Github user koeninger commented on a diff in the pull request:
https://github.com/apache/spark/pull/13945#discussion_r68839964
--- 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
+addition we are also going to set up additional details like checkpoint
+location. Donât worry about them for now, they are explained later in
the guide.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight java %}
+val query = wordCounts
+ .writeStream
+ .outputMode("complete")
+ .format("console")
+ .option("checkpointLocation", checkpointDir)
+ .start()
+
+query.awaitTermination()
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+StreamingQuery query = wordCounts
+ .writeStream()
+ .outputMode("complete")
+ .format("console")
+ .option("checkpointLocation", checkpointDir)
+ .start();
+
+query.awaitTermination();
+{% endhighlight %}
+
+</div>
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+query = wordCounts\
+ .writeStream\
+ .outputMode('complete')\
+ .format('console')\
+ .option('checkpointLocation', checkpointDir)\
+ .start()
+
+query.awaitTermination()
+{% endhighlight %}
+
+</div>
+</div>
+
+Now the streaming computation has started in the background, and the
`query` object is a handle to that active streaming query. Note that we are
also waiting for the query to terminate, to prevent the process from finishing
while the query is active.
+To actually run this code, you can either compile your own Spark
application, or simply run the example once you have downloaded Spark. We are
showing the latter. You will first need to run Netcat (a small utility found in
most Unix-like systems) as a data server by using
+
+ $ nc -lk 9999
+
+Then, in a different terminal, you can start the example by using
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+ $ ./bin/run-example
org.apache.spark.examples.sql.streaming.StructuredNetworkWordCount
+
+</div>
+<div data-lang="java" markdown="1">
+
+ $ ./bin/run-example
org.apache.spark.examples.sql.streaming.JavaStructuredNetworkWordCount
+
+</div>
+<div data-lang="python" markdown="1">
+
+ $ ./bin/spark-submit
examples/src/main/python/sql/streaming/structured_network_wordcount.py
+
+</div>
+</div>
+
+Then, any lines typed in the terminal running the netcat server will be
counted and printed on screen every second. It will look something like the
following.
+
+# Programming Model
+
+The key idea is in Structured Streaming is to treat a live data stream as
a
+table that is being continuously appended. This leads to a new stream
+processing model that is very similar to a batch processing model. You
will
+express your streaming computation as standard batch-like query as on a
static
+table, and Spark runs it as an *incremental* query on the *unbounded*
input
+table. Letâs understand this model in more details.
+
+## Basic Concepts
+Consider the input data stream as the âInput Tableâ. Every data items
that is
+arriving on the stream is like a new row being appended to the Input Table.
+
+
+
+A query on the input will generate the âResult Tableâ. Every trigger
interval (say, every 1 second), new rows gets appended to the Input Table,
which eventually updates the Result Table. Whenever the result table gets
updated, we would want write the changed result rows to an external sink.
+
+
+
+The âOutputâ is defined as what gets written out to the external
storage. The output can be defined in different modes
+
+ - *Complete Mode* - The entire updated Result Table will be written to
the external storage.
+
+ - *Append Mode* - Only the new rows appended in the Result Table since
the last trigger will be written to the external storage. This is applicable
only on queries where existing rows in the Result Table is not expected to
change.
--- End diff --
is not expected => are not expected
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