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From jkr...@apache.org
Subject svn commit: r1580634 - /kafka/site/081/uses.html
Date Sun, 23 Mar 2014 22:09:45 GMT
Author: jkreps
Date: Sun Mar 23 22:09:45 2014
New Revision: 1580634

URL: http://svn.apache.org/r1580634
Log:
Mention event sourcing in the "use cases section of the documentation.


Modified:
    kafka/site/081/uses.html

Modified: kafka/site/081/uses.html
URL: http://svn.apache.org/viewvc/kafka/site/081/uses.html?rev=1580634&r1=1580633&r2=1580634&view=diff
==============================================================================
--- kafka/site/081/uses.html (original)
+++ kafka/site/081/uses.html Sun Mar 23 22:09:45 2014
@@ -1,6 +1,6 @@
 <h3><a id="uses">1.2 Use Cases</a></h3>
 
-Here is a description of a few of the popular use cases for Apache Kafka. For an overview
of a number of these areas in action, see <a href="http://sites.computer.org/debull/A12june/pipeline.pdf">this
paper</a> or <a href="http://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying">this
blog post</a>.
+Here is a description of a few of the popular use cases for Apache Kafka. For an overview
of a number of these areas in action, see <a href="http://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying">this
blog post</a>.
 
 <h4>Messaging</h4>
 
@@ -30,6 +30,10 @@ In comparison to log-centric systems lik
 
 Many users end up doing stage-wise processing of data where data is consumed from topics
of raw data and then aggregated, enriched, or otherwise transformed into new Kafka topics
for further consumption. For example a processing flow for article recommendation might crawl
article content from RSS feeds and publish it to an "articles" topic; further processing might
help normalize or deduplicate this content to a topic of cleaned article content; a final
stage might attempt to match this content to users. This creates a graph of real-time data
flow out of the individual topics. <a href="https://github.com/nathanmarz/storm">Storm</a>
and <a href="http://samza.incubator.apache.org/">Samza</a> are popular frameworks
for implementing these kinds of transformations.
 
+<h4>Event Sourcing</h4>
+
+<a href="http://martinfowler.com/eaaDev/EventSourcing.html">Event sourcing</a>
is a style of application design where state changes are logged as a time-ordered sequence
of records. Kafka's support for very large stored log data makes it an excellent backend for
an application built in this style.
+
 <h4>Commit Log</h4>
 
 Kafka can serve as a kind of external commit-log for a distributed system. The log helps
replicate data between nodes and acts as a re-syncing mechanism for failed nodes to restore
their data. The <a href="/documentation.html#compaction">log compaction</a> feature
in Kafka helps support this usage. In this usage Kafka is similar to <a href="http://zookeeper.apache.org/bookkeeper/">Apache
BookKeeper</a> project.
\ No newline at end of file



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