HyukjinKwon commented on code in PR #36063:
URL: https://github.com/apache/spark/pull/36063#discussion_r846942722
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala:
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@@ -1014,3 +1014,54 @@ case class PercentRank(children: Seq[Expression])
extends RankLike with SizeBase
override protected def withNewChildrenInternal(newChildren:
IndexedSeq[Expression]): PercentRank =
copy(children = newChildren)
}
+
+/**
+ * The Exponentially Weighted Window, MUST only be used in the Pandas API on
Pyspark.
+ * An exponentially weighted window is similar to an expanding window but with
each prior point
+ * being exponentially weighted down relative to the current point.
+ * In general, a weighted moving average is calculated as
+ * y_t = \frac{\sum_{i=0}^t w_i x_{t-i}}{\sum_{i=0}^t w_i},
+ * where x_t is the input, y_t is the result and the w_i are the weights.
+ * See
https://pandas.pydata.org/docs/user_guide/window.html#exponentially-weighted-window
+ * for details.
+ * For now, only function mean is supported. Other functions like sum/var will
be added
+ * in the future.
+ */
+private[sql] case class EWM(input: Expression, alpha: Double)
Review Comment:
We can remove this per SPARK-16813
```suggestion
case class EWM(input: Expression, alpha: Double)
```
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