Author: dmagda
Date: Thu Feb 8 22:02:38 2018
New Revision: 1823603
URL: http://svn.apache.org/viewvc?rev=1823603&view=rev
Log:
completed collocated processing page
Modified:
ignite/site/branches/ignite-7061/collocatedprocessing.html
ignite/site/branches/ignite-7061/features/machinelearning.html
ignite/site/branches/ignite-7061/features/transactions.html
ignite/site/branches/ignite-7061/includes/header.html
Modified: ignite/site/branches/ignite-7061/collocatedprocessing.html
URL:
http://svn.apache.org/viewvc/ignite/site/branches/ignite-7061/collocatedprocessing.html?rev=1823603&r1=1823602&r2=1823603&view=diff
==============================================================================
--- ignite/site/branches/ignite-7061/collocatedprocessing.html (original)
+++ ignite/site/branches/ignite-7061/collocatedprocessing.html Thu Feb 8
22:02:38 2018
@@ -52,7 +52,7 @@ under the License.
<main id="main" role="main" class="container">
<section id="memory-centric" class="page-section">
- <h1 class="first">What is Collocated Processing?</h1>
+ <h1 class="first">Collocated Processing</h1>
<div class="col-sm-12 col-md-12 col-xs-12" style="padding:0 0 20px
0;">
<div class="col-sm-6 col-md-6 col-xs-12"
style="padding-left:0; padding-right:0">
<p>
@@ -122,7 +122,7 @@ under the License.
collocated data. Especially it is crucial for distributed
JOINs supported by Ignite.
</p>
<p>
- Taking the two tables created above as an example, let's get
the most inhabited cities across China,
+ Taking the example of the two tables created above, let's get
the most inhabited cities across China,
Russia and the USA joining the data stored in the
<code>Country</code> and <code>City</code> tables, as follows:
</p>
<div class="tab-content">
@@ -139,20 +139,28 @@ under the License.
</div>
</div>
<p>
- Since all the data was collocated beforehand, Ignite will
execute the query with the JOIN in the collocated
- mode
+ Since all the cities were collocated with their countries
beforehand, Ignite will execute the query and
+ join the data on concrete nodes that store China, Russia and
the USA entries. This approach, again, <i>avoids</i>
+ expensive data movement across the network, needed to complete
the query, because all the data will be available
+ on the nodes locally.
</p>
- <div class="page-heading">Usage Example</div>
+ <div class="page-heading">Distributed Collocated Computations</div>
<p>
- Let's assume that a blizzard is approaching New York. As a
telecommunication company, you have to
- send a warning text message to 8 million New Yorkers.
+ Apache Ignite compute grid and machine learning component
allows to perform computations and execute
+ machine learning algorithms in parallel fashion to gain high
performance, low latency, and linear scalability.
+ Furthermore, both components highly rely on data collocation
(and collocated processing in general) giving
+ the ways to optimize specific tasks or calculations.
+ </p>
+ <p>
+ For instance, let's assume that a blizzard is approaching New
York. As a telecommunication company,
+ you have to send a warning text message to 8 million New
Yorkers.
With the client-server approach the company has to move all
<nobr>8 million (!)</nobr> records
from the database to the client text messaging application,
which does not scale.
</p>
<p>
- A much more efficient approach would be to send the
text-messaging logic to the cluster node responsible
- for storing the New York residents. This approach moves only 1
computation instead of 8 million records
- across the network, and performs a lot better.
+ A much more efficient approach would be to send the
text-messaging logic, implemented with compute grid,
+ to the cluster node responsible for storing the New York
residents.
+ This approach moves only 1 computation instead of 8 million
records across the network, and performs a lot better.
</p>
<p>
@@ -205,9 +213,75 @@ ignite.compute().affinityRun("City", new
</pre>
</div>
</div>
- <p>
- Check more <a
href="https://apacheignite.readme.io/docs/affinity-collocation";
target="_blank">here</a>.
