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of number of workers; number of kafka partitions a multiple of number of spout 
parallelism</li>
-<li>Use one worker per topology per machine</li>
-<li>Start with fewer, larger aggregators, one per machine with workers on 
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-<li>Use the isolation scheduler</li>
-<li>Use one acker per worker -- 0.9 makes that the default, but earlier 
versions do not.</li>
-<li>enable GC logging; you should see very few major GCs if things are in 
reasonable shape.</li>
-<li>set the trident batch millis to about 50% of your typical end-to-end 
latency.</li>
-<li>Start with a max spout pending that is for sure too small -- one for 
trident, or the number of executors for storm -- and increase it until you stop 
seeing changes in the flow. You&#39;ll probably end up with something near 
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-<li>Do you have write access to the log directory</li>
-<li>Are you blowing out your heap?</li>
-<li>Are all the right libraries installed on all of the workers?</li>
-<li>Is the zookeeper hostname still set to localhost?</li>
-<li>Did you supply a correct, unique hostname -- one that resolves back to the 
machine -- to each worker, and put it in the storm conf file?</li>
-<li>Have you opened firewall/securitygroup permissions 
<em>bidirectionally</em> among a) all the workers, b) the storm master, c) 
zookeeper? Also, from the workers to any kafka/kestrel/database/etc that your 
topology accesses? Use netcat to poke the appropriate ports and be sure. </li>
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that you have write permissions to that directory. They are configured in the 
logback/cluster.xml (0.9) and log4j/*.properties in earlier versions.</li>
-<li><strong>final JVM settings</strong> Add the 
<code>-XX+PrintFlagsFinal</code> commandline option in the childopts (see the 
conf file)</li>
-<li><strong>final Java system properties</strong> Add <code>Properties props = 
System.getProperties(); props.list(System.out);</code> near where you build 
your topology.</li>
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number of JVM slots each supervisor will superintend. The thing you set on the 
topology is how many worker slots it will try to claim.</p>
-
-<p>There&#39;s no great reason to use more than one worker per topology per 
machine.</p>
-
-<p>With one topology running on three 8-core nodes, and parallelism hint 24, 
each bolt gets 8 executors per machine, i.e. one for each core. There are three 
big benefits to running three workers (with 8 assigned executors each) compare 
to running say 24 workers (one assigned executor each).</p>
-
-<p>First, data that is repartitioned (shuffles or group-bys) to executors in 
the same worker will not have to hit the transfer buffer. Instead, tuples are 
deposited directly from send to receive buffer. That&#39;s a big win. By 
contrast, if the destination executor were on the same machine in a different 
worker, it would have to go send -&gt; worker transfer -&gt; local socket -&gt; 
worker recv -&gt; exec recv buffer. It doesn&#39;t hit the network card, but 
it&#39;s not as big a win as when executors are in the same worker.</p>
-
-<p>Second, you&#39;re typically better off with three aggregators having very 
large backing cache than having twenty-four aggregators having small backing 
caches. This reduces the effect of skew, and improves LRU efficiency.</p>
-
-<p>Lastly, fewer workers reduces control flow chatter.</p>
-
-<h2 id="topology">Topology</h2>
-
-<h3 id="can-a-trident-topology-have-multiple-streams">Can a Trident topology 
have Multiple Streams?</h3>
-
-<blockquote>
-<p>Can a Trident Topology work like a workflow with conditional paths 
(if-else)? e.g. A Spout (S1) connects to a bolt (B0) which based on certain 
values in the incoming tuple routes them to either bolt (B1) or bolt (B2) but 
not both.</p>
-</blockquote>
-
-<p>A Trident &quot;each&quot; operator returns a Stream object, which you can 
store in a variable. You can then run multiple eaches on the same Stream to 
split it, e.g.: </p>
-<div class="highlight"><pre><code class="language-" data-lang="">    Stream s 
= topology.each(...).groupBy(...).aggregate(...) 
