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The steps are:</p> - -<ol> -<li>Add Storm jars to classpath</li> -<li>If using multilang, add multilang dir to classpath</li> -</ol> - -<p>Follow along to see how to set up the <a href="http://github.com/apache/storm/blob/v0.10.1/examples/storm-starter";>storm-starter</a> project in Eclipse.</p> - -<h3 id="add-storm-jars-to-classpath">Add Storm jars to classpath</h3> - -<p>You'll need the Storm jars on your classpath to develop Storm topologies. Using <a href="Maven.html">Maven</a> is highly recommended. <a href="http://github.com/apache/storm/blob/v0.10.1/examples/storm-starter/pom.xml";>Here's an example</a> of how to setup your pom.xml for a Storm project. If you don't want to use Maven, you can include the jars from the Storm release on your classpath.</p> - -<p>To set up the classpath in Eclipse, create a new Java project, include <code>src/jvm/</code> as a source path, and make sure all the jars in <code>lib/</code> and <code>lib/dev/</code> are in the <code>Referenced Libraries</code> section of the project.</p> - -<h3 id="if-using-multilang-add-multilang-dir-to-classpath">If using multilang, add multilang dir to classpath</h3> - -<p>If you implement spouts or bolts in languages other than Java, then those implementations should be under the <code>multilang/resources/</code> directory of the project. For Storm to find these files in local mode, the <code>resources/</code> dir needs to be on the classpath. You can do this in Eclipse by adding <code>multilang/</code> as a source folder. You may also need to add multilang/resources as a source directory.</p> - -<p>For more information on writing topologies in other languages, see <a href="Using-non-JVM-languages-with-Storm.html">Using non-JVM languages with Storm</a>.</p> - -<p>To test that everything is working in Eclipse, you should now be able to <code>Run</code> the <code>WordCountTopology.java</code> file. 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- <li><a href="http://www.meetup.com/New-York-City-Storm-User-Group/";>NYC Storm User Group</a> <span class="small">(New York, NY)</span></li> - - <li><a href="http://www.meetup.com/Bay-Area-Stream-Processing";>Bay Area Stream Processing</a> <span class="small">(Emeryville, CA)</span></li> - - <li><a href="http://www.meetup.com/Boston-Storm-Users/";>Boston Realtime Data</a> <span class="small">(Boston, MA)</span></li> - - <li><a href="http://www.meetup.com/storm-london";>London Storm User Group</a> <span class="small">(London, UK)</span></li> - - <!-- <li><a href="http://www.meetup.com/Apache-Storm-Kafka-Users/";>Seatle, WA</a> <span class="small">(27 Jun 2015)</span></li> --> - </ul> - </div> - </div> - <div class="col-md-3"> - <div class="footer-widget"> - <h5>About Storm</h5> - <p>Storm integrates with any queueing system and any database system. Storm's spout abstraction makes it easy to integrate a new queuing system. 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Nimbus that schedules workers, supervisors that launch and kill workers, the log viewer that gives access to logs, and the UI that shows the status of a cluster.</p> - -<h2 id="what-happens-when-a-worker-dies">What happens when a worker dies?</h2> - -<p>When a worker dies, the supervisor will restart it. If it continuously fails on startup and is unable to heartbeat to Nimbus, Nimbus will reschedule the worker.</p> - -<h2 id="what-happens-when-a-node-dies">What happens when a node dies?</h2> - -<p>The tasks assigned to that machine will time-out and Nimbus will reassign those tasks to other machines.</p> - -<h2 id="what-happens-when-nimbus-or-supervisor-daemons-die">What happens when Nimbus or Supervisor daemons die?</h2> - -<p>The Nimbus and Supervisor daemons are designed to be fail-fast (process self-destructs whenever any unexpected situation is encountered) and stateless (all state is kept in Zookeeper or on disk). As described in <a href="Setting-up-a-Storm-cluster.html">Setting up a Storm cluster</a>, the Nimbus and Supervisor daemons must be run under supervision using a tool like daemontools or monit. So if the Nimbus or Supervisor daemons die, they restart like nothing happened.</p> - -<p>Most notably, no worker processes are affected by the death of Nimbus or the Supervisors. This is in contrast to Hadoop, where if the JobTracker dies, all the running jobs are lost. </p> - -<h2 id="is-nimbus-a-single-point-of-failure">Is Nimbus a single point of failure?