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Posted to commits@storm.apache.org by bo...@apache.org on 2016/03/22 16:38:34 UTC
[08/51] [partial] storm git commit: STORM-1617: Versioned Docs
http://git-wip-us.apache.org/repos/asf/storm/blob/335bbf94/_site/tutorial.html
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- <h1 class="page-title">Tutorial</h1>
- <div class="row">
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- <!-- Documentation -->
-
-<p class="post-meta"></p>
-
-<p>In this tutorial, you'll learn how to create Storm topologies and deploy them to a Storm cluster. Java will be the main language used, but a few examples will use Python to illustrate Storm's multi-language capabilities.</p>
-
-<h2 id="preliminaries">Preliminaries</h2>
-
-<p>This tutorial uses examples from the <a href="https://github.com/apache/storm/blob/master/examples/storm-starter">storm-starter</a> project. It's recommended that you clone the project and follow along with the examples. Read <a href="/documentation/Setting-up-development-environment.html">Setting up a development environment</a> and <a href="/documentation/Creating-a-new-Storm-project.html">Creating a new Storm project</a> to get your machine set up.</p>
-
-<h2 id="components-of-a-storm-cluster">Components of a Storm cluster</h2>
-
-<p>A Storm cluster is superficially similar to a Hadoop cluster. Whereas on Hadoop you run "MapReduce jobs", on Storm you run "topologies". "Jobs" and "topologies" themselves are very different -- one key difference is that a MapReduce job eventually finishes, whereas a topology processes messages forever (or until you kill it).</p>
-
-<p>There are two kinds of nodes on a Storm cluster: the master node and the worker nodes. The master node runs a daemon called "Nimbus" that is similar to Hadoop's "JobTracker". Nimbus is responsible for distributing code around the cluster, assigning tasks to machines, and monitoring for failures.</p>
-
-<p>Each worker node runs a daemon called the "Supervisor". The supervisor listens for work assigned to its machine and starts and stops worker processes as necessary based on what Nimbus has assigned to it. Each worker process executes a subset of a topology; a running topology consists of many worker processes spread across many machines.</p>
-
-<p><img src="/documentation/images/storm-cluster.png" alt="Storm cluster"></p>
-
-<p>All coordination between Nimbus and the Supervisors is done through a <a href="http://zookeeper.apache.org/">Zookeeper</a> cluster. Additionally, the Nimbus daemon and Supervisor daemons are fail-fast and stateless; all state is kept in Zookeeper or on local disk. This means you can kill -9 Nimbus or the Supervisors and they'll start back up like nothing happened. This design leads to Storm clusters being incredibly stable.</p>
-
-<h2 id="topologies">Topologies</h2>
-
-<p>To do realtime computation on Storm, you create what are called "topologies". A topology is a graph of computation. Each node in a topology contains processing logic, and links between nodes indicate how data should be passed around between nodes.</p>
-
-<p>Running a topology is straightforward. First, you package all your code and dependencies into a single jar. Then, you run a command like the following:</p>
-<div class="highlight"><pre><code class="language-" data-lang="">storm jar all-my-code.jar backtype.storm.MyTopology arg1 arg2
-</code></pre></div>
-<p>This runs the class <code>backtype.storm.MyTopology</code> with the arguments <code>arg1</code> and <code>arg2</code>. The main function of the class defines the topology and submits it to Nimbus. The <code>storm jar</code> part takes care of connecting to Nimbus and uploading the jar.</p>
-
-<p>Since topology definitions are just Thrift structs, and Nimbus is a Thrift service, you can create and submit topologies using any programming language. The above example is the easiest way to do it from a JVM-based language. See <a href="/documentation/Running-topologies-on-a-production-cluster.html">Running topologies on a production cluster</a> for more information on starting and stopping topologies.</p>
-
-<h2 id="streams">Streams</h2>
-
-<p>The core abstraction in Storm is the "stream". A stream is an unbounded sequence of tuples. Storm provides the primitives for transforming a stream into a new stream in a distributed and reliable way. For example, you may transform a stream of tweets into a stream of trending topics.</p>
-
-<p>The basic primitives Storm provides for doing stream transformations are "spouts" and "bolts". Spouts and bolts have interfaces that you implement to run your application-specific logic.</p>
-
-<p>A spout is a source of streams. For example, a spout may read tuples off of a <a href="http://github.com/nathanmarz/storm-kestrel">Kestrel</a> queue and emit them as a stream. Or a spout may connect to the Twitter API and emit a stream of tweets.</p>
-
-<p>A bolt consumes any number of input streams, does some processing, and possibly emits new streams. Complex stream transformations, like computing a stream of trending topics from a stream of tweets, require multiple steps and thus multiple bolts. Bolts can do anything from run functions, filter tuples, do streaming aggregations, do streaming joins, talk to databases, and more.</p>
-
-<p>Networks of spouts and bolts are packaged into a "topology" which is the top-level abstraction that you submit to Storm clusters for execution. A topology is a graph of stream transformations where each node is a spout or bolt. Edges in the graph indicate which bolts are subscribing to which streams. When a spout or bolt emits a tuple to a stream, it sends the tuple to every bolt that subscribed to that stream.</p>
