MarkSfik commented on a change in pull request #344:
URL: https://github.com/apache/flink-web/pull/344#discussion_r434478209
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File path: _posts/2020-05-25-flink-on-zeppelin-part2.md
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+---
+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.
Review comment:
```suggestion
After you define the UDFs, you can use them directly in SQL. e.g.
```
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