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From koeninger <...@git.apache.org>
Subject [GitHub] spark pull request #13945: [SPARK-16256][SQL][STREAMING] Added Structured St...
Date Tue, 28 Jun 2016 20:48:00 GMT
Github user koeninger commented on a diff in the pull request:

    https://github.com/apache/spark/pull/13945#discussion_r68840069
  
    --- 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
    +Let’s say you want maintain a running word count of text data received from a data
server listening on a TCP socket. Let’s 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, let’s 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, let’s 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 “value”, 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 “word”. 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. Don’t 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. Let’s understand this model in more details.
    +
    +## Basic Concepts
    +Consider the input data stream as the “Input Table”. Every data items that is 
    +arriving on the stream is like a new row being appended to the Input Table.
    +
    +![Stream as a Table](img/structured-streaming-stream-as-a-table.png "Stream as a Table")
    +
    +A query on the input will generate the “Result Table”. 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. 
    +
    +![Model](img/structured-streaming-model.png)
    +
    +The “Output” 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.
    --- End diff --
    
    wil be written => will be written


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