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[26/51] [partial] storm git commit: STORM-1617: Versioned Docs
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- <div class="row">
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- <!-- Documentation -->
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-<p class="post-meta"></p>
-
-<p>Running topologies on a production cluster is similar to running in <a href="Local-mode.html">Local mode</a>. Here are the steps:</p>
-
-<p>1) Define the topology (Use <a href="/javadoc/apidocs/backtype/storm/topology/TopologyBuilder.html">TopologyBuilder</a> if defining using Java)</p>
-
-<p>2) Use <a href="/javadoc/apidocs/backtype/storm/StormSubmitter.html">StormSubmitter</a> to submit the topology to the cluster. <code>StormSubmitter</code> takes as input the name of the topology, a configuration for the topology, and the topology itself. For example:</p>
-<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">Config</span> <span class="n">conf</span> <span class="o">=</span> <span class="k">new</span> <span class="n">Config</span><span class="o">();</span>
-<span class="n">conf</span><span class="o">.</span><span class="na">setNumWorkers</span><span class="o">(</span><span class="mi">20</span><span class="o">);</span>
-<span class="n">conf</span><span class="o">.</span><span class="na">setMaxSpoutPending</span><span class="o">(</span><span class="mi">5000</span><span class="o">);</span>
-<span class="n">StormSubmitter</span><span class="o">.</span><span class="na">submitTopology</span><span class="o">(</span><span class="s">"mytopology"</span><span class="o">,</span> <span class="n">conf</span><span class="o">,</span> <span class="n">topology</span><span class="o">);</span>
-</code></pre></div>
-<p>3) Create a jar containing your code and all the dependencies of your code (except for Storm -- the Storm jars will be added to the classpath on the worker nodes).</p>
-
-<p>If you're using Maven, the <a href="http://maven.apache.org/plugins/maven-assembly-plugin/">Maven Assembly Plugin</a> can do the packaging for you. Just add this to your pom.xml:</p>
-<div class="highlight"><pre><code class="language-xml" data-lang="xml"> <span class="nt"><plugin></span>
- <span class="nt"><artifactId></span>maven-assembly-plugin<span class="nt"></artifactId></span>
- <span class="nt"><configuration></span>
- <span class="nt"><descriptorRefs></span>
- <span class="nt"><descriptorRef></span>jar-with-dependencies<span class="nt"></descriptorRef></span>
- <span class="nt"></descriptorRefs></span>
- <span class="nt"><archive></span>
- <span class="nt"><manifest></span>
- <span class="nt"><mainClass></span>com.path.to.main.Class<span class="nt"></mainClass></span>
- <span class="nt"></manifest></span>
- <span class="nt"></archive></span>
- <span class="nt"></configuration></span>
- <span class="nt"></plugin></span>
-</code></pre></div>
-<p>Then run mvn assembly:assembly to get an appropriately packaged jar. Make sure you <a href="http://maven.apache.org/plugins/maven-assembly-plugin/examples/single/including-and-excluding-artifacts.html">exclude</a> the Storm jars since the cluster already has Storm on the classpath.</p>
-
-<p>4) Submit the topology to the cluster using the <code>storm</code> client, specifying the path to your jar, the classname to run, and any arguments it will use:</p>
-
-<p><code>storm jar path/to/allmycode.jar org.me.MyTopology arg1 arg2 arg3</code></p>
-
-<p><code>storm jar</code> will submit the jar to the cluster and configure the <code>StormSubmitter</code> class to talk to the right cluster. In this example, after uploading the jar <code>storm jar</code> calls the main function on <code>org.me.MyTopology</code> with the arguments "arg1", "arg2", and "arg3".</p>
-
-<p>You can find out how to configure your <code>storm</code> client to talk to a Storm cluster on <a href="Setting-up-development-environment.html">Setting up development environment</a>.</p>
-
-<h3 id="common-configurations">Common configurations</h3>
-
-<p>There are a variety of configurations you can set per topology. A list of all the configurations you can set can be found <a href="/javadoc/apidocs/backtype/storm/Config.html">here</a>. The ones prefixed with "TOPOLOGY" can be overridden on a topology-specific basis (the other ones are cluster configurations and cannot be overridden). Here are some common ones that are set for a topology:</p>
-
-<ol>
-<li><strong>Config.TOPOLOGY_WORKERS</strong>: This sets the number of worker processes to use to execute the topology. For example, if you set this to 25, there will be 25 Java processes across the cluster executing all the tasks. If you had a combined 150 parallelism across all components in the topology, each worker process will have 6 tasks running within it as threads.</li>
