Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/10029#discussion_r49562322
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/Covariance.scala
---
@@ -0,0 +1,205 @@
+/*
+ * 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 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.util.TypeUtils
+import org.apache.spark.sql.types._
+
+/**
+ * Compute the covariance between two expressions.
+ * When applied on empty data (i.e., count is zero), it returns NULL.
+ *
+ */
+abstract class Covariance(
+ left: Expression,
+ right: Expression)
+ extends ImperativeAggregate with Serializable {
+ override def children: Seq[Expression] = Seq(left, right)
+
+ override def nullable: Boolean = true
+
+ override def dataType: DataType = DoubleType
+
+ override def inputTypes: Seq[AbstractDataType] = Seq(DoubleType,
DoubleType)
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ if (left.dataType.isInstanceOf[DoubleType] &&
right.dataType.isInstanceOf[DoubleType]) {
+ TypeCheckResult.TypeCheckSuccess
+ } else {
+ TypeCheckResult.TypeCheckFailure(
+ s"covariance requires that both arguments are double type, " +
+ s"not (${left.dataType}, ${right.dataType}).")
+ }
+ }
+
+ override def aggBufferSchema: StructType =
StructType.fromAttributes(aggBufferAttributes)
+
+ override def inputAggBufferAttributes: Seq[AttributeReference] = {
+ aggBufferAttributes.map(_.newInstance())
+ }
+
+ override val aggBufferAttributes: Seq[AttributeReference] = Seq(
+ AttributeReference("xAvg", DoubleType)(),
+ AttributeReference("yAvg", DoubleType)(),
+ AttributeReference("Ck", DoubleType)(),
+ AttributeReference("count", LongType)())
+
+ // Local cache of mutableAggBufferOffset(s) that will be used in update
and merge
+ val xAvgOffset = mutableAggBufferOffset
+ val yAvgOffset = mutableAggBufferOffset + 1
+ val CkOffset = mutableAggBufferOffset + 2
+ val countOffset = mutableAggBufferOffset + 3
+
+ // Local cache of inputAggBufferOffset(s) that will be used in update
and merge
+ val inputXAvgOffset = inputAggBufferOffset
+ val inputYAvgOffset = inputAggBufferOffset + 1
+ val inputCkOffset = inputAggBufferOffset + 2
+ val inputCountOffset = inputAggBufferOffset + 3
+
+ override def initialize(buffer: MutableRow): Unit = {
+ buffer.setDouble(xAvgOffset, 0.0)
+ buffer.setDouble(yAvgOffset, 0.0)
+ buffer.setDouble(CkOffset, 0.0)
+ buffer.setLong(countOffset, 0L)
+ }
+
+ override def update(buffer: MutableRow, input: InternalRow): Unit = {
+ val leftEval = left.eval(input)
+ val rightEval = right.eval(input)
+
+ if (leftEval != null && rightEval != null) {
+ val x = leftEval.asInstanceOf[Double]
+ val y = rightEval.asInstanceOf[Double]
+
+ var xAvg = buffer.getDouble(xAvgOffset)
+ var yAvg = buffer.getDouble(yAvgOffset)
+ var Ck = buffer.getDouble(CkOffset)
+ var count = buffer.getLong(countOffset)
+
+ val deltaX = x - xAvg
+ val deltaY = y - yAvg
+ count += 1
+ xAvg += deltaX / count
+ yAvg += deltaY / count
+ Ck += deltaX * (y - yAvg)
+
+ buffer.setDouble(xAvgOffset, xAvg)
+ buffer.setDouble(yAvgOffset, yAvg)
+ buffer.setDouble(CkOffset, Ck)
+ buffer.setLong(countOffset, count)
+ }
+ }
+
+ // Merge counters from other partitions. Formula can be found at:
+ // http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
+ override def merge(buffer1: MutableRow, buffer2: InternalRow): Unit = {
+ val count2 = buffer2.getLong(inputCountOffset)
+
+ // We only go to merge two buffers if there is at least one record
aggregated in buffer2.
+ // We don't need to check count in buffer1 because if count2 is more
than zero, totalCount
+ // is more than zero too, then we won't get a divide by zero exception.
+ if (count2 > 0) {
+ var xAvg = buffer1.getDouble(xAvgOffset)
+ var yAvg = buffer1.getDouble(yAvgOffset)
+ var Ck = buffer1.getDouble(CkOffset)
+ var count = buffer1.getLong(countOffset)
+
+ val xAvg2 = buffer2.getDouble(inputXAvgOffset)
+ val yAvg2 = buffer2.getDouble(inputYAvgOffset)
+ val Ck2 = buffer2.getDouble(inputCkOffset)
+
+ val totalCount = count + count2
+ val deltaX = xAvg - xAvg2
+ val deltaY = yAvg - yAvg2
+ Ck += Ck2 + deltaX * deltaY * count / totalCount * count2
+ xAvg = (xAvg * count + xAvg2 * count2) / totalCount
+ yAvg = (yAvg * count + yAvg2 * count2) / totalCount
+ count = totalCount
+
+ buffer1.setDouble(xAvgOffset, xAvg)
+ buffer1.setDouble(yAvgOffset, yAvg)
+ buffer1.setDouble(CkOffset, Ck)
+ buffer1.setLong(countOffset, count)
+ }
+ }
+}
+
+case class CovSample(
+ left: Expression,
+ right: Expression,
+ mutableAggBufferOffset: Int = 0,
+ inputAggBufferOffset: Int = 0)
+ extends Covariance(left, right) {
+
+ override def withNewMutableAggBufferOffset(newMutableAggBufferOffset:
Int): ImperativeAggregate =
+ copy(mutableAggBufferOffset = newMutableAggBufferOffset)
+
+ override def withNewInputAggBufferOffset(newInputAggBufferOffset: Int):
ImperativeAggregate =
+ copy(inputAggBufferOffset = newInputAggBufferOffset)
+
+ override def eval(buffer: InternalRow): Any = {
+ val count = buffer.getLong(countOffset)
+ if (count > 0) {
+ if (count > 1) {
+ val Ck = buffer.getDouble(CkOffset)
+ val cov = Ck / (count - 1)
+ if (cov.isNaN) {
+ null
+ } else {
+ cov
+ }
+ } else {
+ 0.0
+ }
+ } else {
+ null
+ }
+ }
+}
+
+case class CovPopulation(
+ left: Expression,
+ right: Expression,
+ mutableAggBufferOffset: Int = 0,
+ inputAggBufferOffset: Int = 0)
+ extends Covariance(left, right) {
+
+ override def withNewMutableAggBufferOffset(newMutableAggBufferOffset:
Int): ImperativeAggregate =
+ copy(mutableAggBufferOffset = newMutableAggBufferOffset)
+
+ override def withNewInputAggBufferOffset(newInputAggBufferOffset: Int):
ImperativeAggregate =
+ copy(inputAggBufferOffset = newInputAggBufferOffset)
+
+ override def eval(buffer: InternalRow): Any = {
+ val count = buffer.getLong(countOffset)
+ if (count > 0) {
+ val Ck = buffer.getDouble(CkOffset)
+ val cov = Ck / count
--- End diff --
I meant it make more sense to check `Ck` directly, because count > 0.
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