apache · caishunfeng · Sep 22, 2022 · Sep 17, 2022 · Sep 22, 2022 · Sep 22, 2022
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
@@ -1,15 +1,15 @@
 <!--Thanks very much for contributing to Apache DolphinScheduler. Please review https://dolphinscheduler.apache.org/en-us/community/development/pull-request.html before opening a pull request.-->
 
-
 ## Purpose of the pull request
 
 <!--(For example: This pull request adds checkstyle plugin).-->
 
 ## Brief change log
 
 <!--*(for example:)*
-  - *Add maven-checkstyle-plugin to root pom.xml*
+- *Add maven-checkstyle-plugin to root pom.xml*
 -->
+
 ## Verify this pull request
 
 <!--*(Please pick either of the following options)*-->
@@ -25,9 +25,9 @@ This pull request is already covered by existing tests, such as *(please describ
 This change added tests and can be verified as follows:
 
 <!--*(example:)*
-  - *Added dolphinscheduler-dao tests for end-to-end.*
-  - *Added CronUtilsTest to verify the change.*
-  - *Manually verified the change by testing locally.* -->
+- *Added dolphinscheduler-dao tests for end-to-end.*
+- *Added CronUtilsTest to verify the change.*
+- *Manually verified the change by testing locally.* -->
 
 (or)
 

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -6,11 +6,11 @@ Start by forking the dolphinscheduler GitHub repository, make changes in a branc
 
 There are three branches in the remote repository currently:
 
-  - `master` : normal delivery branch. After the stable version is released, the code for the stable version branch is merged into the master branch.
-            
-  - `dev` : daily development branch. The daily development branch, the newly submitted code can pull requests to this branch.
-  
-  - `x.x.x-release` : the stable release version.
+- `master` : normal delivery branch. After the stable version is released, the code for the stable version branch is merged into the master branch.
+
+- `dev` : daily development branch. The daily development branch, the newly submitted code can pull requests to this branch.
+
+- `x.x.x-release` : the stable release version.
 
 So, you should fork the `dev` branch.
 
@@ -40,7 +40,6 @@ There will be two repositories at this time: origin (your own warehouse) and ups
 
 Get/update remote repository code (already the latest code, skip it).
 
-
 ```sh
 git fetch upstream
 ```
@@ -91,7 +90,6 @@ After submitting changes to your remote repository, you should click on the new
 <img src = "http://geek.analysys.cn/static/upload/221/2019-04-02/90f3abbf-70ef-4334-b8d6-9014c9cf4c7f.png" width ="60%"/>
 </p>
 
-
 Select the modified local branch and the branch to merge past to create a pull request.
 
 <p align = "center">

diff --git a/README.md b/README.md
@@ -1,16 +1,13 @@
 Dolphin Scheduler Official Website
 [dolphinscheduler.apache.org](https://dolphinscheduler.apache.org)
-============
+==================================================================
 
 [![License](https://img.shields.io/badge/license-Apache%202-4EB1BA.svg)](https://www.apache.org/licenses/LICENSE-2.0.html)
 [![codecov](https://codecov.io/gh/apache/dolphinscheduler/branch/dev/graph/badge.svg)](https://codecov.io/gh/apache/dolphinscheduler/branch/dev)
 [![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=apache-dolphinscheduler&metric=alert_status)](https://sonarcloud.io/dashboard?id=apache-dolphinscheduler)
 [![Twitter Follow](https://img.shields.io/twitter/follow/dolphinschedule.svg?style=social&label=Follow)](https://twitter.com/dolphinschedule)
 [![Slack Status](https://img.shields.io/badge/slack-join_chat-white.svg?logo=slack&style=social)](https://s.apache.org/dolphinscheduler-slack)
 
-
-
-
 [![Stargazers over time](https://starchart.cc/apache/dolphinscheduler.svg)](https://starchart.cc/apache/dolphinscheduler)
 
 [![EN doc](https://img.shields.io/badge/document-English-blue.svg)](README.md)
@@ -21,35 +18,35 @@ Dolphin Scheduler Official Website
 DolphinScheduler is a distributed and extensible workflow scheduler platform with powerful DAG visual interfaces, dedicated to solving complex job dependencies in the data pipeline and providing various types of jobs available `out of the box`.
 
