Author: dsetrakyan
Date: Thu Mar 5 06:08:31 2015
New Revision: 1664233
URL: http://svn.apache.org/r1664233
Log:
fixing features
Modified:
incubator/ignite/site/trunk/features.html
Modified: incubator/ignite/site/trunk/features.html
URL:
http://svn.apache.org/viewvc/incubator/ignite/site/trunk/features.html?rev=1664233&r1=1664232&r2=1664233&view=diff
==============================================================================
--- incubator/ignite/site/trunk/features.html (original)
+++ incubator/ignite/site/trunk/features.html Thu Mar 5 06:08:31 2015
@@ -413,72 +413,10 @@ under the License.
</div>
</section>
- <section id="hadoop" class="feature-section">
- <h2>Hadoop Acceleration</h2>
- <p>
- Hadoop Accelerator enhances existing Hadoop technology to
enable fast data processing using the tools and technology your organization is
already using today.<br/><br/>
- Igniteâs in-memory accelerator for Hadoop is based on the
dual-mode,
- high-performance in-memory file system that is 100% compatible
with Hadoop HDFS, and an in-memory optimized MapReduce implementation.
In-memory HDFS and in-memory MapReduce provide easy to use extensions to
disk-based HDFS and traditional MapReduce, delivering up to 100x faster
performance.
- </p>
- <div class="feature-box">
- <div class="feature-left">
- <div class="features-heading">Features:</div>
- <ul class="features-list">
- <li>100x Faster Performance</li>
- <li>In-Memory MapReduce</li>
- <li>Highly Optimized In-Memory Processing</li>
- <li>Standalone File System</li>
- <li>Optional Caching Layer for HDFS</li>
- <li>Read-Through and Write-Through with HDFS</li>
- </ul>
- </div>
- <div class="feature-right">
- <img src="images/hadoop_sequence.png" width="400px"/>
- </div>
- </div>
-
- <div class="code-examples"> </div>
-
- <div class="feature-links">
- <!--<a target=wiki href="#">Learn More <i class="fa
fa-angle-double-right"></i></a>-->
- <a href="#features">Top <i class="fa
fa-angle-double-up"></i></a>
- </div>
- </section>
-
- <section id="filesystem" class="feature-section">
- <h2>Distributed File System</h2>
- <p>
- One of the unique capabilities of Ignite is a file system
interface to its in-memory data called
- Ignite File System (IGFS). IGFS delivers similar functionality
to Hadoop HDFS, including the ability to
- create a fully functional file system in memory. In fact, IFS
is at the core of
- Igniteâs In-Memory Hadoop Accelerator. <br/><br/>
- The data from each file is split on separate data blocks and
stored in cache.
- Developers can access each fileâs data with a standard Java
streaming API. Moreover, for each part
- of the file a developer can calculate an affinity and process
the fileâs content on corresponding
- nodes to avoid unnecessary networking.
- </p>
- <div class="features-heading">Features:</div>
- <ul class="features-list">
- <li>Provides Typical File System âViewâ on In-Memory
Caches</li>
- <li>List Directories or Get Information for a Single Path</li>
- <li>Create/Move/Delete Files or Directories</li>
- <li>Write/Read Data Streams into/from Files</li>
- </ul>
- <div class="feature-links">
- <a target=wiki
href="http://doc.gridgain.org/latest/GGFS";>Learn More <i class="fa
fa-angle-double-right"></i></a>
- <a href="#features">Top <i class="fa
fa-angle-double-up"></i></a>
- </div>
- </section>
-
<section id="clustering" class="feature-section">
<h2>Advanced Clustering</h2>
<p>
- Ignite In-Memory Data Fabric provides one of the most
sophisticated clustering technologies on
- Java Virtual Machine (JVM). Ignite nodes can automatically
discover each other.