-    Stream branch1 = s.each(..., FilterA) 
-    Stream branch2 = s.each(..., FilterB) 
-</code></pre></div>
-<p>You can join streams with join, merge or multiReduce.</p>
-
-<p>At time of writing, you can&#39;t emit to multiple output streams from 
Trident -- see <a 
href="https://issues.apache.org/jira/browse/STORM-68";>STORM-68</a></p>
-
-<h2 id="spouts">Spouts</h2>
-
-<h3 id="what-is-a-coordinator-and-why-are-there-several">What is a 
coordinator, and why are there several?</h3>
-
-<p>A trident-spout is actually run within a storm <em>bolt</em>. The 
storm-spout of a trident topology is the MasterBatchCoordinator -- it 
coordinates trident batches and is the same no matter what spouts you use. A 
batch is born when the MBC dispenses a seed tuple to each of the 
spout-coordinators. The spout-coordinator bolts know how your particular spouts 
should cooperate -- so in the kafka case, it&#39;s what helps figure out what 
partition and offset range each spout should pull from.</p>
-
-<h3 id="what-can-i-store-into-the-spouts-metadata-record">What can I store 
into the spout&#39;s metadata record?</h3>
-
-<p>You should only store static data, and as little of it as possible, into 
the metadata record (note: maybe you <em>can</em> store more interesting 
things; you shouldn&#39;t, though)</p>
-
-<h3 id="how-often-is-the-emitpartitionbatchnew-function-called">How often is 
the &#39;emitPartitionBatchNew&#39; function called?</h3>
-
-<p>Since the MBC is the actual spout, all the tuples in a batch are just 
members of its tupletree. That means storm&#39;s &quot;max spout pending&quot; 
config effectively defines the number of concurrent batches trident runs. The 
MBC emits a new batch if it has fewer than max-spending tuples pending and if 
at least one <a 
href="https://github.com/apache/storm/blob/master/conf/defaults.yaml#L115";>trident
 batch interval</a>&#39;s worth of seconds has passed since the last batch.</p>
-
-<h3 id="if-nothing-was-emitted-does-trident-slow-down-the-calls">If nothing 
was emitted does Trident slow down the calls?</h3>
-
-<p>Yes, there&#39;s a pluggable &quot;spout wait strategy&quot;; the default 
is to sleep for a <a 
href="https://github.com/apache/storm/blob/master/conf/defaults.yaml#L110";>configurable
 amount of time</a></p>
-
-<h3 id="ok-then-what-is-the-trident-batch-interval-for">OK, then what is the 
trident batch interval for?</h3>
-
-<p>You know how computers of the 486 era had a <a 
href="http://en.wikipedia.org/wiki/Turbo_button";>turbo button</a> on them? 
It&#39;s like that. </p>
-
-<p>Actually, it has two practical uses. One is to throttle spouts that poll a 
remote source without throttling processing. For example, we have a spout that 
looks in a given S3 bucket for a new batch-uploaded file to read, linebreak and 
emit. We don&#39;t want it hitting S3 more than every few seconds: files 
don&#39;t show up more than once every few minutes, and a batch takes a few 
seconds to process.</p>
-
-<p>The other is to limit overpressure on the internal queues during startup or 
under a heavy burst load -- if the spouts spring to life and suddenly jam ten 
batches&#39; worth of records into the system, you could have a mass of 
less-urgent tuples from batch 7 clog up the transfer buffer and prevent the 
$commit tuple from batch 3 to get through (or even just the regular old tuples 
from batch 3). What we do is set the trident batch interval to about half the 
typical end-to-end processing latency -- if it takes 600ms to process a batch, 
it&#39;s OK to only kick off a batch every 300ms.</p>
-
-<p>Note that this is a cap, not an additional delay -- with a period of 300ms, 
if your batch takes 258ms Trident will only delay an additional 42ms.</p>
-
-<h3 id="how-do-you-set-the-batch-size">How do you set the batch size?</h3>
-
-<p>Trident doesn&#39;t place its own limits on the batch count. In the case of 
the Kafka spout, the max fetch bytes size divided by the average record size 
defines an effective records per subbatch partition.</p>
-
-<h3 id="how-do-i-resize-a-batch">How do I resize a batch?</h3>
-
-<p>The trident batch is a somewhat overloaded facility. Together with the 
number of partitions, the batch size is constrained by or serves to define</p>
-
-<ol>
-<li>the unit of transactional safety (tuples at risk vs time)</li>
-<li>per partition, an effective windowing mechanism for windowed stream 
analytics</li>
-<li>per partition, the number of simultaneous queries that will be made by a 
partitionQuery, partitionPersist, etc;</li>
-<li>per partition, the number of records convenient for the spout to dispatch 
at the same time;</li>
-</ol>
-
-<p>You can&#39;t change the overall batch size once generated, but you can 
change the number of partitions -- do a shuffle and then change the parallelism 
hint</p>
-
-<h2 id="time-series">Time Series</h2>
-
-<h3 id="how-do-i-aggregate-events-by-time">How do I aggregate events by 
time?</h3>
-
-<p>If have records with an immutable timestamp, and you would like to count, 
average or otherwise aggregate them into discrete time buckets, Trident is an 
excellent and scalable solution.</p>
-
-<p>Write an <code>Each</code> function that turns the timestamp into a time 
bucket: if the bucket size was &quot;by hour&quot;, then the timestamp 
<code>2013-08-08 12:34:56</code> would be mapped to the <code>2013-08-08 
12:00:00</code> time bucket, and so would everything else in the twelve 
o&#39;clock hour. Then group on that timebucket and use a grouped 
persistentAggregate. The persistentAggregate uses a local cacheMap backed by a 
data store. Groups with many records require very few reads from the data 
store, and use efficient bulk reads and writes; as long as your data feed is 
relatively prompt Trident will make very efficient use of memory and network. 
Even if a server drops off line for a day, then delivers that full day&#39;s 
worth of data in a rush, the old results will be calmly retrieved and updated 
-- and without interfering with calculating the current results.</p>
-
-<h3 
id="how-can-i-know-that-all-records-for-a-time-bucket-have-been-received">How 
can I know that all records for a time bucket have been received?</h3>
-
-<p>You cannot know that all events are collected -- this is an epistemological 
challenge, not a distributed systems challenge. You can:</p>
-
-<ul>
-<li>Set a time limit using domain knowledge</li>
-<li>Introduce a <em>punctuation</em>: a record known to come after all records 
in the given time bucket. Trident uses this scheme to know when a batch is 
complete. If you for instance receive records from a set of sensors, each in 
order for that sensor, then once all sensors have sent you a 3:02:xx or later 
timestamp lets you know you can commit. </li>
-<li>When possible, make your process incremental: each value that comes in 
makes the answer more an more true. A Trident ReducerAggregator is an operator 
that takes a prior result and a set of new records and returns a new result. 
This lets the result be cached and serialized to a datastore; if a server drops 
off line for a day and then comes back with a full day&#39;s worth of data in a 
rush, the old results will be calmly retrieved and updated.</li>
-<li>Lambda architecture: Record all events into an archival store (S3, HBase, 
HDFS) on receipt. in the fast layer, once the time window is clear, process the 
bucket to get an actionable answer, and ignore everything older than the time 
window. Periodically run a global aggregation to calculate a 
&quot;correct&quot; answer.</li>
-</ul>
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-    <h1 class="page-title">Guaranteeing Message Processing</h1>
-          <div class="row">
-               <div class="col-md-12">
-                    <!-- Documentation -->
-
-<p class="post-meta"></p>
-
-<p>Storm guarantees that each message coming off a spout will be fully 
processed. This page describes how Storm accomplishes this guarantee and what 
you have to do as a user to benefit from Storm&#39;s reliability 
capabilities.</p>
-
-<h3 id="what-does-it-mean-for-a-message-to-be-fully-processed">What does it 
mean for a message to be &quot;fully processed&quot;?</h3>
-
-<p>A tuple coming off a spout can trigger thousands of tuples to be created 
based on it. Consider, for example, the streaming word count topology:</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">KestrelSpout</span><span class="o">(</span><span 
class="s">"kestrel.backtype.com"</span><span class="o">,</span>
-                                               <span 
class="mi">22133</span><span class="o">,</span>
-                                               <span 
class="s">"sentence_queue"</span><span class="o">,</span>
-                                               <span class="k">new</span> 
<span class="nf">StringScheme</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">10</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">20</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>This topology reads sentences off of a Kestrel queue, splits the sentences 
into its constituent words, and then emits for each word the number of times it 
has seen that word before. A tuple coming off the spout triggers many tuples 
being created based on it: a tuple for each word in the sentence and a tuple 
for the updated count for each word. The tree of messages looks something like 
this:</p>
-
