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Posted to commits@ignite.apache.org by dm...@apache.org on 2020/03/26 05:46:54 UTC
svn commit: r1875691 - in /ignite/site/branches/ignite-redisign: ./ arch/
features/ includes/ use-cases/
Author: dmagda
Date: Thu Mar 26 05:46:54 2020
New Revision: 1875691
URL: http://svn.apache.org/viewvc?rev=1875691&view=rev
Log:
merged edits by Terry
Added:
ignite/site/branches/ignite-redisign/use-cases/digital-integration-hub.html
- copied, changed from r1875690, ignite/site/branches/ignite-redisign/use-cases/dih.html
Removed:
ignite/site/branches/ignite-redisign/use-cases/dih.html
Modified:
ignite/site/branches/ignite-redisign/arch/clustering.html
ignite/site/branches/ignite-redisign/arch/multi-tier-storage.html
ignite/site/branches/ignite-redisign/arch/persistence.html
ignite/site/branches/ignite-redisign/download.html
ignite/site/branches/ignite-redisign/features/collocatedprocessing.html
ignite/site/branches/ignite-redisign/features/datastructures.html
ignite/site/branches/ignite-redisign/features/machinelearning.html
ignite/site/branches/ignite-redisign/features/manageandmonitor.html
ignite/site/branches/ignite-redisign/features/messaging.html
ignite/site/branches/ignite-redisign/features/multilanguage.html
ignite/site/branches/ignite-redisign/features/rdbmsintegration.html
ignite/site/branches/ignite-redisign/features/servicegrid.html
ignite/site/branches/ignite-redisign/features/sql.html
ignite/site/branches/ignite-redisign/features/streaming.html
ignite/site/branches/ignite-redisign/features/tensorflow.html
ignite/site/branches/ignite-redisign/features/transactions.html
ignite/site/branches/ignite-redisign/includes/header.html
ignite/site/branches/ignite-redisign/index.html
ignite/site/branches/ignite-redisign/use-cases/datagrid.html
ignite/site/branches/ignite-redisign/use-cases/hadoop-acceleration.html
ignite/site/branches/ignite-redisign/use-cases/hpc.html
ignite/site/branches/ignite-redisign/use-cases/in-memory-cache.html
ignite/site/branches/ignite-redisign/use-cases/in-memory-database.html
ignite/site/branches/ignite-redisign/use-cases/key-value-store.html
ignite/site/branches/ignite-redisign/use-cases/spark-acceleration.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=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/arch/clustering.html (original)
+++ ignite/site/branches/ignite-redisign/arch/clustering.html Thu Mar 26 05:46:54 2020
@@ -64,7 +64,7 @@ under the License.
<p>
- Apache Ignite implements the shared-nothing architecture where all 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 is present in most
distributed systems.
Modified: ignite/site/branches/ignite-redisign/arch/multi-tier-storage.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/arch/multi-tier-storage.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/arch/multi-tier-storage.html (original)
+++ ignite/site/branches/ignite-redisign/arch/multi-tier-storage.html Thu Mar 26 05:46:54 2020
@@ -63,7 +63,7 @@ under the License.
<div class="container">
<p>
- Apache Ignite is designed to work with memory, disk, and Intel Optane as active storage tiers.
+ Apache Ignite® is designed to work with memory, disk, and Intel Optane as active storage tiers.
The memory tier allows using DRAM and Intel® Optane⢠operating in the Memory Mode for data storage
and processing needs. The disk tier is optional with the support of two options -- you can
persist data in an external database or keep it in the Ignite native persistence. SSD, Flash,
@@ -213,7 +213,7 @@ under the License.
<p><a href="/use-cases/in-memory-database.html"> Ignite as an In-Memory Database <i class="fa fa-angle-double-right"></i></a></p>
</li>
<li>
- <p><a href="/use-cases/dih.html"> Ignite as a Digital Integration Hub <i class="fa fa-angle-double-right"></i></a></p>
+ <p><a href="/use-cases/digital-integration-hub.html"> Ignite as a Digital Integration Hub <i class="fa fa-angle-double-right"></i></a></p>
</li>
<li>
<p><a href="/use-cases/hpc.html"> Ignite for High Performance Computing <i class="fa fa-angle-double-right"></i></a></p>
Modified: ignite/site/branches/ignite-redisign/arch/persistence.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/arch/persistence.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/arch/persistence.html (original)
+++ ignite/site/branches/ignite-redisign/arch/persistence.html Thu Mar 26 05:46:54 2020
@@ -61,7 +61,7 @@ under the License.
<div class="container">
<p>
- Even though Apache Ignite is broadly used as a caching layer on top of external databases, it
+ Even though Apache Ignite® is broadly used as a caching layer on top of external databases, it
comes with its native persistence - a distributed, ACID, and SQL-compliant disk-based
store. The native persistence integrates into the Ignite multi-tier storage as a disk tier that
can be turned on to let Ignite store more data on disk than it can cache in memory and to enable
Modified: ignite/site/branches/ignite-redisign/download.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/download.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/download.html (original)
+++ ignite/site/branches/ignite-redisign/download.html Thu Mar 26 05:46:54 2020
@@ -170,7 +170,7 @@ under the License.
onclick="ga('send', 'event', 'apache_ignite_usecases', 'menu_click', 'massive_parallel_processing');">
High-Performance Computing</a>
</li>
- <li class="dropdown-item"><a href="/use-cases/dih.html" aria-label="Digital Integration Hub"
+ <li class="dropdown-item"><a href="/use-cases/digital-integration-hub.html" aria-label="Digital Integration Hub"
onclick="ga('send', 'event', 'apache_ignite_usecases', 'menu_click', 'digital_integration_hub');">
Digital Integration Hub</a>
</li>
Modified: ignite/site/branches/ignite-redisign/features/collocatedprocessing.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/collocatedprocessing.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/features/collocatedprocessing.html (original)
+++ ignite/site/branches/ignite-redisign/features/collocatedprocessing.html Thu Mar 26 05:46:54 2020
@@ -63,10 +63,8 @@ under the License.
</div>
</header>
<div class="container">
-
-
<p>
- Apache Ignite supports co-located processing technique for compute-intensive and data-intensive calculations
+ Apache Ignite® supports co-located processing technique for compute-intensive and data-intensive calculations
as well as machine learning algorithms. This technique increases performance by eliminating the impact of
network latency.
</p>
Modified: ignite/site/branches/ignite-redisign/features/datastructures.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/datastructures.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/features/datastructures.html (original)
+++ ignite/site/branches/ignite-redisign/features/datastructures.html Thu Mar 26 05:46:54 2020
@@ -54,7 +54,7 @@ under the License.
</header>
<div class="container">
<p>
- Ignite allows for most of the data structures from <code>java.util.concurrent</code> framework to be used in a distributed fashion. For example, you can take <code>java.util.concurrent.BlockingDeque</code> and add something to it on one node and poll it from another node. Or have a distributed ID generator, which would guarantee uniqueness of IDs on all nodes.
+ Apache Ignite® allows for most of the data structures from <code>java.util.concurrent</code> framework to be used in a distributed fashion. For example, you can take <code>java.util.concurrent.BlockingDeque</code> and add something to it on one node and poll it from another node. Or have a distributed ID generator, which would guarantee uniqueness of IDs on all nodes.
</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=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/features/machinelearning.html (original)
+++ ignite/site/branches/ignite-redisign/features/machinelearning.html Thu Mar 26 05:46:54 2020
@@ -61,7 +61,7 @@ under the License.
</header>
<div class="container">
<p>
- Apache Ignite Machine Learning (ML) is a set of simple, scalable, and efficient tools that
+ Apache Ignite® Machine Learning (ML) is a set of simple, scalable, and efficient tools that
allow building predictive machine learning models without costly data transfers. The rationale for
adding machine and deep learning (DL) to Apache Ignite is quite simple.
Today's data scientists have to deal with two major factors that keep ML from mainstream adoption.
