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Posted to commits@ignite.apache.org by ds...@apache.org on 2018/02/16 14:03:59 UTC
svn commit: r1824488 - in /ignite/site/branches/ignite-7061/features:
collocatedprocessing.html machinelearning.html sql.html transactions.html
Author: dsetrakyan
Date: Fri Feb 16 14:03:58 2018
New Revision: 1824488
URL: http://svn.apache.org/viewvc?rev=1824488&view=rev
Log:
Text updates.
Modified:
ignite/site/branches/ignite-7061/features/collocatedprocessing.html
ignite/site/branches/ignite-7061/features/machinelearning.html
ignite/site/branches/ignite-7061/features/sql.html
ignite/site/branches/ignite-7061/features/transactions.html
Modified: ignite/site/branches/ignite-7061/features/collocatedprocessing.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-7061/features/collocatedprocessing.html?rev=1824488&r1=1824487&r2=1824488&view=diff
==============================================================================
--- ignite/site/branches/ignite-7061/features/collocatedprocessing.html (original)
+++ ignite/site/branches/ignite-7061/features/collocatedprocessing.html Fri Feb 16 14:03:58 2018
@@ -76,13 +76,13 @@ under the License.
<div class="page-heading">Data Collocation</div>
<p>
To start benefiting from the collocated processing, we need to ensure that the data is properly collocated
- at the first place. 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. This technique is also known as
- affinity collocation of the data.
+ in the first place. If the business logic requires to access more than one entry, it is usually best to
+ collocate dependent entries on a single cluster node. This technique is also known as
+ <code>affinity collocation</code> of the data.
</p>
<p>
In the example below, we have <code>Country</code> and <code>City</code> tables and want to collocate
- <code>City</code> entries with <code>Country</code> entries for where a city is located. To achieve this,
+ <code>City</code> entries with their corresponding <code>Country</code> entries. To achieve this,
we use the <code>WITH</code> clause and specify <code>affinityKey=CountryCode</code> as shown below:
</p>
<div class="tab-content">
@@ -119,10 +119,10 @@ under the License.
<div class="page-heading">SQL and Distributed JOINs</div>
<p>
Apache Ignite SQL engine will always perform much more efficiently if a query is run against the
- collocated data. Especially it is crucial for distributed JOINs supported by Ignite.
+ collocated data. It is especially crucial for execution of distributed JOINs within the cluster.
</p>
<p>
- Taking the example of the two tables created above, let's get the most inhabited cities across China,
+ Taking the example of the two tables created above, let's get the most populated 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">
@@ -139,17 +139,15 @@ under the License.
</div>
</div>
<p>
- 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.
+ Since all the cities were collocated with their countries, the JOIN will execute only on the nodes
+ that store China, Russia and the USA entries. This approach <i>avoids</i> expensive data movement
+ across the network, and therefore scales better and provides the fastest performance.
</p>
<div class="page-heading">Distributed Collocated Computations</div>
<p>
- 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.
+ Apache Ignite compute grid and machine learning components allow to perform computations and execute
+ machine learning algorithms in parallel to achieve high performance, low latency, and linear scalability.
+ Furthermore, both components work best with collocated data and collocated processing in general.
</p>
<p>
For instance, let's assume that a blizzard is approaching New York. As a telecommunication company,
@@ -158,9 +156,9 @@ under the License.
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, 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.
+ 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.
</p>
<p>
Modified: ignite/site/branches/ignite-7061/features/machinelearning.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-7061/features/machinelearning.html?rev=1824488&r1=1824487&r2=1824488&view=diff
==============================================================================
--- ignite/site/branches/ignite-7061/features/machinelearning.html (original)
+++ ignite/site/branches/ignite-7061/features/machinelearning.html Fri Feb 16 14:03:58 2018
@@ -58,14 +58,14 @@ under the License.
<p>Apache Ignite 2.0 release introduced first version of its own distributed Machine Learning (ML) library called ML Grid.</p>
<p>
The rationale for building ML Grid is quite simple. Many users employ Ignite as the central high-performance storage and processing
- systems for various data sets. If they wanted to perform ML or Deep Learning (DL) on these data sets (i.e training sets or model inference)
- they had to ETL them first into some other systems like Apache Mahout or Apache Spark.
+ system. If they want to execute ML or Deep Learning (DL) algorithms (i.e training sets or model inference)
+ they can run them directly on Ignite cluster without having to ETL the data into some other system, like Apache Mahout or Apache Spark.
</p>
<p>
- Presently ML Grid supports core distributed algebra implementation based on Ignite co-located distributed processing
- as well as essential machine learning algorithms such as Linear Regression, Decision Trees, K-Means clustering and more.
+ Presently ML Grid supports core distributed algebra implementation based on Ignite co-located distributed processing,
+ as well as other essential machine learning algorithms such as Linear Regression, Decision Trees, K-Means clustering and more.
Future releases will introduce custom DSLs for Python, R and Scala,
- growing collection of optimized ML algorithms as well support for Ignite-optimized Neural Networks.
+ growing collection of optimized ML algorithms as well as support for Ignite-optimized Neural Networks.
</p>
</div>
<div class="col-sm-6 col-md-5 col-xs-12" style="padding-right:0; top: -10px;">
Modified: ignite/site/branches/ignite-7061/features/sql.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-7061/features/sql.html?rev=1824488&r1=1824487&r2=1824488&view=diff
==============================================================================
--- ignite/site/branches/ignite-7061/features/sql.html (original)
+++ ignite/site/branches/ignite-7061/features/sql.html Fri Feb 16 14:03:58 2018
@@ -51,7 +51,7 @@ under the License.
<main id="main" role="main" class="container">
<section id="sql-queries" class="page-section">
- <h1 class="first">Distributed SQL Database</h1>
+ <h1 class="first">Distributed Memory-Centric SQL Database</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-7 col-xs-12" style="padding-left:0;">
<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=1824488&r1=1824487&r2=1824488&view=diff
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