Repository: spark Updated Branches: refs/heads/master b14bfc3f8 -> b93843824
[MINOR][DOCS] Fix spacings in Structured Streaming Programming Guide ## What changes were proposed in this pull request? 1. Omitted space between the sentences: `... on static data.The Spark SQL engine will ...` -> `... on static data. The Spark SQL engine will ...` 2. Omitted colon in Output Model section. ## How was this patch tested? None. Author: Lee Dongjin <dong...@apache.org> Closes #17564 from dongjinleekr/feature/fix-programming-guide. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/b9384382 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/b9384382 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/b9384382 Branch: refs/heads/master Commit: b9384382484a9f5c6b389742e7fdf63865de81c0 Parents: b14bfc3 Author: Lee Dongjin <dong...@apache.org> Authored: Wed Apr 12 09:12:14 2017 +0100 Committer: Sean Owen <so...@cloudera.com> Committed: Wed Apr 12 09:12:14 2017 +0100 ---------------------------------------------------------------------- docs/structured-streaming-programming-guide.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/b9384382/docs/structured-streaming-programming-guide.md ---------------------------------------------------------------------- diff --git a/docs/structured-streaming-programming-guide.md b/docs/structured-streaming-programming-guide.md index 37a1d61..3cf7151 100644 --- a/docs/structured-streaming-programming-guide.md +++ b/docs/structured-streaming-programming-guide.md @@ -8,7 +8,7 @@ title: Structured Streaming Programming Guide {:toc} # Overview -Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. You can express your streaming computation the same way you would express a batch computation on static data.The Spark SQL engine will take care of running it incrementally and continuously and updating the final result as streaming data continues to arrive. You can use the [Dataset/DataFrame API](sql-programming-guide.html) in Scala, Java or Python to express streaming aggregations, event-time windows, stream-to-batch joins, etc. The computation is executed on the same optimized Spark SQL engine. Finally, the system ensures end-to-end exactly-once fault-tolerance guarantees through checkpointing and Write Ahead Logs. In short, *Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing without the user having to reason about streaming.* +Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. You can express your streaming computation the same way you would express a batch computation on static data. The Spark SQL engine will take care of running it incrementally and continuously and updating the final result as streaming data continues to arrive. You can use the [Dataset/DataFrame API](sql-programming-guide.html) in Scala, Java or Python to express streaming aggregations, event-time windows, stream-to-batch joins, etc. The computation is executed on the same optimized Spark SQL engine. Finally, the system ensures end-to-end exactly-once fault-tolerance guarantees through checkpointing and Write Ahead Logs. In short, *Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing without the user having to reason about streaming.* **Structured Streaming is still ALPHA in Spark 2.1** and the APIs are still experimental. In this guide, we are going to walk you through the programming model and the APIs. First, let's start with a simple example - a streaming word count. @@ -362,7 +362,7 @@ A query on the input will generate the "Result Table". Every trigger interval (s  -The "Output" is defined as what gets written out to the external storage. The output can be defined in different modes +The "Output" is defined as what gets written out to the external storage. The output can be defined in a different mode: - *Complete Mode* - The entire updated Result Table will be written to the external storage. It is up to the storage connector to decide how to handle writing of the entire table. --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
