README.md

Easy Scheduler
============
[![License](https://img.shields.io/badge/license-Apache%202-4EB1BA.svg)](https://www.apache.org/licenses/LICENSE-2.0.html)

> 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, cpu
 - Support process running history tree/gantt chart display, support task status statistics, process status statistics
 - Support for complement
 - Support for multi-tenant
 - Support internationalization
 - There are more waiting partners to explore


### Comparison with similar scheduler systems


  | EasyScheduler | Azkaban | Airflow
-- | -- | -- | --
**Stability** |   |   |  
Single point of failure | Decentralized   multi-master and multi-worker | Yes     Single Web and Scheduler Combination Node | Yes.     Single Scheduler
Additional HA requirements | Not   required (HA is supported by itself) | DB | Celery   / Dask / Mesos + Load Balancer + DB
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. | Jammed   the server when there are too many tasks | Jammed   the server when there are too many tasks
**Easy to use** |   |   |  
DAG Monitoring Interface | Visualization   process defines key information such as task status, task type, retry times,   task running machine, visual variables and so on at a glance. | Only   task status can be seen | Can't   visually distinguish task types
Visual process definition | Yes     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. | No     DAG and custom upload via custom DSL | No     DAG is drawn through Python code, which is inconvenient to use, especially   for business people who can't write code.
Quick deployment | One-click   deployment | Complex   clustering deployment | Complex   clustering deployment
**Features** |   |   |  
Suspend and resume | Support   pause, recover operation | No     Can only kill the workflow first and then re-run | No     Can only kill the workflow first and then re-run
Whether to support multiple tenants | 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 | No | No
Task type | Supports   traditional shell tasks, and also support big data platform task scheduling:   MR, Spark, SQL (mysql, postgresql, hive, sparksql), Python, Procedure,   Sub_Process | shell、gobblin、hadoopJava、java、hive、pig、spark、hdfsToTeradata、teradataToHdfs | BashOperator、DummyOperator、MySqlOperator、HiveOperator、EmailOperator、HTTPOperator、SqlOperator
Compatibility | Support   the scheduling of big data jobs like spark, hive, Mr. At the same time, it is   more compatible with big data business because it supports multiple tenants. | Because   it does not support multi-tenant, it is not flexible enough to use business   in big data platform. | Because   it does not support multi-tenant, it is not flexible enough to use business   in big data platform.
**Scalability** |   |   |  
Whether to support custom task types | Yes | Yes | Yes
Is Cluster Extension Supported? | Yes     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. | Yes,   but complicated     Executor horizontal extend | Yes,   but complicated     Executor horizontal extend




### System partial screenshot

![image](https://user-images.githubusercontent.com/48329107/61368744-1f5f3b00-a8c1-11e9-9cf1-10f8557a6b3b.png)

![image](https://user-images.githubusercontent.com/48329107/61368966-9dbbdd00-a8c1-11e9-8dcc-a9469d33583e.png)

![image](https://user-images.githubusercontent.com/48329107/61372146-f347b800-a8c8-11e9-8882-66e8934ada23.png)


### Document

- <a href="https://analysys.github.io/easyscheduler_docs_cn/后端部署文档.html" target="_blank">Backend deployment documentation</a>

- <a href="https://analysys.github.io/easyscheduler_docs_cn/前端部署文档.html" target="_blank">Front-end deployment documentation</a>

- [**User manual**](https://analysys.github.io/easyscheduler_docs_cn/系统使用手册.html?_blank "User manual") 

- [**Upgrade document**](https://analysys.github.io/easyscheduler_docs_cn/升级文档.html?_blank "Upgrade document") 

- <a href="http://52.82.13.76:8888" target="_blank">Online Demo</a> 

More documentation please refer to <a href="https://analysys.github.io/easyscheduler_docs_cn/" 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)

### How to contribute code

Welcome to participate in contributing code, please refer to the process of submitting the code:
https://github.com/analysys/EasyScheduler/blob/master/CONTRIBUTING.md

### 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!

### Help
The fastest way to get response from our developers is to submit issues,  or add our wechat : 510570367