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-          <div class="row">
-               <div class="col-md-12">
-                    <!-- Documentation -->
-
-<p class="post-meta"></p>
-
-<p>Local mode simulates a Storm cluster in process and is useful for 
developing and testing topologies. Running topologies in local mode is similar 
to running topologies <a 
href="Running-topologies-on-a-production-cluster.html">on a cluster</a>. </p>
-
-<p>To create an in-process cluster, simply use the <code>LocalCluster</code> 
class. For example:</p>
-<div class="highlight"><pre><code class="language-java" data-lang="java"><span 
class="kn">import</span> <span 
class="nn">backtype.storm.LocalCluster</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>
-</code></pre></div>
-<p>You can then submit topologies using the <code>submitTopology</code> method 
on the <code>LocalCluster</code> object. Just like the corresponding method on 
<a href="javadocs/backtype/storm/StormSubmitter.html">StormSubmitter</a>, 
<code>submitTopology</code> takes a name, a topology configuration, and the 
topology object. You can then kill a topology using the 
<code>killTopology</code> method which takes the topology name as an 
argument.</p>
-
-<p>To shutdown a local cluster, simple call:</p>
-<div class="highlight"><pre><code class="language-java" data-lang="java"><span 
class="n">cluster</span><span class="o">.</span><span 
class="na">shutdown</span><span class="o">();</span>
-</code></pre></div>
-<h3 id="common-configurations-for-local-mode">Common configurations for local 
mode</h3>
-
-<p>You can see a full list of configurations <a 
href="javadocs/backtype/storm/Config.html">here</a>.</p>
-
-<ol>
-<li><strong>Config.TOPOLOGY_MAX_TASK_PARALLELISM</strong>: This config puts a 
ceiling on the number of threads spawned for a single component. Oftentimes 
production topologies have a lot of parallelism (hundreds of threads) which 
places unreasonable load when trying to test the topology in local mode. This 
config lets you easy control that parallelism.</li>
-<li><strong>Config.TOPOLOGY_DEBUG</strong>: When this is set to true, Storm 
will log a message every time a tuple is emitted from any spout or bolt. This 
is extremely useful for debugging.</li>
-</ol>
-
-
-
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Kafka</a> <span class="small">(Sunnyvale, CA)</span></li>
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http://git-wip-us.apache.org/repos/asf/storm-site/blob/ff14ea94/content/releases/0.10.1/Maven.html
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-          <div class="row">
-               <div class="col-md-12">
-                    <!-- Documentation -->
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-<p class="post-meta"></p>
-
-<p>To develop topologies, you&#39;ll need the Storm jars on your classpath. 
You should either include the unpacked jars in the classpath for your project 
or use Maven to include Storm as a development dependency. Storm is hosted on 
Maven Central. To include Storm in your project as a development dependency, 
add the following to your pom.xml:</p>
-<div class="highlight"><pre><code class="language-xml" data-lang="xml"><span 
class="nt">&lt;dependency&gt;</span>
-  <span class="nt">&lt;groupId&gt;</span>org.apache.storm<span 
class="nt">&lt;/groupId&gt;</span>
-  <span class="nt">&lt;artifactId&gt;</span>storm-core<span 
class="nt">&lt;/artifactId&gt;</span>
-  <span class="nt">&lt;version&gt;</span>0.10.1<span 
class="nt">&lt;/version&gt;</span>
-  <span class="nt">&lt;scope&gt;</span>provided<span 
class="nt">&lt;/scope&gt;</span>
-<span class="nt">&lt;/dependency&gt;</span>
-</code></pre></div>
-<p><a 
href="http://github.com/apache/storm/blob/v0.10.1/examples/storm-starter/pom.xml";>Here&#39;s
 an example</a> of a pom.xml for a Storm project.</p>
-
-<h3 id="developing-storm">Developing Storm</h3>
-
-<p>Please refer to <a 
href="http://github.com/apache/storm/blob/v0.10.1/DEVELOPER.md";>DEVELOPER.md</a>
 for more details.</p>
-
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-                    <ul class="latest-news">
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Kafka</a> <span class="small">(Sunnyvale, CA)</span></li>
-                        
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href="http://www.meetup.com/Apache-Storm-Kafka-Users/";>Apache Storm & Kafka 
Users</a> <span class="small">(Seattle, WA)</span></li>
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href="http://www.meetup.com/New-York-City-Storm-User-Group/";>NYC Storm User 
Group</a> <span class="small">(New York, NY)</span></li>
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Processing</a> <span class="small">(Emeryville, CA)</span></li>
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class="small">(Boston, MA)</span></li>
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class="small">(London, UK)</span></li>
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class="small">(27 Jun 2015)</span></li> -->
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-                    <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. Likewise, integrating Storm with database systems is easy.</p>
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-<p>(Note: this walkthrough is out of date as of 0.8.0. 0.8.0 revamped the 
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-<p>This page walks through how emitting and transferring tuples works in 
Storm.</p>
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-<li><code>refresh-connections</code> is called every 
&quot;task.refresh.poll.secs&quot; or whenever assignment in ZK changes. It 
manages connections to other workers and maintains a mapping from task -&gt; 
worker <a 
href="https://github.com/apache/storm/blob/0.7.1/src/clj/backtype/storm/daemon/worker.clj#L123";>code</a></li>
-<li>Provides a &quot;transfer function&quot; that is used by tasks to send 
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href="https://github.com/apache/storm/blob/0.7.1/src/jvm/backtype/storm/serialization/KryoTupleSerializer.java#L26";>code</a></li>
-<li>The worker has a single thread which drains the transfer queue and sends 
the messages to other workers <a 
href="https://github.com/apache/storm/blob/0.7.1/src/clj/backtype/storm/daemon/worker.clj#L185";>code</a></li>
-<li>Message sending happens through this protocol: <a 
href="https://github.com/apache/storm/blob/0.7.1/src/clj/backtype/storm/messaging/protocol.clj";>code</a></li>
-<li>The implementation for distributed mode uses ZeroMQ <a 
href="https://github.com/apache/storm/blob/0.7.1/src/clj/backtype/storm/messaging/zmq.clj";>code</a></li>
-<li>The implementation for local mode uses in memory Java queues (so that 
it&#39;s easy to use Storm locally without needing to get ZeroMQ installed) <a 
href="https://github.com/apache/storm/blob/0.7.1/src/clj/backtype/storm/messaging/local.clj";>code</a></li>
-</ul></li>
-<li>Receiving messages in tasks works differently in local mode and 
distributed mode
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-<ul>
-<li>In local mode, the tuple is sent directly to an in-memory queue for the 
receiving task <a 
href="https://github.com/apache/storm/blob/0.7.1/src/clj/backtype/storm/messaging/local.clj#L21";>code</a></li>
-<li>In distributed mode, each worker listens on a single TCP port for incoming 
messages and then routes those messages in-memory to tasks. The TCP port is 
called a &quot;virtual port&quot;, because it receives [task id, message] and 
then routes it to the actual task. <a 
href="https://github.com/apache/storm/blob/0.7.1/src/clj/backtype/storm/daemon/worker.clj#L204";>code</a>
-
-<ul>
-<li>The virtual port implementation is here: <a 
href="https://github.com/apache/storm/blob/0.7.1/src/clj/zilch/virtual_port.clj";>code</a></li>
-<li>Tasks listen on an in-memory ZeroMQ port for messages from the virtual 
port <a 
href="https://github.com/apache/storm/blob/0.7.1/src/clj/backtype/storm/daemon/task.clj#L201";>code</a></li>
-<li>Bolts listen here: <a 
href="https://github.com/apache/storm/blob/0.7.1/src/clj/backtype/storm/daemon/task.clj#L489";>code</a></li>
-<li>Spouts listen here: <a 
href="https://github.com/apache/storm/blob/0.7.1/src/clj/backtype/storm/daemon/task.clj#L382";>code</a></li>
-</ul></li>
-</ul></li>
-<li>Tasks are responsible for message routing. A tuple is emitted either to a 
direct stream (where the task id is specified) or a regular stream. In direct 
streams, the message is only sent if that bolt subscribes to that direct 
stream. In regular streams, the stream grouping functions are used to determine 
the task ids to send the tuple to.
-
-<ul>
-<li>Tasks have a routing map from {stream id} -&gt; {component id} -&gt; 
{stream grouping function} <a 
href="https://github.com/apache/storm/blob/0.7.1/src/clj/backtype/storm/daemon/task.clj#L198";>code</a></li>
-<li>The &quot;tasks-fn&quot; returns the task ids to send the tuples to for 
either regular stream emit or direct stream emit <a 
href="https://github.com/apache/storm/blob/0.7.1/src/clj/backtype/storm/daemon/task.clj#L207";>code</a></li>
-<li>After getting the output task ids, bolts and spouts use the transfer-fn 
provided by the worker to actually transfer the tuples
-
-<ul>
-<li>Bolt transfer code here: <a 
href="https://github.com/apache/storm/blob/0.7.1/src/clj/backtype/storm/daemon/task.clj#L429";>code</a></li>
-<li>Spout transfer code here: <a 
href="https://github.com/apache/storm/blob/0.7.1/src/clj/backtype/storm/daemon/task.clj#L329";>code</a></li>
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-
-<p>Metrics have to implement <a 
href="http://github.com/apache/storm/blob/v0.10.1/storm-core/src/jvm/org/apache/storm/metric/api/IMetric.java";><code>IMetric</code></a>
 which contains just one method, <code>getValueAndReset</code> -- do any 
remaining work to find the summary value, and reset back to an initial state. 
For example, the MeanReducer divides the running total by its running count to 
find the mean, then initializes both values back to zero.</p>
-
-<p>Storm gives you these metric types:</p>
-
-<ul>
-<li><a 
href="http://github.com/apache/storm/blob/v0.10.1/storm-core/src/jvm/org/apache/storm/metric/api/AssignableMetric.java";>AssignableMetric</a>
 -- set the metric to the explicit value you supply. Useful if it&#39;s an 
external value or in the case that you are already calculating the summary 
statistic yourself.</li>
-<li><a 
href="http://github.com/apache/storm/blob/v0.10.1/storm-core/src/jvm/org/apache/storm/metric/api/CombinedMetric.java";>CombinedMetric</a>
 -- generic interface for metrics that can be updated associatively. </li>
-<li><a 
href="http://github.com/apache/storm/blob/v0.10.1/storm-core/src/jvm/org/apache/storm/metric/api/CountMetric.java";>CountMetric</a>
 -- a running total of the supplied values. Call <code>incr()</code> to 
increment by one, <code>incrBy(n)</code> to add/subtract the given number.
-
-<ul>
-<li><a 
href="http://github.com/apache/storm/blob/v0.10.1/storm-core/src/jvm/org/apache/storm/metric/api/MultiCountMetric.java";>MultiCountMetric</a>
 -- a hashmap of count metrics.</li>
-</ul></li>
-<li><a 
href="http://github.com/apache/storm/blob/v0.10.1/storm-core/src/jvm/org/apache/storm/metric/api/ReducedMetric.java";>ReducedMetric</a>
-
-<ul>
-<li><a 
href="http://github.com/apache/storm/blob/v0.10.1/storm-core/src/jvm/org/apache/storm/metric/api/MeanReducer.java";>MeanReducer</a>
 -- track a running average of values given to its <code>reduce()</code> 
method. (It accepts <code>Double</code>, <code>Integer</code> or 
<code>Long</code> values, and maintains the internal average as a 
<code>Double</code>.) Despite his reputation, the MeanReducer is actually a 
pretty nice guy in person.</li>
-<li><a 
href="http://github.com/apache/storm/blob/v0.10.1/storm-core/src/jvm/org/apache/storm/metric/api/MultiReducedMetric.java";>MultiReducedMetric</a>
 -- a hashmap of reduced metrics.</li>
-</ul></li>
-</ul>
-
-<h3 id="metrics-consumer">Metrics Consumer</h3>
-
-<p>You can listen and handle the topology metrics via registering Metrics 
Consumer to your topology. </p>
-
-<p>To register metrics consumer to your topology, add to your topology&#39;s 
configuration like:</p>
-<div class="highlight"><pre><code class="language-java" data-lang="java"><span 
class="n">conf</span><span class="o">.</span><span 
class="na">registerMetricsConsumer</span><span class="o">(</span><span 
class="n">org</span><span class="o">.</span><span class="na">apache</span><span 
class="o">.</span><span class="na">storm</span><span class="o">.</span><span 
class="na">metric</span><span class="o">.</span><span 
class="na">LoggingMetricsConsumer</span><span class="o">.</span><span 
class="na">class</span><span class="o">,</span> <span class="mi">1</span><span 
class="o">);</span>
-</code></pre></div>
