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Posted to commits@flink.apache.org by nk...@apache.org on 2020/07/28 14:53:24 UTC
[flink-web] branch asf-site updated (fb6e73a -> 55b6c7c)
This is an automated email from the ASF dual-hosted git repository.
nkruber pushed a change to branch asf-site
in repository https://gitbox.apache.org/repos/asf/flink-web.git.
from fb6e73a fix links and rebuild
new 177fe4f Link blogposts
new 55b6c7c Rebuild website
The 2 revisions listed above as "new" are entirely new to this
repository and will be described in separate emails. The revisions
listed as "add" were already present in the repository and have only
been added to this reference.
Summary of changes:
_posts/2020-01-15-demo-fraud-detection.md | 4 ++--
content/blog/feed.xml | 6 +++---
content/news/2020/01/15/demo-fraud-detection.html | 4 ++--
3 files changed, 7 insertions(+), 7 deletions(-)
[flink-web] 02/02: Rebuild website
Posted by nk...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
nkruber pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/flink-web.git
commit 55b6c7c4379ca3ba6dfca5b720c4aa167ab4f779
Author: Nico Kruber <ni...@gmail.com>
AuthorDate: Tue Jul 28 16:52:43 2020 +0200
Rebuild website
---
content/blog/feed.xml | 6 +++---
content/news/2020/01/15/demo-fraud-detection.html | 4 ++--
2 files changed, 5 insertions(+), 5 deletions(-)
diff --git a/content/blog/feed.xml b/content/blog/feed.xml
index a77152d..4f96e80 100644
--- a/content/blog/feed.xml
+++ b/content/blog/feed.xml
@@ -13,7 +13,7 @@
<p>In the following sections, we describe how to integrate Kafka, MySQL, Elasticsearch, and Kibana with Flink SQL to analyze e-commerce user behavior in real-time. All exercises in this blogpost are performed in the Flink SQL CLI, and the entire process uses standard SQL syntax, without a single line of Java/Scala code or IDE installation. The final result of this demo is shown in the following figure:</p>
<center>
-<img src="/img/blog/2020-05-03-flink-sql-demo/image1.gif" width="650px" alt="Demo Overview" />
+<img src="/img/blog/2020-07-28-flink-sql-demo/image1.gif" width="650px" alt="Demo Overview" />
</center>
<p><br /></p>
@@ -5125,7 +5125,7 @@ However, you need to take care of another aspect, which is providing timestamps
<description><p>In this series of blog posts you will learn about three powerful Flink patterns for building streaming applications:</p>
<ul>
- <li>Dynamic updates of application logic</li>
+ <li><a href="/news/2020/03/24/demo-fraud-detection-2.html">Dynamic updates of application logic</a></li>
<li>Dynamic data partitioning (shuffle), controlled at runtime</li>
<li>Low latency alerting based on custom windowing logic (without using the window API)</li>
</ul>
@@ -5325,7 +5325,7 @@ To understand why this is the case, let us start with articulating a realistic s
</center>
<p><br /></p>
-<p>In the next article, we will see how Flink’s broadcast streams can be utilized to help steer the processing within the Fraud Detection engine at runtime (Dynamic Application Updates pattern).</p>
+<p>In the <a href="/news/2020/03/24/demo-fraud-detection-2.html">next article</a>, we will see how Flink’s broadcast streams can be utilized to help steer the processing within the Fraud Detection engine at runtime (Dynamic Application Updates pattern).</p>
</description>
<pubDate>Wed, 15 Jan 2020 13:00:00 +0100</pubDate>
<link>https://flink.apache.org/news/2020/01/15/demo-fraud-detection.html</link>
diff --git a/content/news/2020/01/15/demo-fraud-detection.html b/content/news/2020/01/15/demo-fraud-detection.html
index 22fe277..dcb51b4 100644
--- a/content/news/2020/01/15/demo-fraud-detection.html
+++ b/content/news/2020/01/15/demo-fraud-detection.html
@@ -200,7 +200,7 @@
<p>In this series of blog posts you will learn about three powerful Flink patterns for building streaming applications:</p>
<ul>
- <li>Dynamic updates of application logic</li>
+ <li><a href="/news/2020/03/24/demo-fraud-detection-2.html">Dynamic updates of application logic</a></li>
<li>Dynamic data partitioning (shuffle), controlled at runtime</li>
<li>Low latency alerting based on custom windowing logic (without using the window API)</li>
</ul>
@@ -400,7 +400,7 @@ To understand why this is the case, let us start with articulating a realistic s
</center>
<p><br /></p>
-<p>In the next article, we will see how Flink’s broadcast streams can be utilized to help steer the processing within the Fraud Detection engine at runtime (Dynamic Application Updates pattern).</p>
+<p>In the <a href="/news/2020/03/24/demo-fraud-detection-2.html">next article</a>, we will see how Flink’s broadcast streams can be utilized to help steer the processing within the Fraud Detection engine at runtime (Dynamic Application Updates pattern).</p>
</article>
</div>
[flink-web] 01/02: Link blogposts
Posted by nk...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
nkruber pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/flink-web.git
commit 177fe4fbe3028b2b9f9ff00e56ce15665ef4a880
Author: Alexander Fedulov <14...@users.noreply.github.com>
AuthorDate: Thu Jul 2 14:38:40 2020 +0200
Link blogposts
This closes #354.
---
_posts/2020-01-15-demo-fraud-detection.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/_posts/2020-01-15-demo-fraud-detection.md b/_posts/2020-01-15-demo-fraud-detection.md
index 96a3c27..291dde7 100644
--- a/_posts/2020-01-15-demo-fraud-detection.md
+++ b/_posts/2020-01-15-demo-fraud-detection.md
@@ -13,7 +13,7 @@ excerpt: In this series of blog posts you will learn about three powerful Flink
In this series of blog posts you will learn about three powerful Flink patterns for building streaming applications:
- - Dynamic updates of application logic
+ - [Dynamic updates of application logic]({{ site.baseurl }}/news/2020/03/24/demo-fraud-detection-2.html)
- Dynamic data partitioning (shuffle), controlled at runtime
- Low latency alerting based on custom windowing logic (without using the window API)
@@ -219,4 +219,4 @@ In the second part of this series, we will describe how the rules make their way
</center>
<br/>
-In the next article, we will see how Flink's broadcast streams can be utilized to help steer the processing within the Fraud Detection engine at runtime (Dynamic Application Updates pattern).
+In the [next article]({{ site.baseurl }}/news/2020/03/24/demo-fraud-detection-2.html), we will see how Flink's broadcast streams can be utilized to help steer the processing within the Fraud Detection engine at runtime (Dynamic Application Updates pattern).