Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71073767
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -620,16 +603,14 @@ df.groupBy("type").count()
### 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.
-Imagine our quick example is modified and the stream now contains lines
along with the time when the line was generated. Instead of running word
counts, we want to count words within 10 minute windows, updating every 5
minutes. That is, word counts in words received between 10 minute windows 12:00
- 12:10, 12:05 - 12:15, 12:10 - 12:20, etc. Note that 12:00 - 12:10 means data
that arrived after 12:00 but before 12:10. Now, consider a word that was
received at 12:07. This word should increment the counts corresponding to two
windows 12:00 - 12:10 and 12:05 - 12:15. So the counts will be indexed by both,
the grouping key (i.e. the word) and the window (can be calculated from the
event-time).
--- End diff --
Added anchor `[quick example](#quick-example)`
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