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`.
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
- 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...
| 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 | |
Please refer the official website document: [QuickStart in Docker](https://dolphinscheduler.apache.org/en-us/docs/latest/user_doc/guide/start/docker.html)
Please refer to the official website document: [QuickStart in Kubernetes](https://dolphinscheduler.apache.org/en-us/docs/latest/user_doc/guide/installation/kubernetes.html)
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!
The community welcomes everyone to contribute, please refer to this page to find out more: [How to contribute](docs/docs/en/contribute/join/contribute.md).
