Github user anabranch commented on a diff in the pull request:
https://github.com/apache/spark/pull/13945#discussion_r68792137
--- Diff: docs/structured-streaming-programming-guide.md ---
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+---
+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.
+
+ - *Update Mode* - Only the rows that were updated in the Result Table
since the last trigger wil be written to the external storage (not available
yet in Spark 2.0). Note that this is different from the Complete Mode in that
this mode does not output the rows that are not changed.
+
+Note that each mode is applicable on certain types of queries. This is
discussed in detail later.
+
+To illustrate the use of this model, letâs understand the model in
context of
+the Quick Example above. The first `lines` DataFrame is the input table,
and
+the final `wordCounts` DataFrame is the result table. Note that the query
on
+streaming `lines` DataFrame to generate `wordCounts` is *exactly the same*
as
+it would be a static DataFrame. However, when this query is started, Spark
+will continuously check for new data from the socket connection. If there
is
+new data, Spark will run an âincrementalâ query that combines the
previous
+running counts with the new data to compute updated counts, as shown below.
+
+
+
+This model is significantly different from many other stream processing
+engines. Many streaming system require the user to maintain running
+aggregations themselves, thus having the reason about fault-tolerance, and
+data consistency (at-least-once, or at-most-once, or exactly-once). In
this
+model, Spark is responsible for updating the Result Table when there is
new
+data, thus relieving the users from reasoning about it. As an example,
letâs
+see how this model handles event-time based processing and late arriving
data.
+
+## Handling Event-time and Late Data
+Event-time is the time embedded in the data itself. For many applications,
you may want to do operate using this event-time. For example, if you want to
get the number of events generated by IoT devices every minute, then you
probably want to use the time when the data was generated (that is, event-time
in the data), rather than the time Spark receives them. This event-time is very
naturally expressed in this model -- each event from the devices is a row in
the table, and event-time a column value in the row. This allows window-based
aggregations (e.g. number of event every minute) to be just a special type of
grouping and aggregation on the even-time column -- each time window is a group
and each row can belong to multiple windows/groups. Therefore, such
event-time-window-based aggregation queries can be defined consistently on both
a static dataset (e.g. from collected device events logs) as well as on
streaming dataset, making the life of the user much easier.
+
+Furthermore this model naturally handles data that has arrived later than
expected based on its event-time. Since Spark is updating the Result Table, it
has full control over updating/cleaning up the aggregates when there is late
data. While not yet implemented in Spark 2.0 yet, event-time watermarking will
be used to manage this data. These are explained in more details in the Window
Operations section.
+
+## Fault Tolerance Semantics
+Delivering end-to-end exactly-once semantics was one of key goals behind
the design of Structured Streaming. To achieve that, we have designed the
Structured Streaming sources, the sinks and the execution engine to reliably
track the exact progress of the processing so that it can handle any kind of
failure by restarting and/or reprocessing. Every streaming source is assumed to
have offsets (similar to Kafka offsets, or Kinesis sequence numbers)
--- End diff --
missing period at the end.
Could be good to define offsets just for people that might not be familiar.
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