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new 1b42933 Add DolphinScheduler Kubernetes Technology in action blog
(#700)
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commit 1b42933f95e4c69259774f84326e05bccf6012dd
Author: lifeng <[email protected]>
AuthorDate: Thu Feb 24 12:13:14 2022 +0800
Add DolphinScheduler Kubernetes Technology in action blog (#700)
* add EN news info
add EN news info
* Update DolphinScheduler_Kubernetes_Technology_in_action.md
* Update .dlc.json
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@@ -2,6 +2,9 @@
"ignorePatterns": [
{
"pattern": "^http://localhost";
+ },
+ {
+ "pattern": "^http://192";
}
],
"replacementPatterns": [
diff --git a/blog/en-us/DolphinScheduler_Kubernetes_Technology_in_action.md
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+---
+title:Technical Practice of Apache DolphinScheduler in Kubernetes System
+keywords: Apache,DolphinScheduler,scheduler,big
data,ETL,airflow,hadoop,orchestration,dataops,Kubernetes
+description:Kubernetes is a cluster system based on container technology
+---
+# Technical Practice of Apache DolphinScheduler in Kubernetes System
+
+<div align=center>
+<img src="/img/2022-02-24/1.jpeg"/>
+</div>
+
+Author | Yang Dian, Data and Algorithm Platform Architect | Shenzhen
Transportation Center
+
+Editor | warrior_
+
+> Editor's note：
+
+> Kubernetes is a cluster system based on container technology, implements
container orchestration, provides microservices and buses, and involves a large
number of knowledge systems.
+
+> Starting from the author's actual work experience, this article shows us the
use and technology sharing of DolphinScheduler in practice scenarios, hoping it
can inspire those who have the same experience.
+
+## Why do we use DolphinSchedule? What value does it bring to us? And what
problems did we encounter?
+
+Apache DolphinScheduler is an excellent distributed and easily scalable visual
workflow task scheduling platform.
+
+In the field I'm working in, the application of DolphinScheduler can quickly
solve the top ten pain points of data development for enterprises:
+
+- Multi-source data connection and access, most common data sources in the
technical field can be accessed, and adding new data sources does not require
too many changes;
+- Diversified +specialized + massive data task management, which really
revolves problems focusing on big data (Hadoop family, Flink, etc.) task
scheduling, and is significantly different from traditional schedulers;
+- Graphical task arrangement, super convenient user experience, competitive
ability with commercial products, especially to most foreign open source
products that cannot directly generate data tasks by dragging and dropping;
+- Task details, rich viewing of tasks, log, and time-running axis display,
which well meet developers' requirement of refined data tasks management,
quickly locating slow SQL and performance bottlenecks;
+- Support for a variety of distributed file systems, enrich users' choices for
unstructured data;
+- Native multi-tenant management to meet the data task management and
isolation requirements of large organizations;
+- Fully automatic distributed scheduling algorithm to balance all scheduling
tasks;
+- The native function of cluster monitoring, which can monitor CPU, memory,
number of connections, Zookeeper status is suitable for one-stop operation and
maintenance for SME;
+- The native task alarm function, which minimizes the risk of task operation;
+- The strong community-based operation, listening to the real voice of
customers, constantly adding new functions, and continuously optimizing the
customer experience.
+
+I also encountered many new challenges in the projects launching various types
of Apache DolphinScheduler:
+- How to deploy Apache DolphinScheduler with less human resources, and can a
fully automatic cluster installation and deployment mode be realized?
+- How to standardize technical component implementation specifications?
+- Can unmanned supervision and system self-healing be achieved?
+- How to install and update the air-gap mode under the network security
control?
+- Can it automatically expand without disturbing?
+- How to build and integrate the monitoring system?
+
+To solve the above challenges, we repeatedly integrated Apache
DolphinScheduler into the existing Kubernetes cloud-native system to tackle
problems and make Apache DolphinScheduler technology more powerful.
+
+## Kubernetes Technical System Bring New Technical Features to Apache
DolphinScheduler
+
+
+After using Kubernetes to manage Apache DolphinScheduler, the overall
technical solutions are quickly enriched and efficient technical features
added, by which the above practical challenges tackled quickly:
+- The development environment and production environment was rapidly
established in various independent deployment projects, and all can be
implemented by one-key deployment and one-key upgrade;
+- Overall supports offline installation, and the installation speed is faster;
+- Unify the installation configuration information as much as possible to
reduce the abnormal configuration of multiple projects. All configuration items
can be managed through the internal git of the enterprise based on different
projects;
+- Combine with object storage technology to unify unstructured data technology;
+- The convenient monitoring system is integrated with the existing prometheus
monitoring system;
+- Mixed-use of multiple schedulers;
+- Fully automatic resource scheduling capability;
+- Fast self-healing ability, automatic abnormal restart, and restart mode
based on probe mode.
+
+The cases in this article are based on Apache DolphinScheduler version 1.3.9.
+
+## Automated and Efficient Deployment Based on Helm tools
+
+First, let's introduce the installation method based on the Helm provided by
the official website. Helm is the best way to find, share and use software to
build Kubernetes, which is one of the graduate projects of cloud-native CNCF.
+
+<div align=center>
+<img src="/img/2022-02-24/2.png"/>
+</div>
+
+There are very detailed configuration files and cases on Apache
DolphinScheduler official website and GitHub. Here I‘ll highlight some of the
FAQs in the community.
+
+Official website document address
+https://dolphinscheduler.apache.org/en-us/docs/1.3.9/user_doc/kubernetes-deployment.html
+
+GitHub folder address
+https://github.com/apache/dolphinscheduler/tree/1.3.9-release/docker/kubernetes/dolphinscheduler
+
+Modify the image in the value.yaml file for offline installation (air-gap
install);
+
+```
+image:
+repository: "apache/dolphinscheduler"
+tag: "1.3.9"
+pullPolicy: "IfNotPresent"
+```
+
+Pull, tag, and push for harbors installed in the company or private warehouses
of other public clouds. Here we assume that the private warehouse address is
harbor.abc.com, the host where the image is built has been docker login
harbor.abc.com, and the new apache project under the private warehouse has been
established and authorized.
+
+execute shell commands
+
+```
+docker pull apache/dolphinscheduler:1.3.9
+dock tag apache/dolphinscheduler:1.3.9
+harbor.abc.com/apache/dolphinscheduler:1.3.9
+docker push apache/dolphinscheduler:1.3.9
+```
+
+
+Then replace the image information in the value file. Here we recommend using
the Always method to pull the image. In the production environment, try to
check whether it is the latest image content every time to ensure the
correctness of the software product. In addition, many coders are used to
writing the tag as latest, and making the image without adding the tag
information, which is very dangerous in the production environment. Because the
image of the latest will be changed once any [...]
+
+```
+image:
+repository: "harbor.abc.com/apache/dolphinscheduler"
+tag: "1.3.9"
+pullPolicy: "Always"
+```
+
+Copy the entire directory of
https://github.com/apache/dolphinscheduler/tree/1.3.9-release/docker/kubernetes/dolphinscheduler
to a host that can execute the Helm command, and then execute
+```
+kubectl create ns ds139
+helm install dolphinscheduler . -n ds139 following the official website
instruction to install offline.
+```
+to install offline.
+
+- To integrate DataX, MySQL, Oracle client components, first download the
following components:
+https://repo1.maven.org/maven2/mysql/mysql-connector-java/5.1.49/mysql-connector-java-5.1.49.jar
+https://repo1.maven.org/maven2/com/oracle/database/jdbc/ojdbc8/
+https://github.com/alibaba/DataX/blob/master/userGuid.md Compile
+
+build and compile according to the prompt, and the file package is located in
{DataX_source_code_home}/target/datax/datax/
+
+
+Create a new dockerfile based on the above plugin components, and the image
that has been pushed to the private warehouse can be applied to the basic image.
+
+```
+FROM harbor.abc.com/apache/dolphinscheduler:1.3.9
+COPY *.jar /opt/dolphinscheduler/lib/
+RUN mkdir -p /opt/soft/datax
+COPY datax /opt/soft/datax
+```
+
+Save the dockerfile and execute the shell command
+
+
+```
+docker build -t
harbor.abc.com/apache/dolphinscheduler:1.3.9-mysql-oracle-datax . #Don't forget
the last point
+docker push harbor.abc.com/apache/dolphinscheduler:1.3.9-mysql-oracle-datax
+```
+
+Modify the value file
+
+```
+image:
+repository: "harbor.abc.com/apache/dolphinscheduler"
+tag: "1.3.9-mysql-oracle-datax"
+pullPolicy: "Always"
+```
+
+Execute helm install dolphinscheduler . -n ds139
+or helm upgrade dolphinscheduler -n ds139, or firstly helm uninstall
dolphinscheduler -n ds139, and then execute helm install dolphinscheduler . -n
ds139.
