MarkSfik commented on a change in pull request #344:
URL: https://github.com/apache/flink-web/pull/344#discussion_r434478626
##########
File path: _posts/2020-05-25-flink-on-zeppelin-part2.md
##########
@@ -0,0 +1,107 @@
+---
+layout: post
+title: "Flink on Zeppelin Notebooks for Interactive Data Analysis - Part 2"
+date: 2020-05-25T08:00:00.000Z
+categories: ecosystem
+authors:
+- zjffdu:
+ name: "Jeff Zhang"
+ twitter: "zjffdu"
+---
+
+In the last post, I introduce the basics of Flink on Zeppelin and how to do
Streaming ETL. This is part-2 where I would talk about how to
+do streaming data visualization via Flink on Zeppelin and how to use flink UDF
in Zeppelin.
+
+# Streaming Data Visualization
+
+In Zeppelin, you can build a realtime streaming dashboard without writing any
line of javascript/html/css code.
+Overall Zeppelin supports 3 kinds of streaming data analytics:
+* Single
+* Update
+* Append
+
+### Single Mode
+Single mode is for the case when the result of sql statement is always one
row, such as the following example.
+The output format is HTML, and you can specify paragraph local property
template for the final output content template.
+And you can use {i} as placeholder for the ith column of result.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin-part2/flink_single_mode.gif"
width="80%" alt="Single Mode"/>
+</center>
+
+### Update Mode
+Update mode is suitable for the case when the output is more than one rows,
+and always will be updated continuously. Here’s one example where we use group
by.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin-part2/flink_update_mode.gif"
width="80%" alt="Update Mode"/>
+</center>
+
+### Append Mode
+Append mode is suitable for the scenario where output data is always appended.
+E.g. the following example which use tumble window.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin-part2/flink_append_mode.gif"
width="80%" alt="Append Mode"/>
+</center>
+
+# UDF
+
+SQL is powerful, especially in expressing data flow. But most of time, you
need to handle complicated business logic that can not be expressed by SQL.
+In these cases, you will need UDF (user defined function). In Zeppelin, you
can write Scala, Python UDF, and also import Scala, Python and Java UDF.
+Here're 2 examples of Scala and Python UDF.
+
+* Scala UDF
+
+```scala
+%flink
+
+class ScalaUpper extends ScalarFunction {
+def eval(str: String) = str.toUpperCase
+}
+btenv.registerFunction("scala_upper", new ScalaUpper())
+
+```
+
+* Python UDF
+
+```python
+
+%flink.pyflink
+
+class PythonUpper(ScalarFunction):
+def eval(self, s):
+ return s.upper()
+
+bt_env.register_function("python_upper", udf(PythonUpper(),
DataTypes.STRING(), DataTypes.STRING()))
+
+```
+
+After you define the UDF, you can use them directly in SQL. e.g.
+
+* Use Scala UDF in SQL
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin-part2/flink_scala_udf.png"
width="100%" alt="Scala UDF"/>
+</center>
+
+* Use Python UDF in SQL
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin-part2/flink_python_udf.png"
width="100%" alt="Python UDF"/>
+</center>
+
+# Summary
+
+In this post, I show you how to do streaming data visualization via Flink on
Zeppelin and how to use UDF.
+Besides that, you can do more in Zeppelin with Flink, such as batch
processing, hive integration and etc.
Review comment:
```suggestion
Besides that, you can do more in Zeppelin with Flink, such as batch
processing, hive integration and more.
```
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org