-
-<p><img src="/documentation/images/topology.png" alt="A Storm topology"></p>
-
-<p>Links between nodes in your topology indicate how tuples should be passed around. For example, if there is a link between Spout A and Bolt B, a link from Spout A to Bolt C, and a link from Bolt B to Bolt C, then everytime Spout A emits a tuple, it will send the tuple to both Bolt B and Bolt C. All of Bolt B's output tuples will go to Bolt C as well.</p>
-
-<p>Each node in a Storm topology executes in parallel. In your topology, you can specify how much parallelism you want for each node, and then Storm will spawn that number of threads across the cluster to do the execution.</p>
-
-<p>A topology runs forever, or until you kill it. Storm will automatically reassign any failed tasks. Additionally, Storm guarantees that there will be no data loss, even if machines go down and messages are dropped.</p>
-
-<h2 id="data-model">Data model</h2>
-
-<p>Storm uses tuples as its data model. A tuple is a named list of values, and a field in a tuple can be an object of any type. Out of the box, Storm supports all the primitive types, strings, and byte arrays as tuple field values. To use an object of another type, you just need to implement <a href="/documentation/Serialization.html">a serializer</a> for the type.</p>
-
-<p>Every node in a topology must declare the output fields for the tuples it emits. For example, this bolt declares that it emits 2-tuples with the fields "double" and "triple":</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">DoubleAndTripleBolt</span> <span class="kd">extends</span> <span class="n">BaseRichBolt</span> <span class="o">{</span>
- <span class="kd">private</span> <span class="n">OutputCollectorBase</span> <span class="n">_collector</span><span class="o">;</span>
-
- <span class="nd">@Override</span>
- <span class="kd">public</span> <span class="kt">void</span> <span class="n">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">OutputCollectorBase</span> <span class="n">collector</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="o">}</span>
-
- <span class="nd">@Override</span>
- <span class="kd">public</span> <span class="kt">void</span> <span class="n">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="kt">int</span> <span class="n">val</span> <span class="o">=</span> <span class="n">input</span><span class="o">.</span><span class="na">getInteger</span><span class="o">(</span><span class="mi">0</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="n">input</span><span class="o">,</span> <span class="k">new</span> <span class="n">Values</span><span class="o">(</span><span class="n">val</span><span class="o">*</span><span class="mi">2</span><span class="o">,</span> <span class="n">val</span><span class="o">*</span><span class="mi">3</span><span class="o">));</span>
- <span class="n">_collector</span><span class="o">.</span><span class="na">ack</span><span class="o">(</span><span class="n">input</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="n">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">"double"</span><span class="o">,</span> <span class="s">"triple"</span><span class="o">));</span>
- <span class="o">}</span>
-<span class="o">}</span>
-</code></pre></div>
-<p>The <code>declareOutputFields</code> function declares the output fields <code>["double", "triple"]</code> for the component. The rest of the bolt will be explained in the upcoming sections.</p>
-
-<h2 id="a-simple-topology">A simple topology</h2>
-
-<p>Let's take a look at a simple topology to explore the concepts more and see how the code shapes up. Let's look at the <code>ExclamationTopology</code> definition from storm-starter:</p>
-<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">TopologyBuilder</span> <span class="n">builder</span> <span class="o">=</span> <span class="k">new</span> <span class="n">TopologyBuilder</span><span class="o">();</span>
-<span class="n">builder</span><span class="o">.</span><span class="na">setSpout</span><span class="o">(</span><span class="s">"words"</span><span class="o">,</span> <span class="k">new</span> <span class="n">TestWordSpout</span><span class="o">(),</span> <span class="mi">10</span><span class="o">);</span>
-<span class="n">builder</span><span class="o">.</span><span class="na">setBolt</span><span class="o">(</span><span class="s">"exclaim1"</span><span class="o">,</span> <span class="k">new</span> <span class="n">ExclamationBolt</span><span class="o">(),</span> <span class="mi">3</span><span class="o">)</span>
- <span class="o">.</span><span class="na">shuffleGrouping</span><span class="o">(</span><span class="s">"words"</span><span class="o">);</span>
-<span class="n">builder</span><span class="o">.</span><span class="na">setBolt</span><span class="o">(</span><span class="s">"exclaim2"</span><span class="o">,</span> <span class="k">new</span> <span class="n">ExclamationBolt</span><span class="o">(),</span> <span class="mi">2</span><span class="o">)</span>
- <span class="o">.</span><span class="na">shuffleGrouping</span><span class="o">(</span><span class="s">"exclaim1"</span><span class="o">);</span>
-</code></pre></div>
-<p>This topology contains a spout and two bolts. The spout emits words, and each bolt appends the string "!!!" to its input. The nodes are arranged in a line: the spout emits to the first bolt which then emits to the second bolt. If the spout emits the tuples ["bob"] and ["john"], then the second bolt will emit the words ["bob!!!!!!"] and ["john!!!!!!"].</p>
-
