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Posted to notifications@dolphinscheduler.apache.org by li...@apache.org on 2019/11/21 05:37:41 UTC
[incubator-dolphinscheduler-website] branch master updated: Replace
quick start and system manual page pictures
This is an automated email from the ASF dual-hosted git repository.
lidongdai pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-dolphinscheduler-website.git
View the commit online:
https://github.com/apache/incubator-dolphinscheduler-website/commit/e9aef164fe823bea8c1f2cc7efab3d815eb82117
The following commit(s) were added to refs/heads/master by this push:
new e9aef16 Replace quick start and system manual page pictures
new fc66f1a Merge pull request #32 from break60/master
e9aef16 is described below
commit e9aef164fe823bea8c1f2cc7efab3d815eb82117
Author: break60 <79...@qq.com>
AuthorDate: Thu Nov 21 13:21:07 2019 +0800
Replace quick start and system manual page pictures
---
docs/zh-cn/user_doc/quick-start.md | 16 ++---
docs/zh-cn/user_doc/system-manual.md | 112 +++++++++++++++++------------------
img/addtenant.png | Bin 0 -> 15441 bytes
img/auth_project.png | Bin 0 -> 17445 bytes
img/auth_user.png | Bin 0 -> 10254 bytes
img/complement.png | Bin 0 -> 58442 bytes
img/create-queue.png | Bin 0 -> 254254 bytes
img/dag1.png | Bin 0 -> 500407 bytes
img/dag2.png | Bin 0 -> 343883 bytes
img/dag3.png | Bin 0 -> 329032 bytes
img/dag4.png | Bin 0 -> 24671 bytes
img/depend-node.png | Bin 0 -> 435609 bytes
img/depend-node2.png | Bin 0 -> 412944 bytes
img/depend-node3.png | Bin 0 -> 473875 bytes
img/dependent_edit.png | Bin 0 -> 65166 bytes
img/file-manage.png | Bin 0 -> 234978 bytes
img/file_create.png | Bin 0 -> 48006 bytes
img/file_detail.png | Bin 0 -> 169913 bytes
img/file_rename.png | Bin 0 -> 9581 bytes
img/file_upload.png | Bin 0 -> 14500 bytes
img/gant-pic.png | Bin 0 -> 238924 bytes
img/global_parameter.png | Bin 0 -> 116141 bytes
img/hive_edit.png | Bin 0 -> 46641 bytes
img/hive_edit2.png | Bin 0 -> 48423 bytes
img/hive_kerberos.png | Bin 0 -> 37052 bytes
img/instance-detail.png | Bin 0 -> 333599 bytes
img/instance-list.png | Bin 0 -> 491317 bytes
img/local_parameter.png | Bin 0 -> 25661 bytes
img/login.jpg | Bin 0 -> 49053 bytes
img/mail_edit.png | Bin 0 -> 14438 bytes
img/master-jk.png | Bin 0 -> 325435 bytes
img/mr_edit.png | Bin 0 -> 136183 bytes
img/mysql-jk.png | Bin 0 -> 208525 bytes
img/mysql_edit.png | Bin 0 -> 48390 bytes
img/postgresql_edit.png | Bin 0 -> 32368 bytes
img/procedure_edit.png | Bin 0 -> 89355 bytes
img/project.png | Bin 0 -> 190310 bytes
img/python_edit.png | Bin 0 -> 467741 bytes
img/run-work.png | Bin 0 -> 43151 bytes
img/shell_edit.png | Bin 0 -> 157618 bytes
img/spark_datesource.png | Bin 0 -> 29955 bytes
img/spark_edit.png | Bin 0 -> 123946 bytes
img/sparksql_kerberos.png | Bin 0 -> 37390 bytes
img/sql-node.png | Bin 0 -> 477610 bytes
img/sql-node2.png | Bin 0 -> 505130 bytes
img/subprocess_edit.png | Bin 0 -> 76964 bytes
img/task-list.png | Bin 0 -> 426472 bytes
img/task-log.png | Bin 0 -> 302312 bytes
img/task-log2.png | Bin 0 -> 334315 bytes
img/task_history.png | Bin 0 -> 109027 bytes
img/time-schedule.png | Bin 0 -> 55284 bytes
img/time-schedule2.png | Bin 0 -> 44594 bytes
img/udf_edit.png | Bin 0 -> 26472 bytes
img/useredit2.png | Bin 0 -> 17285 bytes
img/worker-jk.png | Bin 0 -> 309276 bytes
img/worker1.png | Bin 0 -> 253566 bytes