-<p><img src="images/tuple_tree.png" alt="Tuple tree"></p>
-
-<p>Storm considers a tuple coming off a spout &quot;fully processed&quot; when 
the tuple tree has been exhausted and every message in the tree has been 
processed. A tuple is considered failed when its tree of messages fails to be 
fully processed within a specified timeout. This timeout can be configured on a 
topology-specific basis using the <a 
href="/javadoc/apidocs/backtype/storm/Config.html#TOPOLOGY_MESSAGE_TIMEOUT_SECS">Config.TOPOLOGY_MESSAGE_TIMEOUT_SECS</a>
 configuration and defaults to 30 seconds.</p>
-
-<h3 
id="what-happens-if-a-message-is-fully-processed-or-fails-to-be-fully-processed">What
 happens if a message is fully processed or fails to be fully processed?</h3>
-
-<p>To understand this question, let&#39;s take a look at the lifecycle of a 
tuple coming off of a spout. For reference, here is the interface that spouts 
implement (see the <a 
href="/javadoc/apidocs/backtype/storm/spout/ISpout.html">Javadoc</a> for more 
information):</p>
-<div class="highlight"><pre><code class="language-java" data-lang="java"><span 
class="kd">public</span> <span class="kd">interface</span> <span 
class="nc">ISpout</span> <span class="kd">extends</span> <span 
class="n">Serializable</span> <span class="o">{</span>
-    <span class="kt">void</span> <span class="nf">open</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">SpoutOutputCollector</span> <span class="n">collector</span><span 
class="o">);</span>
-    <span class="kt">void</span> <span class="nf">close</span><span 
class="o">();</span>
-    <span class="kt">void</span> <span class="nf">nextTuple</span><span 
class="o">();</span>
-    <span class="kt">void</span> <span class="nf">ack</span><span 
class="o">(</span><span class="n">Object</span> <span 
class="n">msgId</span><span class="o">);</span>
-    <span class="kt">void</span> <span class="nf">fail</span><span 
class="o">(</span><span class="n">Object</span> <span 
class="n">msgId</span><span class="o">);</span>
-<span class="o">}</span>
-</code></pre></div>
-<p>First, Storm requests a tuple from the <code>Spout</code> by calling the 
<code>nextTuple</code> method on the <code>Spout</code>. The <code>Spout</code> 
uses the <code>SpoutOutputCollector</code> provided in the <code>open</code> 
method to emit a tuple to one of its output streams. When emitting a tuple, the 
<code>Spout</code> provides a &quot;message id&quot; that will be used to 
identify the tuple later. For example, the <code>KestrelSpout</code> reads a 
message off of the kestrel queue and emits as the &quot;message id&quot; the id 
provided by Kestrel for the message. Emitting a message to the 
<code>SpoutOutputCollector</code> looks like this:</p>
-<div class="highlight"><pre><code class="language-java" data-lang="java"><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="s">"field1"</span><span class="o">,</span> <span 
class="s">"field2"</span><span class="o">,</span> <span 
class="mi">3</span><span class="o">)</span> <span class="o">,</span> <span 
class="n">msgId</span><span class="o">);</span>
-</code></pre></div>
-<p>Next, the tuple gets sent to consuming bolts and Storm takes care of 
tracking the tree of messages that is created. If Storm detects that a tuple is 
fully processed, Storm will call the <code>ack</code> method on the originating 
<code>Spout</code> task with the message id that the <code>Spout</code> 
provided to Storm. Likewise, if the tuple times-out Storm will call the 
<code>fail</code> method on the <code>Spout</code>. Note that a tuple will be 
acked or failed by the exact same <code>Spout</code> task that created it. So 
if a <code>Spout</code> is executing as many tasks across the cluster, a tuple 
won&#39;t be acked or failed by a different task than the one that created 
it.</p>
-
-<p>Let&#39;s use <code>KestrelSpout</code> again to see what a 
<code>Spout</code> needs to do to guarantee message processing. When 
<code>KestrelSpout</code> takes a message off the Kestrel queue, it 
&quot;opens&quot; the message. This means the message is not actually taken off 
the queue yet, but instead placed in a &quot;pending&quot; state waiting for 
acknowledgement that the message is completed. While in the pending state, a 
message will not be sent to other consumers of the queue. Additionally, if a 
client disconnects all pending messages for that client are put back on the 
queue. When a message is opened, Kestrel provides the client with the data for 
the message as well as a unique id for the message. The 
<code>KestrelSpout</code> uses that exact id as the &quot;message id&quot; for 
the tuple when emitting the tuple to the <code>SpoutOutputCollector</code>. 