-<p>This code defines the nodes using the <code>setSpout</code> and <code>setBolt</code> methods. These methods take as input a user-specified id, an object containing the processing logic, and the amount of parallelism you want for the node. In this example, the spout is given id "words" and the bolts are given ids "exclaim1" and "exclaim2". </p>
-
-<p>The object containing the processing logic implements the <a href="/javadoc/apidocs/backtype/storm/topology/IRichSpout.html">IRichSpout</a> interface for spouts and the <a href="/javadoc/apidocs/backtype/storm/topology/IRichBolt.html">IRichBolt</a> interface for bolts.</p>
-
-<p>The last parameter, how much parallelism you want for the node, is optional. It indicates how many threads should execute that component across the cluster. If you omit it, Storm will only allocate one thread for that node.</p>
-
-<p><code>setBolt</code> returns an <a href="/javadoc/apidocs/backtype/storm/topology/InputDeclarer.html">InputDeclarer</a> object that is used to define the inputs to the Bolt. Here, component "exclaim1" declares that it wants to read all the tuples emitted by component "words" using a shuffle grouping, and component "exclaim2" declares that it wants to read all the tuples emitted by component "exclaim1" using a shuffle grouping. "shuffle grouping" means that tuples should be randomly distributed from the input tasks to the bolt's tasks. There are many ways to group data between components. These will be explained in a few sections.</p>
-
-<p>If you wanted component "exclaim2" to read all the tuples emitted by both component "words" and component "exclaim1", you would write component "exclaim2"'s definition like this:</p>
-<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">builder</span><span class="o">.</span><span class="na">setBolt</span><span class="o">(</span><span class="s">"exclaim2"</span><span class="o">,</span> <span class="k">new</span> <span class="n">ExclamationBolt</span><span class="o">(),</span> <span class="mi">5</span><span class="o">)</span>
- <span class="o">.</span><span class="na">shuffleGrouping</span><span class="o">(</span><span class="s">"words"</span><span class="o">)</span>
- <span class="o">.</span><span class="na">shuffleGrouping</span><span class="o">(</span><span class="s">"exclaim1"</span><span class="o">);</span>
-</code></pre></div>
-<p>As you can see, input declarations can be chained to specify multiple sources for the Bolt.</p>
-
-<p>Let's dig into the implementations of the spouts and bolts in this topology. Spouts are responsible for emitting new messages into the topology. <code>TestWordSpout</code> in this topology emits a random word from the list ["nathan", "mike", "jackson", "golda", "bertels"] as a 1-tuple every 100ms. The implementation of <code>nextTuple()</code> in TestWordSpout looks like this:</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">nextTuple</span><span class="p">(</span><span class="o">)</span> <span class="o">{</span>
- <span class="n">Utils</span><span class="o">.</span><span class="na">sleep</span><span class="o">(</span><span class="mi">100</span><span class="o">);</span>
- <span class="kd">final</span> <span class="n">String</span><span class="o">[]</span> <span class="n">words</span> <span class="o">=</span> <span class="k">new</span> <span class="n">String</span><span class="o">[]</span> <span class="o">{</span><span class="s">"nathan"</span><span class="o">,</span> <span class="s">"mike"</span><span class="o">,</span> <span class="s">"jackson"</span><span class="o">,</span> <span class="s">"golda"</span><span class="o">,</span> <span class="s">"bertels"</span><span class="o">};</span>
- <span class="kd">final</span> <span class="n">Random</span> <span class="n">rand</span> <span class="o">=</span> <span class="k">new</span> <span class="n">Random</span><span class="o">();</span>
- <span class="kd">final</span> <span class="n">String</span> <span class="n">word</span> <span class="o">=</span> <span class="n">words</span><span class="o">[</span><span class="n">rand</span><span class="o">.</span><span class="na">nextInt</span><span class="o">(</span><span class="n">words</span><span class="o">.</span><span class="na">length</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">word</span><span class="o">));</span>
-<span class="o">}</span>
-</code></pre></div>
-<p>As you can see, the implementation is very straightforward.</p>
-
-<p><code>ExclamationBolt</code> appends the string "!!!" to its input. Let's take a look at the full implementation for <code>ExclamationBolt</code>:</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">ExclamationBolt</span> <span class="kd">implements</span> <span class="n">IRichBolt</span> <span class="o">{</span>
- <span class="n">OutputCollector</span> <span class="n">_collector</span><span class="o">;</span>
-
- <span class="nd">@Override</span>
- <span class="kd">public</span> <span class="kt">void</span> <span class="n">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="n">_collector</span> <span class="o">=</span> <span class="n">collector</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="n">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">_collector</span><span class="o">.</span><span class="na">emit</span><span class="o">(</span><span class="n">tuple</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">getString</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span> <span class="o">+</span> <span class="s">"!!!"</span><span class="o">));</span>
