[incubator-dolphinscheduler-website] branch master updated: Replace quick start and system manual page pictures

Wed, 20 Nov 2019 21:38:24 -0800 

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 <[email protected]>
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![PNG](https://analysys.github.io/easyscheduler_docs_cn/images/toolbar_SHELL.png)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![PNG](https://analysys.github.io/easyscheduler_docs_cn/images/toolbar_SUB_PROCESS.png)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![PNG](https://analysys.github.io/easyscheduler_docs_cn/images/toolbar_DEPENDENT.png)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![PNG](https://analysys.github.io/easyscheduler_docs_cn/images/toolbar_PROCEDURE.png)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![PNG](https://analysys.github.io/easyscheduler_docs_cn/images/toolbar_SQL.png)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![PNG](https://analysys.github.io/easyscheduler_docs_cn/images/toolbar_SPARK.png)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![PNG](https://analysys.github.io/easyscheduler_docs_cn/images/toolbar_PYTHON.png)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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