Github user hvanhovell commented on the pull request:
https://github.com/apache/spark/pull/6104#issuecomment-101519756
Hi,
In the JIRA the following examples is given:
```
df.select(
df.store,
df.date,
df.sales,
avg(df.sales).over.partitionBy(df.store)
.orderBy(df.store)
.rowsFollowing(0) // this means from unbounded
preceding to current row
)
```
Is there a reason for why the aggregation operation has moved from the
beginning (the style in the JIRA), to the end (style above)? Are both still
possible? I'd prefer the former, since it seems a bit shorter, and more
recognizable comming from SQL.
On a related note. Is it also an idea to be able to create a seperate
window (groupBy/orderBy) definition and use this definition in one or more
windowed aggregates. For example:
```
val window = partitionBy($"store").orderBy($"date)
df.select (
$"store"
,$"date"
,sum($"sales").over(window).rowsFollowing(0).as("TotalSales")
,sum($"sales").over(window).rowsFollowing(0).rowsPreceding(2).as("SalesLast3M")
)
```
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