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-    <h1 class="page-title">Common Topology Patterns</h1>
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-<p class="post-meta"></p>
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-<p>This page lists a variety of common patterns in Storm topologies.</p>
-
-<ol>
-<li>Streaming joins</li>
-<li>Batching</li>
-<li>BasicBolt</li>
-<li>In-memory caching + fields grouping combo</li>
-<li>Streaming top N</li>
-<li>TimeCacheMap for efficiently keeping a cache of things that have been 
recently updated</li>
-<li>CoordinatedBolt and KeyedFairBolt for Distributed RPC</li>
-</ol>
-
-<h3 id="joins">Joins</h3>
-
-<p>A streaming join combines two or more data streams together based on some 
common field. Whereas a normal database join has finite input and clear 
semantics for a join, a streaming join has infinite input and unclear semantics 
for what a join should be.</p>
-
-<p>The join type you need will vary per application. Some applications join 
all tuples for two streams over a finite window of time, whereas other 
applications expect exactly one tuple for each side of the join for each join 
field. Other applications may do the join completely differently. The common 
pattern among all these join types is partitioning multiple input streams in 
the same way. This is easily accomplished in Storm by using a fields grouping 
on the same fields for many input streams to the joiner bolt. For example:</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">"join"</span><span class="o">,</span> <span class="k">new</span> 
<span class="n">MyJoiner</span><span class="o">(),</span> <span 
class="n">parallelism</span><span class="o">)</span>
-  <span class="o">.</span><span class="na">fieldsGrouping</span><span 
class="o">(</span><span class="s">"1"</span><span class="o">,</span> <span 
class="k">new</span> <span class="n">Fields</span><span class="o">(</span><span 
class="s">"joinfield1"</span><span class="o">,</span> <span 
class="s">"joinfield2"</span><span class="o">))</span>
-  <span class="o">.</span><span class="na">fieldsGrouping</span><span 
class="o">(</span><span class="s">"2"</span><span class="o">,</span> <span 
class="k">new</span> <span class="n">Fields</span><span class="o">(</span><span 
class="s">"joinfield1"</span><span class="o">,</span> <span 
class="s">"joinfield2"</span><span class="o">))</span>
-  <span class="o">.</span><span class="na">fieldsGrouping</span><span 
class="o">(</span><span class="s">"3"</span><span class="o">,</span> <span 
class="k">new</span> <span class="n">Fields</span><span class="o">(</span><span 
class="s">"joinfield1"</span><span class="o">,</span> <span 
class="s">"joinfield2"</span><span class="o">));</span>
-</code></pre></div>
-<p>The different streams don&#39;t have to have the same field names, of 
course.</p>
-
-<h3 id="batching">Batching</h3>
-
-<p>Oftentimes for efficiency reasons or otherwise, you want to process a group 
of tuples in batch rather than individually. For example, you may want to batch 
updates to a database or do a streaming aggregation of some sort.</p>
-
-<p>If you want reliability in your data processing, the right way to do this 
is to hold on to tuples in an instance variable while the bolt waits to do the 
batching. Once you do the batch operation, you then ack all the tuples you were 
holding onto.</p>
-
-<p>If the bolt emits tuples, then you may want to use multi-anchoring to 
ensure reliability. It all depends on the specific application. See <a 
href="Guaranteeing-message-processing.html">Guaranteeing message processing</a> 
for more details on how reliability works.</p>
-
-<h3 id="basicbolt">BasicBolt</h3>
-
-<p>Many bolts follow a similar pattern of reading an input tuple, emitting 
zero or more tuples based on that input tuple, and then acking that input tuple 
immediately at the end of the execute method. Bolts that match this pattern are 
things like functions and filters. This is such a common pattern that Storm 
exposes an interface called <a 
href="javadocs/backtype/storm/topology/IBasicBolt.html">IBasicBolt</a> that 
automates this pattern for you. See <a 
href="Guaranteeing-message-processing.html">Guaranteeing message processing</a> 
for more information.</p>
-
-<h3 id="in-memory-caching-fields-grouping-combo">In-memory caching + fields 
grouping combo</h3>
-
-<p>It&#39;s common to keep caches in-memory in Storm bolts. Caching becomes 
particularly powerful when you combine it with a fields grouping. For example, 
suppose you have a bolt that expands short URLs (like bit.ly, t.co, etc.) into 
long URLs. You can increase performance by keeping an LRU cache of short URL to 
long URL expansions to avoid doing the same HTTP requests over and over. 
