Author: dmagda
Date: Fri Jan 31 00:31:37 2020
New Revision: 1873391
URL: http://svn.apache.org/viewvc?rev=1873391&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=1873391&r1=1873390&r2=1873391&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
Fri Jan 31 00:31:37 2020
@@ -53,7 +53,7 @@ under the License.
<p>
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
+ and financial instruments 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.
@@ -68,44 +68,97 @@ under the License.
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
+ addition to classic 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 supporting both pessimistic and
optimistic modes. If an
+ cross-cache ACID transactions in both pessimistic and
optimistic modes. Next, 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.
+ utilization by running co-located computations and distributed
machine learning APIs on the cluster
+ nodes that store actual data.
</p>
<p>
- 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).
+ There are two primary deployment strategies for Ignite as an
in-memory cache -- the cache-aside one and
+ read-through/write-through caching. Let's review both
separately.
</p>
-
<div class="page-heading">Cache-Aside Deployment</div>
<p>
- TODO
+ With the cache-aside deployment strategy, the cache is
deployed separately from a primary data store
+ and might not even know about existence of the latter. An
application layer becomes in charge of
+ synchronization between a cache and a primary data store. If a
record gets updated in the data store
+ then either the application or some change-data-capture (CDC)
process needs to refresh a similar
+ record in the cache.
+ </p>
+ <p>
+ This strategy works well when the cached data is rather static
and not updated frequently or
+ temporary data lag/inconsistency is allowed between the
primary store and the in-memory cache. It's
+ assumed that the cache and the store will become consistent
eventually as soon as soon as changes are
+ replicated in full.
+ </p>
+ <p>
+ If Apache Ignite is deployed in this configuration, then its
native persistence can be used as a disk
+ store. This lets eliminate time-consuming data reloading phase
from the primary store as well as
+ a cache warm-up step. Furthermore, the native persistence
allows you to cache a subset of the data
+ by keeping the full copy on disk. If any piece of data is not
cached in RAM then Ignite will take
+ it from disk automatically regardless of the API you use let
it be SQL, key-value or scan queries.
</p>
- <div class="page-heading">Cache-X Deployment (aka. Data Grid)</div>
+ <div class="page-heading">Read-Through/Write-Through Caching</div>
<p>
- TODO
+ The read-through/write-through caching strategy can also be
classified as an in-memory data grid type
+ of deployment. When Apache Ignite is deployed as a data grid,
the application layer starts
+ treating an Ignite cluster as the primary store. The
applications write to and read from Ignite only
+ and it becomes Ignite's responsobility to keep an underlying
external database updated and
+ consistent with the in-memory data.
</p>
+ <p>
+ This strategy is favorable for architectures that need to
accelerate performance or scale external
+ disk-based databases such as RDBMS or NoSQL. Ignite integrates
with many databases out-of-the box and
+ can write-through or write-behind all the changes to them.
This also includes ACID transactions -
+ Ignite will coordinate and commit a transaction across its own
in-memory cluster as well as to a
+ relational database.
+ </p>
+ <p>
+ The read-through capability assumes that a cache can read data
from the external database if a record
+ is missing in memory. Ignite fully supports this capability
for key-value APIs. However, if Ignite
+ SQL is at use then you have to preload an entire data set from
the underlying database first. (Note,
+ that Ignite SQL can query disk if data is persisted in its own
native persistence).
+ </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 href="/features/sql.html">
+ <b>Distributed SQL <i class="fa
fa-angle-double-right"></i></b>
+ </a>
+ </p>
+ <p>
+ <a href="/features/collocatedprocessing.html">
+ <b>Co-located processing <i class="fa
fa-angle-double-right"></i></b>
+ </a>
+ </p>
+ <p>
+ <a href="/features/transactions.html">
+ <b>ACID Transactions <i class="fa
fa-angle-double-right"></i></b>
+ </a>
+ </p>
+ <p>
+ <a
href="https://apacheignite.readme.io/docs/distributed-persistent-store";
target="docs">
+ <b>Native Persistence <i class="fa
fa-angle-double-right"></i></b>
+ </a>
+ </p>
+ <p>
+ <a href="/features/datagrid.html">
+ <b>Ignite as an In-Memory Data Grid <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 href="/use-cases/database/in-memory-database.html">
+ <b>Ignite as an In-Memory Database <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 href="TODO">
+ <b>Ignite as a Digital Integration Hub <i class="fa
fa-angle-double-right"></i></b>
</a>
</p>
</section>
