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Posted to commits@streampark.apache.org by gi...@apache.org on 2024/06/30 15:00:39 UTC
(incubator-streampark-website) branch asf-site updated (22e73bb3 -> b9fb9efd)
This is an automated email from the ASF dual-hosted git repository.
github-bot pushed a change to branch asf-site
in repository https://gitbox.apache.org/repos/asf/incubator-streampark-website.git
discard 22e73bb3 deploy: cb75755a0eda95c9cda698e3570566ae0cb502f5
new b9fb9efd deploy: 5c93bc28455493fb55e156305399d61e43558e17
This update added new revisions after undoing existing revisions.
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branch are not in the new version. This situation occurs
when a user --force pushes a change and generates a repository
containing something like this:
* -- * -- B -- O -- O -- O (22e73bb3)
\
N -- N -- N refs/heads/asf-site (b9fb9efd)
You should already have received notification emails for all of the O
revisions, and so the following emails describe only the N revisions
from the common base, B.
Any revisions marked "omit" are not gone; other references still
refer to them. Any revisions marked "discard" are gone forever.
The 1 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:
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(incubator-streampark-website) 01/01: deploy: 5c93bc28455493fb55e156305399d61e43558e17
Posted by gi...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
github-bot pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/incubator-streampark-website.git
commit b9fb9efd562bba13d288847f65517e66eb9a56ba
Author: wolfboys <wo...@users.noreply.github.com>
AuthorDate: Sun Jun 30 15:00:27 2024 +0000
deploy: 5c93bc28455493fb55e156305399d61e43558e17
---
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zh-CN/user/dyrs.png | Bin 0 -> 84873 bytes
zh-CN/user/esign.png | Bin 0 -> 14140 bytes
zh-CN/user/fy-data.png | Bin 0 -> 17602 bytes
zh-CN/user/gigacloud_technology.png | Bin 0 -> 46507 bytes
zh-CN/user/grow.png | Bin 0 -> 11303 bytes
zh-CN/user/haibo.png | Bin 0 -> 9896 bytes
zh-CN/user/hopson.png | Bin 0 -> 34017 bytes
zh-CN/user/hrfax.png | Bin 0 -> 222703 bytes
zh-CN/user/idataway.png | Bin 0 -> 29381 bytes
zh-CN/user/index.html | 43 +
zh-CN/user/inmobi.png | Bin 0 -> 7094 bytes
zh-CN/user/itcast.png | Bin 0 -> 17174 bytes
zh-CN/user/joy_u.png | Bin 0 -> 13373 bytes
zh-CN/user/joyme.png | Bin 0 -> 10401 bytes
zh-CN/user/kcbebank.png | Bin 0 -> 103653 bytes
zh-CN/user/lccomputing.png | Bin 0 -> 38012 bytes
zh-CN/user/longping.png | Bin 0 -> 199840 bytes
zh-CN/user/lydigi.png | Bin 0 -> 109364 bytes
zh-CN/user/mafengwo.png | Bin 0 -> 31073 bytes
zh-CN/user/relx.png | Bin 0 -> 35305 bytes
zh-CN/user/shudui.png | Bin 0 -> 78870 bytes
zh-CN/user/shunwang.png | Bin 0 -> 223769 bytes
zh-CN/user/sobot.png | Bin 0 -> 43852 bytes
zh-CN/user/supcon.png | Bin 0 -> 6295 bytes
zh-CN/user/tencent.png | Bin 0 -> 11383 bytes
zh-CN/user/tenclass.png | Bin 0 -> 22863 bytes
zh-CN/user/treeroot.png | Bin 0 -> 113310 bytes
zh-CN/user/vixtel.png | Bin 0 -> 3646 bytes
zh-CN/user/vlion.png | Bin 0 -> 113634 bytes
zh-CN/user/vphonor.png | Bin 0 -> 50616 bytes
zh-CN/user/wondersgroup.png | Bin 0 -> 13512 bytes
zh-CN/user/yingzi.png | Bin 0 -> 9145 bytes
zh-CN/user/yipage.png | Bin 0 -> 18854 bytes
zh-CN/user/yonghui.png | Bin 0 -> 18814 bytes
zh-CN/user/yto.png | Bin 0 -> 30092 bytes
zh-CN/user/yucun.png | Bin 0 -> 52464 bytes
zh-CN/user/yunlizhi.png | Bin 0 -> 30235 bytes
zh-CN/user/zehoo.png | Bin 0 -> 66107 bytes
zh-CN/user/zhengzhou.png | Bin 0 -> 15031 bytes
zh-CN/user/ziroom.png | Bin 0 -> 50857 bytes
zh-CN/user/zoomlion.png | Bin 0 -> 55508 bytes
zh-CN/user/zto.png | Bin 0 -> 150344 bytes
2448 files changed, 36709 insertions(+)
diff --git a/.asf.yaml b/.asf.yaml
new file mode 100644
index 00000000..608973a0
--- /dev/null
+++ b/.asf.yaml
@@ -0,0 +1,39 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+github:
+ description: "Apache streampark Website"
+ homepage: https://streampark.apache.org/
+ features:
+ issues: false
+ projects: false
+ wiki: false
+ labels:
+ - streampark
+ enabled_merge_buttons:
+ squash: true
+ merge: false
+ rebase: false
+
+publish:
+ whoami: asf-site
+
+notifications:
+ commits: commits@streampark.apache.org
+ issues: issues@streampark.apache.org
+ pullrequests: issues@streampark.apache.org
+ jobs: builds@streampark.apache.org
diff --git a/.nojekyll b/.nojekyll
new file mode 100644
index 00000000..e69de29b
diff --git a/404.html b/404.html
new file mode 100644
index 00000000..e0d95685
--- /dev/null
+++ b/404.html
@@ -0,0 +1,43 @@
+<!doctype html>
+<html lang="en" dir="ltr" class="plugin-native plugin-id-default" data-has-hydrated="false">
+<head>
+<meta charset="UTF-8">
+<meta name="generator" content="Docusaurus v3.3.2">
+<title data-rh="true">Page Not Found | Apache StreamPark (incubating)</title><meta data-rh="true" name="viewport" content="width=device-width,initial-scale=1"><meta data-rh="true" name="twitter:card" content="summary_large_image"><meta data-rh="true" property="og:url" content="https://streampark.apache.org/404.html"><meta data-rh="true" property="og:locale" content="en"><meta data-rh="true" property="og:locale:alternate" content="zh_CN"><meta data-rh="true" name="docusaurus_locale" conte [...]
+<link rel="alternate" type="application/atom+xml" href="/blog/atom.xml" title="Apache StreamPark (incubating) Atom Feed"><link rel="stylesheet" href="/assets/css/styles.8c72ea81.css">
+<script src="/assets/js/runtime~main.de93b3db.js" defer="defer"></script>
+<script src="/assets/js/main.2e3dadaf.js" defer="defer"></script>
+</head>
+<body class="navigation-with-keyboard">
+<script>!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return localStorage.getItem("theme")}catch(t){}}();null!==e?t(e):window.matchMedia("(prefers-color-scheme: dark)").matches?t("dark"):window.matchMedia("(prefers-color-scheme: light)").matches?t("light"):t("dark")}(),function(){try{const c=new URLSearchParams(window.location.sea [...]
+ <div class="subscribe-box btns">
+ <a class="btn btn-primary" href="https://github.com/apache/incubator-streampark"><i class="fa fa-github"></i><span>Github</span></a>
+ <a class="btn btn-primary" href="https://github.com/apache/incubator-streampark/issues"><i class="fa fa-slack"></i><span>Issue Tracking</span></a>
+ <a class="btn btn-primary" href="javascript:void(0)">
+ <i class="fa fa-wechat"></i>
+ <span>Wechat</span>
+ <div class="wechat-dropdown"><img src="/image/join_wechat.png" alt="weChat"></div>
+ </a>
+ </div>
+ </li></ul></div></div><div class="footer__bottom text--center"><div class="footer__copyright"><div>
+ <div>
+ <div style="margin-bottom: 30px;">
+ <a href="https://incubator.apache.org/" class="footerLogoLink" one-link-mark="yes">
+ <img alt="Apache Incubator logo" class="footer__logo" width="200">
+ </a>
+ </div>
+ <div>
+ <p>
+ Apache StreamPark is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate t [...]
+ </p>
+ </div>
+ </div>
+
+ <div>
+ <span>
+ Copyright © 2022-2024 The Apache Software Foundation. Apache StreamPark, StreamPark, and its feather logo are trademarks of The Apache Software Foundation.
+ </span>
+ </div>
+ </div></div></div></div></footer></div>
+</body>
+</html>
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--- /dev/null
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+@charset "UTF-8";.col,.container{padding:0 var(--ifm-spacing-horizontal)}.markdown>h2,.markdown>h3,.markdown>h4,.markdown>h5,.markdown>h6{margin-bottom:calc(var(--ifm-heading-vertical-rhythm-bottom)*var(--ifm-leading))}pre,table{overflow:auto}a,pre code{background-color:initial}blockquote,pre{margin:0 0 var(--ifm-spacing-vertical)}.breadcrumbs__link,.button{transition-timing-function:var(--ifm-transition-timing-default)}.button,code{vertical-align:middle}.button--outline.button--active,. [...]
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diff --git a/assets/images/dws_business_order_count_op-72bb62943dcef89451b47db142cfe689.png b/assets/images/dws_business_order_count_op-72bb62943dcef89451b47db142cfe689.png
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+ * Snowball JavaScript Library v0.3
+ * http://code.google.com/p/urim/
+ * http://snowball.tartarus.org/
+ *
+ * Copyright 2010, Oleg Mazko
+ * http://www.mozilla.org/MPL/
+ */
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+ * Copyright (C) 2020 Oliver Nightingale
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+ * Copyright (C) 2020 Oliver Nightingale
+ * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt
+ */
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+ * Copyright (C) 2020 Oliver Nightingale
+ */
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+ * lunr.tokenizer
+ * Copyright (C) 2020 Oliver Nightingale
+ */
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+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/*!
+ * lunr.utils
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/*! Bundled license information:
+
+prismjs/prism.js:
+ (**
+ * Prism: Lightweight, robust, elegant syntax highlighting
+ *
+ * @license MIT <https://opensource.org/licenses/MIT>
+ * @author Lea Verou <https://lea.verou.me>
+ * @namespace
+ * @public
+ *)
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+
+/**
+ * @license React
+ * react-dom.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/**
+ * @license React
+ * react-jsx-runtime.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/**
+ * @license React
+ * react.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/**
+ * @license React
+ * scheduler.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/**
+ * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9
+ * Copyright (C) 2020 Oliver Nightingale
+ * @license MIT
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+/** @license React v16.13.1
+ * react-is.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
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diff --git a/blog/archive/index.html b/blog/archive/index.html
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+<!doctype html>
+<html lang="en" dir="ltr" class="plugin-blog plugin-id-default" data-has-hydrated="false">
+<head>
+<meta charset="UTF-8">
+<meta name="generator" content="Docusaurus v3.3.2">
+<title data-rh="true">Archive | Apache StreamPark (incubating)</title><meta data-rh="true" name="viewport" content="width=device-width,initial-scale=1"><meta data-rh="true" name="twitter:card" content="summary_large_image"><meta data-rh="true" property="og:url" content="https://streampark.apache.org/blog/archive"><meta data-rh="true" property="og:locale" content="en"><meta data-rh="true" property="og:locale:alternate" content="zh_CN"><meta data-rh="true" name="docusaurus_locale" content= [...]
+<link rel="alternate" type="application/atom+xml" href="/blog/atom.xml" title="Apache StreamPark (incubating) Atom Feed"><link rel="stylesheet" href="/assets/css/styles.8c72ea81.css">
+<script src="/assets/js/runtime~main.de93b3db.js" defer="defer"></script>
+<script src="/assets/js/main.2e3dadaf.js" defer="defer"></script>
+</head>
+<body class="navigation-with-keyboard">
+<script>!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return localStorage.getItem("theme")}catch(t){}}();null!==e?t(e):window.matchMedia("(prefers-color-scheme: dark)").matches?t("dark"):window.matchMedia("(prefers-color-scheme: light)").matches?t("light"):t("dark")}(),function(){try{const c=new URLSearchParams(window.location.sea [...]
+ <div class="subscribe-box btns">
+ <a class="btn btn-primary" href="https://github.com/apache/incubator-streampark"><i class="fa fa-github"></i><span>Github</span></a>
+ <a class="btn btn-primary" href="https://github.com/apache/incubator-streampark/issues"><i class="fa fa-slack"></i><span>Issue Tracking</span></a>
+ <a class="btn btn-primary" href="javascript:void(0)">
+ <i class="fa fa-wechat"></i>
+ <span>Wechat</span>
+ <div class="wechat-dropdown"><img src="/image/join_wechat.png" alt="weChat"></div>
+ </a>
+ </div>
+ </li></ul></div></div><div class="footer__bottom text--center"><div class="footer__copyright"><div>
+ <div>
+ <div style="margin-bottom: 30px;">
+ <a href="https://incubator.apache.org/" class="footerLogoLink" one-link-mark="yes">
+ <img alt="Apache Incubator logo" class="footer__logo" width="200">
+ </a>
+ </div>
+ <div>
+ <p>
+ Apache StreamPark is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate t [...]
+ </p>
+ </div>
+ </div>
+
+ <div>
+ <span>
+ Copyright © 2022-2024 The Apache Software Foundation. Apache StreamPark, StreamPark, and its feather logo are trademarks of The Apache Software Foundation.
+ </span>
+ </div>
+ </div></div></div></div></footer></div>
+</body>
+</html>
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+<?xml version="1.0" encoding="utf-8"?>
+<feed xmlns="http://www.w3.org/2005/Atom">
+ <id>https://streampark.apache.org/blog</id>
+ <title>Apache StreamPark (incubating) Blog</title>
+ <updated>2024-06-30T14:58:25.000Z</updated>
+ <generator>https://github.com/jpmonette/feed</generator>
+ <link rel="alternate" href="https://streampark.apache.org/blog"/>
+ <subtitle>Apache StreamPark (incubating) Blog</subtitle>
+ <icon>https://streampark.apache.org/image/favicon.ico</icon>
+ <entry>
+ <title type="html"><![CDATA[Apache StreamPark™ Flink on Kubernetes practice]]></title>
+ <id>https://streampark.apache.org/blog/streampark-flink-on-k8s</id>
+ <link href="https://streampark.apache.org/blog/streampark-flink-on-k8s"/>
+ <updated>2024-06-30T14:58:25.000Z</updated>
+ <summary type="html"><![CDATA[Wuxin Technology was founded in January 2018. The current main business includes the research and development, design, manufacturing and sales of RELX brand products. With core technologies and capabilities covering the entire industry chain, RELX is committed to providing users with products that are both high quality and safe]]></summary>
+ <content type="html"><![CDATA[<p>Wuxin Technology was founded in January 2018. The current main business includes the research and development, design, manufacturing and sales of RELX brand products. With core technologies and capabilities covering the entire industry chain, RELX is committed to providing users with products that are both high quality and safe.</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="why-choose-native-kubernetes"><strong>Why Choose Native Kubernetes</strong><a href="https://streampark.apache.org/blog/streampark-flink-on-k8s#why-choose-native-kubernetes" class="hash-link" aria-label="Direct link to why-choose-native-kubernetes" title="Direct link to why-choose-native-kubernetes"></a></h2>
+<p>Native Kubernetes offers the following advantages:</p>
+<ul>
+<li>
+<p>Shorter Failover time</p>
+</li>
+<li>
+<p>Resource hosting can be implemented without the need to manually create TaskManager Pods, which can be automatically destroyed</p>
+</li>
+<li>
+<p>With more convenient HA, in Native Kubernetes mode after Flink version 1.12, you can rely on the Leader election mechanism of native Kubernetes to complete JobManager's HA</p>
+<p>The main difference between Native Kubernetes and Standalone Kubernetes lies in the way Flink interacts with Kubernetes and the resulting series of advantages. Standalone Kubernetes requires users to customize the Kubernetes resource description files of JobManager and TaskManager. When submitting a job, you need to use kubectl combined with the resource description file to start the Flink cluster. The Native Kubernetes mode Flink Client integrates a Kubernetes Client, which can direc [...]
+</li>
+</ul>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/nativekubernetes_architecture-ad376f8ae79ab66d90d95742e8335d53.png" width="1080" height="401" class="img__KcZ"></p>
+<p>When Flink On Kubernetes meets StreamPark</p>
+<p>Flink on Native Kubernetes currently supports Application mode and Session mode. Compared with the two, Application mode deployment avoids the resource isolation problem and client resource consumption problem of Session mode. Therefore, it is recommended to use Application Mode to deploy Flink tasks in ** production environments. **Let’s take a look at the method of using the original script and the process of using StreamPark to develop and deploy a Flink on Native Kubernetes job.
+Deploy Kubernetes using scripts</p>
+<p>In the absence of a platform that supports Flink on Kubernetes task development and deployment, you need to use scripts to submit and stop tasks. This is also the default method provided by Flink. The specific steps are as follows:</p>
+<ol>
+<li>Prepare the kubectl and Docker command running environment on the Flink client node, create the Kubernetes Namespace and Service Account used to deploy the Flink job, and perform RBAC</li>
+<li>Write a Dockerfile file to package the Flink base image and the user’s job Jar together</li>
+</ol>
+<div class="language-dockerfile codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-dockerfile codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">FROM flink:1.13.6-scal [...]
+<ol start="3">
+<li>Use Flink client script to start Flink tasks</li>
+</ol>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">./bin/flink run-application \</s [...]
+<ol start="4">
+<li>Use the Kubectl command to obtain the WebUI access address and JobId of the Flink job.</li>
+</ol>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">kubectl -n flink-cluster get svc</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code to clipboard" t [...]
+<ol start="5">
+<li>Stop the job using Flink command</li>
+</ol>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">./bin/flink cancel</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain"> --target kubernetes-application</span>< [...]
+<p>The above is the process of deploying a Flink task to Kubernetes using the most original script method provided by Flink. Only the most basic task submission is achieved. If it is to reach the production use level, there are still a series of problems that need to be solved, such as: the method is too Originally, it was unable to adapt to large batches of tasks, unable to record task checkpoints and real-time status tracking, difficult to operate and monitor tasks, had no alarm mechan [...]
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="deploy-flink-on-kubernetes-using-apache-streampark"><strong>Deploy Flink on Kubernetes using Apache StreamPark™</strong><a href="https://streampark.apache.org/blog/streampark-flink-on-k8s#deploy-flink-on-kubernetes-using-apache-streampark" class="hash-link" aria-label="Direct link to deploy-flink-on-kubernetes-using-apache-streampark" title="Direct link to deploy-flink-on-kubernetes-using-apache-streampark"></a></h2>
+<p>There will be higher requirements for using Flink on Kubernetes in enterprise-level production environments. Generally, you will choose to build your own platform or purchase related commercial products. No matter which solution meets the product capabilities: large-scale task development and deployment, status tracking, operation and maintenance monitoring , failure alarms, unified task management, high availability, etc. are common demands.</p>
+<p>In response to the above issues, we investigated open source projects in the open source field that support the development and deployment of Flink on Kubernetes tasks. During the investigation, we also encountered other excellent open source projects. After comprehensively comparing multiple open source projects, we came to the conclusion: **StreamPark has great performance in either completness, user experience, or stability, so we finally chose StreamPark as our one-stop real-time [...]
+<p>Let’s take a look at how StreamPark supports Flink on Kubernetes:</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="basic-environment-configuration"><strong>Basic environment configuration</strong><a href="https://streampark.apache.org/blog/streampark-flink-on-k8s#basic-environment-configuration" class="hash-link" aria-label="Direct link to basic-environment-configuration" title="Direct link to basic-environment-configuration"></a></h3>
+<p>Basic environment configuration includes Kubernetes and Docker repository information as well as Flink client information configuration. The simplest way for the Kubernetes basic environment is to directly copy the .kube/config of the Kubernetes node to the StreamPark node user directory, and then use the kubectl command to create a Flink-specific Kubernetes Namespace and perform RBAC configuration.</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain"># Create k8s namespace used by Flink jobs</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">kubectl create ns fl [...]
+<p>Docker account information can be configured directly in the Docker Setting interface:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/docker_setting-80acf43cf64fd390e4d50da8830671c0.png" width="1080" height="586" class="img__KcZ"></p>
+<p>StreamPark can adapt to multi-version Flink job development. The Flink client can be configured directly on the StreamPark Setting interface:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flinkversion_setting-b170a43882590683ea0c7f109f909396.png" width="1080" height="352" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="job-development"><strong>Job development</strong><a href="https://streampark.apache.org/blog/streampark-flink-on-k8s#job-development" class="hash-link" aria-label="Direct link to job-development" title="Direct link to job-development"></a></h3>
+<p>After StreamPark has configured the basic environment, it only takes three steps to develop and deploy a Flink job:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/development_process-476918cfd29159983fe26b36ef487895.png" width="1080" height="271" class="img__KcZ"></p>
+<p>StreamPark supports both Upload Jar and direct writing of Flink SQL jobs. <strong>Flink SQL jobs only need to enter SQL and dependencies. This method greatly improves the development experience and avoids problems such as dependency conflicts.</strong> This article does not focus on this part。</p>
+<p>Here you need to select the deployment mode as kubernetes application, and configure the following parameters on the job development page: The parameters in the red box are the basic parameters of Flink on Kubernetes.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/kubernetes_base_parameters-1a28fec8d9d3dc57744324db4ef58551.png" width="1080" height="1104" class="img__KcZ"></p>
+<p>The following parameters are parameters related to Flink jobs and resources. The choice of Resolve Order is related to the code loading mode. For jobs uploaded by the Upload Jar developed by the DataStream API, choose to use Child-first, and for Flink SQL jobs, choose to use Parent-first loading.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flink_parameters-ff7790882a753bd88fbf5db9b775a0e3.png" width="1080" height="1133" class="img__KcZ"></p>
+<p>Finally, there are the following two heavyweight parameters. For Native Kubernetes, k8s-pod-template generally only requires pod-template configuration. Dynamic Option is a supplement to the pod-template parameters. For some personalized configurations, you can Configured in Dynamic Option. For more Dynamic Option, please directly refer to the Flink official website.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/pod_template-722285f448ec8adc0fa939d0baea2d10.png" width="1080" height="1104" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="job-online"><strong>Job online</strong><a href="https://streampark.apache.org/blog/streampark-flink-on-k8s#job-online" class="hash-link" aria-label="Direct link to job-online" title="Direct link to job-online"></a></h3>
+<p>After the job development is completed, the job comes online. In this step, StreamPark has done a lot of work, as follows:</p>
+<ul>
+<li>Prepare environment</li>
+<li>Dependency download in job</li>
+<li>Build job (JAR package)</li>
+<li>Build image</li>
+<li>Push the image to the remote repository</li>
+</ul>
+<p><strong>For users: Just click the cloud-shaped online button in the task list</strong></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/operation-067a84b9b5b1491206780076f98e6f8d.png" width="1080" height="573" class="img__KcZ"></p>
+<p>We can see a series of work done by StreamPark when building and pushing the image: <strong>Read the configuration, build the image, and push the image to the remote repository...</strong> I want to give StreamPark a big thumbs up!</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/step_details-301b14f2dbfa9c41f4c0e75a9086f0a4.png" width="948" height="1866" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="assignment-submission"><strong>Assignment submission</strong><a href="https://streampark.apache.org/blog/streampark-flink-on-k8s#assignment-submission" class="hash-link" aria-label="Direct link to assignment-submission" title="Direct link to assignment-submission"></a></h3>
+<p>Finally, you only need to click the start Application button in Operation to start a Flink on Kubernetes job. After the job is successfully started, click on the job name to jump to the Jobmanager WebUI page. The whole process is very simple and smooth.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/homework_submit-1f05dacf0fedfd1423f89ca2dec28437.png" width="1080" height="698" class="img__KcZ"></p>
+<p>The entire process only requires the above three steps to complete the development and deployment of a Flink on Kubernetes job on StreamPark. StreamPark's support for Flink on Kubernetes goes far beyond simply submitting a task.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="job-management"><strong>Job management</strong><a href="https://streampark.apache.org/blog/streampark-flink-on-k8s#job-management" class="hash-link" aria-label="Direct link to job-management" title="Direct link to job-management"></a></h3>
+<p><strong>After the job is submitted, StreamPark can obtain the latest checkpoint address of the task, the running status of the task, and the real-time resource consumption information of the cluster in real time. It can very conveniently start and stop the running task with one click, and supports recording the savepoint location when stopping the job. , as well as functions such as restoring the state from savepoint when restarting, thus ensuring the data consistency of the productio [...]
+<p>Next, let’s take a look at how StreamPark supports this capability:</p>
+<ul>
+<li>
+<p><strong>Record checkpoint in real time</strong></p>
+<p>After the job is submitted, sometimes it is necessary to change the job logic but to ensure data consistency, then the platform needs to have the ability to record the location of each checkpoint in real time, as well as the ability to record the last savepoint location when stopped. StreamPark is on Flink on Kubernetes This function is implemented very well. By default, checkpoint information will be obtained and recorded in the corresponding table every 5 seconds, and according to t [...]
+<p>The root path of the default savepoint only needs to be configured in the Flink Home flink-conf.yaml file to automatically identify it. In addition to the default address, the root path of the savepoint can also be customized and specified when stopping.</p>
+</li>
+</ul>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/savepoint-b0288f5293875d156f20dbe768384076.png" width="1080" height="446" class="img__KcZ"></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/checkpoint-acf7379b24a3bac6695a517b425f466b.png" width="1080" height="479" class="img__KcZ"></p>
+<ul>
+<li>
+<p><strong>Track running status in real time</strong></p>
+<p>For challenges in the production environment, a very important point is whether monitoring is in place, especially for Flink on Kubernetes. This is very important and is the most basic capability. StreamPark can monitor the running status of Flink on Kubernetes jobs in real time and display it to users on the platform. Tasks can be easily retrieved based on various running statuses on the page.</p>
+</li>
+</ul>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/run_status-5a663d9169c0bce8f4cfb993db77ae59.png" width="1080" height="617" class="img__KcZ"></p>
+<ul>
+<li>
+<p><strong>Complete alarm mechanism</strong></p>
+<p>In addition, StreamPark also has complete alarm functions: supporting email, DingTalk, WeChat and SMS, etc. This is also an important reason why the company chose StreamPark as the one-stop platform for Flink on Kubernetes after initial research.</p>
+</li>
+</ul>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/alarm-c2104c1839a1b4bb668d48f092f25faa.png" width="1080" height="393" class="img__KcZ"></p>
+<p>From the above, we can see that StreamPark has the capabilities to support the development and deployment process of Flink on Kubernetes, including: ** job development capabilities, deployment capabilities, monitoring capabilities, operation and maintenance capabilities, exception handling capabilities, etc. StreamPark provides a relatively complete set of s solution. And it already has some CICD/DevOps capabilities, and the overall completion level continues to improve. It is a produ [...]
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="apache-streamparks-implementation-in-wuxin-technology"><strong>Apache StreamPark™’s implementation in Wuxin Technology</strong><a href="https://streampark.apache.org/blog/streampark-flink-on-k8s#apache-streamparks-implementation-in-wuxin-technology" class="hash-link" aria-label="Direct link to apache-streamparks-implementation-in-wuxin-technology" title="Direct link to apache-streamparks-implementation-in-wuxin-technology"></a></h2>
+<p>StreamPark was launched late in Wuxin Technology. It is currently mainly used for the development and deployment of real-time data integration jobs and real-time indicator calculation jobs. There are Jar tasks and Flink SQL tasks, all deployed using Native Kubernetes; data sources include CDC, Kafka, etc., and Sink end There are Maxcompute, kafka, Hive, etc. The following is a screenshot of the company's development environment StreamPark platform:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/screenshot-2906914515b810aadb10db951d4f02bd.png" width="1080" height="706" class="img__KcZ"></p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="problems-encountered">Problems encountered<a href="https://streampark.apache.org/blog/streampark-flink-on-k8s#problems-encountered" class="hash-link" aria-label="Direct link to Problems encountered" title="Direct link to Problems encountered"></a></h2>
+<p>Any new technology has a process of exploration and fall into pitfalls. The experience of failure is precious. Here are some pitfalls and experiences that StreamPark has stepped into during the implementation of fog core technology. <strong>The content of this section is not only about StreamPark. I believe it will bring some reference to all friends who use Flink on Kubernetes</strong>.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="faqs-are-summarized-below"><strong>FAQs are summarized below</strong><a href="https://streampark.apache.org/blog/streampark-flink-on-k8s#faqs-are-summarized-below" class="hash-link" aria-label="Direct link to faqs-are-summarized-below" title="Direct link to faqs-are-summarized-below"></a></h3>
+<ul>
+<li>
+<p><strong>Kubernetes pod failed to pull the image</strong></p>
+<p>The main problem is that Kubernetes pod-template lacks docker’s imagePullSecrets</p>
+</li>
+<li>
+<p><strong>Scala version inconsistent</strong></p>
+<p>Since StreamPark deployment requires a Scala environment, and Flink SQL operation requires the Flink SQL Client provided by StreamPark, it is necessary to ensure that the Scala version of the Flink job is consistent with the Scala version of StreamPark.</p>
+</li>
+<li>
+<p><strong>Be aware of class conflicts</strong></p>
+<p>When developing Flink SQL jobs, you need to pay attention to whether there are any class conflicts between the Flink image and the Flink connector and UDF. The best way to avoid class conflicts is to make the Flink image and the commonly used Flink connector and user UDF into a usable basic image. After that, other Flink SQL jobs can be reused directly.</p>
+</li>
+<li>
+<p><strong>How to store checkpoint without Hadoop environment?</strong></p>
+<p>HDFS, Alibaba Cloud OSS/AWS S3 can both perform checkpoint and savepoint storage. The Flink basic image already has support for OSS and S3. If you do not have HDFS, you can use Alibaba Cloud OSS or S3 to store status and checkpoint and savepoint data. You only need to use Flink Simply configure it in the dynamic parameters.</p>
+</li>
+</ul>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">-Dstate.backend=rocksdb</span><b [...]
+<ul>
+<li>
+<p><strong>The changed code did not take effect after it was republished</strong></p>
+<p>This issue is related to the Kubernetes pod image pull policy. It is recommended to set the Pod image pull policy to Always:</p>
+</li>
+</ul>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">-Dkubernetes.container.image.pull-policy=Always</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code [...]
+<ul>
+<li>
+<p><strong>Each restart of the task will result in one more Job instance</strong></p>
+<p>Under the premise that kubernetes-based HA is configured, when you need to stop the Flink task, you need to use cancel of StreamPark. Do not delete the Deployment of the Flink task directly through the kubernetes cluster. Because Flink's shutdown has its own shutdown process, when deleting a pod, the corresponding configuration files in the Configmap will also be deleted. Direct deletion of the pod will result in the remnants of the Configmap. When a task with the same name is restart [...]