Suppose component &quot;urls&quot; emits short URLS, and component 
&quot;expand&quot; expands short URLs into long URLs and keeps a cache 
internally. Consider the difference between the two following snippets of 
code:</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">"expand"</span><span class="o">,</span> <span class="k">new</span> 
<span class="n">ExpandUrl</span><span class="o">(),</span> <span 
class="n">parallelism</span><span class="o">)</span>
-  <span class="o">.</span><span class="na">shuffleGrouping</span><span 
class="o">(</span><span class="mi">1</span><span class="o">);</span>
-</code></pre></div><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">"expand"</span><span class="o">,</span> <span class="k">new</span> 
<span class="n">ExpandUrl</span><span class="o">(),</span> <span 
class="n">parallelism</span><span class="o">)</span>
-  <span class="o">.</span><span class="na">fieldsGrouping</span><span 
class="o">(</span><span class="s">"urls"</span><span class="o">,</span> <span 
class="k">new</span> <span class="n">Fields</span><span class="o">(</span><span 
class="s">"url"</span><span class="o">));</span>
-</code></pre></div>
-<p>The second approach will have vastly more effective caches, since the same 
URL will always go to the same task. This avoids having duplication across any 
of the caches in the tasks and makes it much more likely that a short URL will 
hit the cache.</p>
-
-<h3 id="streaming-top-n">Streaming top N</h3>
-
-<p>A common continuous computation done on Storm is a &quot;streaming top 
N&quot; of some sort. Suppose you have a bolt that emits tuples of the form 
[&quot;value&quot;, &quot;count&quot;] and you want a bolt that emits the top N 
tuples based on count. The simplest way to do this is to have a bolt that does 
a global grouping on the stream and maintains a list in memory of the top N 
items.</p>
-
-<p>This approach obviously doesn&#39;t scale to large streams since the entire 
stream has to go through one task. A better way to do the computation is to do 
many top N&#39;s in parallel across partitions of the stream, and then merge 
those top N&#39;s together to get the global top N. The pattern looks 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">"rank"</span><span class="o">,</span> <span class="k">new</span> 
<span class="n">RankObjects</span><span class="o">(),</span> <span 
class="n">parallelism</span><span class="o">)</span>
-  <span class="o">.</span><span class="na">fieldsGrouping</span><span 
class="o">(</span><span class="s">"objects"</span><span class="o">,</span> 
<span class="k">new</span> <span class="n">Fields</span><span 
class="o">(</span><span class="s">"value"</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">"merge"</span><span class="o">,</span> <span class="k">new</span> 
<span class="n">MergeObjects</span><span class="o">())</span>
-  <span class="o">.</span><span class="na">globalGrouping</span><span 
class="o">(</span><span class="s">"rank"</span><span class="o">);</span>
-</code></pre></div>
-<p>This pattern works because of the fields grouping done by the first bolt 
which gives the partitioning you need for this to be semantically correct. You 
can see an example of this pattern in storm-starter <a 
href="http://github.com/apache/storm/blob/v0.10.1/examples/storm-starter/src/jvm/storm/starter/RollingTopWords.java";>here</a>.</p>
-
-<p>If however you have a known skew in the data being processed it can be 
advantageous to use partialKeyGrouping instead of fieldsGrouping.  This will 
distribute the load for each key between two downstream bolts instead of a 
single one.</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">"count"</span><span class="o">,</span> <span class="k">new</span> 
<span class="n">CountObjects</span><span class="o">(),</span> <span 
class="n">parallelism</span><span class="o">)</span>
-  <span class="o">.</span><span class="na">partialKeyGrouping</span><span 
class="o">(</span><span class="s">"objects"</span><span class="o">,</span> 
<span class="k">new</span> <span class="n">Fields</span><span 
class="o">(</span><span class="s">"value"</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">"rank"</span> 
<span class="k">new</span> <span class="n">AggregateCountsAndRank</span><span 
class="o">(),</span> <span class="n">parallelism</span><span class="o">)</span>
-  <span class="o">.</span><span class="na">fieldsGrouping</span><span 
class="o">(</span><span class="s">"count"</span><span class="o">,</span> <span 
class="k">new</span> <span class="n">Fields</span><span class="o">(</span><span 
class="s">"key"</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">"merge"</span><span class="o">,</span> <span class="k">new</span> 