</h2> - -<p>If you lose the Nimbus node, the workers will still continue to function. Additionally, supervisors will continue to restart workers if they die. However, without Nimbus, workers won't be reassigned to other machines when necessary (like if you lose a worker machine). </p> - -<p>So the answer is that Nimbus is "sort of" a SPOF. In practice, it's not a big deal since nothing catastrophic happens when the Nimbus daemon dies. There are plans to make Nimbus highly available in the future.</p> - -<h2 id="how-does-storm-guarantee-data-processing">How does Storm guarantee data processing?</h2> - -<p>Storm provides mechanisms to guarantee data processing even if nodes die or messages are lost. 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Since Storm topologies are just Thrift structures, and Nimbus is a Thrift daemon, you can create and submit topologies in any language.</p> - -<p>When you create the Thrift structs for spouts and bolts, the code for the spout or bolt is specified in the ComponentObject struct:</p> -<div class="highlight"><pre><code class="language-" data-lang="">union ComponentObject { - 1: binary serialized_java; - 2: ShellComponent shell; - 3: JavaObject java_object; -} -</code></pre></div> -<p>For a Python DSL, you would want to make use of "2" and "3". ShellComponent lets you specify a script to run that component (e.g., your python code). And JavaObject lets you specify native java spouts and bolts for the component (and Storm will use reflection to create that spout or bolt).</p> - -<p>There's a "storm shell" command that will help with submitting a topology. Its usage is like this:</p> -<div class="highlight"><pre><code class="language-" data-lang="">storm shell resources/ python topology.py arg1 arg2 -</code></pre></div> -<p>storm shell will then package resources/ into a jar, upload the jar to Nimbus, and call your topology.py script like this:</p> -<div class="highlight"><pre><code class="language-" data-lang="">python topology.py arg1 arg2 {nimbus-host} {nimbus-port} {uploaded-jar-location} -</code></pre></div> -<p>Then you can connect to Nimbus using the Thrift API and submit the topology, passing {uploaded-jar-location} into the submitTopology method. For reference, here's the submitTopology definition:</p> -<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kt">void</span> <span class="nf">submitTopology</span><span class="o">(</span><span class="mi">1</span><span class="o">:</span> <span class="n">string</span> <span class="n">name</span><span class="o">,</span> <span class="mi">2</span><span class="o">:</span> <span class="n">string</span> <span class="n">uploadedJarLocation</span><span class="o">,</span> <span class="mi">3</span><span class="o">:</span> <span class="n">string</span> <span class="n">jsonConf</span><span class="o">,</span> <span class="mi">4</span><span class="o">:</span> <span class="n">StormTopology</span> <span class="n">topology</span><span class="o">)</span> <span class="kd">throws</span> <span class="o">(</span><span class="mi">1</span><span class="o">:</span> <span class="n">AlreadyAliveException</span> <span class="n">e</span><span class="o">,</span> <span class="mi">2</span><span class="o">:</span> <span class="n">InvalidTop ologyException</span> <span class="n">ite</span><span class="o">);</span> -</code></pre></div> -<p>Finally, one of the key things to do in a non-JVM DSL is make it easy to define the entire topology in one file (the bolts, spouts, and the definition of the topology).</p> - - - - </div> - </div> - </div> -<footer> - <div class="container-fluid"> - <div class="row"> - <div class="col-md-3"> - <div class="footer-widget"> - <h5>Meetups</h5> - <ul class="latest-news"> - - <li><a href="http://www.meetup.com/Apache-Storm-Apache-Kafka/";>Apache Storm & Apache Kafka</a> <span class="small">(Sunnyvale, CA)</span></li> - - <li><a href="http://www.meetup.com/Apache-Storm-Kafka-Users/";>Apache Storm & Kafka Users</a> <span class="small">(Seattle, WA)</span></li> - - <li><a href="http://www.meetup.com/New-York-City-Storm-User-Group/";>NYC Storm User Group</a> <span class="small">(New York, NY)</span></li> - - <li><a href="http://www.meetup.com/Bay-Area-Stream-Processing";>Bay Area Stream Processing</a> <span class="small">(Emeryville, CA)</span></li> - - <li><a href="http://www.meetup.com/Boston-Storm-Users/";>Boston Realtime Data</a> <span class="small">(Boston, MA)</span></li> - - <li><a href="http://www.meetup.com/storm-london";>London Storm User Group</a> <span class="small">(London, UK)</span></li> - - <!-- <li><a href="http://www.meetup.com/Apache-Storm-Kafka-Users/";>Seatle, WA</a> <span class="small">(27 Jun 2015)</span></li> --> - </ul> - </div> - </div> - <div class="col-md-3"> - <div class="footer-widget"> - <h5>About Storm</h5> - <p>Storm integrates with any queueing system and any database system. 