+</li>
+<li>
+<p><strong>How to implement kubernetes pod domain name access</strong></p>
+<p>Domain name configuration only needs to be configured in pod-template according to Kubernetes resources. I can share with you a pod-template.yaml template that I summarized based on the above issues:</p>
+</li>
+</ul>
+<div class="language-yaml codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-yaml codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain" style="display:inline-block"></span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain"></span><span class="token key atru [...]
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="best-practices"><strong>Best Practices</strong><a href="https://streampark.apache.org/blog/streampark-flink-on-k8s#best-practices" class="hash-link" aria-label="Direct link to best-practices" title="Direct link to best-practices"></a></h3>
+<p>Many of RELX's big data components are based on Alibaba Cloud, such as Maxcompute and Alibaba Cloud Redis. At the same time, our Flink SQL jobs need to use some UDFs. At first, we adopted the method of using Flink Base image + maven dependency + upload udf jar, but in practice we encountered some problems such as version conflicts and class conflicts. At the same time, if it is a large-volume job, the development efficiency of this method is relatively low. Finally, we packaged the co [...]
+<p><strong>Let’s share a simple step to create a basic image：</strong></p>
+<p><strong>1. Prepare the required JAR</strong></p>
+<p>Place the commonly used Flink Connector Jar and the user Udf Jar in the same folder lib. The following are some commonly used connector packages in Flink 1.13.6</p>
+<div class="language-jar codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-jar codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">bigdata-udxf-1.0.0-shaded.jar</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">flink-connector-jdbc_2.11-1.13.6.jar [...]
+<p><strong>2. Prepare Dockerfile</strong></p>
+<p>Create a Dockerfile file and place the Dockerfile file in the same folder as the above folder</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">FROM flink:1.13.6-scala_2.11COPY lib $FLINK_HOME/lib/</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy [...]
+<p><strong>3. Create a basic image and push it to a private repository</strong></p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">docker login --username=xxxdocker \</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">build -t udf_flink_1.13.6- [...]
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="future-expectations"><strong>Future Expectations</strong><a href="https://streampark.apache.org/blog/streampark-flink-on-k8s#future-expectations" class="hash-link" aria-label="Direct link to future-expectations" title="Direct link to future-expectations"></a></h2>
+<ul>
+<li>
+<p><strong>StreamPark supports Flink job metric monitoring</strong></p>
+<p>It would be great if StreamPark could connect to Flink Metric data and display Flink’s real-time consumption data at every moment on the StreamPark platform.</p>
+</li>
+<li>
+<p><strong>StreamPark supports Flink job log persistence</strong></p>
+<p>For Flink deployed to YARN, if the Flink program hangs, we can go to YARN to view the historical logs. However, for Kubernetes, if the program hangs, the Kubernetes pod will disappear and there will be no way to check the logs. Therefore, users need to use tools on Kubernetes for log persistence. It would be better if StreamPark supports the Kubernetes log persistence interface.</p>
+</li>
+<li>
+<p><strong>Improvement of the problem of too large image</strong></p>
+<p>StreamPark's current image support for Flink on Kubernetes jobs is to combine the basic image and user code into a Fat image and push it to the Docker repository. The problem with this method is that it takes a long time when the image is too large. It is hoped that the basic image can be restored in the future. There is no need to hit the business code together every time, which can greatly improve development efficiency and save costs.</p>
+</li>
+</ul>]]></content>
+ <category label="StreamPark" term="StreamPark"/>
+ <category label="Production Practice" term="Production Practice"/>
+ <category label="FlinkSQL" term="FlinkSQL"/>
+ <category label="Kubernetes" term="Kubernetes"/>
+ </entry>
+ <entry>
+ <title type="html"><![CDATA[Apache StreamPark™ - Powerful Flink Development Framework]]></title>
+ <id>https://streampark.apache.org/blog/flink-development-framework-streampark</id>
+ <link href="https://streampark.apache.org/blog/flink-development-framework-streampark"/>
+ <updated>2024-06-30T14:58:25.000Z</updated>
+ <summary type="html"><![CDATA[Although the Hadoop system is widely used today, its architecture is complicated, it has a high maintenance complexity, version upgrades are challenging, and due to departmental reasons, data center scheduling is prolonged. We urgently need to explore agile data platform models. With the current popularization of cloud-native architecture and the integration between lake and warehous, we have decided to use Doris as an offline data warehouse and TiDB [...]
+ <content type="html"><![CDATA[<p>Although the Hadoop system is widely used today, its architecture is complicated, it has a high maintenance complexity, version upgrades are challenging, and due to departmental reasons, data center scheduling is prolonged. We urgently need to explore agile data platform models. With the current popularization of cloud-native architecture and the integration between lake and warehous, we have decided to use Doris as an offline data warehouse and T [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/doris-4baaea78343b928b0a798ae9238c489f.png" width="1200" height="738" class="img__KcZ"></p>
+<p>Although the Hadoop system is widely used today, its architecture is complicated, it has a high maintenance complexity, version upgrades are challenging, and due to departmental reasons, data center scheduling is prolonged. We urgently need to explore agile data platform models. With the current popularization of cloud-native architecture and the integration between lake and warehous, we have decided to use Doris as an offline data warehouse and TiDB (which is already in production) a [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/doris-4baaea78343b928b0a798ae9238c489f.png" width="1200" height="738" class="img__KcZ"></p>
+<center style="color:gray">(Borrowing Doris's official architecture diagram here)</center>
+<br>
+<br>
+<h1>2. Challenges Faced</h1>
+<p>For the data engine, we settled on using Spark and Flink:</p>
+<ul>
+<li>Use Spark on K8s client mode for offline data processing.</li>
+<li>Use Flink on K8s Native-Application/Session mode for real-time task stream management.</li>
+</ul>
+<p>Here, there are some challenges we haven't fully resolved:</p>
+<p>Those who have used the Native-Application mode know that each time a task is submitted, a new image must be packaged, pushed to a private repository, and then the Flink Run command is used to communicate with K8s to pull the image and run the Pod. After the task is submitted, you need to check the log on K8s, but:</p>
+<ol>
+<li>How is task runtime monitoring handled?</li>
+<li>Do you use Cluster mode or expose ports using NodePort to access Web UI?</li>
+<li>Can the task submission process be simplified to avoid image packaging?</li>
+<li>How can we reduce the pressure on developers?</li>
+</ol>
+<br>
+<br>
+<h1>3. Solving the Challenges</h1>
+<p>All of the above are challenges that need addressing. If we rely solely on the command line to submit each task, it becomes unrealistic. As the number of tasks increases, it becomes unmanageable. Addressing these challenges became inevitable.</p>
+<br>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="simplifying-image-building">Simplifying Image Building<a href="https://streampark.apache.org/blog/flink-development-framework-streampark#simplifying-image-building" class="hash-link" aria-label="Direct link to Simplifying Image Building" title="Direct link to Simplifying Image Building"></a></h2>
+<p>Firstly, regarding the need for a secondary build of the native Flink image: we utilized MinIO as external storage and mounted it directly on each host node using s3-fuse via DaemonSet. The jar packages we need to submit can all be managed there. In this way, even if we scale the Flink nodes up or down, S3 mounts can automatically scale.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/k8s-9a28cd8f0e9c996501193f591ebe22b0.png" width="1141" height="582" class="img__KcZ"></p>
+<p>From Flink version 1.13 onwards, Pod Template support has been added. We can use volume mounts in the Pod Template to mount host directories into each pod, allowing Flink programs to run directly on K8s without packaging them into images. As shown in the diagram above, we first mount S3 using the s3-fuse Pod to the <code>/mnt/data-s3fs</code> directory on Node 1 and Node 2, and then mount <code>/mnt/data-s3fs</code> into Pod A.</p>
+<p>However, because object storage requires the entire object to be rewritten for random writes or file appends, this method is only suitable for frequent reads. This perfectly fits our current scenario.</p>
+<br>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="introducing-apache-streampark">Introducing Apache StreamPark™<a href="https://streampark.apache.org/blog/flink-development-framework-streampark#introducing-apache-streampark" class="hash-link" aria-label="Direct link to Introducing Apache StreamPark™" title="Direct link to Introducing Apache StreamPark™"></a></h2>
+<p>Previously, when we wrote Flink SQL, we generally used Java to wrap SQL, packed it into a jar package, and submitted it to the S3 platform through the command line. This approach has always been unfriendly; the process is cumbersome, and the costs for development and operations are too high. We hoped to further streamline the process by abstracting the Flink TableEnvironment, letting the platform handle initialization, packaging, and running Flink tasks, and automating the building, t [...]
+<p>This is an era of open-source uprising. Naturally, we turned our attention to the open-source realm: among numerous open-source projects, after comparing various projects, we found that both Zeppelin and StreamPark provide substantial support for Flink and both claim to support Flink on K8s. Eventually, both were shortlisted for our selection. Here's a brief comparison of their support for K8s (if there have been updates since, please kindly correct).</p>
+<table><thead><tr><td>Feature</td><td>Zeppelin</td><td>StreamPark</td></tr></thead><tbody><tr><td>Task Status Monitoring</td><td>Somewhat limited, not suitable as a task status monitoring tool.</td><td>Highly capable</td></tr><tr><td>Task Resource Management</td><td>None</td><td>Exists, but the current version is not very robust.</td></tr><tr><td>Local Deployment</td><td>On the lower side. In on K8s mode, you can only deploy Zeppelin in K8s. Otherwise, you need to connect the Pod and ext [...]
+<center style="color:gray">(PS: This comparison is based on our perspective as evaluators. We hold the utmost respect for the developers of both platforms.)</center>
+<br>
+<p>During our research process, we communicated with the main developers of both tools multiple times. After our repeated studies and assessments, we eventually decided to adopt StreamPark as our primary Flink development tool for now.</p>
+<video src="http://assets.streamxhub.com/streamx-video.mp4" controls="" width="100%" height="100%"></video>
+<center style="color:gray">(StreamPark's official splash screen)</center>
+<br>
+<p>After extended development and testing by our team, StreamPark currently boasts:</p>
+<ul>
+<li>Comprehensive <span style="color:red">SQL validation capabilities</span></li>
+<li>It has achieved <span style="color:red">automatic build/push for images</span></li>
+<li>Using a custom class loader and through the Child-first loading method, it <span style="color:red">addresses both YARN and K8s operational modes</span> and <span style="color:red">supports the seamless switch between multiple Flink versions</span></li>
+<li>It deeply integrates with Flink-Kubernetes, returning a WebUI after task submission, and via remote REST API + remote K8s, it can <span style="color:red">track task execution status</span></li>
+<li>It supports versions like <span style="color:red">Flink 1.12, 1.13, 1.14, and more</span></li>
+</ul>
+<p>This effectively addresses most of the challenges we currently face in development and operations.</p>
+<video src="http://assets.streamxhub.com/streamx-1.2.0.mp4" controls="" width="100%" height="100%"></video>
+<center style="color:gray">(Demo video showcasing StreamPark's support for multiple Flink versions)</center>
+<br>
+<p>In its latest release, version 1.2.0, StreamPark provides robust support for both K8s-Native-Application and K8s-Session-Application modes.</p>
+<video src="http://assets.streamxhub.com/streamx-k8s.mp4" controls="" width="100%" height="100%"></video>
+<center style="color:gray">(StreamPark's K8s deployment demo video)</center>
+<br>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="k8s-native-application-mode">K8s Native Application Mode<a href="https://streampark.apache.org/blog/flink-development-framework-streampark#k8s-native-application-mode" class="hash-link" aria-label="Direct link to K8s Native Application Mode" title="Direct link to K8s Native Application Mode"></a></h3>
+<p>Within StreamPark, all we need to do is configure the relevant parameters, fill in the corresponding dependencies in the Maven POM, or upload the dependency jar files. Once we click on 'Apply', the specified dependencies will be generated. This implies that we can also compile all the UDFs we use into jar files, as well as various connector.jar files, and use them directly in SQL. As illustrated below:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/dependency-a3b9ff29795acb8a1fd4ed6bb773d53e.png" width="1080" height="461" class="img__KcZ"></p>
+<p>The SQL validation capability is roughly equivalent to that of Zeppelin:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/sqlverify-7e12cf343c9c81fcbc2e20f8d7588f1b.png" width="1080" height="619" class="img__KcZ"></p>
+<p>We can also specify resources, designate dynamic parameters within Flink Run as Dynamic Options, and even integrate these parameters with a Pod Template.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/pod-d46370aaff2c34c4fe6a584c0524b28e.png" width="1080" height="574" class="img__KcZ"></p>
+<p>After saving the program, when clicking to run, we can also specify a savepoint. Once the task is successfully submitted, StreamPark will, based on the FlinkPod's network Exposed Type (be it loadBalancer, NodePort, or ClusterIp), return the corresponding WebURL, seamlessly enabling a WebUI redirect. However, as of now, due to security considerations within our online private K8s cluster, there hasn't been a connection established between the Pod and client node network (and there's cu [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/start-71fbb288851d022c450a6bd34e8b4dc2.png" width="1080" height="522" class="img__KcZ"></p>
+<p>Note: If the K8s master uses a vip for load balancing, the Flink 1.13 version will return the vip's IP address. This issue has been rectified in the 1.14 version.</p>
+<p>Below is the specific submission process in the K8s Application mode:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flow-c2227ba0cc1f59f78e2164fdb3657223.png" width="1080" height="949" class="img__KcZ"></p>
+<center style="color:gray">(The above is a task submission flowchart, drawn based on personal understanding. If there are inaccuracies, your understanding is appreciated.)</center>
+<br>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="k8s-native-session-mode">K8s Native Session Mode<a href="https://streampark.apache.org/blog/flink-development-framework-streampark#k8s-native-session-mode" class="hash-link" aria-label="Direct link to K8s Native Session Mode" title="Direct link to K8s Native Session Mode"></a></h3>
+<p>StreamPark also offers robust support for the <span style="color:red"> K8s Native-Session mode</span>, which lays a solid technical foundation for our subsequent offline FlinkSQL development or for segmenting certain resources.</p>
+<p>To use the Native-Session mode, one must first use the Flink command to create a Flink cluster that operates within K8s. For instance:</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">./kubernetes-session.sh \</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">-Dkubernetes.cluster-id=flink-on-k8s [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flinksql-13b242feb3803b15e6698635a79065b4.png" width="1080" height="664" class="img__KcZ"></p>
+<p>As shown in the image above, we use that ClusterId as the Kubernetes ClusterId task parameter for StreamPark. Once the task is saved and submitted, it quickly transitions to a 'Running' state:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/detail-e9ee4c14e45068bea5e1edabec596bee.png" width="1080" height="529" class="img__KcZ"></p>
+<p>Following the application info's WebUI link:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/dashboard-78745d8d3ebe422b166a17631bfbe622.png" width="1080" height="520" class="img__KcZ"></p>
+<p>It becomes evident that StreamPark essentially uploads the jar package to the Flink cluster through REST API and then schedules the task for execution.</p>
+<br>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="custom-code-mode">Custom Code Mode<a href="https://streampark.apache.org/blog/flink-development-framework-streampark#custom-code-mode" class="hash-link" aria-label="Direct link to Custom Code Mode" title="Direct link to Custom Code Mode"></a></h3>
+<p>To our delight, StreamPark also provides support for coding DataStream/FlinkSQL tasks. For special requirements, we can achieve our implementations in Java/Scala. You can compose tasks following the scaffold method recommended by StreamPark or write a standard Flink task. By adopting this approach, we can delegate code management to git, utilizing the platform for automated compilation, packaging, and deployment. Naturally, if functionality can be achieved via SQL, we would prefer not [...]
+<br>
+<br>
+<h1>4. Feedback and Future Directions</h1>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="suggestions-for-improvement">Suggestions for Improvement<a href="https://streampark.apache.org/blog/flink-development-framework-streampark#suggestions-for-improvement" class="hash-link" aria-label="Direct link to Suggestions for Improvement" title="Direct link to Suggestions for Improvement"></a></h2>
+<p>StreamPark, similar to any other new tools, does have areas for further enhancement based on our current evaluations:</p>
+<ul>
+<li><strong>Strengthening Resource Management</strong>: Features like multi-file system jar resources and robust task versioning are still awaiting additions.</li>
+<li><strong>Enriching Frontend Features</strong>: For instance, once a task is added, functionalities like copying could be integrated.</li>
+<li><strong>Visualization of Task Submission Logs</strong>: The process of task submission involves loading class files, jar packaging, building and submitting images, and more. A failure at any of these stages could halt the task. However, error logs are not always clear, or due to some anomaly, the exceptions aren't thrown as expected, leaving users puzzled about rectifications.</li>
+</ul>
+<p>It's a universal truth that innovations aren't perfect from the outset. Although minor issues exist and there are areas for improvement with StreamPark, its merits outweigh its limitations. As a result, we've chosen StreamPark as our Flink DevOps platform. We're also committed to collaborating with its main developers to refine StreamPark further. We wholeheartedly invite others to use it and contribute towards its advancement.</p>
+<br>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="future-prospects">Future Prospects<a href="https://streampark.apache.org/blog/flink-development-framework-streampark#future-prospects" class="hash-link" aria-label="Direct link to Future Prospects" title="Direct link to Future Prospects"></a></h2>
+<ul>
+<li>We'll keep our focus on Doris and plan to unify business data with log data in Doris, leveraging Flink to realize lakehouse capabilities.</li>
+<li>Our next step is to explore integrating StreamPark with DolphinScheduler 2.x. This would enhance DolphinScheduler's offline tasks, and gradually we aim to replace Spark with Flink for a unified batch-streaming solution.</li>
+<li>Drawing from our own experiments with S3, after building the fat-jar, we're considering bypassing image building. Instead, we'll mount PVC directly to the Flink Pod's directory using Pod Template, refining the code submission process even further.</li>
+<li>We plan to persistently implement StreamPark in our production environment. Collaborating with community developers, we aim to boost StreamPark's Flink stream development, deployment, and monitoring capabilities. Our collective vision is to evolve StreamPark into a holistic stream data DevOps platform.</li>
+</ul>
+<p>Resources:</p>
+<p>StreamPark GitHub: <a href="https://github.com/apache/incubator-streampark" target="_blank" rel="noopener noreferrer">https://github.com/apache/incubator-streampark</a> <br>
+Doris GitHub: <a href="https://github.com/apache/doris" target="_blank" rel="noopener noreferrer">https://github.com/apache/doris</a></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/author-c3dabbb31d7cea1b5164a75a94ca3008.png" width="900" height="500" class="img__KcZ"></p>]]></content>
+ <category label="StreamPark" term="StreamPark"/>
+ <category label="DataStream" term="DataStream"/>
+ <category label="FlinkSQL" term="FlinkSQL"/>
+ </entry>
+ <entry>
+ <title type="html"><![CDATA[Ziroom implements the best practice of one-key data input into the lake based on Apache StreamPark™ + Paimon]]></title>
+ <id>https://streampark.apache.org/blog/streampark-flink-with-paimon-in-ziru</id>
+ <link href="https://streampark.apache.org/blog/streampark-flink-with-paimon-in-ziru"/>
+ <updated>2024-06-30T14:58:25.000Z</updated>
+ <summary type="html"><![CDATA[Introduction：This article mainly introduces the architecture upgrade and evolution of the self-migrating MySQL data to Hive, the original architecture involves many components, complex links, and encounters many challenges, and effectively solves the dilemmas and challenges encountered in data integration after using the combination of StreamPark + Paimon, and shares the specific practical solutions of StreamPark + Paimon in practical applications, a [...]
+ <content type="html"><![CDATA[<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/new_cover-5991f0d6d5ee86bc8716862748caf484.png" width="1080" height="460" class="img__KcZ"></p>
+<p><strong>Introduction</strong>：This article mainly introduces the architecture upgrade and evolution of the self-migrating MySQL data to Hive, the original architecture involves many components, complex links, and encounters many challenges, and effectively solves the dilemmas and challenges encountered in data integration after using the combination of StreamPark + Paimon, and shares the specific practical solutions of StreamPark + Paimon in practical applications, as well as the adva [...]
+<p>StreamPark: <a href="https://github.com/apache/streampark" target="_blank" rel="noopener noreferrer">https://github.com/apache/streampark</a></p>
+<p>Paimon: <a href="https://github.com/apache/paimon" target="_blank" rel="noopener noreferrer">https://github.com/apache/paimon</a></p>
+<p>Welcome to follow, star, fork, and participate in contributions</p>
+<p>Contributor｜Beijing Ziru Information Technology Co., Ltd.</p>
+<p>Authors of the article｜Liu Tao, Liang Yansheng, Wei Linzi</p>
+<p>Article compilation｜Yang Linwei</p>
+<p>Content proofreading｜Pan Yuepeng</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="1data-integration-business-background"><strong>1.Data integration business background</strong><a href="https://streampark.apache.org/blog/streampark-flink-with-paimon-in-ziru#1data-integration-business-background" class="hash-link" aria-label="Direct link to 1data-integration-business-background" title="Direct link to 1data-integration-business-background"></a></h2>
+<p>The data integration scenario of Ziroom's rental business mainly comes from the need to synchronize MySQL tables of each business line to Hive tables. This requirement includes more than 4,400 MySQL business tables synchronized every day and more than 8,000 Hive ETL processing tasks. The amount of new data generated every day is 50T, and these numbers are still growing. According to the freshness requirements of the data, it is divided into two types: low freshness (T+1 day) and high [...]
+<p>Ziroom's data integration solutions can be mainly divided into two types according to business usage scenarios:</p>
+<ul>
+<li>
+<p><strong>Low freshness</strong>: The timeliness requirement of low freshness for data is <strong>T+1day</strong>, and the Hive jdbc handler is used to pull the full amount of MySQL data to Hive at 00:00 every day. The basic The process is shown in the figure below:</p>
+<p><img decoding="async" loading="lazy" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAWkAAAApCAYAAAD3VNiWAAAKCUlEQVR4Xu2dWahNbRjH9+25UOfi5EqdcuHChQullJSU5MiRRBIRKYSPZCzz8GUqGTJTh5BMIdJBIZllJvM8H9MxT+93fu/ez7L2e9be56y97OF8nl+97bXfNf/3+/zXs561HLFYMmUlJSWVRUVF1TXTRltyQxf0qZkuiymKEhb1lzStTn8pLi6uKC0tfbdmzRrz5MkTo9QGXdAHndDL1VBRlGDUX+omrb/QUV5e/s5dSUkNetUSUlGUWqi/hMf1lzKc211IqRt0Qz/feFQUJRn1lwzx/IUaCCm2Eh50S9SQFEUJQP0lczx/oVitNaLMQLfEQxBFUQJQf8kcv7+485QQoJ8zLhVF+Y0bMkoI0E9FjIiIqChKIG7IK [...]
+</li>
+<li>
+<p><strong>High freshness</strong>: In this scenario, the required data effectiveness is <strong>T+10minutes</strong>. We reused the snapshot pull method of the low freshness scenario to obtain the full amount of data and initialized it to MySQL. Synchronously use Canal to parse the logs and collect them into Kafka, then use Flink to read the data in kafka and write it to HDFS, and finally use Airflow for scheduling to merge the incremental data into Hive. The basic logic is as follows:</p>
+<p><img decoding="async" loading="lazy" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAnIAAAApCAYAAACx+MSIAAAO00lEQVR4Xu2dSagdRRuG7zaLQBbBlRBw4cKFC0EQRBDBhREjIoqIoiCCivKrwRHn6XdGUHGMiwgqEsUJRaKCijiLs+I8xilRYzSTQ/15ylMnfd97zr2p091fdd//e6C5p6u7z/n6faurvq6uc+7U1HSWLl68ePWCBQs2bHsdfJm+oAv6bHu9dKpbuG+zLO5bPxf3rZ+L+9atpcN+1MG9HMWiRYtWLlmyZP2KFSvCmjVrgjMTdEEfdEIv1bAE7tvcuG/9xH3rJ+5bt+iiH3VwL8d4ScGyZcvW60HOeNBrhpDGuG/5uG/9xH3rJ+5bt+iCH3VwL7ejXi4lu9OdnLlBN/Sr1DNL3LcJcd/6ifvWT9y3blHYjzq4l8LQS [...]
+</li>
+</ul>
+<p>However, there are many challenges and pressures in the current architecture. Firstly, the operation and maintenance costs are high, and secondly, the computing pressure, storage pressure and network pressure are all very high. In addition, although the resources are underutilized during the system running time from 0:00 to 1:00, other time periods face resource shortages. In this regard, Ziroom decided to update the data integration architecture to improve the efficiency and stabilit [...]
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="2challenges-encountered"><strong>2.Challenges encountered</strong><a href="https://streampark.apache.org/blog/streampark-flink-with-paimon-in-ziru#2challenges-encountered" class="hash-link" aria-label="Direct link to 2challenges-encountered" title="Direct link to 2challenges-encountered"></a></h2>
+<p>In the above two scenarios, we encountered the following challenges during the data integration process:</p>
+<ul>
+<li>
+<p><strong>Network bandwidth overload problem</strong>: Since the pull task reached <strong>4000+</strong>, too many mirror full data pulls put great pressure on the database network bandwidth.</p>
+</li>
+<li>
+<p><strong>Inefficient resource utilization</strong>: After the upstream data is synchronized from MySQL to the ODS layer table, the downstream processing table can be started. As a result, the CPU and memory resources of the Hadoop cluster are not available between 0:00 and 1:00. be fully utilized.</p>
+</li>
+<li>
+<p><strong>High maintenance costs</strong>: When the database table structure changes, the Airflow script needs to be modified simultaneously. Otherwise, incomplete fields will appear, causing online data anomalies.</p>
+</li>
+<li>
+<p><strong>Difficulty in troubleshooting</strong>: The data link is long. When data anomalies occur, troubleshooting costs are high. The problem may occur in any link in Canal, Kafka, Flink, and Airflow scheduling, resulting in a long recovery time. .</p>
+</li>
+<li>
+<p><strong>Flink jobs are difficult to manage in a unified manner</strong>: Flink itself does not provide good deployment and development capabilities. As the number of Flink tasks increases, the time cost of management and maintenance also increases.</p>
+</li>
+</ul>
+<p>In order to solve the above problems, after a series of investigations, we decided to adopt the "<strong>StreamPark+Paimon</strong>" strategy. So what are the reasons for choosing them? We can first look at their characteristics.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="paimons-core-features"><strong>Paimon’s core features</strong><a href="https://streampark.apache.org/blog/streampark-flink-with-paimon-in-ziru#paimons-core-features" class="hash-link" aria-label="Direct link to paimons-core-features" title="Direct link to paimons-core-features"></a></h3>
+<p><strong>After research and comprehensive evaluation of several data lake frameworks such as Apache Hudi/Iceberg/Paimon, we decided to use Apache Paimon</strong>. Apache Paimon is a streaming data lake storage technology that can provide users with high throughput and low cost. Delayed data ingestion, streaming subscription and real-time query capabilities support the use of Flink and Spark to build a real-time Lakehouse architecture, support batch/stream data processing operations, in [...]
