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Posted to commits@ignite.apache.org by dm...@apache.org on 2020/03/10 23:45:11 UTC
svn commit: r1875068 - in /ignite/site/branches/ignite-redisign:
arch/clustering.html features/machinelearning.html
features/manageandmonitor.html features/multilanguage.html
Author: dmagda
Date: Tue Mar 10 23:45:10 2020
New Revision: 1875068
URL: http://svn.apache.org/viewvc?rev=1875068&view=rev
Log:
a new chunck of edited pages
Modified:
ignite/site/branches/ignite-redisign/arch/clustering.html
ignite/site/branches/ignite-redisign/features/machinelearning.html
ignite/site/branches/ignite-redisign/features/manageandmonitor.html
ignite/site/branches/ignite-redisign/features/multilanguage.html
Modified: ignite/site/branches/ignite-redisign/arch/clustering.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/arch/clustering.html?rev=1875068&r1=1875067&r2=1875068&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/arch/clustering.html (original)
+++ ignite/site/branches/ignite-redisign/arch/clustering.html Tue Mar 10 23:45:10 2020
@@ -61,19 +61,20 @@ under the License.
<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-6 col-xs-12" style="padding-left:0; padding-right:0;">
<p>
- Apache Ignite implements the shared-nothing architecture when all the cluster nodes are equal
+ Apache Ignite implements the shared-nothing architecture where all cluster nodes are equal
and there is no single point of failure or bottleneck.
- Ignite does NOT have a component such as a master node or name node that presents in many
+ Ignite does NOT have a component such as a master node or name node that is present in most
distributed systems.
</p>
<p>
- Ignite nodes discover each automatically in your environment, and the cluster can be scaled out or in easily.
+ Ignite nodes discover each other automatically in your environment, and the cluster can be
+ scaled out or in easily.
</p>
<p>
Apache Ignite can run on bare metal, virtual machines, Docker, Kubernetes, and cloud
environments. Technically there are no limitations in regards to deployment environments --
- as long as the nodes can auto-discover each other via the TCP/IP interface, the cluster can be
- launched anywhere.
+ since Ignite nodes can auto-discover each other via the TCP/IP interface, you can launch a
+ cluster anywhere.
</p>
</div>
<div class="col-sm-6 col-md-6 col-xs-12" style="padding-right:0;">
@@ -87,8 +88,8 @@ under the License.
A server node is the base computational and data storage unit. Typically, you start a single server
node per machine or container and it will scale vertically by utilizing all of the CPU, RAM, and other
- resources available unless specified differently. Those resources are pooled and become available to
- Ignite applications once the server node joins a cluster of other server nodes.
+ resources available unless specified differently. These resources are pooled and become available to
+ Ignite applications once the server node joins the cluster of other server nodes.
</p>
<p>
A cluster is a group of server nodes interconnected together in order to provide shared resources
@@ -96,20 +97,20 @@ under the License.
</p>
<p>
Client nodes (aka. thick clients) are your connection endpoints and gateways from the application layer
- to the cluster of server nodes. You always embed a client into your application code and execute
+ to the cluster of server nodes. You always embed a client into your application code and execute the
required APIs. The clients shield all the complexity of Igniteâs distributed nature from application
developers who will see the cluster as a single unit. Itâs as simple as connecting to an RDBMS via a
JDBC driver or Spring Data framework.
</p>
<p>
- In addition to the thick clients nodes, you can access the cluster with Ignite thin clients,
- JDBC and ODBC drivers, or REST API.
+ In addition to the thick clients, you can access the cluster with Ignite thin clients,
+ JDBC and ODBC drivers, or the REST API.
</p>
<div class="page-heading">Learn More</div>
<p>
<a href="https://apacheignite.readme.io/docs/clustering" target="docs">
- <b>Ignite Clustering Documentation <i class="fa fa-angle-double-right"></i></b>
+ <b>Ignite Clustering <i class="fa fa-angle-double-right"></i></b>
</a>
</p>
<p>
Modified: ignite/site/branches/ignite-redisign/features/machinelearning.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/machinelearning.html?rev=1875068&r1=1875067&r2=1875068&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/features/machinelearning.html (original)
+++ ignite/site/branches/ignite-redisign/features/machinelearning.html Tue Mar 10 23:45:10 2020
@@ -86,11 +86,11 @@ under the License.