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The Storm topology takes in as input a stream of function arguments, and it emits an output stream of the results for each of those function calls. </p> - -<p>DRPC is not so much a feature of Storm as it is a pattern expressed from Storm's primitives of streams, spouts, bolts, and topologies. DRPC could have been packaged as a separate library from Storm, but it's so useful that it's bundled with Storm.</p> - -<h3 id="high-level-overview">High level overview</h3> - -<p>Distributed RPC is coordinated by a "DRPC server" (Storm comes packaged with an implementation of this). The DRPC server coordinates receiving an RPC request, sending the request to the Storm topology, receiving the results from the Storm topology, and sending the results back to the waiting client. From a client's perspective, a distributed RPC call looks just like a regular RPC call. For example, here's how a client would compute the results for the "reach" function with the argument "<a href="http://twitter.com%22:";>http://twitter.com":</a></p> -<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">DRPCClient</span> <span class="n">client</span> <span class="o">=</span> <span class="k">new</span> <span class="n">DRPCClient</span><span class="o">(</span><span class="s">"drpc-host"</span><span class="o">,</span> <span class="mi">3772</span><span class="o">);</span> -<span class="n">String</span> <span class="n">result</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="na">execute</span><span class="o">(</span><span class="s">"reach"</span><span class="o">,</span> <span class="s">"http://twitter.com";</span><span class="o">);</span> -</code></pre></div> -<p>The distributed RPC workflow looks like this:</p> - -<p><img src="images/drpc-workflow.png" alt="Tasks in a topology"></p> - -<p>A client sends the DRPC server the name of the function to execute and the arguments to that function. The topology implementing that function uses a <code>DRPCSpout</code> to receive a function invocation stream from the DRPC server. Each function invocation is tagged with a unique id by the DRPC server. The topology then computes the result and at the end of the topology a bolt called <code>ReturnResults</code> connects to the DRPC server and gives it the result for the function invocation id. The DRPC server then uses the id to match up that result with which client is waiting, unblocks the waiting client, and sends it the result.</p> - -<h3 id="lineardrpctopologybuilder">LinearDRPCTopologyBuilder</h3> - -<p>Storm comes with a topology builder called <a href="javadocs/backtype/storm/drpc/LinearDRPCTopologyBuilder.html">LinearDRPCTopologyBuilder</a> that automates almost all the steps involved for doing DRPC. These include:</p> - -<ol> -<li>Setting up the spout</li> -<li>Returning the results to the DRPC server</li> -<li>Providing functionality to bolts for doing finite aggregations over groups of tuples</li> -</ol> - -<p>Let's look at a simple example. Here's the implementation of a DRPC topology that returns its input argument with a "!" appended:</p> -<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kd">public</span> <span class="kd">static</span> <span class="kd">class</span> <span class="nc">ExclaimBolt</span> <span class="kd">extends</span> <span class="n">BaseBasicBolt</span> <span class="o">{</span> - <span class="kd">public</span> <span class="kt">void</span> <span class="nf">execute</span><span class="o">(</span><span class="n">Tuple</span> <span class="n">tuple</span><span class="o">,</span> <span class="n">BasicOutputCollector</span> <span class="n">collector</span><span class="o">)</span> <span class="o">{</span> - <span class="n">String</span> <span class="n">input</span> <span class="o">=</span> <span class="n">tuple</span><span class="o">.</span><span class="na">getString</span><span class="o">(</span><span class="mi">1</span><span class="o">);</span> - <span class="n">collector</span><span class="o">.</span><span class="na">emit</span><span class="o">(</span><span class="k">new</span> <span class="n">Values</span><span class="o">(</span><span class="n">tuple</span><span class="o">.</span><span class="na">getValue</span><span class="o">(</span><span class="mi">0</span><span class="o">),</span> <span class="n">input</span> <span class="o">+</span> <span class="s">"!"