+<ul>
+<li>
+<p><strong>Unified batch and stream processing</strong>: Paimon supports batch writing, batch reading and streaming operations, providing flexible data processing methods.</p>
+</li>
+<li>
+<p><strong>Data Lake Features</strong>: As a data lake storage system, Paimon has the characteristics of low cost, high reliability and scalable metadata.</p>
+</li>
+<li>
+<p><strong>Rich merging engines</strong>: Paimon provides a variety of merging engines, and you can choose to retain the latest data, perform partial updates, or perform aggregation operations according to your needs.</p>
+</li>
+<li>
+<p><strong>Automatically generate change logs</strong>: Paimon supports a variety of Changelog producers and can automatically generate correct and complete change logs to simplify streaming task analysis.</p>
+</li>
+<li>
+<p><strong>Rich table types</strong>: Paimon supports primary key tables and append-only tables, as well as multiple table types such as internal tables, external tables, partitioned tables, and temporary tables.</p>
+</li>
+<li>
+<p><strong>Support table structure change synchronization</strong>: When the data source table structure changes, Paimon can automatically identify and synchronize these changes.</p>
+</li>
+</ul>
+<p>Paimon can be used in conjunction with Apache Spark. Our scenario is Paimon combined with Flink. In this way, "<strong>How to manage 4000+ Flink data synchronization jobs</strong>" will be a new problem we face. After a comprehensive investigation of related projects and a comprehensive evaluation of various dimensions, <strong>we decided to use StreamPark</strong>. So why did we choose StremaPark?</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="streamparks-core-features"><strong>StreamPark’s core features</strong><a href="https://streampark.apache.org/blog/streampark-flink-with-paimon-in-ziru#streamparks-core-features" class="hash-link" aria-label="Direct link to streamparks-core-features" title="Direct link to streamparks-core-features"></a></h3>
+<p>Apache StreamPark is a stream processing development and management framework that provides a set of fast APIs for developing Flink/Spark jobs. In addition, it also provides a one-stop stream processing job development and management platform, covering the entire life cycle from stream processing job development to launch. Cycles are supported. StreamPark mainly includes the following core features:</p>
+<ul>
+<li>
+<p><strong>Stream processing application development framework</strong>: Based on StreamPark, developers can easily build and manage stream processing applications, and better utilize Apache Flink® to write stream processing applications.</p>
+</li>
+<li>
+<p><strong>Perfect management capabilities</strong>: StreamPark provides a one-stop streaming task development and management platform that supports the full life cycle of Flink/Spark from application development to debugging, deployment, operation and maintenance, allowing Flink/Spark jobs to Make it simple.</p>
+</li>
+<li>
+<p><strong>High degree of completion</strong>: StreamPark supports multiple versions of Flink, allowing flexible switching of one platform. It also supports Flink’s deployment mode, effectively solving the problem of too cumbersome Flink on YARN/K8s deployment. Through automated processes, It simplifies the process of building, testing and deploying tasks and improves development efficiency.</p>
+</li>
+<li>
+<p><strong>Rich management API</strong>: StreamPark provides APIs for job operations, including job creation, copy, build, deployment, stop and start based on checkpoint/savepoint, etc., making it easy to implement external system calls to Apache Flink® tasks. .</p>
+</li>
+</ul>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="3-streampark--paimon-practice"><strong>3. StreamPark + Paimon Practice</strong><a href="https://streampark.apache.org/blog/streampark-flink-with-paimon-in-ziru#3-streampark--paimon-practice" class="hash-link" aria-label="Direct link to 3-streampark--paimon-practice" title="Direct link to 3-streampark--paimon-practice"></a></h2>
+<p>Next, we will continue to share how Ziroom optimized the architecture based on <strong>StreamPark + Paimon</strong>. Let’s first look at the comparison before and after the architecture upgrade.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="31-before-architecture-upgrade"><strong>3.1 Before architecture upgrade</strong><a href="https://streampark.apache.org/blog/streampark-flink-with-paimon-in-ziru#31-before-architecture-upgrade" class="hash-link" aria-label="Direct link to 31-before-architecture-upgrade" title="Direct link to 31-before-architecture-upgrade"></a></h3>
+<p>The system interaction process of the data integration module before the transformation is as follows:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/system_interaction_process-0fa72272598ce1fbf180e4f859cf59dc.png" width="1080" height="291" class="img__KcZ"></p>
+<p><strong>Step1</strong> (User initiates access application): First, the user selects a table on the data access platform, and then clicks the Apply for Access button:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/data_access_platform-a2d62b477382c15128b76c60e9c6be53.png" width="1080" height="245" class="img__KcZ"></p>
+<p><strong>Step2</strong> (Initiate OA system approval process): When the OA system receives the access application, it will initiate workflow approval. If the approval fails, the application will be rejected. Only if the approval is passed will the next step be continued.</p>
+<p><strong>Step3</strong> (The data access platform processes the approval event): The data access platform calls the Canal interface to deploy the Canal task and transfer the Binlog data in the table to Kafka:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/canal_job-fbfcee343ac2473e1b22d199da463ad4.png" width="1041" height="760" class="img__KcZ"></p>
+<p><strong>Step4</strong> (Flink task deployment): Manually use the Flink Session Submit UI to deploy the Flink template job, which is responsible for parsing the Binlog change data in Kafka, writing the data to HDFS, and mapping it into a Hive external incremental table:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flink_job-2769695ec6b0a9446b8d5f4828976b7d.png" width="946" height="331" class="img__KcZ"></p>
+<p><strong>Step5</strong> (Airflow scheduling initialization table): Create Hive mapping incremental table and full table and use Airflow scheduling to complete the first initialization:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/first_initialization-f270b827e85b0ff840bf3db25686349c.png" width="1080" height="884" class="img__KcZ"></p>
+<p><strong>Step6</strong> (Merge data into Hive full table): Configure scheduling to merge data from Hive external incremental tables into Hive full table through Hive Merge method</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/data_merging-b8ac385c5de9f96c13163aa65e382496.png" width="1075" height="972" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="32-after-the-architecture-upgrade"><strong>3.2 After the architecture upgrade</strong><a href="https://streampark.apache.org/blog/streampark-flink-with-paimon-in-ziru#32-after-the-architecture-upgrade" class="hash-link" aria-label="Direct link to 32-after-the-architecture-upgrade" title="Direct link to 32-after-the-architecture-upgrade"></a></h3>
+<p>The modified system interaction flow chart is as follows:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/after_the_transformation-8b3717c58b603d12e8e87c8267068672.png" width="1080" height="291" class="img__KcZ"></p>
+<p>Comparing the interaction flow charts before and after the transformation, it can be seen that the process of the first two steps (table access application and approval) is the same, but the only difference is the event monitoring and processing method after the approval is passed.</p>
+<ul>
+<li>
+<p><strong>Before transformation</strong>: Call the Canal interface to deploy Canal tasks (old logic);</p>
+</li>
+<li>
+<p><strong>After transformation</strong>: Call StreamPark’s API interface to complete Flink Paimon’s task deployment.</p>
+</li>
+</ul>
+<p>Next, let’s look at using Paimon to complete data integration.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="33-paimon-implements-one-key-hive-input"><strong>3.3 Paimon implements one-key Hive input</strong><a href="https://streampark.apache.org/blog/streampark-flink-with-paimon-in-ziru#33-paimon-implements-one-key-hive-input" class="hash-link" aria-label="Direct link to 33-paimon-implements-one-key-hive-input" title="Direct link to 33-paimon-implements-one-key-hive-input"></a></h3>
+<p>After Apache Paimon version 0.5, CDC data integration capabilities are provided. Data from MySQL, Kafka, Mongo, etc. can be easily ingested into Paimon in real time through the officially provided paimon-action jar. We are using paimon- flink-action uses <strong>mysql-sync-database (whole database synchronization)</strong> and selects the tables to be synchronized through the "<strong>—including_tables</strong>" parameter. This synchronization mode effectively saves a lot of resource [...]
+<p>paimon-flink-action provides the function of automatically creating Paimon tables and supports Schema Evolution (for example, when the MySQL table fields change, the Paimon table will change accordingly without additional operations). The entire operation process is efficient and smooth, which solves the unnecessary operation and maintenance costs caused by adding fields to the original architecture. The specific use of paimon-action is as follows. For more information, please refer t [...]
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain"><FLINK_HOME>/bin/flink run \</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">/path/to/paimon-flink-actio [...]
+<p>The introduction of Paimon shortens the entire data access link, eliminates dependence on Canal, Kafka, and Airflow, allowing MySQL to directly connect to Hive at minute speeds, and the overall environment is clean and efficient. In addition, Paimon is fully compatible with Hive-side data reading, with extremely low conversion costs and good compatibility with the use of original architecture scripts. Paimon also supports the Tag function, which can be regarded as a lightweight snapsh [...]
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="34-streampark--paimon-implementation-practice"><strong>3.4 StreamPark + Paimon implementation practice</strong><a href="https://streampark.apache.org/blog/streampark-flink-with-paimon-in-ziru#34-streampark--paimon-implementation-practice" class="hash-link" aria-label="Direct link to 34-streampark--paimon-implementation-practice" title="Direct link to 34-streampark--paimon-implementation-practice"></a></h3>
+<p>StreamPark has better support for JAR type jobs in version 2.1.2, making Paimon type data integration jobs simpler. The following example demonstrates how to use StreamPark to quickly develop Paimon data migration type jobs:</p>
+<p>//Video link (StreamPark deployment Paimon data integration job example)</p>
+<p>It is true that StreamPark has specifically supported Paimon data integration jobs after version 2.1.2, but this method of manually creating jobs and entering job parameters still does not meet our actual needs. We need more flexible job creation, which can be done through Quickly complete job creation and startup by calling API... After our research, we found that StreamPark has completely opened various operation APIs for jobs, such as job copy, creation, deployment, start, stop, et [...]
+<p><strong>Step1</strong>：First, the api copy template interface will be called and parameters will be passed in. The relevant screenshots are as follows:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/api_copy-ceff34954a24abe2b3e04bc6d10d4f26.png" width="737" height="484" class="img__KcZ"></p>
+<p>In this way, the job will be created quickly, and the parameters will be passed in through the copy interface. The specific parameters are as follows:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/parameter-d7feafe0cd25f249bec4b2115d64ec00.png" width="780" height="959" class="img__KcZ"></p>
+<p><strong>Step2</strong>：Next, call the API to build the job image:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/job_mirroring-8cbc7decc438aa15ca8166ba9a0bd7c1.png" width="870" height="522" class="img__KcZ"></p>
+<p><strong>Step3</strong>：Continue to call the API to start the job:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/start_the_job-036fdf864d022cf8de2282af72d4b3ca.png" width="855" height="641" class="img__KcZ"></p>
+<p>Finally, after the task is successfully started, you can see the relevant status information of the task and the resource overview of the overall task on the StreamPark platform:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/resource_overview-b7f0b5ccafd202f82c515c64e48cd377.png" width="1080" height="618" class="img__KcZ"></p>
+<p>You can also click Application Name to schedule the Flink web UI:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flink_ui-1a5557a19b30e92802eab1b75cc4a574.png" width="1080" height="702" class="img__KcZ"></p>
+<p>Finally, you can directly query the Paimon table on the Hive side to obtain the required data. The Paimon table is a data synchronization target table with low latency processed by Flink. The screenshot of Hive query Paimon table in Hue is as follows</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/hive_query_paimon-9203a5f6debb35ce41f9f51be06e1446.png" width="2000" height="1346" class="img__KcZ"></p>
+<p>Through the above steps, the business party can easily initiate an access application. After the approval process, a Flink job is created and deployed through StreamPark. The data is entered into the Paimon table through the Flink job, allowing users to easily perform query operations on the Hive side. The entire process is simplified. User operation improves the efficiency and maintainability of data access.</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="4-benefits"><strong>4. Benefits</strong><a href="https://streampark.apache.org/blog/streampark-flink-with-paimon-in-ziru#4-benefits" class="hash-link" aria-label="Direct link to 4-benefits" title="Direct link to 4-benefits"></a></h2>
+<p>By using Paimon and StreamPark, Ziroom has brought the following advantages and benefits:</p>
+<ul>
+<li>
+<p><strong>Network resources and database pressure optimization</strong>: By obtaining data snapshots directly from the Paimon table, the problem of network resource constraints and excessive database pressure caused by pulling data from the business database in the early morning is solved, while data storage costs are reduced.</p>
+</li>
+<li>
+<p><strong>Job management interface improves efficiency</strong>: Using StreamPark's job management interface easily solves the problem of manual deployment of Flink tasks, eliminates the situation where tasks are not deployed in time due to personnel dependence on time, improves efficiency, and reduces communication costs. Improved the management efficiency of Flink jobs.</p>
+</li>
+<li>
+<p><strong>Reduce development and maintenance costs</strong>: It solves the problems of high maintenance costs and slow problem location caused by long links in the previous solution. It also solves the problem of field inconsistency caused by field changes and realizes the unified flow and batch of data access. , reducing development and maintenance costs.</p>
+</li>
+<li>
+<p><strong>Reduced usage costs of data integration scheduling resources and computing resources</strong>: No longer relies on external scheduling systems for incremental data merging, reducing the use of scheduling resources. It no longer relies on Hive resources for merge operations, reducing the cost of merging incremental data on Hive computing resources.</p>
+</li>
+</ul>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="5-conclusion--expectations"><strong>5. Conclusion & Expectations</strong><a href="https://streampark.apache.org/blog/streampark-flink-with-paimon-in-ziru#5-conclusion--expectations" class="hash-link" aria-label="Direct link to 5-conclusion--expectations" title="Direct link to 5-conclusion--expectations"></a></h2>
+<p>We would like to sincerely thank the <strong>Apache StreamPark</strong> community for their generous help as we use the StreamPark API. Their professional service spirit and user-oriented attitude allow us to use this powerful framework more efficiently and smoothly. As an excellent framework, StreamPark not only has excellent functions, but also supports a series of operations such as task copying, creation, deployment, start, stop and status monitoring, and has significantly played [...]
+<p>At the same time, we would like to express our gratitude to the <strong>Apache Paimon</strong> community for their patience and professional guidance during the testing phase of Ziroom MySQL into Paimon. This is an important support for us to successfully complete the test. As a project with great potential, Paimon has demonstrated extraordinary qualities both in terms of feature implementation and team collaboration.</p>
+<p>Finally, we are full of expectations and confidence in the future of Apache StreamPark and Apache Paimon, and firmly believe that they will become excellent projects for Apache in the future. Their excellent functions and harmonious community cooperation model have laid a solid foundation for their maturity in the open source community. We expect that the StreamPark and Paimon communities will continue to maintain a professional attitude and good teamwork spirit, continue to advance t [...]
+ <category label="StreamPark" term="StreamPark"/>
+ <category label="Production Practice" term="Production Practice"/>
+ </entry>
+ <entry>
+ <title type="html"><![CDATA[Apache StreamPark™ helps Tianyancha real-time platform construction｜Multiple-fold increase in efficiency]]></title>
+ <id>https://streampark.apache.org/blog/streampark-usercase-tianyancha</id>
+ <link href="https://streampark.apache.org/blog/streampark-usercase-tianyancha"/>
+ <updated>2024-06-30T14:58:25.000Z</updated>
+ <summary type="html"><![CDATA[Introduction: This paper mainly introduces the challenges of job development and management faced by Tianyancha in the operation and maintenance of nearly 1,000 Flink jobs in the real-time computing business, and solves these challenges by introducing Apache StreamPark. It introduces some of the problems encountered in the process of introducing StreamPark, and how to solve these problems and successfully land the project, and finally greatly reduces [...]
+ <content type="html"><![CDATA[<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/new_cover-4883e1aa52dc856ae160abd142daf867.png" width="1080" height="460" class="img__KcZ"></p>
+<p><strong>Introduction</strong>: This paper mainly introduces the challenges of job development and management faced by Tianyancha in the operation and maintenance of nearly 1,000 Flink jobs in the real-time computing business, and solves these challenges by introducing Apache StreamPark. It introduces some of the problems encountered in the process of introducing StreamPark, and how to solve these problems and successfully land the project, and finally greatly reduces operation and mai [...]
+<p>Github: <a href="https://github.com/apache/streampark" target="_blank" rel="noopener noreferrer">https://github.com/apache/streampark</a></p>
+<p>Welcome to follow, star, fork, and participate in contributions</p>
+<p>Contributor | Beijing Tianyancha</p>
+<p>Author | Li Zhilin</p>
+<p>Article compilation｜Yang Linwei</p>
+<p>Content proofreading｜Pan Yuepeng</p>
+<p>Tianyancha is China's leading business search platform. Since its inception in 2014, Tianyancha has continued to maintain rapid growth, with an industry penetration rate of more than 77%, a monthly active user base of 35 million, and a cumulative user base of 600 million+, making Tianyancha a super symbol in the business field, and one of the first companies to obtain the qualification of enterprise credit collection filed with the Central Bank. After years of deep cultivation, the pl [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/enterprise-c8641726b9205b14e3e330d10e73bb68.png" width="1080" height="748" class="img__KcZ"></p>
+<p>In this paper, we will introduce the challenges faced by the real-time computing business of Tianyancha, how these challenges are addressed by the Apache StreamPark platform, as well as the significant benefits and future plans.</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="business-background-and-challenges"><strong>Business background and challenges</strong><a href="https://streampark.apache.org/blog/streampark-usercase-tianyancha#business-background-and-challenges" class="hash-link" aria-label="Direct link to business-background-and-challenges" title="Direct link to business-background-and-challenges"></a></h2>
+<p>Tianyancha has a huge user base and diverse business dimensions, we use Apache Flink, a powerful real-time computing engine, to provide users with better products and service experience, our real-time computing business mainly covers the following scenarios:</p>
+<ul>
+<li>
+<p>Real-time data ETL processing and data transmission.</p>
+</li>
+<li>
+<p>C-end dimensional data statistics, including user behavior, PV/UV analysis.</p>
+</li>
+<li>
+<p>Real-time correlation analysis of C-end business data with business information, risk information, property rights, equity, shareholders, cases and other information.</p>
+</li>
+</ul>
+<p>With the rapid development of the business of Tianyancha, real-time operations have increased to 800+, and will soon be thousands in the future. Facing such a large volume of operations, we have encountered challenges in development and management, which are roughly as follows:</p>
+<ul>
+<li>
+<p><strong>Difficulty in job operation and maintenance</strong>: Jobs cover multiple Flink versions, running and upgrading multi-version Flink jobs are costly due to API changes, and job submission and parameter tuning rely on complex scripts, which greatly increases the maintenance difficulty and cost.</p>
+</li>
+<li>
+<p><strong>Inefficient job development</strong>: From local IDE debugging to test environment deployment, it takes time-consuming and inefficient steps such as compilation, file transfer and command execution.</p>
+</li>
+<li>
+<p><strong>Lack of automatic retry mechanism</strong>: Multi-table correlation operation of real-time business is susceptible to network and IO fluctuations, which leads to Flink program failure and requires manual or additional program intervention for recovery, increasing development and maintenance costs.</p>
+</li>
+<li>
+<p><strong>High Maintenance Costs</strong>: Lack of centralized logging, version control, and configuration management tools makes maintaining a large number of JAR packages, commit scripts, and Flink client configurations increasingly complex as the task volume rises.</p>
+</li>
+</ul>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/challenges-2c1babbcd18fc83fb47c4447fa413a95.png" width="1069" height="982" class="img__KcZ"></p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="streampark-on-the-ground-practice"><strong>StreamPark on-the-ground practice</strong><a href="https://streampark.apache.org/blog/streampark-usercase-tianyancha#streampark-on-the-ground-practice" class="hash-link" aria-label="Direct link to streampark-on-the-ground-practice" title="Direct link to streampark-on-the-ground-practice"></a></h2>
+<p>In order to solve the above problems, our company decided to introduce StreamPark, we started to pay attention to the StreamPark project when it was first open-sourced, and the project name was StreamX at that time, and since then we have been closely following the development of the project, witnessing its development from open-sourcing to joining the incubation of the Apache Software Foundation, and we have seen that its community activity, user reputation, and the number of times o [...]
+<ul>
+<li>
+<p><strong>Git-style project management</strong>: provides a convenient version control and deployment process for jobs.</p>
+</li>
+<li>
+<p><strong>Powerful job O&M capabilities</strong>: significantly improves the efficiency of job management and maintenance.</p>
+</li>
+<li>
+<p><strong>Precise second-level alarm system</strong>: provides real-time monitoring and rapid response capability.</p>
+</li>
+<li>
+<p><strong>One-stop task development platform</strong>: provides strong protection for development, testing and deployment</p>
+</li>
+</ul>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="1-git-style-project-management"><strong>1. Git-style project management</strong><a href="https://streampark.apache.org/blog/streampark-usercase-tianyancha#1-git-style-project-management" class="hash-link" aria-label="Direct link to 1-git-style-project-management" title="Direct link to 1-git-style-project-management"></a></h3>
+<p>Tianyancha more than 90% are JAR jobs, management of hundreds of JAR jobs is a headache, in order to facilitate the maintenance and management of the previous we named the JAR file name through the “developer name-project name-module-environment”, which brings another problem, there is no way to intuitively differentiate between the maintenance of the operation in that There is no way to visually distinguish which module the job is maintained in, and if a non-owner maintains the job, [...]
+<p>In StreamPark, we were pleasantly surprised to find a project management feature, similar to the Git integration, that solved our pain point by simplifying the naming convention to isolate projects, people, branches and environments. Now, we can instantly locate the code and documents of any job, which greatly improves the maintainability and development efficiency of jobs, and significantly improves our operation and maintenance efficiency.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/git-90b5c4fc3c5c732e3e2757fc83ecb1d4.png" width="1080" height="525" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="2-seconds-accurate-alerts-and-automated-om"><strong>2. Seconds-accurate alerts and automated O&M</strong><a href="https://streampark.apache.org/blog/streampark-usercase-tianyancha#2-seconds-accurate-alerts-and-automated-om" class="hash-link" aria-label="Direct link to 2-seconds-accurate-alerts-and-automated-om" title="Direct link to 2-seconds-accurate-alerts-and-automated-om"></a></h3>
+<p>Below is one of our Flink online tasks, during the query process to access the third-party side, Connection Time out and other errors occurred (Note: itself Flink set the restart policy interval restart three times, but ultimately failed)</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/alarm-609ca86190f86b8ad9a8c4d212705add.png" width="1080" height="904" class="img__KcZ"></p>
+<p>As shown above, when the job alert was received, and our fellow developers responded to 1 in the group, we realized that the <strong>job had been successfully pulled up by StreamPark, and it took less than 15 seconds to resubmit the job and run it</strong>!</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="3-platform-user-rights-control"><strong>3. Platform user rights control</strong><a href="https://streampark.apache.org/blog/streampark-usercase-tianyancha#3-platform-user-rights-control" class="hash-link" aria-label="Direct link to 3-platform-user-rights-control" title="Direct link to 3-platform-user-rights-control"></a></h3>
+<p>StreamPark provides a complete set of permission management mechanism, which allows us to manage the fine-grained permissions of jobs and users on the StreamPark platform, effectively avoiding possible misoperation due to excessive user permissions, and reducing the impact on the stability of job operation and the accuracy of the environment configuration, which is very necessary for enterprise-level users.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/permissions-5879a5913ed1c6f93481b0951b139fc3.png" width="1080" height="525" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="4-flink-sql-online-development"><strong>4. Flink SQL online development</strong><a href="https://streampark.apache.org/blog/streampark-usercase-tianyancha#4-flink-sql-online-development" class="hash-link" aria-label="Direct link to 4-flink-sql-online-development" title="Direct link to 4-flink-sql-online-development"></a></h3>
+<p>For many development scenarios where Flink SQL jobs need to be submitted, we can now completely eliminate the tedious steps of writing code in IDEA. Simply use the StreamPark platform,<strong>which makes it easy to develop Flink SQL jobs</strong>, and here's a simple example.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/sql_job-8e9bfa3d04866e5bf5e05103048f0e09.png" width="1080" height="651" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="5-well-established-operations-management-capabilities"><strong>5. Well-established operations management capabilities</strong><a href="https://streampark.apache.org/blog/streampark-usercase-tianyancha#5-well-established-operations-management-capabilities" class="hash-link" aria-label="Direct link to 5-well-established-operations-management-capabilities" title="Direct link to 5-well-established-operations-management-capabilities"></a></h3>
+<p>StreamPark also has more complete job operation records, for some abnormal jobs at the time of submission, you can now view the failure log completely through the platform, including historical log information.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/historical_logs-815b75ba2fe1a1d1d90c284999e35f3d.png" width="1080" height="525" class="img__KcZ"></p>
+<p>Of course, StreamPark also provides us with a more complete and user-friendly task management interface to visualize how tasks are running.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/task_management-d7b65ce685ba87d759bcae290b0cbbe1.png" width="1080" height="525" class="img__KcZ"></p>
+<p>Our internal real-time projects are highly abstracted code development jobs, and each time we develop a new job, we only need to modify some external incoming parameters. With the StreamPark replicated job feature, we can say goodbye to a lot of repetitive work, and just replicate the existing job on the platform, and slightly modify the running parameters of the program in order to submit a new job!</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/successful-5bef1e14d16c3e90b504d99e28e70d2e.png" width="552" height="520" class="img__KcZ"></p>
+<p>Of course, there are more, not to list here, StreamPark in the use of simple enough to use, within minutes you can get started to use, the function is focused enough to be reliable, the introduction of StreamPark to solve the problem we face in managing Flink jobs, bringing real convenience, significantly improve the efficiency of the people, to give the creative team a great praise!</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="problems-encountered"><strong>Problems encountered</strong><a href="https://streampark.apache.org/blog/streampark-usercase-tianyancha#problems-encountered" class="hash-link" aria-label="Direct link to problems-encountered" title="Direct link to problems-encountered"></a></h2>
+<p>In the StreamPark landing practice, we also encountered some problems, which are documented here, expecting to bring some inputs and references to the users in the community.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="huawei-cloud-has-compatibility-issues-with-open-source-hadoop"><strong>Huawei Cloud has compatibility issues with open source Hadoop</strong><a href="https://streampark.apache.org/blog/streampark-usercase-tianyancha#huawei-cloud-has-compatibility-issues-with-open-source-hadoop" class="hash-link" aria-label="Direct link to huawei-cloud-has-compatibility-issues-with-open-source-hadoop" title="Direct link to huawei-cloud-has-compatibility-i [...]
+<p>Our jobs are in Flink on Yarn mode and deployed in Huawei Cloud. During the process of using StreamPark to deploy jobs, we found that the jobs can be successfully deployed to Huawei Hadoop cluster, but the request for Yarn ResourceManager was rejected when getting the job status information, so we communicated with the community in time to find a solution, and logged the issue.
+<a href="https://github.com/apache/incubator-streampark/issues/3566" target="_blank" rel="noopener noreferrer">https://github.com/apache/incubator-streampark/issues/3566</a></p>
+<p>We communicated with the community to seek a solution, and recorded the issue: . Finally, with the assistance of Mr. Huajie, a community PMC member, and Huawei Cloud students, we successfully resolved the issue: StreamPark could not access the cloud ResourceManager properly due to the internal security authentication mechanism of the cloud product itself.</p>
+<p>Solution (for reference only - add different dependencies according to different environments)</p>
+<p>Remove two HADOOP related packages from the STREAMPARK_HOME/lib directory:</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">hadoop-client-api-3.3.4.jar</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">hadoop-client-runtime-3.3.4.jar</s [...]
+<p>Replace the following Huawei Cloud HADOOP dependencies.</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">commons-configuration2-2.1.1.jar</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">commons-lang-2.6.0.wso2v1.jar [...]
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="other-bugs"><strong>Other Bugs</strong><a href="https://streampark.apache.org/blog/streampark-usercase-tianyancha#other-bugs" class="hash-link" aria-label="Direct link to other-bugs" title="Direct link to other-bugs"></a></h3>
+<p>In the process of intensive use, we also found some problems. In order to better improve and optimize StreamPark's functionality, we have come up with some specific suggestions and solutions, such as:</p>
+<ul>
+<li>
+<p><strong>Dependency conflict</strong>: conflict resolution when loading Flink dependencies (see: Pull Request #3568).</p>
+</li>
+<li>
+<p><strong>Service Stability</strong>: Intercept user program JVM exits to prevent the StreamPark platform from exiting abnormally due to user programs (see: Pull Request #3659).</p>
+</li>
+<li>
+<p><strong>Resource Optimization</strong>: In order to reduce the burden on resources, we limit the number of concurrent StremaPark builds, and control the maximum number of builds through configuration parameters (see: Pull Request #3696).</p>
+</li>
+</ul>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="benefits-derived"><strong>Benefits derived</strong><a href="https://streampark.apache.org/blog/streampark-usercase-tianyancha#benefits-derived" class="hash-link" aria-label="Direct link to benefits-derived" title="Direct link to benefits-derived"></a></h2>
+<p>Apache StreamPark has brought us significant benefits,<strong>mainly in its one-stop service capability, which enables business developers to complete the development, compilation, submission and management of Flink jobs</strong> on a unified platform. It greatly saves our time on Flink job development and deployment, significantly improves development efficiency, and realizes full-process automation from user rights management to Git deployment, task submission, alerting, and automat [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/earnings-40d9739c55527d669e4f90c739571049.png" width="1080" height="989" class="img__KcZ"></p>
+<p>Currently on the Tianyancha platform, specific results from StreamPark include:</p>
+<ul>
+<li>
+<p>Real-time job go-live and test deployment processes are simplified by <strong>70%</strong>.</p>
+</li>
+<li>
+<p>The operation and maintenance costs of real-time jobs have been reduced by <strong>80%</strong>, allowing developers to focus more on code logic.</p>
+</li>
+<li>
+<p>Alert timeliness has improved by more than <strong>5x</strong>, from minutes to seconds, enabling task exceptions to be sensed and handled within <strong>5 seconds</strong>.</p>
+</li>
+<li>
+<p>StreamPark automatically recovers from task failures without human intervention.</p>
+</li>
+<li>
+<p>The integration of GitLab with StreamPark simplifies the process of compiling projects and submitting jobs, dramatically reducing development and maintenance costs.</p>
+</li>
+</ul>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="future-planning"><strong>Future planning</strong><a href="https://streampark.apache.org/blog/streampark-usercase-tianyancha#future-planning" class="hash-link" aria-label="Direct link to future-planning" title="Direct link to future-planning"></a></h2>
+<p>In the future, we plan to migrate all 300+ Flink jobs deployed on our internal real-time computing platform and 500+ Flink jobs maintained in other ways to StreamPark for unified management and maintenance. When we encounter some optimization or enhancement points in the future, we will communicate with the community developers in a timely manner and contribute to the community, so as to make StreamPark easier and more usable! We will make StreamPark easier and better to use.</p>
+<p>At the end of this article, we would like to thank the Apache Streampark community, especially Mr. Huajie, a member of the PMC, for his continuous follow-up and valuable technical support during the process of using StreamPark, and we deeply feel the serious attitude and enthusiasm of the Apache StreamPark community. We are also very grateful to Huawei Cloud students for their support and troubleshooting on cloud deployment.</p>
+<p>We look forward to continuing our cooperation with the StreamPark community in the future to jointly promote the development of real-time computing technology, and hope that StreamPark will do better and better in the future, and graduate to become a representative of the new Apache project soon!</p>]]></content>
+ <category label="StreamPark" term="StreamPark"/>
+ <category label="Production Practice" term="Production Practice"/>
+ </entry>
+ <entry>
+ <title type="html"><![CDATA[China Union's Flink Real-Time Computing Platform Ops Practice]]></title>
+ <id>https://streampark.apache.org/blog/streampark-usercase-chinaunion</id>
+ <link href="https://streampark.apache.org/blog/streampark-usercase-chinaunion"/>
+ <updated>2024-06-30T14:58:25.000Z</updated>
+ <summary type="html"><![CDATA[Abstract]]></summary>
+ <content type="html"><![CDATA[<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/overall_architecture_en-34d3339cff0ab62d179ea3f205e008c9.png" width="1920" height="1080" class="img__KcZ"></p>
+<p><strong>Abstract:</strong> This article is compiled from the sharing of Mu Chunjin, the head of China Union Data Science's real-time computing team and Apache StreamPark Committer, at the Flink Forward Asia 2022 platform construction session. The content of this article is mainly divided into four parts:</p>
+<ul>
+<li>Introduction to the Real-Time Computing Platform Background</li>
+<li>Operational Challenges of Flink Real-Time Jobs</li>
+<li>Integrated Management Based on StreamPark</li>
+<li>Future Planning and Evolution</li>
+</ul>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="introduction-to-the-real-time-computing-platform-background"><strong>Introduction to the Real-Time Computing Platform Background</strong><a href="https://streampark.apache.org/blog/streampark-usercase-chinaunion#introduction-to-the-real-time-computing-platform-background" class="hash-link" aria-label="Direct link to introduction-to-the-real-time-computing-platform-background" title="Direct link to introduction-to-the-real-time-computing- [...]
