mirror of
https://gitee.com/dolphinscheduler/DolphinScheduler.git
synced 2024-12-03 04:39:00 +08:00
bao liang
GitHub
commit
d79efa2a15
@ -1,20 +1,11 @@
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# 前端部署文档
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- ##### 1. 开发环境搭建
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- ##### 2. 自动化部署
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- ##### 3. 手动部署
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- ##### 4. Liunx下使用node启动并且守护进程
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### 1.开发环境搭建
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- #### node安装
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- #### 安装node
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Node包下载 (注意版本 8.9.4) `https://nodejs.org/download/release/v8.9.4/`
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- #### 前端项目构建
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- #### 构建项目
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用命令行模式 `cd` 进入 `escheduler-ui`项目目录并执行 `npm install` 拉取项目依赖包
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> 如果 `npm install` 速度非常慢
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@ -23,8 +14,6 @@ Node包下载 (注意版本 8.9.4) `https://nodejs.org/download/release/v8.9.4/`
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> 运行 `cnpm install`
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> ##### ！！！这里特别注意 项目如果在拉取依赖包的过程中报 " node-sass error " 错误，请在执行完后再次执行以下命令
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```
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npm install node-sass --unsafe-perm //单独安装node-sass依赖
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@ -44,6 +33,7 @@ API_BASE = http://192.168.220.204:12345
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- `npm run build` 项目打包 (打包后根目录会创建一个名为dist文件夹，用于发布线上Nginx)
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### 2.自动部署方式
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### 2.自动化部署`
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@ -61,6 +51,11 @@ esc_proxy_port="http://192.168.220.154:12345"
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前端自动部署基于`yum`操作，部署之前请先安装更新`yum
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在项目`escheduler-ui`根目录下，修改install.sh中的参数，执行`./install(线上环境).sh`
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### 3.手动部署方式
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在项目`escheduler-ui`根目录执行`./install(线上环境).sh`
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@ -167,11 +162,8 @@ systemctl restart nginx
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│ npm │ 0 │ N/A │ fork │ 6168 │ online │ 31 │ 0s │ 0% │ 5.6 MB │ root │ disabled │
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└──────────┴────┴─────────┴──────┴──────┴────────┴─────────┴────────┴─────┴──────────┴──────┴──────────┘
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Use `pm2 show <id|name>` to get more details about an app
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## FAQ
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```
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## 问题
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#### 1. 上传文件大小限制
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编辑配置文件 `vi /etc/nginx/nginx.conf`
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```
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@ -6,7 +6,7 @@
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* [Mysql](https://blog.csdn.net/u011886447/article/details/79796802) (5.5+) : 必装
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* [JDK](https://www.oracle.com/technetwork/java/javase/downloads/index.html) (1.8+) : 必装
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* [ZooKeeper](https://www.jianshu.com/p/de90172ea680)(3.4.6) ：必装
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* [Hadoop](https://blog.csdn.net/Evankaka/article/details/51612437)(2.7.3) ：选装， 如果需要使用到资源上传功能，MapReduce任务提交则需要配置Hadoop(上传的资源文件目前保存在Hdfs上)
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* [Hadoop](https://blog.csdn.net/Evankaka/article/details/51612437)(2.6+) ：选装， 如果需要使用到资源上传功能，MapReduce任务提交则需要配置Hadoop(上传的资源文件目前保存在Hdfs上)
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* [Hive](https://staroon.pro/2017/12/09/HiveInstall/)(1.2.1) : 选装，hive任务提交需要安装
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* Spark(1.x,2.x) : 选装，Spark任务提交需要安装
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* PostgreSQL(8.2.15+) : 选装，PostgreSQL PostgreSQL存储过程需要安装
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@ -27,15 +27,6 @@
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正常编译完后，会在当前目录生成 target/escheduler-{version}/
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```
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bin
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conf
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lib
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script
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sql
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install.sh
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```
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- 说明
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```
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@ -74,7 +65,7 @@ mysql -h {host} -u {user} -p{password} -D {db} < quartz.sql
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## 创建部署用户
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因为escheduler worker都是以 sudo -u {linux-user} 方式来执行作业，所以部署用户需要有 sudo 权限，而且是免密的。
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- 在所有需要部署调度的机器上创建部署用户，因为worker服务是以 sudo -u {linux-user} 方式来执行作业，所以部署用户需要有 sudo 权限，而且是免密的。
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```部署账号
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vi /etc/sudoers
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@ -86,386 +77,73 @@ escheduler ALL=(ALL) NOPASSWD: NOPASSWD: ALL
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#Default requiretty
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```
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## 配置文件说明
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## ssh免密配置
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在部署机器和其他安装机器上配置ssh免密登录，如果要在部署机上安装调度，需要配置本机免密登录自己