- <span class="n">_collector</span><span class="o">.</span><span class="na">ack</span><span class="o">(</span><span class="n">tuple</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="n">cleanup</span><span class="o">()</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="n">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">"word"</span><span class="o">));</span>
- <span class="o">}</span>
-
- <span class="nd">@Override</span>
- <span class="kd">public</span> <span class="n">Map</span> <span class="n">getComponentConfiguration</span><span class="o">()</span> <span class="o">{</span>
- <span class="k">return</span> <span class="kc">null</span><span class="o">;</span>
- <span class="o">}</span>
-<span class="o">}</span>
-</code></pre></div>
-<p>The <code>prepare</code> method provides the bolt with an <code>OutputCollector</code> that is used for emitting tuples from this bolt. Tuples can be emitted at anytime from the bolt -- in the <code>prepare</code>, <code>execute</code>, or <code>cleanup</code> methods, or even asynchronously in another thread. This <code>prepare</code> implementation simply saves the <code>OutputCollector</code> as an instance variable to be used later on in the <code>execute</code> method.</p>
-
-<p>The <code>execute</code> method receives a tuple from one of the bolt's inputs. The <code>ExclamationBolt</code> grabs the first field from the tuple and emits a new tuple with the string "!!!" appended to it. If you implement a bolt that subscribes to multiple input sources, you can find out which component the <a href="/javadoc/apidocs/backtype/storm/tuple/Tuple.html">Tuple</a> came from by using the <code>Tuple#getSourceComponent</code> method.</p>
-
-<p>There's a few other things going on in the <code>execute</code> method, namely that the input tuple is passed as the first argument to <code>emit</code> and the input tuple is acked on the final line. These are part of Storm's reliability API for guaranteeing no data loss and will be explained later in this tutorial. </p>
-
-<p>The <code>cleanup</code> method is called when a Bolt is being shutdown and should cleanup any resources that were opened. There's no guarantee that this method will be called on the cluster: for example, if the machine the task is running on blows up, there's no way to invoke the method. The <code>cleanup</code> method is intended for when you run topologies in <a href="/documentation/Local-mode.html">local mode</a> (where a Storm cluster is simulated in process), and you want to be able to run and kill many topologies without suffering any resource leaks.</p>
-
-<p>The <code>declareOutputFields</code> method declares that the <code>ExclamationBolt</code> emits 1-tuples with one field called "word".</p>
-
-<p>The <code>getComponentConfiguration</code> method allows you to configure various aspects of how this component runs. This is a more advanced topic that is explained further on <a href="/documentation/Configuration.html">Configuration</a>.</p>
-
-<p>Methods like <code>cleanup</code> and <code>getComponentConfiguration</code> are often not needed in a bolt implementation. You can define bolts more succinctly by using a base class that provides default implementations where appropriate. <code>ExclamationBolt</code> can be written more succinctly by extending <code>BaseRichBolt</code>, like so:</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">ExclamationBolt</span> <span class="kd">extends</span> <span class="n">BaseRichBolt</span> <span class="o">{</span>
- <span class="n">OutputCollector</span> <span class="n">_collector</span><span class="o">;</span>
-
- <span class="nd">@Override</span>
- <span class="kd">public</span> <span class="kt">void</span> <span class="n">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="n">_collector</span> <span class="o">=</span> <span class="n">collector</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="n">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">_collector</span><span class="o">.</span><span class="na">emit</span><span class="o">(</span><span class="n">tuple</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">getString</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span> <span class="o">+</span> <span class="s">"!!!"</span><span class="o">));</span>
- <span class="n">_collector</span><span class="o">.</span><span class="na">ack</span><span class="o">(</span><span class="n">tuple</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="n">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">"word"</span><span class="o">));</span>
- <span class="o">}</span>
-<span class="o">}</span>
-</code></pre></div>
-<h2 id="running-exclamationtopology-in-local-mode">Running ExclamationTopology in local mode</h2>
-
-<p>Let's see how to run the <code>ExclamationTopology</code> in local mode and see that it's working.</p>
-
-<p>Storm has two modes of operation: local mode and distributed mode. In local mode, Storm executes completely in process by simulating worker nodes with threads. Local mode is useful for testing and development of topologies. When you run the topologies in storm-starter, they'll run in local mode and you'll be able to see what messages each component is emitting. You can read more about running topologies in local mode on <a href="/documentation/Local-mode.html">Local mode</a>.</p>
-
-<p>In distributed mode, Storm operates as a cluster of machines. When you submit a topology to the master, you also submit all the code necessary to run the topology. The master will take care of distributing your code and allocating workers to run your topology. If workers go down, the master will reassign them somewhere else. You can read more about running topologies on a cluster on <a href="/documentation/Running-topologies-on-a-production-cluster.html">Running topologies on a production cluster</a>. </p>