Modified: ignite/site/branches/ignite-redisign/features/manageandmonitor.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/manageandmonitor.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/features/manageandmonitor.html (original)
+++ ignite/site/branches/ignite-redisign/features/manageandmonitor.html Thu Mar 26 05:46:54 2020
@@ -52,7 +52,7 @@ under the License.
</header>
<div class="container">
<p>
- Apache Ignite® exposes metrics in JMX and OpenCensus formats making it possible to monitor the
+ Apache Ignite® exposes metrics in JMX and OpenCensus formats making it possible to monitor the
clusters with a broad range of tools, including Zabbix, Prometheus, Grafana, App Dynamics.
</p>
<p>
Modified: ignite/site/branches/ignite-redisign/features/messaging.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/messaging.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/features/messaging.html (original)
+++ ignite/site/branches/ignite-redisign/features/messaging.html Thu Mar 26 05:46:54 2020
@@ -55,7 +55,7 @@ under the License.
<div class="container">
<p>
- Ignite provides <b>high-performance cluster-wide messaging</b> functionality to exchange data via publish-subscribe and direct point-to-point communication models. Messages can be exchanged in an ordered or unordered fashion. Ordered messages are slightly slower, but when used, Ignite guarantees that messages will be received in the same order they were sent.
+ Apache Ignite® provides <b>high-performance cluster-wide messaging</b> functionality to exchange data via publish-subscribe and direct point-to-point communication models. Messages can be exchanged in an ordered or unordered fashion. Ordered messages are slightly slower, but when used, Ignite guarantees that messages will be received in the same order they were sent.
</p>
<p> Ignite <b>distributed events</b> functionality allows applications to receive notifications when a variety of events occur in the distributed grid environment. You can automatically get notified for task executions,
Modified: ignite/site/branches/ignite-redisign/features/multilanguage.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/multilanguage.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
Binary files - no diff available.
Modified: ignite/site/branches/ignite-redisign/features/rdbmsintegration.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/rdbmsintegration.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/features/rdbmsintegration.html (original)
+++ ignite/site/branches/ignite-redisign/features/rdbmsintegration.html Thu Mar 26 05:46:54 2020
@@ -55,7 +55,7 @@ under the License.
<div class="container">
<p>
- Ignite provides support for integrating with a variety of persistence stores.
+ Apache Ignite® provides support for integrating with a variety of persistence stores.
It allows you to connect to the database, import schemas, configure indexed types, and automatically generate all the
required XML OR-mapping configuration and Java domain model POJOs that you can easily download and copy into your project.
</p>
Modified: ignite/site/branches/ignite-redisign/features/servicegrid.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/servicegrid.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/features/servicegrid.html (original)
+++ ignite/site/branches/ignite-redisign/features/servicegrid.html Thu Mar 26 05:46:54 2020
@@ -56,7 +56,7 @@ under the License.
<p>
- Ignite Service Grid allows for deployments of arbitrary user-defined services on the cluster.
+ Apache Ignite® Service Grid allows for deployments of arbitrary user-defined services on the cluster.
You can implement and deploy any service, such as custom counters, ID generators,
hierarchical maps, etc.
</p>
Modified: ignite/site/branches/ignite-redisign/features/sql.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/sql.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/features/sql.html (original)
+++ ignite/site/branches/ignite-redisign/features/sql.html Thu Mar 26 05:46:54 2020
@@ -57,10 +57,8 @@ under the License.
</div>
</header>
<div class="container">
-
-
<p>
- Apache Ignite comes with a ANSI-99 compliant, horizontally scalable, and fault-tolerant SQL engine
+ Apache Ignite® comes with a ANSI-99 compliant, horizontally scalable, and fault-tolerant SQL engine
that allows you to interact with Ignite as with a regular SQL database using JDBC, ODBC drivers, or
native SQL APIs available for Java, C#, C++, Python, and other programming languages.
</p>
Modified: ignite/site/branches/ignite-redisign/features/streaming.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/streaming.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/features/streaming.html (original)
+++ ignite/site/branches/ignite-redisign/features/streaming.html Thu Mar 26 05:46:54 2020
@@ -56,7 +56,7 @@ under the License.
<p>
- Ignite data loading and streaming capabilities allow ingesting large finite as well as
+ Apache Ignite® data loading and streaming capabilities allow ingesting large finite as well as
never-ending volumes of data in a scalable and fault-tolerant way into the cluster.
The rate at which data can be injected into Ignite is very high and easily exceeds millions
of events per second on a moderately sized cluster.
Modified: ignite/site/branches/ignite-redisign/features/tensorflow.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/tensorflow.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
Binary files - no diff available.
Modified: ignite/site/branches/ignite-redisign/features/transactions.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/transactions.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
Binary files - no diff available.
Modified: ignite/site/branches/ignite-redisign/includes/header.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/includes/header.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/includes/header.html (original)
+++ ignite/site/branches/ignite-redisign/includes/header.html Thu Mar 26 05:46:54 2020
@@ -97,7 +97,7 @@
onclick="ga('send', 'event', 'apache_ignite_usecases', 'menu_click', 'massive_parallel_processing');">
High-Performance Computing</a>
</li>
- <li class="dropdown-item"><a href="/use-cases/dih.html" aria-label="Digital Integration Hub"
+ <li class="dropdown-item"><a href="/use-cases/digital-integration-hub.html" aria-label="Digital Integration Hub"
onclick="ga('send', 'event', 'apache_ignite_usecases', 'menu_click', 'digital_integration_hub');">
Digital Integration Hub</a>
</li>
Modified: ignite/site/branches/ignite-redisign/index.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/index.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/index.html (original)
+++ ignite/site/branches/ignite-redisign/index.html Thu Mar 26 05:46:54 2020
@@ -301,7 +301,7 @@ under the License.
functionally rich, unified and consistent APIs.
</p>
- <a href="/use-cases/dih.html" class="btn btn-primary"
+ <a href="/use-cases/digital-integration-hub.html" class="btn btn-primary"
onclick="ga('send', 'event', 'apache_ignite_use_cases', 'homepage_click', 'digital_hub');">
Learn More</a>
</div>
Modified: ignite/site/branches/ignite-redisign/use-cases/datagrid.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/use-cases/datagrid.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/use-cases/datagrid.html (original)
+++ ignite/site/branches/ignite-redisign/use-cases/datagrid.html Thu Mar 26 05:46:54 2020
@@ -50,120 +50,121 @@ under the License.
<body>
<!--#include virtual="/includes/header.html" -->
<article>
- <header><div class="container">
- <h1>In-Memory Data Grid <strong>With SQL, <br />ACID Transactions and Compute APIs</strong></h1>
- </div>
- </header>
-<div class="container">
-
- <p>
- Apache Ignite as an in-memory data grid that accelerates and scales your databases, services, and
- APIs. It supports key-value and ANSI SQL APIs, ACID transactions, co-located compute, and machine
- learning libraries required for real-time applications.
- </p>
- <p>
- An in-memory data grid type of deployment is also known as a read-through/write-through caching
- strategy, in which case the application layer starts treating the data grid as the primary store.
- While the application layer writes to and reads from Ignite, the latter ensures that any underlying
- database stays updated and consistent with in-memory data.
- </p>
- <img class="img-fluid diagram-right" src="/images/svg-diagrams/data_grid.svg" />
- <p>
- As an in-memory data grid, Ignite provides all essential APIs needed to simplify its adoption.
- The APIs include distributed key-value and ANSI SQL queries, ACID transactions, co-located
- computations, and machine learning models. While key-value and SQL calls let you request, join, and
- group distributed data sets, the compute and machine learning components help to eliminate data
- shuffling over the network, thus, boosting compute and data-intensive calculations.
- </p>
-
- <p>
- Ignite is capable of storing data both in memory and on disk with two options for data persistence
- -- you can persist changes in an external database or let Ignite keep data in its native persistence.
