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* rename from DatasourceUserMapper to DataSourceUserMapper * add unit test in UserMapper and WorkerGroupMapper * change cn.escheduler to org.apache.dolphinscheduler * add unit test in UdfFuncMapperTest * add unit test in UdfFuncMapperTest * remove DatabaseConfiguration * add ConnectionFactoryTest * cal duration in processInstancesList * change desc to description * change table name in mysql ddl * change table name in mysql ddl * change escheduler to dolphinscheduler * change escheduler to dolphinscheduler * change escheduler to dolphinscheduler * remove log4j-1.2-api and modify AlertMapperTest * remove log4j-1.2-api * Add alertDao to spring management * Add alertDao to spring management * get SqlSessionFactory from MybatisSqlSessionFactoryBean * get processDao by DaoFactory * read druid properties in ConneciontFactory * read druid properties in ConneciontFactory * change get alertDao by spring to DaoFactory * add log4j to resolve #967 * resole verify udf name error and delete udf error * Determine if principal is empty * Determine whether the logon user has the right to delete the project * Fixed an issue that produced attatch file named such as ATT00002.bin * fix too many connection in upgrade or create * fix NEED_FAULT_TOLERANCE and WAITTING_THREAD count fail * Added a judgment on whether the currently login user is an administrator |
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README_zh_CN.md | ||
README.md |
Easy Scheduler
Easy Scheduler for Big Data
Design features:
A distributed and easy-to-expand 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 Easy 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 easyscheduler 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
Document
More documentation please refer to [EasyScheduler online documentation]
Recent R&D plan
Work plan of Easy Scheduler: R&D plan, where In Develop
card is the features of 1.1.0 version , TODO card is to be done (including feature ideas)
How to contribute code
Welcome to participate in contributing code, please refer to the process of submitting the code: [How to contribute code]
Thanks
Easy 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 Easy 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, so we decided to contribute to easy scheduling and promised long-term updates. 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
- Submit an issue
- Mail list: dev@dolphinscheduler.apache.org. Mail to dev-subscribe@dolphinscheduler.apache.org, follow the reply to subscribe the mail list.
- Contact WeChat group manager, ID 510570367. This is for Mandarin(CN) discussion.
License
Please refer to LICENSE file.