 Its main objectives are as follows:
- -  Highly Reliable,
+-  Highly Reliable,
 DolphinScheduler adopts a decentralized multi-master and multi-worker architecture design, which naturally supports easy expansion and high availability (not restricted by a single point of bottleneck), and its performance increases linearly with the increase of machines
- - High performance, supporting tens of millions of tasks every day
- - Support multi-tenant.
- - Cloud Native, DolphinScheduler supports multi-cloud/data center workflow management, also
+- High performance, supporting tens of millions of tasks every day
+- Support multi-tenant.
+- Cloud Native, DolphinScheduler supports multi-cloud/data center workflow management, also
 supports Kubernetes, Docker deployment and custom task types, distributed
 scheduling, with overall scheduling capability increased linearly with the
 scale of the cluster
- - Support various task types: Shell, MR, Spark, SQL (MySQL, PostgreSQL, hive, spark SQL), Python, Sub_Process, Procedure, etc.
- - Support scheduling of workflows and dependencies, manual scheduling to pause/stop/recover task, support failure task retry/alarm, recover specified nodes from failure, kill task, etc.
- - Associate the tasks according to the dependencies of the tasks in a DAG graph, which can visualize the running state of the task in real-time.
- - WYSIWYG online editing tasks
- - Support the priority of workflows & tasks, task failover, and task timeout alarm or failure.
- - Support workflow global parameters and node customized parameter settings.
- - Support online upload/download/management of resource files, etc. Support online file creation and editing.
- - Support task log online viewing and scrolling and downloading, etc.
- - Support the viewing of Master/Worker CPU load, memory, and CPU usage metrics.
- - Support displaying workflow history in tree/Gantt chart, as well as statistical analysis on the task status & process status in each workflow.
- - Support back-filling data.
- - Support internationalization.
- - More features waiting for partners to explore...
+- Support various task types: Shell, MR, Spark, SQL (MySQL, PostgreSQL, hive, spark SQL), Python, Sub_Process, Procedure, etc.
+- Support scheduling of workflows and dependencies, manual scheduling to pause/stop/recover task, support failure task retry/alarm, recover specified nodes from failure, kill task, etc.
+- Associate the tasks according to the dependencies of the tasks in a DAG graph, which can visualize the running state of the task in real-time.
+- WYSIWYG online editing tasks
+- Support the priority of workflows & tasks, task failover, and task timeout alarm or failure.
+- Support workflow global parameters and node customized parameter settings.
+- Support online upload/download/management of resource files, etc. Support online file creation and editing.
+- Support task log online viewing and scrolling and downloading, etc.
+- Support the viewing of Master/Worker CPU load, memory, and CPU usage metrics.
+- Support displaying workflow history in tree/Gantt chart, as well as statistical analysis on the task status & process status in each workflow.
+- Support back-filling data.
+- Support internationalization.
+- More features waiting for partners to explore...
 
 ## What's in DolphinScheduler
 
- Stability | Accessibility | Features | Scalability |
- --------- | ------------- | -------- | ------------|
-Decentralized multi-master and multi-worker | Visualization of workflow key information, such as task status, task type, retry times, task operation machine information, visual variables, and so on at a glance.  |  Support pause, recover operation | Support customized task types
-support HA | Visualization of all workflow operations, dragging tasks to draw DAGs, configuring data sources and resources. At the same time, for third-party systems, provide API mode operations. | Users on DolphinScheduler can achieve many-to-one or one-to-one mapping relationship through tenants and Hadoop users, which is very important for scheduling large data jobs.  | The scheduler supports distributed scheduling, and the overall scheduling capability will increase linearly with the scale of the cluster. Master and Worker support dynamic adjustment.
-Overload processing: By using the task queue mechanism, the number of schedulable tasks on a single machine can be flexibly configured. Machine jam can be avoided with high tolerance to numbers of tasks cached in task queue. | One-click deployment | Support traditional shell tasks, and big data platform task scheduling: MR, Spark, SQL (MySQL, PostgreSQL, hive, spark SQL), Python, Procedure, Sub_Process |  |
+|                                                                                                            Stability                                                                                                             |                                                                                     Accessibility                                                                                      |                                                                                    Features                                                                                    |                                                                                       Scalability                                                                                        |
+|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Decentralized multi-master and multi-worker                                                                                                                                                                                      | Visualization of workflow key information, such as task status, task type, retry times, task operation machine information, visual variables, and so on at a glance.                   |  Support pause, recover operation                                                                                                                                              | Support customized task types                                                                                                                                                            |
+| support HA                                                                                                                                                                                                                       | Visualization of all workflow operations, dragging tasks to draw DAGs, configuring data sources and resources. At the same time, for third-party systems, provide API mode operations. | Users on DolphinScheduler can achieve many-to-one or one-to-one mapping relationship through tenants and Hadoop users, which is very important for scheduling large data jobs. | The scheduler supports distributed scheduling, and the overall scheduling capability will increase linearly with the scale of the cluster. Master and Worker support dynamic adjustment. |
+| Overload processing: By using the task queue mechanism, the number of schedulable tasks on a single machine can be flexibly configured. Machine jam can be avoided with high tolerance to numbers of tasks cached in task queue. | One-click deployment                                                                                                                                                                   | Support traditional shell tasks, and big data platform task scheduling: MR, Spark, SQL (MySQL, PostgreSQL, hive, spark SQL), Python, Procedure, Sub_Process                    |                                                                                                                                                                                          |
 
 ## User Interface Screenshots
 

diff --git a/README_zh_CN.md b/README_zh_CN.md
@@ -1,12 +1,11 @@
 Dolphin Scheduler Official Website
 [dolphinscheduler.apache.org](https://dolphinscheduler.apache.org)
-============
+==================================================================
 