-<p>You can refer <a 
href="javadocs/org/apache/storm/Config.html#registerMetricsConsumer-java.lang.Class-">Config#registerMetricsConsumer</a>
 and overloaded methods from javadoc.</p>
-
-<p>Otherwise edit the storm.yaml config file:</p>
-<div class="highlight"><pre><code class="language-yaml" data-lang="yaml"><span 
class="s">topology.metrics.consumer.register</span><span class="pi">:</span>
-  <span class="pi">-</span> <span class="na">class</span><span 
class="pi">:</span> <span class="s2">"</span><span 
class="s">org.apache.storm.metric.LoggingMetricsConsumer"</span>
-    <span class="s">parallelism.hint</span><span class="pi">:</span> <span 
class="s">1</span>
-  <span class="pi">-</span> <span class="na">class</span><span 
class="pi">:</span> <span class="s2">"</span><span 
class="s">org.apache.storm.metric.HttpForwardingMetricsConsumer"</span>
-    <span class="s">parallelism.hint</span><span class="pi">:</span> <span 
class="s">1</span>
-    <span class="na">argument</span><span class="pi">:</span> <span 
class="s2">"</span><span 
class="s">http://example.com:8080/metrics/my-topology/";</span>
-</code></pre></div>
-<p>Storm appends MetricsConsumerBolt to your topology per each registered 
metrics consumer internally, and each MetricsConsumerBolt subscribes to receive 
metrics from all tasks. The parallelism for that Bolt is set to 
<code>parallelism.hint</code> and <code>component id</code> for that Bolt is 
set to <code>__metrics_&lt;metrics consumer class name&gt;</code>. If you 
register same class name more than once, postfix <code>#&lt;sequence 
number&gt;</code> is appended to component id.</p>
-
-<p>Storm provides some built-in metrics consumers for you to try out to see 
which metrics are provided in your topology.</p>
-
-<ul>
-<li><a 
href="http://github.com/apache/storm/blob/v0.10.1/storm-core/src/jvm/org/apache/storm/metric/LoggingMetricsConsumer.java";><code>LoggingMetricsConsumer</code></a>
 -- listens for all metrics and dumps them to log file with TSV (Tab Separated 
Values).</li>
-<li><a 
href="http://github.com/apache/storm/blob/v0.10.1/storm-core/src/jvm/org/apache/storm/metric/HttpForwardingMetricsConsumer.java";><code>HttpForwardingMetricsConsumer</code></a>
 -- listens for all metrics and POSTs them serialized to a configured URL via 
HTTP. Storm also provides <a 
href="http://github.com/apache/storm/blob/v0.10.1/storm-core/src/jvm/org/apache/storm/metric/HttpForwardingMetricsServer.java";><code>HttpForwardingMetricsServer</code></a>
 as abstract class so you can extend this class and run as a HTTP server, and 
handle metrics sent by HttpForwardingMetricsConsumer.</li>
-</ul>
-
-<p>Also, Storm exposes the interface <a 
href="http://github.com/apache/storm/blob/v0.10.1/storm-core/src/jvm/org/apache/storm/metric/api/IMetricsConsumer.java";><code>IMetricsConsumer</code></a>
 for implementing Metrics Consumer so you can create custom metrics consumers 
and attach to their topologies, or use other great implementation of Metrics 
Consumers provided by Storm community. Some of examples are <a 
href="https://github.com/verisign/storm-graphite";>versign/storm-graphite</a>, 
and <a 
href="https://github.com/endgameinc/storm-metrics-statsd";>storm-metrics-statsd</a>.</p>
-
-<p>When you implement your own metrics consumer, <code>argument</code> is 
passed to Object when <a 
href="javadocs/org/apache/storm/metric/api/IMetricsConsumer.html#prepare-java.util.Map-java.lang.Object-org.apache.storm.task.TopologyContext-org.apache.storm.task.IErrorReporter-">IMetricsConsumer#prepare</a>
 is called, so you need to infer the Java type of configured value on yaml, and 
do explicit type casting.</p>
-
-<p>Please keep in mind that MetricsConsumerBolt is just a kind of Bolt, so 
whole throughput of the topology will go down when registered metrics consumers 
cannot keep up handling incoming metrics, so you may want to take care of those 
Bolts like normal Bolts. One of idea to avoid this is making your 
implementation of Metrics Consumer as <code>non-blocking</code> fashion.</p>
-
-<h3 id="build-your-own-metric-task-level">Build your own metric (task 
level)</h3>
-
-<p>You can measure your own metric by registering <code>IMetric</code> to 