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- [将 **主机器** 和各个其它机器SSH打通](http://geek.analysys.cn/topic/113)
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## 部署
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### 1. 修改安装目录权限
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- 安装目录如下：
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```
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说明：配置文件位于 target/escheduler-{version}/conf 下面
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```
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### escheduler-alert
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配置邮件告警信息
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* alert.properties
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bin
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conf
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install.sh
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lib
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script
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sql
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```
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#以qq邮箱为例，如果是别的邮箱，请更改对应配置
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#alert type is EMAIL/SMS
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alert.type=EMAIL
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- 修改权限(deployUser修改为对应部署用户)
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# mail server configuration
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mail.protocol=SMTP
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mail.server.host=smtp.exmail.qq.com
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mail.server.port=25
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mail.sender=xxxxxxx@qq.com
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mail.passwd=xxxxxxx
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`sudo chown -R deployUser:deployUser *`
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# xls file path, need manually create it before use if not exist
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xls.file.path=/opt/xls
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### 2. 修改环境变量文件
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- 根据业务需求，修改conf/env/目录下的**escheduler_env.py**，**.escheduler_env.sh**两个文件中的环境变量
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### 3. 修改部署参数
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- 修改 **install.sh**中的参数，替换成自身业务所需的值
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- 如果使用hdfs相关功能，需要拷贝**hdfs-site.xml**和**core-site.xml**到conf目录下
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### 4. 一键部署
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- 安装zookeeper工具
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`pip install kazoo`
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- 切换到部署用户，一键部署
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`sh install.sh`
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- jps查看服务是否启动
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```aidl
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MasterServer ----- master服务
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WorkerServer ----- worker服务
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LoggerServer ----- logger服务
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ApiApplicationServer ----- api服务
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AlertServer ----- alert服务
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```
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## 日志查看
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日志统一存放于指定文件夹内
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### escheduler-common
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通用配置文件配置，队列选择及地址配置，通用文件目录配置
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- common/common.properties
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```
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#task queue implementation, default "zookeeper"
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escheduler.queue.impl=zookeeper
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# user data directory path, self configuration, please make sure the directory exists and have read write permissions
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data.basedir.path=/tmp/escheduler
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# directory path for user data download. self configuration, please make sure the directory exists and have read write permissions
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data.download.basedir.path=/tmp/escheduler/download
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# process execute directory. self configuration, please make sure the directory exists and have read write permissions
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process.exec.basepath=/tmp/escheduler/exec
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# data base dir, resource file will store to this hadoop hdfs path, self configuration, please make sure the directory exists on hdfs and have read write permissions。"/escheduler" is recommended
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data.store2hdfs.basepath=/escheduler
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# whether hdfs starts
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hdfs.startup.state=true
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# system env path. self configuration, please make sure the directory and file exists and have read write execute permissions
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escheduler.env.path=/opt/.escheduler_env.sh
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escheduler.env.py=/opt/escheduler_env.py
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#resource.view.suffixs
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resource.view.suffixs=txt,log,sh,conf,cfg,py,java,sql,hql,xml
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# is development state? default "false"
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development.state=false
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```日志路径
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logs/
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├── escheduler-alert-server.log
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├── escheduler-master-server.log
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|—— escheduler-worker-server.log
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|—— escheduler-api-server.log
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|—— escheduler-logger-server.log
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```
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SHELL任务 环境变量配置
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```
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说明：配置文件位于 target/escheduler-{version}/conf/env 下面，这个会是Worker执行任务时加载的环境
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```
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.escheduler_env.sh
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```
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export HADOOP_HOME=/opt/soft/hadoop
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export HADOOP_CONF_DIR=/opt/soft/hadoop/etc/hadoop
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export SPARK_HOME1=/opt/soft/spark1
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export SPARK_HOME2=/opt/soft/spark2
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export PYTHON_HOME=/opt/soft/python
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export JAVA_HOME=/opt/soft/java
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export HIVE_HOME=/opt/soft/hive
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export PATH=$HADOOP_HOME/bin:$SPARK_HOME1/bin:$SPARK_HOME2/bin:$PYTHON_HOME/bin:$JAVA_HOME/bin:$HIVE_HOME/bin:$PATH
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```
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||||
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Python任务 环境变量配置
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||||
```
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说明：配置文件位于 target/escheduler-{version}/conf/env 下面
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```
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escheduler_env.py
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```
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import os
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HADOOP_HOME="/opt/soft/hadoop"
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SPARK_HOME1="/opt/soft/spark1"
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SPARK_HOME2="/opt/soft/spark2"
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PYTHON_HOME="/opt/soft/python"
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JAVA_HOME="/opt/soft/java"
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HIVE_HOME="/opt/soft/hive"
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PATH=os.environ['PATH']
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PATH="%s/bin:%s/bin:%s/bin:%s/bin:%s/bin:%s/bin:%s"%(HIVE_HOME,HADOOP_HOME,SPARK_HOME1,SPARK_HOME2,JAVA_HOME,PYTHON_HOME,PATH)
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os.putenv('PATH','%s'%PATH)
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```
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hadoop 配置文件
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- common/hadoop/hadoop.properties
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```
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# ha or single namenode,If namenode ha needs to copy core-site.xml and hdfs-site.xml to the conf directory
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fs.defaultFS=hdfs://mycluster:8020
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#resourcemanager ha note this need ips , this empty if single
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yarn.resourcemanager.ha.rm.ids=192.168.xx.xx,192.168.xx.xx
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# If it is a single resourcemanager, you only need to configure one host name. If it is resourcemanager HA, the default configuration is fine
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yarn.application.status.address=http://ark1:8088/ws/v1/cluster/apps/%s
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```
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定时器配置文件
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- quartz.properties
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|
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```
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#============================================================================
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# Configure Main Scheduler Properties
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#============================================================================
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org.quartz.scheduler.instanceName = EasyScheduler
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org.quartz.scheduler.instanceId = AUTO
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org.quartz.scheduler.makeSchedulerThreadDaemon = true
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org.quartz.jobStore.useProperties = false
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#============================================================================
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# Configure ThreadPool
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#============================================================================
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org.quartz.threadPool.class = org.quartz.simpl.SimpleThreadPool
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org.quartz.threadPool.makeThreadsDaemons = true
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org.quartz.threadPool.threadCount = 25
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org.quartz.threadPool.threadPriority = 5
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#============================================================================
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# Configure JobStore