- </p>
+
+ <div class="page-heading">More on Collocated Processing</div>
+ <table class="formatted" name="More on Ignite Transactions">
+ <thead>
+ <tr>
+ <th width="35%" class="left">Feature</th>
+ <th>Description</th>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td class="features-left">Affinity Collocation</td>
+ <td>
+ <p>
+ If business logic requires to access more than one
entry it can be reasonable to
+ collocate dependent entries by storing them on a
single cluster node:
+ </p>
+ <div class="page-links">
+ <a
href="https://apacheignite.readme.io/docs/affinity-collocation";
target="docs">Docs for this feature <i class="fa fa-angle-double-right"></i></a>
+ </div>
+ </td>
+ </tr>
+ <tr>
+ <td class="features-left">Collocated Computations</td>
+ <td>
+ <p>
+ It is also possible to route computations to the
nodes where the data is stored:
+ </p>
+ <div class="page-links">
+ <a
href="https://apacheignite.readme.io/docs/collocate-compute-and-data";
target="docs">Docs for this feature <i class="fa fa-angle-double-right"></i></a>
+ </div>
+ </td>
+ </tr>
+ <tr>
+ <td class="features-left">Compute Grid</td>
+ <td>
+ <p>
+ Distributed computations are performed in parallel
fashion to gain high performance, low latency, and linear scalability:
+ </p>
+ <div class="page-links">
+ <a
href="https://apacheignite.readme.io/docs/compute-grid"; target="docs">Docs for
this feature <i class="fa fa-angle-double-right"></i></a>
+ </div>
+ </td>
+ </tr>
+ <tr>
+ <td class="features-left">Distributed JOINs</td>
+ <td>
+ <p>
+ Ignite supports collocated and non-collocated
distributed SQL joins:
+ </p>
+ <div class="page-links">
+ <a
href="https://apacheignite-sql.readme.io/docs/distributed-joins";
target="docs">Docs for this feature <i class="fa fa-angle-double-right"></i></a>
+ </div>
+ </td>
+ </tr>
+ <tr>
+ <td class="features-left">Machine Learning</td>
+ <td>
+ <p>
+ Ignite machine learning component allows users to
run ML/DL training and inference directly
+ on the data stored in an Ignite cluster and
provides ML and DL algorithms:
+ </p>
+ <div class="page-links">
+ <a
href="https://apacheignite.readme.io/docs/machine-learning"; target="docs">Docs
for this feature <i class="fa fa-angle-double-right"></i></a>
+ </div>
+ </td>
+ </tr>
+ </tbody>
+ </table>
</section>
</main>
Modified: ignite/site/branches/ignite-7061/features/machinelearning.html
URL:
http://svn.apache.org/viewvc/ignite/site/branches/ignite-7061/features/machinelearning.html?rev=1823603&r1=1823602&r2=1823603&view=diff
==============================================================================
--- ignite/site/branches/ignite-7061/features/machinelearning.html (original)
+++ ignite/site/branches/ignite-7061/features/machinelearning.html Thu Feb 8
22:02:38 2018
@@ -52,7 +52,7 @@ under the License.
<main id="main" role="main" class="container">
<section id="machine-learning" class="page-section">
- <h1 class="first">Machine Learning<sup><span style="font-size:
20px;">βeta</span></sup></h1>
+ <h1 class="first">Machine Learning</h1>
<div class="col-sm-12 col-md-12 col-xs-12" style="padding-left:0;
padding-right:0;">
<div class="col-sm-6 col-md-7 col-xs-12"
style="padding-left:0; padding-right:0;">
<p>Apache Ignite 2.0 release introduced first version of
its own distributed Machine Learning (ML) library called ML Grid.</p>
Modified: ignite/site/branches/ignite-7061/features/transactions.html
URL:
http://svn.apache.org/viewvc/ignite/site/branches/ignite-7061/features/transactions.html?rev=1823603&r1=1823602&r2=1823603&view=diff
==============================================================================
Binary files - no diff available.
Modified: ignite/site/branches/ignite-7061/includes/header.html
URL:
http://svn.apache.org/viewvc/ignite/site/branches/ignite-7061/includes/header.html?rev=1823603&r1=1823602&r2=1823603&view=diff
==============================================================================
--- ignite/site/branches/ignite-7061/includes/header.html (original)
+++ ignite/site/branches/ignite-7061/includes/header.html Thu Feb 8 22:02:38
2018
@@ -53,24 +53,24 @@
<li class="dropdown">
<a href="/features.html">Features<span
class="caret"></span></a>
<ul class="dropdown-menu" role="menu">
+ <li role="presentation"
class="submenu-header">Overview</li>
<li><a href="/whatisignite.html">What
is Ignite™?</a></li>
<!-- Ignite main features. -->
<li role="presentation"
class="submenu-header">Features</li>
- <li><a
href="/features/sql.html">SQL</a></li>
- <li><a href="#">Key-Value</a></li>
+ <li><a
href="/features/sql.html">Distributed SQL</a></li>
+ <li><a href="#">Distributed
Key-Value</a></li>
<li><a
href="/features/transactions.html">ACID Transactions</a></li>
<li><a
href="/collocatedprocessing.html">Collocated Processing</a></li>
- <li><a
href="/features/machinelearning.html">Machine
Learning<sup>βeta</sup></a></li>
+ <li><a
href="/features/machinelearning.html">Machine Learning</a></li>
<li><a
href="/features/multilanguage.html">Multi-Language</a></li>
<li><a href="/features.html"><i>More
Features</i></a></li>
<li class="divider">
<!-- Ignite architecture overview. -->
<li role="presentation"
class="submenu-header">Architecture</li>
- <li><a href="#">Overview</a></li>
<li><a href="#">Clustering and
Deployment</a></li>
- <li><a href="#">Distributed
Database</a></li>
+ <li><a href="#">Memory-Centric
Storage</a></li>
<li><a
href="/features/durablememory.html">Durable Memory</a></li>
<li class="divider">