- Let's review both of these options.
- </p>
-
- <h2>Ignite and External Databases</h2>
-
- <p>
- Ignite can improve the performance and scalability of any external database such as RDBMS,
- NoSQL or Hadoop, by sliding in as an in-memory cache between the application and the database
- layer. When an application writes data to the cache, Ignite automatically writes-through or
- writes-behind all data modifications to the underlying external store. Ignite also performs
- ACID transactions where it coordinates and commits a transaction across the cluster as well as
- the database.
- </p>
- <p>
- Additionally, Ignite can be deployed as a shared and unified in-memory layer that stores data
- sets originating from disjointed databases. Your applications can consume all the data from
- Ignite as a single store while Ignite can keep the original databases in sync whenever in-memory
- data gets updated.
- </p>
- <p>
- However, there are some limitations if an external database is used as a persistence layer for
- Ignite deployments. For instance, if you run Ignite SQL or scan queries, you need to ensure that
- all the data has been preloaded to the in-memory cluster. Note that Ignite SQL or scan queries
- can read data from disk only if it is stored in the native persistence.
- </p>
-
-
-
- <p> </p>
- <h2 >Ignite Native Persistence</h2>
- <img class="img-fluid diagram-right" src="/images/native_persistence_key_value.png"/>
- <p>Ignite native persistence is a distributed ACID and SQL-compliant disk store that transparently integrates with Ignite in-memory layer. When the native persistence is enabled, Ignite stores both data and indexes on disk and eliminates the time-consuming cache warm-up step. Since the native persistence always keeps a full copy of data on disk, you are free to cache a subset of records in memory. If a required data record is missing in memory, then Ignite reads it from the disk automatically regardless of the API you use -- be it SQL, key-value, or scan queries.</p>
-
-
-
-
-
- <div class="jumbotron jumbotron-fluid">
+ <header>
<div class="container">
- <div class="display-6 title">Learn More</div>
- <hr class="my-4">
- <div class="row">
- <div class="col-sm-6">
- <ul>
- <li>
- <a href="/features/sql.html">
- Distributed SQL <i class="fa fa-angle-double-right"></i>
- </a>
- </li>
- <li>
- <a href="/features/collocatedprocessing.html">
- Co-located Processing <i class="fa fa-angle-double-right"></i>
- </a>
- </li>
- <li><a href="/features/transactions.html">
- ACID Transactions <i class="fa fa-angle-double-right"></i>
- </a></li>
- <li><a href="/features/machinelearning.html">
- Machine and Deep Learning <i class="fa fa-angle-double-right"></i>
- </a></li>
- </ul>
- </div>
- <div class="col-sm-6">
- <ul>
- <li>
- <a href="/arch/persistence.html">
- Native Persistence <i class="fa fa-angle-double-right"></i>
- </a>
- </li>
- <li><a href="/use-cases/in-memory-database.html">
- Ignite as an In-Memory Database <i class="fa fa-angle-double-right"></i>
- </a></li>
- <li><a href="/use-cases/dih.html">
- Ignite as a Digital Integration Hub <i class="fa fa-angle-double-right"></i>
- </a></li>
- </ul>
+ <h1>Apache Ignite as an <strong>In-Memory Data Grid</strong></h1>
+ </div>
+ </header>
+ <div class="container">
+
+ <p>
+ Apache Ignite® is an in-memory data grid that accelerates and scales your databases, services, and APIs.
+ It supports key-value and ANSI SQL APIs, ACID transactions, co-located compute, and machine learning
+ libraries required for real-time applications.
+ </p>
+ <p>
+ An in-memory data grid deployment is a read-through/write-through caching strategy, in which the application
+ layer treats the data grid as the primary data store. The application layer writes to and reads from Ignite.
+ Ignite ensures that any underlying database stays updated and consistent with the in-memory data.
+ </p>
+ <img class="img-fluid diagram-right" src="/images/svg-diagrams/data_grid.svg"/>
+ <p>
+ As an in-memory data grid, Ignite provides all essential APIs needed to simplify its adoption.
+ The APIs include distributed key-value and ANSI SQL queries, ACID transactions, co-located
+ computations, and machine learning models. While key-value and SQL calls let you request, join, and
+ group distributed data sets, the compute and machine learning components help to eliminate data
+ shuffling over the network, thus, boosting compute and data-intensive calculations.
+ </p>
+
+ <p>
+ Ignite is capable of storing data both in memory and on disk with two options for data persistence
+ -- you can persist changes in an external database or let Ignite keep data in its native persistence.
+ Let's review both of these options.
+ </p>
+
+ <h2>Ignite and External Databases</h2>
+
+ <p>
+ Ignite can improve the performance and scalability of any external database such as RDBMS,
+ NoSQL or Hadoop, by sliding in as an in-memory cache between the application and the database
+ layer. When an application writes data to the cache, Ignite automatically writes-through or
+ writes-behind all data modifications to the underlying external store. Ignite also performs
+ ACID transactions where it coordinates and commits a transaction across the cluster as well as
+ the database.
+ </p>
+ <p>
+ Additionally, Ignite can be deployed as a shared and unified in-memory layer that stores data
+ sets originating from disjointed databases. Your applications can consume all the data from
+ Ignite as a single store while Ignite can keep the original databases in sync whenever in-memory
+ data gets updated.
+ </p>
+ <p>
+ However, there are some limitations if an external database is used as a persistence layer for
+ Ignite deployments. For instance, if you run Ignite SQL or scan queries, you need to ensure that
+ all the data has been preloaded to the in-memory cluster. Note that Ignite SQL or scan queries
+ can read data from disk only if it is stored in the native persistence.
+ </p>
+
+
+ <p> </p>
+ <h2>Ignite Native Persistence</h2>
+ <img class="img-fluid diagram-right" src="/images/native_persistence_key_value.png"/>
+ <p>Ignite native persistence is a distributed ACID and SQL-compliant disk store that transparently integrates
+ with Ignite in-memory layer. When the native persistence is enabled, Ignite stores both data and indexes on
+ disk and eliminates the time-consuming cache warm-up step. Since the native persistence always keeps a full
+ copy of data on disk, you are free to cache a subset of records in memory. If a required data record is
+ missing in memory, then Ignite reads it from the disk automatically regardless of the API you use -- be it
+ SQL, key-value, or scan queries.</p>
+
+
+ <div class="jumbotron jumbotron-fluid">
+ <div class="container">
+ <div class="display-6 title">Learn More</div>
+ <hr class="my-4">
+ <div class="row">
+ <div class="col-sm-6">
+ <ul>
+ <li>
+ <a href="/features/sql.html">
+ Distributed SQL <i class="fa fa-angle-double-right"></i>
+ </a>
+ </li>
+ <li>
+ <a href="/features/collocatedprocessing.html">
+ Co-located Processing <i class="fa fa-angle-double-right"></i>
+ </a>
+ </li>
+ <li><a href="/features/transactions.html">
+ ACID Transactions <i class="fa fa-angle-double-right"></i>
+ </a></li>
+ <li><a href="/features/machinelearning.html">
+ Machine and Deep Learning <i class="fa fa-angle-double-right"></i>
+ </a></li>
+ </ul>
+ </div>
+ <div class="col-sm-6">
+ <ul>
+ <li>
+ <a href="/arch/persistence.html">
+ Native Persistence <i class="fa fa-angle-double-right"></i>
+ </a>
+ </li>
+ <li><a href="/use-cases/in-memory-database.html">
+ Ignite as an In-Memory Database <i class="fa fa-angle-double-right"></i>
+ </a></li>
+ <li><a href="/use-cases/digital-integration-hub.html">
+ Ignite as a Digital Integration Hub <i class="fa fa-angle-double-right"></i>
+ </a></li>
+ </ul>
+ </div>
+ </div>
</div>
- </div>
</div>
- </div>
-
-
-</div>
+
+
+ </div>
</article>
- <!--#include virtual="/includes/footer.html" -->
+<!--#include virtual="/includes/footer.html" -->
<!--#include virtual="/includes/scripts.html" -->
</body>
</html>
Copied: ignite/site/branches/ignite-redisign/use-cases/digital-integration-hub.html (from r1875690, ignite/site/branches/ignite-redisign/use-cases/dih.html)
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/use-cases/digital-integration-hub.html?p2=ignite/site/branches/ignite-redisign/use-cases/digital-integration-hub.html&p1=ignite/site/branches/ignite-redisign/use-cases/dih.html&r1=1875690&r2=1875691&rev=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/use-cases/dih.html (original)
+++ ignite/site/branches/ignite-redisign/use-cases/digital-integration-hub.html Thu Mar 26 05:46:54 2020
@@ -33,7 +33,7 @@ under the License.
