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编写于
3月 28, 2019
作者:
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<a
href=
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Easyscheduler简介
</a>
</li>
<li
class=
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data-level=
"1.2"
>
<span>
前端文档
</span>
<ul
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data-path=
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项目生产环境Nginx配置
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data-path=
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>
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Easyscheduler简介
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Easy Scheduler
</h3>
<p><a
href=
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target=
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src=
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></a></p>
<blockquote>
<p>
Easy Scheduler for Big Data
</p>
</blockquote>
<p><strong>
设计特点:
</strong>
一个分布式易扩展的可视化
DAG
工作流任务调度系统。致力于解决数据处理流程中错综复杂的依赖关系,使调度系统在数据处理流程中
<code>
开箱即用
</code>
。
其主要目标如下:
</p>
<ul>
<li>
以
DAG
图的方式将
Task
按照任务的依赖关系关联起来,可实时可视化监控任务的运行状态
</li>
<li>
支持丰富的任务类型:
Shell
、
MR
、
Spark
、
SQL(mysql
、
postgresql
、
hive
、
sparksql),Python,Sub_Process
、
Procedure
等
</li>
<li>
支持工作流定时调度、依赖调度、手动调度、手动暂停
/
停止
/
恢复,同时支持失败重试
/
告警、从指定节点恢复失败、
Kill
任务等操作
</li>
<li>
支持工作流优先级、任务优先级及任务的故障转移及任务超时告警
/
失败
</li>
<li>
支持工作流全局参数及节点自定义参数设置
</li>
<li>
支持资源文件的在线上传
/
下载,管理等,支持在线文件创建、编辑
</li>
<li>
支持任务日志在线查看及滚动、在线下载日志等
</li>
<li>
实现集群
HA
,通过
Zookeeper
实现
Master
集群和
Worker
集群去中心化
</li>
<li>
支持对
<code>
Master/Worker
</code>
cpu load
,
memory
,
cpu
在线查看
</li>
<li>
支持工作流运行历史树形
/
甘特图展示、支持任务状态统计、流程状态统计
</li>
<li>
支持补数
</li>
<li>
支持多租户
</li>
<li>
支持国际化
</li>
<li>
还有更多等待伙伴们探索
</li>
</ul>
<h3
id=
"与同类调度系统的对比"
>
与同类调度系统的对比
</h3>
<style>
table
th
:first-of-type
{
width
:
200px
;
}
</style>
<table>
<thead>
<tr>
<th
style=
"text-align:left"
></th>
<th
style=
"text-align:left"
>
EasyScheduler
</th>
<th
style=
"text-align:left"
>
Azkaban
</th>
<th
style=
"text-align:left"
>
Airflow
</th>
</tr>
</thead>
<tbody>
<tr>
<td
style=
"text-align:left"
><h6
id=
"稳定性"
>
稳定性
</h6></td>
<td
style=
"text-align:left"
></td>
<td
style=
"text-align:left"
></td>
<td
style=
"text-align:left"
></td>
</tr>
<tr>
<td
style=
"text-align:left"
>
单点故障
</td>
<td
style=
"text-align:left"
>
去中心化的多
Master
和多
Worker
</td>
<td
style=
"text-align:left"
>
是
<br>
单个
Web
和调度程序组合
</td>
<td
style=
"text-align:left"
>
是
<br>
单一调度程序
</td>
</tr>
<tr>
<td
style=
"text-align:left"
>
HA
额外要求
</td>
<td
style=
"text-align:left"
>
不需要
(
本身就支持
HA)
</td>
<td
style=
"text-align:left"
>
DB
</td>
<td
style=
"text-align:left"
>
Celery / Dask / Mesos + Load Balancer + DB
</td>
</tr>
<tr>
<td
style=
"text-align:left"
>
过载处理
</td>
<td
style=
"text-align:left"
>
任务队列机制,单个机器上可调度的任务数量可以灵活配置,当任务过多时会缓存在任务队列中,不会造成机器卡死
</td>
<td
style=
"text-align:left"
>
任务太多时会卡死服务器
</td>
<td
style=
"text-align:left"
>
任务太多时会卡死服务器
</td>
</tr>
<tr>
<td
style=
"text-align:left"
><h6
id=
"易用性"
>
易用性
</h6></td>
<td
style=
"text-align:left"
></td>
<td
style=
"text-align:left"
></td>
<td
style=
"text-align:left"
></td>
</tr>
<tr>
<td
style=
"text-align:left"
>
DAG
监控界面
</td>
<td
style=
"text-align:left"
>
任务状态、任务类型、重试次数、任务运行机器、可视化变量等关键信息一目了然