img/zk-jk.png | Bin 0 -> 194588 bytes
57 files changed, 64 insertions(+), 64 deletions(-)
diff --git a/docs/zh-cn/user_doc/quick-start.md b/docs/zh-cn/user_doc/quick-start.md
index 7aa4f65..8e02bcd 100755
--- a/docs/zh-cn/user_doc/quick-start.md
+++ b/docs/zh-cn/user_doc/quick-start.md
@@ -4,29 +4,29 @@
>地址：192.168.xx.xx:8888 用户名密码：admin/dolphinscheduler123
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/login.jpg" width="60%" />
+ <img src="/img/login.jpg" width="60%" />
</p>
* 创建队列
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/create-queue.png" width="60%" />
+ <img src="/img/create-queue.png" width="60%" />
</p>
* 创建租户
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/addtenant.png" width="60%" />
+ <img src="/img/addtenant.png" width="60%" />
</p>
* 创建普通用户
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/useredit2.png" width="60%" />
+ <img src="/img/useredit2.png" width="60%" />
</p>
* 创建告警组
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/mail_edit.png" width="60%" />
+ <img src="/img/mail_edit.png" width="60%" />
</p>
* 使用普通用户登录
@@ -34,17 +34,17 @@
* 项目管理->创建项目->点击项目名称
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/project.png" width="60%" />
+ <img src="/img/project.png" width="60%" />
</p>
* 点击工作流定义->创建工作流定义->上线工作流定义
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/dag1.png" width="60%" />
+ <img src="/img/dag1.png" width="60%" />
</p>
* 运行工作流定义->点击工作流实例->点击工作流实例名称->双击任务节点->查看任务执行日志
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/task-log.png" width="60%" />
+ <img src="/img/task-log.png" width="60%" />
</p>
\ No newline at end of file
diff --git a/docs/zh-cn/user_doc/system-manual.md b/docs/zh-cn/user_doc/system-manual.md
index 7f03ac2..e5c4c0f 100755
--- a/docs/zh-cn/user_doc/system-manual.md
+++ b/docs/zh-cn/user_doc/system-manual.md
@@ -12,7 +12,7 @@
- 点击“项目管理->创建项目”，输入项目名称，项目描述，点击“提交”，创建新的项目。
- 点击项目名称，进入项目首页。
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/project.png" width="60%" />
+ <img src="/img/project.png" width="60%" />
</p>
> 项目首页其中包含任务状态统计，流程状态统计、工作流定义统计
@@ -31,24 +31,24 @@
- 超时告警， 填写”超时时长“，当任务执行时间超过**超时时长**可以告警并且超时失败。
- 填写"自定义参数",参考[自定义参数](#用户自定义参数)
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/dag1.png" width="60%" />
+ <img src="/img/dag1.png" width="60%" />
</p>
- 增加节点之间执行的先后顺序： 点击”线条连接“；如图示，任务1和任务3并行执行，当任务1执行完，任务2、3会同时执行。
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/dag2.png" width="60%" />
+ <img src="/img/dag2.png" width="60%" />
</p>
- 删除依赖关系： 点击箭头图标”拖动节点和选中项“，选中连接线，点击删除图标，删除节点间依赖关系。
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/dag3.png" width="60%" />
+ <img src="/img/dag3.png" width="60%" />
</p>
- 点击”保存“，输入工作流定义名称，工作流定义描述，设置全局参数,参考[自定义参数](#用户自定义参数)。
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/dag4.png" width="60%" />
+ <img src="/img/dag4.png" width="60%" />
</p>
- 其他类型节点，请参考 [任务节点类型和参数设置](#任务节点类型和参数设置)
@@ -68,12 +68,12 @@
* 收件人：输入邮箱后按回车键保存。当流程结束、发生容错时，会发送告警邮件到收件人列表。
* 抄送人：输入邮箱后按回车键保存。当流程结束、发生容错时，会抄送告警邮件到抄送人列表。
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/run-work.png" width="60%" />
+ <img src="/img/run-work.png" width="60%" />
</p>
* 补数： 执行指定日期的工作流定义，可以选择补数时间范围（目前只支持针对连续的天进行补数)，比如要补5月1号到5月10号的数据，如图示：
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/complement.png" width="60%" />
+ <img src="/img/complement.png" width="60%" />
</p>
> 补数执行模式有**串行执行、并行执行**，串行模式下，补数会从5月1号到5月10号依次执行；并行模式下，会同时执行5月1号到5月10号的任务。
@@ -82,12 +82,12 @@