<!DOCTYPE html>
<html lang="en">
<head>
- <link rel="canonical" href="https://ignite.apache.org/use-cases/dih.html"/>
+ <link rel="canonical" href="https://ignite.apache.org/use-cases/digital-integration-hub.html"/>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
@@ -48,115 +48,122 @@ under the License.
<!--#include virtual="/includes/sh.html" -->
</head>
<body>
- <!--#include virtual="/includes/header.html" -->
- <article>
- <header><div class="container">
- <h1>Building Digital Integration Hub <strong>With Apache Ignite</strong></h1>
- </div></header>
- <div class="container">
-
- <p>
- Apache Ignite is used as a low-latency and shared store of your digital integration hub
- architecture that caches and persists data sets scattered across many disjointed back-end databases
- and systems. A digital integration hub (DIH) is an advanced platform architecture that aggregates multiple
- back-end systems and databases into a low-latency and shared data store.
- </p>
- <img class="diagram-right img-fluid" src="/images/svg-diagrams/digital_hub.svg"/>
-
- <p>
- Applications typically access Ignite via an API service layer and get substantial performance
- growth by requesting data from only this distributed store, which keeps all the records in
- memory and offloads the back-end systems.
- </p>
-
- <p>
- The primary purpose of Ignite as a DIH component is:
- </p>
- <ul>
- <li>To enable implementations of large-scale and high-throughput architectures that prevent the back-end
- systems from getting overwhelmed with excessive workloads.</li>
- <li>To avoid complex integrations between the back-end databases and the front-end API services.</li>
- </ul>
-
- <h2>Synchronization of Apache Ignite and Back-End Systems</h2>
- <p>
- Ignite, as the high-performance data store, needs to be synchronized with the back-end databases via
- streaming, event-based, change data capture (CDC), or other techniques.
- </p>
-
- <p>
- Ignite provides the <code>CacheStore</code> interface for uni-directional synchronization between an
- Ignite cluster and an external store supporting relational databases and some NoSQL stores. The interface
- allows Ignite to write-through or write-behind all the changes to the backend-systems automatically.
- It also includes transactions - Ignite coordinates and commits a transaction across its in-memory
- cluster as well as an external transactional database.
- </p>
-
- <p>
- For bi-directional synchronization, you can consider various streaming, CDC, and event-based technologies.
- For instance, Kafka, Spark, and Debezium are widely used to keep Ignite in sync with other databases.
- </p>
-
- <h2>Real-Time Analytics</h2>
- <p>
- Although real-time analytics is not a defining characteristic of digital integration hub architectures, in some situations, you can end up consolidating operational and analytical data silos in Apache Ignite. If this happens, you can tap into Ignite SQL, compute, and machine learning capabilities for real-time analytics needs.
- </p>
-
-
- <div class="jumbotron jumbotron-fluid">
+<!--#include virtual="/includes/header.html" -->
+<article>
+ <header>
<div class="container">
- <div class="title display-6">Learn More</div>
- <hr class="my-4">
- <div class="row">
- <div class="col-sm-6">
- <ul>
- <li>
- <a href="https://apacheignite-mix.readme.io/docs/overview" target="docs">
- Ignite and Streaming Technologies <i class="fa fa-angle-double-right"></i>
- </a>
- </li>
- <li>
- <a href="https://apacheignite-fs.readme.io/docs/overview" target="docs">
- Ignite and Spark Integration <i class="fa fa-angle-double-right"></i>
- </a>
- </li>
- <li>
- <a href="/features/sql.html">
- Distributed SQL <i class="fa fa-angle-double-right"></i>
- </a>
- </li>
- </ul>
- </div>
- <div class="col-sm-6">
- <ul>
- <li>
- <a href="/features/collocatedprocessing.html">
- Co-located Processing <i class="fa fa-angle-double-right"></i>
- </a>
- </li>
- <li>
-
- <a href="/features/machinelearning.html">
- Machine and Deep Learning <i class="fa fa-angle-double-right"></i>
- </a>
-
- </li>
- <li>
-
- <a href="/arch/multi-tier-storage.html">
- Multi-Tier Storage <i class="fa fa-angle-double-right"></i>
- </a>
-
- </li>
- </ul>
+ <h1><strong>Digital Integration Hub</strong></h1>
+ </div>
+ </header>
+ <div class="container">
+ <p>
+ A digital integration hub (DIH) is an advanced platform architecture that aggregates multiple back-end
+ systems and databases into a low-latency and shared data store. Apache Ignite® functions as such a store
+ that caches and persists data sets scattered across many disjointed back-end databases and makes
+ them available through high-performance APIs to your applications.
+ </p>
+ <img class="diagram-right img-fluid" src="/images/svg-diagrams/digital_hub.svg"/>
+
+ <p>
+ Applications access Ignite via an API service layer and experience substantial performance improvements by
+ requesting data from only the Ignite distributed store.
+ </p>
+
+ <p>
+ As a digital integration hub component, Apache Ignite:
+ </p>
+ <ul>
+ <li>
+ Enables large-scale and high-throughput architectures that prevent back-end systems from getting
+ overwhelmed with excessive workloads
+ </li>
+ <li>
+ Avoids complex integrations between back-end databases and front-end API services
+ </li>
+ </ul>
+
+ <h2>Synchronization of Apache Ignite and Back-End Systems</h2>
+ <p>
+ Ignite, as the high-performance data store, needs to be synchronized with the back-end databases via
+ streaming, event-based, change data capture (CDC), or other techniques.
+ </p>
+
+ <p>
+ Ignite provides the <code>CacheStore</code> interface for uni-directional synchronization between an
+ Ignite cluster and an external store supporting relational databases and some NoSQL stores. The interface
+ allows Ignite to write-through or write-behind all the changes to the backend-systems automatically.
+ It also includes transactions - Ignite coordinates and commits a transaction across its in-memory
+ cluster as well as an external transactional database.
+ </p>
+
+ <p>
+ For bi-directional synchronization, you can consider various streaming, CDC, and event-based technologies.
+ For instance, Kafka, Spark, and Debezium are widely used to keep Ignite in sync with other databases.
+ </p>
+
+ <h2>Real-Time Analytics</h2>
+ <p>
+ Although real-time analytics is not a defining characteristic of digital integration hub architectures, in
+ some situations, you can end up consolidating operational and analytical data silos in Apache Ignite. If
+ this happens, you can tap into Ignite SQL, compute, and machine learning capabilities for real-time
+ analytics needs.