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#============================================================================
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org.quartz.jobStore.class = org.quartz.impl.jdbcjobstore.JobStoreTX
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org.quartz.jobStore.driverDelegateClass = org.quartz.impl.jdbcjobstore.StdJDBCDelegate
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org.quartz.jobStore.tablePrefix = QRTZ_
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org.quartz.jobStore.isClustered = true
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org.quartz.jobStore.misfireThreshold = 60000
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org.quartz.jobStore.clusterCheckinInterval = 5000
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org.quartz.jobStore.dataSource = myDs
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#============================================================================
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# Configure Datasources
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#============================================================================
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org.quartz.dataSource.myDs.driver = com.mysql.jdbc.Driver
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org.quartz.dataSource.myDs.URL = jdbc:mysql://192.168.xx.xx:3306/escheduler?characterEncoding=utf8&useSSL=false
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org.quartz.dataSource.myDs.user = xx
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org.quartz.dataSource.myDs.password = xx
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org.quartz.dataSource.myDs.maxConnections = 10
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org.quartz.dataSource.myDs.validationQuery = select 1
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```
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||||
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zookeeper 配置文件
|
||||
|
||||
|
||||
- zookeeper.properties
|
||||
|
||||
```
|
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#zookeeper cluster
|
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zookeeper.quorum=192.168.xx.xx:2181,192.168.xx.xx:2181,192.168.xx.xx:2181
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|
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#escheduler root directory
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zookeeper.escheduler.root=/escheduler
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|
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#zookeeper server dirctory
|
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zookeeper.escheduler.dead.servers=/escheduler/dead-servers
|
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zookeeper.escheduler.masters=/escheduler/masters
|
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zookeeper.escheduler.workers=/escheduler/workers
|
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|
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#zookeeper lock dirctory
|
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zookeeper.escheduler.lock.masters=/escheduler/lock/masters
|
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zookeeper.escheduler.lock.workers=/escheduler/lock/workers
|
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|
||||
#escheduler failover directory
|
||||
zookeeper.escheduler.lock.masters.failover=/escheduler/lock/failover/masters
|
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zookeeper.escheduler.lock.workers.failover=/escheduler/lock/failover/workers
|
||||
|
||||
#escheduler failover directory
|
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zookeeper.session.timeout=300
|
||||
zookeeper.connection.timeout=300
|
||||
zookeeper.retry.sleep=1000
|
||||
zookeeper.retry.maxtime=5
|
||||
|
||||
```
|
||||
|
||||
|
||||
|
||||
### escheduler-dao
|
||||
|
||||
dao数据源配置
|
||||
|
||||
- dao/data_source.properties
|
||||
|
||||
```
|
||||
# base spring data source configuration
|
||||
spring.datasource.type=com.alibaba.druid.pool.DruidDataSource
|
||||
spring.datasource.driver-class-name=com.mysql.jdbc.Driver
|
||||
spring.datasource.url=jdbc:mysql://192.168.xx.xx:3306/escheduler?characterEncoding=UTF-8
|
||||
spring.datasource.username=xx
|
||||
spring.datasource.password=xx
|
||||
|
||||
# connection configuration
|
||||
spring.datasource.initialSize=5
|
||||
# min connection number
|
||||
spring.datasource.minIdle=5
|
||||
# max connection number
|
||||
spring.datasource.maxActive=50
|
||||
|
||||
# max wait time for get a connection in milliseconds. if configuring maxWait, fair locks are enabled by default and concurrency efficiency decreases.
|
||||
# If necessary, unfair locks can be used by configuring the useUnfairLock attribute to true.
|
||||
spring.datasource.maxWait=60000
|
||||
|
||||
# milliseconds for check to close free connections
|
||||
spring.datasource.timeBetweenEvictionRunsMillis=60000
|
||||
|
||||
# the Destroy thread detects the connection interval and closes the physical connection in milliseconds if the connection idle time is greater than or equal to minEvictableIdleTimeMillis.
|
||||
spring.datasource.timeBetweenConnectErrorMillis=60000
|
||||
|
||||
# the longest time a connection remains idle without being evicted, in milliseconds
|
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spring.datasource.minEvictableIdleTimeMillis=300000
|
||||
|
||||
#the SQL used to check whether the connection is valid requires a query statement. If validation Query is null, testOnBorrow, testOnReturn, and testWhileIdle will not work.
|
||||
spring.datasource.validationQuery=SELECT 1
|
||||
#check whether the connection is valid for timeout, in seconds
|
||||
spring.datasource.validationQueryTimeout=3
|
||||
|
||||
# when applying for a connection, if it is detected that the connection is idle longer than time Between Eviction Runs Millis,
|
||||
# validation Query is performed to check whether the connection is valid
|
||||
spring.datasource.testWhileIdle=true
|
||||
|
||||
#execute validation to check if the connection is valid when applying for a connection
|
||||
spring.datasource.testOnBorrow=true
|
||||
#execute validation to check if the connection is valid when the connection is returned