- 创建定时："工作流定义->定时”
- 选择起止时间，在起止时间范围内，定时正常工作，超过范围，就不会再继续产生定时工作流实例了。
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/time-schedule.png" width="60%" />
+ <img src="/img/time-schedule.png" width="60%" />
</p>
- 添加一个每天凌晨5点执行一次的定时，如图示：
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/time-schedule2.png" width="60%" />
+ <img src="/img/time-schedule2.png" width="60%" />
</p>
- 定时上线，**新创建的定时是下线状态，需要点击“定时管理->上线”，定时才能正常工作**。
@@ -98,26 +98,26 @@
> 点击工作流名称，查看任务执行状态。
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/instance-detail.png" width="60%" />
+ <img src="/img/instance-detail.png" width="60%" />
</p>
> 点击任务节点，点击“查看日志”，查看任务执行日志。
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/task-log.png" width="60%" />
+ <img src="/img/task-log.png" width="60%" />
</p>
> 点击任务实例节点，点击**查看历史**，可以查看该工作流实例运行的该任务实例列表
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/task_history.png" width="60%" />
+ <img src="/img/task_history.png" width="60%" />
</p>
> 对工作流实例的操作：
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/instance-list.png" width="60%" />
+ <img src="/img/instance-list.png" width="60%" />
</p>
* 编辑：可以对已经终止的流程进行编辑，编辑后保存的时候，可以选择是否更新到工作流定义。
@@ -129,20 +129,20 @@
* 删除：删除工作流实例及工作流实例下的任务实例
* 甘特图：Gantt图纵轴是某个工作流实例下的任务实例的拓扑排序，横轴是任务实例的运行时间,如图示：
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/gant-pic.png" width="60%" />
+ <img src="/img/gant-pic.png" width="60%" />
</p>
### 查看任务实例
> 点击“任务实例”，进入任务列表页，查询任务执行情况
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/task-list.png" width="60%" />
+ <img src="/img/task-list.png" width="60%" />
</p>
> 点击操作列中的“查看日志”，可以查看任务执行的日志情况。
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/task-log2.png" width="60%" />
+ <img src="/img/task-log2.png" width="60%" />
</p>
### 创建数据源
@@ -163,7 +163,7 @@
- Jdbc连接参数：用于MySQL连接的参数设置，以JSON形式填写
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/mysql_edit.png" width="60%" />
+ <img src="/img/mysql_edit.png" width="60%" />
</p>
> 点击“测试连接”，测试数据源是否可以连接成功。
@@ -181,7 +181,7 @@
- Jdbc连接参数：用于POSTGRESQL连接的参数设置，以JSON形式填写
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/postgresql_edit.png" width="60%" />
+ <img src="/img/postgresql_edit.png" width="60%" />
</p>
#### 创建、编辑HIVE数据源
@@ -189,7 +189,7 @@
1.使用HiveServer2方式连接
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/hive_edit.png" width="60%" />
+ <img src="/img/hive_edit.png" width="60%" />
</p>
- 数据源：选择HIVE
@@ -205,13 +205,13 @@
2.使用HiveServer2 HA Zookeeper方式连接
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/hive_edit2.png" width="60%" />
+ <img src="/img/hive_edit2.png" width="60%" />
</p>
注意：如果开启了**kerberos**，则需要填写 **Principal**
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/hive_kerberos.png" width="60%" />
+ <img src="/img/hive_kerberos.png" width="60%" />
</p>
@@ -220,7 +220,7 @@
#### 创建、编辑Spark数据源
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/spark_datesource.png" width="60%" />
+ <img src="/img/spark_datesource.png" width="60%" />
</p>
- 数据源：选择Spark
@@ -238,7 +238,7 @@
注意：如果开启了**kerberos**，则需要填写 **Principal**
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/sparksql_kerberos.png" width="60%" />
+ <img src="/img/sparksql_kerberos.png" width="60%" />
</p>
### 上传资源
@@ -257,14 +257,14 @@ conf/common/hadoop.properties
> 是对各种资源文件的管理，包括创建基本的txt/log/sh/conf等文件、上传jar包等各种类型文件，以及编辑、下载、删除等操作。
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/file-manage.png" width="60%" />
