Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/18065#discussion_r118390436
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -10,7 +10,7 @@ title: Structured Streaming Programming Guide
# 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, Python or R 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.*
--- End diff --
haha, good catch
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