-
-<p>Here's the code that runs <code>ExclamationTopology</code> in local mode:</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">setDebug</span><span class="o">(</span><span class="kc">true</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">2</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">"test"</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">createTopology</span><span class="o">());</span>
-<span class="n">Utils</span><span class="o">.</span><span class="na">sleep</span><span class="o">(</span><span class="mi">10000</span><span class="o">);</span>
-<span class="n">cluster</span><span class="o">.</span><span class="na">killTopology</span><span class="o">(</span><span class="s">"test"</span><span class="o">);</span>
-<span class="n">cluster</span><span class="o">.</span><span class="na">shutdown</span><span class="o">();</span>
-</code></pre></div>
-<p>First, the code defines an in-process cluster by creating a <code>LocalCluster</code> object. Submitting topologies to this virtual cluster is identical to submitting topologies to distributed clusters. It submits a topology to the <code>LocalCluster</code> by calling <code>submitTopology</code>, which takes as arguments a name for the running topology, a configuration for the topology, and then the topology itself.</p>
-
-<p>The name is used to identify the topology so that you can kill it later on. A topology will run indefinitely until you kill it.</p>
-
-<p>The configuration is used to tune various aspects of the running topology. The two configurations specified here are very common:</p>
-
-<ol>
-<li><strong>TOPOLOGY_WORKERS</strong> (set with <code>setNumWorkers</code>) specifies how many <em>processes</em> you want allocated around the cluster to execute the topology. Each component in the topology will execute as many <em>threads</em>. The number of threads allocated to a given component is configured through the <code>setBolt</code> and <code>setSpout</code> methods. Those <em>threads</em> exist within worker <em>processes</em>. Each worker <em>process</em> contains within it some number of <em>threads</em> for some number of components. For instance, you may have 300 threads specified across all your components and 50 worker processes specified in your config. Each worker process will execute 6 threads, each of which of could belong to a different component. You tune the performance of Storm topologies by tweaking the parallelism for each component and the number of worker processes those threads should run within.</li>
-<li><strong>TOPOLOGY_DEBUG</strong> (set with <code>setDebug</code>), when set to true, tells Storm to log every message every emitted by a component. This is useful in local mode when testing topologies, but you probably want to keep this turned off when running topologies on the cluster.</li>
-</ol>
-
-<p>There's many other configurations you can set for the topology. The various configurations are detailed on <a href="/javadoc/apidocs/backtype/storm/Config.html">the Javadoc for Config</a>.</p>
-
-<p>To learn about how to set up your development environment so that you can run topologies in local mode (such as in Eclipse), see <a href="/documentation/Creating-a-new-Storm-project.html">Creating a new Storm project</a>.</p>
-
-<h2 id="stream-groupings">Stream groupings</h2>
-
-<p>A stream grouping tells a topology how to send tuples between two components. Remember, spouts and bolts execute in parallel as many tasks across the cluster. If you look at how a topology is executing at the task level, it looks something like this:</p>
-
-<p><img src="/documentation/images/topology-tasks.png" alt="Tasks in a topology"></p>
-
-<p>When a task for Bolt A emits a tuple to Bolt B, which task should it send the tuple to?</p>
-
-<p>A "stream grouping" answers this question by telling Storm how to send tuples between sets of tasks. Before we dig into the different kinds of stream groupings, let's take a look at another topology from <a href="http://github.com/apache/storm/blob/master/examples/storm-starter">storm-starter</a>. This <a href="https://github.com/apache/storm/blob/master/examples/storm-starter/src/jvm/storm/starter/WordCountTopology.java">WordCountTopology</a> reads sentences off of a spout and streams out of <code>WordCountBolt</code> the total number of times it has seen that word before:</p>
-<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="n">TopologyBuilder</span> <span class="n">builder</span> <span class="o">=</span> <span class="k">new</span> <span class="n">TopologyBuilder</span><span class="o">();</span>
-
-<span class="n">builder</span><span class="o">.</span><span class="na">setSpout</span><span class="o">(</span><span class="s">"sentences"</span><span class="o">,</span> <span class="k">new</span> <span class="n">RandomSentenceSpout</span><span class="o">(),</span> <span class="mi">5</span><span class="o">);</span>
-<span class="n">builder</span><span class="o">.</span><span class="na">setBolt</span><span class="o">(</span><span class="s">"split"</span><span class="o">,</span> <span class="k">new</span> <span class="n">SplitSentence</span><span class="o">(),</span> <span class="mi">8</span><span class="o">)</span>
- <span class="o">.</span><span class="na">shuffleGrouping</span><span class="o">(</span><span class="s">"sentences"</span><span class="o">);</span>
-<span class="n">builder</span><span class="o">.</span><span class="na">setBolt</span><span class="o">(</span><span class="s">"count"</span><span class="o">,</span> <span class="k">new</span> <span class="n">WordCount</span><span class="o">(),</span> <span class="mi">12</span><span class="o">)</span>