- This helps to scale the cluster when needed, without having to
restart the whole cluster. Developers
- can also leverage from Igniteâs hybrid cloud support that
allows establishing connection
- between private cloud and public clouds such as Amazon Web
Services, providing them
- with best of both worlds. <br/><br/>
+ Ignite In-Memory Data Fabric provides one of the most
sophisticated clustering technologies on Java Virtual Machine (JVM). Ignite
nodes can automatically discover each other. This helps to scale the cluster
when needed, without having to restart the whole cluster.Developers can also
leverage from Igniteâs hybrid cloud support that allows establishing
connection between private cloud and public clouds such as Amazon Web Services,
providing them with best of both worlds. <br/><br/>
</p>
<div class="feature-box">
<div class="feature-left">
@@ -565,7 +503,7 @@ under the License.
</div>
<div class="feature-links">
- <a target=wiki
href="http://doc.gridgain.org/latest/Basic+Concepts";>Learn More <i class="fa
fa-angle-double-right"></i></a>
+ <a target=wiki
href="http://apacheignite.readme.io/v1.0/docs/cluster";>Learn More <i class="fa
fa-angle-double-right"></i></a>
<a href="#features">Top <i class="fa
fa-angle-double-up"></i></a>
</div>
</section>
@@ -573,17 +511,20 @@ under the License.
<section id="messaging" class="feature-section">
<h2>Distributed Messaging</h2>
<p>
- Apache Ignite provides high-performance cluster-wide messaging
functionality to exchange data
- via publish-subscribe and direct point-to-point communication
models.
+ Apache Ignite provides high-performance cluster-wide messaging
functionality to exchange data via publish-subscribe and direct point-to-point
communication models.
</p>
- <div class="features-heading">Features:</div>
- <ul class="features-list">
- <li>Support for Topic-Based Publish-Subscribe Model</li>
- <li>Support for Direct Point-to-Point Communication</li>
- <li>Pluggable Communication Transport Layer</li>
- <li>Support for Message Ordering</li>
- <li>Cluster-Aware Message Listener Auto-Deployment </li>
- </ul>
+ <div class="feature-box">
+ <div class="feature-left">
+ <div class="features-heading">Features:</div>
+ <ul class="features-list">
+ <li>Support for Topic-Based Publish-Subscribe
Model</li>
+ <li>Support for Direct Point-to-Point
Communication</li>
+ <li>Pluggable Communication Transport Layer</li>
+ <li>Support for Message Ordering</li>
+ <li>Cluster-Aware Message Listener Auto-Deployment
</li>
+ </ul>
+ </div>
+ </div>
<div class="code-examples">
<div class="examples-heading">Examples:</div>
@@ -596,43 +537,16 @@ under the License.
<!-- Tab panes -->
<div class="tab-content">
<div class="tab-pane active"
id="messaging-example-ordered">
- <br/>
- <p>
- Send and receive ordered messages
- </p>
<pre class="brush:java">
- Ignite ignite = Ignition.ignite();
-
- // Add listener for ordered messages on all nodes.
- ignite.message().remoteListen("MyOrderedTopic",
new IgniteBiPredicate<UUID, String>() {
- @Override public boolean apply(UUID nodeId,
String msg) {
- System.out.println("Received ordered
message [msg=" + msg + ", from=" + nodeId + ']');
-
- return true; // Return true to continue
listening.
- }
- });
-
- // Send ordered messages to remote nodes nodes.
- for (int i = 0; i < 10; i++)
- ignite.message(ignite.cluster().forRemotes()).
- sendOrdered("MyOrderedTopic",
Integer.toString(i), 0);
- </pre>
+
</div>
<div class="tab-pane" id="messaging-example-unordered">
- <br/>
- <p>
- Send and receive unordered messages
- </p>
<pre class="brush:java">
- Ignite ignite = Ignition.ignite();
-
- // Add listener for unordered messages on all
nodes.
- ignite.message().remoteListen("MyUnOrderedTopic",
new IgniteBiPredicate<UUID, String>() {
- @Override public boolean apply(UUID nodeId,
String msg) {
- System.out.println("Received unordered
message [msg=" + msg + ", from=" + nodeId + ']');
+ // Add listener for unordered messages on all
remote nodes.