+<p>The image above depicts the overall architecture of the real-time computing platform. At the bottom layer, we have the data sources. Due to some sensitive information, the detailed information of the data sources is not listed. It mainly includes three parts: business databases, user behavior logs, and user location. China Union has a vast number of data sources, with just the business databases comprising tens of thousands of tables. The data is primarily processed through Flink SQL [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/data_processing_processes_en-8cfb9567159e06c1ffd041785a699342.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>The image above provides a detailed workflow of data processing.</p>
+<p>The first part is collection and parsing. Our data sources come from business databases, including OGG and DTS format messages, log messages, user behavior, and user location data, totaling over 50 different data sources. This number is expected to gradually increase. All data sources are processed in real-time using Flink, and Metrics have been added to monitor the latency of data sources.</p>
+<p>The second part is real-time computing. This stage deals with a massive amount of data, in the trillions, supporting over 10,000 real-time data subscriptions. There are more than 200 Flink tasks. We encapsulate a certain type of business into a scenario, and a single Flink job can support multiple subscriptions in the same scenario. Currently, the number of Flink jobs is continuously increasing, and in the future, it might increase to over 500. One of the major challenges faced here i [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/platform_evolution_en-8accacb1629486423dd06666918a0166.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>In 2018, we adopted a third-party black-box computing engine, which did not support flexible customization of personalized functions, and depended heavily on external systems, resulting in high loads on these external systems and complex operations and maintenance. In 2019, we utilized Spark Streaming's micro-batch processing. From 2020, we began to use Flink for stream computing. Starting from 2021, almost all Spark Streaming micro-batch processing tasks have been replaced by Flink. [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/platform_background_en-b2f833807aec53e4cd8153ac59766d4e.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>To summarize the platform background, it mainly includes the following parts:</p>
+<ul>
+<li>Large data volume: processing an average of trillions of data per day.</li>
+<li>Numerous data sources: integrated with more than 50 types of real-time data sources.</li>
+<li>Numerous subscriptions: supported more than 10,000 data service subscriptions.</li>
+<li>Numerous users: supported the usage of more than 30 internal and external users.</li>
+</ul>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/operational_background_en-921078815a2efaf6de311d061ea51c86.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>The operational maintenance background can also be divided into the following parts:</p>
+<ul>
+<li>High support demand: More than 50 types of data sources, and over 10,000 data service subscriptions.</li>
+<li>Numerous real-time jobs: Currently, there are 200+ Flink production jobs, and the number is continuously and rapidly increasing, potentially reaching 500+ in the future.</li>
+<li>High frequency of launches: There are new or enhanced Flink jobs going live every day.</li>
+<li>Numerous developers: Over 50 R&D personnel are involved in developing Flink real-time computing tasks.</li>
+<li>Numerous users: Over 30 internal and external organizations' users are utilizing the platform.</li>
+<li>Low monitoring latency: Once an issue is identified, we must address it immediately to avoid user complaints.</li>
+</ul>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="flink-real-time-job-operation-and-maintenance-challenges"><strong>Flink Real-Time Job Operation and Maintenance Challenges</strong><a href="https://streampark.apache.org/blog/streampark-usercase-chinaunion#flink-real-time-job-operation-and-maintenance-challenges" class="hash-link" aria-label="Direct link to flink-real-time-job-operation-and-maintenance-challenges" title="Direct link to flink-real-time-job-operation-and-maintenance-challe [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/difficulties_en-51da13514ddd61386799291d4c9d85d1.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>Given the platform and operational maintenance background, particularly with the increasing number of Flink jobs, we have encountered significant challenges in two main areas: job operation and maintenance dilemmas, and business support difficulties.</p>
+<p>In terms of job operation and maintenance dilemmas, firstly, the job deployment process is lengthy and inefficient. Under China Union's principle that security is the top priority, deploying programs on servers involves connecting to a VPN, logging in through the 4A system, packaging and compiling, deploying, and then starting the program. This entire process is quite long. Initially, when developing Flink, we started jobs using scripts, leading to uncontrollable code branches. After [...]
+<p>Due to various factors in the job operation and maintenance difficulties, business support challenges arise, such as a high rate of failures during launch, impact on data quality, lengthy launch times, high data latency, and issues with missed alarm handling, leading to complaints. In addition, the impact on our business is unclear, and once a problem arises, addressing the issue becomes the top priority.</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="integrated-management-based-on-apache-streampark"><strong>Integrated Management based on Apache StreamPark™</strong><a href="https://streampark.apache.org/blog/streampark-usercase-chinaunion#integrated-management-based-on-apache-streampark" class="hash-link" aria-label="Direct link to integrated-management-based-on-apache-streampark" title="Direct link to integrated-management-based-on-apache-streampark"></a></h2>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/job_management_en-4c44c79f18f1fd4e478ed5905a58db25.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>In response to the two dilemmas mentioned above, we have resolved many issues through StreamPark's integrated management. First, let's take a look at the dual evolution of StreamPark, which includes Flink Job Management and Flink Job DevOps Platform. In terms of job management, StreamPark supports deploying Flink real-time jobs to different clusters, such as Flink's native Standalone mode, and the Session, Application, and PerJob modes of Flink on Yarn. In the latest version, it will [...]
+<ul>
+<li>Project Management: When deploying a Flink program, you can fill in the git address in project management and select the branch you want to deploy.</li>
+<li>Job Management: You can specify the execution mode of the Flink job, such as which type of cluster you want to submit to. You can also configure some resources, such as the number of TaskManagers, the memory size of TaskManager/JobManager, parallelism, etc. Additionally, you can set up some fault tolerance measures; for instance, if a Flink job fails, StreamPark can support automatic restarts, and it also supports the input of some dynamic parameters.</li>
+<li>Cluster Management: You can add and manage big data clusters through the interface.</li>
+<li>Team Management: In the actual production process of an enterprise, there are multiple teams, and these teams are isolated from each other.</li>
+<li>Variable Management: You can maintain some variables in one place. For example, you can define Kafka's Broker address as a variable. When configuring Flink jobs or SQL, you can replace the Broker's IP with a variable. Moreover, if this Kafka needs to be decommissioned later, you can also use this variable to check which jobs are using this cluster, facilitating some subsequent processes.</li>
+<li>Alarm Management: Supports multiple alarm modes, such as WeChat, DingTalk, SMS, and email.</li>
+</ul>
+<p>StreamPark supports the submission of Flink SQL and Flink Jar, allows for resource configuration, and supports state tracking, indicating whether the state is running, failed, etc. Additionally, it provides a metrics dashboard and supports the viewing of various logs.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/devops_platform_en-02d9f0b165baf303a20d7c9a8eb091c6.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>The Flink Job DevOps platform primarily consists of the following parts:</p>
+<ul>
+<li>Teams: StreamPark supports multiple teams, each with its team administrator who has all permissions. There are also team developers who only have a limited set of permissions.</li>
+<li>Compilation and Packaging: When creating a Flink project, you can configure the git address, branch, and packaging commands in the project, and then compile and package with a single click of the build button.</li>
+<li>Release and Deployment: During release and deployment, a Flink job is created. Within the Flink job, you can choose the execution mode, deployment cluster, resource settings, fault tolerance settings, and fill in variables. Finally, the Flink job can be started or stopped with a single click.</li>
+<li>State Monitoring: After the Flink job is started, real-time tracking of its state begins, including Flink's running status, runtime duration, Checkpoint information, etc. There is also support for one-click redirection to Flink's Web UI.</li>
+<li>Logs and Alerts: This includes logs from the build and start-up processes and supports alerting methods such as DingTalk, WeChat, email, and SMS.</li>
+</ul>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/multi_team_support_en-f7f607d42182ae6e2c41f706e0c0a743.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>Companies generally have multiple teams working on real-time jobs simultaneously. In our company, this includes a real-time data collection team, a data processing team, and a real-time marketing team. StreamPark supports resource isolation for multiple teams.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/platformized_management_en-ebe6e86784e9489b29cf9a434b4510db.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>Management of the Flink job platform faces the following challenges:</p>
+<ul>
+<li>Numerous scripts: There are several hundred scripts on the platform, scattered across multiple servers.</li>
+<li>Various types of scripts: When starting Flink jobs, there are start scripts, stop scripts, and daemon scripts, and it is very difficult to control operation permissions.</li>
+<li>Inconsistent scripts: The scripts on the server are inconsistent with the scripts on git.</li>
+<li>Difficult to ascertain script ownership: It is unclear who is responsible for the Flink jobs and their purpose.</li>
+<li>Uncontrollable branches: When starting a job, you need to specify the git branch in the script, resulting in untraceable branches.</li>
+</ul>
+<p>Based on the challenges mentioned above, StreamPark has addressed the issues of unclear ownership and untraceable branches through project management. This is because when creating a project, you need to manually specify certain branches. Once the packaging is successful, these branches are recorded. Job management centralizes configurations, preventing scripts from being too dispersed. Additionally, there is strict control over the permissions for starting and stopping jobs, preventi [...]
+<p>Referring to the image above, you can see at the bottom of the diagram that packaging is conducted through project management, configuration is done via job management, and then it is released. This process allows for one-click start and stop operations, and jobs can be submitted through the API.</p>
+<p><img decoding="async" loading="lazy" alt="图片" src="https://streampark.apache.org/assets/images/development_efficiency_en-5beb07a0a515b5b2f304a6f8099096cc.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>In the early stages, we needed to go through seven steps for deployment, including connecting to a VPN, logging in through 4A, executing compile scripts, executing start scripts, opening Yarn, searching for the job name, and entering the Flink UI. StreamPark supports one-click deployment for four of these steps, including one-click packaging, one-click release, one-click start, and one-click access to the Flink UI.</p>
+<p><img decoding="async" loading="lazy" alt="图片" src="https://streampark.apache.org/assets/images/submission_process_en-46dc2e25232a2915b2eec367fcd62e08.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>The image above illustrates the job submission process of our StreamPark platform. Firstly, StreamPark proceeds to release the job, during which some resources are uploaded. Following that, the job is submitted, accompanied by various configured parameters, and it is published to the cluster using the Flink Submit method via an API call. At this point, there are multiple Flink Submit instances corresponding to different execution modes, such as Yarn Session, Yarn Application, Kubernet [...]
+<p><img decoding="async" loading="lazy" alt="图片" src="https://streampark.apache.org/assets/images/status_acquisition_bottleneck_en-652976e1111719f2f1c32ffe44e45a71.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>As mentioned above, in the case of Flink on Yarn jobs, two IDs are acquired upon job submission: the Application ID and the Job ID. These IDs are used to retrieve the job status. However, when there is a large number of Flink jobs, certain issues may arise. StreamPark utilizes a status retriever that periodically sends requests to the ResourceManager every five seconds, using the Application ID or Job ID stored in our database. If there are a considerable number of jobs, during each p [...]
+<p>In the diagram mentioned earlier, the connection count to the ResourceManager shows periodic and sustained increases, indicating that the ResourceManager is in a relatively critical state. This is evidenced by monitoring data from the server, which indeed shows a higher number of connections to the ResourceManager.</p>
+<p><img decoding="async" loading="lazy" alt="图片" src="https://streampark.apache.org/assets/images/state_optimization_en-8a96d3d3392cb7d9b91a9f5b97dacd96.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>To address the issues mentioned above, we have made some optimizations in StreamPark. Firstly, after submitting a job, StreamPark saves the Application ID or Job ID, and it also retrieves and stores the direct access address of the Job Manager in the database. Therefore, instead of polling the ResourceManager for job status, it can directly call the addresses of individual Job Managers to obtain the real-time status. This significantly reduces the number of connections to the Resource [...]
+<p><img decoding="async" loading="lazy" alt="图片" src="https://streampark.apache.org/assets/images/state_recovery_en-9fd0c4443346771b7aafba64da4fb95d.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>Another issue that StreamPark resolves is safeguarding Flink's state recovery. In the past, when we used scripts for operations and maintenance, especially during business upgrades, it was necessary to recover from the latest checkpoint when starting Flink. However, developers often forgot to recover from the previous checkpoint, leading to significant data quality issues and complaints. StreamPark's process is designed to mitigate this issue. Upon the initial start of a Flink job, it [...]
+<p><img decoding="async" loading="lazy" alt="图片" src="https://streampark.apache.org/assets/images/multiple_environments_and_components_en-e4758211d3a79798f5e19aa8e8c03b0e.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>StreamPark also addresses the challenges associated with referencing multiple components across various environments. In a corporate setting, there are typically multiple environments, such as development, testing, and production. Each environment generally includes multiple components, such as Kafka, HBase, Redis, etc. Additionally, within a single environment, there may be multiple instances of the same component. For example, in a real-time computing platform at China Union, when c [...]
+<p><img decoding="async" loading="lazy" alt="图片" src="https://streampark.apache.org/assets/images/multiple_execution_modes_en-6c7c16b64108f8485e811833d7282e86.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>StreamPark supports multiple execution modes for Flink, including three deployment modes based on Yarn: Application, Perjob, and Session. Additionally, it supports two deployment modes for Kubernetes: Application and Session, as well as some Remote modes.</p>
+<p><img decoding="async" loading="lazy" alt="图片" src="https://streampark.apache.org/assets/images/versioning_en-5b907bf416e5c968917ed091b198bb31.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>StreamPark also supports multiple versions of Flink. For example, while our current version is 1.14.x, we would like to experiment with the new 1.16.x release. However, it’s not feasible to upgrade all existing jobs to 1.16.x. Instead, we can opt to upgrade only the new jobs to 1.16.x, allowing us to leverage the benefits of the new version while maintaining compatibility with the older version.</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="future-planning-and-evolution"><strong>Future Planning and Evolution</strong><a href="https://streampark.apache.org/blog/streampark-usercase-chinaunion#future-planning-and-evolution" class="hash-link" aria-label="Direct link to future-planning-and-evolution" title="Direct link to future-planning-and-evolution"></a></h2>
+<p><img decoding="async" loading="lazy" alt="图片" src="https://streampark.apache.org/assets/images/contribution_and_enhancement_en-2d749c52f2c4255dff234b275433f25c.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>In the future, we will increase our involvement in the development of StreamPark, and we have planned the following directions for enhancement:</p>
+<ul>
+<li>High Availability: StreamPark currently does not support high availability, and this aspect needs further strengthening.</li>
+<li>State Management: In enterprise practices, each operator in a Flink job has a UID. If the Flink UID is not set, it could lead to situations where state recovery is not possible when upgrading the Flink job. This issue cannot be solved through the platform at the moment. Therefore, we plan to add this functionality to the platform. We will introduce a feature that checks whether the operator has a UID set when submitting a Flink Jar. If not, a reminder will be issued to avoid state re [...]
+<li>More Detailed Monitoring: Currently, StreamPark supports sending alerts when a Flink job fails. We hope to also send alerts when a Task fails, and need to know the reason for the failure. In addition, enhancements are needed in job backpressure monitoring alerts, Checkpoint timeout alerts, failure alerts, and performance metric collection.</li>
+<li>Stream-Batch Integration: Explore a platform that integrates both streaming and batch processing, combining the Flink stream-batch unified engine with data lake storage that supports stream-batch unification.</li>
+</ul>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/road_map_en-ea1ba2536fecd2828b882dd40ffe5bf4.png" width="1920" height="1080" class="img__KcZ"></p>
+<p>The above diagram represents the Roadmap for StreamPark.</p>
+<ul>
+<li>Data Source: StreamPark will support rapid integration with more data sources, achieving one-click data onboarding.</li>
+<li>Operation Center: Acquire more Flink Metrics to further enhance the capabilities in monitoring and operation.</li>
+<li>K8S-operator: The existing Flink on K8S is somewhat cumbersome, having gone through the processes of packaging Jars, building images, and pushing images. There is a need for future improvements and optimization, and we are actively embracing the upstream K8S-operator integration.</li>
+<li>Streaming Data Warehouse: Enhance support for Flink SQL job capabilities, simplify the submission of Flink SQL jobs, and plan to integrate with Flink SQL Gateway. Enhance capabilities in the SQL data warehouse domain, including metadata storage, unified table creation syntax validation, runtime testing, and interactive queries, while actively embracing Flink upstream to explore real-time data warehouses and streaming data warehouses.</li>
+</ul>]]></content>
+ <category label="StreamPark" term="StreamPark"/>
+ <category label="Production Practice" term="Production Practice"/>
+ <category label="FlinkSQL" term="FlinkSQL"/>
+ </entry>
+ <entry>
+ <title type="html"><![CDATA[Based on Apache Paimon + Apache StreamPark™'s Streaming Data Warehouse Practice by Bondex]]></title>
+ <id>https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon</id>
+ <link href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon"/>
+ <updated>2024-06-30T14:58:25.000Z</updated>
+ <summary type="html"><![CDATA[Foreword: This article mainly introduces the implementation of a streaming data warehouse by Bondex, a supply chain logistics service provider, in the process of digital transformation using the Paimon + StreamPark platform. We provide an easy-to-follow operational manual with the Apache StreamPark integrated stream-batch platform to help users submit Flink tasks and quickly master the use of Paimon.]]></summary>
+ <content type="html"><![CDATA[<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/Bondex-b9280a2451e6984d91f7747acaa10b97.png" width="1080" height="460" class="img__KcZ"></p>
+<p>**Foreword: **This article mainly introduces the implementation of a streaming data warehouse by Bondex, a supply chain logistics service provider, in the process of digital transformation using the Paimon + StreamPark platform. We provide an easy-to-follow operational manual with the Apache StreamPark integrated stream-batch platform to help users submit Flink tasks and quickly master the use of Paimon.</p>
+<ul>
+<li>Introduction to Company Business</li>
+<li>Pain Points and Selection in Big Data Technology</li>
+<li>Production Practice</li>
+<li>Troubleshooting Analysis</li>
+<li>Future Planning</li>
+</ul>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="01-introduction-to-company-business">01 Introduction to Company Business<a href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon#01-introduction-to-company-business" class="hash-link" aria-label="Direct link to 01 Introduction to Company Business" title="Direct link to 01 Introduction to Company Business"></a></h2>
+<p>Bondex Group has always focused on the field of supply chain logistics. By creating an excellent international logistics platform, it provides customers with end-to-end one-stop intelligent supply chain logistics services. The group currently has over 2,000 employees, an annual turnover of more than 12 billion RMB, a network covering over 200 ports globally, and more than 80 branches and subsidiaries at home and abroad, aiding Chinese enterprises to connect with the world.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="business-background"><strong>Business Background</strong><a href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon#business-background" class="hash-link" aria-label="Direct link to business-background" title="Direct link to business-background"></a></h3>
+<p>As the company continues to expand and the complexity of its business increases, in order to better achieve resource optimization and process improvement, the Operations and Process Management Department needs to monitor the company's business operations in real time to ensure the stability and efficiency of business processes.</p>
+<p>The Operations and Process Management Department is responsible for overseeing the execution of various business processes within the company, including the volume of orders for sea, air, and rail transport across different regions and business divisions, large customer order volumes, route order volumes, the amount of business entrusted to each operation site for customs, warehousing, and land transportation, as well as the actual revenue and expenses of each region and business divi [...]
+<p><strong>Real-Time Data Warehouse Architecture:</strong></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/realtime_warehouse-418beb6d713966065d731eb8417b8a6e.png" width="1080" height="447" class="img__KcZ"></p>
+<p>The current system requires direct collection of real-time data from the production system, but there are multiple data sources that need to be associated for queries. The Fanruan report is not user-friendly when dealing with multiple data sources and cannot re-aggregate multiple data sources. Scheduled queries to the production system database can put pressure on it, affecting the stable operation of the production system. Therefore, we need to introduce a warehouse that can handle r [...]
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="02-big-data-technology-pain-points-and-selection">02 Big Data Technology Pain Points and Selection<a href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon#02-big-data-technology-pain-points-and-selection" class="hash-link" aria-label="Direct link to 02 Big Data Technology Pain Points and Selection" title="Direct link to 02 Big Data Technology Pain Points and Selection"></a></h2>
+<p>Since its establishment, the Bondex big data team has always focused on using efficient operational tools or platforms to achieve effective staffing arrangements, optimize repetitive labor, and reduce manual operations.</p>
+<p>While offline batch data processing has been able to support the group's basic cockpit and management reporting, the Transportation Management Department of the group has proposed the need for real-time statistics on order quantities and operational volumes. The finance department has expressed the need for a real-time display of cash flow. In this context, a big data-based integrated stream-batch solution became imperative.</p>
+<p>Although the big data department has already utilized <a href="https://github.com/apache/doris" target="_blank" rel="noopener noreferrer">Apache Doris</a> for integrated storage and computing of lakehouse architecture and has published articles on lakehouse construction in the Doris community, there are some issues that need to be addressed, such as the inability to reuse streaming data storage, the inaccessibility of intermediate layer data, and the inability to perform real-time agg [...]
+<p>Sorted by the evolution of architecture over recent years, the common architectural solutions are as follows:</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="hadoop-architecture"><strong>Hadoop Architecture</strong><a href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon#hadoop-architecture" class="hash-link" aria-label="Direct link to hadoop-architecture" title="Direct link to hadoop-architecture"></a></h3>
+<p>The demarcation point between traditional data warehouses and internet data warehouses dates back to the early days of the internet when the requirements for data analysis were not high, mainly focusing on reports with low real-time needs to support decision-making. This gave rise to offline data analysis solutions.</p>
+<p>**Advantages: **Supports a rich variety of data types, capable of massive computations, low requirements for machine configurations, low timeliness, fault-tolerant.</p>
+<p>**Disadvantages: **Does not support real-time; complex to maintain; the query optimizer is not as good as MPP, slow response.</p>
+<p>Selection Basis: Does not support real-time; maintenance is complex, which does not conform to the principle of streamlined staffing; poor performance.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="lambda-architecture"><strong>Lambda Architecture</strong><a href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon#lambda-architecture" class="hash-link" aria-label="Direct link to lambda-architecture" title="Direct link to lambda-architecture"></a></h3>
+<p>Lambda Architecture is a real-time big data processing framework proposed by Nathan Marz, the author of Storm. Marz developed the famous real-time big data processing framework <a href="https://github.com/apache/storm" target="_blank" rel="noopener noreferrer">Apache Storm</a> while working at Twitter, and the Lambda Architecture is the culmination of his years of experience in distributed big data systems.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/lambda-e357a7e34ced4e82971d4b86acc790b4.png" width="764" height="413" class="img__KcZ"></p>
+<p>Data stream processing is divided into three layers: Serving Layer, Speed Layer, and Batch Layer:</p>
+<ul>
+<li>Batch Layer: Processes offline data and eventually provides a view service to the business;</li>
+<li>Speed Layer: Processes real-time incremental data and eventually provides a view service to the business;</li>
+<li>Serving Layer: Responds to user requests, performs aggregation calculations on offline and incremental data, and ultimately provides the service;</li>
+</ul>
+<p>The advantage is that offline and real-time computations are separated, using two sets of frameworks, which makes the architecture stable.</p>
+<p>The disadvantage is that it is difficult to maintain consistency between offline and real-time data, and operational staff need to maintain two sets of frameworks and three layers of architecture. Developers need to write three sets of code.</p>
+<p>Selection Basis: Data consistency is uncontrollable; operations and development require significant workload, which does not conform to the principle of streamlined staffing.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="kappa-architecture"><strong>Kappa Architecture</strong><a href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon#kappa-architecture" class="hash-link" aria-label="Direct link to kappa-architecture" title="Direct link to kappa-architecture"></a></h3>
+<p>The Kappa Architecture uses a single stream-processing framework to address both offline and real-time data, solving all problems with a real-time stream, with the goal of providing fast and reliable query access to results. It is highly suitable for various data processing workloads, including continuous data pipelines, real-time data processing, machine learning models and real-time analytics, IoT systems, and many other use cases with a single technology stack.</p>
+<p>It is typically implemented using a streaming processing engine such as Apache Flink, Apache Storm, Apache Kinesis, Apache Kafka, designed to handle large data streams and provide fast and reliable query results.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/kappa-924eb8691ece5d996d9aa740e5b79230.png" width="1080" height="281" class="img__KcZ"></p>
+<p>**Advantages: **Single data stream processing framework.</p>
+<p>**Disadvantages: **Although its architecture is simpler compared to Lambda Architecture, the setup and maintenance of the streaming processing framework are relatively complex, and it lacks true offline data processing capabilities; storing large amounts of data in streaming platforms can be costly.</p>
+<p>Selection Basis: The capability for offline data processing needs to be retained, and costs controlled.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="iceberg"><strong>Iceberg</strong><a href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon#iceberg" class="hash-link" aria-label="Direct link to iceberg" title="Direct link to iceberg"></a></h3>
+<p>Therefore, we also researched <a href="https://github.com/apache/iceberg" target="_blank" rel="noopener noreferrer">Apache Iceberg</a>, whose snapshot feature can to some extent achieve streaming-batch integration. However, the issue with it is that the real-time table layer based on Kafka is either not queryable or cannot reuse existing tables, with a strong dependency on Kafka. It requires the use of Kafka to write intermediate results to Iceberg tables, increasing system complexity [...]
+<p>Selection Basis: A Kafka-free real-time architecture has been implemented, intermediate data cannot be made queryable or reusable.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="streaming-data-warehouse-continuation-of-the-kappa-architecture"><strong>Streaming Data Warehouse (Continuation of the Kappa Architecture)</strong><a href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon#streaming-data-warehouse-continuation-of-the-kappa-architecture" class="hash-link" aria-label="Direct link to streaming-data-warehouse-continuation-of-the-kappa-architecture" title="Direct link to streaming-data [...]
+<p>Since the FTS0.3.0 version, the BonDex big data team has participated in the construction of a streaming data warehouse, aiming to further reduce the complexity of the data processing framework and streamline personnel configuration. The initial principle was to get involved with the trend, to continuously learn and improve, and to move closer to cutting-edge technology. The team unanimously believes that it is essential to embrace challenges and "cross the river by feeling the stones [...]
+<p><strong>The architecture of the streaming data warehouse is as follows:</strong></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/streaming_warehouse-ab83b309ef461f3907c2df023143c817.png" width="854" height="494" class="img__KcZ"></p>
+<p>Continuing the characteristics of the Kappa architecture with a single stream processing framework, the advantage lies in the fact that the underlying Paimon technology support makes the data traceable throughout the entire chain. The data warehouse layer architecture can be reused, while also considering the processing capabilities of both offline and real-time data, reducing the waste of storage and computing resources.</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="03-production-practices">03 Production Practices<a href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon#03-production-practices" class="hash-link" aria-label="Direct link to 03 Production Practices" title="Direct link to 03 Production Practices"></a></h2>
+<p>This solution adopts Flink Application on K8s clusters, with Flink CDC for real-time ingestion of relational database data from business systems. Tasks for Flink + Paimon Streaming Data Warehouse are submitted through the StreamPark task platform, with the Trino engine ultimately used to access Finereport for service provision and developer queries. Paimon's underlying storage supports the S3 protocol, and as the company's big data services rely on Alibaba Cloud, Object Storage Servic [...]
+<p><a href="https://github.com/apache/incubator-streampark" target="_blank" rel="noopener noreferrer">StreamPark</a> is a real-time computing platform that leverages the powerful capabilities of <a href="https://github.com/apache/incubator-paimon" target="_blank" rel="noopener noreferrer">Paimon</a> to process real-time data streams. This platform offers the following key features:</p>
+<p>**Real-time Data Processing: **StreamPark supports the submission of real-time data stream tasks, capable of real-time acquisition, transformation, filtering, and analysis of data. This is extremely important for applications that require rapid response to real-time data, such as real-time monitoring, real-time recommendations, and real-time risk control.</p>
+<p>**Scalability: **Capable of efficiently processing large-scale real-time data with good scalability. It can operate in a distributed computing environment, automatically handling parallelization, fault recovery, and load balancing to ensure efficient and reliable data processing.</p>
+<p>**Flink Integration: **Built on <a href="https://github.com/apache/flink" target="_blank" rel="noopener noreferrer">Apache Flink</a>, it leverages Flink’s powerful stream processing engine for high performance and robustness. Users can fully utilize the features and ecosystem of Flink, such as its extensive connectors, state management, and event-time processing.</p>
+<p>**Ease of Use: **Provides a straightforward graphical interface and simplified API, enabling easy construction and deployment of data processing tasks without needing to delve into underlying technical details.</p>
+<p>By submitting Paimon tasks on the StreamPark platform, we can establish a full-link real-time flowing, queryable, and layered reusable Pipline.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/pipline-60b68a9487092b6866020ce983a85ac0.png" width="1080" height="496" class="img__KcZ"></p>
+<p>The main components versions used are as follows:</p>
+<ul>
+<li>flink-1.16.0-scala-2.12</li>
+<li>paimon-flink-1.16-0.4-20230424.001927-40.jar</li>
+<li>apache-streampark_2.12-2.0.0</li>
+<li>kubernetes v1.18.3</li>
+</ul>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="environment-setup"><strong>Environment Setup</strong><a href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon#environment-setup" class="hash-link" aria-label="Direct link to environment-setup" title="Direct link to environment-setup"></a></h3>
+<p>Download flink-1.16.0-scala-2.12.tar.gz which can be obtained from the official Flink website. Download the corresponding version of the package to the StreamPark deployment server.</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain"># Unzip</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">tar zxvf flink-1.16.0-scala-2.12.tar.gz</span><br></sp [...]
+<p>It is suggested to add FLINK_HOME locally for convenient troubleshooting before deploying on k8s.</p>
+<p>vim /etc/profile</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">#FLINK</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">export FLINK_HOME=/data/src/flink-1.16.0-scala-2.12</sp [...]
+<p>In StreamPark, add Flink conf:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flink_conf-1c8b9773c6678621a140320ae2a8525f.jpg" width="1080" height="427" class="img__KcZ"></p>
+<p>To build the Flink 1.16.0 base image, pull the corresponding version of the image from Docker Hub.</p>
+<div class="language-dockerfile codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-dockerfile codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain"># Pull the image</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">docker pull flink:1.16.0-scala_2.12 [...]
+<p>Create a Dockerfile & target directory to place the flink-oss-fs-hadoop JAR package in that directory. Download the shaded Hadoop OSS file system jar package from:</p>
+<p><a href="https://repository.apache.org/snapshots/org/apache/paimon/paimon-oss/" target="_blank" rel="noopener noreferrer">https://repository.apache.org/snapshots/org/apache/paimon/paimon-oss/</a></p>
+<p>.</p>
+<p>├── Dockerfile</p>
+<p>└── target</p>
+<p>└── flink-oss-fs-hadoop-1.16.0.jar</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">touch Dockerfile</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain" style="display:inline-block"></span><br></spa [...]
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="build-base-image"><strong>Build base image</strong><a href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon#build-base-image" class="hash-link" aria-label="Direct link to build-base-image" title="Direct link to build-base-image"></a></h3>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">docker build -t flink-table-store:v1.16.0 .</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain" style="display:inl [...]
+<p>Next, prepare the Paimon jar package. You can download the corresponding version from the Apache <a href="https://repository.apache.org/content/groups/snapshots/org/apache/paimon" target="_blank" rel="noopener noreferrer">Repository</a>. It's important to note that it should be consistent with the major version of Flink.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="managing-jobs-with-apache-streampark"><strong>Managing Jobs with Apache StreamPark™</strong><a href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon#managing-jobs-with-apache-streampark" class="hash-link" aria-label="Direct link to managing-jobs-with-apache-streampark" title="Direct link to managing-jobs-with-apache-streampark"></a></h3>
+<p><strong>Prerequisites:</strong></p>
+<ul>
+<li>Kubernetes client connection configuration</li>
+<li>Kubernetes RBAC configuration</li>
+<li>Container image repository configuration (the free version of Alibaba Cloud image is used in this case)</li>
+<li>Create a PVC resource to mount checkpoints/savepoints</li>
+</ul>
+<p><strong>Kubernetes Client Connection Configuration:</strong></p>
+<p>Copy the k8s master node's <code>~/.kube/config</code> configuration directly to the directory on the StreamPark server, then execute the following command on the StreamPark server to display the k8s cluster as running, which indicates successful permission and network verification.</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">kubectl cluster-info</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code to clipboard" title="Copy" [...]
+<p>Kubernetes RBAC Configuration, create the streampark namespace:</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">kubectl create ns streampark</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code to clipboard" title [...]
