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<p>A library for writing and reading data from MQTT Servers 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-mqtt" % "2.3.3"
</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-mqtt_2.11&lt;/artifactId&gt;
&lt;version&gt;2.3.3&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-mqtt_2.11:2.3.3
</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>SQL Stream can be created with data streams received through MQTT Server using:</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>sqlContext.readStream
.format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
.option("topic", "mytopic")
.load("tcp://localhost:1883")
</code></pre></div></div>
<p>SQL Stream may be also transferred into MQTT messages using:</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>sqlContext.writeStream
.format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSinkProvider")
.option("checkpointLocation", "/path/to/localdir")
.outputMode("complete")
.option("topic", "mytopic")
.load("tcp://localhost:1883")
</code></pre></div></div>
<h2 id="source-recovering-from-failures">Source recovering from failures</h2>
<p>Setting values for option <code class="language-plaintext highlighter-rouge">localStorage</code> and <code class="language-plaintext highlighter-rouge">clientId</code> helps in recovering in case of source 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.mqtt.MQTTStreamSourceProvider")
.option("topic", "mytopic")
.option("localStorage", "/path/to/localdir")
.option("clientId", "some-client-id")
.load("tcp://localhost:1883")
</code></pre></div></div>
<h2 id="configuration-options">Configuration options</h2>
<p>This connector uses <a href="https://eclipse.org/paho/clients/java/">Eclipse Paho Java Client</a>. Client API documentation is located <a href="http://www.eclipse.org/paho/files/javadoc/index.html">here</a>.</p>
<ul>
<li><code class="language-plaintext highlighter-rouge">brokerUrl</code> An URL MqttClient connects to. Set this or <code class="language-plaintext highlighter-rouge">path</code> as the URL of the Mqtt Server. e.g. tcp://localhost:1883.</li>
<li><code class="language-plaintext highlighter-rouge">persistence</code> By default it is used for storing incoming messages on disk. If <code class="language-plaintext highlighter-rouge">memory</code> is provided as value for this option, then recovery on restart is not supported.</li>
<li><code class="language-plaintext highlighter-rouge">topic</code> Topic MqttClient subscribes to.</li>
<li><code class="language-plaintext highlighter-rouge">clientId</code> clientId, this client is associated with. Provide the same value to recover a stopped source client. MQTT sink ignores client identifier, because Spark batch can be distributed across multiple workers whereas MQTT broker does not allow simultanous connections with same ID from multiple hosts.</li>
<li><code class="language-plaintext highlighter-rouge">QoS</code> The maximum quality of service to subscribe each topic at. Messages published at a lower quality of service will be received at the published QoS. Messages published at a higher quality of service will be received using the QoS specified on the subscribe.</li>
<li><code class="language-plaintext highlighter-rouge">username</code> Sets the user name to use for the connection to Mqtt Server. Do not set it, if server does not need this. Setting it empty will lead to errors.</li>
<li><code class="language-plaintext highlighter-rouge">password</code> Sets the password to use for the connection.</li>
<li><code class="language-plaintext highlighter-rouge">cleanSession</code> Setting it true starts a clean session, removes all checkpointed messages by a previous run of this source. This is set to false by default.</li>
<li><code class="language-plaintext highlighter-rouge">connectionTimeout</code> Sets the connection timeout, a value of 0 is interpretted as wait until client connects. See <code class="language-plaintext highlighter-rouge">MqttConnectOptions.setConnectionTimeout</code> for more information.</li>
<li><code class="language-plaintext highlighter-rouge">keepAlive</code> Same as <code class="language-plaintext highlighter-rouge">MqttConnectOptions.setKeepAliveInterval</code>.</li>
<li><code class="language-plaintext highlighter-rouge">mqttVersion</code> Same as <code class="language-plaintext highlighter-rouge">MqttConnectOptions.setMqttVersion</code>.</li>
<li><code class="language-plaintext highlighter-rouge">maxInflight</code> Same as <code class="language-plaintext highlighter-rouge">MqttConnectOptions.setMaxInflight</code></li>
<li><code class="language-plaintext highlighter-rouge">autoReconnect</code> Same as <code class="language-plaintext highlighter-rouge">MqttConnectOptions.setAutomaticReconnect</code></li>
</ul>
<h2 id="environment-variables">Environment variables</h2>
<p>Custom environment variables allowing to manage MQTT connectivity performed by sink connector:</p>
<ul>
<li><code class="language-plaintext highlighter-rouge">spark.mqtt.client.connect.attempts</code> Number of attempts sink will try to connect to MQTT broker before failing.</li>
<li><code class="language-plaintext highlighter-rouge">spark.mqtt.client.connect.backoff</code> Delay in milliseconds to wait before retrying connection to the server.</li>
<li><code class="language-plaintext highlighter-rouge">spark.mqtt.connection.cache.timeout</code> Sink connector caches MQTT connections. Idle connections will be closed after timeout milliseconds.</li>
<li><code class="language-plaintext highlighter-rouge">spark.mqtt.client.publish.attempts</code> Number of attempts to publish the message before failing the task.</li>
<li><code class="language-plaintext highlighter-rouge">spark.mqtt.client.publish.backoff</code> Delay in milliseconds to wait before retrying send operation.</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 mqtt server
val lines = spark.readStream
.format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
.option("topic", topic)
.load(brokerUrl).selectExpr("CAST(payload AS STRING)").as[String]
// 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">MQTTStreamWordCount.scala</code> for full example. Review <code class="language-plaintext highlighter-rouge">MQTTSinkWordCount.scala</code>, if interested in publishing data to MQTT broker.</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 mqtt server.
