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href="/blog/kafka-meets-wayang-2">Apache Kafka meets Apache Wayang - Part 2</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/blog/kafka-meets-wayang-1">Apache Kafka meets Apache Wayang - Part 1</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/blog/website_update">Website updated</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><article itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="The third part of this article series is an activity log."><header><h1 class="title_f1Hy" itemprop="headline">Apache Kafka meets Apache Wayang - Part 3</h1><div class="container_mt6G margin-vert--md"><time datetime="2024-03-10T00:00:00.000Z" itemprop="datePublished">March 10, 2024</time> · <!-- -->5 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><a href="https://github.com/kamir" target="_blank" rel="noopener noreferrer" class="avatar__photo-link"><img class="avatar__photo" src="https://avatars.githubusercontent.com/u/1241122?v=4" alt="Mirko Kämpf" itemprop="image"></a><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><a href="https://github.com/kamir" target="_blank" rel="noopener noreferrer" itemprop="url"><span itemprop="name">Mirko Kämpf</span></a></div><small class="avatar__subtitle" itemprop="description">Apache Committer</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><p>The third part of this article series is an activity log. |
| Motivated by the learnings from last time, I stated implementing a Kafka Source component and a Kafka Sink component for the Apache Spark platform in Apache Wayang. |
| In our previous article we shared the results of the work on the frist Apache Kafka integration using the Java Platform.</p> |
| <p>Let's see how it goes this time with Apache Spark.</p> |
| <h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-goal-of-this-implementation">The goal of this implementation<a href="#the-goal-of-this-implementation" class="hash-link" aria-label="Direct link to The goal of this implementation" title="Direct link to The goal of this implementation"></a></h2> |
| <p>We want to process data from Apache Kafka topics, which are hosted on Confluent cloud. |
| In our example scenario, the data is available in multiple different clusters, in different regions and owned by different organizations.</p> |
| <p>We assume, that the operator of our job has been granted appropriate permissions, and the topic owner already provided the configuration properties, including access coordinates and credentials.</p> |
| <p><img decoding="async" loading="lazy" alt="images/image-1.png" src="/assets/images/image-1-9cc35d5aea2b867d7e5759a96bd02334.png" width="904" height="550" class="img_ev3q"></p> |
| <p>This illustration has already been introduced in part one. |
| We focus on <strong>Job 4</strong> in the image and start to implement it. |
| This time we expect the processing load to be higher so that we want to utilize the scalability capabilities of Apache Spark.</p> |
| <p>Again, we start with a <strong>WayangContext</strong>, as shown by examples in the Wayang code repository.</p> |
| <div class="codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#393A34;--prism-background-color:#f6f8fa"><div class="codeBlockContent_biex"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#393A34;background-color:#f6f8fa"><code class="codeBlockLines_e6Vv"><span class="token-line" style="color:#393A34"><span class="token plain">WayangContext wayangContext = new WayangContext().with(Spark.basicPlugin());</span><br></span></code></pre><div class="buttonGroup__atx"><button type="button" aria-label="Copy code to clipboard" title="Copy" class="clean-btn"><span class="copyButtonIcons_eSgA" aria-hidden="true"><svg viewBox="0 0 24 24" class="copyButtonIcon_y97N"><path fill="currentColor" d="M19,21H8V7H19M19,5H8A2,2 0 0,0 6,7V21A2,2 0 0,0 8,23H19A2,2 0 0,0 21,21V7A2,2 0 0,0 19,5M16,1H4A2,2 0 0,0 2,3V17H4V3H16V1Z"></path></svg><svg viewBox="0 0 24 24" class="copyButtonSuccessIcon_LjdS"><path fill="currentColor" d="M21,7L9,19L3.5,13.5L4.91,12.09L9,16.17L19.59,5.59L21,7Z"></path></svg></span></button></div></div></div> |
| <p>We simply switched the backend system towards Apache Spark by using the <em>WayangContext</em> with <em>Spark.basicPlugin()</em>. |
| The <strong>JavaPlanBuilder</strong> and all other logic of our example job won't be touched.</p> |
| <p>In order to make this working we will now implement the Mappings and the Operators for the Apache Spark platform module.</p> |
| <h2 class="anchor anchorWithStickyNavbar_LWe7" id="implementation-of-input--and-output-operators">Implementation of Input- and Output Operators<a href="#implementation-of-input--and-output-operators" class="hash-link" aria-label="Direct link to Implementation of Input- and Output Operators" title="Direct link to Implementation of Input- and Output Operators"></a></h2> |
| <p>We reuse the Kafka Source and Kafka Sink components which have been created for the JavaKafkaSource and JavaKafkaSink. |
| Hence we work with Wayang's Java API.</p> |
| <p><strong>Level 1 – Wayang execution plan with abstract operators</strong></p> |
| <p>Since the <em>JavaPlanBuilder</em> already exposes the function for selecting a Kafka topic as source |
| and the <em>DataQuantaBuilder</em> class exposes the <em>writeKafkaTopic</em> function we can move on quickly.</p> |
| <p>Remember, in this API layer we use the Scala programming language, but we utilize the Java classes, implemented in the layer below.</p> |
| <p><strong>Level 2 – Wiring between Platform Abstraction and Implementation</strong></p> |
| <p>As in the case with the Java Platform, in the second layer we build a bridge between the WayangContext and the PlanBuilders, which work together with DataQuanta and the DataQuantaBuilder.</p> |