-<li><strong>Config.TOPOLOGY_ACKER_EXECUTORS</strong>: This sets the number of executors that will track tuple trees and detect when a spout tuple has been fully processed. Ackers are an integral part of Storm's reliability model and you can read more about them on <a href="Guaranteeing-message-processing.html">Guaranteeing message processing</a>. By not setting this variable or setting it as null, Storm will set the number of acker executors to be equal to the number of workers configured for this topology. If this variable is set to 0, then Storm will immediately ack tuples as soon as they come off the spout, effectively disabling reliability.</li>
-<li><strong>Config.TOPOLOGY_MAX_SPOUT_PENDING</strong>: This sets the maximum number of spout tuples that can be pending on a single spout task at once (pending means the tuple has not been acked or failed yet). It is highly recommended you set this config to prevent queue explosion.</li>
-<li><strong>Config.TOPOLOGY_MESSAGE_TIMEOUT_SECS</strong>: This is the maximum amount of time a spout tuple has to be fully completed before it is considered failed. This value defaults to 30 seconds, which is sufficient for most topologies. See <a href="Guaranteeing-message-processing.html">Guaranteeing message processing</a> for more information on how Storm's reliability model works.</li>
-<li><strong>Config.TOPOLOGY_SERIALIZATIONS</strong>: You can register more serializers to Storm using this config so that you can use custom types within tuples.</li>
-</ol>
-
-<h3 id="killing-a-topology">Killing a topology</h3>
-
-<p>To kill a topology, simply run:</p>
-
-<p><code>storm kill {stormname}</code></p>
-
-<p>Give the same name to <code>storm kill</code> as you used when submitting the topology.</p>
-
-<p>Storm won't kill the topology immediately. Instead, it deactivates all the spouts so that they don't emit any more tuples, and then Storm waits Config.TOPOLOGY_MESSAGE_TIMEOUT_SECS seconds before destroying all the workers. This gives the topology enough time to complete any tuples it was processing when it got killed.</p>
-
-<h3 id="updating-a-running-topology">Updating a running topology</h3>
-
-<p>To update a running topology, the only option currently is to kill the current topology and resubmit a new one. A planned feature is to implement a <code>storm swap</code> command that swaps a running topology with a new one, ensuring minimal downtime and no chance of both topologies processing tuples at the same time. </p>
-
-<h3 id="monitoring-topologies">Monitoring topologies</h3>
-
-<p>The best place to monitor a topology is using the Storm UI. The Storm UI provides information about errors happening in tasks and fine-grained stats on the throughput and latency performance of each component of each running topology.</p>
-
-<p>You can also look at the worker logs on the cluster machines.</p>
-
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http://git-wip-us.apache.org/repos/asf/storm/blob/335bbf94/_site/documentation/Serialization-(prior-to-0.6.0).html
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- <!-- Documentation -->
-
-<p class="post-meta"></p>
-
-<p>Tuples can be comprised of objects of any types. Since Storm is a distributed system, it needs to know how to serialize and deserialize objects when they're passed between tasks. By default Storm can serialize ints, shorts, longs, floats, doubles, bools, bytes, strings, and byte arrays, but if you want to use another type in your tuples, you'll need to implement a custom serializer.</p>
-
-<h3 id="dynamic-typing">Dynamic typing</h3>
-
-<p>There are no type declarations for fields in a Tuple. You put objects in fields and Storm figures out the serialization dynamically. Before we get to the interface for serialization, let's spend a moment understanding why Storm's tuples are dynamically typed.</p>
-
-<p>Adding static typing to tuple fields would add large amount of complexity to Storm's API. Hadoop, for example, statically types its keys and values but requires a huge amount of annotations on the part of the user. Hadoop's API is a burden to use and the "type safety" isn't worth it. Dynamic typing is simply easier to use.</p>
-
-<p>Further than that, it's not possible to statically type Storm's tuples in any reasonable way. Suppose a Bolt subscribes to multiple streams. The tuples from all those streams may have different types across the fields. When a Bolt receives a <code>Tuple</code> in <code>execute</code>, that tuple could have come from any stream and so could have any combination of types. There might be some reflection magic you can do to declare a different method for every tuple stream a bolt subscribes to, but Storm opts for the simpler, straightforward approach of dynamic typing.</p>