<span class="n">MergeRanksObjects</span><span class="o">())</span>
-  <span class="o">.</span><span class="na">globalGrouping</span><span 
class="o">(</span><span class="s">"rank"</span><span class="o">);</span>
-</code></pre></div>
-<p>The topology needs an extra layer of processing to aggregate the partial 
counts from the upstream bolts but this only processes aggregated values now so 
the bolt it is not subject to the load caused by the skewed data. You can see 
an example of this pattern in storm-starter <a 
href="http://github.com/apache/storm/blob/v0.10.1/examples/storm-starter/src/jvm/storm/starter/SkewedRollingTopWords.java";>here</a>.</p>
-
-<h3 
id="timecachemap-for-efficiently-keeping-a-cache-of-things-that-have-been-recently-updated">TimeCacheMap
 for efficiently keeping a cache of things that have been recently updated</h3>
-
-<p>You sometimes want to keep a cache in memory of items that have been 
recently &quot;active&quot; and have items that have been inactive for some 
time be automatically expires. <a 
href="javadocs/backtype/storm/utils/TimeCacheMap.html">TimeCacheMap</a> is an 
efficient data structure for doing this and provides hooks so you can insert 
callbacks whenever an item is expired.</p>
-
-<h3 id="coordinatedbolt-and-keyedfairbolt-for-distributed-rpc">CoordinatedBolt 
and KeyedFairBolt for Distributed RPC</h3>
-
-<p>When building distributed RPC applications on top of Storm, there are two 
common patterns that are usually needed. These are encapsulated by <a 
href="javadocs/backtype/storm/task/CoordinatedBolt.html">CoordinatedBolt</a> 
and <a href="javadocs/backtype/storm/task/KeyedFairBolt.html">KeyedFairBolt</a> 
which are part of the &quot;standard library&quot; that ships with the Storm 
codebase.</p>
-
-<p><code>CoordinatedBolt</code> wraps the bolt containing your logic and 
figures out when your bolt has received all the tuples for any given request. 
It makes heavy use of direct streams to do this.</p>
-
-<p><code>KeyedFairBolt</code> also wraps the bolt containing your logic and 
makes sure your topology processes multiple DRPC invocations at the same time, 
instead of doing them serially one at a time.</p>
-
-<p>See <a href="Distributed-RPC.html">Distributed RPC</a> for more details.</p>
-
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-<p class="post-meta"></p>
-
-<p>This page lists the main concepts of Storm and links to resources where you 
can find more information. The concepts discussed are:</p>
-
-<ol>
-<li>Topologies</li>
-<li>Streams</li>
-<li>Spouts</li>
-<li>Bolts</li>
-<li>Stream groupings</li>
-<li>Reliability</li>
-<li>Tasks</li>
-<li>Workers</li>
-</ol>
-
-<h3 id="topologies">Topologies</h3>
-
-<p>The logic for a realtime application is packaged into a Storm topology. A 
Storm topology is analogous to a MapReduce job. One key difference is that a 
MapReduce job eventually finishes, whereas a topology runs forever (or until 
you kill it, of course). A topology is a graph of spouts and bolts that are 
connected with stream groupings. These concepts are described below.</p>
-
-<p><strong>Resources:</strong></p>
-
-<ul>
-<li><a 
href="javadocs/backtype/storm/topology/TopologyBuilder.html">TopologyBuilder</a>:
 use this class to construct topologies in Java</li>
-<li><a href="Running-topologies-on-a-production-cluster.html">Running 
topologies on a production cluster</a></li>
-<li><a href="Local-mode.html">Local mode</a>: Read this to learn how to 
develop and test topologies in local mode.</li>
-</ul>
-
-<h3 id="streams">Streams</h3>
-
-<p>The stream is the core abstraction in Storm. A stream is an unbounded 
sequence of tuples that is processed and created in parallel in a distributed 
fashion. Streams are defined with a schema that names the fields in the 
stream&#39;s tuples. By default, tuples can contain integers, longs, shorts, 
bytes, strings, doubles, floats, booleans, and byte arrays. You can also define 
your own serializers so that custom types can be used natively within 
tuples.</p>
-
-<p>Every stream is given an id when declared. Since single-stream spouts and 
bolts are so common, <a 
href="javadocs/backtype/storm/topology/OutputFieldsDeclarer.html">OutputFieldsDeclarer</a>
 has convenience methods for declaring a single stream without specifying an 
id. In this case, the stream is given the default id of &quot;default&quot;.</p>
-
-<p><strong>Resources:</strong></p>
-
-<ul>
-<li><a href="javadocs/backtype/storm/tuple/Tuple.html">Tuple</a>: streams are 
composed of tuples</li>
-<li><a 
href="javadocs/backtype/storm/topology/OutputFieldsDeclarer.html">OutputFieldsDeclarer</a>:
