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 <p>A library for reading data from Akka Actors using Spark SQL Streaming ( or Structured streaming.).</p>

 <h2 id="linking">Linking</h2>

 <p>Using SBT:</p>

 <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>libraryDependencies += "org.apache.bahir" %% "spark-sql-streaming-akka" % "2.2.1"
 </code></pre></div></div>

 <p>Using Maven:</p>

 <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>&lt;dependency&gt;
     &lt;groupId&gt;org.apache.bahir&lt;/groupId&gt;
     &lt;artifactId&gt;spark-sql-streaming-akka_2.11&lt;/artifactId&gt;
     &lt;version&gt;2.2.1&lt;/version&gt;
 &lt;/dependency&gt;
 </code></pre></div></div>

 <p>This library can also be added to Spark jobs launched through <code class="language-plaintext highlighter-rouge">spark-shell</code> or <code class="language-plaintext highlighter-rouge">spark-submit</code> by using the <code class="language-plaintext highlighter-rouge">--packages</code> command line option.
 For example, to include it when starting the spark shell:</p>

 <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>$ bin/spark-shell --packages org.apache.bahir:spark-sql-streaming-akka_2.11:2.2.1
 </code></pre></div></div>

 <p>Unlike using <code class="language-plaintext highlighter-rouge">--jars</code>, using <code class="language-plaintext highlighter-rouge">--packages</code> ensures that this library and its dependencies will be added to the classpath.
 The <code class="language-plaintext highlighter-rouge">--packages</code> argument can also be used with <code class="language-plaintext highlighter-rouge">bin/spark-submit</code>.</p>

 <p>This library is compiled for Scala 2.11 only, and intends to support Spark 2.0 onwards.</p>

 <h2 id="examples">Examples</h2>

 <p>A SQL Stream can be created with data streams received from Akka Feeder actor using,</p>

 <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>    sqlContext.readStream
             .format("org.apache.bahir.sql.streaming.akka.AkkaStreamSourceProvider")
             .option("urlOfPublisher", "feederActorUri")
             .load()
 </code></pre></div></div>

 <h2 id="enable-recovering-from-failures">Enable recovering from failures.</h2>

 <p>Setting values for option <code class="language-plaintext highlighter-rouge">persistenceDirPath</code> helps in recovering in case of a restart, by restoring the state where it left off before the shutdown.</p>

 <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>    sqlContext.readStream
             .format("org.apache.bahir.sql.streaming.akka.AkkaStreamSourceProvider")
             .option("urlOfPublisher", "feederActorUri")
             .option("persistenceDirPath", "/path/to/localdir")
             .load()
 </code></pre></div></div>

 <h2 id="configuration-options">Configuration options.</h2>

 <p>This source uses <a href="http://doc.akka.io/api/akka/2.4/akka/actor/Actor.html">Akka Actor api</a>.</p>

 <ul>
   <li><code class="language-plaintext highlighter-rouge">urlOfPublisher</code> The url of Publisher or Feeder actor that the Receiver actor connects to. Set this as the tcp url of the Publisher or Feeder actor.</li>
   <li><code class="language-plaintext highlighter-rouge">persistenceDirPath</code> By default it is used for storing incoming messages on disk.</li>
 </ul>

 <h3 id="scala-api">Scala API</h3>

 <p>An example, for scala API to count words from incoming message stream.</p>

 <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>    // Create DataFrame representing the stream of input lines from connection
     // to publisher or feeder actor
     val lines = spark.readStream
                 .format("org.apache.bahir.sql.streaming.akka.AkkaStreamSourceProvider")
                 .option("urlOfPublisher", urlOfPublisher)
                 .load().as[(String, Timestamp)]

     // Split the lines into words
     val words = lines.map(_._1).flatMap(_.split(" "))

     // Generate running word count
     val wordCounts = words.groupBy("value").count()

     // Start running the query that prints the running counts to the console
     val query = wordCounts.writeStream
                 .outputMode("complete")
                 .format("console")
                 .start()

     query.awaitTermination()
 </code></pre></div></div>

 <p>Please see <code class="language-plaintext highlighter-rouge">AkkaStreamWordCount.scala</code> for full example.</p>

 <h3 id="java-api">Java API</h3>

 <p>An example, for Java API to count words from incoming message stream.</p>

 <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>    // Create DataFrame representing the stream of input lines from connection
     // to publisher or feeder actor
     Dataset&lt;String&gt; lines = spark
                             .readStream()
                             .format("org.apache.bahir.sql.streaming.akka.AkkaStreamSourceProvider")
                             .option("urlOfPublisher", urlOfPublisher)
                             .load().select("value").as(Encoders.STRING());

     // Split the lines into words
     Dataset&lt;String&gt; words = lines.flatMap(new FlatMapFunction&lt;String, String&gt;() {
       @Override
       public Iterator&lt;String&gt; call(String s) throws Exception {
         return Arrays.asList(s.split(" ")).iterator();
       }
     }, Encoders.STRING());

     // Generate running word count
     Dataset&lt;Row&gt; wordCounts = words.groupBy("value").count();

     // Start running the query that prints the running counts to the console
     StreamingQuery query = wordCounts.writeStream()
                             .outputMode("complete")
                             .format("console")
                             .start();

     query.awaitTermination();
 </code></pre></div></div>

 <p>Please see <code class="language-plaintext highlighter-rouge">JavaAkkaStreamWordCount.java</code> for full example.</p>