Sometime later on, when <code>ack</code> or <code>fail</code> are called on the 
<code>KestrelSpout</code>, the <code>Kes
 trelSpout</code> sends an ack or fail message to Kestrel with the message id 
to take the message off the queue or have it put back on.</p>
-
-<h3 id="what-is-storms-reliability-api">What is Storm&#39;s reliability 
API?</h3>
-
-<p>There&#39;s two things you have to do as a user to benefit from Storm&#39;s 
reliability capabilities. First, you need to tell Storm whenever you&#39;re 
creating a new link in the tree of tuples. Second, you need to tell Storm when 
you have finished processing an individual tuple. By doing both these things, 
Storm can detect when the tree of tuples is fully processed and can ack or fail 
the spout tuple appropriately. Storm&#39;s API provides a concise way of doing 
both of these tasks. </p>
-
-<p>Specifying a link in the tuple tree is called <em>anchoring</em>. Anchoring 
is done at the same time you emit a new tuple. Let&#39;s use the following bolt 
as an example. This bolt splits a tuple containing a sentence into a tuple for 
each word:</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">SplitSentence</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="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="n">_collector</span> <span class="o">=</span> <span 
class="n">collector</span><span class="o">;</span>
-        <span class="o">}</span>
-
-        <span class="kd">public</span> <span class="kt">void</span> <span 
class="nf">execute</span><span class="o">(</span><span class="n">Tuple</span> 
<span class="n">tuple</span><span class="o">)</span> <span class="o">{</span>
-            <span class="n">String</span> <span class="n">sentence</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="k">for</span><span class="o">(</span><span 
class="n">String</span> <span class="nl">word:</span> <span 
class="n">sentence</span><span class="o">.</span><span 
class="na">split</span><span class="o">(</span><span class="s">" "</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">word</span><span 
class="o">));</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="kd">public</span> <span class="kt">void</span> <span 
class="nf">declareOutputFields</span><span class="o">(</span><span 
class="n">OutputFieldsDeclarer</span> <span class="n">declarer</span><span 
class="o">)</span> <span class="o">{</span>
-            <span class="n">declarer</span><span class="o">.</span><span 
class="na">declare</span><span class="o">(</span><span class="k">new</span> 
<span class="n">Fields</span><span class="o">(</span><span 
class="s">"word"</span><span class="o">));</span>
-        <span class="o">}</span>        
-    <span class="o">}</span>
-</code></pre></div>
-<p>Each word tuple is <em>anchored</em> by specifying the input tuple as the 
first argument to <code>emit</code>. Since the word tuple is anchored, the 
spout tuple at the root of the tree will be replayed later on if the word tuple 
failed to be processed downstream. In contrast, let&#39;s look at what happens 
if the word tuple is emitted like this:</p>
-<div class="highlight"><pre><code class="language-java" data-lang="java"><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>
-</code></pre></div>
-<p>Emitting the word tuple this way causes it to be <em>unanchored</em>. If 
the tuple fails be processed downstream, the root tuple will not be replayed. 