 used to declare streams and their schemas</li>
-<li><a href="Serialization.html">Serialization</a>: Information about 
Storm&#39;s dynamic typing of tuples and declaring custom serializations</li>
-</ul>
-
-<h3 id="spouts">Spouts</h3>
-
-<p>A spout is a source of streams in a topology. Generally spouts will read 
tuples from an external source and emit them into the topology (e.g. a Kestrel 
queue or the Twitter API). Spouts can either be <strong>reliable</strong> or 
<strong>unreliable</strong>. A reliable spout is capable of replaying a tuple 
if it failed to be processed by Storm, whereas an unreliable spout forgets 
about the tuple as soon as it is emitted.</p>
-
-<p>Spouts can emit more than one stream. To do so, declare multiple streams 
using the <code>declareStream</code> method of <a 
href="javadocs/backtype/storm/topology/OutputFieldsDeclarer.html">OutputFieldsDeclarer</a>
 and specify the stream to emit to when using the <code>emit</code> method on 
<a 
href="javadocs/backtype/storm/spout/SpoutOutputCollector.html">SpoutOutputCollector</a>.</p>
-
-<p>The main method on spouts is <code>nextTuple</code>. <code>nextTuple</code> 
either emits a new tuple into the topology or simply returns if there are no 
new tuples to emit. It is imperative that <code>nextTuple</code> does not block 
for any spout implementation, because Storm calls all the spout methods on the 
same thread.</p>
-
-<p>The other main methods on spouts are <code>ack</code> and 
<code>fail</code>. These are called when Storm detects that a tuple emitted 
from the spout either successfully completed through the topology or failed to 
be completed. <code>ack</code> and <code>fail</code> are only called for 
reliable spouts. See <a href="javadocs/backtype/storm/spout/ISpout.html">the 
Javadoc</a> for more information.</p>
-
-<p><strong>Resources:</strong></p>
-
-<ul>
-<li><a href="javadocs/backtype/storm/topology/IRichSpout.html">IRichSpout</a>: 
this is the interface that spouts must implement.</li>
-<li><a href="Guaranteeing-message-processing.html">Guaranteeing message 
processing</a></li>
-</ul>
-
-<h3 id="bolts">Bolts</h3>
-
-<p>All processing in topologies is done in bolts. Bolts can do anything from 
filtering, functions, aggregations, joins, talking to databases, and more. </p>
-
-<p>Bolts can do simple stream transformations. Doing complex stream 
transformations often requires multiple steps and thus multiple bolts. For 
example, transforming a stream of tweets into a stream of trending images 
requires at least two steps: a bolt to do a rolling count of retweets for each 
image, and one or more bolts to stream out the top X images (you can do this 
particular stream transformation in a more scalable way with three bolts than 
with two). </p>
-
-<p>Bolts can emit more than one stream. To do so, declare multiple streams 
using the <code>declareStream</code> method of <a 
href="javadocs/backtype/storm/topology/OutputFieldsDeclarer.html">OutputFieldsDeclarer</a>
 and specify the stream to emit to when using the <code>emit</code> method on 
<a 
href="javadocs/backtype/storm/task/OutputCollector.html">OutputCollector</a>.</p>
-
-<p>When you declare a bolt&#39;s input streams, you always subscribe to 
specific streams of another component. If you want to subscribe to all the 
streams of another component, you have to subscribe to each one individually. 
<a href="javadocs/backtype/storm/topology/InputDeclarer.html">InputDeclarer</a> 
has syntactic sugar for subscribing to streams declared on the default stream 
id. Saying <code>declarer.shuffleGrouping(&quot;1&quot;)</code> subscribes to 
the default stream on component &quot;1&quot; and is equivalent to 
<code>declarer.shuffleGrouping(&quot;1&quot;, DEFAULT_STREAM_ID)</code>.</p>
-
-<p>The main method in bolts is the <code>execute</code> method which takes in 
as input a new tuple. Bolts emit new tuples using the <a 
href="javadocs/backtype/storm/task/OutputCollector.html">OutputCollector</a> 
object. Bolts must call the <code>ack</code> method on the 
<code>OutputCollector</code> for every tuple they process so that Storm knows 
when tuples are completed (and can eventually determine that its safe to ack 
the original spout tuples). For the common case of processing an input tuple, 
emitting 0 or more tuples based on that tuple, and then acking the input tuple, 
Storm provides an <a 
href="javadocs/backtype/storm/topology/IBasicBolt.html">IBasicBolt</a> 
interface which does the acking automatically.</p>
-
-<p>Please note that <a 
href="javadocs/backtype/storm/task/OutputCollector.html">OutputCollector</a> is 
not thread-safe, and all emits, acks, and fails must happen on the same thread. 