</span><span class="o">));</span> - <span class="o">}</span> - - <span class="kd">public</span> <span class="kt">void</span> <span class="nf">declareOutputFields</span><span class="o">(</span><span class="n">OutputFieldsDeclarer</span> <span class="n">declarer</span><span class="o">)</span> <span class="o">{</span> - <span class="n">declarer</span><span class="o">.</span><span class="na">declare</span><span class="o">(</span><span class="k">new</span> <span class="n">Fields</span><span class="o">(</span><span class="s">"id"</span><span class="o">,</span> <span class="s">"result"</span><span class="o">));</span> - <span class="o">}</span> -<span class="o">}</span> - -<span class="kd">public</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">main</span><span class="o">(</span><span class="n">String</span><span class="o">[]</span> <span class="n">args</span><span class="o">)</span> <span class="kd">throws</span> <span class="n">Exception</span> <span class="o">{</span> - <span class="n">LinearDRPCTopologyBuilder</span> <span class="n">builder</span> <span class="o">=</span> <span class="k">new</span> <span class="n">LinearDRPCTopologyBuilder</span><span class="o">(</span><span class="s">"exclamation"</span><span class="o">);</span> - <span class="n">builder</span><span class="o">.</span><span class="na">addBolt</span><span class="o">(</span><span class="k">new</span> <span class="n">ExclaimBolt</span><span class="o">(),</span> <span class="mi">3</span><span class="o">);</span> - <span class="c1">// ...</span> -<span class="o">}</span> -</code></pre></div> -<p>As you can see, there's very little to it. When creating the <code>LinearDRPCTopologyBuilder</code>, you tell it the name of the DRPC function for the topology. A single DRPC server can coordinate many functions, and the function name distinguishes the functions from one another. The first bolt you declare will take in as input 2-tuples, where the first field is the request id and the second field is the arguments for that request. <code>LinearDRPCTopologyBuilder</code> expects the last bolt to emit an output stream containing 2-tuples of the form [id, result]. Finally, all intermediate tuples must contain the request id as the first field.</p> - -<p>In this example, <code>ExclaimBolt</code> simply appends a "!" to the second field of the tuple. <code>LinearDRPCTopologyBuilder</code> handles the rest of the coordination of connecting to the DRPC server and sending results back.</p> - -<h3 id="local-mode-drpc">Local mode DRPC</h3> - -<p>DRPC can be run in local mode. Here's how to run the above example in local mode:</p> -<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">LocalDRPC</span> <span class="n">drpc</span> <span class="o">=</span> <span class="k">new</span> <span class="n">LocalDRPC</span><span class="o">();</span> -<span class="n">LocalCluster</span> <span class="n">cluster</span> <span class="o">=</span> <span class="k">new</span> <span class="n">LocalCluster</span><span class="o">();</span> - -<span class="n">cluster</span><span class="o">.</span><span class="na">submitTopology</span><span class="o">(</span><span class="s">"drpc-demo"</span><span class="o">,</span> <span class="n">conf</span><span class="o">,</span> <span class="n">builder</span><span class="o">.</span><span class="na">createLocalTopology</span><span class="o">(</span><span class="n">drpc</span><span class="o">));</span> - -<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Results for 'hello':"</span> <span class="o">+</span> <span class="n">drpc</span><span class="o">.</span><span class="na">execute</span><span class="o">(</span><span class="s">"exclamation"</span><span class="o">,</span> <span class="s">"hello"</span><span class="o">));</span> - -<span class="n">cluster</span><span class="o">.</span><span class="na">shutdown</span><span class="o">();</span> -<span class="n">drpc</span><span class="o">.</span><span class="na">shutdown</span><span class="o">();</span> -</code></pre></div> -<p>First you create a <code>LocalDRPC</code> object. This object simulates a DRPC server in process, just like how <code>LocalCluster</code> simulates a Storm cluster in process. Then you create the <code>LocalCluster</code> to run the topology in local mode. <code>LinearDRPCTopologyBuilder</code> has separate methods for creating local topologies and remote topologies. In local mode the <code>LocalDRPC</code> object does not bind to any ports so the topology needs to know about the object to communicate with it. This is why <code>createLocalTopology</code> takes in the <code>LocalDRPC</code> object as input.</p> - -<p>After launching the topology, you can do DRPC invocations using the <code>execute</code> method on <code>LocalDRPC</code>.