@ -17,32 +17,33 @@ Dolphin Scheduler Official Website
### 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.
Dolphin Scheduler is a distributed and easy-to-extend visual DAG workflow scheduling system. It dedicates to solving the complex dependencies in data processing to make the scheduling system `out of the box` for the data processing process.
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.
- 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 many task types: Shell, MR, Spark, SQL (MySQL, PostgreSQL, hive, spark SQL), 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 the priority of process & task, task failover, and task timeout alarm or 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
- Implement cluster HA, decentralize Master cluster and Worker cluster through Zookeeper.
- Support the viewing of Master/Worker CPU load, memory, and CPU usage metrics.
- Support presenting tree or Gantt chart of workflow history as well as the statistics results of task & process status in each workflow.
- Support backfilling data.
- Support multi-tenant.
- Support internationalization.
- There are more waiting for 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 | |
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 Dolphin Scheduler 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: 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 | |
### System partial screenshot
@ -55,17 +56,14 @@ Overload processing: Task queue mechanism, the number of schedulable tasks on a
Work plan of Dolphin Scheduler: [R&D plan](https://github.com/apache/incubator-dolphinscheduler/projects/1), Under the `In Develop` card is what is currently being developed, TODO card is to be done (including feature ideas)
The work plan of Dolphin Scheduler: [R&D plan](https://github.com/apache/incubator-dolphinscheduler/projects/1), which `In Develop` card shows the features that are currently being developed and TODO card lists what needs 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](https://dolphinscheduler.apache.org/en-us/docs/development/contribute.html)]
Welcome to participate in contributing, please refer to this website to find out more: [[How to contribute](https://dolphinscheduler.apache.org/en-us/docs/development/contribute.html)]
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!
Dolphin Scheduler 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 which contribute to making the dream of Dolphin Scheduler comes true. We hope that we are not only the beneficiaries of open-source, but also give back to the community. Besides, we expect the partners who have the same passion and conviction to open-source will join in and contribute to the open-source community!
### Get Help
1. Submit an issue
1. Subscribe the mail list : https://dolphinscheduler.apache.org/en-us/docs/development/subscribe.html. then send mail to dev@dolphinscheduler.apache.org
QUERY_AUTHORIZED_AND_USER_CREATED_PROJECT_ERROR(10162,"query authorized and user created project error error","查询授权的和用户创建的项目错误"),
DELETE_PROCESS_DEFINITION_BY_ID_FAIL(10163,"delete process definition by id fail, for there are {0} process instances in executing using it","删除工作流定义失败，有[{0}]个运行中的工作流实例正在使用"),
CHECK_OS_TENANT_CODE_ERROR(10164,"Please enter the English os tenant code","请输入英文操作系统租户"),
TASK_INSTANCE_STATE_OPERATION_ERROR(10166,"the status of task instance {0} is {1},Cannot perform force success operation","任务实例[{0}]的状态是[{1}]，无法执行强制成功操作"),
UDF_FUNCTION_NOT_EXIST(20001,"UDF function not found","UDF函数不存在"),
UDF_FUNCTION_EXISTS(20002,"UDF function already exists","UDF函数已存在"),
@ -247,7 +250,7 @@ public enum Status {
BATCH_DELETE_PROCESS_DEFINE_BY_IDS_ERROR(50026,"batch delete process definition by ids {0} error","批量删除工作流定义[{0}]错误"),
TENANT_NOT_SUITABLE(50027,"there is not any tenant suitable, please choose a tenant available.","没有合适的租户，请选择可用的租户"),
EXPORT_PROCESS_DEFINE_BY_ID_ERROR(50028,"export process definition by id error","导出工作流定义错误"),
BATCH_EXPORT_PROCESS_DEFINE_BY_IDS_ERROR(50028,"batch export process definition by ids error","批量导出工作流定义错误"),
BATCH_EXPORT_PROCESS_DEFINE_BY_IDS_ERROR(50028,"batch export process definition by ids error","批量导出工作流定义错误"),
IMPORT_PROCESS_DEFINE_ERROR(50029,"import process definition error","导入工作流定义错误"),
HDFS_NOT_STARTUP(60001,"hdfs not startup","hdfs未启用"),
break60
4 years ago