+
+- Generally, it is recommended to use an independent external PostgreSQL as
the management database in the production environment, and use the
independently installed Zookeeper environment (I used the Zookeeper operator
https://github.com/pravega/zookeeper-operator in this case, which is scheduled
in the same Kubernetes cluster as Apache DolphinScheduler ). We found that
after using the external database, completely deleting and redeploying the
Apache DolphinScheduler in Kubernetes, th [...]
+
+```
+## If not exists external database, by default, Dolphinscheduler's database
will use it.
+postgresql:
+enabled: false
+postgresqlUsername: "root"
+postgresqlPassword: "root"
+postgresqlDatabase: "dolphinscheduler"
+persistence:
+ enabled: false
+ size: "20Gi"
+ storageClass: "-"
+
+## If exists external database, and set postgresql.enable value to false.
+## external database will be used, otherwise Dolphinscheduler's database will
be used.
+externalDatabase:
+type: "postgresql"
+driver: "org.postgresql.Driver"
+host: "192.168.1.100"
+port: "5432"
+username: "admin"
+password: "password"
+database: "dolphinscheduler"
+params: "characterEncoding=utf8"
+
+## If not exists external zookeeper, by default, Dolphinscheduler's zookeeper
will use it.
+zookeeper:
+enabled: false
+fourlwCommandsWhitelist: "srvr,ruok,wchs,cons"
+persistence:
+ enabled: false
+ size: "20Gi"
+ storageClass: "storage-nfs"
+zookeeperRoot: "/dolphinscheduler"
+
+## If exists external zookeeper, and set zookeeper.enable value to false.
+## If zookeeper.enable is false, Dolphinscheduler's zookeeper will use it.
+externalZookeeper:
+zookeeperQuorum:
"zookeeper-0.zookeeper-headless.zookeeper.svc.cluster.local:2181,zookeeper-1.zookeeper-headless.zookeeper.svc.cluster.local:2181,zookeeper-2.zookeeper-headless.zookeeper.svc.cluster.local:2181"
+zookeeperRoot: "/dolphinscheduler"
+```
+
+## How to deploy GitOps based on Argo CD
+
+Argo CD is a declarative GitOps continuous delivery tool based on Kubernetes.
GitOps is an incubation project of CNCF and a best practice tool for GitOps.
For more details of GitOps, please refer to
https://about.gitlab.com/topics/gitops/
+
+<div align=center>
+<img src="/img/2022-02-24/3.png"/>
+</div>
+
+GitOps can bring the following advantages to the implementation of Apache
DolphinScheduler.
+- Graphical & one-click installation of clustered software;
+- Git records the full release process, one-click rollback;
+- Convenient DolphinScheduler tool log viewing.
+
+Implementation installation steps using Argo CD:
+- Download the Apache DolphinScheduler source code from GitHub, modify the
value file, and refer to the content that needs to be modified in the helm
installation in the previous chapter;
+- Create a new git project in the modified source code directory, and push it
to the company's internal GitLab. The directory name of the GitHub source code
is docker/kubernetes/dolphinscheduler;
+- Configure GitLab information in Argo CD, we use https mode here;
+
+<div align=center>
+<img src="/img/2022-02-24/4.png"/>
+</div>
+
+- Argo CD Create a new deployment project and fill in the relevant information
+
+<div align=center>
+<img src="/img/2022-02-24/5.png"/>
+</div>
+
+<div align=center>
+<img src="/img/2022-02-24/6.png"/>
+</div>
+
+Refresh and pull the deployment information in git to complete the final
deployment work. You can see that pod, configmap, secret, service, ingress and
other resources are automatically pulled up, and Argo CD displays the commit
information and submitter username used by git push before, which completely
records all release event information. At the same time, you can also roll back
to the historical version with one click.