+ ignite.message().remoteListen("MyUnOrderedTopic",
(nodeId, msg) -> {
+ System.out.println("Received unordered message
[msg=" + msg + ", from=" + nodeId + ']');
- return true; // Return true to continue
listening.
- }
+ return true; // Return true to continue
listening.
});
// Send unordered messages to remote nodes.
@@ -645,7 +559,7 @@ under the License.
</div>
<div class="feature-links">
- <a target=wiki
href="http://doc.gridgain.org/latest/Distributed+Messaging";>Learn More <i
class="fa fa-angle-double-right"></i></a>
+ <a target=wiki
href="http://apacheignite.readme.io/v1.0/docs/messaging";>Learn More <i
class="fa fa-angle-double-right"></i></a>
<a href="#features">Top <i class="fa
fa-angle-double-up"></i></a>
</div>
</section>
@@ -816,6 +730,64 @@ under the License.
<a href="#features">Top <i class="fa
fa-angle-double-up"></i></a>
</div>
</section>
+
+ <section id="filesystem" class="feature-section">
+ <h2>Distributed File System</h2>
+ <p>
+ One of the unique capabilities of Ignite is a file system
interface to its in-memory data called Ignite File System (IGFS). IGFS delivers
similar functionality to Hadoop HDFS, but only in memory. In fact, IGFS is at
the core of Igniteâs In-Memory Hadoop Accelerator. <br/><br/>
+ The data from each file is split on separate data blocks and
stored in cache.
+ Developers can access each fileâs data with a standard Java
streaming API. Moreover, for each part of the file a developer can calculate an
affinity and process the fileâs content on corresponding nodes to avoid
unnecessary networking.
+ </p>
+ <div class="feature-box">
+ <div class="feature-left">
+ <div class="features-heading">Features:</div>
+ <ul class="features-list">
+ <li>In-Memory File System</li>
+ <li>List Directories</li>
+ <li>Get Information for a Single Path</li>
+ <li>Create/Move/Delete Files or Directories</li>
+ <li>Write/Read Data Streams into/from Files</li>
+ </ul>
+ </div>
+ </div>
+
+ <div class="feature-links">
+ <!--<a target=wiki
href="http://doc.gridgain.org/latest/GGFS";>Learn More <i class="fa
fa-angle-double-right"></i></a>-->
+ <a href="#features">Top <i class="fa
fa-angle-double-up"></i></a>
+ </div>
+ </section>
+
+ <section id="hadoop" class="feature-section">
+ <h2>Hadoop Acceleration</h2>
+ <p>
+ Hadoop Accelerator enhances existing Hadoop technology to
enable fast data processing using the tools and technology your organization is
already using today.<br/><br/>
+ Igniteâs in-memory accelerator for Hadoop is based on the
dual-mode,
+ high-performance in-memory file system that is 100% compatible
with Hadoop HDFS, and an in-memory optimized MapReduce implementation.
In-memory HDFS and in-memory MapReduce provide easy to use extensions to
disk-based HDFS and traditional MapReduce, delivering up to 100x faster
performance.
+ </p>
+ <div class="feature-box">
+ <div class="feature-left">
+ <div class="features-heading">Features:</div>
+ <ul class="features-list">
+ <li>100x Faster Performance</li>
+ <li>In-Memory MapReduce</li>
+ <li>Highly Optimized In-Memory Processing</li>
+ <li>Standalone File System</li>
+ <li>Optional Caching Layer for HDFS</li>
+ <li>Read-Through and Write-Through with HDFS</li>
+ </ul>
+ </div>
+ <div class="feature-right">
+ <img src="images/hadoop_sequence.png" width="400px"/>
+ </div>
+ </div>
+
+ <div class="code-examples"> </div>
+
+ <div class="feature-links">
+ <!--<a target=wiki href="#">Learn More <i class="fa
fa-angle-double-right"></i></a>-->
+ <a href="#features">Top <i class="fa
fa-angle-double-up"></i></a>
+ </div>
+ </section>
</main>
<!--#include virtual="/includes/footer.html" -->