Metric Registry. </p>
-
-<p>Suppose we would like to measure execution count of Bolt#execute. Let&#39;s 
start with defining metric instance. CountMetric seems to fit our use case.</p>
-<div class="highlight"><pre><code class="language-java" data-lang="java"><span 
class="kd">private</span> <span class="kd">transient</span> <span 
class="n">CountMetric</span> <span class="n">countMetric</span><span 
class="o">;</span>
-</code></pre></div>
-<p>Notice we define it as transient. IMertic is not Serializable so we defined 
as transient to avoid any serialization issues.</p>
-
-<p>Next, let&#39;s initialize and register the metric instance.</p>
-<div class="highlight"><pre><code class="language-java" data-lang="java"><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">OutputCollector</span> <span 
class="n">collector</span><span class="o">)</span> <span class="o">{</span>
-    <span class="c1">// other intialization here.</span>
-    <span class="n">countMetric</span> <span class="o">=</span> <span 
class="k">new</span> <span class="n">CountMetric</span><span 
class="o">();</span>
-    <span class="n">context</span><span class="o">.</span><span 
class="na">registerMetric</span><span class="o">(</span><span 
class="s">"execute_count"</span><span class="o">,</span> <span 
class="n">countMetric</span><span class="o">,</span> <span 
class="mi">60</span><span class="o">);</span>
-<span class="o">}</span>
-</code></pre></div>
-<p>The meaning of first and second parameters are straightforward, metric name 
and instance of IMetric. Third parameter of <a 
href="javadocs/org/apache/storm/task/TopologyContext.html#registerMetric-java.lang.String-T-int-">TopologyContext#registerMetric</a>
 is the period (seconds) to publish and reset the metric.</p>
-
-<p>Last, let&#39;s increment the value when Bolt.execute() is executed.</p>
-<div class="highlight"><pre><code class="language-java" data-lang="java"><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">input</span><span class="o">)</span> <span class="o">{</span>
-    <span class="n">countMetric</span><span class="o">.</span><span 
class="na">incr</span><span class="o">();</span>
-    <span class="c1">// handle tuple here.   </span>
-<span class="o">}</span>
-</code></pre></div>
-<p>Note that sample rate for topology metrics is not applied to custom metrics 
since we&#39;re calling incr() ourselves.</p>
-
-<p>Done! <code>countMetric.getValueAndReset()</code> is called every 60 
seconds as we registered as period, and pair of (&quot;execute_count&quot;, 
value) will be pushed to MetricsConsumer.</p>
-
-<h3 id="build-your-own-metrics-worker-level">Build your own metrics (worker 
level)</h3>
-
-<p>You can register your own worker level metrics by adding them to 
<code>Config.WORKER_METRICS</code> for all workers in cluster, or 
<code>Config.TOPOLOGY_WORKER_METRICS</code> for all workers in specific 
topology.</p>
-
-<p>For example, we can add <code>worker.metrics</code> to storm.yaml in 
cluster,</p>
-<div class="highlight"><pre><code class="language-yaml" data-lang="yaml"><span 
class="s">worker.metrics</span><span class="pi">:</span> 
-  <span class="na">metricA</span><span class="pi">:</span> <span 
class="s2">"</span><span class="s">aaa.bbb.ccc.ddd.MetricA"</span>
-  <span class="na">metricB</span><span class="pi">:</span> <span 
class="s2">"</span><span class="s">aaa.bbb.ccc.ddd.MetricB"</span>
-  <span class="s">...</span>
-</code></pre></div>
-<p>or put <code>Map&lt;String, String&gt;</code> (metric name, metric class 
name) with key <code>Config.TOPOLOGY_WORKER_METRICS</code> to config map.</p>
-
-<p>There&#39;re some restrictions for worker level metric instances: </p>
-
-<p>A) Metrics for worker level should be kind of gauge since it is initialized 
and registered from SystemBolt and not exposed to user tasks.</p>
-
-<p>B) Metrics will be initialized with default constructor, and no injection 
for configuration or object will be performed.</p>
-
-<p>C) Bucket size (seconds) for metrics is fixed to 
<code>Config.TOPOLOGY_BUILTIN_METRICS_BUCKET_SIZE_SECS</code>.</p>
-
-<h3 id="builtin-metrics">Builtin Metrics</h3>
-
-<p>The <a 