| <p>We must provide the mapping between the abstract components and the specific implementations in this layer.</p> |
| <p>Therefore, the mappings package in project <strong>wayang-platforms/wayang-spark</strong> has a class <em>Mappings</em> in which |
| our <em>KafkaTopicSinkMapping</em> and <em>KafkaTopicSourceMapping</em> will be registered.</p> |
| <p>Again, these classes allow the Apache Wayang framework to use the Java implementation of the KafkaTopicSource component (and KafkaTopicSink respectively).</p> |
| <p>While the Wayang execution plan uses the higher abstractions, here on the “platform level” we have to link the specific implementation for the target platform. |
| In this case this leads to an Apache Spark job, running on a Spark cluster which is set up by the Apache Wayang framework using the logical components of the execution plan, and the Apache Spark configuration provided at runtime.</p> |
| <p>A mapping links an operator implementation to the abstraction used in an execution plan. |
| We define two new mappings for our purpose, namely KafkaTopicSourceMapping, and KafkaTopicSinkMapping, both could be reused from last round.</p> |
| <p>For the Spark platform we simply replace the occurences of <em>JavaPlatform</em> with <em>SparkPlatform</em>.</p> |
| <p>Furthermore, we create an implementation of the <em>SparkKafkaTopicSource</em> and <em>SparkKafkaTopicSink</em>.</p> |
| <p><strong>Layer 3 – Input/Output Connector Layer</strong></p> |
| <p>Let's quickly recap, how does Apache Spark interacts with Apache Kafka?</p> |
| <p>There is already an integration which gives us a DataSet using the Spark SQL framework. |
| For Spark Streaming, there is also a Kafka integration using the <em>SparkSession</em>'s <em>readStream()</em> function. |
| Kafka client properties are provided as key value pairs <em>k</em> and <em>v</em> by using the <em>option( k, v )</em> function. |
| For writing into a topic, we can use the <em>writeStream()</em> function. |
| But from a first look, it seems to be not the best fit.</p> |
| <p>Another approach is possible. |
| We can use simple RDDs to process data previously consumed from Apache Kafka. |
| This is a more low-level approach compared to using Datasets with Spark Structured Streaming, |
| and it typically involves using the Kafka RDD API provided by Spark.</p> |
| <p>This approach is less common with newer versions of Spark, as Structured Streaming provides a higher-level abstraction that simplifies stream processing. |
| However, we might need that approach for the integration with Apache Wayang.</p> |
| <p>For now, we will focus on the lower level approach and plan to consume data from Kafka using a Kafka client, and then |
| we parallelize the records in an RDD.</p> |
| <p>This allows us to reuse <em>KafkaTopicSource</em> and <em>KafkaTopicSink</em> classes we built last time. |
| Those were made specifically for a simple non parallel Java program, using one Consumer and one Producer.</p> |
| <p>The selected approach does not yet fully take advantage from Spark's parallelism at load time. |
| For higher loads and especially for streaming processing we would have to investigate another approache, using a <em>SparkStreamingContext</em>, but this is out of scope for now.</p> |
| <p>Since we can't reuse the <em>JavaKafkaTopicSource</em> and <em>JavaKafkaTopicSink</em> we rather implement <em>SparkKafkaTopicSource</em> and <em>SparkKafkaTopicSink</em> based on given <em>SparkTextFileSource</em> and <em>SparkTextFileSink</em> which both cary all needed RDD specific logic.</p> |
| <h2 class="anchor anchorWithStickyNavbar_LWe7" id="summary">Summary<a href="#summary" class="hash-link" aria-label="Direct link to Summary" title="Direct link to Summary"></a></h2> |
| <p>As expected, the integration of Apache Spark with Apache Wayang was no magic, thanks to a fluent API design and a well structured architecture of Apache Wayang. |
| We could easily follow the pattern we have worked out in the previous exercise.</p> |
| <p>But a bunch of much more interesting work will follow next. |
| More testing, more serialization schemes, and Kafka Schema Registry support should follow, and full parallelization as well.</p> |
| <p>The code has been submitted to the Apache Wayang repository.</p> |
| <h2 class="anchor anchorWithStickyNavbar_LWe7" id="outlook">Outlook<a href="#outlook" class="hash-link" aria-label="Direct link to Outlook" title="Direct link to Outlook"></a></h2> |
| <p>The next part of the article series will cover the real world example as described in image 1. |
| We will show how analysts and developers can use the Apache Kafka integration for Apache Wayang to solve cross organizational collaboration issues. |
| Therefore, we will bring all puzzles together, and show the full implementation of the multi organizational data collaboration use case.</p></div><footer class="row docusaurus-mt-lg blogPostFooterDetailsFull_mRVl"><div class="col"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/blog/tags/wayang">wayang</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/blog/tags/kafka">kafka</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/blog/tags/spark">spark</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/blog/tags/cross-organization-data-collaboration">cross organization data collaboration</a></li></ul></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Blog post page navigation"><a class="pagination-nav__link pagination-nav__link--prev" href="/blog/wayang-python-api"><div class="pagination-nav__sublabel">Newer 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