Github user zsxwing commented on a diff in the pull request:
https://github.com/apache/spark/pull/13945#discussion_r68811047
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -0,0 +1,888 @@
+---
+layout: global
+displayTitle: Structured Streaming Programming Guide [Alpha]
+title: Structured Streaming Programming Guide
+---
+
+* This will become a table of contents (this text will be scraped).
+{: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
by
+thinking you are running a batch computation on a static dataset, and the
+Spark SQL engine takes care of running it incrementally and continuously
+updating the final result as streaming data keeps arriving. 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, *Stuctured Streaming
provides
+fast, scalable, fault-tolerant, end-to-end exactly-once stream processing
+without the user having to reason about streaming.*
+
+**Spark 2.0 is the ALPHA RELEASE of Structured Streaming** and the APIs
are still experimental. In this guide, we are going to walk you through the
programming model and the APIs. First, lets start with a simple example - a
streaming word count.
+
+# Quick Example
+Letâs say you want maintain a running word count of text data received
from a data server listening on a TCP socket. Letâs see how you can express
this using Structured Streaming. You can see the full code in
Scala/Java/Python. And if you download Spark, you can directly run the example.
In any case, letâs walk through the example step-by-step and understand how
it is works. First, we have to import the names of the necessary classes and
create a local SparkSession, the starting point of all functionalities related
to Spark.
--- End diff --
nit: want maintain -> want **to** maintain
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