Github user anabranch commented on a diff in the pull request:
https://github.com/apache/spark/pull/13945#discussion_r68790342
--- 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.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.SparkSession
+
+val spark = SparkSession
+ .builder
+ .appName("StructuredNetworkWordCount")
+ .getOrCreate()
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+import org.apache.spark.sql.*;
+import org.apache.spark.sql.streaming.StreamingQuery;
+
+SparkSession spark = SparkSession
+ .builder()
+ .appName("JavaStructuredNetworkWordCount")
+ .getOrCreate();
+{% endhighlight %}
+
+</div>
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+from pyspark.sql import SparkSession
+from pyspark.sql.functions import explode
+from pyspark.sql.functions import split
+
+spark = SparkSession\
+ .builder()\
+ .appName("StructuredNetworkWordCount")\
+ .getOrCreate()
+{% endhighlight %}
+
+</div>
+</div>
+
+Next, letâs create a streaming DataFrame that represents text data
received from a server listening on localhost:9999, and transform the DataFrame
to calculate word counts.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+val lines = spark.readStream
+ .format("socket")
+ .option("host", "localhost")
+ .option("port", 9999)
+ .load()
+
+val words = lines.select(
+ explode(
+ split(lines.col("value"), " ")
+ ).alias("word")
+)
+
+val wordCounts = words.groupBy("word").count()
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+Dataset<Row> lines = spark
+ .readStream()
+ .format("socket")
+ .option("host", "localhost")
+ .option("port", 9999)
+ .load();
+
+Dataset<Row> words = lines.select(
+ functions.explode(
+ functions.split(lines.col("value"), " ")
+ ).alias("word")
+);
+
+Dataset<Row> wordCounts = words.groupBy("word").count();
+{% endhighlight %}
+
+</div>
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+lines = spark\
+ .readStream\
+ .format('socket')\
+ .option('host', 'localhost')\
+ .option('port', 9999)\
+ .load()
+
+words = lines.select(
+ explode(
+ split(lines.value, ' ')
+ ).alias('word')
+)
+
+wordCounts = words.groupBy('word').count()
+{% endhighlight %}
+
+</div>
+</div>
+
+This `lines` DataFrame is like an unbounded table containing the streaming
+text data. This table contains one column of string named âvalueâ, and
each
+line in the streaming text data is like a row in this table. Note, that
this
+is not currently receiving any data as we are just setting up the
+transformation, and have not yet started it. Next, we have used to
built-in
+SQL functions - split and explode, to split each line into multiple rows
with
+a word each. In addition, we use the function `alias` to name the new
column
+as âwordâ. Finally, we have defined the running counts, by grouping
the `words`
+DataFrame by the column `word` and count on that grouping.
+
+We have now set up the query on the streaming data. All that is left is to
+actually start receiving data and computing the counts. To do this, we set
it
+up to output the counts to the console every time they are updated. In
--- End diff --
Think we can clean up these last two sentences.
> There are other details that will be mentioned on in the guide that are
relevant for this problem like the specific checkpoint location where
checkpoint data will be stored.
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