Dolphin Scheduler Official Website

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    Dolphin Scheduler for Big Data

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    Design features:

    A distributed and easy-to-extend visual DAG workflow scheduling system. Dedicated to solving the complex dependencies in data processing, making the scheduling system out of the box for data processing. 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 task in real time.
    • Support for many task types: Shell, MR, Spark, SQL (mysql, postgresql, hive, sparksql), Python, Sub_Process, Procedure, etc.
    • Support process scheduling, dependency scheduling, manual scheduling, manual pause/stop/recovery, support for failed retry/alarm, recovery from specified nodes, Kill task, etc.
    • Support process priority, task priority and task failover and task timeout alarm/failure
    • Support process global parameters and node custom parameter settings
    • Support online upload/download of resource files, management, etc. Support online file creation and editing
    • Support task log online viewing and scrolling, online download log, etc.
    • Implement cluster HA, decentralize Master cluster and Worker cluster through Zookeeper
    • Support online viewing of Master/Worker cpu load, memory
    • Support process running history tree/gantt chart display, support task status statistics, process status statistics
    • Support backfilling data
    • Support multi-tenant
    • Support internationalization
    • There are more waiting partners to explore

    What's in Dolphin Scheduler

    Stability Easy to use Features Scalability
    Decentralized multi-master and multi-worker Visualization process defines key information such as task status, task type, retry times, task running machine, visual variables and so on at a glance.   Support pause, recover operation support custom task types
    HA is supported by itself All process definition operations are visualized, dragging tasks to draw DAGs, configuring data sources and resources. At the same time, for third-party systems, the api mode operation is provided. 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 uses distributed scheduling, and the overall scheduling capability will increase linearly with the scale of the cluster. Master and Worker support dynamic online and offline.
    Overload processing: Task queue mechanism, the number of schedulable tasks on a single machine can be flexibly configured, when too many tasks will be cached in the task queue, will not cause machine jam. One-click deployment Supports traditional shell tasks, and also support big data platform task scheduling: MR, Spark, SQL (mysql, postgresql, hive, sparksql), Python, Procedure, Sub_Process

    System partial screenshot

    home page dag process definition list page view task log online resource management monitor security treeview

    Online Demo

    Recent R&D plan

    Work plan of Dolphin Scheduler: R&D plan, Under the In Develop card is what is currently being developed, TODO card is to be done (including feature ideas)

    How to contribute

    Welcome to participate in contributing, please refer to the process of submitting the code: [How to contribute]

    How to Build

    ./mvnw clean install -Prelease


    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} Source code package of DolphinScheduler


    Dolphin Scheduler uses a lot of excellent open source projects, such as google guava, guice, grpc, netty, ali bonecp, quartz, and many open source projects of apache, etc. It is because of the shoulders of these open source projects that the birth of the Dolphin Scheduler is possible. We are very grateful for all the open source software used! We also hope that we will not only be the beneficiaries of open source, but also be open source contributors. We also hope that partners who have the same passion and conviction for open source will join in and contribute to open source!

    Get Help

    1. Submit an issue
    2. Subscribe the mail list : then send mail to
    3. Slack channel: Slack Status
    4. Contact WeChat(dailidong66). This is just for Mandarin(CN) discussion.


    Please refer to LICENSE file.





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