Depending on the fault-tolerance guarantees you need in your topology, 
sometimes it&#39;s appropriate to emit an unanchored tuple.</p>
-
-<p>An output tuple can be anchored to more than one input tuple. This is 
useful when doing streaming joins or aggregations. A multi-anchored tuple 
failing to be processed will cause multiple tuples to be replayed from the 
spouts. Multi-anchoring is done by specifying a list of tuples rather than just 
a single tuple. For example:</p>
-<div class="highlight"><pre><code class="language-java" data-lang="java"><span 
class="n">List</span><span class="o">&lt;</span><span 
class="n">Tuple</span><span class="o">&gt;</span> <span 
class="n">anchors</span> <span class="o">=</span> <span class="k">new</span> 
<span class="n">ArrayList</span><span class="o">&lt;</span><span 
class="n">Tuple</span><span class="o">&gt;();</span>
-<span class="n">anchors</span><span class="o">.</span><span 
class="na">add</span><span class="o">(</span><span class="n">tuple1</span><span 
class="o">);</span>
-<span class="n">anchors</span><span class="o">.</span><span 
class="na">add</span><span class="o">(</span><span class="n">tuple2</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">anchors</span><span class="o">,</span> <span class="k">new</span> 
<span class="n">Values</span><span class="o">(</span><span 
class="mi">1</span><span class="o">,</span> <span class="mi">2</span><span 
class="o">,</span> <span class="mi">3</span><span class="o">));</span>
-</code></pre></div>
-<p>Multi-anchoring adds the output tuple into multiple tuple trees. Note that 
it&#39;s also possible for multi-anchoring to break the tree structure and 
create tuple DAGs, like so:</p>
-
-<p><img src="images/tuple-dag.png" alt="Tuple DAG"></p>
-
-<p>Storm&#39;s implementation works for DAGs as well as trees (pre-release it 
only worked for trees, and the name &quot;tuple tree&quot; stuck).</p>
-
-<p>Anchoring is how you specify the tuple tree -- the next and final piece to 
Storm&#39;s reliability API is specifying when you&#39;ve finished processing 
an individual tuple in the tuple tree. This is done by using the 
<code>ack</code> and <code>fail</code> methods on the 
<code>OutputCollector</code>. If you look back at the 
<code>SplitSentence</code> example, you can see that the input tuple is acked 
after all the word tuples are emitted.</p>
-
-<p>You can use the <code>fail</code> method on the 
<code>OutputCollector</code> to immediately fail the spout tuple at the root of 
the tuple tree. For example, your application may choose to catch an exception 
from a database client and explicitly fail the input tuple. By failing the 
tuple explicitly, the spout tuple can be replayed faster than if you waited for 
the tuple to time-out.</p>
-
-<p>Every tuple you process must be acked or failed. Storm uses memory to track 
each tuple, so if you don&#39;t ack/fail every tuple, the task will eventually 
run out of memory. </p>
-
-<p>A lot of bolts follow a common pattern of reading an input tuple, emitting 
tuples based on it, and then acking the tuple at the end of the 
<code>execute</code> method. These bolts fall into the categories of filters 
and simple functions. Storm has an interface called <code>BasicBolt</code> that 
encapsulates this pattern for you. The <code>SplitSentence</code> example can 
be written as a <code>BasicBolt</code> like follows:</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">SplitSentence</span> <span class="kd">extends</span> <span 
class="n">BaseBasicBolt</span> <span class="o">{</span>
-        <span class="kd">public</span> <span class="kt">void</span> <span 
class="nf">execute</span><span class="o">(</span><span class="n">Tuple</span> 
<span class="n">tuple</span><span class="o">,</span> <span 
class="n">BasicOutputCollector</span> <span class="n">collector</span><span 
class="o">)</span> <span class="o">{</span>
-            <span class="n">String</span> <span class="n">sentence</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="k">for</span><span class="o">(</span><span 
class="n">String</span> <span class="nl">word:</span> <span 
class="n">sentence</span><span class="o">.</span><span 
class="na">split</span><span class="o">(</span><span class="s">" "</span><span 
class="o">))</span> <span class="o">{</span>
-                <span class="n">collector</span><span class="o">.</span><span 
