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| <h1> |
| <a href="/2020/06/23/flink-on-zeppelin-notebooks-for-interactive-data-analysis-part-2/">Flink on Zeppelin Notebooks for Interactive Data Analysis - Part 2</a> |
| </h1> |
| |
| |
| |
| June 23, 2020 - |
| |
| |
| |
| Jeff Zhang |
| |
| <a href="https://twitter.com/zjffdu">(@zjffdu)</a> |
| |
| |
| |
| |
| <p><p>In a previous post, we introduced the basics of Flink on Zeppelin and how to do Streaming ETL. In this second part of the “Flink on Zeppelin” series of posts, I will share how to |
| perform streaming data visualization via Flink on Zeppelin and how to use Apache Flink UDFs in Zeppelin.</p> |
| <h1 id="streaming-data-visualization"> |
| Streaming Data Visualization |
| <a class="anchor" href="#streaming-data-visualization">#</a> |
| </h1> |
| <p>With <a href="https://zeppelin.apache.org/">Zeppelin</a>, you can build a real time streaming dashboard without writing any line of javascript/html/css code.</p> |
| <p>Overall, Zeppelin supports 3 kinds of streaming data analytics:</p> |
| <ul> |
| <li>Single Mode</li> |
| <li>Update Mode</li> |
| <li>Append Mode</li> |
| </ul> |
| <h3 id="single-mode"> |
| Single Mode |
| <a class="anchor" href="#single-mode">#</a> |
| </h3> |
| <p>Single mode is used for cases when the result of a SQL statement is always one row, such as the following example. |
| The output format is translated in HTML, and you can specify a paragraph local property template for the final output content template. |
| And you can use <code>{i}</code> as placeholder for the {i}th column of the result.</p> |
| <center> |
| <img src="/img/blog/2020-06-23-flink-on-zeppelin-part2/flink_single_mode.gif" width="80%" alt="Single Mode"/> |
| </center> |
| <h3 id="update-mode"> |
| Update Mode |
| <a class="anchor" href="#update-mode">#</a> |
| </h3> |
| <p>Update mode is suitable for the cases when the output format is more than one row, |
| and will always be continuously updated. Here’s one example where we use <code>GROUP BY</code>.</p> |
| <center> |
| <img src="/img/blog/2020-06-23-flink-on-zeppelin-part2/flink_update_mode.gif" width="80%" alt="Update Mode"/> |
| </center> |
| <h3 id="append-mode"> |
| Append Mode |
| <a class="anchor" href="#append-mode">#</a> |
| </h3> |
| <p>Append mode is suitable for the cases when the output data is always appended. |
| For instance, the example below uses a tumble window.</p> |
| <center> |
| <img src="/img/blog/2020-06-23-flink-on-zeppelin-part2/flink_append_mode.gif" width="80%" alt="Append Mode"/> |
| </center> |
| <h1 id="udf"> |
| UDF |
| <a class="anchor" href="#udf">#</a> |
| </h1> |
| <p>SQL is a very powerful language, especially in expressing data flow. But most of the time, you need to handle complicated business logic that cannot be expressed by SQL. |
| In these cases UDFs (user-defined functions) come particularly handy. In Zeppelin, you can write Scala or Python UDFs, while you can also import Scala, Python and Java UDFs. |
| Here are 2 examples of Scala and Python UDFs:</p> |
| <ul> |
| <li>Scala UDF</li> |
| </ul> |
| <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-scala" data-lang="scala"><span class="line"><span class="cl"><span class="o">%</span><span class="n">flink</span> |
| </span></span><span class="line"><span class="cl"> |
| </span></span><span class="line"><span class="cl"><span class="k">class</span> <span class="nc">ScalaUpper</span> <span class="k">extends</span> <span class="nc">ScalarFunction</span> <span class="o">{</span> |
| </span></span><span class="line"><span class="cl"><span class="k">def</span> <span class="n">eval</span><span class="o">(</span><span class="n">str</span><span class="k">:</span> <span class="kt">String</span><span class="o">)</span> <span class="k">=</span> <span class="n">str</span><span class="o">.</span><span class="n">toUpperCase</span> |