- <span class="o">.</span><span class="na">fieldsGrouping</span><span class="o">(</span><span class="s">"split"</span><span class="o">,</span> <span class="k">new</span> <span class="n">Fields</span><span class="o">(</span><span class="s">"word"</span><span class="o">));</span>
-</code></pre></div>
-<p><code>SplitSentence</code> emits a tuple for each word in each sentence it receives, and <code>WordCount</code> keeps a map in memory from word to count. Each time <code>WordCount</code> receives a word, it updates its state and emits the new word count.</p>
-
-<p>There's a few different kinds of stream groupings.</p>
-
-<p>The simplest kind of grouping is called a "shuffle grouping" which sends the tuple to a random task. A shuffle grouping is used in the <code>WordCountTopology</code> to send tuples from <code>RandomSentenceSpout</code> to the <code>SplitSentence</code> bolt. It has the effect of evenly distributing the work of processing the tuples across all of <code>SplitSentence</code> bolt's tasks.</p>
-
-<p>A more interesting kind of grouping is the "fields grouping". A fields grouping is used between the <code>SplitSentence</code> bolt and the <code>WordCount</code> bolt. It is critical for the functioning of the <code>WordCount</code> bolt that the same word always go to the same task. Otherwise, more than one task will see the same word, and they'll each emit incorrect values for the count since each has incomplete information. A fields grouping lets you group a stream by a subset of its fields. This causes equal values for that subset of fields to go to the same task. Since <code>WordCount</code> subscribes to <code>SplitSentence</code>'s output stream using a fields grouping on the "word" field, the same word always goes to the same task and the bolt produces the correct output.</p>
-
-<p>Fields groupings are the basis of implementing streaming joins and streaming aggregations as well as a plethora of other use cases. Underneath the hood, fields groupings are implemented using mod hashing.</p>
-
-<p>There's a few other kinds of stream groupings. You can read more about them on <a href="/documentation/Concepts.html">Concepts</a>. </p>
-
-<h2 id="defining-bolts-in-other-languages">Defining Bolts in other languages</h2>
-
-<p>Bolts can be defined in any language. Bolts written in another language are executed as subprocesses, and Storm communicates with those subprocesses with JSON messages over stdin/stdout. The communication protocol just requires an ~100 line adapter library, and Storm ships with adapter libraries for Ruby, Python, and Fancy. </p>
-
-<p>Here's the definition of the <code>SplitSentence</code> bolt from <code>WordCountTopology</code>:</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">SplitSentence</span> <span class="kd">extends</span> <span class="n">ShellBolt</span> <span class="kd">implements</span> <span class="n">IRichBolt</span> <span class="o">{</span>
- <span class="kd">public</span> <span class="n">SplitSentence</span><span class="o">()</span> <span class="o">{</span>
- <span class="kd">super</span><span class="o">(</span><span class="s">"python"</span><span class="o">,</span> <span class="s">"splitsentence.py"</span><span class="o">);</span>
- <span class="o">}</span>
-
- <span class="kd">public</span> <span class="kt">void</span> <span class="n">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">"word"</span><span class="o">));</span>
- <span class="o">}</span>
-<span class="o">}</span>
-</code></pre></div>
-<p><code>SplitSentence</code> overrides <code>ShellBolt</code> and declares it as running using <code>python</code> with the arguments <code>splitsentence.py</code>. Here's the implementation of <code>splitsentence.py</code>:</p>
-<div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">import</span> <span class="nn">storm</span>
-
-<span class="k">class</span> <span class="nc">SplitSentenceBolt</span><span class="p">(</span><span class="n">storm</span><span class="o">.</span><span class="n">BasicBolt</span><span class="p">):</span>
- <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tup</span><span class="p">):</span>
- <span class="n">words</span> <span class="o">=</span> <span class="n">tup</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">" "</span><span class="p">)</span>
- <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">words</span><span class="p">:</span>
- <span class="n">storm</span><span class="o">.</span><span class="n">emit</span><span class="p">([</span><span class="n">word</span><span class="p">])</span>
-
-<span class="n">SplitSentenceBolt</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
-</code></pre></div>
-<p>For more information on writing spouts and bolts in other languages, and to learn about how to create topologies in other languages (and avoid the JVM completely), see <a href="/documentation/Using-non-JVM-languages-with-Storm.html">Using non-JVM languages with Storm</a>.</p>
-
-<h2 id="guaranteeing-message-processing">Guaranteeing message processing</h2>
-
-<p>Earlier on in this tutorial, we skipped over a few aspects of how tuples are emitted. Those aspects were part of Storm's reliability API: how Storm guarantees that every message coming off a spout will be fully processed. See <a href="/documentation/Guaranteeing-message-processing.html">Guaranteeing message processing</a> for information on how this works and what you have to do as a user to take advantage of Storm's reliability capabilities.</p>
-
-<h2 id="transactional-topologies">Transactional topologies</h2>
