Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/15544#discussion_r140140577
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervals.scala
---
@@ -0,0 +1,235 @@
+/*
+ * 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.util
+
+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.{AttributeReference,
ExpectsInputTypes, Expression}
+import org.apache.spark.sql.catalyst.util.{ArrayData, GenericArrayData,
HyperLogLogPlusPlusHelper}
+import org.apache.spark.sql.types._
+
+/**
+ * This function counts the approximate number of distinct values (ndv) in
+ * intervals constructed from endpoints specified in
`endpointsExpression`. The endpoints should be
+ * sorted into ascending order. E.g., given an array of endpoints
+ * (endpoint_1, endpoint_2, ... endpoint_N), returns the approximate ndv's
for intervals
+ * [endpoint_1, endpoint_2], (endpoint_2, endpoint_3], ... (endpoint_N-1,
endpoint_N].
+ * To count ndv's in these intervals, apply the HyperLogLogPlusPlus
algorithm in each of them.
+ * @param child to estimate the ndv's of.
+ * @param endpointsExpression to construct the intervals, should be sorted
into ascending order.
+ * @param relativeSD The maximum estimation error allowed in the
HyperLogLogPlusPlus algorithm.
+ */
+case class ApproxCountDistinctForIntervals(
+ child: Expression,
+ endpointsExpression: Expression,
+ relativeSD: Double = 0.05,
+ mutableAggBufferOffset: Int = 0,
+ inputAggBufferOffset: Int = 0)
+ extends ImperativeAggregate with ExpectsInputTypes {
+
+ def this(child: Expression, endpointsExpression: Expression) = {
+ this(
+ child = child,
+ endpointsExpression = endpointsExpression,
+ relativeSD = 0.05,
+ mutableAggBufferOffset = 0,
+ inputAggBufferOffset = 0)
+ }
+
+ def this(child: Expression, endpointsExpression: Expression, relativeSD:
Expression) = {
+ this(
+ child = child,
+ endpointsExpression = endpointsExpression,
+ relativeSD = HyperLogLogPlusPlus.validateDoubleLiteral(relativeSD),
+ mutableAggBufferOffset = 0,
+ inputAggBufferOffset = 0)
+ }
+
+ override def inputTypes: Seq[AbstractDataType] = {
+ Seq(TypeCollection(NumericType, TimestampType, DateType), ArrayType)
+ }
+
+ // Mark as lazy so that endpointsExpression is not evaluated during tree
transformation.
+ lazy val endpoints: Array[Double] =
+ (endpointsExpression.dataType, endpointsExpression.eval()) match {
+ case (ArrayType(baseType: NumericType, _), arrayData: ArrayData) =>
+ val numericArray = arrayData.toObjectArray(baseType)
+ numericArray.map { x =>
+ baseType.numeric.toDouble(x.asInstanceOf[baseType.InternalType])
+ }
+ }
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ val defaultCheck = super.checkInputDataTypes()
+ if (defaultCheck.isFailure) {
+ defaultCheck
+ } else if (!endpointsExpression.foldable) {
+ TypeCheckFailure("The intervals provided must be constant literals")
+ } else if (endpoints.length < 2) {
+ TypeCheckFailure("The number of endpoints must be >= 2 to construct
intervals")
+ } else {
+ TypeCheckSuccess
+ }
+ }
+
+ // N endpoints construct N-1 intervals, creating a HLLPP for each
interval
+ private lazy val hllppArray = {
+ val array = new Array[HyperLogLogPlusPlusHelper](endpoints.length - 1)
+ for (i <- array.indices) {
+ array(i) = new HyperLogLogPlusPlusHelper(relativeSD)
+ }
+ // `numWords` in each HLLPPHelper should be the same because it is
determined by `relativeSD`
+ // which is shared among all HLLPPHelpers.
+ assert(array.map(_.numWords).distinct.length == 1)
+ array
+ }
+
+ private lazy val numWordsPerHllpp = hllppArray.head.numWords
+
+ private lazy val totalNumWords = numWordsPerHllpp * hllppArray.length
+
+ /** Allocate enough words to store all registers. */
+ override lazy val aggBufferAttributes: Seq[AttributeReference] = {
+ Seq.tabulate(totalNumWords) { i =>
+ AttributeReference(s"MS[$i]", LongType)()
+ }
+ }
+
+ override def aggBufferSchema: StructType =
StructType.fromAttributes(aggBufferAttributes)
+
+ // Note: although this simply copies aggBufferAttributes, this common
code can not be placed
+ // in the superclass because that will lead to initialization ordering
issues.
+ override lazy val inputAggBufferAttributes: Seq[AttributeReference] =
+ aggBufferAttributes.map(_.newInstance())
+
+ /** Fill all words with zeros. */
+ override def initialize(buffer: InternalRow): Unit = {
+ var word = 0
+ while (word < totalNumWords) {
+ buffer.setLong(mutableAggBufferOffset + word, 0)
+ word += 1
+ }
+ }
+
+ override def update(buffer: InternalRow, input: InternalRow): Unit = {
+ val value = child.eval(input)
+ // Ignore empty rows
+ if (value != null) {
+ // convert the value into a double value for searching in the double
array
+ val doubleValue = child.dataType match {
+ case n: NumericType =>
+ n.numeric.toDouble(value.asInstanceOf[n.InternalType])
+ case _: DateType =>
+ value.asInstanceOf[Int].toDouble
+ case _: TimestampType =>
+ value.asInstanceOf[Long].toDouble
+ }
+
+ // endpoints are sorted into ascending order already
+ if (endpoints.head > doubleValue || endpoints.last < doubleValue) {
+ // ignore if the value is out of the whole range
+ return
+ }
+
+ val hllppIndex = findHllppIndex(doubleValue)
+ val offset = mutableAggBufferOffset + hllppIndex * numWordsPerHllpp
+ hllppArray(hllppIndex).update(buffer, offset, value, child.dataType)
+ }
+ }
+
+ // Find which interval (HyperLogLogPlusPlusHelper) should receive the
given value.
+ def findHllppIndex(value: Double): Int = {
+ var index = util.Arrays.binarySearch(endpoints, value)
+ if (index >= 0) {
+ // The value is found.
+ if (index == 0) {
+ 0
+ } else {
+ // If the endpoints contains multiple elements with the specified
value, there is no
+ // guarantee which one binarySearch will return. We remove this
uncertainty by moving the
+ // index to the first position of these elements.
--- End diff --
We'd better explain this in the expression comment.
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