-
-<p>Finally, another reason for using dynamic typing is so Storm can be used in a straightforward manner from dynamically typed languages like Clojure and JRuby.</p>
-
-<h3 id="custom-serialization">Custom serialization</h3>
-
-<p>Let's dive into Storm's API for defining custom serializations. There are two steps you need to take as a user to create a custom serialization: implement the serializer, and register the serializer to Storm.</p>
-
-<h4 id="creating-a-serializer">Creating a serializer</h4>
-
-<p>Custom serializers implement the <a href="/javadoc/apidocs/backtype/storm/serialization/ISerialization.html">ISerialization</a> interface. Implementations specify how to serialize and deserialize types into a binary format.</p>
-
-<p>The interface looks like this:</p>
-<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kd">public</span> <span class="kd">interface</span> <span class="nc">ISerialization</span><span class="o"><</span><span class="n">T</span><span class="o">></span> <span class="o">{</span>
- <span class="kd">public</span> <span class="kt">boolean</span> <span class="n">accept</span><span class="o">(</span><span class="n">Class</span> <span class="n">c</span><span class="o">);</span>
- <span class="kd">public</span> <span class="kt">void</span> <span class="n">serialize</span><span class="o">(</span><span class="n">T</span> <span class="n">object</span><span class="o">,</span> <span class="n">DataOutputStream</span> <span class="n">stream</span><span class="o">)</span> <span class="kd">throws</span> <span class="n">IOException</span><span class="o">;</span>
- <span class="kd">public</span> <span class="n">T</span> <span class="n">deserialize</span><span class="o">(</span><span class="n">DataInputStream</span> <span class="n">stream</span><span class="o">)</span> <span class="kd">throws</span> <span class="n">IOException</span><span class="o">;</span>
-<span class="o">}</span>
-</code></pre></div>
-<p>Storm uses the <code>accept</code> method to determine if a type can be serialized by this serializer. Remember, Storm's tuples are dynamically typed so Storm determines what serializer to use at runtime.</p>
-
-<p><code>serialize</code> writes the object out to the output stream in binary format. The field must be written in a way such that it can be deserialized later. For example, if you're writing out a list of objects, you'll need to write out the size of the list first so that you know how many elements to deserialize.</p>
-
-<p><code>deserialize</code> reads the serialized object off of the stream and returns it.</p>
-
-<p>You can see example serialization implementations in the source for <a href="https://github.com/apache/storm/blob/0.5.4/src/jvm/backtype/storm/serialization/SerializationFactory.java">SerializationFactory</a></p>
-
-<h4 id="registering-a-serializer">Registering a serializer</h4>
-
-<p>Once you create a serializer, you need to tell Storm it exists. This is done through the Storm configuration (See <a href="Concepts.html">Concepts</a> for information about how configuration works in Storm). You can register serializations either through the config given when submitting a topology or in the storm.yaml files across your cluster.</p>
-
-<p>Serializer registrations are done through the Config.TOPOLOGY_SERIALIZATIONS config and is simply a list of serialization class names.</p>
-
-<p>Storm provides helpers for registering serializers in a topology config. The <a href="/javadoc/apidocs/backtype/storm/Config.html">Config</a> class has a method called <code>addSerialization</code> that takes in a serializer class to add to the config.</p>
-
-<p>There's an advanced config called Config.TOPOLOGY_SKIP_MISSING_SERIALIZATIONS. If you set this to true, Storm will ignore any serializations that are registered but do not have their code available on the classpath. Otherwise, Storm will throw errors when it can't find a serialization. This is useful if you run many topologies on a cluster that each have different serializations, but you want to declare all the serializations across all topologies in the <code>storm.yaml</code> files.</p>
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-<p>This page is about how the serialization system in Storm works for versions 0.6.0 and onwards. Storm used a different serialization system prior to 0.6.0 which is documented on <a href="Serialization-(prior-to-0.6.0).html">Serialization (prior to 0.6.0)</a>. </p>
-