-
-<p>Storm guarantees that every message will be played through the topology at least once. A common question asked is "how do you do things like counting on top of Storm? Won't you overcount?" Storm has a feature called transactional topologies that let you achieve exactly-once messaging semantics for most computations. Read more about transactional topologies <a href="/documentation/Transactional-topologies.html">here</a>. </p>
-
-<h2 id="distributed-rpc">Distributed RPC</h2>
-
-<p>This tutorial showed how to do basic stream processing on top of Storm. There's lots more things you can do with Storm's primitives. One of the most interesting applications of Storm is Distributed RPC, where you parallelize the computation of intense functions on the fly. Read more about Distributed RPC <a href="/documentation/Distributed-RPC.html">here</a>. </p>
-
-<h2 id="conclusion">Conclusion</h2>
-
-<p>This tutorial gave a broad overview of developing, testing, and deploying Storm topologies. The rest of the documentation dives deeper into all the aspects of using Storm.</p>
-
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http://git-wip-us.apache.org/repos/asf/storm/blob/335bbf94/about.md
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diff --git a/about.md b/about.md
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+++ /dev/null
@@ -1,44 +0,0 @@
----
-layout: page
-title: Project Information
-items:
- -
- - "/about/integrates.html"
- - "Integrates"
- -
- - "/about/simple-api.html"
- - "Simple API"
- -
- - "/about/scalable.html"
- - "Scalable"
- -
- - "/about/fault-tolerant.html"
- - "Fault tolerant"
- -
- - "/about/guarantees-data-processing.html"
- - "Guarantees data processing"
- -
- - "/about/multi-language.html"
- - "Use with any language"
- -
- - "/about/deployment.html"
- - "Easy to deploy and operate"
- -
- - "/about/free-and-open-source.html"
- - "Free and open source"
----
-{% for post in page.items %}
-<div class="download-block">
- <div class="row">
- <div class="col-md-3 remove-custom-padding">
- <h4><a class="{% if page.url == post[0] then %}current{% else %}{% endif %}" href="{{ post[0] }}">{{ post[1] }}</a></h4>
- </div>
- <div class="col-md-9 remove-custom-padding">
- <div class="download-info">
- <h4>{{page.title}}</h4>
- {{ post[1].meta }}
- </div>
- </div>
- </div>
-</div>
-{% endfor %}
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/storm/blob/335bbf94/about/deployment.md
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--- a/about/deployment.md
+++ /dev/null
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----
-layout: about
-title: Easy to deploy and operate
----
-
-Storm clusters are easy to deploy, requiring a minimum of setup and configuration to get up and running. Storm's out of the box configurations are suitable for production. Read more about how to deploy a Storm cluster [here](/documentation/Setting-up-a-Storm-cluster.html).
-
-If you're on EC2, the [storm-deploy](https://github.com/nathanmarz/storm-deploy) project can provision, configure, and install a Storm cluster from scratch at just the click of a button.
-
-Additionally, Storm is easy to operate once deployed. Storm has been designed to be [extremely robust](/about/fault-tolerant.html) – the cluster will just keep on running, month after month.
http://git-wip-us.apache.org/repos/asf/storm/blob/335bbf94/about/fault-tolerant.md
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----
-layout: about
----
-
-Storm is fault-tolerant: when workers die, Storm will automatically restart them. If a node dies, the worker will be restarted on another node.
-
-The Storm daemons, Nimbus and the Supervisors, are designed to be stateless and fail-fast. So if they die, they will restart like nothing happened. This means you can *kill -9* the Storm daemons without affecting the health of the cluster or your topologies.
-
-Read more about Storm's fault-tolerance [on the manual](/documentation/Fault-tolerance.html).
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/storm/blob/335bbf94/about/free-and-open-source.md
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diff --git a/about/free-and-open-source.md b/about/free-and-open-source.md
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-layout: about
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-
-Apache Storm is a free and open source project licensed under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0.html)
-
-
-Storm has a large and growing ecosystem of libraries and tools to use in conjunction with Storm including everything from:
-
-1. *Spouts*: These spouts integrate with queueing systems such as JMS, Kafka, Redis pub/sub, and more.
-2. *storm-state*: storm-state makes it easy to manage large amounts of in-memory state in your computations in a reliable by using a distributed filesystem for persistence
-3. *Database integrations*: There are helper bolts for integrating with various databases, such as MongoDB, RDBMS's, Cassandra, and more.
-4. Other miscellaneous utilities
-
-The [Storm documentation](/documentation.html) has links to notable Storm-related projects hosted outside of Apache.
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/storm/blob/335bbf94/about/guarantees-data-processing.md
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----
-layout: about
----
-
-Storm guarantees every tuple will be fully processed. One of Storm's core mechanisms is the ability to track the lineage of a tuple as it makes its way through the topology in an extremely efficient way. Read more about how this works [here](/documentation/Guaranteeing-message-processing.html).
