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04_spark_streaming.html
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<!doctype html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
<title>Apache Spark 2 Workshop — Spark Streaming</title>
<meta name="description" content="Apache Spark 2 Workshop — Spark Streaming">
<meta name="author" content="Jacek Laskowski">
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<body>
<div class="reveal">
<div class="slides">
<div class="footer">
<small>Copyright ©2017 Jacek Laskowski</small>
</div>
<section class="intro" data-transition="zoom">
<p>
<img width="25%" style="background:none; border:none; box-shadow:none;" data-src="images/spark-logo.png">
<img width="15%" src="images/jacek_laskowski_20141201_512px.png" style="border: 0">
</p>
<h1>Spark Streaming</h1>
<h2>Apache Spark 2</h2>
<h4><a href="https://twitter.com/jaceklaskowski">@jaceklaskowski</a> / <a href="http://stackoverflow.com/users/1305344/jacek-laskowski">StackOverflow</a> / <a href="https://github.com/jaceklaskowski">GitHub</a> / <a href="http://bit.ly/mastering-apache-spark">Mastering Apache Spark 2</a></h4>
</section>
<section id="agenda" data-markdown>
<script type="text/template">
## Agenda
1. [Spark Streaming](#/intro)
1. [StreamingContext](#/streaming-context)
1. [Discretized Streams (DStreams)](#/dstreams)
1. [DStream Operators](#/dstream-operators)
1. [Web UI](#/web-ui)
1. [Spark Streaming Demo](#/demo)
1. [Exercise](#/exercise-socket-dstream): Reading Text Data From Socket (Standalone Application)
</script>
</section>
<section id="warning">
<h2>Heads-up</h2>
<blockquote>
Spark Streaming had almost no notable changes between Spark 1.6 and Spark 2.0.
<b>Jacek</b> believes that it will be marked deprecated after the modern <b>Structured Streaming</b> has been announced ready for production use <br/>
(which has happened at SparkSummit 2017 in San Francisco)
</blockquote>
</section>
<section>
<section id="intro">
<h2>Spark Streaming</h2>
<ol>
<li><b>Spark Streaming</b> is the incremental stream processing framework for Apache Spark.</li>
<li>Scalable, high-throughput, fault-tolerant, batch-oriented</li>
<li><b>Discretized Streams</b> (DStreams) of continuous data</li>
<li>Integration with other Spark modules
<ul>
<li>Spark SQL and MLlib</li>
</ul>
</li>
<li>Switch to Mastering Apache Spark 2
<ul>
<li><a href="https://jaceklaskowski.gitbooks.io/spark-streaming/content/spark-streaming.html">Spark Streaming</a></li>
</ul>
</li>
</ol>
</section>
<section id="spark-streaming-architecture">
<p>
<img width="100%" src="images/spark-streaming-architecture.png" style="border: 0">
</p>
<small>From <a href="http://spark.apache.org/docs/latest/streaming-programming-guide.html">the official documentation of Apache Spark</a></small>
</section>
</section>
<section id="streaming-context">
<h2>StreamingContext</h2>
<ol>
<li><b>StreamingContext</b> is the entry point for all Spark Streaming functionality.
<pre><code>
val ssc = new StreamingContext(sc, Seconds(5))
</code></pre>
</li>
<li>Used to create built-in <b>DStreams</b> <small>(covered next)</small></li>
<li>Switch to Mastering Apache Spark 2
<ul>
<li><a href="https://jaceklaskowski.gitbooks.io/spark-streaming/content/spark-streaming-streamingcontext.html">StreamingContext — The Entry Point to Spark Streaming</a></li>
</ul>
</li>
</ol>
</section>
<section id="dstreams">
<h2>Discretized Streams (DStreams)</h2>
<ol>
<li><b>Discretized Stream</b> (DStream) is the fundamental concept of Spark Streaming.</li>
<li>A stream of RDDs with input records per batch.</li>
<li>Created using <b>StreamingContext</b> or custom factories, e.g. <b>KafkaUtils</b>.
<pre><code>
val dstream = KafkaUtils.createDirectStream[String, String](...)