<div class="page-heading">Problem #2: Lack of Horizontal Scalability</div>
<p>
- The second factor relates to scalability. ML and DL algorithms that have to process data sets that no
- longer fit within a single server unit are continually growing. That requires data scientists to come
+ The second factor relates to scalability. ML and DL algorithms have to process data sets that no
+ longer fit within a single server unit are continually growing. This requires data scientists to come
up with sophisticated solutions or turn to distributed computing platforms such as Apache Spark and
TensorFlow. However, those platforms mostly solve only a part of the puzzle, which is the models
- training, making it a burden of the developers to decide how do deploy the models in production later.
+ training, making it a burden for the developers to decide how to deploy the models in production later.
</p>
<div class="page-heading">Zero ETL and Massive Scalability</div>
@@ -98,7 +98,7 @@ under the License.
<p>
Ignite Machine Learning relies on Ignite's multi-tier storage that brings massive scalability
for ML and DL tasks and eliminates the wait imposed by ETL between the different systems.
- For instance, it allows users to run ML/DL training and inference directly on data stored across
+ For instance, it allows users to run ML/DL training and inference directly on the data stored across
memory and disk in an Ignite cluster. Next, Ignite provides a host
of ML and DL algorithms that are optimized for Ignite's collocated distributed processing.
These implementations deliver in-memory speed and unlimited horizontal scalability when running
@@ -110,10 +110,10 @@ under the License.
<div class="page-heading">Fault Tolerance and Continuous Learning</div>
<p>
- Apache Ignite Machine Learning is tolerant to node failures. That means that in the case of node
- failures
- during the learning process, all recovery procedures will be transparent to the user, learning processes
- won't be interrupted, and we will get results in the time similar to the case when all nodes work fine.
+ Ignite Machine Learning is tolerant to node failures. This means that in the case of node
+ failures during the learning process, all recovery procedures will be transparent to the user,
+ learning processes won't be interrupted, and you will get results in the time similar to the case when
+ all nodes are up and running.
</p>
<p><a href="https://apacheignite.readme.io/docs/machine-learning" target="_blank" rel="noopener">Read more</a></p>
</section>
@@ -124,13 +124,13 @@ under the License.
<h2 style="padding-bottom: 5px; margin-bottom: 20px;">Genetic Algorithms</h2>
<p>
- Machine learning component goes with a set of genetic algorithms (GA) which is a method of
- solving optimization problems by simulating the process of biological evolution.
+ The Ignite Machine Learning component comes with a set of genetic algorithms (GA), which is a
+ method of solving optimization problems by simulating the process of biological evolution.
</p>
<p>
GAs are excellent for searching through large and complex data sets for an optimal solution.
- Real world applications of GAs include: automotive design, computer gaming, robotics, investments,
- traffic/shipment routing and more.
+ Real-world applications of GAs include: automotive design, computer gaming, robotics, investments,
+ traffic/shipment routing, and more.
</p>
<div class="page-links">
Modified: ignite/site/branches/ignite-redisign/features/manageandmonitor.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/manageandmonitor.html?rev=1875068&r1=1875067&r2=1875068&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/features/manageandmonitor.html (original)
+++ ignite/site/branches/ignite-redisign/features/manageandmonitor.html Tue Mar 10 23:45:10 2020
@@ -77,7 +77,7 @@ under the License.
<li>Execute SQL queries over distributed data sets and view the execution plans.</li>
</ul>
- <p>Refer to <a href="https://apacheignite-tools.readme.io/docs/ignite-web-console" target="_blank"
+ <p>Refer to the <a href="https://apacheignite-tools.readme.io/docs/ignite-web-console" target="_blank"
rel="noopener">Ignite Web Console</a> documentation for more details.</p>
</div>
@@ -100,13 +100,13 @@ under the License.
<p>GridGain Web Console is a free management and monitoring tool,
built on top of Apache Ignite Web Console that allows you to do the following:</p>
<ul class="page-list">
- <li>All the capabilities of Ignite Web Console.</li>
+ <li>Use all the capabilities of Ignite Web Console.</li>
<li>Manage and Monitor Ignite nodes and caches.</li>
<li>View heap, CPU, and other useful node and cache metrics.</li>
</ul>
<p>
- Refer to <a href="https://www.gridgain.com/docs/web-console/" target="_blank" rel="noopener">GridGain Web Console</a>
+ Refer to the <a href="https://www.gridgain.com/docs/web-console/" target="_blank" rel="noopener">GridGain Web Console</a>
documentation for more details and check out a
<a href="https://console.gridgain.com" target="_blank" rel="noopener">hosted version</a>
skipping the installation phase.
Modified: ignite/site/branches/ignite-redisign/features/multilanguage.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/multilanguage.html?rev=1875068&r1=1875067&r2=1875068&view=diff
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