href="http://github.com/apache/storm/blob/v0.10.1/storm-core/src/clj/org/apache/storm/daemon/builtin_metrics.clj";>builtin
 metrics</a> instrument Storm itself.</p>
-
-<p><a 
href="http://github.com/apache/storm/blob/v0.10.1/storm-core/src/clj/org/apache/storm/daemon/builtin_metrics.clj";>builtin_metrics.clj</a>
 sets up data structures for the built-in metrics, and facade methods that the 
other framework components can use to update them. The metrics themselves are 
calculated in the calling code -- see for example <a 
href="http://github.com/apache/storm/blob/v0.10.1/storm-core/src/clj/org/apache/storm/daemon/executor.clj#358";><code>ack-spout-msg</code></a>
  in <code>clj/b/s/daemon/daemon/executor.clj</code></p>
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-<h1 id="storm-multi-language-protocol">Storm Multi-Language Protocol</h1>
-
-<h2 id="shell-components">Shell Components</h2>
-
-<p>Support for multiple languages is implemented via the ShellBolt,
-ShellSpout, and ShellProcess classes.  These classes implement the
-IBolt and ISpout interfaces and the protocol for executing a script or
-program via the shell using Java&#39;s ProcessBuilder class.</p>
-
-<h2 id="output-fields">Output fields</h2>
-
-<p>Output fields are part of the Thrift definition of the topology. This means 
that when you multilang in Java, you need to create a bolt that extends 
ShellBolt, implements IRichBolt, and declare the fields in 
<code>declareOutputFields</code> (similarly for ShellSpout).</p>
-
-<p>You can learn more about this on <a href="Concepts.html">Concepts</a></p>
-
-<h2 id="protocol-preamble">Protocol Preamble</h2>
-
-<p>A simple protocol is implemented via the STDIN and STDOUT of the
-executed script or program. All data exchanged with the process is
-encoded in JSON, making support possible for pretty much any language.</p>
-
-<h1 id="packaging-your-stuff">Packaging Your Stuff</h1>
-
-<p>To run a shell component on a cluster, the scripts that are shelled
-out to must be in the <code>resources/</code> directory within the jar 
submitted
-to the master.</p>
-
-<p>However, during development or testing on a local machine, the resources
-directory just needs to be on the classpath.</p>
-
-<h2 id="the-protocol">The Protocol</h2>
-
-<p>Notes:</p>
-
-<ul>
-<li>Both ends of this protocol use a line-reading mechanism, so be sure to
-trim off newlines from the input and to append them to your output.</li>
-<li>All JSON inputs and outputs are terminated by a single line containing 
&quot;end&quot;. Note that this delimiter is not itself JSON encoded.</li>
-<li>The bullet points below are written from the perspective of the script 
writer&#39;s
-STDIN and STDOUT.</li>
-</ul>
-
-<h3 id="initial-handshake">Initial Handshake</h3>
-
-<p>The initial handshake is the same for both types of shell components:</p>
-
-<ul>
-<li>STDIN: Setup info. This is a JSON object with the Storm configuration, a 
PID directory, and a topology context, like this:</li>
-</ul>
-<div class="highlight"><pre><code class="language-" data-lang="">{
-    "conf": {
-        "topology.message.timeout.secs": 3,
-        // etc
-    },
-    "pidDir": "...",
-    "context": {
-        "task-&gt;component": {
-            "1": "example-spout",
-            "2": "__acker",
-            "3": "example-bolt1",
-            "4": "example-bolt2"
-        },
-        "taskid": 3,
-        // Everything below this line is only available in Storm 0.10.0+
-        "componentid": "example-bolt"
-        "stream-&gt;target-&gt;grouping": {
-            "default": {
-                "example-bolt2": {
-                    "type": "SHUFFLE"}}},
-        "streams": ["default"],
-        "stream-&gt;outputfields": {"default": ["word"]},
-        "source-&gt;stream-&gt;grouping": {
-            "example-spout": {
-                "default": {
-                    "type": "FIELDS",
-                    "fields": ["word"]
-                }
-            }
-        }
-        "source-&gt;stream-&gt;fields": {
-            "example-spout": {
-                "default": ["word"]
-            }
-        }
-    }
-}
-</code></pre></div>
-<p>Your script should create an empty file named with its PID in this 
directory. e.g.
