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Posted to commits@ignite.apache.org by dm...@apache.org on 2020/01/30 21:00:28 UTC
svn commit: r1873382 -
/ignite/site/branches/ignite-redisign/use-cases/caching/database-caching.html
Author: dmagda
Date: Thu Jan 30 21:00:28 2020
New Revision: 1873382
URL: http://svn.apache.org/viewvc?rev=1873382&view=rev
Log:
Working on the in-memory cache use case
Modified:
ignite/site/branches/ignite-redisign/use-cases/caching/database-caching.html
Modified: ignite/site/branches/ignite-redisign/use-cases/caching/database-caching.html
URL: http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/use-cases/caching/database-caching.html?rev=1873382&r1=1873381&r2=1873382&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/use-cases/caching/database-caching.html (original)
+++ ignite/site/branches/ignite-redisign/use-cases/caching/database-caching.html Thu Jan 30 21:00:28 2020
@@ -36,7 +36,7 @@ under the License.
<link rel="canonical" href="https://ignite.apache.org/use-cases/caching/database-caching.html" />
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
- <title>Database Caching Use Case - Apache Ignite</title>
+ <title>Distributed In-Memory Cache With SQL and ACID Transactions - Apache Ignite</title>
<!--#include virtual="/includes/styles.html" -->
<!--#include virtual="/includes/sh.html" -->
@@ -47,50 +47,66 @@ under the License.
<main id="main" role="main" class="container">
<section id="database-caching" class="page-section">
- <h1 class="first">Database Caching</h1>
+ <h1 class="first">Distributed In-Memory Cache With SQL and ACID Transactions</h1>
<div class="col-sm-12 col-md-12 col-xs-12" style="padding:0 0 20px 0;">
<div class="col-sm-6 col-md-6 col-xs-12" style="padding-left:0;">
<p>
- Ignite <code>database caching</code> is implemented in the
- <nobr><a href="/features/datagrid.html">data grid</a></nobr>
- component. It enables users to keep the most frequently accessed data in memory, by either
- partitioning or replicating it across a cluster of computers.
- </p>
- <div class="page-heading">Write-Through & Read-Through</div>
- <p>
- Whenever an update happens in the in-memory cache, Ignite can optionally propagate it
- to the underlying database. Also, whenever the data is requested from cache and is not
- there, Ignite can automatically load it from the underlying database. Such behavior
- is called <code class="text-nowrap">write-through</code> and
- <code class="text-nowrap">read-through</code> correspondingly.
+ Distributed in-memory cache that supports ANSI SQL, ACID transactions and co-located
+ computations is one of the usage scenarios Apache Ignite is selected for. From web sessions
+ and financial securities caching to external APIs and existing databases acceleration, Ignite
+ provides all essential components needed to speed up enterprise applications as well as
+ microservices-based architectures that require more than standard key-value look-ups for data
+ processing.
</p>
</div>
<div class="col-sm-6 col-md-5 col-xs-12" style="padding-right:0">
<img class="img-responsive" src="/images/in_memory_data.png" width="400px" style="float:right;"/>
</div>
</div>
- <div class="page-heading">Write-Behind</div>
+
<p>
- In a simple write-through mode each cache put and remove operation will involve a
- corresponding request to the persistent storage and therefore the overall duration of
- the cache update might be relatively long. To mitigate such cases, Ignite offers an
- option to perform an asynchronous persistent store update also known as
- <code class="text-nowrap">write-behind</code>.
+ As a typical distributed cache, you would span Ignite cluster across several interconnected
+ physical or virtual machines letting it utilize all the available memory and CPU resources.
+ However, the difference comes in a way you can utilize the cluster once it's up and running. In
+ addition to classic key-value APIs you can run distributed ANSI SQL queries joining and grouping
+ various data sets. If strong consistency is required, you can execute multi-records and
+ cross-cache ACID transactions supporting both pessimistic and optimistic modes. 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 APIs.
</p>
- <div class="page-heading">Automatic Persistence</div>
+
<p>
- Ignite ships with its own database schema mapping wizard which provides automatic support for
- integrating with persistence stores. This utility automatically connects to the underlying database
- and generates all the required XML OR-mapping configuration and Java domain model POJOs.
+ There are two primary deployment patterns for Ignite as an in-memory cache which are a cache-aside
+ and data grid modes. TODO (mention that if the cache-aside architecture is selected then Ignite
+ persistence can be used).
</p>
- <div class="page-heading">In-Memory Data Grid</div>
+
+
+ <div class="page-heading">Cache-Aside Deployment</div>
<p>
- See <a href="/features/datagrid.html" class="text-nowrap" target="_blank" rel="noopener"><b>in-memory data grid</b></a>
- for more information.
+ TODO
</p>
+ <div class="page-heading">Cache-X Deployment (aka. Data Grid)</div>
<p>
+ TODO
+ </p>
+ <div class="page-heading">Learn More</div>
+ <p>
+ <a href="https://apacheignite-fs.readme.io/docs/installation-deployment" target="docs">
+ <b>Ignite and Spark Installation and Deployment <i class="fa fa-angle-double-right"></i></b>
+ </a>
+ </p>
+ <p>
+ <a href="https://apacheignite-fs.readme.io/docs/ignitecontext-igniterdd" target="docs">
+ <b>Ignite RDDs in Details <i class="fa fa-angle-double-right"></i></b>
+ </a>
+ </p>
+ <p>
+ <a href="https://apacheignite-fs.readme.io/docs/ignite-data-frame" target="docs">
+ <b>Ignite DataFrames in Details <i class="fa fa-angle-double-right"></i></b>
+ </a>
</p>
</section>
</main>