+
+<div align=center>
+<img src="/img/2022-02-24/7.png"/>
+</div>
+<div align=center>
+<img src="/img/2022-02-24/8.png"/>
+</div>
+
+- Relevant resource information can be seen through the kubectl command;
+```
+[root@tpk8s-master01 ~]# kubectl get po -n ds139
+NAME READY STATUS RESTARTS AGE
+dolphinscheduler-alert-96c74dc84-72cc9 1/1 Running 0 22m
+dolphinscheduler-api-78db664b7b-gsltq 1/1 Running 0 22m
+dolphinscheduler-master-0 1/1 Running 0 22m
+dolphinscheduler-master-1 1/1 Running 0 22m
+dolphinscheduler-master-2 1/1 Running 0 22m
+dolphinscheduler-worker-0 1/1 Running 0 22m
+dolphinscheduler-worker-1 1/1 Running 0 22m
+dolphinscheduler-worker-2 1/1 Running 0 22m
+
+[root@tpk8s-master01 ~]# kubectl get statefulset -n ds139
+NAME READY AGE
+dolphinscheduler-master 3/3 22m
+dolphinscheduler-worker 3/3 22m
+
+[root@tpk8s-master01 ~]# kubectl get cm -n ds139
+NAME DATA AGE
+dolphinscheduler-alert 15 23m
+dolphinscheduler-api 1 23m
+dolphinscheduler-common 29 23m
+dolphinscheduler-master 10 23m
+dolphinscheduler-worker 7 23m
+
+[root@tpk8s-master01 ~]# kubectl get service -n ds139
+NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
+dolphinscheduler-api ClusterIP 10.43.238.5 <none> 12345/TCP 23m
+dolphinscheduler-master-headless ClusterIP None <none> 5678/TCP 23m
+dolphinscheduler-worker-headless ClusterIP None <none> 1234/TCP,50051/TCP 23m
+
+[root@tpk8s-master01 ~]# kubectl get ingress -n ds139
+NAME CLASS HOSTS ADDRESS
+dolphinscheduler <none> ds139.abc.com
+```
+
+- You can see that all pods are scattered on different hosts in the Kubernetes
cluster, for example, workers 1 and 2 are on different nodes.
+
+<div align=center>
+<img src="/img/2022-02-24/9.png"/>
+</div>
+<div align=center>
+<img src="/img/2022-02-24/10.png"/>
+</div>
+
+We have configured ingress, and the company can easily use the domain name for
access by configuring the pan-domain name within the company;
+<div align=center>
+<img src="/img/2022-02-24/11.png"/>
+</div>
+
+You can log in to the domain name for access：
+
+http:ds.139.abc.com/dolphinscheduler/ui/#/home
+
+
+- The specific configuration can modify the content in the value file:
+```
+ingress:
+enabled: true
+host: "ds139.abc.com"
+path: "/dolphinscheduler"
+tls:
+ enabled: false
+ secretName: "dolphinscheduler-tls"
+
+```
+- It is convenient to view the internal logs of each component of Apache
DolphinScheduler:
+
+<div align=center>
+<img src="/img/2022-02-24/13.png"/>
+</div>
+
+- Check the deployed system, 3 masters, 3 workers, and Zookeeper are all
configured normally;
+
+<div align=center>
+<img src="/img/2022-02-24/14.png"/>
+</div>
+<div align=center>
+<img src="/img/2022-02-24/15.png"/>
+</div>
+
+- Using Argo CD, it is very convenient to modify the number of replicas of
components such as master, worker, api, alert, etc. Apache DolphinScheduler's
helm configuration also reserves the setting information of CPU and memory.
Here we modify the copy value in value. After modification, git push it to the
company's internal GitLab.
+```
+master:
+
+ ## PodManagementPolicy controls how pods are created during initial scale up,
when replacing pods on nodes, or when scaling down.
+podManagementPolicy: "Parallel"
+ ## Replicas is the desired number of replicas of the given Template.
+replicas: "5"
+
+worker:
+ ## PodManagementPolicy controls how pods are created during initial scale up,
when replacing pods on nodes, or when scaling down.
+podManagementPolicy: "Parallel"
+ ## Replicas is the desired number of replicas of the given Template.
+replicas: "5"
+
+
+alert:
+ ## Number of desired pods. This is a pointer to distinguish between explicit
zero and not specified. Defaults to 1.
+replicas: "3"
+
+api:
+ ## Number of desired pods. This is a pointer to distinguish between explicit
zero and not specified. Defaults to 1.
+replicas: "3"
+```
+
+- Just click sync on Argo CD to synchronize, and the corresponding pods will
be added as required
+
+<div align=center>
+<img src="/img/2022-02-24/17.png"/>
+</div>
+<div align=center>
+<img src="/img/2022-02-24/18.png"/>
+</div>
+
+```
+[root@tpk8s-master01 ~]# kubectl get po -n ds139
+NAME READY STATUS RESTARTS AGE
+dolphinscheduler-alert-96c74dc84-72cc9 1/1 Running 0 43m
+dolphinscheduler-alert-96c74dc84-j6zdh 1/1 Running 0 2m27s
+dolphinscheduler-alert-96c74dc84-rn9wb 1/1 Running 0 2m27s
+dolphinscheduler-api-78db664b7b-6j8rj 1/1 Running 0 2m27s
+dolphinscheduler-api-78db664b7b-bsdgv 1/1 Running 0 2m27s
+dolphinscheduler-api-78db664b7b-gsltq 1/1 Running 0 43m
+dolphinscheduler-master-0 1/1 Running 0 43m
+dolphinscheduler-master-1 1/1 Running 0 43m
+dolphinscheduler-master-2 1/1 Running 0 43m
+dolphinscheduler-master-3 1/1 Running 0 2m27s
+dolphinscheduler-master-4 1/1 Running 0 2m27s
+dolphinscheduler-worker-0 1/1 Running 0 43m
+dolphinscheduler-worker-1 1/1 Running 0 43m
+dolphinscheduler-worker-2 1/1 Running 0 43m
+dolphinscheduler-worker-3 1/1 Running 0 2m27s
+dolphinscheduler-worker-4 1/1 Running 0 2m27s
+
+```
+
+## Apache DolphinScheduler integrates with S3 Object Storage Technology
+
+How to configure the integration of s3 minio is one of the FAQs in the
community. Here is the method of helm configuration based on Kubernetes.
+
+- Modify the s3 part of value, it is recommended to use ip+port to point to
the minio server.
+```
+common:
+ ##Configmap
+configmap:
+ DOLPHINSCHEDULER_OPTS: ""
+ DATA_BASEDIR_PATH: "/tmp/dolphinscheduler"
+ RESOURCE_STORAGE_TYPE: "S3"
+ RESOURCE_UPLOAD_PATH: "/dolphinscheduler"
+ FS_DEFAULT_FS: "s3a://dfs"
+ FS_S3A_ENDPOINT: "http://192.168.1.100:9000";
+ FS_S3A_ACCESS_KEY: "admin"
+ FS_S3A_SECRET_KEY: "password"
+```
+
+- The name of the bucket that stores dolphin files in minio is
dolphinscheduler. I create new folders and files for testing here. The
directory of the minio is under the tenant of the upload operation.
+
+<div align=center>
+<img src="/img/2022-02-24/19.png"/>
+</div>
+
+## Apache DolphinScheduler Integrates with Kube-Prometheus Technology
+
+
+
+- We use the Kube-prometheus operator technology in Kubernetes to
automatically monitor the resources of each component of Apache
DolphinScheduler after deploying.
+
+- Please pay attention to that the version of kube-prometheus needs to
correspond to the major version of Kubernetes.
https://github.com/prometheus-operator/kube-prometheus
+
+<div align=center>
+<img src="/img/2022-02-24/20.png"/>
+</div>
+<div align=center>
+<img src="/img/2022-02-24/21.png"/>
+</div>
+<div align=center>
+<img src="/img/2022-02-24/22.png"/>
+</div>
+
+## Technical Integration of Apache DolphinScheduler and Service Mesh
+
+- Through the service mesh technology, the observability analysis of API
external service calls and internal calls of Apache DolphinScheduler can be
realized to optimize the Apache DolphinScheduler product services.
+
+We use linkerd as a service mesh product for integration, which is also one of
CNCF's excellent graduate projects.
