Dolphin Scheduler Official Website dolphinscheduler.apache.org

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:

• 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.
• 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.
• 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.
• Have implemented cluster HA, decentralize Master cluster and Worker cluster through Zookeeper.
• 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 multi-tenant.
• Support internationalization.
• More features waiting for partners to explore...

What's in DolphinScheduler

Stability	Easy to use	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

QuickStart in Docker

Please referer the official website document:[QuickStart in Docker]

How to Build

./mvnw clean install -Prelease

Artifact:

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

Thanks

DolphinScheduler is based on a lot of excellent open-source projects, such as google guava, guice, grpc, netty, ali bonecp, 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. Subscribe to the mail list: https://dolphinscheduler.apache.org/en-us/docs/development/subscribe.html, then email dev@dolphinscheduler.apache.org

How to Contribute

The community welcomes everyone to participate in contributing, please refer to this website to find out more: [How to contribute]

License

Please refer to the LICENSE file.