+ </p>
+
+
+ <div class="jumbotron jumbotron-fluid">
+ <div class="container">
+ <div class="title display-6">Learn More</div>
+ <hr class="my-4">
+ <div class="row">
+ <div class="col-sm-6">
+ <ul>
+ <li>
+ <a href="https://apacheignite-mix.readme.io/docs/overview" target="docs">
+ Ignite and Streaming Technologies <i class="fa fa-angle-double-right"></i>
+ </a>
+ </li>
+ <li>
+ <a href="https://apacheignite-fs.readme.io/docs/overview" target="docs">
+ Ignite and Spark Integration <i class="fa fa-angle-double-right"></i>
+ </a>
+ </li>
+ <li>
+ <a href="/features/sql.html">
+ Distributed SQL <i class="fa fa-angle-double-right"></i>
+ </a>
+ </li>
+ </ul>
+ </div>
+ <div class="col-sm-6">
+ <ul>
+ <li>
+ <a href="/features/collocatedprocessing.html">
+ Co-located Processing <i class="fa fa-angle-double-right"></i>
+ </a>
+ </li>
+ <li>
+
+ <a href="/features/machinelearning.html">
+ Machine and Deep Learning <i class="fa fa-angle-double-right"></i>
+ </a>
+
+ </li>
+ <li>
+
+ <a href="/arch/multi-tier-storage.html">
+ Multi-Tier Storage <i class="fa fa-angle-double-right"></i>
+ </a>
+
+ </li>
+ </ul>
+ </div>
+ </div>
</div>
- </div>
</div>
- </div>
-
-
- </div>
-</article>
+
+
+ </div>
+</article>
<!--#include virtual="/includes/footer.html" -->
<!--#include virtual="/includes/scripts.html" -->
</body>
Modified: ignite/site/branches/ignite-redisign/use-cases/hadoop-acceleration.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/use-cases/hadoop-acceleration.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/use-cases/hadoop-acceleration.html (original)
+++ ignite/site/branches/ignite-redisign/use-cases/hadoop-acceleration.html Thu Mar 26 05:46:54 2020
@@ -33,7 +33,7 @@ under the License.
<!DOCTYPE html>
<html lang="en">
<head>
-<link rel="canonical" href="https://ignite.apache.org/use-cases/hadoop-acceleration.html"/>
+ <link rel="canonical" href="https://ignite.apache.org/use-cases/hadoop-acceleration.html"/>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
@@ -42,153 +42,148 @@ under the License.
Apache Hadoop deployments. Ignite serves as an in-memory computing platform designated for low-latency and
real-time operations while Hadoop continues to be used for long-running OLAP workloads."/>
- <title>Apache Hadoop Performance Acceleration With Apache Ignite</title>
+ <title>Apache Hadoop Performance Acceleration</title>
<!--#include virtual="/includes/styles.html" -->
<!--#include virtual="/includes/sh.html" -->
</head>
<body>
-<!--#include virtual="/includes/header.html" -->
+<!--#include virtual="/includes/header.html" -->
<article>
- <header><div class="container">
-
- <h1>Apache Hadoop Performance <br />Acceleration <strong>With Apache Ignite</strong></h1>
- </div>
- </header>
-<div class="container">
-
-
- <p>
- Apache Ignite enables real-time analytics across operational and historical silos for
- existing Apache Hadoop deployments by serving as an in-memory computing platform designated for
- low-latency and high-throughput operations while Hadoop continues to be used for long-running
- OLAP workloads.
- </p>
- <img class="diagram-right img-fluid" src="/images/svg-diagrams/hadoop_acceleration.svg"/>
-
- <p>
- As the architecture diagram on the right suggests, you can achieve the performance acceleration
- of Hadoop-based systems by deploying Ignite as a separate distributed storage that keeps data
- sets needed for your low-latency operations or real-time reports.
- </p>
-
-
- <p>
- First, depending on the data volume and available memory capacity, you can enable Ignite native persistence to
- store historical data sets on disk while dedicating a memory space for operational records. You can
- continue to use Hadoop as storage for less frequently used data or for long-running and ad-hoc
- analytical queries.
- </p>
-
- <p>
- Next, as the architecture suggests, your applications and services should use Ignite native APIs to
- process the data residing in the in-memory cluster. Ignite provides SQL, compute (aka. map-reduce),
- and machine learning APIs for various data processing needs.
- </p>
-
- <p>
- Finally, consider using Apache Spark DataFrames APIs if an application needs to run federated or
- cross-database across Ignite and Hadoop clusters. Ignite is integrated with Spark, which natively
- supports Hive/Hadoop. Cross-database queries should be considered only for a limited number of
- scenarios when neither Ignite nor Hadoop contains the entire data set.
- </p>
-
-
- <h2>How to split data and operations between Ignite and Hadoop?</h2>
- <p>
- Consider using this approach:
- </p>
- <ul >
- <li>
- Use Apache Ignite for tasks that require low-latency response time (microseconds,
- milliseconds, seconds), high throughput operations (thousands and millions of
- operations per second), and real-time processing.
- </li>
- <li>
- Continue using Apache Hadoop for high-latency operations (dozens of seconds, minutes, hours) and
- batch processing.
- </li>
- </ul>
-
-
- <h2>Getting Started Checklist</h2>
- <p>
- Follow the steps below to implement the discussed architecture in practice:
- </p>
- <ul >
- <li>
- Download and install Apache Ignite in your system.
- </li>
- <li>
- Select a list of operations/reports to be executed against Ignite. The best candidates are
- operations that require low-latency response time, high-throughput, and real-time analytics.
- </li>
- <li>
- Depending on the data volume and available memory space, consider using Ignite native
- persistence. Alternatively, you can use Ignite as a pure in-memory cache or in-memory data grid
- that persists changes to Hadoop or another external database.
- </li>
- <li>
- Update your applications to ensure they use Ignite native APIs to process Ignite data and Spark
- for federated queries.
- </li>
- </ul>
-
-
-
-
-
- <div class="jumbotron jumbotron-fluid">
+ <header>
<div class="container">
- <div class="title display-6">Learn More</div>
- <hr class="my-4">
- <div class="row">
- <div class="col-sm-6">
- <ul>
- <li>
- <a href="/features/sql.html">
- Distributed SQL <i class="fa fa-angle-double-right"></i>
- </a>
- </li>
- <li>
- <a href="/features/collocatedprocessing.html">
- Co-located Processing <i class="fa fa-angle-double-right"></i>
- </a>
- </li>
- <li><a href="/features/transactions.html">
- ACID Transactions <i class="fa fa-angle-double-right"></i>
- </a></li>
- <li><a href="/arch/persistence.html">
- Native Persistence <i class="fa fa-angle-double-right"></i>
- </a></li>
- </ul>
- </div>
- <div class="col-sm-6">
- <ul>
- <li>
- <a href="/features/machinelearning.html">
- Machine and Deep Learning <i class="fa fa-angle-double-right"></i>
- </a>
- </li>
- <li>
- <a href="/use-cases/datagrid.html">
- Ignite as an In-Memory Data Grid <i class="fa fa-angle-double-right"></i>
- </a>
- </li>
- <li><a href="/use-cases/in-memory-database.html">
- Ignite as an In-Memory Database <i class="fa fa-angle-double-right"></i>
- </a></li>
- <li><a href="/use-cases/dih.html">
- Ignite as a Digital Integration Hub <i class="fa fa-angle-double-right"></i>
- </a></li>
- </ul>
+
+ <h1><strong>Apache Hadoop Performance Acceleration</strong></h1>
+ </div>
+ </header>
+ <div class="container">
+ <p>
+ Apache Ignite® enables real-time analytics across Apache⢠Hadoop® operational and historical data silos. The
+ Ignite in-memory computing platform provides low-latency and high-throughput operations while Hadoop
+ continues to be used for long-running OLAP workloads.
+ </p>
+ <img class="diagram-right img-fluid" src="/images/svg-diagrams/hadoop_acceleration.svg"/>
+
+ <p>
+ As the architecture diagram on the right suggests, you can achieve the performance acceleration
+ of Hadoop-based systems by deploying Ignite as a separate distributed storage that maintains the data
+ sets required for your low-latency operations or real-time reports.
+ </p>
+
+ <p>
+ First, depending on the data volume and available memory capacity, you can enable Ignite native persistence
+ to
+ store historical data sets on disk while dedicating a memory space for operational records. You can
+ continue to use Hadoop as storage for less frequently used data or for long-running and ad-hoc
+ analytical queries.