</p> - -<h3 id="remote-mode-drpc">Remote mode DRPC</h3> - -<p>Using DRPC on an actual cluster is also straightforward. There's three steps:</p> - -<ol> -<li>Launch DRPC server(s)</li> -<li>Configure the locations of the DRPC servers</li> -<li>Submit DRPC topologies to Storm cluster</li> -</ol> - -<p>Launching a DRPC server can be done with the <code>storm</code> script and is just like launching Nimbus or the UI:</p> -<div class="highlight"><pre><code class="language-" data-lang="">bin/storm drpc -</code></pre></div> -<p>Next, you need to configure your Storm cluster to know the locations of the DRPC server(s). This is how <code>DRPCSpout</code> knows from where to read function invocations. This can be done through the <code>storm.yaml</code> file or the topology configurations. Configuring this through the <code>storm.yaml</code> looks something like this:</p> -<div class="highlight"><pre><code class="language-yaml" data-lang="yaml"><span class="s">drpc.servers</span><span class="pi">:</span> - <span class="pi">-</span> <span class="s2">"</span><span class="s">drpc1.foo.com"</span> - <span class="pi">-</span> <span class="s2">"</span><span class="s">drpc2.foo.com"</span> -</code></pre></div> -<p>Finally, you launch DRPC topologies using <code>StormSubmitter</code> just like you launch any other topology. To run the above example in remote mode, you do something like this:</p> -<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">StormSubmitter</span><span class="o">.</span><span class="na">submitTopology</span><span class="o">(</span><span class="s">"exclamation-drpc"</span><span class="o">,</span> <span class="n">conf</span><span class="o">,</span> <span class="n">builder</span><span class="o">.</span><span class="na">createRemoteTopology</span><span class="o">());</span> -</code></pre></div> -<p><code>createRemoteTopology</code> is used to create topologies suitable for Storm clusters.</p> - -<h3 id="a-more-complex-example">A more complex example</h3> - -<p>The exclamation DRPC example was a toy example for illustrating the concepts of DRPC. Let's look at a more complex example which really needs the parallelism a Storm cluster provides for computing the DRPC function. The example we'll look at is computing the reach of a URL on Twitter.</p> - -<p>The reach of a URL is the number of unique people exposed to a URL on Twitter. To compute reach, you need to:</p> - -<ol> -<li>Get all the people who tweeted the URL</li> -<li>Get all the followers of all those people</li> -<li>Unique the set of followers</li> -<li>Count the unique set of followers</li> -</ol> - -<p>A single reach computation can involve thousands of database calls and tens of millions of follower records during the computation. It's a really, really intense computation. As you're about to see, implementing this function on top of Storm is dead simple. On a single machine, reach can take minutes to compute; on a Storm cluster, you can compute reach for even the hardest URLs in a couple seconds.</p> - -<p>A sample reach topology is defined in storm-starter <a href="http://github.com/apache/storm/blob/v0.10.1/examples/storm-starter/src/jvm/storm/starter/ReachTopology.java";>here</a>. Here's how you define the reach topology:</p> -<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">LinearDRPCTopologyBuilder</span> <span class="n">builder</span> <span class="o">=</span> <span class="k">new</span> <span class="n">LinearDRPCTopologyBuilder</span><span class="o">(</span><span class="s">"reach"</span><span class="o">);</span> -<span class="n">builder</span><span class="o">.</span><span class="na">addBolt</span><span class="o">(</span><span class="k">new</span> <span class="n">GetTweeters</span><span class="o">(),</span> <span class="mi">3</span><span class="o">);</span> -<span class="n">builder</span><span class="o">.</span><span class="na">addBolt</span><span class="o">(</span><span class="k">new</span> <span class="n">GetFollowers</span><span class="o">(),</span> <span class="mi">12</span><span class="o">)</span> - <span class="o">.</span><span class="na">shuffleGrouping</span><span class="o">();</span> -<span class="n">builder</span><span class="o">.</span><span class="na">addBolt</span><span class="o">(</span><span class="k">new</span> <span class="n">PartialUniquer</span><span class="o">(),</span> <span class="mi">6</span><span class="o">)</span> - <span class="o">.</span><span class="na">fieldsGrouping</span><span class="o">(</span><span class="k">new</span> <span class="n">Fields</span><span class="o">(</span><span class="s">"id"</span><span class="o">,</span> <span class="s">"follower"</span><span class="o">));</span> -<span class="n">builder</span><span class="o">.