Please refer <a href="troubleshooting.html">Troubleshooting</a> for more 
details.</p>
-
-<p><strong>Resources:</strong></p>
-
-<ul>
-<li><a href="javadocs/backtype/storm/topology/IRichBolt.html">IRichBolt</a>: 
this is general interface for bolts.</li>
-<li><a href="javadocs/backtype/storm/topology/IBasicBolt.html">IBasicBolt</a>: 
this is a convenience interface for defining bolts that do filtering or simple 
functions.</li>
-<li><a 
href="javadocs/backtype/storm/task/OutputCollector.html">OutputCollector</a>: 
bolts emit tuples to their output streams using an instance of this class</li>
-<li><a href="Guaranteeing-message-processing.html">Guaranteeing message 
processing</a></li>
-</ul>
-
-<h3 id="stream-groupings">Stream groupings</h3>
-
-<p>Part of defining a topology is specifying for each bolt which streams it 
should receive as input. A stream grouping defines how that stream should be 
partitioned among the bolt&#39;s tasks.</p>
-
-<p>There are eight built-in stream groupings in Storm, and you can implement a 
custom stream grouping by implementing the <a 
href="javadocs/backtype/storm/grouping/CustomStreamGrouping.html">CustomStreamGrouping</a>
 interface:</p>
-
-<ol>
-<li><strong>Shuffle grouping</strong>: Tuples are randomly distributed across 
the bolt&#39;s tasks in a way such that each bolt is guaranteed to get an equal 
number of tuples.</li>
-<li><strong>Fields grouping</strong>: The stream is partitioned by the fields 
specified in the grouping. For example, if the stream is grouped by the 
&quot;user-id&quot; field, tuples with the same &quot;user-id&quot; will always 
go to the same task, but tuples with different &quot;user-id&quot;&#39;s may go 
to different tasks.</li>
-<li><strong>Partial Key grouping</strong>: The stream is partitioned by the 
fields specified in the grouping, like the Fields grouping, but are load 
balanced between two downstream bolts, which provides better utilization of 
resources when the incoming data is skewed. <a 
href="https://melmeric.files.wordpress.com/2014/11/the-power-of-both-choices-practical-load-balancing-for-distributed-stream-processing-engines.pdf";>This
 paper</a> provides a good explanation of how it works and the advantages it 
provides.</li>
-<li><strong>All grouping</strong>: The stream is replicated across all the 
bolt&#39;s tasks. Use this grouping with care.</li>
-<li><strong>Global grouping</strong>: The entire stream goes to a single one 
of the bolt&#39;s tasks. Specifically, it goes to the task with the lowest 
id.</li>
-<li><strong>None grouping</strong>: This grouping specifies that you don&#39;t 
care how the stream is grouped. Currently, none groupings are equivalent to 
shuffle groupings. Eventually though, Storm will push down bolts with none 
groupings to execute in the same thread as the bolt or spout they subscribe 
from (when possible).</li>
-<li><strong>Direct grouping</strong>: This is a special kind of grouping. A 
stream grouped this way means that the <strong>producer</strong> of the tuple 
decides which task of the consumer will receive this tuple. Direct groupings 
can only be declared on streams that have been declared as direct streams. 