+ </p>
+
+ <p>
+ Next, your applications and services should use Ignite native APIs to
+ process the data residing in the in-memory cluster. Ignite provides SQL, compute (aka. map-reduce),
+ and machine learning APIs for various data processing needs.
+ </p>
+
+ <p>
+ Finally, consider using Apache Spark DataFrames APIs if an application needs to run federated or
+ cross-database across Ignite and Hadoop clusters. Ignite is integrated with Spark, which natively
+ supports Hive/Hadoop. Cross-database queries should be considered only for a limited number of
+ scenarios when neither Ignite nor Hadoop contains the entire data set.
+ </p>
+
+
+ <h2>How to split data and operations between Ignite and Hadoop?</h2>
+ <p>
+ Consider using this approach:
+ </p>
+ <ul>
+ <li>
+ Use Apache Ignite for tasks that require low-latency response time (microseconds,
+ milliseconds, seconds), high throughput operations (thousands and millions of
+ operations per second), and real-time processing.
+ </li>
+ <li>
+ Continue using Apache Hadoop for high-latency operations (dozens of seconds, minutes, hours) and
+ batch processing.
+ </li>
+ </ul>
+
+
+ <h2>Getting Started Checklist</h2>
+ <p>
+ Follow the steps below to implement the discussed architecture in practice:
+ </p>
+ <ul>
+ <li>
+ Download and install Apache Ignite in your system.
+ </li>
+ <li>
+ Select a list of operations/reports to be executed against Ignite. The best candidates are
+ operations that require low-latency response time, high-throughput, and real-time analytics.
+ </li>
+ <li>
+ Depending on the data volume and available memory space, consider using Ignite native
+ persistence. Alternatively, you can use Ignite as a pure in-memory cache or in-memory data grid
+ that persists changes to Hadoop or another external database.
+ </li>
+ <li>
+ Update your applications to ensure they use Ignite native APIs to process Ignite data and Spark
+ for federated queries.
+ </li>
+ </ul>
+
+
+ <div class="jumbotron jumbotron-fluid">
+ <div class="container">
+ <div class="title display-6">Learn More</div>
+ <hr class="my-4">
+ <div class="row">
+ <div class="col-sm-6">
+ <ul>
+ <li>
+ <a href="/features/sql.html">
+ Distributed SQL <i class="fa fa-angle-double-right"></i>
+ </a>
+ </li>
+ <li>
+ <a href="/features/collocatedprocessing.html">
+ Co-located Processing <i class="fa fa-angle-double-right"></i>
+ </a>
+ </li>
+ <li><a href="/features/transactions.html">
+ ACID Transactions <i class="fa fa-angle-double-right"></i>
+ </a></li>
+ <li><a href="/arch/persistence.html">
+ Native Persistence <i class="fa fa-angle-double-right"></i>
+ </a></li>
+ </ul>
+ </div>
+ <div class="col-sm-6">
+ <ul>
+ <li>
+ <a href="/features/machinelearning.html">
+ Machine and Deep Learning <i class="fa fa-angle-double-right"></i>
+ </a>
+ </li>
+ <li>
+ <a href="/use-cases/datagrid.html">
+ Ignite as an In-Memory Data Grid <i class="fa fa-angle-double-right"></i>
+ </a>
+ </li>
+ <li><a href="/use-cases/in-memory-database.html">
+ Ignite as an In-Memory Database <i class="fa fa-angle-double-right"></i>
+ </a></li>
+ <li><a href="/use-cases/digital-integration-hub.html">
+ Ignite as a Digital Integration Hub <i class="fa fa-angle-double-right"></i>
+ </a></li>
+ </ul>
+ </div>
+ </div>
</div>
- </div>
</div>
- </div>
-
- </div>
+
+ </div>
</article>
<!--#include virtual="/includes/footer.html" -->
Modified: ignite/site/branches/ignite-redisign/use-cases/hpc.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/use-cases/hpc.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/use-cases/hpc.html (original)
+++ ignite/site/branches/ignite-redisign/use-cases/hpc.html Thu Mar 26 05:46:54 2020
@@ -41,7 +41,7 @@ under the License.
content="Apache Ignite enables high-performance computing by providing APIs for data and
compute-intensive calculations. Turn your commodity hardware or cloud environment into a distributed supercomputer."/>
- <title>High-Performance Computing With Apache Ignite</title>
+ <title>High-Performance Computing</title>
<!--#include virtual="/includes/styles.html" -->
@@ -51,27 +51,22 @@ under the License.
<!--#include virtual="/includes/header.html" -->
<article>
-<header><div class="container">
- <h1>High-Performance Computing <strong>With Apache Ignite</strong></h1></header>
-<div class="container">
+ <header>
+ <div class="container">
+ <h1><strong>High-Performance Computing</strong></h1>
+ </div>
+ </header>
+ <div class="container">
<p>
- Apache Ignite enables high-performance computing by providing APIs for data and
- compute-intensive calculations. The APIs implement the MapReduce paradigm and let you run
- arbitrary tasks across the cluster of Ignite nodes. High-performance computing (HPC) is the ability to
- process data and
- perform complex calculations at high speeds and with Ignite you can turn your commodity hardware or cloud
- environment into a distributed supercomputer.
+ High-performance computing (HPC) is the ability to process data and perform complex calculations at high
+ speeds.
+ Using Apache Ignite® as a high-performance compute cluster, you can turn a group of commodity machines or a
+ cloud environment into a distributed supercomputer of interconnected Ignite nodes. Ignite enables speed and scale
+ by processing records in memory and reducing network utilization with APIs for data and compute-intensive
+ calculations. Those APIs implement the MapReduce paradigm and allow you to run arbitrary tasks across the
+ cluster of nodes.
</p>
<img class="diagram-right img-fluid" src="/images/svg-diagrams/high_performance_compute.svg"/>
- <p>
- Having Ignite as a high-performance compute cluster, you can turn a group of commodity
- machines or a cloud environment into a distributed supercomputer of interconnected Ignite
- nodes.
- </p>
- <p>
- Ignite enables speed and scale for HPC scenarios by processing records in memory and reducing
- data shuffling and network utilization.
- </p>
<h2>Co-located Processing</h2>
Modified: ignite/site/branches/ignite-redisign/use-cases/in-memory-cache.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/use-cases/in-memory-cache.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/use-cases/in-memory-cache.html (original)
+++ ignite/site/branches/ignite-redisign/use-cases/in-memory-cache.html Thu Mar 26 05:46:54 2020
@@ -51,23 +51,22 @@ under the License.
<!--#include virtual="/includes/header.html" -->
<article>
<header><div class="container">
- <h1>In-Memory Cache <strong>With SQL, <br/>ACID Transactions and Compute APIs</strong></h1>
+ <h1>Apache Ignite as an <strong>In-Memory Cache</strong></h1>
</div></header>
<div class="container">
<p>
- Apache Ignite is used as a distributed in-memory cache that supports ANSI SQL,
- ACID transactions, co-located computations and machine learning libraries. From APIs and sessions caching
- to databases and microservices acceleration, Ignite provides all essential components required to speed up
- applications.
+ Apache Ignite® is a distributed in-memory cache that supports ANSI SQL, ACID transactions, co-located
+ computations and machine learning libraries. Ignite provides all essential components required to speed up
+ applications including APIs and sessions caching and acceleration for databases and microservices.
</p>
<img class="img-fluid diagram-right" src="/images/svg-diagrams/apps_acceleration.svg"/>
<p>
- As with classic distributed caches, you can span an Ignite cluster across several interconnected
- physical or virtual machines letting it utilize all the available memory and CPU resources. But the
- difference lies in the way you can use the cluster. In addition to standard key-value APIs, you can
- run distributed SQL queries joining and grouping various data sets. If strong consistency is required,
+ An Apache Ignite cluster can span several interconnected physical or virtual machines, allowing it to utilize
+ all the available memory and CPU resources, like a classic distributed cache. The difference between Ignite
+ and a classic distributed cache lies in the way you can use the cluster. With Ignite, in addition to standard
+ key-value APIs, you can run distributed SQL queries joining and grouping various data sets. If strong consistency is required,
you can execute multi-records and cross-cache ACID transactions in both pessimistic and optimistic
modes. Additionally, if an application runs compute or data-intensive logic, you can minimize data
shuffling and network utilization by running co-located computations and distributed machine learning
@@ -165,7 +164,7 @@ under the License.