+<p>Use the default account to create the clusterrolebinding resource:</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">kubectl create secret docker-registry streamparksecret </span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">--dock [...]
+<p><strong>Container Image Registry Configuration:</strong></p>
+<p>In this case, Alibaba Cloud's Container Registry Service (ACR) is used, but you can also substitute it with a self-hosted image service such as Harbor.</p>
+<p>Create a namespace named StreamPark (set the security setting to private).</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/aliyun-43dec052b5792743675fb5a0e5980ebc.png" width="1080" height="316" class="img__KcZ"></p>
+<p>Configure the image repository in StreamPark; task build images will be pushed to the repository.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/dockersystem_setting-60d5b7d9e930185cf1a9a404beab9dc0.png" width="1080" height="360" class="img__KcZ"></p>
+<p>Create a k8s secret key to pull images from ACR; streamparksecret is the name of the secret, customizable.</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">kubectl create secret docker-registry streamparksecret </span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">--dock [...]
+<p>Creation of PVC Resources for Checkpoints/Savepoints, Utilizing Alibaba Cloud's OSS for K8S Persistence</p>
+<p><strong>OSS CSI Plugin:</strong></p>
+<p>The OSS CSI plugin can be used to help simplify storage management. You can use the CSI configuration to create a PV, and define PVCs and pods as usual. For the YAML file reference, visit:
+<a href="https://bondextest.oss-cn-zhangjiakou.aliyuncs.com/ossyaml.zip" target="_blank" rel="noopener noreferrer">https://bondextest.oss-cn-zhangjiakou.aliyuncs.com/ossyaml.zip</a></p>
+<p><strong>Configuration Requirements:</strong></p>
+<p>- Create a service account with the necessary RBAC permissions, please refer as below:</p>
+<p><a href="https://github.com/kubernetes-sigs/alibaba-cloud-csi-driver/blob/master/docs/oss.md" target="_blank" rel="noopener noreferrer">https://github.com/kubernetes-sigs/alibaba-cloud-csi-driver/blob/master/docs/oss.md</a></p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">kubectl -f rbac.yaml</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code to clipboard" title="Copy" [...]
+<p>- Deploy the OSS CSI Plugin:</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">kubectl -f oss-plugin.yaml</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code to clipboard" title=" [...]
+<p>- Create PV for CP (Checkpoints) & SP (Savepoints):</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">kubectl -f checkpoints_pv.yaml kubectl -f savepoints_pv.yaml</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-labe [...]
+<p>- Create PVC for CP & SP:</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">kubectl -f checkpoints_pvc.yaml kubectl -f savepoints_pvc.yaml</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-la [...]
+<p>Once the dependent environment is configured, we can start using Paimon for layered development of the streaming data warehouse.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="case-study"><strong>Case Study</strong><a href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon#case-study" class="hash-link" aria-label="Direct link to case-study" title="Direct link to case-study"></a></h3>
+<p>Real-time calculation of sea and air freight consignment volumes.</p>
+<p>Job Submission: Initialize Paimon catalog configuration.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/paimon_catalog_setting-aa6a826f1cb1a7d9c90fc3fae71a0ef3.png" width="592" height="637" class="img__KcZ"></p>
+<div class="language-sql codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-sql codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token keyword" style="color:rgb(86, 156, 214)">SET</span><span class="token plain"> </span><span class="token string" style="color:rgb(206, 145, 120)">'execution.runtime [...]
+<p>A single job extracts table data from postgres, mysql, and sqlserver databases and writes it into Paimon.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/application_setting-48542837b22debfeca15eddaf6ea93a0.png" width="1080" height="533" class="img__KcZ"></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/pom-949e43be1f8e611776858b7573047de2.jpg" width="1080" height="597" class="img__KcZ"></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/pod_template-4a36a24383f78f09d3090e5ddf7fe1fa.png" width="1080" height="623" class="img__KcZ"></p>
+<p><strong>Details are as follows:</strong></p>
+<div class="codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-text codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">Development Mode：Flink SQL</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain" style="display:inline-block"></span><br></span><spa [...]
+<p><strong>pod template:</strong></p>
+<div class="language-yaml codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-yaml codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token key atrule">apiVersion</span><span class="token punctuation" style="color:rgb(212, 212, 212)">:</span><span class="token plain"> v1</span><br></span><span class= [...]
+<p><strong>Flink sql：</strong></p>
+<ol>
+<li>Establish the relationship between the source table and the ODS table in Paimon, here it is a one-to-one mapping between the source table and the target table.</li>
+</ol>
+<div class="language-sql codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-sql codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token comment" style="color:rgb(106, 153, 85)">-- PostgreSQL database example</span><span class="token plain"></span><br></span><span class="token-line" style="color:#9C [...]
+<p>The real-time data writing effect of the business table into the Paimon ods table is as follows:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/ods-bc0d5ecbef808b0ca349311569803f18.png" width="1080" height="422" class="img__KcZ"></p>
+<ol start="2">
+<li>Flatten the data from the ods layer tables and write it into the dwd layer. This process is essentially about merging the related business tables from the ods layer into the dwd layer. The main focus here is on processing the value of the count_order field. Since the data in the source table may be logically or physically deleted, using the count function for statistics may lead to issues. Therefore, we use the sum aggregate to calculate the order quantity. Each reference_no correspo [...]
+</ol>
+<p>For the dim dimension table, we directly use the dimension table processed in Doris, as the update frequency of the dimension table is low, so there was no need for secondary development within Paimon.</p>
+<div class="language-sql codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-sql codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token comment" style="color:rgb(106, 153, 85)">-- Create a wide table at the paimon-dwd layer</span><span class="token plain"></span><br></span><span class="token-line" [...]
+<p>In the Flink UI, you can see the ods data is joined and cleansed in real-time through Paimon to the table dwd_business_order.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/dwd_business_order-a017b7fa6dac855794cf9720a0421090.png" width="1080" height="646" class="img__KcZ"></p>
+<ol start="2">
+<li>The data from the dwd layer is lightly aggregated to the dwm layer, and the related data is written into the dwm.dwm_business_order_count table. The data in this table will perform a sum on the aggregated fields based on the primary key, with the sum_orderCount field being the result of the aggregation. Paimon will automatically handle the data that is physically deleted during the sum.</li>
+</ol>
+<div class="language-sql codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-sql codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token comment" style="color:rgb(106, 153, 85)">-- Create a lightweight summary table on the dwm layer, summarizing the order volume by date, sales, operation, business c [...]
+<p>The Flink UI effect is as follows: the data from dwd_business_orders is aggregated into dwm_business_order_count:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/dwm_business_order_count-1abbcf7d6b31f15e49f2e035ecfdb8d7.png" width="194" height="649" class="img__KcZ"></p>
+<ol start="4">
+<li>Aggregate the data from the dwm layer to the dws layer, where the dws layer performs summaries at even finer dimensions.</li>
+</ol>
+<div class="language-sql codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-sql codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token comment" style="color:rgb(106, 153, 85)">-- Create a table to aggregate daily order counts by operator and business type</span><span class="token plain"></span><br [...]
+<p>The Flink UI reflects the following: Data from <code>dws_business_order_count_op</code> is written to <code>dws_business_order_count_op</code>:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/dws_business_order_count_op-72bb62943dcef89451b47db142cfe689.png" width="191" height="630" class="img__KcZ"></p>
+<p>Overall Data Flow Example</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/all_datastream-f095c45ce30fe21e178eac323c6a7a37.jpg" width="1080" height="515" class="img__KcZ"></p>
+<p>Source Table:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/source-afa30e0d3ab0116582cc4bd7831ce2ca.png" width="1080" height="100" class="img__KcZ"></p>
+<p>paimon-ods:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/paimon-ods-876cf67ff375c0b5cb67d4a0e555b1a3.png" width="1080" height="94" class="img__KcZ"></p>
+<p>paimon-dwd:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/paimon-dwd-d3b020d2bd2b78c26944a6928be39e4e.png" width="1080" height="95" class="img__KcZ"></p>
+<p>paimon-dwm:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/paimon-dwm-778a748d6b64c9e61d73e6b6da1d625f.png" width="1080" height="39" class="img__KcZ"></p>
+<p>paimon-dws:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/paimon-dws-632e1291e21c4f628f7e87ca03ae5c6f.png" width="1080" height="56" class="img__KcZ"></p>
+<p>A special reminder: When extracting from SQL Server databases, if the source table is too large, a full extraction will lock the table. It is recommended to use incremental extraction if the business allows. For a full extraction, SQL Server can use an interim approach where the full data is imported into MySQL, then from MySQL to Paimon-ODS, and later incremental extraction is done through SQL Server.</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="04-troubleshooting-and-analysis">04 Troubleshooting and Analysis<a href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon#04-troubleshooting-and-analysis" class="hash-link" aria-label="Direct link to 04 Troubleshooting and Analysis" title="Direct link to 04 Troubleshooting and Analysis"></a></h2>
+<p><strong>1. Inaccurate Aggregated Data Calculation</strong></p>
+<p>SQL Server CDC collects data into the Paimon table, explanation:</p>
+<p><strong>DWD table:</strong></p>
+<p>'changelog-producer' = 'input'</p>
+<p><strong>ADS table:</strong></p>
+<p>'merge-engine' = 'aggregation', -- Using aggregation to compute sum</p>
+<p>'fields.sum_amount.aggregate-function' = 'sum'</p>
+<p>The ADS layer's aggregated table uses agg sum, which can result in the DWD data stream not generating update_before, creating an incorrect data stream with update_after. For example, if the upstream source table updates from 10 to 30, the DWD layer's data will change to 30, and the ADS aggregation layer's data will also change to 30. But now it turns into an append data, resulting in incorrect data of 10+30=40.</p>
+<p>Solution:</p>
+<p>By specifying 'changelog-producer' = 'full-compaction',
+Table Store will compare the results between full compactions and produce the differences as changelog.
+The latency of changelog is affected by the frequency of full compactions.</p>
+<p>By specifying changelog-producer.compaction-interval table property (default value 30min),
+users can define the maximum interval between two full compactions to ensure latency.
+This table property does not affect normal compactions and they may still be performed once in a while by writers to reduce reader costs.</p>
+<p>This approach can solve the above mentioned issue. However, it has led to a new problem. The default changelog-producer.compaction-interval is 30 minutes, meaning that it takes 30 minutes for changes upstream to be reflected in the ads query. During production, it has been found that changing the compaction interval to 1 minute or 2 minutes can cause inaccuracies in the ADS layer aggregation data again.</p>
+<div class="language-sql codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-sql codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token string" style="color:rgb(206, 145, 120)">'changelog-producer.compaction-interval'</span><span class="token plain"> </span><span class="token operator" style="color [...]
+<p>When writing into the Flink Table Store, it is necessary to configure table.exec.sink.upsert-materialize= none to avoid generating an upsert stream, ensuring that the Flink Table Store can retain a complete changelog for subsequent stream read operations.</p>
+<div class="language-sql codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-sql codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token keyword" style="color:rgb(86, 156, 214)">set</span><span class="token plain"> </span><span class="token string" style="color:rgb(206, 145, 120)">'table.exec.sink.u [...]
+<p><strong>2. The same sequence.field causes the dwd detailed wide table to not receive data updates</strong></p>
+<p><strong>mysql cdc collects data into the paimon table</strong></p>
+<p>Explanation:</p>
+<p>After executing an update on the MySQL source, the data modifications are successfully synchronized to the dwd_orders table.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/dwd_orders-8343ec8277c69354f8ee7cac0c4c9f91.png" width="1080" height="558" class="img__KcZ"></p>
+<p>However, upon examining the dwd_enriched_orders table data, it is found to be unsynchronized. When starting the stream mode to observe the data, it is discovered that there is no data flow.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/log-42ef92b8036ed3698fb0fbb1abdaecab.png" width="1080" height="357" class="img__KcZ"></p>
+<p><strong>Solution:</strong></p>
+<p>Upon investigation, it was discovered that the issue was caused by the configuration of the parameter 'sequence.field' = 'o_orderdate' (using o_orderdate to generate a sequence ID, and when merging records with the same primary key, the record with the larger sequence ID is chosen). Since the o_orderdate field does not change when modifying the price, the 'sequence.field' remains the same, leading to an uncertain order. Therefore, for ROW1 and ROW2, since their o_orderdate are the sam [...]
+<p><strong>3. Aggregate function 'last_non_null_value' does not support retraction</strong></p>
+<p>Error:</p>
+<div class="codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-text codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">Caused by: java.lang.UnsupportedOperationException: Aggregate function 'last_non_null_value' does not support retraction, If you allow this function to ignore retractio [...]
+<p>An explanation can be found in the official documentation:</p>
+<div class="codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-text codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">Only sum supports retraction (UPDATE_BEFORE and DELETE), other aggregate functions do not support retraction.</span><br></span></code></pre><div class="buttonGroup_inAn [...]
+<p>This can be understood as: except for the SUM function, other Agg functions do not support Retraction. To avoid errors when receiving DELETE and UPDATEBEFORE messages, it is necessary to configure <code>'fields.${field_name}.ignore-retract'='true'</code> for the specified field to ignore retraction and solve this error.</p>
+<div class="language-sql codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-sql codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token keyword" style="color:rgb(86, 156, 214)">WITH</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(212, 212, 212)">(</span><spa [...]
+<p><strong>4. Paimon Task Interruption Failure</strong></p>
+<p>Task abnormal interruption leads to pod crash, viewing loki logs shows akka.pattern.AskTimeoutException: Ask timed out on</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/loki-372203296a8af19f7a32fcd2162fdc29.png" width="1080" height="288" class="img__KcZ"></p>
+<p>java.util.concurrent.TimeoutException: Invocation of [RemoteRpcInvocation(JobMasterGateway.updateTaskExecutionState(TaskExecutionState))] at recipient [akka.tcp://flink@fts-business-order-count.streampark:6123/user/rpc/jobmanager_2] timed out. This is usually caused by: 1) Akka failed sending the message silently, due to problems like oversized payload or serialization failures. In that case, you should find detailed error information in the logs. 2) The recipient needs more time for [...]
+<p>The preliminary judgment is that the triggering of akka's timeout mechanism is likely due to the above two reasons. Therefore, adjust the cluster's akka timeout settings and carry out individual task segmentation or increase resource configuration.</p>
+<p>To proceed, let's first see how to modify the parameters:</p>
+<table><thead><tr><th>key</th><th>default</th><th>describe</th></tr></thead><tbody><tr><td>akka.ask.timeout</td><td>10s</td><td>Timeout used for all futures and blocking Akka calls. If Flink fails due to timeouts then you should try to increase this value. Timeouts can be caused by slow machines or a congested network. The timeout value requires a time-unit specifier (ms/s/min/h/d).</td></tr><tr><td>web.timeout</td><td>600000</td><td>Timeout for asynchronous operations by the web monitor [...]
+<p>In <code>conf/flink-conf.yaml</code>, add the following parameters at the end:</p>
+<p><strong>akka.ask.timeout: 100s</strong></p>
+<p><strong>web.timeout:1000000</strong></p>
+<p>Then manually refresh the <code>flink-conf.yaml</code> in Streampark to verify if the parameters have synchronized successfully.</p>
+<p><strong>5. snapshot no such file or directory</strong></p>
+<p>It was discovered that <code>cp</code> (checkpoint) is failing.</p>
+<p>[Image showing failure of cp]</p>
+<p>Corresponding logs at the time show Snapshot missing, with the task status shown as running, but data from the MySQL source table cannot be written into the paimon ods table.</p>
+<p>[Image showing status log]</p>
+<p>The reason for cp failure is identified as: due to high computation and CPU intensity, threads within the TaskManager are constantly processing elements, without having time to perform the checkpoint.</p>
+<p>The reason for being unable to read the Snapshot is: insufficient resources in the Flink cluster, leading to competition between Writer and Committer. During Full-Compaction, an incomplete Snapshot of an expired part was read. The official response to this issue has been fixed:</p>
+<p><a href="https://github.com/apache/incubator-paimon/pull/1308" target="_blank" rel="noopener noreferrer">https://github.com/apache/incubator-paimon/pull/1308</a></p>
+<p>And the solution to the checkpoint failure is to increase parallelism, by adding more deployment taskmanager slots and enhancing the jobmanager CPU resources.</p>
+<div class="codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-text codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">-D kubernetes.jobmanager.cpu=0.8</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain" style="display:inline-block"></span><br></spa [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/dynamic_properties-33cd67364d80285f0e480974a60d52ff.jpg" width="1080" height="244" class="img__KcZ"></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flink_dashboard-4bcca1ae4f103249bd7c6c48625a4959.png" width="1080" height="487" class="img__KcZ"></p>
+<p>In complex real-time tasks, resources can be increased by modifying dynamic parameters.</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="05-future-planning">05 Future Planning<a href="https://streampark.apache.org/blog/streampark-usercase-bondex-with-paimon#05-future-planning" class="hash-link" aria-label="Direct link to 05 Future Planning" title="Direct link to 05 Future Planning"></a></h2>
+<ul>
+<li>Our self-built data platform, Bondata, is integrating Paimon's metadata information, data metric system, lineage, and one-click pipeline features. This integration aims to form HCBondata's data assets and will serve as the foundation for a one-stop data governance initiative.</li>
+<li>Subsequently, we will integrate with Trino Catalog to access Doris, realizing a one-service solution for both offline and real-time data.</li>
+<li>We will continue to advance the pace of building an integrated streaming and batch data warehouse within the group, adopting the architecture of Doris + Paimon.</li>
+</ul>
+<p>Here, I would like to thank Teacher Zhixin and the StreamPark community for their strong support during the use of StreamPark + Paimon. The problems encountered in the learning process are promptly clarified and resolved. We will also actively participate in community exchanges and contributions in the future, enabling Paimon to provide more developers and enterprises with integrated stream and batch data lake solutions.</p>]]></content>
+ <category label="StreamPark" term="StreamPark"/>
+ <category label="Production Practice" term="Production Practice"/>
+ <category label="paimon" term="paimon"/>
+ <category label="streaming-warehouse" term="streaming-warehouse"/>
+ </entry>
+ <entry>
+ <title type="html"><![CDATA[Apache StreamPark™ in the Large-Scale Production Practice at Shunwang Technology]]></title>
+ <id>https://streampark.apache.org/blog/streampark-usercase-shunwang</id>
+ <link href="https://streampark.apache.org/blog/streampark-usercase-shunwang"/>
+ <updated>2024-06-30T14:58:25.000Z</updated>
+ <summary type="html"><![CDATA[Preface: This article primarily discusses the challenges encountered by Shunwang Technology in using the Flink computation engine, and how StreamPark is leveraged as a real-time data platform to address these challenges, thus supporting the company's business on a large scale.]]></summary>
+ <content type="html"><![CDATA[<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/autor-4fee09aa3abf8842dcdbb7ada1aa9409.png" width="1080" height="460" class="img__KcZ"></p>
+<p><strong>Preface:</strong> This article primarily discusses the challenges encountered by Shunwang Technology in using the Flink computation engine, and how StreamPark is leveraged as a real-time data platform to address these challenges, thus supporting the company's business on a large scale.</p>
+<ul>
+<li>Introduction to the company's business</li>
+<li>Challenges encountered</li>
+<li>Why choose StreamPark</li>
+<li>Implementation in practice</li>
+<li>Benefits Brought</li>
+<li>Future planning</li>
+</ul>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="introduction-to-the-companys-business"><strong>Introduction to the Company's Business</strong><a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#introduction-to-the-companys-business" class="hash-link" aria-label="Direct link to introduction-to-the-companys-business" title="Direct link to introduction-to-the-companys-business"></a></h2>
+<p>Hangzhou Shunwang Technology Co., Ltd. was established in 2005. Upholding the corporate mission of connecting happiness through technology, it is one of the influential pan-entertainment technology service platforms in China. Over the years, the company has always been driven by products and technology, dedicated to creating immersive entertainment experiences across all scenes through digital platform services.</p>
+<p>Since its inception, Shunwang Technology has experienced rapid business growth, serving 80,000 offline physical stores, owning more than 50 million internet users, reaching over 140 million netizens annually, with 7 out of every 10 public internet service venues using Shunwang Technology's products.</p>
+<p>With a vast user base, Shunwang Technology has been committed to providing a superior product experience and achieving digital transformation of the enterprise. Since 2015, it has vigorously developed big data capabilities, with Flink playing a crucial role in Shunwang Technology’s real-time computing. At Shunwang Technology, real-time computing is roughly divided into four application scenarios:</p>
+<ul>
+<li>Real-time update of user profiles: This includes internet cafe profiles and netizen profiles.</li>
+<li>Real-time risk control: This includes activity anti-brushing, monitoring of logins from different locations, etc.</li>
+<li>Data synchronization: This includes data synchronization from Kafka to Hive / Iceberg / ClickHouse, etc.</li>
+<li>Real-time data analysis: This includes real-time big screens for games, voice, advertising, live broadcasts, and other businesses.</li>
+</ul>
+<p>To date, Shunwang Technology has to process TB-level data daily, with a total of more than 700 real-time tasks, of which FlinkSQL tasks account for over 95%. With the rapid development of the company's business and the increased demand for data timeliness, it is expected that the number of Flink tasks will reach 900+ by the end of this year.</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="challenges-encountered"><strong>Challenges Encountered</strong><a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#challenges-encountered" class="hash-link" aria-label="Direct link to challenges-encountered" title="Direct link to challenges-encountered"></a></h2>
+<p>Flink, as one of the most popular real-time computing frameworks currently, boasts powerful features such as high throughput, low latency, and stateful computations. However, in our exploration, we found that while Flink has strong computational capabilities, the community has not provided effective solutions for job development management and operational issues. We have roughly summarized some of the pain points we encountered in Flink job development management into four aspects, as [...]
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/pain-4d2aee42ea7aae80eaeac17a6d51c090.png" width="1080" height="744" class="img__KcZ"></p>
+<p>Facing a series of pain points in the management and operation of Flink jobs, we have been looking for suitable solutions to lower the barrier to entry for our developers using Flink and improve work efficiency.</p>
+<p>Before we encountered StreamPark, we researched some companies' Flink management solutions and found that they all developed and managed Flink jobs through self-developed real-time job platforms. Consequently, we decided to develop our own real-time computing management platform to meet the basic needs of our developers for Flink job management and operation. Our platform is called Streaming-Launcher, with the following main functions:</p>
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/launcher-766611cb0dddb99d8437ead44d0665d4.png" width="1080" height="670" class="img__KcZ"></p>
+<p>However, as our developers continued to use Streaming-Launcher, they discovered quite a few deficiencies: the development cost for Flink remained too high, work efficiency was poor, and troubleshooting was difficult. We summarized the defects of Streaming-Launcher as follows:</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="cumbersome-sql-development-process"><strong>Cumbersome SQL Development Process</strong><a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#cumbersome-sql-development-process" class="hash-link" aria-label="Direct link to cumbersome-sql-development-process" title="Direct link to cumbersome-sql-development-process"></a></h3>
+<p>Business developers need multiple tools to complete the development of a single SQL job, increasing the barrier to entry for our developers.</p>
+<p><img decoding="async" loading="lazy" alt="cc0b1414ed43942e0ef5e9129c2bf817" src="https://streampark.apache.org/assets/images/sql_develop-ef85f6a001fb814d494c9b39eb2dfba9.png" width="1080" height="229" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="drawbacks-of-sql-client"><strong>Drawbacks of SQL-Client</strong><a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#drawbacks-of-sql-client" class="hash-link" aria-label="Direct link to drawbacks-of-sql-client" title="Direct link to drawbacks-of-sql-client"></a></h3>
+<p>The SQL-Client provided by Flink currently has certain drawbacks regarding the support for job execution modes.</p>
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/sql_client-32fd9eb063198e6f35349187eed61546.png" width="1080" height="721" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="lack-of-unified-job-management"><strong>Lack of Unified Job Management</strong><a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#lack-of-unified-job-management" class="hash-link" aria-label="Direct link to lack-of-unified-job-management" title="Direct link to lack-of-unified-job-management"></a></h3>
+<p>Within Streaming-Launcher, there is no provision of a unified job management interface. Developers cannot intuitively see the job running status and can only judge the job situation through alarm information, which is very unfriendly to developers. If a large number of tasks fail at once due to Yarn cluster stability problems or network fluctuations and other uncertain factors, it is easy for developers to miss restoring a certain task while manually recovering jobs, which can lead to [...]
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="cumbersome-problem-diagnosis-process"><strong>Cumbersome Problem Diagnosis Process</strong><a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#cumbersome-problem-diagnosis-process" class="hash-link" aria-label="Direct link to cumbersome-problem-diagnosis-process" title="Direct link to cumbersome-problem-diagnosis-process"></a></h3>
+<p>To view logs for a job, developers must go through multiple steps, which to some extent reduces their work efficiency.</p>
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/step-d945592bbe32c0e093289c6265472f1c.png" width="1080" height="122" class="img__KcZ"></p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="why-use-apache-streampark"><strong>Why Use Apache StreamPark™</strong><a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#why-use-apache-streampark" class="hash-link" aria-label="Direct link to why-use-apache-streampark" title="Direct link to why-use-apache-streampark"></a></h2>
+<p>Faced with the defects of our self-developed platform Streaming-Launcher, we have been considering how to further lower the barriers to using Flink and improve work efficiency. Considering the cost of human resources and time, we decided to seek help from the open-source community and look for an appropriate open-source project to manage and maintain our Flink tasks.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="01--apache-streampark-a-powerful-tool-for-solving-flink-issues">01 <strong>Apache StreamPark™: A Powerful Tool for Solving Flink Issues</strong><a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#01--apache-streampark-a-powerful-tool-for-solving-flink-issues" class="hash-link" aria-label="Direct link to 01--apache-streampark-a-powerful-tool-for-solving-flink-issues" title="Direct link to 01--apache-streampark-a-powe [...]
+<p>Fortunately, in early June 2022, we stumbled upon StreamPark on GitHub and embarked on a preliminary exploration full of hope. We found that StreamPark's capabilities can be broadly divided into three areas: user permission management, job operation and maintenance management, and development scaffolding.</p>
+<p><strong>User Permission Management</strong></p>
+<p>In the StreamPark platform, to avoid the risk of users having too much authority and making unnecessary misoperations that affect job stability and the accuracy of environmental configurations, some user permission management functions are provided. This is very necessary for enterprise-level users.</p>
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/permission-affd274acd10773ad4531da9102f3f91.png" width="1080" height="721" class="img__KcZ"></p>
+<p><strong>Job Operation and Maintenance Management</strong></p>
+<p><strong>Our main focus during the research on StreamPark was its capability to manage the entire lifecycle of jobs:</strong> from development and deployment to management and problem diagnosis. <strong>Fortunately, StreamPark excels in this aspect, relieving developers from the pain points associated with Flink job management and operation.</strong> Within StreamPark’s job operation and maintenance management, there are three main modules: basic job management functions, Jar job manag [...]
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/homework_manager-96dc37860dd87f12ed55311b38116931.png" width="1080" height="542" class="img__KcZ"></p>
+<p><strong>Development Scaffolding</strong></p>
+<p>Further research revealed that StreamPark is not just a platform but also includes a development scaffold for Flink jobs. It offers a better solution for code-written Flink jobs, standardizing program configuration, providing a simplified programming model, and a suite of Connectors that lower the barrier to entry for DataStream development.</p>
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/connectors-d405c391d0cfdd753e7b329433275117.png" width="1080" height="720" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="02--how-apache-streampark-addresses-issues-of-the-self-developed-platform">02 <strong>How Apache StreamPark™ Addresses Issues of the Self-Developed Platform</strong><a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#02--how-apache-streampark-addresses-issues-of-the-self-developed-platform" class="hash-link" aria-label="Direct link to 02--how-apache-streampark-addresses-issues-of-the-self-developed-platform" title=" [...]
+<p>We briefly introduced the core capabilities of StreamPark above. During the technology selection process at Shunwang Technology, we found that StreamPark not only includes the basic functions of our existing Streaming-Launcher but also offers a more complete set of solutions to address its many shortcomings. Here, we focus on the solutions provided by StreamPark for the deficiencies of our self-developed platform, Streaming-Launcher.</p>
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/function-c2d3ad419e1676a0253d933935921a71.png" width="1080" height="720" class="img__KcZ"></p>
+<p><strong>One-Stop Flink Job Development Capability</strong></p>
+<p>To lower the barriers to Flink job development and improve developers' work efficiency, <strong>StreamPark provides features like FlinkSQL IDE, parameter management, task management, code management, one-click compilation, and one-click job deployment and undeployment</strong>. Our research found that these integrated features of StreamPark could further enhance developers’ work efficiency. To some extent, developers no longer need to concern themselves with the difficulties of Flink [...]
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/application-c99d4b0512fdb7d214e5429769629930.png" width="1080" height="959" class="img__KcZ"></p>
+<p><strong>Support for Multiple Deployment Modes</strong></p>
+<p>The Streaming-Launcher was not flexible for developers since it only supported the Yarn Session mode. StreamPark provides a comprehensive solution for this aspect. <strong>StreamPark fully supports all of Flink's deployment modes: Remote, Yarn Per-Job, Yarn Application, Yarn Session, K8s Session, and K8s Application,</strong> allowing developers to freely choose the appropriate running mode for different business scenarios.</p>
+<p><strong>Unified Job Management Center</strong></p>
+<p>For developers, job running status is one of their primary concerns. In Streaming-Launcher, due to the lack of a unified job management interface, developers had to rely on alarm information and Yarn application status to judge the state of tasks, which was very unfriendly. StreamPark addresses this issue by providing a unified job management interface, allowing for a clear view of the running status of each task.</p>
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/management-9eb1dec5b2fa480e897cf1c12d1425d8.png" width="1080" height="572" class="img__KcZ"></p>
+<p>In the Streaming-Launcher, developers had to go through multiple steps to diagnose job issues and locate job runtime logs. StreamPark offers a one-click jump feature that allows quick access to job runtime logs.</p>
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/logs-e4e9f2084f1fbcf0a4afe079433cef0a.png" width="1080" height="575" class="img__KcZ"></p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="practical-implementation">Practical Implementation<a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#practical-implementation" class="hash-link" aria-label="Direct link to Practical Implementation" title="Direct link to Practical Implementation"></a></h2>
+<p>When introducing StreamPark to Shunwang Technology, due to the company's business characteristics and some customized requirements from the developers, we made some additions and optimizations to the functionalities of StreamPark. We have also summarized some problems encountered during the use and corresponding solutions.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="01--leveraging-the-capabilities-of-flink-sql-gateway">01 <strong>Leveraging the Capabilities of Flink-SQL-Gateway</strong><a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#01--leveraging-the-capabilities-of-flink-sql-gateway" class="hash-link" aria-label="Direct link to 01--leveraging-the-capabilities-of-flink-sql-gateway" title="Direct link to 01--leveraging-the-capabilities-of-flink-sql-gateway"></a></h3>
+<p>At Shunwang Technology, we have developed the ODPS platform based on the Flink-SQL-Gateway to facilitate business developers in managing the metadata of Flink tables. Business developers perform DDL operations on Flink tables in ODPS, and then carry out analysis and query operations on the created Flink tables in StreamPark. Throughout the entire business development process, we have decoupled the creation and analysis of Flink tables, making the development process appear more straig [...]
