Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/17037#discussion_r102830753
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -647,7 +647,7 @@ df.groupBy("deviceType").count()
</div>
### Window Operations on Event Time
-Aggregations over a sliding event-time window are straightforward with
Structured Streaming. The key idea to understand about window-based
aggregations are very similar to grouped aggregations. In a grouped
aggregation, aggregate values (e.g. counts) are maintained for each unique
value in the user-specified grouping column. In case of window-based
aggregations, aggregate values are maintained for each window the event-time of
a row falls into. Let's understand this with an illustration.
+Aggregations over a sliding event-time window are straightforward with
Structured Streaming. The key idea to understand window-based aggregations is
very similar to grouped aggregations. In a grouped aggregation, aggregate
values (e.g. counts) are maintained for each unique value in the user-specified
grouping column. In case of window-based aggregations, aggregate values are
maintained for each window the event-time of a row falls into. Let's understand
this with an illustration.
--- End diff --
agreed.
"The key idea to understand is that window-based aggregations are very
similar to grouped aggregations."
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