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Dolphin Scheduler Official Website dolphinscheduler.apache.org

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Design Features

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, 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 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...

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

User Interface Screenshots

dag data-source home master workflow-tree

QuickStart in Docker

Please refer the official website document: QuickStart in Docker

QuickStart in Kubernetes

Please refer to the official website document: QuickStart in Kubernetes

How to Build

./mvnw clean install -Prelease


dolphinscheduler-dist/target/apache-dolphinscheduler-${latest.release.version}-bin.tar.gz: Binary package of DolphinScheduler
dolphinscheduler-dist/target/apache-dolphinscheduler-${latest.release.version}-src.tar.gz: Source code package of DolphinScheduler


DolphinScheduler is based on a lot of excellent open-source projects, such as Google guava, grpc, netty, quartz, and many open-source projects of Apache and so on. We would like to express our deep gratitude to all the open-source projects used in Dolphin Scheduler. We hope that we are not only the beneficiaries of open-source, but also give back to the community. Besides, we hope everyone who have the same enthusiasm and passion for open source could join in and contribute to the open-source community!

Get Help

  1. Submit an issue
  2. Join our slack and send your question to channel #troubleshooting


You are very welcome to communicate with the developers and users of Dolphin Scheduler. There are two ways to find them:

  1. Join the Slack channel Slack.
  2. Follow the Twitter account of DolphinScheduler and get the latest news on time.

How to Contribute

The community welcomes everyone to contribute, please refer to this page to find out more: How to contribute.



DolphinScheduler enriches the CNCF CLOUD NATIVE Landscape.


Please refer to the LICENSE file.

Repository Comments ( 234 )

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🔥 Apache DolphinScheduler是一个新一代分布式大数据工作流任务调度系统,致力于“解决大数据任务之间错综复杂的依赖关系,整个数据处理开箱即用”。它以 DAG(有向无环图) 的方式将任务连接起来,可实时监控任务的运行状态,同时支持重试、从指定节点恢复失败、暂停及 Kill 任务等操作。目前已经有像IBM、阿里、腾讯、360 等 1000 多家公司生产上使用,下载安装包请去官网 expand collapse





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