@ -37,7 +37,7 @@ Its main objectives are as follows:
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. " Supports traditional shell tasks, while supporting large data platform task scheduling: MR, Spark, SQL (mysql, postgresql, hive, sparksql), Python, Procedure, Sub_Process | 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.
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 | |
@ -62,7 +62,7 @@ Overload processing: Task queue mechanism, the number of schedulable tasks on a
@ -28,7 +28,7 @@ A: Support most mailboxes, qq, 163, 126, 139, outlook, aliyun, etc. are supporte
## Q: What are the common system variable time parameters and how do I use them?
A: Please refer to https://analysys.github.io/easyscheduler_docs_cn/%E7%B3%BB%E7%BB%9F%E4%BD%BF%E7%94%A8%E6%89%8B%E5%86%8C.html#%E7%B3%BB%E7%BB%9F%E5%8F%82%E6%95%B0
A: Please refer to 'System parameter' in the system-manual
---
@ -46,7 +46,7 @@ A: Use **the administrator** to create a Worker group, **specify the Worker grou
## Q: Priority of the task
A: We also support t**he priority of processes and tasks**. Priority We have five levels of **HIGHEST, HIGH, MEDIUM, LOW and LOWEST**. **You can set the priority between different process instances, or you can set the priority of different task instances in the same process instance.** For details, please refer to the task priority design https://analysys.github.io/easyscheduler_docs_cn/%E7%B3%BB%E7%BB%9F%E6%9E%B6%E6%9E%84%E8%AE%BE%E8%AE%A1.html#%E7%B3%BB%E7%BB%9F%E6%9E%B6%E6%9E%84%E8%AE%BE%E8%AE%A1
A: We also support **the priority of processes and tasks**. Priority We have five levels of **HIGHEST, HIGH, MEDIUM, LOW and LOWEST**. **You can set the priority between different process instances, or you can set the priority of different task instances in the same process instance.** For details, please refer to the task priority design in the architecture-design.
2，If the Worker service is lost, the Master will monitor that the Worker service is gone. If there is a Yarn task, the Kill Yarn task will be retried.
Please see the fault-tolerant design for details：https://analysys.github.io/easyscheduler_docs_cn/%E7%B3%BB%E7%BB%9F%E6%9E%B6%E6%9E%84%E8%AE%BE%E8%AE%A1.html#%E7%B3%BB%E7%BB%9F%E6%9E%B6%E6%9E%84%E8%AE%BE%E8%AE%A1
Please see the fault-tolerant design in the architecture for details.
---
@ -189,7 +189,7 @@ A: Yes, **if the timing start and end time is the same time, then this timing wi
A： 1, the task dependency between **DAG**, is **from the zero degree** of the DAG segmentation
2, there are **task dependent nodes**, you can achieve cross-process tasks or process dependencies, please refer to the (DEPENDENT) node：https://analysys.github.io/easyscheduler_docs_cn/%E7%B3%BB%E7%BB%9F%E4%BD%BF%E7%94%A8%E6%89%8B%E5%86%8C.html#%E4%BB%BB%E5%8A%A1%E8%8A%82%E7%82%B9%E7%B1%BB%E5%9E%8B%E5%92%8C%E5%8F%82%E6%95%B0%E8%AE%BE%E7%BD%AE
2, there are **task dependent nodes**, you can achieve cross-process tasks or process dependencies, please refer to the (DEPENDENT) node design in the system-manual.
Note: **Cross-project processes or task dependencies are not supported**
@ -248,7 +248,7 @@ If it is a Spark task --queue mode specifies the queue
## Q : Master or Worker reports the following alarm
@ -38,7 +38,7 @@ Its main objectives are as follows:
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. " Supports traditional shell tasks, while supporting large data platform task scheduling: MR, Spark, SQL (mysql, postgresql, hive, sparksql), Python, Procedure, Sub_Process | 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.
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 | |
@ -55,17 +55,17 @@ Overload processing: Task queue mechanism, the number of schedulable tasks on a
More documentation please refer to <ahref="https://analysys.github.io/easyscheduler_docs_cn/"target="_blank">[EasyScheduler online documentation]</a>
More documentation please refer to <ahref="https://analysys.github.io/easyscheduler_docs/"target="_blank">[EasyScheduler online documentation]</a>
### Recent R&D plan
Work plan of Easy Scheduler: [R&D plan](https://github.com/analysys/EasyScheduler/projects/1), where `In Develop` card is the features of 1.1.0 version , TODO card is to be done (including feature ideas)
Download the latest version of the installation package, download address： [gitee download](https://gitee.com/easyscheduler/EasyScheduler/attach_files/) ， download escheduler-backend-x.x.x.tar.gz(back-end referred to as escheduler-backend),escheduler-ui-x.x.x.tar.gz(front-end referred to as escheduler-ui)
Download the latest version of the installation package, download address： [gitee download](https://gitee.com/easyscheduler/EasyScheduler/attach_files/) or [github download](https://github.com/analysys/EasyScheduler/releases), download escheduler-backend-x.x.x.tar.gz(back-end referred to as escheduler-backend),escheduler-ui-x.x.x.tar.gz(front-end referred to as escheduler-ui)
@ -27,9 +27,9 @@ Download the latest version of the installation package, download address： [gi
#### Preparations 2: Create deployment users
- Deployment users are created on all machines that require deployment scheduling, because the worker service executes jobs in sudo-u {linux-user}, so deployment users need sudo privileges and are confidential.