-<p>Tuples can be comprised of objects of any types. Since Storm is a distributed system, it needs to know how to serialize and deserialize objects when they're passed between tasks.</p>
-
-<p>Storm uses <a href="https://github.com/EsotericSoftware/kryo">Kryo</a> for serialization. Kryo is a flexible and fast serialization library that produces small serializations.</p>
-
-<p>By default, Storm can serialize primitive types, strings, byte arrays, ArrayList, HashMap, HashSet, and the Clojure collection types. If you want to use another type in your tuples, you'll need to register a custom serializer.</p>
-
-<h3 id="dynamic-typing">Dynamic typing</h3>
-
-<p>There are no type declarations for fields in a Tuple. You put objects in fields and Storm figures out the serialization dynamically. Before we get to the interface for serialization, let's spend a moment understanding why Storm's tuples are dynamically typed.</p>
-
-<p>Adding static typing to tuple fields would add large amount of complexity to Storm's API. Hadoop, for example, statically types its keys and values but requires a huge amount of annotations on the part of the user. Hadoop's API is a burden to use and the "type safety" isn't worth it. Dynamic typing is simply easier to use.</p>
-
-<p>Further than that, it's not possible to statically type Storm's tuples in any reasonable way. Suppose a Bolt subscribes to multiple streams. The tuples from all those streams may have different types across the fields. When a Bolt receives a <code>Tuple</code> in <code>execute</code>, that tuple could have come from any stream and so could have any combination of types. There might be some reflection magic you can do to declare a different method for every tuple stream a bolt subscribes to, but Storm opts for the simpler, straightforward approach of dynamic typing.</p>
-
-<p>Finally, another reason for using dynamic typing is so Storm can be used in a straightforward manner from dynamically typed languages like Clojure and JRuby.</p>
-
-<h3 id="custom-serialization">Custom serialization</h3>
-
-<p>As mentioned, Storm uses Kryo for serialization. To implement custom serializers, you need to register new serializers with Kryo. It's highly recommended that you read over <a href="https://github.com/EsotericSoftware/kryo">Kryo's home page</a> to understand how it handles custom serialization.</p>
-
-<p>Adding custom serializers is done through the "topology.kryo.register" property in your topology config. It takes a list of registrations, where each registration can take one of two forms:</p>
-
-<ol>
-<li>The name of a class to register. In this case, Storm will use Kryo's <code>FieldsSerializer</code> to serialize the class. This may or may not be optimal for the class -- see the Kryo docs for more details.</li>
-<li>A map from the name of a class to register to an implementation of <a href="https://github.com/EsotericSoftware/kryo/blob/master/src/com/esotericsoftware/kryo/Serializer.java">com.esotericsoftware.kryo.Serializer</a>.</li>
-</ol>
-
-<p>Let's look at an example.</p>
-<div class="highlight"><pre><code class="language-" data-lang="">topology.kryo.register:
- - com.mycompany.CustomType1
- - com.mycompany.CustomType2: com.mycompany.serializer.CustomType2Serializer
- - com.mycompany.CustomType3
-</code></pre></div>
-<p><code>com.mycompany.CustomType1</code> and <code>com.mycompany.CustomType3</code> will use the <code>FieldsSerializer</code>, whereas <code>com.mycompany.CustomType2</code> will use <code>com.mycompany.serializer.CustomType2Serializer</code> for serialization.</p>
-
-<p>Storm provides helpers for registering serializers in a topology config. The <a href="/javadoc/apidocs/backtype/storm/Config.html">Config</a> class has a method called <code>registerSerialization</code> that takes in a registration to add to the config.</p>
-
-<p>There's an advanced config called <code>Config.TOPOLOGY_SKIP_MISSING_KRYO_REGISTRATIONS</code>. If you set this to true, Storm will ignore any serializations that are registered but do not have their code available on the classpath. Otherwise, Storm will throw errors when it can't find a serialization. This is useful if you run many topologies on a cluster that each have different serializations, but you want to declare all the serializations across all topologies in the <code>storm.yaml</code> files.</p>
-
-<h3 id="java-serialization">Java serialization</h3>
-
-<p>If Storm encounters a type for which it doesn't have a serialization registered, it will use Java serialization if possible. If the object can't be serialized with Java serialization, then Storm will throw an error.</p>
-