-the PID is 1234, so an empty file named 1234 is created in the directory. This
-file lets the supervisor know the PID so it can shutdown the process later 
on.</p>
-
-<p>As of Storm 0.10.0, the context sent by Storm to shell components has been
-enhanced substantially to include all aspects of the topology context available
-to JVM components.  One key addition is the ability to determine a shell
-component&#39;s source and targets (i.e., inputs and outputs) in the topology 
via
-the <code>stream-&gt;target-&gt;grouping</code> and 
<code>source-&gt;stream-&gt;grouping</code> dictionaries.  At
-the innermost level of these nested dictionaries, groupings are represented as
-a dictionary that minimally has a <code>type</code> key, but can also have a 
<code>fields</code> key
-to specify which fields are involved in a <code>FIELDS</code> grouping.</p>
-
-<ul>
-<li>STDOUT: Your PID, in a JSON object, like <code>{&quot;pid&quot;: 
1234}</code>. The shell component will log the PID to its log.</li>
-</ul>
-
-<p>What happens next depends on the type of component:</p>
-
-<h3 id="spouts">Spouts</h3>
-
-<p>Shell spouts are synchronous. The rest happens in a while(true) loop:</p>
-
-<ul>
-<li>STDIN: Either a next, ack, or fail command.</li>
-</ul>
-
-<p>&quot;next&quot; is the equivalent of ISpout&#39;s <code>nextTuple</code>. 
It looks like:</p>
-<div class="highlight"><pre><code class="language-" data-lang="">{"command": 
"next"}
-</code></pre></div>
-<p>&quot;ack&quot; looks like:</p>
-<div class="highlight"><pre><code class="language-" data-lang="">{"command": 
"ack", "id": "1231231"}
-</code></pre></div>
-<p>&quot;fail&quot; looks like:</p>
-<div class="highlight"><pre><code class="language-" data-lang="">{"command": 
"fail", "id": "1231231"}
-</code></pre></div>
-<ul>
-<li>STDOUT: The results of your spout for the previous command. This can
-be a sequence of emits and logs.</li>
-</ul>
-
-<p>An emit looks like:</p>
-<div class="highlight"><pre><code class="language-" data-lang="">{
-    "command": "emit",
-    // The id for the tuple. Leave this out for an unreliable emit. The id can
-    // be a string or a number.
-    "id": "1231231",
-    // The id of the stream this tuple was emitted to. Leave this empty to 
emit to default stream.
-    "stream": "1",
-    // If doing an emit direct, indicate the task to send the tuple to
-    "task": 9,
-    // All the values in this tuple
-    "tuple": ["field1", 2, 3]
-}
-</code></pre></div>
-<p>If not doing an emit direct, you will immediately receive the task ids to 
which the tuple was emitted on STDIN as a JSON array.</p>
-
-<p>A &quot;log&quot; will log a message in the worker log. It looks like:</p>
-<div class="highlight"><pre><code class="language-" data-lang="">{
-    "command": "log",
-    // the message to log
-    "msg": "hello world!"