<li><p><a href="/use-cases/in-memory-database.html">
Ignite as an In-Memory Database <i class="fa fa-angle-double-right"></i>
</a></p></li>
- <li><p><a href="/use-cases/dih.html">
+ <li><p><a href="/use-cases/digital-integration-hub.html">
Ignite as a Digital Integration Hub <i class="fa fa-angle-double-right"></i>
</a></p></li>
</ul>
Modified: ignite/site/branches/ignite-redisign/use-cases/in-memory-database.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/use-cases/in-memory-database.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/use-cases/in-memory-database.html (original)
+++ ignite/site/branches/ignite-redisign/use-cases/in-memory-database.html Thu Mar 26 05:46:54 2020
@@ -54,25 +54,24 @@ under the License.
<article>
<header>
<div class="container">
- <h1>In-Memory Database <strong>With Multi-Tier Storage</strong></h1>
+ <h1>Apache Ignite as an <strong>In-Memory Database</strong></h1>
</div>
</header>
<div class="container">
<p>
- Apache Ignite is used as a distributed in-memory database that scales horizontally across memory and disk
- tiers and supports ACID transactions, ANSI SQL, key-value, compute, machine learning, and other data
- processing APIs. As a database, Ignite uses memory, disk or Intel Optane as active storage tiers with
- no need for caching of all the data and memory warm-ups.
+ Apache Ignite® is a distributed in-memory database that scales horizontally across memory and disk tiers.
+ Ignite supports ACID transactions, ANSI-99 SQL, key-value, compute, machine learning, and other data
+ processing APIs. As a database, Ignite uses memory, disk or Intel® Optane⢠as active storage tiers and
+ removes the need to cache all the data and the need for memory warm-ups.
</p>
<img class="diagram-right img-responsive" src="/images/svg-diagrams/database.svg" />
<h2>Multi-Tier Storage</h2>
<p>
- Apache Ignite is designed to work with memory, disk, and Intel® Optane⢠as active storage tiers.
- Such architecture lets you combine the advantages of in-memory computing with disk durability and
- strong consistency in one system.
+ Apache Ignite works with memory, disk, and Intel Optane as active storage tiers. This architecture combines,
+ in one system, the advantages of in-memory computing with disk durability and strong consistency.
</p>
<p>
When the native persistence is enabled, Ignite allows you to control the amount of memory it should
@@ -150,7 +149,7 @@ under the License.
class="fa fa-angle-double-right"></i></a></li>
<li><a href="/use-cases/in-memory-cache.html">Ignite as an In-Memory Cache <i
class="fa fa-angle-double-right"></i></a></li>
- <li><a href="/use-cases/dih.html">Ignite as a Digital Integration Hub <i
+ <li><a href="/use-cases/digital-integration-hub.html">Ignite as a Digital Integration Hub <i
class="fa fa-angle-double-right"></i></a></li>
</ul>
</div>
Modified: ignite/site/branches/ignite-redisign/use-cases/key-value-store.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/use-cases/key-value-store.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/use-cases/key-value-store.html (original)
+++ ignite/site/branches/ignite-redisign/use-cases/key-value-store.html Thu Mar 26 05:46:54 2020
@@ -57,14 +57,11 @@ under the License.
</div>
</header>
<div class="container">
-
-
<p>
- Apache Ignite can operate as a distributed key-value store that stores data both in memory
- and on disk. In this deployment mode, Ignite functions as a distributed partitioned
- hash map with every cluster node owning a portion of the overall data set. As with
- standard key-value stores, you can access the cluster with key-value requests or take
- advantage of APIs available exclusively in Ignite - distributed ACID transactions, SQL,
+ Apache Ignite® is a distributed key-value store that stores data in memory and on disk. Ignite functions
+ as a distributed partitioned hash map with every cluster node owning a portion of the overall data set
+ in this deployment mode. You can access the cluster with key-value requests or take advantage of APIs
+ available exclusively in Ignite which include distributed ACID transactions, SQL,
co-located computations, and machine learning.
</p>
@@ -72,7 +69,7 @@ under the License.
<h2>JCache and Extended Key-Value APIs</h2>
<p>
- Ignite key-value APIs comply with JCache (JSR 107) specification supporting the following:
+ Ignite key-value APIs comply with the JCache (JSR 107) specification and support:
</p>
<ul class="page-list">
@@ -83,11 +80,11 @@ under the License.
</ul>
<p>
- In addition to that, Ignite extends JCache specification supporting distributed key-value ACID
- transactions, scan and continuous queries, co-located computations, and much more. For instance,
- continuous queries are handy for cases where you want an application to be notified whenever a record
- gets updated on the server nodes. While ACID transactions let you update a set of records stored in
- different caches/tables consistently.
+ Ignite also extends the JCache specification and supports distributed key-value ACID transactions,
+ scan and continuous queries, co-located computations, and much more. For instance, continuous
+ queries are useful if you want an application to be notified whenever a record gets updated on
+ the server nodes. The ACID transactions support lets you update a set of records stored in different
+ caches/tables with data consistency.
</p>
<h2>Near Cache</h2>
Modified: ignite/site/branches/ignite-redisign/use-cases/spark-acceleration.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/use-cases/spark-acceleration.html?rev=1875691&r1=1875690&r2=1875691&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/use-cases/spark-acceleration.html (original)
+++ ignite/site/branches/ignite-redisign/use-cases/spark-acceleration.html Thu Mar 26 05:46:54 2020
@@ -33,7 +33,7 @@ under the License.
<!DOCTYPE html>
<html lang="en">
<head>
-<link rel="canonical" href="https://ignite.apache.org/use-cases/spark-acceleration.html"/>
+ <link rel="canonical" href="https://ignite.apache.org/use-cases/spark-acceleration.html"/>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
@@ -41,121 +41,117 @@ under the License.
content="Apache Ignite integrates with Apache Spark to accelerate the performance of Spark applications
and APIs by keeping data in a shared in-memory cluster."/>
- <title>Apache Spark Performance Acceleration With Apache Ignite</title>
+ <title>Apache Spark Performance Acceleration</title>
<!--#include virtual="/includes/styles.html" -->
<!--#include virtual="/includes/sh.html" -->
</head>
<body>
-<!--#include virtual="/includes/header.html" -->
+<!--#include virtual="/includes/header.html" -->
<article>
- <header><div class="container">
- <h1>Apache Spark Performance <br />Acceleration <strong>With Apache Ignite</strong></h1>
-</div>
-</header>
-<div class="container">
-
-
- <p>
- Apache Ignite integrates with Apache Spark to accelerate the performance of Spark applications
- and APIs by keeping data in a shared in-memory cluster. Spark users can use Ignite as a data
- source in a way similar to Hadoop or a relational database. Just start an Ignite cluster, set
- it as a data source for Spark workers, and keep using Spark RDDs or DataFrames APIs or gain
- even more speed by running Ignite SQL or compute APIs directly.
- </p>
- <img class="img-fluid diagram-right" src="/images/svg-diagrams/spark_acceleration.svg" />
-
- <p>
- In addition to the performance acceleration of Spark applications, Ignite is used as a shared
- in-memory layer by those Spark workers that need to share both data and state.