+<p>If developers wish to query real-time data in ODPS, we need to provide a Flink SQL runtime environment. We have used StreamPark to run a Yarn Session Flink environment to support ODPS in conducting real-time queries.</p>
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/homework-96030dec4ea9db88b42668873f5a176d.png" width="1080" height="541" class="img__KcZ"></p>
+<p>Currently, the StreamPark community is intergrating with Flink-SQL-Gateway to further lower the barriers to developing real-time jobs.</p>
+<p><a href="https://github.com/apache/streampark/issues/2274" target="_blank" rel="noopener noreferrer">https://github.com/apache/streampark/issues/2274</a></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="02--enhancing-flink-cluster-management-capabilities">02 <strong>Enhancing Flink Cluster Management Capabilities</strong><a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#02--enhancing-flink-cluster-management-capabilities" class="hash-link" aria-label="Direct link to 02--enhancing-flink-cluster-management-capabilities" title="Direct link to 02--enhancing-flink-cluster-management-capabilities"></a></h3>
+<p>At Shunwang Technology, there are numerous jobs synchronizing data from Kafka to Iceberg / PG / Clickhouse / Hive. These jobs do not have high resource requirements and timeliness demands on Yarn. However, if Yarn Application and per-job modes are used for all tasks, where each task starts a JobManager, this would result in a waste of Yarn resources. For this reason, we decided to run these numerous data synchronization jobs in Yarn Session mode.</p>
+<p>In practice, we found it difficult for business developers to intuitively know how many jobs are running in each Yarn Session, including the total number of jobs and the number of jobs that are running. Based on this, to facilitate developers to intuitively observe the number of jobs in a Yarn Session, we added All Jobs and Running Jobs in the Flink Cluster interface to indicate the total number of jobs and the number of running jobs in a Yarn Session.</p>
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/cluster-96bd791c1c4f3d6144fbd1d134d89cd6.png" width="1080" height="543" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="03--enhancing-alert-capabilities">03 <strong>Enhancing Alert Capabilities</strong><a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#03--enhancing-alert-capabilities" class="hash-link" aria-label="Direct link to 03--enhancing-alert-capabilities" title="Direct link to 03--enhancing-alert-capabilities"></a></h3>
+<p>Since each company's SMS alert platform is implemented differently, the StreamPark community has not abstracted a unified SMS alert function. Here, we have implemented the SMS alert function through the Webhook method.</p>
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/alarm-8fa330c6a2d27cfd62878dcd20cd524c.png" width="1080" height="601" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="04--adding-a-blocking-queue-to-solve-throttling-issues">04 <strong>Adding a Blocking Queue to Solve Throttling Issues</strong><a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#04--adding-a-blocking-queue-to-solve-throttling-issues" class="hash-link" aria-label="Direct link to 04--adding-a-blocking-queue-to-solve-throttling-issues" title="Direct link to 04--adding-a-blocking-queue-to-solve-throttling-issues"></a></h3>
+<p>In production practice, we found that when a large number of tasks fail at the same time, such as when a Yarn Session cluster goes down, platforms like Feishu/Lark and WeChat have throttling issues when multiple threads call the alert interface simultaneously. Consequently, only a portion of the alert messages will be sent by StreamPark due to the throttling issues of platforms like Feishu/Lark and WeChat, which can easily mislead developers in troubleshooting. At Shunwang Technology, [...]
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/queue-83552f1862acff8811b74488e8fe7c05.png" width="1080" height="320" class="img__KcZ"></p>
+<p>When the job monitoring scheduler detects an abnormality in a job, it generates a job exception message and sends it to the blocking queue. The alert thread will continuously consume messages from the blocking queue. Upon receiving a job exception message, it will send the alert to different platforms sequentially according to the user-configured alert information. Although this method might delay the alert delivery to users, in practice, we found that even with 100+ Flink jobs failin [...]
+<p><a href="https://github.com/apache/streampark/issues/2142" target="_blank" rel="noopener noreferrer">https://github.com/apache/streampark/issues/2142</a></p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="benefits-brought">Benefits Brought<a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#benefits-brought" class="hash-link" aria-label="Direct link to Benefits Brought" title="Direct link to Benefits Brought"></a></h2>
+<p>We started exploring and using StreamX 1.2.3 (the predecessor of StreamPark) and after more than a year of running in, we found that StreamPark truly resolves many pain points in the development management and operation and maintenance of Flink jobs.</p>
+<p>The greatest benefit that StreamPark has brought to Shunwang Technology is the lowered threshold for using Flink and improved development efficiency. Previously, our business development colleagues had to use multiple tools such as vscode, GitLab, and a scheduling platform in the original Streaming-Launcher to complete a FlinkSQL job development. The process was tedious, going through multiple tools from development to compilation to release. StreamPark provides one-stop service, allo [...]
+<p><strong>At present, StreamPark has been massively deployed in the production environment at Shunwang Technology, with the number of FlinkSQL jobs managed by StreamPark increasing from the initial 500+ to nearly 700, while also managing more than 10 Yarn Session Clusters.</strong></p>
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/achievements1-9eb1dec5b2fa480e897cf1c12d1425d8.png" width="1080" height="572" class="img__KcZ"></p>
+<p><img decoding="async" loading="lazy" alt="Image" src="https://streampark.apache.org/assets/images/achievements2-5aa9b3c14892f9b1d8b054e82f3b4ad3.png" width="1080" height="545" class="img__KcZ"></p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="future-plans">Future Plans<a href="https://streampark.apache.org/blog/streampark-usercase-shunwang#future-plans" class="hash-link" aria-label="Direct link to Future Plans" title="Direct link to Future Plans"></a></h2>
+<p>As one of the early users of StreamPark, Shunwang Technology has been communicating with the community for a year and participating in the polishing of StreamPark's stability. We have submitted the Bugs encountered in production operations and new Features to the community. In the future, we hope to manage the metadata information of Flink tables on StreamPark, and implement cross-data-source query analysis functions based on the Flink engine through multiple Catalogs. Currently, Stre [...]
+<p>Since Shunwang Technology primarily runs jobs in Yarn Session mode, we hope that StreamPark can provide more support for Remote clusters, Yarn Session clusters, and K8s Session clusters, such as monitoring and alerts, and optimizing operational processes.</p>
+<p>Considering the future, as the business develops, we may use StreamPark to manage more Flink real-time jobs, and StreamPark in single-node mode may not be safe. Therefore, we are also looking forward to the High Availability (HA) of StreamPark.</p>
+<p>We will also participate in the construction of the capabilities of StreamPark is integrating with Flink-SQL-Gateway, enriching Flink Cluster functionality, and StreamPark HA.</p>]]></content>
+ <category label="StreamPark" term="StreamPark"/>
+ <category label="Production Practice" term="Production Practice"/>
+ <category label="FlinkSQL" term="FlinkSQL"/>
+ </entry>
+ <entry>
+ <title type="html"><![CDATA[Apache StreamPark™'s Best Practices at Dustess, Simplifying Complexity for the Ultimate Experience]]></title>
+ <id>https://streampark.apache.org/blog/streampark-usercase-dustess</id>
+ <link href="https://streampark.apache.org/blog/streampark-usercase-dustess"/>
+ <updated>2024-06-30T14:58:25.000Z</updated>
+ <summary type="html"><![CDATA[Abstract]]></summary>
+ <content type="html"><![CDATA[<p><strong>Abstract:</strong> This article originates from the production practices of StreamPark at Dustess Information, written by the senior data development engineer, Gump. The main content includes:</p>
+<ol>
+<li>Technology selection</li>
+<li>Practical implementation</li>
+<li>Business support & capability opening</li>
+<li>Future planning</li>
+<li>Closing remarks</li>
+</ol>
+<p>Dustess Information is a one-stop private domain operation management solution provider based on the WeChat Work ecosystem. It is committed to becoming the leading expert in private domain operation and management across all industries, helping enterprises build a new model of private domain operation management in the digital age, and promoting high-quality development for businesses.</p>
+<p>Currently, Dustess has established 13 city centers nationwide, covering five major regions: North China, Central China, East China, South China, and Southwest China, providing digital marketing services to over 10,000 enterprises across more than 30 industries.</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="01-technology-selection"><strong>01 Technology Selection</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#01-technology-selection" class="hash-link" aria-label="Direct link to 01-technology-selection" title="Direct link to 01-technology-selection"></a></h2>
+<p>Dustess Information entered a rapid development phase in 2021. With the increase in service industries and corporate clients, the demand for real-time solutions became more pressing, necessitating the immediate implementation of a real-time computing platform.</p>
+<p>As the company is in a phase of rapid growth, with urgent and rapidly changing needs, the team's technology selection followed these principles:</p>
+<ul>
+<li><strong>Speed:</strong> Due to urgent business needs, we required a quick implementation of the planned technology selection into production.</li>
+<li><strong>Stability:</strong> On the basis of speed, the chosen technology must provide stable service for the business.</li>
+<li><strong>Innovation:</strong> On the basis of the above, the selected technology should also be as modern as possible.</li>
+<li><strong>Comprehensiveness:</strong> The selected technology should meet the company's rapidly developing and changing business needs, be in line with the team's long-term development goals, and support quick and efficient secondary development.</li>
+</ul>
+<p>Firstly, in terms of the computing engine: We chose Flink for the following reasons:</p>
+<ul>
+<li>Team members have an in-depth understanding of Flink and are well-versed in its source code.</li>
+<li>Flink supports both batch and stream processing. Although the company's current batch processing architecture is based on Hive, Spark, etc., using Flink for stream computing facilitates the subsequent construction of unified batch and stream processing and lake-house architecture.</li>
+<li>The domestic ecosystem of Flink has become increasingly mature, and Flink is starting to break boundaries towards the development of stream-based data warehousing.</li>
+</ul>
+<p>At the platform level, we comprehensively compared StreamPark, Apache Zeppelin, and flink-streaming-platform-web, also thoroughly read their source code and conducted an analysis of their advantages and disadvantages. We won’t elaborate on the latter two projects in this article, but those interested can search for them on GitHub. We ultimately chose StreamPark for the following reasons:</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="high-completion"><strong>High Completion</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#high-completion" class="hash-link" aria-label="Direct link to high-completion" title="Direct link to high-completion"></a></h3>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="1-supports-multiple-flink-versions"><strong>1. Supports Multiple Flink Versions</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#1-supports-multiple-flink-versions" class="hash-link" aria-label="Direct link to 1-supports-multiple-flink-versions" title="Direct link to 1-supports-multiple-flink-versions"></a></h4>
+<p>//Video link (Flink Multi-Version Support Demo)</p>
+<p>When creating a task, you can <strong>freely choose the Flink version</strong>. The Flink binary package will be automatically uploaded to HDFS (if using Yarn for submission), and only one copy of a version's binary package will be saved on HDFS. When the task is initiated, the Flink binary package in HDFS will be automatically loaded according to the context, which is very elegant. This can meet the needs for coexistence of multiple versions and for testing new versions of Flink duri [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flink_home-0a6f4f2014cc87b074ef259088af2b98.png" width="1080" height="223" class="img__KcZ"></p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="2-supports-multiple-deployment-modes"><strong>2. Supports Multiple Deployment Modes</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#2-supports-multiple-deployment-modes" class="hash-link" aria-label="Direct link to 2-supports-multiple-deployment-modes" title="Direct link to 2-supports-multiple-deployment-modes"></a></h4>
+<p>StreamPark supports <strong>all the mainstream submission modes</strong> for Flink, such as standalone, yarn-session, yarn application, yarn-perjob, kubernetes-session, kubernetes-application. Moreover, StreamPark does not simply piece together Flink run commands to submit tasks. Instead, it introduces the Flink Client source package and directly calls the Flink Client API for task submission. The advantages of this approach include modular code, readability, ease of extension, stabil [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/execution_mode-1182aeb8efe9572ec98c2a2b95293dc1.png" width="1080" height="324" class="img__KcZ"></p>
+<p>Flink SQL can greatly improve development efficiency and the popularity of Flink. StreamPark’s support for <strong>Flink SQL is very comprehensive</strong>, with an excellent SQL editor, dependency management, multi-version task management, etc. The StreamPark official website states that it will introduce metadata management integration for Flink SQL in the future. Stay tuned.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flink_sql-13f6952f92585140c5fc640b490918b0.png" width="1080" height="779" class="img__KcZ"></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flink_sql_version-a02b0f0eac9c5d6c0281b7471e438b78.png" width="1080" height="736" class="img__KcZ"></p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="4-online-building-of-java-tasks"><strong>4. Online Building of JAVA Tasks</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#4-online-building-of-java-tasks" class="hash-link" aria-label="Direct link to 4-online-building-of-java-tasks" title="Direct link to 4-online-building-of-java-tasks"></a></h4>
+<p>//Video link (JAVA Task Building Demo)</p>
+<p>Although Flink SQL is now powerful enough, using JVM languages like Java and Scala to develop Flink tasks can be more flexible, more customizable, and better for tuning and improving resource utilization. The biggest problem with submitting tasks via Jar packages, compared to SQL, is the management of the Jar uploads. Without excellent tooling products, this can significantly reduce development efficiency and increase maintenance costs.</p>
+<p>Besides supporting Jar uploads, StreamPark also provides an <strong>online update build</strong> feature, which elegantly solves the above problems:</p>
+<ol>
+<li>
+<p>Create Project: Fill in the GitHub/Gitlab (supports enterprise private servers) address and username/password, and StreamPark can Pull and Build the project.</p>
+</li>
+<li>
+<p>When creating a StreamPark Custom-Code task, refer to the Project, specify the main class, and optionally automate Pull, Build, and bind the generated Jar when starting the task, which is very elegant!</p>
+</li>
+</ol>
+<p>At the same time, the StreamPark community is also perfecting the entire task compilation and launch process. The future StreamPark will be even more refined and professional on this foundation.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/system_list-6e9c13318d5aa2cfa4cdc11ac42c5844.png" width="1080" height="362" class="img__KcZ"></p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="5-comprehensive-task-parameter-configuration"><strong>5. Comprehensive Task Parameter Configuration</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#5-comprehensive-task-parameter-configuration" class="hash-link" aria-label="Direct link to 5-comprehensive-task-parameter-configuration" title="Direct link to 5-comprehensive-task-parameter-configuration"></a></h4>
+<p>For data development using Flink, the parameters submitted with Flink run are almost impossible to maintain. StreamPark has also <strong>elegantly solved</strong> this kind of problem, mainly because, as mentioned above, StreamPark directly calls the Flink Client API and has connected the entire process through the StreamPark product frontend.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/parameter_configuration-94fb5d7ee0c9c04ed6d79ddb1cd3c1c7.png" width="1080" height="1001" class="img__KcZ"></p>
+<p>As you can see, StreamPark's task parameter settings cover all the mainstream parameters, and every parameter has been thoughtfully provided with an introduction and an optimal recommendation based on best practices. This is also very beneficial for newcomers to Flink, helping them to avoid common pitfalls!</p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="6-excellent-configuration-file-design"><strong>6. Excellent Configuration File Design</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#6-excellent-configuration-file-design" class="hash-link" aria-label="Direct link to 6-excellent-configuration-file-design" title="Direct link to 6-excellent-configuration-file-design"></a></h4>
+<p>In addition to the native parameters for Flink tasks, which are covered by the task parameters above, StreamPark also provides a powerful <strong>Yaml configuration file</strong> mode and <strong>programming model</strong>.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/extended_parameters-75c3f87809d0675c2fc82bc8d2ec096e.jpg" width="1080" height="2245" class="img__KcZ"></p>
+<ol>
+<li>
+<p>For Flink SQL tasks, you can configure the parameters that StreamPark has already built-in, such as <strong>CheckPoint, retry mechanism, State Backend, table planner, mode</strong>, etc., directly using the task's Yaml configuration file.</p>
+</li>
+<li>
+<p>For Jar tasks, StreamPark offers a generic programming model that encapsulates the native Flink API. Combined with the wrapper package provided by StreamPark, it can very elegantly retrieve custom parameters from the configuration file. For more details, see the documentation:</p>
+</li>
+</ol>
+<p>Programming model:</p>
+<div class="codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-text codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">https://streampark.apache.org/docs/development/dev-model</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code to clip [...]
+<p>Built-in Configuration File Parameters:</p>
+<div class="codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-text codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">https://streampark.apache.org/docs/development/config</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code to clipboa [...]
+<p>In addition:</p>
+<p>StreamPark also <strong>supports Apache Flink® native tasks</strong>. The parameter configuration can be statically maintained within the Java task internal code, covering a wide range of scenarios, such as seamless migration of existing Flink tasks, etc.</p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="7-checkpoint-management"><strong>7. Checkpoint Management</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#7-checkpoint-management" class="hash-link" aria-label="Direct link to 7-checkpoint-management" title="Direct link to 7-checkpoint-management"></a></h4>
+<p>Regarding Flink's Checkpoint (Savepoint) mechanism, the greatest difficulty is maintenance. StreamPark has also elegantly solved this problem:</p>
+<ul>
+<li>StreamPark will <strong>automatically maintain</strong> the task Checkpoint directory and versions in the system for easy retrieval.</li>
+<li>When users need to update and restart an application, they can choose whether to save a Savepoint.</li>
+<li>When restarting a task, it is possible to choose to recover from a specified version of Checkpoint/Savepoint.</li>
+</ul>
+<p>As shown below, developers can very intuitively and conveniently upgrade or deal with exceptional tasks, which is very powerful.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/checkpoint-e9edd22da076247770a0b40595626fb7.png" width="1080" height="483" class="img__KcZ"></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/recover-f1de53fdbe66c50b465d58d0a66050de.jpg" width="1053" height="391" class="img__KcZ"></p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="8-comprehensive-alerting-features"><strong>8. Comprehensive Alerting Features</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#8-comprehensive-alerting-features" class="hash-link" aria-label="Direct link to 8-comprehensive-alerting-features" title="Direct link to 8-comprehensive-alerting-features"></a></h4>
+<p>For streaming computations, which are 7*24H resident tasks, monitoring and alerting are very important. StreamPark also has a <strong>comprehensive solution</strong> for these issues:</p>
+<ul>
+<li>It comes with an email-based alerting method, which has zero development cost and can be used once configured.</li>
+<li>Thanks to the excellent modularity of the StreamPark source code, it's possible to enhance the code at the Task Track point and introduce the company's internal SDK for telephone, group, and other alerting methods, all with a very low development cost.</li>
+</ul>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/alarm_email-fd4c9ba1995ec69b7557bd1378dce737.png" width="1009" height="1340" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="excellent-source-code"><strong>Excellent Source Code</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#excellent-source-code" class="hash-link" aria-label="Direct link to excellent-source-code" title="Direct link to excellent-source-code"></a></h3>
+<p>Following the principle of technology selection, a new technology must be sufficiently understood in terms of underlying principles and architectural ideas before it is considered for production use. Before choosing StreamPark, its architecture and source code were subjected to in-depth study and reading. It was found that the underlying technologies used by StreamPark are very familiar to Chinese developers: MySQL, Spring Boot, Mybatis Plus, Vue, etc. The code style is unified and el [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/code_notebook-542046feb8a312b5f6c057af551421c6.png" width="1080" height="527" class="img__KcZ"></p>
+<p>In November 2021, StreamPark was successfully selected by Open Source China as a GVP - Gitee "Most Valuable Open Source Project," which speaks volumes about its quality and potential.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/certificate-2f5b95ebb0816ead327ec169c12996b6.png" width="1080" height="684" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="03-active-community"><strong>03 Active Community</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#03-active-community" class="hash-link" aria-label="Direct link to 03-active-community" title="Direct link to 03-active-community"></a></h3>
+<p>The community is currently very active. Since the end of November 2021, when StreamPark (based on 1.2.0-release) was implemented, StreamPark had just started to be recognized by everyone, and there were some minor bugs in the user experience (not affecting core functionality). At that time, in order to go live quickly, some features were disabled and some minor bugs were fixed. Just as we were preparing to contribute back to the community, we found that these had already been fixed, i [...]
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="02-implementation-practice"><strong>02 Implementation Practice</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#02-implementation-practice" class="hash-link" aria-label="Direct link to 02-implementation-practice" title="Direct link to 02-implementation-practice"></a></h2>
+<p>StreamPark's environment setup is very straightforward, following the official website's building tutorial you can complete the setup within a few hours. It now supports a front-end and back-end separation packaging deployment model, which can meet the needs of more companies, and there has already been a Docker Build related PR, suggesting that StreamPark's compilation and deployment will become even more convenient and quick in the future. Related documentation is as follows:</p>
+<div class="codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-text codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">https://streampark.apache.org/docs/user-guide/deployment</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code to clip [...]
+<p>For rapid implementation and production use, we chose the reliable On Yarn resource management mode (even though StreamPark already supports K8S quite well), and there are already many companies that have deployed using StreamPark on K8S, which you can refer to:</p>
+<div class="codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-text codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">https://streampark.apache.org/blog/flink-development-framework-streampark</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label=" [...]
+<p>Integrating StreamPark with the Hadoop ecosystem can be said to be zero-cost (provided that Flink is integrated with the Hadoop ecosystem according to the Flink official website, and tasks can be launched via Flink scripts).</p>
+<p>Currently, we are also conducting K8S testing and solution design, and will be migrating to K8S in the future.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="01-implementing-flinksql-tasks"><strong>01 Implementing FlinkSQL Tasks</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#01-implementing-flinksql-tasks" class="hash-link" aria-label="Direct link to 01-implementing-flinksql-tasks" title="Direct link to 01-implementing-flinksql-tasks"></a></h3>
+<p>At present, our company's tasks based on Flink SQL are mainly for simple real-time ETL and computing scenarios, with about 10 tasks, all of which have been very stable since they went live.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/online_flinksql-ca82a6f42e04e54e9f6da2b1e391b073.png" width="1080" height="118" class="img__KcZ"></p>
+<p>StreamPark has thoughtfully prepared a demo SQL task that can be run directly on a newly set up platform. This attention to detail demonstrates the community's commitment to user experience. Initially, our simple tasks were written and executed using Flink SQL, and StreamPark's support for Flink SQL is excellent, with a superior SQL editor and innovative POM and Jar package dependency management that can meet many SQL scenario needs.</p>
+<p>Currently, we are researching and designing solutions related to metadata, permissions, UDFs, etc.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="02-implementing-jar-tasks"><strong>02 Implementing Jar Tasks</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#02-implementing-jar-tasks" class="hash-link" aria-label="Direct link to 02-implementing-jar-tasks" title="Direct link to 02-implementing-jar-tasks"></a></h3>
+<p>Since most of the data development team members have a background in Java and Scala, we've implemented Jar-based builds for more flexible development, transparent tuning of Flink tasks, and to cover more scenarios. Our implementation was in two phases:</p>
+<p><strong>First Phase:</strong> StreamPark provides support for native Apache Flink® projects. We configured our existing tasks' Git addresses in StreamPark, used Maven to package them as Jar files, and created StreamPark Apache Flink® tasks for seamless migration. In this process, StreamPark was merely used as a platform tool for task submission and state maintenance, without leveraging the other features mentioned above.</p>
+<p><strong>Second Phase:</strong> After migrating tasks to StreamPark in the first phase and having them run on the platform, the tasks' configurations, such as checkpoint, fault tolerance, and adjustments to business parameters within Flink tasks, required source code modifications, pushes, and builds. This was very inefficient and opaque.</p>
+<p>Therefore, following StreamPark's QuickStart, we quickly integrated StreamPark's programming model, which is an encapsulation for StreamPark Flink tasks (for Apache Flink).</p>
+<p>Example：</p>
+<div class="codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-text codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">StreamingContext = ParameterTool + StreamExecutionEnvironment</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code to [...]
+<ul>
+<li>StreamingContext is the encapsulation object for StreamPark</li>
+<li>ParameterTool is the parameter object after parsing the configuration file</li>
+</ul>
+<div class="codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-text codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain"> String value = ParameterTool.get("${user.custom.key}")</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code to clipb [...]
+<ul>
+<li>StreamExecutionEnvironment is the native task context for Apache Flink</li>
+</ul>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="03-business-support--capability-opening"><strong>03 Business Support & Capability Opening</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#03-business-support--capability-opening" class="hash-link" aria-label="Direct link to 03-business-support--capability-opening" title="Direct link to 03-business-support--capability-opening"></a></h2>
+<p>Currently, Dustess Info's real-time computing platform based on StreamPark has been online since the end of November last year and has launched 50+ Flink tasks, including 10+ Flink SQL tasks and 40+ Jar tasks. At present, it is mainly used internally by the data team, and the real-time computing platform will be opened up for use by business teams across the company shortly, which will significantly increase the number of tasks.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/online_jar-48549248f657388c7aebc0c8491660fa.png" width="1080" height="445" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="01-real-time-data-warehouse"><strong>01 Real-Time Data Warehouse</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#01-real-time-data-warehouse" class="hash-link" aria-label="Direct link to 01-real-time-data-warehouse" title="Direct link to 01-real-time-data-warehouse"></a></h3>
+<p>The real-time data warehouse mainly uses Jar tasks because the model is more generic. Using Jar tasks can generically handle a large number of data table synchronization and calculations, and even achieve configuration-based synchronization. Our real-time data warehouse mainly uses Apache Doris for storage, with Flink handling the cleaning and calculations (the goal being storage-computation separation).</p>
+<p>Using StreamPark to integrate other components is also very straightforward, and we have also abstracted the configuration related to Apache Doris and Kafka into the configuration file, which greatly enhances our development efficiency and flexibility.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="02-capability-opening"><strong>02 Capability Opening</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#02-capability-opening" class="hash-link" aria-label="Direct link to 02-capability-opening" title="Direct link to 02-capability-opening"></a></h3>
+<p>Other business teams outside the data team also have many stream processing scenarios. Hence, after secondary development of the real-time computing platform based on StreamPark, we opened up the following capabilities to all business teams in the company:</p>
+<ul>
+<li>Business capability opening: The upstream real-time data warehouse collects all business tables through log collection and writes them into Kafka. Business teams can base their business-related development on Kafka, or they can perform OLAP analysis through the real-time data warehouse (Apache Doris).</li>
+<li>Computing capability opening: The server resources of the big data platform are made available for use by business teams.</li>
+<li>Solution opening: The mature Connectors of the Flink ecosystem and support for Exactly Once semantics can reduce the development and maintenance costs related to stream processing for business teams.</li>
+</ul>
+<p>Currently, StreamPark does not support multi-business group functions. The multi-business group function will be abstracted and contributed to the community.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/manager-07ba2a4bc979cd2dd86fc9e07384ec61.png" width="1080" height="235" class="img__KcZ"></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/task_retrieval-eee86f9c0af117cbf15d2af5d528b2cc.png" width="1080" height="382" class="img__KcZ"></p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="04-future-planning"><strong>04 Future Planning</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#04-future-planning" class="hash-link" aria-label="Direct link to 04-future-planning" title="Direct link to 04-future-planning"></a></h2>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="01-flink-on-k8s"><strong>01 Flink on K8S</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#01-flink-on-k8s" class="hash-link" aria-label="Direct link to 01-flink-on-k8s" title="Direct link to 01-flink-on-k8s"></a></h3>
+<p>Currently, all our company's Flink tasks run on Yarn, which meets current needs, but Flink on Kubernetes has the following advantages:</p>
+<ul>
+<li><strong>Unified Operations</strong>. The company has unified operations with a dedicated department managing K8S.</li>
+<li><strong>CPU Isolation</strong>. There is CPU isolation between K8S Pods, so real-time tasks do not affect each other, leading to more stability.</li>
+<li><strong>Separation of Storage and Computation</strong>. Flink's computational resources and state storage are separated; computational resources can be mixed with other component resources, improving machine utilization.</li>
+<li><strong>Elastic Scaling</strong>. It is capable of elastic scaling, better saving manpower and material costs.</li>
+</ul>
+<p>I am also currently organizing and implementing related technical architectures and solutions and have completed the technical verification of Flink on Kubernetes using StreamPark in an experimental environment. With the support of the StreamPark platform and the enthusiastic help of the community, I believe that production implementation is not far off.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="02-stream-batch-unification-construction"><strong>02 Stream-Batch Unification Construction</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#02-stream-batch-unification-construction" class="hash-link" aria-label="Direct link to 02-stream-batch-unification-construction" title="Direct link to 02-stream-batch-unification-construction"></a></h3>
+<p>Personally, I think the biggest difference between batch and stream processing lies in the scheduling strategy of operator tasks and the data transfer strategy between operators:</p>
+<ul>
+<li><strong>Batch processing</strong>: Upstream and downstream operator tasks have sequential scheduling (upstream tasks end and release resources), and data has a Shuffle strategy (landing on disk). The downside is lower timeliness and no intermediate state in computation, but the upside is high throughput, suitable for offline computation of super-large data volumes.</li>
+<li><strong>Stream processing</strong>: Upstream and downstream operator tasks start at the same time (occupying resources simultaneously), and data is streamed between nodes through the network. The downside is insufficient throughput, but the advantage is high timeliness and intermediate state in computation, suitable for real-time and incremental computation scenarios.</li>
+</ul>
+<p>As mentioned above, I believe that choosing <strong>batch or stream processing</strong> <strong>is a tuning method for data development according to different data volumes and business scenarios</strong>. However, currently, because the computing engine and platform distinguish offline and real-time, it causes development and maintenance fragmentation, with prohibitively high costs. To achieve stream-batch unification, the following aspects must be realized:</p>
+<ul>
+<li>Unified storage (unification of metadata): Supports batch and stream writing/reading.</li>
+<li>Unified computing engine: Able to use a set of APIs or SQL to develop offline and real-time tasks.</li>
+<li>Unified data platform: Able to support the persistent real-time tasks, as well as offline scheduling strategies.</li>
+</ul>
+<p>Regarding the unification of stream and batch, I am also currently researching and organizing, and I welcome interested friends to discuss and study the project together.</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="05-closing-words"><strong>05 Closing Words</strong><a href="https://streampark.apache.org/blog/streampark-usercase-dustess#05-closing-words" class="hash-link" aria-label="Direct link to 05-closing-words" title="Direct link to 05-closing-words"></a></h2>
+<p>That's all for the sharing of StreamPark in the production practice at Dustess Info. Thank you all for reading this far. The original intention of writing this article was to bring a bit of StreamPark's production practice experience and reference to everyone, and together with the buddies in the StreamPark community, to jointly build StreamPark. In the future, I plan to participate and contribute more. A big thank you to the developers of StreamPark for providing such an excellent pr [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/author-487ad3c1ad9a397cd4c2614f54976368.png" width="844" height="439" class="img__KcZ"></p>]]></content>
+ <category label="StreamPark" term="StreamPark"/>
+ <category label="Production Practice" term="Production Practice"/>
+ <category label="FlinkSQL" term="FlinkSQL"/>
+ </entry>
+ <entry>
+ <title type="html"><![CDATA[Apache StreamPark™'s Production Practice in Joyme]]></title>
+ <id>https://streampark.apache.org/blog/streampark-usercase-joyme</id>
+ <link href="https://streampark.apache.org/blog/streampark-usercase-joyme"/>
+ <updated>2024-06-30T14:58:25.000Z</updated>
+ <summary type="html"><![CDATA[Abstract]]></summary>
+ <content type="html"><![CDATA[<p><strong>Abstract:</strong> This article presents the production practices of StreamPark at Joyme, written by Qin Jiyong, a big data engineer at Joyme. The main contents include:</p>
+<ul>
+<li>Encountering StreamPark</li>
+<li>Flink SQL job development</li>
+<li>Custom code job development</li>
+<li>Monitoring and alerting</li>
+<li>Common issues</li>
+<li>Community impressions</li>
+<li>Summary</li>
+</ul>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="1-encountering-apache-streampark">1 Encountering Apache StreamPark™<a href="https://streampark.apache.org/blog/streampark-usercase-joyme#1-encountering-apache-streampark" class="hash-link" aria-label="Direct link to 1 Encountering Apache StreamPark™" title="Direct link to 1 Encountering Apache StreamPark™"></a></h2>
+<p>Encountering StreamPark was inevitable. Based on our existing real-time job development mode, we had to find an open-source platform to support our company's real-time business. Our current situation was as follows:</p>
+<ul>
+<li>We wrote jobs and packaged them to servers, then executed the Flink run command to submit them, which was a cumbersome and inefficient process.</li>
+<li>Flink SQL was submitted through a self-developed old platform. The developers of the old platform had left, and no one maintained the subsequent code, even if someone did, they would have to face the problem of high maintenance costs.</li>
+<li>Some of the authors were SparkStreaming jobs, with two sets of streaming engines and frameworks not unified, resulting in high development costs.</li>
+<li>Real-time jobs were developed in Scala and Java, with languages and technology stacks not unified.</li>
+</ul>
+<p>For all these reasons, we needed an open-source platform to manage our real-time jobs, and we also needed to refactor to unify the development mode and language and centralize project management.</p>
+<p>The first encounter with StreamPark basically confirmed our choice. We quickly deployed and installed according to the official documentation, performed some operations after setting up, and were greeted with a user-friendly interface. StreamPark's support for multiple versions of Flink, permission management, job monitoring, and other series of functions already met our needs well. Further understanding revealed that its community is also very active. We have witnessed the process of [...]