</span><span class="na">addBolt</span><span class="o">(</span><span class="k">new</span> <span class="n">CountAggregator</span><span class="o">(),</span> <span class="mi">2</span><span class="o">)</span> - <span class="o">.</span><span class="na">fieldsGrouping</span><span class="o">(</span><span class="k">new</span> <span class="n">Fields</span><span class="o">(</span><span class="s">"id"</span><span class="o">));</span> -</code></pre></div> -<p>The topology executes as four steps:</p> - -<ol> -<li><code>GetTweeters</code> gets the users who tweeted the URL. It transforms an input stream of <code>[id, url]</code> into an output stream of <code>[id, tweeter]</code>. Each <code>url</code> tuple will map to many <code>tweeter</code> tuples.</li> -<li><code>GetFollowers</code> gets the followers for the tweeters. It transforms an input stream of <code>[id, tweeter]</code> into an output stream of <code>[id, follower]</code>. Across all the tasks, there may of course be duplication of follower tuples when someone follows multiple people who tweeted the same URL.</li> -<li><code>PartialUniquer</code> groups the followers stream by the follower id. This has the effect of the same follower going to the same task. So each task of <code>PartialUniquer</code> will receive mutually independent sets of followers. Once <code>PartialUniquer</code> receives all the follower tuples directed at it for the request id, it emits the unique count of its subset of followers.</li> -<li>Finally, <code>CountAggregator</code> receives the partial counts from each of the <code>PartialUniquer</code> tasks and sums them up to complete the reach computation.</li> -</ol> - -<p>Let's take a look at the <code>PartialUniquer</code> bolt:</p> -<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kd">public</span> <span class="kd">class</span> <span class="nc">PartialUniquer</span> <span class="kd">extends</span> <span class="n">BaseBatchBolt</span> <span class="o">{</span> - <span class="n">BatchOutputCollector</span> <span class="n">_collector</span><span class="o">;</span> - <span class="n">Object</span> <span class="n">_id</span><span class="o">;</span> - <span class="n">Set</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">_followers</span> <span class="o">=</span> <span class="k">new</span> <span class="n">HashSet</span><span class="o"><</span><span class="n">String</span><span class="o">>();</span> - - <span class="nd">@Override</span> - <span class="kd">public</span> <span class="kt">void</span> <span class="nf">prepare</span><span class="o">(</span><span class="n">Map</span> <span class="n">conf</span><span class="o">,</span> <span class="n">TopologyContext</span> <span class="n">context</span><span class="o">,</span> <span class="n">BatchOutputCollector</span> <span class="n">collector</span><span class="o">,</span> <span class="n">Object</span> <span class="n">id</span><span class="o">)</span> <span class="o">{</span> - <span class="n">_collector</span> <span class="o">=</span> <span class="n">collector</span><span class="o">;</span> - <span class="n">_id</span> <span class="o">=</span> <span class="n">id</span><span class="o">;</span> - <span class="o">}</span> - - <span class="nd">@Override</span> - <span class="kd">public</span> <span class="kt">void</span> <span class="nf">execute</span><span class="o">(</span><span class="n">Tuple</span> <span class="n">tuple</span><span class="o">)</span> <span class="o">{</span> - <span class="n">_followers</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">tuple</span><span class="o">.</span><span class="na">getString</span><span class="o">(</span><span class="mi">1</span><span class="o">));</span> - <span class="o">}</span> - - <span class="nd">@Override</span> - <span class="kd">public</span> <span class="kt">void</span> <span class="nf">finishBatch</span><span class="o">()</span> <span class="o">{</span> - <span class="n">_collector</span><span class="o">.</span><span class="na">emit</span><span class="o">(</span><span class="k">new</span> <span class="n">Values</span><span class="o">(</span><span class="n">_id</span><span class="o">,</span> <span class="n">_followers</span><span class="o">.</span><span class="na">size</span><span class="o">()));</span> - <span class="o">}</span> - - <span class="nd">@Override</span> - <span class="kd">public</span> <span class="kt">void</span> <span class="nf">declareOutputFields</span><span class="o">(</span><span class="n">OutputFieldsDeclarer</span> <span class="n">declarer</span><span class="o">)</span> <span class="o">{</span> - <span class="n">declarer</span><span class="o">.