- </p>
-
-
- <p>
- The performance increase is achievable for several reasons. First, Ignite is designed to store data sets
- in memory across a cluster of nodes reducing latency of Spark operations that usually need to pull date
- from disk-based systems. Second, Ignite tries to minimize data shuffling over the network between its
- store and Spark applications by running certain Spark tasks, produced by RDDs or DataFrames APIs,
- in-place on Ignite nodes. This optimization helps to reduce the effect of network latency on the
- performance of Spark calls. Finally, the network impact can be further reduced if the native
- Ignite APIs, such as SQL, are called from Spark applications directly. By doing so, you can eliminate
- data shuffling between Spark and Ignite as long as Ignite SQL queries are always executed on
- Ignite nodes returning a much smaller final result set to the application layer.
- </p>
-
- <h2>Ignite Shared RDDs</h2>
- <p>
- Apache Ignite provides an implementation of the Spark RDD, which allows any data and state to be shared
- in memory as RDDs across Spark jobs. The Ignite RDD provides a shared, mutable view of the data stored
- in Ignite caches across different Spark jobs, workers, or applications.
- </p>
-
- <p>
- The Ignite RDD is implemented as a view over a distributed Ignite table (aka. cache). It can be deployed
- with an Ignite node either within the Spark job executing process, on a Spark worker, or in a separate
- Ignite cluster. This means that depending on the chosen deployment mode, the shared state may either
- exist only during the lifespan of a Spark application (embedded mode), or it may out-survive the Spark
- application (standalone mode).
- </p>
-
- <h2>Ignite DataFrames</h2>
- <p>
- The Apache Spark DataFrame API introduced the concept of a schema to describe the data,
- allowing Spark to manage the schema and organize the data into a tabular format. To put it simply,
- a DataFrame is a distributed collection of data organized into named columns. It is conceptually
- equivalent to a table in a relational database and allows Spark to leverage the Catalyst query
- optimizer to produce much more efficient query execution plans in comparison to RDDs, which are
- collections of elements partitioned across the nodes of the cluster.
- </p>
- <p>
- Ignite supports DataFrame APIs allowing Spark to write to and read from Ignite through that interface.
- Furthermore, Ignite analyses execution plans produced by Spark's Catalyst engine and can execute
- parts of the plan on Ignite nodes directly, which will reduce data shuffling and consequently make your
- SparkSQL perform better.
- </p>
-
-
- <div class="jumbotron jumbotron-fluid">
+ <header>
<div class="container">
- <div class="title display-6">Learn More</div>
- <hr class="my-4">
- <div class="row">
- <div class="col-sm-6">
- <ul>
- <li>
- <a href="https://apacheignite-fs.readme.io/docs/installation-deployment" target="docs">
- Ignite and Spark Installation and Deployment <i class="fa fa-angle-double-right"></i>
- </a>
- </li>
- <li>
- <a href="https://apacheignite-fs.readme.io/docs/ignitecontext-igniterdd" target="docs">
- Ignite RDDs in Details <i class="fa fa-angle-double-right"></i>
- </a>
- </li>
- </ul>
- </div>
- <div class="col-sm-6">
- <ul>
- <li>
- <a href="https://apacheignite-fs.readme.io/docs/ignite-data-frame" target="docs">
- Ignite DataFrames in Details <i class="fa fa-angle-double-right"></i>
- </a>
- </li>
- <li>
-
- <a href="/use-cases/dih.html">
- Ignite as a Digital Integration Hub <i class="fa fa-angle-double-right"></i>
- </a>
-
- </li>
- </ul>
+ <h1><strong>Apache Spark Performance Acceleration</strong></h1>
+ </div>
+ </header>
+ <div class="container">
+ <p>
+ The performance of Apache Spark® applications can be accelerated by keeping data in a shared
+ Apache Ignite® in-memory cluster. Spark works with Ignite as a data source similar to how it uses Hadoop or a
+ relational database. You can start an Ignite cluster, set it as a data source for Spark workers, and
+ continue using Spark RDDs or DataFrames APIs. You can gain even more speed by running Ignite SQL or
+ compute APIs directly on the Spark dataset. Ignite can also be used as a distributed in-memory layer by Spark
+ workers that need to share both data and state.
+ </p>
+ <img class="img-fluid diagram-right" src="/images/svg-diagrams/spark_acceleration.svg"/>
+
+
+ <p>
+ The performance increase is achievable for several reasons. First, Ignite is designed to store data sets
+ in memory across a cluster of nodes reducing latency of Spark operations that usually need to pull date
+ from disk-based systems. Second, Ignite tries to minimize data shuffling over the network between its
+ store and Spark applications by running certain Spark tasks, produced by RDDs or DataFrames APIs,
+ in-place on Ignite nodes. This optimization helps to reduce the effect of network latency on the
+ performance of Spark calls. Finally, the network impact can be further reduced if the native
+ Ignite APIs, such as SQL, are called from Spark applications directly. By doing so, you can eliminate
+ data shuffling between Spark and Ignite as long as Ignite SQL queries are always executed on
+ Ignite nodes returning a much smaller final result set to the application layer.
+ </p>
+
+ <h2>Ignite Shared RDDs</h2>
+ <p>
+ Apache Ignite provides an implementation of the Spark RDD, which allows any data and state to be shared
+ in memory as RDDs across Spark jobs. The Ignite RDD provides a shared, mutable view of the data stored
+ in Ignite caches across different Spark jobs, workers, or applications.
+ </p>
+
+ <p>
+ The Ignite RDD is implemented as a view over a distributed Ignite table (aka. cache). It can be deployed
+ with an Ignite node either within the Spark job executing process, on a Spark worker, or in a separate
+ Ignite cluster. This means that depending on the chosen deployment mode, the shared state may either
+ exist only during the lifespan of a Spark application (embedded mode), or it may out-survive the Spark
+ application (standalone mode).
+ </p>
+
+ <h2>Ignite DataFrames</h2>
+ <p>
+ The Apache Spark DataFrame API introduced the concept of a schema to describe the data,
+ allowing Spark to manage the schema and organize the data into a tabular format. To put it simply,
+ a DataFrame is a distributed collection of data organized into named columns. It is conceptually
+ equivalent to a table in a relational database and allows Spark to leverage the Catalyst query
+ optimizer to produce much more efficient query execution plans in comparison to RDDs, which are
+ collections of elements partitioned across the nodes of the cluster.
+ </p>
+ <p>
+ Ignite supports DataFrame APIs allowing Spark to write to and read from Ignite through that interface.
+ Furthermore, Ignite analyses execution plans produced by Spark's Catalyst engine and can execute
+ parts of the plan on Ignite nodes directly, which will reduce data shuffling and consequently make your
+ SparkSQL perform better.
+ </p>
+
+
+ <div class="jumbotron jumbotron-fluid">
+ <div class="container">
+ <div class="title display-6">Learn More</div>
+ <hr class="my-4">
+ <div class="row">
+ <div class="col-sm-6">
+ <ul>
+ <li>
+ <a href="https://apacheignite-fs.readme.io/docs/installation-deployment" target="docs">
+ Ignite and Spark Installation and Deployment <i
+ class="fa fa-angle-double-right"></i>
+ </a>
+ </li>
+ <li>
+ <a href="https://apacheignite-fs.readme.io/docs/ignitecontext-igniterdd" target="docs">
+ Ignite RDDs in Details <i class="fa fa-angle-double-right"></i>
+ </a>
+ </li>
+ </ul>
+ </div>
+ <div class="col-sm-6">
+ <ul>
+ <li>
+ <a href="https://apacheignite-fs.readme.io/docs/ignite-data-frame" target="docs">
+ Ignite DataFrames in Details <i class="fa fa-angle-double-right"></i>
+ </a>
+ </li>
+ <li>
+
+ <a href="/use-cases/digital-integration-hub.html">
+ Ignite as a Digital Integration Hub <i class="fa fa-angle-double-right"></i>
+ </a>
+
+ </li>
+ </ul>
+ </div>
+ </div>
</div>
- </div>
</div>
- </div>
-
- </div>
+
+ </div>
</article>
<!--#include virtual="/includes/footer.html" -->