-<p>Beware that Java serialization is extremely expensive, both in terms of CPU cost as well as the size of the serialized object. It is highly recommended that you register custom serializers when you put the topology in production. The Java serialization behavior is there so that it's easy to prototype new topologies.</p>
-
-<p>You can turn off the behavior to fall back on Java serialization by setting the <code>Config.TOPOLOGY_FALL_BACK_ON_JAVA_SERIALIZATION</code> config to false.</p>
-
-<h3 id="component-specific-serialization-registrations">Component-specific serialization registrations</h3>
-
-<p>Storm 0.7.0 lets you set component-specific configurations (read more about this at <a href="Configuration.html">Configuration</a>). Of course, if one component defines a serialization that serialization will need to be available to other bolts -- otherwise they won't be able to receive messages from that component!</p>
-
-<p>When a topology is submitted, a single set of serializations is chosen to be used by all components in the topology for sending messages. This is done by merging the component-specific serializer registrations with the regular set of serialization registrations. If two components define serializers for the same class, one of the serializers is chosen arbitrarily.</p>
-
-<p>To force a serializer for a particular class if there's a conflict between two component-specific registrations, just define the serializer you want to use in the topology-specific configuration. The topology-specific configuration has precedence over component-specific configurations for serialization registrations.</p>
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- <h1 class="page-title">Setting up a Storm Cluster</h1>
- <div class="row">
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- <!-- Documentation -->
-
-<p class="post-meta"></p>
-
-<p>This page outlines the steps for getting a Storm cluster up and running. If you're on AWS, you should check out the <a href="https://github.com/nathanmarz/storm-deploy/wiki">storm-deploy</a> project. <a href="https://github.com/nathanmarz/storm-deploy/wiki">storm-deploy</a> completely automates the provisioning, configuration, and installation of Storm clusters on EC2. It also sets up Ganglia for you so you can monitor CPU, disk, and network usage.</p>
-
-<p>If you run into difficulties with your Storm cluster, first check for a solution is in the <a href="Troubleshooting.html">Troubleshooting</a> page. Otherwise, email the mailing list.</p>
-
-<p>Here's a summary of the steps for setting up a Storm cluster:</p>
-
-<ol>
-<li>Set up a Zookeeper cluster</li>
-<li>Install dependencies on Nimbus and worker machines</li>
-<li>Download and extract a Storm release to Nimbus and worker machines</li>
-<li>Fill in mandatory configurations into storm.yaml</li>
-<li>Launch daemons under supervision using "storm" script and a supervisor of your choice</li>
-</ol>
-
-<h3 id="set-up-a-zookeeper-cluster">Set up a Zookeeper cluster</h3>
-
-<p>Storm uses Zookeeper for coordinating the cluster. Zookeeper <strong>is not</strong> used for message passing, so the load Storm places on Zookeeper is quite low. Single node Zookeeper clusters should be sufficient for most cases, but if you want failover or are deploying large Storm clusters you may want larger Zookeeper clusters. Instructions for deploying Zookeeper are <a href="http://zookeeper.apache.org/doc/r3.3.3/zookeeperAdmin.html">here</a>. </p>
-
-<p>A few notes about Zookeeper deployment:</p>
-
-<ol>
-<li>It's critical that you run Zookeeper under supervision, since Zookeeper is fail-fast and will exit the process if it encounters any error case. See <a href="http://zookeeper.apache.org/doc/r3.3.3/zookeeperAdmin.html#sc_supervision">here</a> for more details. </li>
-<li>It's critical that you set up a cron to compact Zookeeper's data and transaction logs. The Zookeeper daemon does not do this on its own, and if you don't set up a cron, Zookeeper will quickly run out of disk space. See <a href="http://zookeeper.apache.org/doc/r3.3.3/zookeeperAdmin.html#sc_maintenance">here</a> for more details.</li>
-</ol>
-
-<h3 id="install-dependencies-on-nimbus-and-worker-machines">Install dependencies on Nimbus and worker machines</h3>
-
-<p>Next you need to install Storm's dependencies on Nimbus and the worker machines. These are:</p>
-
-<ol>
-<li>Java 6</li>
-<li>Python 2.6.6</li>
-</ol>
-
-<p>These are the versions of the dependencies that have been tested with Storm. Storm may or may not work with different versions of Java and/or Python.</p>
-
-<h3 id="download-and-extract-a-storm-release-to-nimbus-and-worker-machines">Download and extract a Storm release to Nimbus and worker machines</h3>
-
-<p>Next, download a Storm release and extract the zip file somewhere on Nimbus and each of the worker machines. The Storm releases can be downloaded <a href="http://github.com/apache/storm/releases">from here</a>.</p>
-
-<h3 id="fill-in-mandatory-configurations-into-storm-yaml">Fill in mandatory configurations into storm.yaml</h3>
-
-<p>The Storm release contains a file at <code>conf/storm.yaml</code> that configures the Storm daemons. You can see the default configuration values <a href="https://github.com/apache/storm/blob/master/conf/defaults.yaml">here</a>. storm.yaml overrides anything in defaults.yaml. There's a few configurations that are mandatory to get a working cluster:</p>
-
-<p>1) <strong>storm.zookeeper.servers</strong>: This is a list of the hosts in the Zookeeper cluster for your Storm cluster. It should look something like:</p>
-<div class="highlight"><pre><code class="language-yaml" data-lang="yaml"><span class="s">storm.zookeeper.servers</span><span class="pi">:</span>
- <span class="pi">-</span> <span class="s2">"</span><span class="s">111.222.333.444"</span>
- <span class="pi">-</span> <span class="s2">"</span><span class="s">555.666.777.888"</span>
-</code></pre></div>
-<p>If the port that your Zookeeper cluster uses is different than the default, you should set <strong>storm.zookeeper.port</strong> as well.</p>
-
-<p>2) <strong>storm.local.dir</strong>: The Nimbus and Supervisor daemons require a directory on the local disk to store small amounts of state (like jars, confs, and things like that). You should create that directory on each machine, give it proper permissions, and then fill in the directory location using this config. For example:</p>
-<div class="highlight"><pre><code class="language-yaml" data-lang="yaml"><span class="s">storm.local.dir</span><span class="pi">:</span> <span class="s2">"</span><span class="s">/mnt/storm"</span>
-</code></pre></div>
-<p>3) <strong>nimbus.host</strong>: The worker nodes need to know which machine is the master in order to download topology jars and confs. For example:</p>
-<div class="highlight"><pre><code class="language-yaml" data-lang="yaml"><span class="s">nimbus.host</span><span class="pi">:</span> <span class="s2">"</span><span class="s">111.222.333.44"</span>
-</code></pre></div>
-<p>4) <strong>supervisor.slots.ports</strong>: For each worker machine, you configure how many workers run on that machine with this config. Each worker uses a single port for receiving messages, and this setting defines which ports are open for use. If you define five ports here, then Storm will allocate up to five workers to run on this machine. If you define three ports, Storm will only run up to three. By default, this setting is configured to run 4 workers on the ports 6700, 6701, 6702, and 6703. For example:</p>
-<div class="highlight"><pre><code class="language-yaml" data-lang="yaml"><span class="s">supervisor.slots.ports</span><span class="pi">:</span>
- <span class="pi">-</span> <span class="s">6700</span>
- <span class="pi">-</span> <span class="s">6701</span>
- <span class="pi">-</span> <span class="s">6702</span>
- <span class="pi">-</span> <span class="s">6703</span>
-</code></pre></div>
-<h3 id="configure-external-libraries-and-environmental-variables-optional">Configure external libraries and environmental variables (optional)</h3>
-
-<p>If you need support from external libraries or custom plugins, you can place such jars into the extlib/ and extlib-daemon/ directories. Note that the extlib-daemon/ directory stores jars used only by daemons (Nimbus, Supervisor, DRPC, UI, Logviewer), e.g., HDFS and customized scheduling libraries. Accordingly, two environmental variables STORM_EXT_CLASSPATH and STORM_EXT_CLASSPATH_DAEMON can be configured by users for including the external classpath and daemon-only external classpath.</p>
-
-<h3 id="launch-daemons-under-supervision-using-quot-storm-quot-script-and-a-supervisor-of-your-choice">Launch daemons under supervision using "storm" script and a supervisor of your choice</h3>
-
-<p>The last step is to launch all the Storm daemons. It is critical that you run each of these daemons under supervision. Storm is a <strong>fail-fast</strong> system which means the processes will halt whenever an unexpected error is encountered. Storm is designed so that it can safely halt at any point and recover correctly when the process is restarted. This is why Storm keeps no state in-process -- if Nimbus or the Supervisors restart, the running topologies are unaffected. Here's how to run the Storm daemons:</p>
-
-<ol>
-<li><strong>Nimbus</strong>: Run the command "bin/storm nimbus" under supervision on the master machine.</li>