+ <img src="/img/file-manage.png" width="60%" />
</p>
* 创建文件
> 文件格式支持以下几种类型：txt、log、sh、conf、cfg、py、java、sql、xml、hql
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/file_create.png" width="60%" />
+ <img src="/img/file_create.png" width="60%" />
</p>
* 上传文件
@@ -272,7 +272,7 @@ conf/common/hadoop.properties
> 上传文件：点击上传按钮进行上传，将文件拖拽到上传区域，文件名会自动以上传的文件名称补全
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/file_upload.png" width="60%" />
+ <img src="/img/file_upload.png" width="60%" />
</p>
@@ -281,7 +281,7 @@ conf/common/hadoop.properties
> 对可查看的文件类型，点击 文件名称 可以查看文件详情
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/file_detail.png" width="60%" />
+ <img src="/img/file_detail.png" width="60%" />
</p>
* 下载文件
@@ -291,7 +291,7 @@ conf/common/hadoop.properties
* 文件重命名
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/file_rename.png" width="60%" />
+ <img src="/img/file_rename.png" width="60%" />
</p>
#### 删除
@@ -317,7 +317,7 @@ conf/common/hadoop.properties
- UDF资源：设置创建的UDF对应的资源文件
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/udf_edit.png" width="60%" />
+ <img src="/img/udf_edit.png" width="60%" />
</p>
## 安全中心（权限系统）
@@ -329,7 +329,7 @@ conf/common/hadoop.properties
- 队列是在执行spark、mapreduce等程序，需要用到“队列”参数时使用的。
- “安全中心”->“队列管理”->“创建队列”
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/create-queue.png" width="60%" />
+ <img src="/img/create-queue.png" width="60%" />
</p>
@@ -338,7 +338,7 @@ conf/common/hadoop.properties
- 租户编码：**租户编码是Linux上的用户，唯一，不能重复**
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/addtenant.png" width="60%" />
+ <img src="/img/addtenant.png" width="60%" />
</p>
### 创建普通用户
@@ -347,7 +347,7 @@ conf/common/hadoop.properties
* 普通用户可以**创建项目和对工作流定义的创建，编辑，执行**等操作。
* 注意：**如果该用户切换了租户，则该用户所在租户下所有资源将复制到切换的新租户下**
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/useredit2.png" width="60%" />
+ <img src="/img/useredit2.png" width="60%" />
</p>
### 创建告警组
@@ -355,7 +355,7 @@ conf/common/hadoop.properties
- 新建、编辑告警组
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/mail_edit.png" width="60%" />
+ <img src="/img/mail_edit.png" width="60%" />
</p>
### 创建worker分组
@@ -363,7 +363,7 @@ conf/common/hadoop.properties
- worker分组内多个ip地址（**不能写别名**），以**英文逗号**分隔
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/worker1.png" width="60%" />
+ <img src="/img/worker1.png" width="60%" />
</p>
### 令牌管理
@@ -413,13 +413,13 @@ conf/common/hadoop.properties
- 1.点击指定人的授权按钮，如下图：
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/auth_user.png" width="60%" />
+ <img src="/img/auth_user.png" width="60%" />
</p>
- 2.选中项目按钮，进行项目授权
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/auth_project.png" width="60%" />
+ <img src="/img/auth_project.png" width="60%" />
</p>
@@ -431,28 +431,28 @@ conf/common/hadoop.properties
#### master监控
- 主要是master的相关信息。
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/master-jk.png" width="60%" />
+ <img src="/img/master-jk.png" width="60%" />
</p>
#### worker监控
- 主要是worker的相关信息。
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/worker-jk.png" width="60%" />
+ <img src="/img/worker-jk.png" width="60%" />
</p>
#### Zookeeper监控
- 主要是zookpeeper中各个worker和master的相关配置信息。
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/zk-jk.png" width="60%" />
+ <img src="/img/zk-jk.png" width="60%" />
</p>
-#### Mysql监控
- - 主要是mysql的健康状况
+#### DB监控
+ - 主要是DB的健康状况
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/mysql-jk.png" width="60%" />