-}
-</code></pre></div>
-<ul>
-<li>STDOUT: a &quot;sync&quot; command ends the sequence of emits and logs. It 
looks like:</li>
-</ul>
-<div class="highlight"><pre><code class="language-" data-lang="">{"command": 
"sync"}
-</code></pre></div>
-<p>After you sync, ShellSpout will not read your output until it sends another 
next, ack, or fail command.</p>
-
-<p>Note that, similarly to ISpout, all of the spouts in the worker will be 
locked up after a next, ack, or fail, until you sync. Also like ISpout, if you 
have no tuples to emit for a next, you should sleep for a small amount of time 
before syncing. ShellSpout will not automatically sleep for you.</p>
-
-<h3 id="bolts">Bolts</h3>
-
-<p>The shell bolt protocol is asynchronous. You will receive tuples on STDIN 
as soon as they are available, and you may emit, ack, and fail, and log at any 
time by writing to STDOUT, as follows:</p>
-
-<ul>
-<li>STDIN: A tuple! This is a JSON encoded structure like this:</li>
-</ul>
-<div class="highlight"><pre><code class="language-" data-lang="">{
-    // The tuple's id - this is a string to support languages lacking 64-bit 
precision
-    "id": "-6955786537413359385",
-    // The id of the component that created this tuple
-    "comp": "1",
-    // The id of the stream this tuple was emitted to
-    "stream": "1",
-    // The id of the task that created this tuple
-    "task": 9,
-    // All the values in this tuple
-    "tuple": ["snow white and the seven dwarfs", "field2", 3]
-}
-</code></pre></div>
-<ul>
-<li>STDOUT: An ack, fail, emit, or log. Emits look like:</li>
-</ul>
-<div class="highlight"><pre><code class="language-" data-lang="">{
-    "command": "emit",
-    // The ids of the tuples this output tuples should be anchored to
-    "anchors": ["1231231", "-234234234"],
-    // The id of the stream this tuple was emitted to. Leave this empty to 
emit to default stream.
-    "stream": "1",
-    // If doing an emit direct, indicate the task to send the tuple to
-    "task": 9,
-    // All the values in this tuple
-    "tuple": ["field1", 2, 3]
-}
-</code></pre></div>
-<p>If not doing an emit direct, you will receive the task ids to which
-the tuple was emitted on STDIN as a JSON array. Note that, due to the
-asynchronous nature of the shell bolt protocol, when you read after
-emitting, you may not receive the task ids. You may instead read the
-task ids for a previous emit or a new tuple to process. You will
-receive the task id lists in the same order as their corresponding
-emits, however.</p>
-
-<p>An ack looks like:</p>
-<div class="highlight"><pre><code class="language-" data-lang="">{
-    "command": "ack",
-    // the id of the tuple to ack
-    "id": "123123"
-}
-</code></pre></div>
-<p>A fail looks like:</p>
-<div class="highlight"><pre><code class="language-" data-lang="">{
-    "command": "fail",
-    // the id of the tuple to fail
-    "id": "123123"
-}
-</code></pre></div>
-<p>A &quot;log&quot; will log a message in the worker log. It looks like:</p>
-<div class="highlight"><pre><code class="language-" data-lang="">{
-    "command": "log",
-    // the message to log
-    "msg": "hello world!"
-}
-</code></pre></div>
-<ul>
-<li>Note that, as of version 0.7.1, there is no longer any need for a
-shell bolt to &#39;sync&#39;.</li>
-</ul>
-
-<h3 id="handling-heartbeats-0-9-3-and-later">Handling Heartbeats (0.9.3 and 
later)</h3>
-
-<p>As of Storm 0.9.3, heartbeats have been between ShellSpout/ShellBolt and 
their
-multi-lang subprocesses to detect hanging/zombie subprocesses.  Any libraries
-for interfacing with Storm via multi-lang must take the following actions
-regarding hearbeats:</p>
-
-<h4 id="spout">Spout</h4>
-
-<p>Shell spouts are synchronous, so subprocesses always send <code>sync</code> 
commands at the
-end of <code>next()</code>,  so you should not have to do much to support 
heartbeats for
-spouts.  That said, you must not let subprocesses sleep more than the worker
-timeout during <code>next()</code>.</p>
-
-<h4 id="bolt">Bolt</h4>
-
-<p>Shell bolts are asynchronous, so a ShellBolt will send heartbeat tuples to 
its
-subprocess periodically.  Heartbeat tuple looks like:</p>
-<div class="highlight"><pre><code class="language-" data-lang="">{
-    "id": "-6955786537413359385",
-    "comp": "1",
-    "stream": "__heartbeat",
-    // this shell bolt's system task id
-    "task": -1,
-    "tuple": []
-}
-</code></pre></div>
-<p>When subprocess receives heartbeat tuple, it must send a <code>sync</code> 
command back to
-ShellBolt.</p>
-
-
-
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