class="na">emit</span><span class="o">(</span><span class="k">new</span> <span 
class="n">Values</span><span class="o">(</span><span class="n">word</span><span 
class="o">));</span>
-            <span class="o">}</span>
-        <span class="o">}</span>
-
-        <span class="kd">public</span> <span class="kt">void</span> <span 
class="nf">declareOutputFields</span><span class="o">(</span><span 
class="n">OutputFieldsDeclarer</span> <span class="n">declarer</span><span 
class="o">)</span> <span class="o">{</span>
-            <span class="n">declarer</span><span class="o">.</span><span 
class="na">declare</span><span class="o">(</span><span class="k">new</span> 
<span class="n">Fields</span><span class="o">(</span><span 
class="s">"word"</span><span class="o">));</span>
-        <span class="o">}</span>        
-    <span class="o">}</span>
-</code></pre></div>
-<p>This implementation is simpler than the implementation from before and is 
semantically identical. Tuples emitted to <code>BasicOutputCollector</code> are 
automatically anchored to the input tuple, and the input tuple is acked for you 
automatically when the execute method completes.</p>
-
-<p>In contrast, bolts that do aggregations or joins may delay acking a tuple 
until after it has computed a result based on a bunch of tuples. Aggregations 
and joins will commonly multi-anchor their output tuples as well. These things 
fall outside the simpler pattern of <code>IBasicBolt</code>.</p>
-
-<h3 
id="how-do-i-make-my-applications-work-correctly-given-that-tuples-can-be-replayed">How
 do I make my applications work correctly given that tuples can be 
replayed?</h3>
-
-<p>As always in software design, the answer is &quot;it depends.&quot; Storm 
0.7.0 introduced the &quot;transactional topologies&quot; feature, which 
enables you to get fully fault-tolerant exactly-once messaging semantics for 
most computations. Read more about transactional topologies <a 
href="Transactional-topologies.html">here</a>. </p>
-
-<h3 id="how-does-storm-implement-reliability-in-an-efficient-way">How does 
Storm implement reliability in an efficient way?</h3>
-
-<p>A Storm topology has a set of special &quot;acker&quot; tasks that track 
the DAG of tuples for every spout tuple. When an acker sees that a DAG is 
complete, it sends a message to the spout task that created the spout tuple to 
ack the message. You can set the number of acker tasks for a topology in the 
topology configuration using <a 
href="/javadoc/apidocs/backtype/storm/Config.html#TOPOLOGY_ACKERS">Config.TOPOLOGY_ACKERS</a>.
 Storm defaults TOPOLOGY_ACKERS to one task -- you will need to increase this 
number for topologies processing large amounts of messages.</p>
-
-<p>The best way to understand Storm&#39;s reliability implementation is to 
look at the lifecycle of tuples and tuple DAGs. When a tuple is created in a 
topology, whether in a spout or a bolt, it is given a random 64 bit id. These 
ids are used by ackers to track the tuple DAG for every spout tuple.</p>
-
-<p>Every tuple knows the ids of all the spout tuples for which it exists in 
their tuple trees. When you emit a new tuple in a bolt, the spout tuple ids 
from the tuple&#39;s anchors are copied into the new tuple. When a tuple is 
acked, it sends a message to the appropriate acker tasks with information about 
how the tuple tree changed. In particular it tells the acker &quot;I am now 
completed within the tree for this spout tuple, and here are the new tuples in 
the tree that were anchored to me&quot;. </p>
-
-<p>For example, if tuples &quot;D&quot; and &quot;E&quot; were created based 
on tuple &quot;C&quot;, here&#39;s how the tuple tree changes when 
&quot;C&quot; is acked: </p>
-
-<p><img src="images/ack_tree.png" alt="What happens on an ack"></p>
-
-<p>Since &quot;C&quot; is removed from the tree at the same time that 
&quot;D&quot; and &quot;E&quot; are added to it, the tree can never be 
prematurely completed.</p>
-
-<p>There are a few more details to how Storm tracks tuple trees. As mentioned 
already, you can have an arbitrary number of acker tasks in a topology. This 
leads to the following question: when a tuple is acked in the topology, how 
does it know to which acker task to send that information? </p>
-
-<p>Storm uses mod hashing to map a spout tuple id to an acker task. Since 
