Repository: predictionio-site
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href="#">System Architecture</a><span class="spacer">&gt;</span></li><li><span 
class="last">Architecture Overview</span></li></ul></div><div 
id="page-title"><h1>System Architecture and Dependencies</h1></div></div><div 
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class="hidden-sm hidden xs"><ul><li><a href="#">System Architecture</a><span 
class="spacer">&gt;</span></li><li><span class="last">Architecture 
Overview</span></li></ul></div><div id="page-title"><h1>System Architecture and 
Dependencies</h1></div></div><div class="content"> <p>During the <a 
href="/install">installation</a>, you have installed the latest stable versions 
of the following software:</p> <ul> <li>Apache Hadoop up to 2.7.2 (required 
only if YARN and HDFS are needed)</li> <li>Apache HBase up to 1.2.4</li> 
<li>Apache Spark up to 1.6.3 for Hadoop 2.6 (not Spark 2.x version)</li> 
<li>Elasticsearch up to 1.7.5 (not the Elasticsearch 2.x version)</li> </ul> 
<p>This section explains general ru
 les-of-thumb for how they are used in PredictionIO. The actual implementation 
of the Template will define how much of this applies. PredictionIO is flexible 
about much of this configuration but its Templates generally fit the Lambda 
model for integrating real-time serving with background periodic model 
updates.</p><p><img alt="PredictionIO Systems" 
src="/images/pio-architecture-a1e2a38c.svg"/></p><p><strong>HBase</strong>: 
Event Server uses Apache HBase (or JDBC DB for small data) as the data store. 
It stores imported events. If you are not using the PredictionIO Event Server, 
you do not need to install HBase.</p><p><strong>Apache Spark</strong>: Spark is 
a large-scale data processing engine that powers the data preparation and input 
to the algorithm, training, and sometimes the serving processing. PredictionIO 
allows for different engines to be used in training but many algorithms come 
from Spark&#39;s MLlib.</p><p><strong>HDFS</strong>: is a distributed 
filesystem from Hadoop. It 
 allows storage to be shared among clustered machines. It is used to stage data 
for batch import into PIO, for export of Event Server datasets, and for storage 
of some models (see your template for details).</p><p>The output of training 
has two parts: a model and its meta-data. The model is then stored in HDFS, a 
local file system, or Elasticsearch. See the details of your 
algorithm.</p><p><strong>Elasticsearch</strong>: stores metadata such as model 
versions, engine versions, access key and app id mappings, evaluation results, 
etc. For some templates it may store the 
model.</p></div></div></div></div><footer><div class="container"><div 
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 ion Metrics</span></a></li><li class="level-2"><a class="final" 
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 inal" href="/templates/recommendation/evaluation/"><span>Evaluation 
Explained</span></a></li><li class="level-3"><a class="final" 
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class="level-3"><a class="final" 
href="/templates/recommendation/reading-custom-events/"><span>Read Custom 
Events</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/customize-data-prep/"><span>Customize Data 
Preparator</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/customize-serving/"><span>Customize 
Serving</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/training-with-implicit-preference/"><span>Train 
with Implicit Preference</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/blacklist-items/"><span>Filter Recommended 
Items by Blacklist in Query</span></a></li><li class="level-3"><a class="final" 
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 <span>Batch Persistable Evaluator</span></a></li></ul></li><li 
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href="/templates/ecommercerecommendation/how-to/"><span>How-To</span></a></li><li
 class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/train-with-rate-event/"><span>Train 
with Rate Event</span></a></li><li class="level-3"><a class="final" 
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 ss="level-3"><a class="final" 
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class="level-3"><a class="final" 
href="/templates/similarproduct/multi-events-multi-algos/"><span>Multiple 
Events and Multiple Algorithms</span></a></li><li class="level-3"><a 
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href="/templates/similarproduct/return-item-properties/"><span>Returns Item 
Properties</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/train-with-rate-event/"><span>Train with Rate 
Event</span></a></li><li class="level-3"><a class="final" 
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for Users</span></a></li><li class="level-3"><a class="final" 
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Users</span></a></li></ul></li><li class="level-2"><a class="expandible" 
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 class="level-3"><a class="final" 
href="/templates/classification/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/add-algorithm/"><span>Use Alternative 
Algorithm</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/reading-custom-properties/"><span>Read Custom 
Properties</span></a></li></ul></li></ul></li><li class="level-1"><a 
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 n>Demo Tutorials</span></a><ul><li class="level-2"><a class="final" 
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hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">System Architecture</a><span class="spacer">&gt;</span></li><li><span 
class="last">Architecture Overview</span></li></ul></div><div 
id="page-title"><h1>System Architecture and Dependencies</h1></div></div><div 
id="table
 -of-content-wrapper"><a id="edit-page-link" 
href="https://github.com/apache/predictionio/tree/livedoc/docs/manual/source/system/index.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="#">System Architecture</a><span 
class="spacer">&gt;</span></li><li><span class="last">Architecture 
Overview</span></li></ul></div><div id="page-title"><h1>System Architecture and 
Dependencies</h1></div></div><div class="content"> <p>During the <a 
href="/install">installation</a>, you have installed the latest stable versions 
of the following software:</p> <ul> <li>Apache Hadoop up to 2.7.2 (required 
only if YARN and HDFS are needed)</li> <li>Apache HBase up to 1.2.4</li> 
<li>Apache Spark up to 1.6.3 for Hadoop 2.6 (not Spark 2.x version)</li> 
<li>Elasticsearch up to 1.7.5 (not the Elasticsearch 2.x version)</li> </ul> 
<p>This section explains general rules
 -of-thumb for how they are used in PredictionIO. The actual implementation of 
the Template will define how much of this applies. PredictionIO is flexible 
about much of this configuration but its Templates generally fit the Lambda 
model for integrating real-time serving with background periodic model 
updates.</p><p><img alt="PredictionIO Systems" 
src="/images/pio-architecture-a1e2a38c.svg"/></p><p><strong>HBase</strong>: 
Event Server uses Apache HBase (or JDBC DB for small data) as the data store. 
It stores imported events. If you are not using the PredictionIO Event Server, 
you do not need to install HBase.</p><p><strong>Apache Spark</strong>: Spark is 
a large-scale data processing engine that powers the data preparation and input 
to the algorithm, training, and sometimes the serving processing. PredictionIO 
allows for different engines to be used in training but many algorithms come 
from Spark&#39;s MLlib.</p><p><strong>HDFS</strong>: is a distributed 
filesystem from Hadoop. It all
 ows storage to be shared among clustered machines. It is used to stage data 
for batch import into PIO, for export of Event Server datasets, and for storage 
of some models (see your template for details).</p><p>The output of training 
has two parts: a model and its meta-data. The model is then stored in HDFS, a 
local file system, or Elasticsearch. See the details of your 
algorithm.</p><p><strong>Elasticsearch</strong>: stores metadata such as model 
versions, engine versions, access key and app id mappings, evaluation results, 
etc. For some templates it may store the 
model.</p></div></div></div></div><footer><div class="container"><div 
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