Tuples emitted to a direct stream must be emitted using one of the 
[emitDirect](javadocs/backtype/storm/task/OutputCollector.html#emitDirect(int, 
int, java.util.List) methods. A bolt can get the task ids of its consumers by 
either using the provided <a 
href="javadocs/backtype/storm/task/TopologyContext.html">TopologyContext</a> or 
by keeping track of the output of the <code>emit</code> method in <a 
href="javadocs/backtype/storm/task/OutputCollector.html">OutputCollector</a> 
(which returns the task ids that the tuple was sent to).</li>
-<li><strong>Local or shuffle grouping</strong>: If the target bolt has one or 
more tasks in the same worker process, tuples will be shuffled to just those 
in-process tasks. Otherwise, this acts like a normal shuffle grouping.</li>
-</ol>
-
-<p><strong>Resources:</strong></p>
-
-<ul>
-<li><a 
href="javadocs/backtype/storm/topology/TopologyBuilder.html">TopologyBuilder</a>:
 use this class to define topologies</li>
-<li><a 
href="javadocs/backtype/storm/topology/InputDeclarer.html">InputDeclarer</a>: 
this object is returned whenever <code>setBolt</code> is called on 
<code>TopologyBuilder</code> and is used for declaring a bolt&#39;s input 
streams and how those streams should be grouped</li>
-</ul>
-
-<h3 id="reliability">Reliability</h3>
-
-<p>Storm guarantees that every spout tuple will be fully processed by the 
topology. It does this by tracking the tree of tuples triggered by every spout 
tuple and determining when that tree of tuples has been successfully completed. 
Every topology has a &quot;message timeout&quot; associated with it. If Storm 
fails to detect that a spout tuple has been completed within that timeout, then 
it fails the tuple and replays it later. </p>
-
-<p>To take advantage of Storm&#39;s reliability capabilities, you must tell 
Storm when new edges in a tuple tree are being created and tell Storm whenever 
you&#39;ve finished processing an individual tuple. These are done using the <a 
href="javadocs/backtype/storm/task/OutputCollector.html">OutputCollector</a> 
object that bolts use to emit tuples. Anchoring is done in the 
<code>emit</code> method, and you declare that you&#39;re finished with a tuple 
using the <code>ack</code> method.</p>
-
-<p>This is all explained in much more detail in <a 
href="Guaranteeing-message-processing.html">Guaranteeing message 
processing</a>. </p>
-
-<h3 id="tasks">Tasks</h3>
-
-<p>Each spout or bolt executes as many tasks across the cluster. Each task 
corresponds to one thread of execution, and stream groupings define how to send 
tuples from one set of tasks to another set of tasks. You set the parallelism 
for each spout or bolt in the <code>setSpout</code> and <code>setBolt</code> 
methods of <a 
href="javadocs/backtype/storm/topology/TopologyBuilder.html">TopologyBuilder</a>.</p>
-
-<h3 id="workers">Workers</h3>
-
-<p>Topologies execute across one or more worker processes. Each worker process 
is a physical JVM and executes a subset of all the tasks for the topology. For 
example, if the combined parallelism of the topology is 300 and 50 workers are 
allocated, then each worker will execute 6 tasks (as threads within the 
worker). Storm tries to spread the tasks evenly across all the workers.</p>
-
-<p><strong>Resources:</strong></p>
-
-<ul>
-<li><a 
href="javadocs/backtype/storm/Config.html#TOPOLOGY_WORKERS">Config.TOPOLOGY_WORKERS</a>:
 this config sets the number of workers to allocate for executing the 
topology</li>
-</ul>
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other configurations can be modified per topology. </p>
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href="http://github.com/apache/storm/blob/v0.10.1/conf/defaults.yaml";>defaults.yaml</a>
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storm.yaml in the classpath of Nimbus and the supervisors. Finally, you can 
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&quot;TOPOLOGY&quot;.</p>
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-<li>&quot;topology.max.spout.pending&quot;</li>
-<li>&quot;topology.max.task.parallelism&quot;</li>
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than the other ones, since the serializations will be available to all 
components in the topology. More details on <a 
href="Serialization.html">Serialization</a>. </li>
-</ol>
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ways:</p>
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any spout or bolt and return the component-specific configuration map.</li>
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<code>TopologyBuilder</code> return an object with methods 
<code>addConfiguration</code> and <code>addConfigurations</code> that can be 
used to override the configurations for the component.</li>
-</ol>
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-<p>The preference order for configuration values is defaults.yaml &lt; 
storm.yaml &lt; topology specific configuration &lt; internal component 
specific configuration &lt; external component specific configuration. </p>
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-<p><strong>Resources:</strong></p>
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with the &quot;Newbie&quot; label. If you&#39;re interesting in contributing to 
Storm but don&#39;t know where to begin, these are good issues to start with. 
These issues are a great way to get your feet wet with learning the codebase 
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href="https://issues.apache.org/jira/browse/STORM";>issue tracker</a> if one 
doesn&#39;t exist already</li>
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what pieces of the codebase you&#39;re going to touch and how everything is 
going to fit together.</li>
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the mailing list proposing to make it part of @stormprocessor. Then the 
community can discuss whether it&#39;s useful enough to be part of 
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