</span><span class="na">declare</span><span class="o">(</span><span class="k">new</span> <span class="n">Fields</span><span class="o">(</span><span class="s">"id"</span><span class="o">,</span> <span class="s">"partial-count"</span><span class="o">));</span> - <span class="o">}</span> -<span class="o">}</span> -</code></pre></div> -<p><code>PartialUniquer</code> implements <code>IBatchBolt</code> by extending <code>BaseBatchBolt</code>. A batch bolt provides a first class API to processing a batch of tuples as a concrete unit. A new instance of the batch bolt is created for each request id, and Storm takes care of cleaning up the instances when appropriate. </p> - -<p>When <code>PartialUniquer</code> receives a follower tuple in the <code>execute</code> method, it adds it to the set for the request id in an internal <code>HashSet</code>. </p> - -<p>Batch bolts provide the <code>finishBatch</code> method which is called after all the tuples for this batch targeted at this task have been processed. In the callback, <code>PartialUniquer</code> emits a single tuple containing the unique count for its subset of follower ids.</p> - -<p>Under the hood, <code>CoordinatedBolt</code> is used to detect when a given bolt has received all of the tuples for any given request id. <code>CoordinatedBolt</code> makes use of direct streams to manage this coordination.</p> - -<p>The rest of the topology should be self-explanatory. As you can see, every single step of the reach computation is done in parallel, and defining the DRPC topology was extremely simple.</p> - -<h3 id="non-linear-drpc-topologies">Non-linear DRPC topologies</h3> - -<p><code>LinearDRPCTopologyBuilder</code> only handles "linear" DRPC topologies, where the computation is expressed as a sequence of steps (like reach). It's not hard to imagine functions that would require a more complicated topology with branching and merging of the bolts. For now, to do this you'll need to drop down into using <code>CoordinatedBolt</code> directly. Be sure to talk about your use case for non-linear DRPC topologies on the mailing list to inform the construction of more general abstractions for DRPC topologies.</p> - -<h3 id="how-lineardrpctopologybuilder-works">How LinearDRPCTopologyBuilder works</h3> - -<ul> -<li>DRPCSpout emits [args, return-info]. return-info is the host and port of the DRPC server as well as the id generated by the DRPC server</li> -<li>constructs a topology comprising of: - -<ul> -<li>DRPCSpout</li> -<li>PrepareRequest (generates a request id and creates a stream for the return info and a stream for the args)</li> -<li>CoordinatedBolt wrappers and direct groupings</li> -<li>JoinResult (joins the result with the return info)</li> -<li>ReturnResult (connects to the DRPC server and returns the result)</li> -</ul></li> -<li>LinearDRPCTopologyBuilder is a good example of a higher level abstraction built on top of Storm's primitives</li> -</ul> - -<h3 id="advanced">Advanced</h3> - -<ul> -<li>KeyedFairBolt for weaving the processing of multiple requests at the same time</li> -<li>How to use <code>CoordinatedBolt</code> directly</li> -</ul> - - - - </div> - </div> - </div> -<footer> - <div class="container-fluid"> - <div class="row"> - <div class="col-md-3"> - <div class="footer-widget"> - <h5>Meetups</h5> - <ul class="latest-news"> - - <li><a href="http://www.meetup.com/Apache-Storm-Apache-Kafka/";>Apache Storm & Apache Kafka</a> <span class="small">(Sunnyvale, CA)</span></li> - - <li><a href="http://www.meetup.com/Apache-Storm-Kafka-Users/";>Apache Storm & Kafka Users</a> <span class="small">(Seattle, WA)</span></li> - - <li><a href="http://www.meetup.com/New-York-City-Storm-User-Group/";>NYC Storm User Group</a> <span class="small">(New York, NY)</span></li> - - <li><a href="http://www.meetup.com/Bay-Area-Stream-Processing";>Bay Area Stream Processing</a> <span class="small">(Emeryville, CA)</span></li> - - <li><a href="http://www.meetup.com/Boston-Storm-Users/";>Boston Realtime Data</a> <span class="small">(Boston, MA)</span></li> - - <li><a href="http://www.meetup.com/storm-london";>London Storm User Group</a> <span class="small">(London, UK)</span></li> - - <!-- <li><a href="http://www.meetup.com/Apache-Storm-Kafka-Users/";>Seatle, WA</a> <span class="small">(27 Jun 2015)</span></li> --> - </ul> - </div> - </div> - <div class="col-md-3"> - <div class="footer-widget"> - <h5>About Storm</h5> - <p>Storm integrates with any queueing system and any database system. 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