- Deployment users are created on all machines that require deployment scheduling, because the worker service executes jobs in `sudo-u {linux-user}`, so deployment users need sudo privileges and are confidential.
```Deployment account
```
vi /etc/sudoers
# For example, the deployment user is an escheduler account
@ -50,7 +50,7 @@ Configure SSH secret-free login on deployment machines and other installation ma
Execute the following command to create database and account
```sql
```
CREATE DATABASE escheduler DEFAULT CHARACTER SET utf8 DEFAULT COLLATE utf8_general_ci;
GRANT ALL PRIVILEGES ON escheduler.* TO '{user}'@'%' IDENTIFIED BY '{password}';
GRANT ALL PRIVILEGES ON escheduler.* TO '{user}'@'localhost' IDENTIFIED BY '{password}';
@ -65,7 +65,9 @@ Configure SSH secret-free login on deployment machines and other installation ma
spring.datasource.username
spring.datasource.password
```
Execute scripts for creating tables and importing basic data
- For other types of nodes, refer to [task node types and parameter settings](#task node types and parameter settings)
@ -66,13 +61,15 @@
* Notification group: When the process ends or fault tolerance occurs, process information is sent to all members of the notification group by mail.
* Recipient: Enter the mailbox and press Enter key to save. When the process ends and fault tolerance occurs, an alert message is sent to the recipient list.
* Cc: Enter the mailbox and press Enter key to save. When the process is over and fault-tolerant occurs, alarm messages are copied to the copier list.
* Complement: To implement the workflow definition of a specified date, you can select the time range of the complement (currently only support for continuous days), such as the data from May 1 to May 10, as shown in the figure:
> Complement execution mode includes serial execution and parallel execution. In serial mode, the complement will be executed sequentially from May 1 to May 10. In parallel mode, the tasks from May 1 to May 10 will be executed simultaneously.
@ -80,8 +77,9 @@
### Timing Process Definition
- Create Timing: "Process Definition - > Timing"
- Choose start-stop time, in the start-stop time range, regular normal work, beyond the scope, will not continue to produce timed workflow instances.
- The security has the functions of queue management, tenant management, user management, warning group management, worker group manager, token manage and other functions. It can also authorize resources, data sources, projects, etc.
- The shell node, when the worker executes, generates a temporary shell script, which is executed by a Linux user with the same name as the tenant.
> Drag the  task node in the toolbar onto the palette and double-click the task node as follows:
> Drag the  task node in the toolbar onto the palette and double-click the task node as follows:
- Dependent nodes are **dependent checking nodes**. For example, process A depends on the successful execution of process B yesterday, and the dependent node checks whether process B has a successful execution instance yesterday.
> Drag the  ask node in the toolbar onto the palette and double-click the task node as follows:
> Drag the  ask node in the toolbar onto the palette and double-click the task node as follows:
> Dependent nodes provide logical judgment functions, such as checking whether yesterday's B process was successful or whether the C process was successfully executed.
> For example, process A is a weekly task and process B and C are daily tasks. Task A requires that task B and C be successfully executed every day of the week, as shown in the figure:
> For example, process A is a weekly task and process B and C are daily tasks. Task A requires that task B and C be successfully executed every day of the last week, as shown in the figure:
- The procedure is executed according to the selected data source.
> Drag the  task node in the toolbar onto the palette and double-click the task node as follows:
> Drag the  task node in the toolbar onto the palette and double-click the task node as follows:
- Executing the query SQL function, you can choose to send mail in the form of tables and attachments to the designated recipients.
> Drag the  task node in the toolbar onto the palette and double-click the task node as follows:
> Drag the  task node in the toolbar onto the palette and double-click the task node as follows:
- Through SPARK node, SPARK program can be directly executed. For spark node, worker will use `spark-submit` mode to submit tasks.
> Drag the  task node in the toolbar onto the palette and double-click the task node as follows:
> Drag the  task node in the toolbar onto the palette and double-click the task node as follows:
>
>
@ -595,7 +588,7 @@ Note: JAVA and Scala are just used for identification, no difference. If it's a
- Using MR nodes, MR programs can be executed directly. For Mr nodes, worker submits tasks using `hadoop jar`
> Drag the  task node in the toolbar onto the palette and double-click the task node as follows:
> Drag the  task node in the toolbar onto the palette and double-click the task node as follows:
1. JAVA program
@ -631,7 +624,7 @@ Note: JAVA and Scala are just used for identification, no difference. If it's a
> Drag the  task node in the toolbar onto the palette and double-click the task node as follows:
> Drag the  task node in the toolbar onto the palette and double-click the task node as follows:
@ -690,9 +683,8 @@ Note: JAVA and Scala are just used for identification, no difference. If it's a
> User-defined parameters are divided into global parameters and local parameters. Global parameters are the global parameters passed when the process definition and process instance are saved. Global parameters can be referenced by local parameters of any task node in the whole process.
bao liang
5 years ago
committed by
dailidong