Dataset&lt;String&gt; lines = spark
.readStream()
.format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
.option("topic", topic)
.load(brokerUrl)
.selectExpr("CAST(payload AS STRING)").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 x) {
return Arrays.asList(x.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">JavaMQTTStreamWordCount.java</code> for full example. Review <code class="language-plaintext highlighter-rouge">JavaMQTTSinkWordCount.java</code>, if interested in publishing data to MQTT broker.</p>
<h2 id="best-practices">Best Practices.</h2>
<ol>
<li>Turn Mqtt into a more reliable messaging service.</li>
</ol>
<blockquote>
<p><em>MQTT is a machine-to-machine (M2M)/”Internet of Things” connectivity protocol. It was designed as an extremely lightweight publish/subscribe messaging transport.</em></p>
</blockquote>
<p>The design of Mqtt and the purpose it serves goes well together, but often in an application it is of utmost value to have reliability. Since mqtt is not a distributed message queue and thus does not offer the highest level of reliability features. It should be redirected via a kafka message queue to take advantage of a distributed message queue. In fact, using a kafka message queue offers a lot of possibilities including a single kafka topic subscribed to several mqtt sources and even a single mqtt stream publishing to multiple kafka topics. Kafka is a reliable and scalable message queue.</p>
<ol>
<li>Often the message payload is not of the default character encoding or contains binary that needs to be parsed using a particular parser. In such cases, spark mqtt payload should be processed using the external parser. For example:</li>
</ol>
<ul>
<li>Scala API example:
<div class="language-scala highlighter-rouge"><div class="highlight"><pre class="highlight"><code> <span class="c1">// Create DataFrame representing the stream of binary messages</span>
<span class="k">val</span> <span class="nv">lines</span> <span class="k">=</span> <span class="nv">spark</span><span class="o">.</span><span class="py">readStream</span>
<span class="o">.</span><span class="py">format</span><span class="o">(</span><span class="s">"org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider"</span><span class="o">)</span>
<span class="o">.</span><span class="py">option</span><span class="o">(</span><span class="s">"topic"</span><span class="o">,</span> <span class="n">topic</span><span class="o">)</span>
<span class="o">.</span><span class="py">load</span><span class="o">(</span><span class="n">brokerUrl</span><span class="o">).</span><span class="py">select</span><span class="o">(</span><span class="s">"payload"</span><span class="o">).</span><span class="py">as</span><span class="o">[</span><span class="kt">Array</span><span class="o">[</span><span class="kt">Byte</span><span class="o">]].</span><span class="py">map</span><span class="o">(</span><span class="nf">externalParser</span><span class="o">(</span><span class="k">_</span><span class="o">))</span>
</code></pre></div> </div>
</li>
<li>
<p>Java API example
```java
// Create DataFrame representing the stream of binary messages
Dataset&lt;byte[]&gt; lines = spark
.readStream()
.format(“org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider”)
.option(“topic”, topic)
.load(brokerUrl).selectExpr(“CAST(payload AS BINARY)”).as(Encoders.BINARY());</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code> // Split the lines into words
Dataset&lt;String&gt; words = lines.map(new MapFunction&lt;byte[], String&gt;() {
@Override
public String call(byte[] bytes) throws Exception {
return new String(bytes); // Plug in external parser here.
}
}, Encoders.STRING()).flatMap(new FlatMapFunction&lt;String, String&gt;() {
@Override
public Iterator&lt;String&gt; call(String x) {
return Arrays.asList(x.split(" ")).iterator();
}
}, Encoders.STRING());
</code></pre></div> </div>
</li>
</ul>
<p>```</p>
<ol>
<li>What is the solution for a situation when there are a large number of varied mqtt sources, each with different schema and throughput characteristics.</li>
</ol>
<p>Generally, one would create a lot of streaming pipelines to solve this problem. This would either require a very sophisticated scheduling setup or will waste a lot of resources, as it is not certain which stream is using more amount of data.</p>
<p>The general solution is both less optimum and is more cumbersome to operate, with multiple moving parts incurs a high maintenance overall. As an alternative, in this situation, one can setup a single topic kafka-spark stream, where message from each of the varied stream contains a unique tag separating one from other streams. This way at the processing end, one can distinguish the message from one another and apply the right kind of decoding and processing. Similarly while storing, each message can be distinguished from others by a tag that distinguishes.</p>
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