| </span></span><span class="line"><span class="cl"><span class="o">}</span> |
| </span></span><span class="line"><span class="cl"><span class="n">btenv</span><span class="o">.</span><span class="n">registerFunction</span><span class="o">(</span><span class="s">"scala_upper"</span><span class="o">,</span> <span class="k">new</span> <span class="nc">ScalaUpper</span><span class="o">())</span> |
| </span></span></code></pre></div><ul> |
| <li>Python UDF</li> |
| </ul> |
| <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="cl"> |
| </span></span><span class="line"><span class="cl"><span class="o">%</span><span class="n">flink</span><span class="o">.</span><span class="n">pyflink</span> |
| </span></span><span class="line"><span class="cl"> |
| </span></span><span class="line"><span class="cl"><span class="k">class</span> <span class="nc">PythonUpper</span><span class="p">(</span><span class="n">ScalarFunction</span><span class="p">):</span> |
| </span></span><span class="line"><span class="cl"><span class="k">def</span> <span class="nf">eval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span> |
| </span></span><span class="line"><span class="cl"> <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> |
| </span></span><span class="line"><span class="cl"> |
| </span></span><span class="line"><span class="cl"><span class="n">bt_env</span><span class="o">.</span><span class="n">register_function</span><span class="p">(</span><span class="s2">"python_upper"</span><span class="p">,</span> <span class="n">udf</span><span class="p">(</span><span class="n">PythonUpper</span><span class="p">(),</span> <span class="n">DataTypes</span><span class="o">.</span><span class="n">STRING</span><span class="p">(),</span> <span class="n">DataTypes</span><span class="o">.</span><span class="n">STRING</span><span class="p">()))</span> |
| </span></span></code></pre></div><p>After you define the UDFs, you can use them directly in SQL:</p> |
| <ul> |
| <li>Use Scala UDF in SQL</li> |
| </ul> |
| <center> |
| <img src="/img/blog/2020-06-23-flink-on-zeppelin-part2/flink_scala_udf.png" width="100%" alt="Scala UDF"/> |
| </center> |
| <ul> |
| <li>Use Python UDF in SQL</li> |
| </ul> |
| <center> |
| <img src="/img/blog/2020-06-23-flink-on-zeppelin-part2/flink_python_udf.png" width="100%" alt="Python UDF"/> |
| </center> |
| <h1 id="summary"> |
| Summary |
| <a class="anchor" href="#summary">#</a> |
| </h1> |
| <p>In this post, we explained how to perform streaming data visualization via Flink on Zeppelin and how to use UDFs. |
| Besides that, you can do more in Zeppelin with Flink, such as batch processing, Hive integration and more. |
| You can check the following articles for more details and here’s a list of <a href="https://www.youtube.com/watch?v=YxPo0Fosjjg&list=PL4oy12nnS7FFtg3KV1iS5vDb0pTz12VcX">Flink on Zeppelin tutorial videos</a> for your reference.</p> |
| <h1 id="references"> |
| References |
| <a class="anchor" href="#references">#</a> |
| </h1> |
| <ul> |
| <li><a href="http://zeppelin.apache.org">Apache Zeppelin official website</a></li> |
| <li>Flink on Zeppelin tutorials - <a href="https://medium.com/@zjffdu/flink-on-zeppelin-part-1-get-started-2591aaa6aa47">Part 1</a></li> |
| <li>Flink on Zeppelin tutorials - <a href="https://medium.com/@zjffdu/flink-on-zeppelin-part-2-batch-711731df5ad9">Part 2</a></li> |
| <li>Flink on Zeppelin tutorials - <a href="https://medium.com/@zjffdu/flink-on-zeppelin-part-3-streaming-5fca1e16754">Part 3</a></li> |
| <li>Flink on Zeppelin tutorials - <a href="https://medium.com/@zjffdu/flink-on-zeppelin-part-4-advanced-usage-998b74908cd9">Part 4</a></li> |
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