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	<h1></h1>
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	<div class="col-md-12">
	<!--

	-->

	<p>A library for reading data from Akka Actors using Spark SQL Streaming ( or Structured streaming.).</p>

	<h2 id="linking">Linking</h2>

	<p>Using SBT:</p>

	<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>libraryDependencies += "org.apache.bahir" %% "spark-sql-streaming-akka" % "2.2.1"
	</code></pre></div></div>

	<p>Using Maven:</p>

	<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code><dependency>
	<groupId>org.apache.bahir</groupId>
	<artifactId>spark-sql-streaming-akka_2.11</artifactId>
	<version>2.2.1</version>
	</dependency>
	</code></pre></div></div>

	<p>This library can also be added to Spark jobs launched through <code class="language-plaintext highlighter-rouge">spark-shell</code> or <code class="language-plaintext highlighter-rouge">spark-submit</code> by using the <code class="language-plaintext highlighter-rouge">--packages</code> command line option.
	For example, to include it when starting the spark shell:</p>

	<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>$ bin/spark-shell --packages org.apache.bahir:spark-sql-streaming-akka_2.11:2.2.1
	</code></pre></div></div>

	<p>Unlike using <code class="language-plaintext highlighter-rouge">--jars</code>, using <code class="language-plaintext highlighter-rouge">--packages</code> ensures that this library and its dependencies will be added to the classpath.
	The <code class="language-plaintext highlighter-rouge">--packages</code> argument can also be used with <code class="language-plaintext highlighter-rouge">bin/spark-submit</code>.</p>

	<p>This library is compiled for Scala 2.11 only, and intends to support Spark 2.0 onwards.</p>

	<h2 id="examples">Examples</h2>

	<p>A SQL Stream can be created with data streams received from Akka Feeder actor using,</p>

	<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code> sqlContext.readStream
	.format("org.apache.bahir.sql.streaming.akka.AkkaStreamSourceProvider")
	.option("urlOfPublisher", "feederActorUri")
	.load()
	</code></pre></div></div>

	<h2 id="enable-recovering-from-failures">Enable recovering from failures.</h2>

	<p>Setting values for option <code class="language-plaintext highlighter-rouge">persistenceDirPath</code> helps in recovering in case of a restart, by restoring the state where it left off before the shutdown.</p>

	<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code> sqlContext.readStream
	.format("org.apache.bahir.sql.streaming.akka.AkkaStreamSourceProvider")
	.option("urlOfPublisher", "feederActorUri")
	.option("persistenceDirPath", "/path/to/localdir")
	.load()
	</code></pre></div></div>

	<h2 id="configuration-options">Configuration options.</h2>

	<p>This source uses <a href="http://doc.akka.io/api/akka/2.4/akka/actor/Actor.html">Akka Actor api</a>.</p>

	<ul>
	<li><code class="language-plaintext highlighter-rouge">urlOfPublisher</code> The url of Publisher or Feeder actor that the Receiver actor connects to. Set this as the tcp url of the Publisher or Feeder actor.</li>
	<li><code class="language-plaintext highlighter-rouge">persistenceDirPath</code> By default it is used for storing incoming messages on disk.</li>
	</ul>

	<h3 id="scala-api">Scala API</h3>

	<p>An example, for scala API to count words from incoming message stream.</p>

	<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code> // Create DataFrame representing the stream of input lines from connection
	// to publisher or feeder actor
	val lines = spark.readStream
	.format("org.apache.bahir.sql.streaming.akka.AkkaStreamSourceProvider")
	.option("urlOfPublisher", urlOfPublisher)
	.load().as[(String, Timestamp)]

	// Split the lines into words
	val words = lines.map(_._1).flatMap(_.split(" "))

	// Generate running word count
	val wordCounts = words.groupBy("value").count()

	// Start running the query that prints the running counts to the console
	val query = wordCounts.writeStream
	.outputMode("complete")
	.format("console")
	.start()

	query.awaitTermination()
	</code></pre></div></div>

	<p>Please see <code class="language-plaintext highlighter-rouge">AkkaStreamWordCount.scala</code> for full example.</p>

	<h3 id="java-api">Java API</h3>

	<p>An example, for Java API to count words from incoming message stream.</p>

	<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code> // Create DataFrame representing the stream of input lines from connection
	// to publisher or feeder actor
	Dataset<String> lines = spark
	.readStream()
	.format("org.apache.bahir.sql.streaming.akka.AkkaStreamSourceProvider")
	.option("urlOfPublisher", urlOfPublisher)
	.load().select("value").as(Encoders.STRING());

	// Split the lines into words
	Dataset<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
	@Override
	public Iterator<String> call(String s) throws Exception {
	return Arrays.asList(s.split(" ")).iterator();
	}
	}, Encoders.STRING());

	// Generate running word count
	Dataset<Row> wordCounts = words.groupBy("value").count();

	// Start running the query that prints the running counts to the console
	StreamingQuery query = wordCounts.writeStream()
	.outputMode("complete")
	.format("console")
	.start();

	query.awaitTermination();
	</code></pre></div></div>

	<p>Please see <code class="language-plaintext highlighter-rouge">JavaAkkaStreamWordCount.java</code> for full example.</p>

	</div>
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