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="2-development-of-flink-sql-jobs">2 Development of Flink SQL Jobs<a href="https://streampark.apache.org/blog/streampark-usercase-joyme#2-development-of-flink-sql-jobs" class="hash-link" aria-label="Direct link to 2 Development of Flink SQL Jobs" title="Direct link to 2 Development of Flink SQL Jobs"></a></h2>
+<p>The Flink SQL development mode has brought great convenience. For some simple metric developments, it is possible to complete them with just a few SQL statements, without the need to write a single line of code. Flink SQL has facilitated the development work for many colleagues, especially since writing code can be somewhat difficult for those who work on warehouses.</p>
+<p>To add a new task, you open the task addition interface of StreamPark, where the default Development Mode is Flink SQL mode. You can write the SQL logic directly in the Flink SQL section.</p>
+<p>For the Flink SQL part, you can progressively write the logic SQL following the Flink official website's documentation. Generally, for our company, it consists of three parts: the Source connection, intermediate logic processing, and finally the Sink. Essentially, the Source is consuming data from Kafka, the logic layer will involve MySQL for dimension table queries, and the Sink part is mostly Elasticsearch, Redis, or MySQL.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="1-writing-sql"><strong>1. Writing SQL</strong><a href="https://streampark.apache.org/blog/streampark-usercase-joyme#1-writing-sql" class="hash-link" aria-label="Direct link to 1-writing-sql" title="Direct link to 1-writing-sql"></a></h3>
+<div class="language-sql codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-sql codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token comment" style="color:rgb(106, 153, 85)">-- Connect kafka</span><span class="token plain"></span><br></span><span class="token-line" style="color:#9CDCFE"><span cl [...]
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="2-add-dependency"><strong>2. Add Dependency</strong><a href="https://streampark.apache.org/blog/streampark-usercase-joyme#2-add-dependency" class="hash-link" aria-label="Direct link to 2-add-dependency" title="Direct link to 2-add-dependency"></a></h3>
+<p>In terms of dependencies, it's an unique feature to Streampark. A complete Flink SQL job is innovatively split into two components within StreamPark: the SQL and the dependencies. The SQL part is easy to understand and requires no further explanation, but the dependencies are the various Connector JARs needed within the SQL, such as Kafka and MySQL Connectors. If these are used within the SQL, then these Connector dependencies must be introduced. In StreamPark, there are two ways to a [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/add_dependency-281888d96a8f3ce6e9af01efef9de643.png" width="1080" height="469" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="3-parameter-configuration"><strong>3. Parameter Configuration</strong><a href="https://streampark.apache.org/blog/streampark-usercase-joyme#3-parameter-configuration" class="hash-link" aria-label="Direct link to 3-parameter-configuration" title="Direct link to 3-parameter-configuration"></a></h3>
+<p>The task addition and modification page has listed some common parameter settings, and for more extensive configuration options, a yaml configuration file is provided. Here, we've only set up checkpoint and savepoint configurations. The first is the location of the checkpoint, and the second is the frequency of checkpoint execution. We haven't changed other configurations much; users can customize these settings as per their needs.</p>
+<p>The rest of the parameter settings should be configured according to the specific circumstances of the job. If the volume of data processed is large or the logic is complex, it might require more memory and higher parallelism. Sometimes, adjustments need to be made multiple times based on the job's operational performance.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/checkpoint_configuration-861a4f0439b1a0f8aade35e10e6b60c5.png" width="1080" height="610" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="4-dynamic-parameter-settings"><strong>4. Dynamic Parameter Settings</strong><a href="https://streampark.apache.org/blog/streampark-usercase-joyme#4-dynamic-parameter-settings" class="hash-link" aria-label="Direct link to 4-dynamic-parameter-settings" title="Direct link to 4-dynamic-parameter-settings"></a></h3>
+<p>Since our deployment mode is on Yarn, we configured the name of the Yarn queue in the dynamic options. Some configurations have also been set to enable incremental checkpoints and state TTL (time-to-live), all of which can be found on Flink's official website. Before, some jobs frequently encountered out-of-memory issues. After adding the incremental parameter and TTL, the job operation improved significantly. Also, in cases where the Flink SQL job involves larger states and complex l [...]
+<ul>
+<li>-Dyarn.application.queue=Yarn queue name</li>
+<li>-Dstate.backend.incremental=true</li>
+<li>-Dtable.exec.state.ttl=expiration time</li>
+</ul>
+<p>After completing the configuration, submit & deploy it from the application interface.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/application_job-5518b58af2b481d7bdb36a7bae252c41.png" width="1080" height="422" class="img__KcZ"></p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="3-custom-code-job-development">3 Custom Code Job Development<a href="https://streampark.apache.org/blog/streampark-usercase-joyme#3-custom-code-job-development" class="hash-link" aria-label="Direct link to 3 Custom Code Job Development" title="Direct link to 3 Custom Code Job Development"></a></h2>
+<p>For streaming jobs, we use Flink Java for development, having refactored previous Spark Scala, Flink Scala, and Flink Java jobs and then integrated these projects together. The purpose of this integration is to facilitate maintenance. Custom code jobs require the submission of code to Git, followed by project configuration:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/project_configuration-add3d079bba91ac4977b830d3c3fe8f7.png" width="1080" height="365" class="img__KcZ"></p>
+<p>Once the configuration is completed, compile the corresponding project to finish the packaging phase. Thus, the Custom code jobs can also reference it. Compilation is required every time the code needs to go live; otherwise, only the last compiled code is available. Here's an issue: for security reasons, our company’s GitLab account passwords are regularly updated. This leads to a situation where the StreamPark projects have the old passwords configured, resulting in a failure when pu [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flink_system-02dee8b704254087aae900731ae47076.png" width="1080" height="214" class="img__KcZ"></p>
+<p>To create a new task, select Custom code, choose the Flink version, select the project and the module Jar package, and choose the development application mode as Apache Flink® (standard Flink program), program main function entry class, and the task's name.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/add_projectconfiguration-26b97212de577a0cbc46b59f1eceea0b.png" width="1080" height="536" class="img__KcZ"></p>
+<p>As well as the task’s parallelism, monitoring method, etc., memory size should be configured based on the needs of the task. Program Args, the program parameters, are defined according to the program's needs. For example: If our unified startup class is StartJobApp, to start a job, it's necessary to pass the full name of the job, informing the startup class which class to find to launch the task—essentially, which is a reflection mechanism. After the job configuration is complete, it [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/application_interface-51c8f96e343842f32b56c3c889064cba.png" width="1080" height="500" class="img__KcZ"></p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="4-monitoring-and-alerts">4 Monitoring and Alerts<a href="https://streampark.apache.org/blog/streampark-usercase-joyme#4-monitoring-and-alerts" class="hash-link" aria-label="Direct link to 4 Monitoring and Alerts" title="Direct link to 4 Monitoring and Alerts"></a></h2>
+<p>The monitoring in StreamPark requires configuration in the setting module to set up the basic information for sending emails.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/system_setting-6b5b21f22fc4a32b973ede0a5f21ebc2.png" width="1080" height="380" class="img__KcZ"></p>
+<p>Then, within the tasks, configure the restart strategy: monitor how many times an exception occurs within a certain time, and then decide whether the strategy is to alert or restart, as well as which email address should receive the alert notifications. The version currently used by our company is 1.2.1, which supports only email sending.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/email_setting-7b8ddba6688c75cfad32f5d16e93354f.png" width="1080" height="243" class="img__KcZ"></p>
+<p>When our jobs fail, we can receive alerts through email. These alerts are quite clear, showing which job is in what state. You can also click on the specific address below to view details.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/alarm_eamil-29b2875dbfcbeb071fef815ab751786a.png" width="1080" height="1517" class="img__KcZ"></p>
+<p>Regarding alerts, we have developed a scheduled task based on StreamPark's t_flink_app table. Why do this? Because most people might not check their emails promptly when it comes to email notifications. Therefore, we opted to monitor the status of each task and send corresponding monitoring information to our Lark (Feishu) alert group, enabling us to promptly identify and address issues with the tasks. It's a simple Python script, then configured with crontab to execute at scheduled t [...]
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="5-common-issues">5 Common Issues<a href="https://streampark.apache.org/blog/streampark-usercase-joyme#5-common-issues" class="hash-link" aria-label="Direct link to 5 Common Issues" title="Direct link to 5 Common Issues"></a></h2>
+<p>When it comes to the abnormal issues of jobs, we have summarized them into the following categories:</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="1-job-launch-failure"><strong>1. Job Launch Failure</strong><a href="https://streampark.apache.org/blog/streampark-usercase-joyme#1-job-launch-failure" class="hash-link" aria-label="Direct link to 1-job-launch-failure" title="Direct link to 1-job-launch-failure"></a></h3>
+<p>The issue of job launch failure refers to the situation where the job does not start upon initiation. In this case, one needs to check the detailed information logs on the interface. There is an eye-shaped button in our task list; by clicking on it, one can find the submitted job log information in the start logs. If there is clear prompt information, the issue can be directly addressed. If not, one has to check the streamx.out file under the logs/ directory of the backend deployment [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/start_log-6ce6b3125c8693db96197193241d6807.png" width="1080" height="83" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="2-job-running-failure"><strong>2. Job Running Failure</strong><a href="https://streampark.apache.org/blog/streampark-usercase-joyme#2-job-running-failure" class="hash-link" aria-label="Direct link to 2-job-running-failure" title="Direct link to 2-job-running-failure"></a></h3>
+<p>If the task has started but fails during the running phase, this situation might seem similar to the first, but is actually entirely different. This means that the task has been submitted to the cluster, but the task itself has problems running. One can still apply the troubleshooting method from the first scenario to open the specific logs of the job, find the task information on yarn, and then go to yarn's logs with the yarn's tackurl recorded in the logs to look for the specific re [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/yarn_log-100e18b484ec81cf3165736de8259365.png" width="1080" height="82" class="img__KcZ"></p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="6-community-impression">6 Community Impression<a href="https://streampark.apache.org/blog/streampark-usercase-joyme#6-community-impression" class="hash-link" aria-label="Direct link to 6 Community Impression" title="Direct link to 6 Community Impression"></a></h2>
+<p>Often when we discuss issues in the StreamPark user group, we get immediate responses from community members. Issues that cannot be resolved at the moment are generally fixed in the next version or the latest code branch. In the group, we also see many non-community members actively helping each other out. There are many big names from other communities as well, and many members actively join the community development work. The whole community feels very active to me!</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="7-conclusion">7 Conclusion<a href="https://streampark.apache.org/blog/streampark-usercase-joyme#7-conclusion" class="hash-link" aria-label="Direct link to 7 Conclusion" title="Direct link to 7 Conclusion"></a></h2>
+<p>Currently, our company runs 60 real-time jobs online, with Flink SQL and custom code each making up about half. More real-time tasks will be put online subsequently. Many colleagues worry about the stability of StreamPark, but based on several months of production practice in our company, StreamPark is just a platform to help you develop, deploy, monitor, and manage jobs. Whether it is stable or not depends on the stability of our own Hadoop Yarn cluster (we use the onyan mode) and ha [...]
+<p>That is all the content shared by StreamPark at Joyme. Thank you for reading this article. We are very grateful for such an excellent product provided by StreamPark, which is a true act of benefiting others. From version 1.0 to 1.2.1, the bugs encountered are promptly fixed, and every issue is taken seriously. We are still using the on yarn deployment mode. Restarting yarn will still cause jobs to be lost, but restarting yarn is not something we do every day. The community will also l [...]
+ <category label="StreamPark" term="StreamPark"/>
+ <category label="Production Practice" term="Production Practice"/>
+ <category label="FlinkSQL" term="FlinkSQL"/>
+ </entry>
+ <entry>
+ <title type="html"><![CDATA[An All-in-One Computation Tool in Haibo Tech's Production Practice and facilitation in Smart City Construction]]></title>
+ <id>https://streampark.apache.org/blog/streampark-usercase-haibo</id>
+ <link href="https://streampark.apache.org/blog/streampark-usercase-haibo"/>
+ <updated>2024-06-30T14:58:25.000Z</updated>
+ <summary type="html"><![CDATA[Summary An All-in-One Computation Tool in Haibo Tech's Production Practice and facilitation in Smart City Construction," is the Big Data Architect at Haibo Tech. The main topics covered include:]]></summary>
+ <content type="html"><![CDATA[<p><strong>Summary:</strong> The author of this article, "StreamPark: An All-in-One Computation Tool in Haibo Tech's Production Practice and facilitation in Smart City Construction," is the Big Data Architect at Haibo Tech. The main topics covered include:</p>
+<ol>
+<li>Choosing StreamPark</li>
+<li>Getting Started Quickly</li>
+<li>Application Scenarios</li>
+<li>Feature Extensions</li>
+<li>Future Expectations</li>
+</ol>
+<p>Haibo Tech is an industry-leading company offering AI IoT products and solutions. Currently, they provide full-stack solutions, including algorithms, software, and hardware products, to clients nationwide in public safety, smart cities, and smart manufacturing domains.</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="01-choosing-apache-streampark"><strong>01. Choosing Apache StreamPark™</strong><a href="https://streampark.apache.org/blog/streampark-usercase-haibo#01-choosing-apache-streampark" class="hash-link" aria-label="Direct link to 01-choosing-apache-streampark" title="Direct link to 01-choosing-apache-streampark"></a></h2>
+<p>Haibo Tech started using Flink SQL to aggregate and process various real-time IoT data since 2020. With the accelerated pace of smart city construction in various cities, the types and volume of IoT data to be aggregated are also increasing. This has resulted in an increasing number of Flink SQL tasks being maintained online, making a dedicated platform for managing numerous Flink SQL tasks an urgent need.</p>
+<p>After evaluating Apache Zeppelin and StreamPark, we chose StreamPark as our real-time computing platform. Compared to Apache Zeppelin, StreamPark may not be as well-known. However, after experiencing the initial release of StreamPark and reading its design documentation, we recognized that its all-in-one design philosophy covers the entire lifecycle of Flink task development. This means that configuration, development, deployment, and operations can all be accomplished on a single pla [...]
+<p>// Video link (streampark official video)</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="02-practical-implementation"><strong>02. Practical Implementation</strong><a href="https://streampark.apache.org/blog/streampark-usercase-haibo#02-practical-implementation" class="hash-link" aria-label="Direct link to 02-practical-implementation" title="Direct link to 02-practical-implementation"></a></h2>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="1-quick-start"><strong>1. Quick Start</strong><a href="https://streampark.apache.org/blog/streampark-usercase-haibo#1-quick-start" class="hash-link" aria-label="Direct link to 1-quick-start" title="Direct link to 1-quick-start"></a></h3>
+<p>Using StreamPark to accomplish a real-time aggregation task is as simple as putting an elephant into a fridge, and it can be done in just three steps:</p>
+<ul>
+<li>Edit SQL</li>
+</ul>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flink_sql-a8811954a9f765640bb08ad9cd139a15.png" width="1080" height="578" class="img__KcZ"></p>
+<ul>
+<li>Upload dependency packages</li>
+</ul>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/dependency-172169d43c50f455fb7e2c7e08de32a2.png" width="1080" height="449" class="img__KcZ"></p>
+<ul>
+<li>Deploy and run</li>
+</ul>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/deploy-d486a5c0eb238a9f2a6ce5f2b3013500.png" width="1080" height="538" class="img__KcZ"></p>
+<p>With just the above three steps, you can complete the aggregation task from Mysql to Elasticsearch, significantly improving data access efficiency.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="2-production-practice"><strong>2. Production Practice</strong><a href="https://streampark.apache.org/blog/streampark-usercase-haibo#2-production-practice" class="hash-link" aria-label="Direct link to 2-production-practice" title="Direct link to 2-production-practice"></a></h3>
+<p>StreamPark is primarily used at Haibo for running real-time Flink SQL tasks: reading data from Kafka, processing it, and outputting to Clickhouse or Elasticsearch.</p>
+<p>Starting from October 2021, the company gradually migrated Flink SQL tasks to the StreamPark platform for centralized management. It supports the aggregation, computation, and alerting of our real-time IoT data.</p>
+<p>As of now, StreamPark has been deployed in various government and public security production environments, aggregating and processing real-time IoT data, as well as capturing data on people and vehicles. Below is a screenshot of the StreamPark platform deployed on a city's dedicated network:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/application-b12eb5d36d548a02e52ec12a28ddcec0.png" width="1080" height="613" class="img__KcZ"></p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="03-application-scenarios"><strong>03. Application Scenarios</strong><a href="https://streampark.apache.org/blog/streampark-usercase-haibo#03-application-scenarios" class="hash-link" aria-label="Direct link to 03-application-scenarios" title="Direct link to 03-application-scenarios"></a></h2>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="1-real-time-iot-sensing-data-aggregation"><strong>1. Real-time IoT Sensing Data Aggregation</strong><a href="https://streampark.apache.org/blog/streampark-usercase-haibo#1-real-time-iot-sensing-data-aggregation" class="hash-link" aria-label="Direct link to 1-real-time-iot-sensing-data-aggregation" title="Direct link to 1-real-time-iot-sensing-data-aggregation"></a></h4>
+<p>For aggregating real-time IoT sensing data, we directly use StreamPark to develop Flink SQL tasks. For methods not provided by Flink SQL, StreamPark also supports UDF-related functionalities. Users can upload UDF packages through StreamPark, and then call the relevant UDF in SQL to achieve more complex logical operations.</p>
+<p>The "SQL+UDF" approach meets most of our data aggregation scenarios. If business changes in the future, we only need to modify the SQL statement in StreamPark to complete business changes and deployment.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/data_aggregation-e5caba8eee38a8444df6cb3382a1d978.png" width="1080" height="607" class="img__KcZ"></p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="2-flink-cdc-database-synchronization"><strong>2. Flink CDC Database Synchronization</strong><a href="https://streampark.apache.org/blog/streampark-usercase-haibo#2-flink-cdc-database-synchronization" class="hash-link" aria-label="Direct link to 2-flink-cdc-database-synchronization" title="Direct link to 2-flink-cdc-database-synchronization"></a></h4>
+<p>To achieve synchronization between various databases and data warehouses, we use StreamPark to develop Flink CDC SQL tasks. With the capabilities of Flink CDC, we've implemented data synchronization between Oracle and Oracle, as well as synchronization between Mysql/Postgresql and Clickhouse.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flink_cdc-318444c917eb66f591a5c75db9662e78.png" width="794" height="232" class="img__KcZ"></p>
+<p><strong>3. Data Analysis Model Management</strong></p>
+<p>For tasks that can't use Flink SQL and need Flink code development, such as real-time control models and offline data analysis models, StreamPark offers a Custom code approach, allowing users to upload executable Flink Jar packages and run them.</p>
+<p>Currently, we have uploaded over 20 analysis models, such as personnel and vehicles, to StreamPark, which manages and operates them.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/data_aggregation-e5caba8eee38a8444df6cb3382a1d978.png" width="1080" height="607" class="img__KcZ"></p>
+<p><strong>In Summary:</strong> Whether it's Flink SQL tasks or Custom code tasks, StreamPark provides excellent support to meet various business scenarios. However, StreamPark lacks task scheduling capabilities. If you need to schedule tasks regularly, StreamPark currently cannot meet this need. Community members are actively developing scheduling-related modules, and the soon-to-be-released version 1.2.3 will support task scheduling capabilities, so stay tuned.</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="04-feature-extension"><strong>04. Feature Extension</strong><a href="https://streampark.apache.org/blog/streampark-usercase-haibo#04-feature-extension" class="hash-link" aria-label="Direct link to 04-feature-extension" title="Direct link to 04-feature-extension"></a></h2>
+<p>Datahub is a metadata management platform developed by Linkedin, offering data source management, data lineage, data quality checks, and more. Haibo Tech has developed an extension based on StreamPark and Datahub, implementing table-level/field-level lineage features. With the data lineage feature, users can check the field lineage relationship of Flink SQL and save the lineage relationship to the Linkedin/Datahub metadata management platform.</p>
+<p>// Two video links (Data lineage feature developed based on streampark)</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="05-future-expectations"><strong>05. Future Expectations</strong><a href="https://streampark.apache.org/blog/streampark-usercase-haibo#05-future-expectations" class="hash-link" aria-label="Direct link to 05-future-expectations" title="Direct link to 05-future-expectations"></a></h2>
+<p>Currently, the StreamPark community's Roadmap indicates that StreamPark 1.3.0 will usher in a brand new Workbench experience, a unified resource management center (unified management of JAR/UDF/Connectors), batch task scheduling, and more. These are also some of the brand-new features we are eagerly anticipating.</p>
+<p>The Workbench will use a new workbench-style SQL development style. By selecting a data source, SQL can be generated automatically, further enhancing Flink task development efficiency. The unified UDF resource center will solve the current problem where each task has to upload its dependency package. The batch task scheduling feature will address StreamPark's current inability to schedule tasks.</p>
+<p>Below is a prototype designed by StreamPark developers, so please stay tuned.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/data_source-5f9b710b4186a08571192ae81ab350d1.png" width="830" height="518" class="img__KcZ"></p>]]></content>
+ <category label="StreamPark" term="StreamPark"/>
+ <category label="Production Practice" term="Production Practice"/>
+ <category label="FlinkSQL" term="FlinkSQL"/>
+ </entry>
+ <entry>
+ <title type="html"><![CDATA[Ziroom's Real-Time Computing Platform Practice Based on Apache StreamPark™]]></title>
+ <id>https://streampark.apache.org/blog/streampark-usercase-ziru</id>
+ <link href="https://streampark.apache.org/blog/streampark-usercase-ziru"/>
+ <updated>2024-06-30T14:58:25.000Z</updated>
+ <summary type="html"><![CDATA[Introduction: Ziroom, an O2O internet company focusing on providing rental housing products and services, has built an online, data-driven, and intelligent platform that covers the entire chain of urban living. Real-time computing has always played an important role in Ziroom. To date, Ziroom processes TB-level data daily. This article, brought by the real-time computing team from Ziroom, introduces the in-depth practice of Ziroom's real-time computi [...]
+ <content type="html"><![CDATA[<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/cover-87ff8832da8f5d9398ed5a1e2f283d0d.png" width="1080" height="460" class="img__KcZ"></p>
+<p><strong>Introduction:</strong> Ziroom, an O2O internet company focusing on providing rental housing products and services, has built an online, data-driven, and intelligent platform that covers the entire chain of urban living. Real-time computing has always played an important role in Ziroom. To date, Ziroom processes TB-level data daily. This article, brought by the real-time computing team from Ziroom, introduces the in-depth practice of Ziroom's real-time computing platform based [...]
+<ul>
+<li>Challenges in real-time computing</li>
+<li>The journey to the solution</li>
+<li>In-depth practice based on StreamPark</li>
+<li>Summary of practical experience and examples</li>
+<li>Benefits brought by the implementation</li>
+<li>Future plans</li>
+</ul>
+<p>As an O2O internet brand offering rental housing products and services, Ziroom was established in October 2011. To date, Ziroom has served nearly 500,000 landlords and 5 million customers, managing over 1 million housing units. As of March 2021, Ziroom has expanded to 10 major cities including Beijing, Shanghai, Shenzhen, Hangzhou, Nanjing, Guangzhou, Chengdu, Tianjin, Wuhan, and Suzhou. Ziroom has created an online, data-driven, and intelligent platform for quality residential produc [...]
+<p>With a vast user base, Ziroom has been committed to providing superior product experiences and achieving digital transformation of the enterprise. Since 2021, real-time computing, particularly Flink, has played an important role in Ziroom. To date, Ziroom processes TB-level data daily, with over 500 real-time jobs supporting more than 10 million data calls per day.</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="challenges-in-real-time-computing"><strong>Challenges in Real-Time Computing</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#challenges-in-real-time-computing" class="hash-link" aria-label="Direct link to challenges-in-real-time-computing" title="Direct link to challenges-in-real-time-computing"></a></h2>
+<p>At Ziroom, real-time computing is mainly divided into two application scenarios:</p>
+<ul>
+<li>
+<p>Data synchronization: Includes Kafka, MySQL, and MongoDB data synchronization to Hive / Paimon / ClickHouse, etc.</p>
+</li>
+<li>
+<p>Real-time data warehouse: Includes real-time indicators for businesses like rentals, acquisitions, and home services.</p>
+</li>
+</ul>
+<p>In the process of implementing real-time computing, we faced several challenges, roughly as follows:</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="01-low-efficiency-in-job-deployment"><strong>01 Low Efficiency in Job Deployment</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#01-low-efficiency-in-job-deployment" class="hash-link" aria-label="Direct link to 01-low-efficiency-in-job-deployment" title="Direct link to 01-low-efficiency-in-job-deployment"></a></h3>
+<p>The process of developing and deploying real-time jobs at Ziroom is as follows: data warehouse developers embed Flink SQL code in the program, debug locally, compile into FatJar, and then submit the job as a work order and JAR package to the operation team. The operation team member responsible for job deployment then deploys the job to the online Kubernetes session environment through the command line. This process involves many steps, each requiring manual intervention, resulting in [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/job_goes_online-ae38a4f939687b0e80e8e5677885fe0d.png" width="1080" height="356" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="02-unclear-job-ownership-information"><strong>02 Unclear Job Ownership Information</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#02-unclear-job-ownership-information" class="hash-link" aria-label="Direct link to 02-unclear-job-ownership-information" title="Direct link to 02-unclear-job-ownership-information"></a></h3>
+<p>Due to the lack of unified management of the real-time computing platform, business code is managed by GitLab. Although this solved some problems, we found deficiencies between the repository code and the management of online Flink jobs: lack of clear ownership, lack of grouping and effective permission control, leading to chaotic job management and difficult responsibility tracing. To ensure the consistency and controllability of code and online jobs, there is an urgent need to estab [...]
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="03-difficulty-in-job-maintenance"><strong>03 Difficulty in Job Maintenance</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#03-difficulty-in-job-maintenance" class="hash-link" aria-label="Direct link to 03-difficulty-in-job-maintenance" title="Direct link to 03-difficulty-in-job-maintenance"></a></h3>
+<p>At Ziroom, multiple versions of Flink jobs are running. Due to frequent API changes and lack of backward compatibility in major version upgrades of Apache Flink, the cost of upgrading project code becomes very high. Therefore, managing these different versions of jobs has become a headache.</p>
+<p>Without a</p>
+<p>unified job platform, these jobs are submitted using scripts. Jobs vary in importance and data volume, requiring different resources and runtime parameters, necessitating corresponding modifications. Modifications can be made by editing the submission script or directly setting parameters in the code, but this makes configuration information difficult to access, especially when jobs restart or fail and FlinkUI is unavailable, turning configuration information into a black box. Therefo [...]
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="04-difficulty-in-job-development-and-debugging"><strong>04 Difficulty in Job Development and Debugging</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#04-difficulty-in-job-development-and-debugging" class="hash-link" aria-label="Direct link to 04-difficulty-in-job-development-and-debugging" title="Direct link to 04-difficulty-in-job-development-and-debugging"></a></h3>
+<p>In our previous development process, we typically embedded SQL code within program code in the local IDEA environment for job development and debugging, verifying the correctness of the program. However, this approach has several disadvantages:</p>
+<ol>
+<li>
+<p>Difficulty in multi-data source debugging. Often, a requirement involves multiple different data sources. For local environment debugging, developers need to apply for white-list access to data, which is both time-consuming and cumbersome.</p>
+</li>
+<li>
+<p>SQL code is hard to read and modify. As SQL code is embedded in program code, it's difficult to read and inconvenient to modify. More challenging is debugging through SQL segments, as the lack of SQL version control and syntax verification makes it hard for developers to locate specific SQL lines in client logs to identify the cause of execution failure.</p>
+</li>
+</ol>
+<p>Therefore, there is a need to improve the efficiency of development and debugging.</p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="the-journey-to-the-solution"><strong>The Journey to the Solution</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#the-journey-to-the-solution" class="hash-link" aria-label="Direct link to the-journey-to-the-solution" title="Direct link to the-journey-to-the-solution"></a></h2>
+<p>In the early stages of platform construction, we comprehensively surveyed almost all relevant projects in the industry, covering both commercial paid versions and open-source versions, starting from early 2022. After investigation and comparison, we found that these projects have their limitations to varying extents, and their usability and stability could not be effectively guaranteed.</p>
+<p>Overall, StreamPark performed best in our evaluation. It was the only project without major flaws and with strong extensibility: supporting both SQL and JAR jobs, with the most complete and stable deployment mode for Flink jobs. Its unique architectural design not only avoids locking in specific Flink versions but also supports convenient version switching and parallel processing, effectively solving job dependency isolation and conflict issues. The job management & operations cap [...]
