Repository: spark Updated Branches: refs/heads/master c62263340 -> 5d00a7bc1
[SPARK-16256][DOCS] Fix window operation diagram Author: Tathagata Das <tathagata.das1...@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 <tathagata.das1...@gmail.com> Authored: Thu Jun 30 14:01:34 2016 -0700 Committer: Tathagata Das <tathagata.das1...@gmail.com> Committed: Thu Jun 30 14:01:34 2016 -0700 ---------------------------------------------------------------------- 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(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/5d00a7bc/docs/img/structured-streaming-late-data.png ---------------------------------------------------------------------- diff --git a/docs/img/structured-streaming-late-data.png b/docs/img/structured-streaming-late-data.png index 5276b47..2283f67 100644 Binary files a/docs/img/structured-streaming-late-data.png and b/docs/img/structured-streaming-late-data.png differ http://git-wip-us.apache.org/repos/asf/spark/blob/5d00a7bc/docs/img/structured-streaming-window.png ---------------------------------------------------------------------- diff --git a/docs/img/structured-streaming-window.png b/docs/img/structured-streaming-window.png index be9d3fb..c1842b1 100644 Binary files a/docs/img/structured-streaming-window.png and b/docs/img/structured-streaming-window.png differ http://git-wip-us.apache.org/repos/asf/spark/blob/5d00a7bc/docs/img/structured-streaming.pptx ---------------------------------------------------------------------- diff --git a/docs/img/structured-streaming.pptx b/docs/img/structured-streaming.pptx index c278323..6aad2ed 100644 Binary files a/docs/img/structured-streaming.pptx and b/docs/img/structured-streaming.pptx differ http://git-wip-us.apache.org/repos/asf/spark/blob/5d00a7bc/docs/structured-streaming-programming-guide.md ---------------------------------------------------------------------- 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. --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