-<li><strong>Supervisor</strong>: Run the command "bin/storm supervisor" under supervision on each worker machine. The supervisor daemon is responsible for starting and stopping worker processes on that machine.</li>
-<li><strong>UI</strong>: Run the Storm UI (a site you can access from the browser that gives diagnostics on the cluster and topologies) by running the command "bin/storm ui" under supervision. The UI can be accessed by navigating your web browser to http://{nimbus host}:8080. </li>
-</ol>
-
-<p>As you can see, running the daemons is very straightforward. The daemons will log to the logs/ directory in wherever you extracted the Storm release.</p>
-
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- <div class="row">
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- <!-- Documentation -->
-
-<p class="post-meta"></p>
-
-<p>This page outlines what you need to do to get a Storm development environment set up. In summary, the steps are:</p>
-
-<ol>
-<li>Download a <a href="..//downloads.html">Storm release</a> , unpack it, and put the unpacked <code>bin/</code> directory on your PATH</li>
-<li>To be able to start and stop topologies on a remote cluster, put the cluster information in <code>~/.storm/storm.yaml</code></li>
-</ol>
-
-<p>More detail on each of these steps is below.</p>
-
-<h3 id="what-is-a-development-environment">What is a development environment?</h3>
-
-<p>Storm has two modes of operation: local mode and remote mode. In local mode, you can develop and test topologies completely in process on your local machine. In remote mode, you submit topologies for execution on a cluster of machines.</p>
-
-<p>A Storm development environment has everything installed so that you can develop and test Storm topologies in local mode, package topologies for execution on a remote cluster, and submit/kill topologies on a remote cluster.</p>
-
-<p>Let's quickly go over the relationship between your machine and a remote cluster. A Storm cluster is managed by a master node called "Nimbus". Your machine communicates with Nimbus to submit code (packaged as a jar) and topologies for execution on the cluster, and Nimbus will take care of distributing that code around the cluster and assigning workers to run your topology. Your machine uses a command line client called <code>storm</code> to communicate with Nimbus. The <code>storm</code> client is only used for remote mode; it is not used for developing and testing topologies in local mode.</p>
-
-<h3 id="installing-a-storm-release-locally">Installing a Storm release locally</h3>
-
-<p>If you want to be able to submit topologies to a remote cluster from your machine, you should install a Storm release locally. Installing a Storm release will give you the <code>storm</code> client that you can use to interact with remote clusters. To install Storm locally, download a release <a href="https://github.com/apache/storm/releases">from here</a> and unzip it somewhere on your computer. Then add the unpacked <code>bin/</code> directory onto your <code>PATH</code> and make sure the <code>bin/storm</code> script is executable.</p>
-
-<p>Installing a Storm release locally is only for interacting with remote clusters. For developing and testing topologies in local mode, it is recommended that you use Maven to include Storm as a dev dependency for your project. You can read more about using Maven for this purpose on <a href="Maven.html">Maven</a>. </p>
-
-<h3 id="starting-and-stopping-topologies-on-a-remote-cluster">Starting and stopping topologies on a remote cluster</h3>
-
-<p>The previous step installed the <code>storm</code> client on your machine which is used to communicate with remote Storm clusters. Now all you have to do is tell the client which Storm cluster to talk to. To do this, all you have to do is put the host address of the master in the <code>~/.storm/storm.yaml</code> file. It should look something like this:</p>
-<div class="highlight"><pre><code class="language-" data-lang="">nimbus.host: "123.45.678.890"
-</code></pre></div>
-<p>Alternatively, if you use the <a href="https://github.com/nathanmarz/storm-deploy">storm-deploy</a> project to provision Storm clusters on AWS, it will automatically set up your ~/.storm/storm.yaml file. You can manually attach to a Storm cluster (or switch between multiple clusters) using the "attach" command, like so:</p>
-<div class="highlight"><pre><code class="language-" data-lang="">lein run :deploy --attach --name mystormcluster
-</code></pre></div>
-<p>More information is on the storm-deploy <a href="https://github.com/nathanmarz/storm-deploy/wiki">wiki</a></p>
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