every tuple carries with it the spout tuple ids of all the trees they exist 
within, they know which acker tasks to communicate with. </p>
-
-<p>Another detail of Storm is how the acker tasks track which spout tasks are 
responsible for each spout tuple they&#39;re tracking. When a spout task emits 
a new tuple, it simply sends a message to the appropriate acker telling it that 
its task id is responsible for that spout tuple. Then when an acker sees a tree 
has been completed, it knows to which task id to send the completion 
message.</p>
-
-<p>Acker tasks do not track the tree of tuples explicitly. For large tuple 
trees with tens of thousands of nodes (or more), tracking all the tuple trees 
could overwhelm the memory used by the ackers. Instead, the ackers take a 
different strategy that only requires a fixed amount of space per spout tuple 
(about 20 bytes). This tracking algorithm is the key to how Storm works and is 
one of its major breakthroughs.</p>
-
-<p>An acker task stores a map from a spout tuple id to a pair of values. The 
first value is the task id that created the spout tuple which is used later on 
to send completion messages. The second value is a 64 bit number called the 
&quot;ack val&quot;. The ack val is a representation of the state of the entire 
tuple tree, no matter how big or how small.  It is simply the xor of all tuple 
ids that have been created and/or acked in the tree.</p>
-
-<p>When an acker task sees that an &quot;ack val&quot; has become 0, then it 
knows that the tuple tree is completed. Since tuple ids are random 64 bit 
numbers, the chances of an &quot;ack val&quot; accidentally becoming 0 is 
extremely small. If you work the math, at 10K acks per second, it will take 
50,000,000 years until a mistake is made. And even then, it will only cause 
data loss if that tuple happens to fail in the topology.</p>
-
-<p>Now that you understand the reliability algorithm, let&#39;s go over all 
the failure cases and see how in each case Storm avoids data loss:</p>
-
-<ul>
-<li><strong>A tuple isn&#39;t acked because the task died</strong>: In this 
case the spout tuple ids at the root of the trees for the failed tuple will 
time out and be replayed.</li>
-<li><strong>Acker task dies</strong>: In this case all the spout tuples the 
acker was tracking will time out and be replayed.</li>
-<li><strong>Spout task dies</strong>: In this case the source that the spout 
talks to is responsible for replaying the messages. For example, queues like 
Kestrel and RabbitMQ will place all pending messages back on the queue when a 
client disconnects.</li>
-</ul>
-
-<p>As you have seen, Storm&#39;s reliability mechanisms are completely 
distributed, scalable, and fault-tolerant. </p>
-
-<h3 id="tuning-reliability">Tuning reliability</h3>
-
-<p>Acker tasks are lightweight, so you don&#39;t need very many of them in a 
topology. You can track their performance through the Storm UI (component id 
&quot;__acker&quot;). If the throughput doesn&#39;t look right, you&#39;ll need 
to add more acker tasks. </p>
-
-<p>If reliability isn&#39;t important to you -- that is, you don&#39;t care 
about losing tuples in failure situations -- then you can improve performance 
by not tracking the tuple tree for spout tuples. Not tracking a tuple tree 
halves the number of messages transferred since normally there&#39;s an ack 
message for every tuple in the tuple tree. Additionally, it requires fewer ids 
to be kept in each downstream tuple, reducing bandwidth usage.</p>
-
-<p>There are three ways to remove reliability. The first is to set 
Config.TOPOLOGY_ACKERS to 0. In this case, Storm will call the <code>ack</code> 
method on the spout immediately after the spout emits a tuple. The tuple tree 
won&#39;t be tracked.</p>
-
-<p>The second way is to remove reliability on a message by message basis. You 
can turn off tracking for an individual spout tuple by omitting a message id in 
the <code>SpoutOutputCollector.emit</code> method.</p>
-
-<p>Finally, if you don&#39;t care if a particular subset of the tuples 
downstream in the topology fail to be processed, you can emit them as 
unanchored tuples. Since they&#39;re not anchored to any spout tuples, they 
won&#39;t cause any spout tuples to fail if they aren&#39;t acked.</p>
-
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