+
+<div align=center>
+<img src="/img/2022-02-24/23.png"/>
+</div>
+
+Just modify the annotations in the value file of the Apache DolphinScheduler
helm and redeploy, you can quickly inject the mesh proxy sidecar, as well as
master, worker, API, alert and other components.
+
+```
+annotations: #{}
+ linkerd.io/inject: enabled
+```
+You can observe the quality of service communication between components, the
number of requests per second, etc.
+
+<div align=center>
+<img src="/img/2022-02-24/24.png"/>
+</div>
+<div align=center>
+<img src="/img/2022-02-24/25.png"/>
+</div>
+
+## Prospects on Apache DolphinScheduler Based on cloud-native Technology
+
+As a new-generation Cloud-Native big data tool, Apache DolphinScheduler is
expected to integrate more excellent tools and features in the Kubernetes
ecosystem in the future to meet more requirements of diversified user groups
and scenarios.
+
+- Integration with Argo-workflow, users can call Argo-workflow single job, dag
job, and periodic job in Apache DolphinScheduler through api, cli, etc.;
+- Use HPA to automatically expand and shrink workers to achieve unattended
horizontal expansion;
+- Integrate the Spark operator and Flink operator tools of Kubernetes to
achieve comprehensive cloud-native;
+- Implement multi-cloud and multi-cluster distributed job scheduling, and
strengthen the architectural attributes of serverless+faas classes;
+- Use sidecar to periodically delete worker job logs to realize carefree
operation and maintenance;
+
+Finally, I strongly recommend you to use Slack to communicate with the Apache
DolphinScheduler community, which is officially recommended！
+
+<div align=center>
+<img src="/img/2022-02-24/26.png"/>
+</div>
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diff --git a/img/2022-02-24/8.png b/img/2022-02-24/8.png
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diff --git a/site_config/blog.js b/site_config/blog.js
index 6046cad..2432ccf 100644
--- a/site_config/blog.js
+++ b/site_config/blog.js
@@ -4,7 +4,14 @@ export default {
postsTitle: 'All posts',
list: [
{
- title: '# Sign Up to Apache DolphinScheduler Meetup Online |
We Are Waiting For You to Join the Grand Gathering on 2.26 2022!\n',
+ title: 'Technical Practice of Apache DolphinScheduler in
Kubernetes System',
+ author: 'Debra Chen',
+ dateStr: '2022-2-24',
+ desc: 'Kubernetes is a cluster system based on container
technology, ',
+ link:
'/en-us/blog/DolphinScheduler_Kubernetes_Technology_in_action.html',
+ },
+ {
+ title: 'Sign Up to Apache DolphinScheduler Meetup Online | We
Are Waiting For You to Join the Grand Gathering on 2.26 2022!',
author: 'Debra Chen',
dateStr: '2022-2-18',
desc: 'Apache DolphinScheduler Meetup Online ',
diff --git a/site_config/home.jsx b/site_config/home.jsx
index fb724a8..f6620e8 100644
--- a/site_config/home.jsx
+++ b/site_config/home.jsx
@@ -547,6 +547,14 @@ export default {
title: 'Events & News',
list: [
{
+
+ img: '/img/2022-02-24/1.jpeg',
+ title: 'Technical Practice of Apache DolphinScheduler in Kubernetes
System',
+ content: 'Kubernetes is a cluster system based on container
technology....',
+ dateStr: '2022-2-24',
+ link:
'/en-us/blog/DolphinScheduler_Kubernetes_Technology_in_action.html',
+ },
+ {
img: '/img/2022-02-26/08.png',
title: ' Sign Up to Apache DolphinScheduler Meetup Online | We Are
Waiting For You to Join the Grand Gathering on 2.26 2022!\n',
content: 'Hello the community! After having adjusted ourselves from
the pleasure of...',
@@ -560,14 +568,7 @@ export default {
dateStr: '2022-1-27',
link: '/en-us/blog/Apache_dolphinScheduler_2.0.3.html',
},
- {
- img: '/img/2022-1-13/640.png',
- title: 'Apache DolphinScheduler Board Report: Community Runs Well,
Commit Number Grows over 123%',
- content: 'Since graduating from the Apache Incubator on March 17,
2021....',
- dateStr: '2022-1-13',
- link: '/en-us/blog/Board_of_Directors_Report.html',
- },
],
},
userreview: {