 [![License](https://img.shields.io/badge/license-Apache%202-4EB1BA.svg)](https://www.apache.org/licenses/LICENSE-2.0.html)
 [![codecov](https://codecov.io/gh/apache/dolphinscheduler/branch/dev/graph/badge.svg)](https://codecov.io/gh/apache/dolphinscheduler/branch/dev)
 [![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=apache-dolphinscheduler&metric=alert_status)](https://sonarcloud.io/dashboard?id=apache-dolphinscheduler)
 
-
 [![Stargazers over time](https://starchart.cc/apache/dolphinscheduler.svg)](https://starchart.cc/apache/dolphinscheduler)
 
 [![CN doc](https://img.shields.io/badge/文档-中文版-blue.svg)](README_zh_CN.md)
@@ -18,20 +17,20 @@ Dolphin Scheduler Official Website
 
 其主要目标如下：
 
- - 以DAG图的方式将Task按照任务的依赖关系关联起来，可实时可视化监控任务的运行状态
- - 支持丰富的任务类型：Shell、MR、Spark、SQL(mysql、postgresql、hive、sparksql)、Python、Sub_Process、Procedure等
- - 支持工作流定时调度、依赖调度、手动调度、手动暂停/停止/恢复，同时支持失败重试/告警、从指定节点恢复失败、Kill任务等操作
- - 支持工作流优先级、任务优先级及任务的故障转移及任务超时告警/失败
- - 支持工作流全局参数及节点自定义参数设置
- - 支持资源文件的在线上传/下载，管理等，支持在线文件创建、编辑
- - 支持任务日志在线查看及滚动、在线下载日志等
- - 实现集群HA，通过Zookeeper实现Master集群和Worker集群去中心化
- - 支持对`Master/Worker` cpu load，memory，cpu在线查看
- - 支持工作流运行历史树形/甘特图展示、支持任务状态统计、流程状态统计
- - 支持补数
- - 支持多租户
- - 支持国际化
- - 还有更多等待伙伴们探索
+- 以DAG图的方式将Task按照任务的依赖关系关联起来，可实时可视化监控任务的运行状态
+- 支持丰富的任务类型：Shell、MR、Spark、SQL(mysql、postgresql、hive、sparksql)、Python、Sub_Process、Procedure等
+- 支持工作流定时调度、依赖调度、手动调度、手动暂停/停止/恢复，同时支持失败重试/告警、从指定节点恢复失败、Kill任务等操作
+- 支持工作流优先级、任务优先级及任务的故障转移及任务超时告警/失败
+- 支持工作流全局参数及节点自定义参数设置
+- 支持资源文件的在线上传/下载，管理等，支持在线文件创建、编辑
+- 支持任务日志在线查看及滚动、在线下载日志等
+- 实现集群HA，通过Zookeeper实现Master集群和Worker集群去中心化
+- 支持对`Master/Worker` cpu load，memory，cpu在线查看
+- 支持工作流运行历史树形/甘特图展示、支持任务状态统计、流程状态统计
+- 支持补数
+- 支持多租户
+- 支持国际化
+- 还有更多等待伙伴们探索
 
 ## 系统部分截图
 

diff --git a/deploy/README.md b/deploy/README.md
@@ -2,3 +2,4 @@
 
 * [Start Up DolphinScheduler with Docker](https://dolphinscheduler.apache.org/en-us/docs/latest/user_doc/guide/start/docker.html)
 * [Start Up DolphinScheduler with Kubernetes](https://dolphinscheduler.apache.org/en-us/docs/latest/user_doc/guide/installation/kubernetes.html)
+
diff --git a/docs/docs/en/DSIP.md b/docs/docs/en/DSIP.md
@@ -55,11 +55,11 @@ Here is the template for mail
 
   ```text
   Hi community,
-  
+
   <CHANGE-TO-YOUR-PROPOSAL-DETAIL>
-  
+
   I already add a GitHub Issue for my proposal, which you could see in <CHANGE-TO-YOUR-GITHUB-ISSUE-LINK>.
-  
+
   Looking forward any feedback for this thread.
   ```
 
@@ -89,3 +89,4 @@ closed and transfer from [current DSIPs][current-DSIPs] to [past DSIPs][past-DSI
 [github-issue-choose]: https://github.com/apache/dolphinscheduler/issues/new/choose
 [mail-to-dev]: mailto:[email protected]
 [DSIP-1]: https://github.com/apache/dolphinscheduler/issues/6407
+
diff --git a/docs/docs/en/about/features.md b/docs/docs/en/about/features.md
@@ -16,4 +16,5 @@
 
 ## High Scalability
 
-- **Scalability**: Supports multitenancy and online resource management. Stable operation of 100,000 data tasks per day is supported.
+- **Scalability**: Supports multitenancy and online resource management. Stable operation of 100,000 data tasks per day is supported.
+
diff --git a/docs/docs/en/about/glossary.md b/docs/docs/en/about/glossary.md
@@ -71,4 +71,3 @@ process fails and ends
 From the perspective of scheduling, this article preliminarily introduces the architecture principles and implementation
 ideas of the big data distributed workflow scheduling system-DolphinScheduler. To be continued
 
-