-
-Storm's basic abstractions provide an at-least-once processing guarantee, the same guarantee you get when using a queueing system. Messages are only replayed when there are failures.
-
-Using [Trident](/documentation/Trident-tutorial.html), a higher level abstraction over Storm's basic abstractions, you can achieve exactly-once processing semantics.
-
http://git-wip-us.apache.org/repos/asf/storm/blob/335bbf94/about/integrates.md
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-layout: about
----
-
-Storm integrates with any queueing system and any database system. Storm's [spout](/apidocs/backtype/storm/spout/ISpout.html) abstraction makes it easy to integrate a new queuing system. Example queue integrations include:
-
-1. [Kestrel](https://github.com/nathanmarz/storm-kestrel)
-2. [RabbitMQ / AMQP](https://github.com/Xorlev/storm-amqp-spout)
-3. [Kafka](https://github.com/apache/storm/tree/master/external/storm-kafka)
-4. [JMS](https://github.com/ptgoetz/storm-jms)
-5. [Amazon Kinesis](https://github.com/awslabs/kinesis-storm-spout)
-
-Likewise, integrating Storm with database systems is easy. Simply open a connection to your database and read/write like you normally would. Storm will handle the parallelization, partitioning, and retrying on failures when necessary.
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/storm/blob/335bbf94/about/multi-language.md
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-layout: about
----
-
-Storm was designed from the ground up to be usable with any programming language. At the core of Storm is a [Thrift](http://thrift.apache.org/) [definition](https://github.com/apache/storm/blob/master/storm-core/src/storm.thrift) for defining and submitting topologies. Since Thrift can be used in any language, topologies can be defined and submitted from any language.
-
-Similarly, spouts and bolts can be defined in any language. Non-JVM spouts and bolts communicate to Storm over a [JSON-based protocol](/documentation/Multilang-protocol.html) over stdin/stdout. Adapters that implement this protocol exist for [Ruby](https://github.com/apache/storm/blob/master/storm-multilang/ruby/src/main/resources/resources/storm.rb), [Python](https://github.com/apache/storm/blob/master/storm-multilang/python/src/main/resources/resources/storm.py), [Javascript](https://github.com/apache/storm/blob/master/storm-multilang/javascript/src/main/resources/resources/storm.js), [Perl](https://github.com/dan-blanchard/io-storm).
-
-*storm-starter* has an [example topology](https://github.com/apache/storm/blob/master/examples/storm-starter/src/jvm/storm/starter/WordCountTopology.java) that implements one of the bolts in Python.
http://git-wip-us.apache.org/repos/asf/storm/blob/335bbf94/about/scalable.md
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-layout: about
----
-
-Storm topologies are inherently parallel and run across a cluster of machines. Different parts of the topology can be scaled individually by tweaking their parallelism. The "rebalance" command of the "storm" command line client can adjust the parallelism of running topologies on the fly.
-
-Storm's inherent parallelism means it can process very high throughputs of messages with very low latency. Storm was benchmarked at processing **one million 100 byte messages per second per node** on hardware with the following specs:
-
- * **Processor:** 2x Intel E5645@2.4Ghz
- * **Memory:** 24 GB
http://git-wip-us.apache.org/repos/asf/storm/blob/335bbf94/about/simple-api.md
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diff --git a/about/simple-api.md b/about/simple-api.md
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-layout: about
----
-
-Storm has a simple and easy to use API. When programming on Storm, you manipulate and transform streams of tuples, and a tuple is a named list of values. Tuples can contain objects of any type; if you want to use a type Storm doesn't know about it's [very easy](/documentation/Serialization.html) to register a serializer for that type.
-
-There are just three abstractions in Storm: spouts, bolts, and topologies. A **spout** is a source of streams in a computation. Typically a spout reads from a queueing broker such as Kestrel, RabbitMQ, or Kafka, but a spout can also generate its own stream or read from somewhere like the Twitter streaming API. Spout implementations already exist for most queueing systems.
-
-A **bolt** processes any number of input streams and produces any number of new output streams. Most of the logic of a computation goes into bolts, such as functions, filters, streaming joins, streaming aggregations, talking to databases, and so on.
-
-A **topology** is a network of spouts and bolts, with each edge in the network representing a bolt subscribing to the output stream of some other spout or bolt. A topology is an arbitrarily complex multi-stage stream computation. Topologies run indefinitely when deployed.
-
-Storm has a "local mode" where a Storm cluster is simulated in-process. This is useful for development and testing. The "storm" command line client is used when ready to submit a topology for execution on an actual cluster.
-
-The [storm-starter](https://github.com/apache/storm/tree/master/examples/storm-starter) project contains example topologies for learning the basics of Storm. Learn more about how to use Storm by reading the [tutorial](/tutorial.html) and the [documentation](/documentation.html).
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http://git-wip-us.apache.org/repos/asf/storm/blob/335bbf94/assets/css/bootstrap-theme.css
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