+<p>In the latest 2.2 version, the community has already restructured this part.</p>
+<p>After analyzing the pros and cons of many open-source projects, we decided to participate in projects with excellent architecture, development potential, and an actively dedicated core team. Based on this understanding, we made the following decisions:</p>
+<ol>
+<li>
+<p>In terms of job deployment mode, we decided to adopt the On Kubernetes mode. Real-time jobs have dynamic resource consumption, creating a strong need for Kubernetes' elastic scaling, which helps us better cope with data output fluctuations and ensure job stability.</p>
+</li>
+<li>
+<p>In the selection of open-source components, after comprehensive comparison and evaluation of various indicators, we finally chose what was then StreamX. Subsequent close communication with the community allowed us to deeply appreciate the serious and responsible attitude of the founders and the united and friendly atmosphere of the community. We also witnessed the project's inclusion in the Apache Incubator in September 2022, making us hopeful for its future.</p>
+</li>
+<li>
+<p>On the basis of StreamPark, we aim to promote integration with the existing ecosystem of the company to better meet our business needs.</p>
+</li>
+</ol>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="in-depth-practice-based-on-apache-streampark"><strong>In-depth Practice Based on Apache StreamPark™</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#in-depth-practice-based-on-apache-streampark" class="hash-link" aria-label="Direct link to in-depth-practice-based-on-apache-streampark" title="Direct link to in-depth-practice-based-on-apache-streampark"></a></h2>
+<p>Based on the above decisions, we initiated the evolution of the real-time computing platform, oriented by "pain point needs," and built a stable, efficient, and easy-to-maintain real-time computing platform based on StreamPark. Since the beginning of 2022, we have participated in the construction of the community while officially scheduling our internal platform construction.</p>
+<p>First, we further improved related functionalities on the basis of StreamPark:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/platform_construction-b73e12b57a4a9f558ada77038496bf50.png" width="1080" height="522" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="01-ldap-login-support"><strong>01 LDAP Login Support</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#01-ldap-login-support" class="hash-link" aria-label="Direct link to 01-ldap-login-support" title="Direct link to 01-ldap-login-support"></a></h3>
+<p>On the basis of StreamPark, we further improved related functionalities, including support for LDAP, so that in the future we can fully open up real-time capabilities, allowing analysts from the company's four business lines to use the platform, expected to reach about 170 people. With the increase in numbers, account management becomes increasingly important, especially in the case of personnel changes, account cancellation, and application become frequent and time-consuming operatio [...]
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step1-fill-in-the-corresponding-ldap">step1: Fill in the corresponding LDAP<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step1-fill-in-the-corresponding-ldap" class="hash-link" aria-label="Direct link to step1: Fill in the corresponding LDAP" title="Direct link to step1: Fill in the corresponding LDAP"></a></h4>
+<p>configuration:</p>
+<p>Edit the application.yml file, setting the LDAP basic information as follows:</p>
+<div class="language-yaml codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-yaml codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token key atrule">ldap</span><span class="token punctuation" style="color:rgb(212, 212, 212)">:</span><span class="token plain"></span><br></span><span class="token-li [...]
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step2-ldap-login">step2: LDAP Login<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step2-ldap-login" class="hash-link" aria-label="Direct link to step2: LDAP Login" title="Direct link to step2: LDAP Login"></a></h4>
+<p>On the login interface, click LDAP login method, then enter the corresponding account and password, and click to log in.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/ldap-39a8f66b7bba105117ecbfc54551dc95.png" width="1080" height="581" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="02-automatic-ingress-generation-for-job-submission"><strong>02 Automatic Ingress Generation for Job Submission</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#02-automatic-ingress-generation-for-job-submission" class="hash-link" aria-label="Direct link to 02-automatic-ingress-generation-for-job-submission" title="Direct link to 02-automatic-ingress-generation-for-job-submission"></a></h3>
+<p>Due to the company's network security policy, only port 80 is opened on the Kubernetes host machines by the operation team, making it impossible to directly access the job WebUI on Kubernetes via "domain + random port." To solve this problem, we needed to use Ingress to add a proxy layer to the access path, achieving the effect of access routing. In StreamPark version 2.0, we contributed the functionality related to Ingress [3]. We adopted a strategy pattern implementation, initially [...]
+<p>The specific configuration steps are as follows:</p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step1-click-to-enter-setting--choose-ingress-setting-fill-in-the-domain-name">step1: Click to enter Setting-> Choose Ingress Setting, fill in the domain name<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step1-click-to-enter-setting--choose-ingress-setting-fill-in-the-domain-name" class="hash-link" aria-label="Direct link to step1: Click to enter Setting-> Choose Ingress Setting, fill in the domain name" title="D [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/ingress_setting-2b8e1403aae4ff708a05f61f87d34733.png" width="1080" height="384" class="img__KcZ"></p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step2-submit-a-job">step2: Submit a job<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step2-submit-a-job" class="hash-link" aria-label="Direct link to step2: Submit a job" title="Direct link to step2: Submit a job"></a></h4>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/k8s_job-1759da059df126162f2b7e0c7b9b121a.png" width="1080" height="367" class="img__KcZ"></p>
+<p>Upon entering the K8s management platform, you can observe that submitting a Flink job also corresponds to submitting an Ingress with the same name.</p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step3-click-to-enter-flinks-webui">step3: Click to enter Flink's WebUI<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step3-click-to-enter-flinks-webui" class="hash-link" aria-label="Direct link to step3: Click to enter Flink's WebUI" title="Direct link to step3: Click to enter Flink's WebUI"></a></h4>
+<p>You will notice that the generated address consists of three parts: domain + job submission namespace + job name.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flink_webui-ca719f066a60a19004af7d6761eae27d.png" width="1080" height="451" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="03-support-for-viewing-job-deployment-logs"><strong>03 Support for Viewing Job Deployment Logs</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#03-support-for-viewing-job-deployment-logs" class="hash-link" aria-label="Direct link to 03-support-for-viewing-job-deployment-logs" title="Direct link to 03-support-for-viewing-job-deployment-logs"></a></h3>
+<p>In the process of continuous job deployment, we gradually realized that without logs, we cannot perform effective operations. Log retention, archiving, and viewing became an important part in our later problem-solving process. Therefore, in StreamPark version 2.0, we contributed the ability to archive startup logs in On Kubernetes mode and view them on the page [4]. Now, by clicking the log viewing button in the job list, it is very convenient to view the real-time logs of the job.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/k8s_log-aafb7f2dff97d584b187ab4a22de9eaf.png" width="1080" height="473" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="04-integration-of-grafana-monitoring-chart-links"><strong>04 Integration of Grafana Monitoring Chart Links</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#04-integration-of-grafana-monitoring-chart-links" class="hash-link" aria-label="Direct link to 04-integration-of-grafana-monitoring-chart-links" title="Direct link to 04-integration-of-grafana-monitoring-chart-links"></a></h3>
+<p>In actual use, as the number of jobs increased, the number of users rose, and more departments were involved, we faced the problem of difficulty in self-troubleshooting. Our team's operational capabilities are actually very limited. Due to the difference in professional fields, when we tell users to view charts and logs on Grafana and ELK, users often feel at a loss and do not know how to find information related to their jobs.</p>
+<p>To solve this problem, we proposed a demand in the community: we hoped that each job could directly jump to the corresponding monitoring chart and log archive page through a hyperlink, so that users could directly view the monitoring information and logs related to their jobs. This avoids tedious searches in complex system interfaces, thus improving the efficiency of troubleshooting.</p>
+<p>We had a discussion in the community, and it was quickly responded to, as everyone thought it was a common need. Soon, a developer contributed a design and related PR, and the issue was quickly resolved. Now, to enable this feature in StreamPark has become very simple:</p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step1-create-a-badge-label">step1: Create a badge label<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step1-create-a-badge-label" class="hash-link" aria-label="Direct link to step1: Create a badge label" title="Direct link to step1: Create a badge label"></a></h4>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/create_badge_label-a6542822bd5a1e9274ca0017d25a9ccd.png" width="1080" height="649" class="img__KcZ"></p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step2-associate-the-badge-label-with-a-redirect-link">step2: Associate the badge label with a redirect link<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step2-associate-the-badge-label-with-a-redirect-link" class="hash-link" aria-label="Direct link to step2: Associate the badge label with a redirect link" title="Direct link to step2: Associate the badge label with a redirect link"></a></h4>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/relevancy-a1914717d6479d16e07f7f1e6193f409.png" width="1062" height="400" class="img__KcZ"></p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step3-click-the-badge-label-for-link-redirection">step3: Click the badge label for link redirection<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step3-click-the-badge-label-for-link-redirection" class="hash-link" aria-label="Direct link to step3: Click the badge label for link redirection" title="Direct link to step3: Click the badge label for link redirection"></a></h4>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/link_jump-f353a479fac3e5576056edcaab705dda.png" width="1080" height="381" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="05-integration-of-flink-sql-security-for-permission-control"><strong>05 Integration of Flink SQL Security for Permission Control</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#05-integration-of-flink-sql-security-for-permission-control" class="hash-link" aria-label="Direct link to 05-integration-of-flink-sql-security-for-permission-control" title="Direct link to 05-integration-of-flink-sql-security-for-permi [...]
+<p>In our system, lineage management is based on Apache Atlas, and permission management is based on the open-source project Flink-sql-security, which supports user-level data desensitization and row-level data access control, allowing specific users to only access desensitized data or authorized rows.</p>
+<p>This design is to handle some complex inheritance logic. For example, when joining encrypted field age in Table A with Table B to obtain Table C, the age field in Table C should inherit the encryption logic of Table A to ensure the encryption status of data is not lost during processing. This way, we can better protect data security and ensure that data complies with security standards throughout the processing process.</p>
+<p>For permission control, we developed a Flink-sql-security-streampark plugin based on Flink-s</p>
+<p>ql-security. The basic implementation is as follows:</p>
+<ol>
+<li>
+<p>During submission check, the system parses the submitted SQL, obtaining InputTable and OutputTable datasets.</p>
+</li>
+<li>
+<p>The system queries the remote permission service to obtain the user's bound RBAC (Role-Based Access Control) permissions.</p>
+</li>
+<li>
+<p>Based on the RBAC permissions, the system gets the encryption rules for the corresponding tables.</p>
+</li>
+<li>
+<p>The system rewrites the SQL, wrapping the original SQL query fields with a preset encryption algorithm, thereby reorganizing the logic.</p>
+</li>
+<li>
+<p>Finally, the system submits according to the reorganized logic.</p>
+</li>
+</ol>
+<p>Through this integration and plugin development, we implemented permission control for user query requests, thereby ensuring data security.</p>
+<p><strong>01 Row-level Permission Conditions</strong></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/row_level_permissions-cd7665f926dd3c92e8479be34f9084d5.png" width="1080" height="348" class="img__KcZ"></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/row_level_permissions_table-780239498d6b6d09c8a5c1ffe4ad44ef.png" width="1080" height="151" class="img__KcZ"></p>
+<p>Input SQL</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">SELECT * FROM orders;</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code to clipboard" title="Copy" [...]
+<p>User A's actual execution SQL:</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">SELECT * FROM orders WHERE region = 'beijing';</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code t [...]
+<p>User B's actual execution SQL:</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">SELECT * FROM orders WHERE region = 'hangzhou';</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code [...]
+<p><strong>02 Field Desensitization Conditions</strong></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/field_desensitization-3005c6f1d9c2f7ad0530d09a5a9aecea.png" width="1080" height="269" class="img__KcZ"></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/field_desensitization_table-e200cbbf0081e2d416411659a188bb90.png" width="1080" height="160" class="img__KcZ"></p>
+<p>Input SQL</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">SELECT name, age, price, phone FROM user;</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code to cli [...]
+<p>Execution SQL:</p>
+<p>User A's actual execution SQL:</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">SELECT Encryption_function(name), age, price, Sensitive_field_functions(phone) FROM user;</span><br></span></code></pre><div class="buttonGroup_inAn"><b [...]
+<p>User B's actual execution SQL:</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">SELECT name, Encryption_function(age), price, Sensitive_field_functions(phone) FROM user;</span><br></span></code></pre><div class="buttonGroup_inAn"><b [...]
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="06-data-synchronization-platform-based-on-apache-streampark"><strong>06 Data Synchronization Platform Based on Apache StreamPark™</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#06-data-synchronization-platform-based-on-apache-streampark" class="hash-link" aria-label="Direct link to 06-data-synchronization-platform-based-on-apache-streampark" title="Direct link to 06-data-synchronization-platform-based-on-apa [...]
+<p>With the successful implementation of StreamPark's technical solutions in the company, we achieved deep support for Flink jobs, bringing a qualitative leap in data processing. This prompted us to completely revamp our past data synchronization logic, aiming to reduce operational costs through technical optimization and integration. Therefore, we gradually replaced historical Sqoop jobs, Canal jobs, and Hive JDBC Handler jobs with Flink CDC jobs, Flink stream, and batch jobs. In this p [...]
+<p>External system integration with StreamPark is simple, requiring only a few steps:</p>
+<ol>
+<li>First, create a token for API access:</li>
+</ol>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/token-94cb364497fe129fc62be3bbb7193390.png" width="1080" height="288" class="img__KcZ"></p>
+<ol start="2">
+<li>View the external call link of the Application:</li>
+</ol>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/call_link-da9d24e3a1993590cf25da174d5fb676.png" width="1080" height="390" class="img__KcZ"></p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">curl -X POST '/flink/app/start' \</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">-H 'Authorization: $token' \ [...]
+<ol start="3">
+<li>Configure Http scheduling in DolphinScheduler</li>
+</ol>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/http_scheduling-267e8ae0aa04d5901cd98bea50370d69.png" width="1080" height="576" class="img__KcZ"></p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="summary-of-practical-experience"><strong>Summary of Practical Experience</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#summary-of-practical-experience" class="hash-link" aria-label="Direct link to summary-of-practical-experience" title="Direct link to summary-of-practical-experience"></a></h2>
+<p>During our in-depth use of StreamPark, we summarized some common issues and explored solutions in the practice process, which we have compiled into examples for reference.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="01-building-base-images"><strong>01 Building Base Images</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#01-building-base-images" class="hash-link" aria-label="Direct link to 01-building-base-images" title="Direct link to 01-building-base-images"></a></h3>
+<p>To deploy a Flink job on Kubernetes using StreamPark, you first need to prepare a Base image built on Flink. Then, on the Kubernetes platform, the user-provided image is used to start the Flink job. If we continue to use the official "bare image," it is far from sufficient for actual development. Business logic developed by users often involves multiple upstream and downstream data sources, requiring related data source Connectors and dependencies like Hadoop. Therefore, these depende [...]
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step1-first-create-a-folder-containing-two-folders-and-a-dockerfile-file">step1: First, create a folder containing two folders and a Dockerfile file<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step1-first-create-a-folder-containing-two-folders-and-a-dockerfile-file" class="hash-link" aria-label="Direct link to step1: First, create a folder containing two folders and a Dockerfile file" title="Direct link to step1: [...]
+<p><img decoding="async" loading="lazy" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAxwAAACmCAIAAADf3B7RAAAbi0lEQVR4Xu3d6VcUZ77A8fl/7tzLzJu598w5PYlRSVwQJYqoIIsLakwEWdwSQEGR3Q0RFMG4g0rTTQLIZJmcyWYmM4lJjNmMW2KMMUbjvt6n+2kfi3qaprCrGqS/dT5nDlT9ajFv5nu6iuo/xMw5AgAAgDD9QV8FAACAgSKqAAAAbEBUAQAA2ICoAgAAsAFRBQAAYAOiCgAAwAZEFQAAgA2GeVStbni9JJjlG7vmlr4xIus9fRcAAIDHMMyjSs+pXupfn/LKP/S9AAAABiq6o8pvakGEuqq+44eej3/t/PAXfZPtuo5cFOeq85zVN0VG/MtHY/M/0dcDADBcEVWR66r/fPf7gwcPbt25r2+y3d1798W5/vX1FX2To [...]
+<p>conf folder: Contains HDFS configuration files, mainly for configuring Flink's Checkpoint writing and using Hive metadata in FlinkSQL</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/hdfs_conf-8af6d1660d09b58fc28a6fec1bff604b.png" width="824" height="284" class="img__KcZ"></p>
+<p>lib folder: Contains the related Jar dependency packages, as follows:</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/lib-faa76eddded5bdcb4228bf65c2d3122d.png" width="842" height="722" class="img__KcZ"></p>
+<p>Dockerfile file for defining image construction</p>
+<div class="language-dockerfile codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-dockerfile codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">FROM apache/flink:1.14.5-scala_2.11-java8</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">ENV TIME_Z [...]
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step2-image-build-command-using-multi-architecture-build-mode-as-follows">step2: Image build command using multi-architecture build mode, as follows:<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step2-image-build-command-using-multi-architecture-build-mode-as-follows" class="hash-link" aria-label="Direct link to step2: Image build command using multi-architecture build mode, as follows:" title="Direct link to step2 [...]
+<div class="language-dockerfile codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-dockerfile codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">docker buildx build --push --platform linux/amd64 -t ${private image repository address}</span><br></span></code></pre><div class="buttonGroup [...]
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="02-base-image-integration-with-arthas-example"><strong>02 Base Image Integration with Arthas Example</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#02-base-image-integration-with-arthas-example" class="hash-link" aria-label="Direct link to 02-base-image-integration-with-arthas-example" title="Direct link to 02-base-image-integration-with-arthas-example"></a></h3>
+<p>As more jobs are released and go live within our company, we often encounter performance degradation in long-running jobs, such as reduced Kafka consumption capacity, increased memory usage, and extended GC time. We recommend using Arthas, an open-source Java diagnostic tool by Alibaba. It allows real-time global viewing of Java application load, memory, GC, thread status, and without modifying application code, it enables viewing method call parameters, exceptions, monitoring method [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/arthas-31f8df2a167d53420d2b4a828003a449.png" width="1080" height="526" class="img__KcZ"></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/advanced-e5c462ae9a888deddb5939a7c8065732.png" width="1080" height="416" class="img__KcZ"></p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/arthas_log-fbc7dc0891690e569c6ad63359fc52fd.png" width="1080" height="390" class="img__KcZ"></p>
+<p>Therefore, we integrated Arthas into the base image to facilitate runtime problem troubleshooting.</p>
+<div class="language-dockerfile codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-dockerfile codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">FROM apache/flink:1.14.5-scala_2.11-java8</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">ENV TIME_Z [...]
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="03-resolution-of-dependency-conflicts-in-images"><strong>03 Resolution of Dependency Conflicts in Images</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#03-resolution-of-dependency-conflicts-in-images" class="hash-link" aria-label="Direct link to 03-resolution-of-dependency-conflicts-in-images" title="Direct link to 03-resolution-of-dependency-conflicts-in-images"></a></h3>
+<p>In the process of using StreamPark, we often encounter dependency conflict exceptions like NoClassDefFoundError, ClassNotFoundException, and NoSuchMethodError in Flink jobs running on base images. The troubleshooting approach is to find the package path of the conflicting class indicated in the error. For example, if the error class is in org.apache.orc<!-- -->:orc-core<!-- -->, go to the corresponding module directory, run <code>mvn dependency::tree</code>, search for orc-core, see w [...]
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step1-clone-the-flink-shaded-project-locally">step1: Clone the flink-shaded project locally👇<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step1-clone-the-flink-shaded-project-locally" class="hash-link" aria-label="Direct link to step1: Clone the flink-shaded project locally👇" title="Direct link to step1: Clone the flink-shaded project locally👇"></a></h4>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">git clone https://github.com/apache/flink-shaded.git</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flink_shaded-f21909c81059b2ea4ebd8307998cbc26.png" width="1080" height="529" class="img__KcZ"></p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step2-load-the-project-into-idea">step2: Load the project into IDEA<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step2-load-the-project-into-idea" class="hash-link" aria-label="Direct link to step2: Load the project into IDEA" title="Direct link to step2: Load the project into IDEA"></a></h4>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/idea-d654040d5fad6fefd756ede9a9ac8a4b.png" width="1080" height="586" class="img__KcZ"></p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step3-exclude-the-conflicting-parts-and-then-package-them">step3: Exclude the conflicting parts and then package them.<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step3-exclude-the-conflicting-parts-and-then-package-them" class="hash-link" aria-label="Direct link to step3: Exclude the conflicting parts and then package them." title="Direct link to step3: Exclude the conflicting parts and then package them."></a></h4>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="04-centralized-job-configuration-example"><strong>04 Centralized Job Configuration Example</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#04-centralized-job-configuration-example" class="hash-link" aria-label="Direct link to 04-centralized-job-configuration-example" title="Direct link to 04-centralized-job-configuration-example"></a></h3>
+<p>One of the great conveniences of using StreamPark is centralized configuration management. You can configure all settings in the conf file in the Flink directory bound to the platform.</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">cd /flink-1.14.5/conf</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">vim flink-conf.yaml</span><br></span></c [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/conf-ff77bc47a1f6af137a3326cc05cfdfee.png" width="1080" height="562" class="img__KcZ"></p>
+<p>After completing the configuration, save it. Then go to the platform's Setting, and click on the Flink Conf icon.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/flink_conf-db4fdbcbac08f9e19eaf550c1332de25.png" width="1080" height="351" class="img__KcZ"></p>
+<p>Clicking Sync Conf will synchronize the global configuration file, and new jobs will be submitted with the new configuration.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/sync_conf-9d2b45b6f42376859fda062127e33d82.png" width="1080" height="485" class="img__KcZ"></p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="05-apache-streampark-dns-resolution-configuration"><strong>05 Apache StreamPark™ DNS Resolution Configuration</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#05-apache-streampark-dns-resolution-configuration" class="hash-link" aria-label="Direct link to 05-apache-streampark-dns-resolution-configuration" title="Direct link to 05-apache-streampark-dns-resolution-configuration"></a></h3>
+<p>A correct and reasonable DNS resolution configuration is very important when submitting FlinkSQL on the StreamPark platform. It mainly involves the following points:</p>
+<ol>
+<li>
+<p>Flink jobs' Checkpoint writing to HDFS requires a snapshot write to an HDFS node obtained through ResourceManager. If there are expansions in the Hadoop cluster in the enterprise, and these new nodes are not covered by the DNS resolution service, this will directly lead to Checkpoint failure, affecting online stability.</p>
+</li>
+<li>
+<p>Flink jobs typically need to configure connection strings for different internal data sources. Configuring the database's real IP address often leads to job exits due to IP changes during database migration. Therefore, in production, connection strings are often composed of domain names and attribute parameters, with DNS services resolving them to real IP addresses for access.</p>
+</li>
+</ol>
+<p>Initially, we maintained DNS configuration through Pod Template.</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">apiVersion: v1</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">kind: Pod</span><br></span><span class="token-l [...]
+<p>Although theoretically feasible, we encountered a series of problems in practice. When expanding HDFS, we found failures in Flink's Checkpoint function, and database migration also faced connection failures, causing sudden online service outages. After in-depth investigation, we found that the root cause was in DNS resolution.</p>
+<p>Previously, we used hostAliases to maintain the mapping between domain names and IP addresses. However, this method was costly in practice, as every update of hostAliases required stopping all Flink jobs, undoubtedly increasing our operational costs. To seek a more flexible and reliable method to manage DNS resolution configuration and ensure the normal operation of Flink jobs, we decided to build dnsmasq for bidirectional DNS resolution.</p>
+<p>After configuring and installing dnsmasq, we first needed to override the resolv.conf configuration file in the /etc directory of the Flink image. However, since resolv.conf is a read-only file, if we want to override it, we need to use mounting. Therefore, we first configured resolv.conf as a ConfigMap for use during the override. This way, we can more flexibly and reliably manage DNS resolution configuration, ensuring stable operation of Flink jobs.</p>
+<div class="language-yaml codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-yaml codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token key atrule">apiVersion</span><span class="token punctuation" style="color:rgb(212, 212, 212)">:</span><span class="token plain"> v1</span><br></span><span class= [...]
+<p>Mounting it through Pod Template.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/pod_template-d8a9864bc8486d493025cc5e8b0e6dd1.png" width="1080" height="476" class="img__KcZ"></p>
+<p>This way, DNS related to the big data platform can be maintained on dnsmasq, while the host machine running Flink jobs can follow the DNS resolution process.</p>
+<ol>
+<li>
+<p>First, check the local hosts file to see if there is a corresponding relationship, read the record for resolution, and proceed to the next step if not.</p>
+</li>
+<li>
+<p>The operating system checks the local DNS cache, and if not found, moves to the next step.</p>
+</li>
+<li>
+<p>The operating system searches the DNS server address defined in our network configuration.</p>
+</li>
+</ol>
+<p>This achieves dynamic recognition of DNS changes.</p>
+<h3 class="anchor anchorWithStickyNavbar_myWT" id="06-multi-instance-deployment-practice"><strong>06 Multi-Instance Deployment Practice</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#06-multi-instance-deployment-practice" class="hash-link" aria-label="Direct link to 06-multi-instance-deployment-practice" title="Direct link to 06-multi-instance-deployment-practice"></a></h3>
+<p>In actual production environments, we often need to operate multiple clusters, including a set for testing and a set for official online use. Tasks are first verified and performance tested in the test cluster, then released to the official online cluster after ensuring accuracy.</p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step1-modify-the-port-number-to-avoid-conflicts-between-multiple-service-ports">step1: Modify the port number to avoid conflicts between multiple service ports<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step1-modify-the-port-number-to-avoid-conflicts-between-multiple-service-ports" class="hash-link" aria-label="Direct link to step1: Modify the port number to avoid conflicts between multiple service ports" title=" [...]
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/port_number-e421801c946def0b0e2df3747239fec6.png" width="1080" height="622" class="img__KcZ"></p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step2-modify-workspace">step2: Modify workspace<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step2-modify-workspace" class="hash-link" aria-label="Direct link to step2: Modify workspace" title="Direct link to step2: Modify workspace"></a></h4>
+<p>Different instance services need to configure different workspaces to avoid resource interference leading to strange bugs.</p>
+<h4 class="anchor anchorWithStickyNavbar_myWT" id="step3-launch-multi-instance-services">step3: Launch multi-instance services<a href="https://streampark.apache.org/blog/streampark-usercase-ziru#step3-launch-multi-instance-services" class="hash-link" aria-label="Direct link to step3: Launch multi-instance services" title="Direct link to step3: Launch multi-instance services"></a></h4>
+<p>To achieve isolation between production and testing environments, we introduced a key step at the beginning of the startup process. We input the command (for the Hadoop B cluster):</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">export HADOOP_CONF_DIR=/home/streamx/conf</span><br></span></code></pre><div class="buttonGroup_inAn"><button type="button" aria-label="Copy code to cli [...]
+<p>This effectively cut off the default logic of Flink on K8s loading HDFS configuration. This operation ensures that A StreamPark only connects to A Hadoop environment, while B StreamPark connects to B Hadoop environment, thus achieving complete isolation between testing and production environments.</p>
+<p>Specifically, after this command takes effect, we can ensure that Flink jobs submitted on port 10002 connect to the B Hadoop environment. Thus, the B Hadoop environment is isolated from the Hadoop environment used by Flink jobs submitted on port 10000 in the past, effectively preventing interference between different environments and ensuring system stability and reliability.</p>
+<p>The following content is an analysis of Flink's logic for loading the Hadoop environment:</p>
+<div class="language-yaml codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-yaml codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">// Process of finding Hadoop configuration files</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">//1. First</spa [...]
+<p>Then conduct process port occupancy queries:</p>
+<div class="language-shell codeBlockContainer_APcc theme-code-block"><div class="codeBlockContent_IhPH"><pre tabindex="0" class="prism-code language-shell codeBlock_tTXe thin-scrollbar" style="color:#9CDCFE;background-color:#1E1E1E"><code class="codeBlockLines_qImF"><span class="token-line" style="color:#9CDCFE"><span class="token plain">netstat -tlnp | grep 10000</span><br></span><span class="token-line" style="color:#9CDCFE"><span class="token plain">netstat -tlnp | grep 10002</span><b [...]
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="benefits-brought"><strong>Benefits Brought</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#benefits-brought" class="hash-link" aria-label="Direct link to benefits-brought" title="Direct link to benefits-brought"></a></h2>
+<p>Our team has been using StreamX (the predecessor of StreamPark) and, after more than a year of practice and refinement, StreamPark has significantly improved our challenges in developing, managing, and operating Apache Flink® jobs. As a one-stop service platform, StreamPark greatly simplifies the entire development process. Now, we can complete job development, compilation, and release directly on the StreamPark platform, not only lowering the management and deployment threshold of Fl [...]
+<p>Since deploying StreamPark, we have been using the platform on a large scale in a production environment. From initially managing over 50 FlinkSQL jobs to nearly 500 jobs now, as shown in the diagram, StreamPark is divided into 7 teams, each with dozens of jobs. This transformation not only demonstrates StreamPark's scalability and efficiency but also fully proves its strong practical value in actual business.</p>
+<p><img decoding="async" loading="lazy" src="https://streampark.apache.org/assets/images/production_environment-4f155f3b14a4e95c5367ba99a6c5a6f8.png" width="1080" height="522" class="img__KcZ"></p>
+<h2 class="anchor anchorWithStickyNavbar_myWT" id="future-expectations"><strong>Future Expectations</strong><a href="https://streampark.apache.org/blog/streampark-usercase-ziru#future-expectations" class="hash-link" aria-label="Direct link to future-expectations" title="Direct link to future-expectations"></a></h2>
+<p>As one of the early users of StreamPark, we have maintained close communication with the community, participating in the stability improvement of StreamPark. We have submitted bugs encountered in production operation and new features to the community. In the future, we hope to manage the metadata information of Apache Paimon lake tables and the capability of auxiliary jobs for</p>
+<p>Paimon's Actions on StreamPark. Based on the Flink engine, by interfacing with the Catalog of lake tables and Action jobs, we aim to realize the management and optimization of lake table jobs in one integrated capability. Currently, StreamPark is working on integrating the capabilities with Paimon data, which will greatly assist in real-time data lake ingestion in the future.</p>
+<p>We are very grateful for the technical support that the StreamPark team has provided us all along. We wish Apache StreamPark continued success, more users, and its early graduation to become a top-level Apache project.</p>]]></content>
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