| tag | 2d592641a7d86f68bba4da7345e55ac00d04cdb4 | |
|---|---|---|
| tagger | zhuangchong <zhuang.kerwin@gmail.com> | Mon May 22 16:05:29 2023 +0800 |
| object | b570a187f6eb92b20d6daacae01eeca17c774007 |
[maven-release-plugin] copy for tag 3.1.7
| commit | b570a187f6eb92b20d6daacae01eeca17c774007 | [log] [tgz] |
|---|---|---|
| author | zhuangchong <zhuang.kerwin@gmail.com> | Mon May 22 16:05:29 2023 +0800 |
| committer | zhuangchong <zhuang.kerwin@gmail.com> | Mon May 22 16:05:29 2023 +0800 |
| tree | 65c7a7933713d8b098c41cf9963a0030f7a56224 | |
| parent | cdf2b21e7eaac17732057f7b80562c7c6cdba0bd [diff] |
[maven-release-plugin] prepare release 3.1.7
Apache DolphinScheduler is the modern data orchestration platform. Agile to create high performance workflow with low-code. It is also provided powerful user interface, dedicated to solving complex task dependencies in the data pipeline and providing various types of jobs available out of the box
The key features for DolphinScheduler are as follows:
| 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 |
Homepage: Project and workflow overview, including the latest workflow instance and task instance status statistics. 
Workflow Definition: Create and manage workflow by drag and drop, easy to build and maintain complex workflow, support bulk of tasks out of box. 
Workflow Tree View: Abstract tree structure could clearer understanding of the relationship between tasks 
Data source: Manage support multiple external data sources, provide unified data access capabilities for such as MySQL, PostgreSQL, Hive, Trino, etc. 
Monitor: View the status of the master, worker and database in real time, including server resource usage and load, do quick health check without logging in to the server. 
Follow this guide to report your suggestions or bugs.
The community welcomes everyone to contribute, please refer to this page to find out more: How to contribute, find the good first issue in here if you are new to DolphinScheduler.
Welcome to join the Apache DolphinScheduler community by: