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Posted to commits@spark.apache.org by td...@apache.org on 2016/06/30 21:01:39 UTC
spark git commit: [SPARK-16256][DOCS] Fix window operation diagram
Repository: spark
Updated Branches:
refs/heads/master c62263340 -> 5d00a7bc1
[SPARK-16256][DOCS] Fix window operation diagram
Author: Tathagata Das <ta...@gmail.com>
Closes #14001 from tdas/SPARK-16256-2.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/5d00a7bc
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/5d00a7bc
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/5d00a7bc
Branch: refs/heads/master
Commit: 5d00a7bc19ddeb1b5247733b55095a03ee7b1a30
Parents: c622633
Author: Tathagata Das <ta...@gmail.com>
Authored: Thu Jun 30 14:01:34 2016 -0700
Committer: Tathagata Das <ta...@gmail.com>
Committed: Thu Jun 30 14:01:34 2016 -0700
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docs/img/structured-streaming-late-data.png | Bin 138931 -> 138226 bytes
docs/img/structured-streaming-window.png | Bin 128930 -> 132875 bytes
docs/img/structured-streaming.pptx | Bin 1105315 -> 1105413 bytes
docs/structured-streaming-programming-guide.md | 2 +-
4 files changed, 1 insertion(+), 1 deletion(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/5d00a7bc/docs/img/structured-streaming-late-data.png
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http://git-wip-us.apache.org/repos/asf/spark/blob/5d00a7bc/docs/img/structured-streaming.pptx
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http://git-wip-us.apache.org/repos/asf/spark/blob/5d00a7bc/docs/structured-streaming-programming-guide.md
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diff --git a/docs/structured-streaming-programming-guide.md b/docs/structured-streaming-programming-guide.md
index 5932566..7949396 100644
--- a/docs/structured-streaming-programming-guide.md
+++ b/docs/structured-streaming-programming-guide.md
@@ -620,7 +620,7 @@ 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 the quick example is modified and the stream 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).
+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).
The result tables would look something like the following.
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