+ <img src="/img/mysql-jk.png" width="60%" />
</p>
## 任务节点类型和参数设置
@@ -462,7 +462,7 @@ conf/common/hadoop.properties
> 拖动工具栏中的任务节点到画板中，双击任务节点，如下图：
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/shell_edit.png" width="60%" />
+ <img src="/img/shell_edit.png" width="60%" />
</p>
- 节点名称：一个工作流定义中的节点名称是唯一的
@@ -479,7 +479,7 @@ conf/common/hadoop.properties
> 拖动工具栏中的任务节点到画板中，双击任务节点，如下图：
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/subprocess_edit.png" width="60%" />
+ <img src="/img/subprocess_edit.png" width="60%" />
</p>
- 节点名称：一个工作流定义中的节点名称是唯一的
@@ -493,25 +493,25 @@ conf/common/hadoop.properties
> 拖动工具栏中的任务节点到画板中，双击任务节点，如下图：
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/dependent_edit.png" width="60%" />
+ <img src="/img/dependent_edit.png" width="60%" />
</p>
> 依赖节点提供了逻辑判断功能，比如检查昨天的B流程是否成功，或者C流程是否执行成功。
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/depend-node.png" width="80%" />
+ <img src="/img/depend-node.png" width="80%" />
</p>
> 例如，A流程为周报任务，B、C流程为天任务，A任务需要B、C任务在上周的每一天都执行成功，如图示：
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/depend-node2.png" width="80%" />
+ <img src="/img/depend-node2.png" width="80%" />
</p>
> 假如，周报A同时还需要自身在上周二执行成功：
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/depend-node3.png" width="80%" />
+ <img src="/img/depend-node3.png" width="80%" />
</p>
### 存储过程节点
@@ -519,7 +519,7 @@ conf/common/hadoop.properties
> 拖动工具栏中的任务节点到画板中，双击任务节点，如下图：
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/procedure_edit.png" width="60%" />
+ <img src="/img/procedure_edit.png" width="60%" />
</p>
- 数据源：存储过程的数据源类型支持MySQL和POSTGRESQL两种，选择对应的数据源
@@ -529,14 +529,14 @@ conf/common/hadoop.properties
### SQL节点
- 执行非查询SQL功能
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/sql-node.png" width="60%" />
+ <img src="/img/sql-node.png" width="60%" />
</p>
- 执行查询SQL功能，可以选择通过表格和附件形式发送邮件到指定的收件人。
> 拖动工具栏中的任务节点到画板中，双击任务节点，如下图：
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/sql-node2.png" width="60%" />
+ <img src="/img/sql-node2.png" width="60%" />
</p>
- 数据源：选择对应的数据源
@@ -552,7 +552,7 @@ conf/common/hadoop.properties
> 拖动工具栏中的任务节点到画板中，双击任务节点，如下图：
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/spark_edit.png" width="60%" />
+ <img src="/img/spark_edit.png" width="60%" />
</p>
- 程序类型：支持JAVA、Scala和Python三种语言
@@ -591,7 +591,7 @@ conf/common/hadoop.properties
2. Python程序
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/mr_edit.png" width="60%" />
+ <img src="/img/mr_edit.png" width="60%" />
</p>
- 程序类型：选择Python语言
@@ -609,7 +609,7 @@ conf/common/hadoop.properties
> 拖动工具栏中的任务节点到画板中，双击任务节点，如下图：
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/python_edit.png" width="60%" />
+ <img src="/img/python_edit.png" width="60%" />
</p>
- 脚本：用户开发的Python程序
@@ -663,13 +663,13 @@ conf/common/hadoop.properties
> 例如：
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/local_parameter.png" width="60%" />
+ <img src="/img/local_parameter.png" width="60%" />
</p>
> global_bizdate为全局参数，引用的是系统参数。
<p align="center">
- <img src="https://analysys.github.io/easyscheduler_docs_cn/images/global_parameter.png" width="60%" />
+ <img src="/img/global_parameter.png" width="60%" />
</p>
> 任务中local_param_bizdate通过${global_bizdate}来引用全局参数，对于脚本可以通过${local_param_bizdate}来引用变量local_param_bizdate的值，或通过JDBC直接将local_param_bizdate的值set进去
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