): Boolean = {
+ if (baseMap.contains(value)) {
--- End diff --
Isn't this just
`mergeMap(baseMap, (value -> 1), numBins)`
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