</code></pre>
</li>
<li>Switch to Mastering Apache Spark 2
<ul>
<li><a href="https://jaceklaskowski.gitbooks.io/spark-streaming/content/spark-streaming-dstreams.html">DStream — Discretized Stream</a></li>
</ul>
</li>
</ol>
</section>
<section id="dstream-operators">
<h2>DStream Operators</h2>
<ol>
<li><b>Stream operators</b> allow for <b>transformations</b> to the records from input DStreams and ultimately trigger computations using <b>output operators</b>.
</li>
<li>Switch to Mastering Apache Spark 2
<ul>
<li><a href="https://jaceklaskowski.gitbooks.io/spark-streaming/content/spark-streaming-operators.html">Stream Operators</a></li>
</ul>
</li>
</ol>
</section>
<section id="web-ui">
<h2>Web UI</h2>
<ol>
<li>Spark Streaming applications have their own web UI with <b>Streaming Statistics Page</b></li>
<li>Switch to Mastering Apache Spark 2
<ul>
<li><a href="https://jaceklaskowski.gitbooks.io/spark-streaming/content/spark-streaming-webui.html">web UI and Streaming Statistics Page</a></li>
</ul>
</li>
</ol>
</section>
<section>
<section id="demo">
<h2>Spark Streaming Demo</h2>
<ol>
<li>Reading text records from socket</li>
<li>Uses <b>StreamingContext.socketTextStream</b> and <a href="https://en.wikipedia.org/wiki/Netcat">netcat</a></li>
</ol>
</section>
<section id="more-streaming-examples">
<h2>More Streaming Examples</h2>
<ol>
<li>"Official" examples in <a href="https://github.com/apache/spark/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming">examples/streaming</a> repo</li>
<li>Use <b>run-example streaming.*</b>
<ul>
<li>NetworkWordCount</li>
<li>SqlNetworkWordCount</li>
<li>StatefulNetworkWordCount</li>
</ul>
</li>
</ol>
</section>
</section>
<section id="exercise-socket-dstream" data-markdown>
<script type="text/template">
## Exercise: Reading Text Data From Socket (Standalone Application)
1. Create a sbt-managed Scala project using IntelliJ IDEA
1. Define a library dependency on Spark Streaming
1. Use [StreamingContext.socketTextStream](http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.streaming.StreamingContext)
1. **print** Streaming Data
1. Start **StreamingContext** and...**awaitTerminate**
1. Time: **30 mins**
</script>
</section>
<section id="recap" data-markdown>
<script type="text/template">
## Recap
1. [Spark Streaming](#/intro)
1. [StreamingContext](#/streaming-context)
1. [Discretized Streams (DStreams)](#/dstreams)
1. [DStream Operators](#/dstream-operators)
1. [Web UI](#/web-ui)
1. [Spark Streaming Demo](#/demo)
1. [Exercise](#/exercise-socket-dstream): Reading Text Data From Socket (Standalone Application)
</script>
</section>
<section id="questions" style="text-align: left">
<h1>Questions?</h1>
<p>
<ul>
<li>Read <a href="https://bit.ly/mastering-apache-spark">Mastering Apache Spark 2</a>
<ul>
<li><a href="https://bit.ly/mastering-apache-spark">https://bit.ly/mastering-apache-spark</a></li>
</ul>
</li>
<li>Follow <a href="https://twitter.com/jaceklaskowski">@jaceklaskowski</a> on twitter</li>
<li>Upvote <a href="http://stackoverflow.com/users/1305344/jacek-laskowski">my questions and answers on StackOverflow</a></li>
<li>Use <a href="https://github.com/jaceklaskowski">Jacek's code at GitHub</a></li>
<li>Read <a href="https://medium.com/@jaceklaskowski">my blog posts on Medium</a></li>
<li>Upvote <a href="https://www.quora.com/profile/Jacek-Laskowski">my answers on Quora</a></li>
<li>Connect on <a href="https://www.linkedin.com/in/jaceklaskowski/">LinkedIn</a></li>
<li>Visit <a href="https://blog.jaceklaskowski.pl">Jacek Laskowski's blog</a></li>
</ul>
</p>
</section>
</div>
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