</td>
<td
style=
"text-align:left"
>
只能看到任务状态
</td>
<td
style=
"text-align:left"
>
不能直观区分任务类型
</td>
</tr>
<tr>
<td
style=
"text-align:left"
>
可视化流程定义
</td>
<td
style=
"text-align:left"
>
是
<br>
所有流程定义操作都是可视化的,通过拖拽任务来绘制
DAG,
配置数据源及资源。同时对于第三方系统,提供
api
方式的操作。
</td>
<td
style=
"text-align:left"
>
否
<br>
通过自定义
DSL
绘制
DAG
并打包上传
</td>
<td
style=
"text-align:left"
>
否
<br>
通过
python
代码来绘制
DAG
,使用不便,特别是对不会写代码的业务人员基本无法使用。
</td>
</tr>
<tr>
<td
style=
"text-align:left"
>
快速部署
</td>
<td
style=
"text-align:left"
>
一键部署
</td>
<td
style=
"text-align:left"
>
集群化部署复杂
</td>
<td
style=
"text-align:left"
>
集群化部署复杂
</td>
</tr>
<tr>
<td
style=
"text-align:left"
><h6
id=
"功能"
>
功能
</h6></td>
<td
style=
"text-align:left"
></td>
<td
style=
"text-align:left"
></td>
<td
style=
"text-align:left"
></td>
</tr>
<tr>
<td
style=
"text-align:left"
>
是否能暂停和恢复
</td>
<td
style=
"text-align:left"
>
支持暂停,恢复操作
</td>
<td
style=
"text-align:left"
>
否
<br>
需将工作流杀死再运行
</td>
<td
style=
"text-align:left"
>
否
<br>
需将工作流杀死再运行
</td>
</tr>
<tr>
<td
style=
"text-align:left"
>
是否支持多租户
</td>
<td
style=
"text-align:left"
>
支持
<br>
easyscheduler
上的用户可以通过租户和
hadoop
用户实现多对一或一对一的映射关系,这对大数据作业的调度是非常重要的。
</td>
<td
style=
"text-align:left"
>
否
</td>
<td
style=
"text-align:left"
>
否
</td>
</tr>
<tr>
<td
style=
"text-align:left"
>
任务类型
</td>
<td
style=
"text-align:left"
>
支持传统的
shell
任务,同时支持大数据平台任务调度:
MR
、
Spark
、
SQL(mysql
、
postgresql
、
hive
、
sparksql)
、
Python
、
Procedure
、
Sub_Process
</td>
<td
style=
"text-align:left"
>
shell
、
gobblin
、
hadoopJava
、
java
、
hive
、
pig
、
spark
、
hdfsToTeradata
、
teradataToHdfs
</td>
<td
style=
"text-align:left"
>
BashOperator
、
DummyOperator
、
MySqlOperator
、
HiveOperator
、
EmailOperator
、
HTTPOperator
、
SqlOperator
</td>
</tr>
<tr>
<td
style=
"text-align:left"
>
契合度
</td>
<td
style=
"text-align:left"
>
支持大数据作业
spark,hive,mr
的调度,同时由于支持多租户,与大数据业务更加契合
</td>
<td
style=
"text-align:left"
>
由于不支持多租户,在大数据平台业务使用不够灵活
</td>
<td
style=
"text-align:left"
>
由于不支持多租户,在大数据平台业务使用不够灵活
</td>
</tr>
<tr>
<td
style=
"text-align:left"
><h6
id=
"扩展性"
>
扩展性
</h6></td>
<td
style=
"text-align:left"
></td>
<td
style=
"text-align:left"
></td>
<td
style=
"text-align:left"
></td>
</tr>
<tr>
<td
style=
"text-align:left"
><center>
是否支持自定义任务类型
</center></td>
<td
style=
"text-align:left"
>
是
</td>
<td
style=
"text-align:left"
>
是
</td>
<td
style=
"text-align:left"
>
是
</td>
</tr>
<tr>
<td
style=
"text-align:left"
>
是否支持集群扩展
</td>
<td
style=
"text-align:left"
>
是
<br>
调度器使用分布式调度,整体的调度能力会随便集群的规模线性增长,
Master
和
Worker
支持动态上下线
</td>
<td
style=
"text-align:left"
>
是,但是复杂
<br>
Executor
水平扩展
</td>
<td
style=
"text-align:left"
>
是,但是复杂
<br>
Executor
水平扩展
</td>
</tr>
</tbody>
</table>
<h3
id=
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系统部分截图
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<p><img
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<p><a
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使用手册
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<p>
更多文档请参考
XXX
</p>
<h3
id=
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帮助
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The fastest way to get response from our developers is to submit issues, or add our wechat : 510570367
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