|
||||
spring.datasource.testOnReturn=false
|
||||
spring.datasource.defaultAutoCommit=true
|
||||
spring.datasource.keepAlive=true
|
||||
|
||||
# open PSCache, specify count PSCache for every connection
|
||||
spring.datasource.poolPreparedStatements=true
|
||||
spring.datasource.maxPoolPreparedStatementPerConnectionSize=20
|
||||
```
|
||||
|
||||
|
||||
|
||||
### escheduler-server
|
||||
|
||||
master配置文件
|
||||
|
||||
- master.properties
|
||||
|
||||
```
|
||||
# master execute thread num
|
||||
master.exec.threads=100
|
||||
|
||||
# master execute task number in parallel
|
||||
master.exec.task.number=20
|
||||
|
||||
# master heartbeat interval
|
||||
master.heartbeat.interval=10
|
||||
|
||||
# master commit task retry times
|
||||
master.task.commit.retryTimes=5
|
||||
|
||||
# master commit task interval
|
||||
master.task.commit.interval=100
|
||||
|
||||
|
||||
# only less than cpu avg load, master server can work. default value : the number of cpu cores * 2
|
||||
master.max.cpuload.avg=10
|
||||
|
||||
# only larger than reserved memory, master server can work. default value : physical memory * 1/10, unit is G.
|
||||
master.reserved.memory=1
|
||||
```
|
||||
|
||||
|
||||
|
||||
worker配置文件
|
||||
|
||||
- worker.properties
|
||||
|
||||
```
|
||||
# worker execute thread num
|
||||
worker.exec.threads=100
|
||||
|
||||
# worker heartbeat interval
|
||||
worker.heartbeat.interval=10
|
||||
|
||||
# submit the number of tasks at a time
|
||||
worker.fetch.task.num = 10
|
||||
|
||||
|
||||
# only less than cpu avg load, worker server can work. default value : the number of cpu cores * 2
|
||||
worker.max.cpuload.avg=10
|
||||
|
||||
# only larger than reserved memory, worker server can work. default value : physical memory * 1/6, unit is G.
|
||||
worker.reserved.memory=1
|
||||
```
|
||||
|
||||
|
||||
|
||||
### escheduler-api
|
||||
|
||||
web配置文件
|
||||
|
||||
- application.properties
|
||||
|
||||
```
|
||||
# server port
|
||||
server.port=12345
|
||||
|
||||
# session config
|
||||
server.session.timeout=7200
|
||||
|
||||
server.context-path=/escheduler/
|
||||
|
||||
# file size limit for upload
|
||||
spring.http.multipart.max-file-size=1024MB
|
||||
spring.http.multipart.max-request-size=1024MB
|
||||
|
||||
# post content
|
||||
server.max-http-post-size=5000000
|
||||
```
|
||||
|
||||
|
||||
|
||||
## 伪分布式部署
|
||||
|
||||
### 1，创建部署用户
|
||||
|
||||
如上 **创建部署用户**
|
||||
|
||||
### 2，根据实际需求来创建HDFS根路径
|
||||
|
||||
根据 **common/common.properties** 中 **hdfs.startup.state** 的配置来判断是否启动HDFS，如果启动，则需要创建HDFS根路径，并将 **owner** 修改为**部署用户**，否则忽略此步骤
|
||||
|
||||
### 3，项目编译
|
||||
|
||||
如上进行 **项目编译**
|
||||
|
||||
### 4，修改配置文件
|
||||
|
||||
根据 **配置文件说明** 修改配置文件和 **环境变量** 文件
|
||||
|
||||
### 5，创建目录并将环境变量文件复制到指定目录
|
||||
|
||||
- 创建 **common/common.properties** 下的data.basedir.path、data.download.basedir.path和process.exec.basepath路径
|
||||
|
||||
- 将**.escheduler_env.sh** 和 **escheduler_env.py** 两个环境变量文件复制到 **common/common.properties**配置的**escheduler.env.path** 和 **escheduler.env.py** 的目录下，并将 **owner** 修改为**部署用户**
|
||||
|
||||
### 6，启停服务
|
||||
## 启停服务
|
||||
|
||||
* 启停Master
|
||||
|
||||
@ -500,68 +178,3 @@ sh ./bin/escheduler-daemon.sh start alert-server
|
||||
sh ./bin/escheduler-daemon.sh stop alert-server
|
||||
```
|
||||
|
||||
|
||||
|
||||
## 分布式部署
|
||||
|
||||
### 1，创建部署用户
|
||||
|
||||
- 在需要部署调度的机器上如上 **创建部署用户**
|
||||
- [将 **主机器** 和各个其它机器SSH打通](https://blog.csdn.net/thinkmore1314/article/details/22489203)
|
||||
|
||||
### 2，根据实际需求来创建HDFS根路径
|
||||
|
||||
根据 **common/common.properties** 中 **hdfs.startup.state** 的配置来判断是否启动HDFS，如果启动，则需要创建HDFS根路径，并将 **owner** 修改为**部署用户**，否则忽略此步骤
|
||||
|
||||
### 3，项目编译
|
||||
|
||||
如上进行 **项目编译**
|
||||
|
||||
### 4，将环境变量文件复制到指定目录
|
||||
|
||||
将**.escheduler_env.sh** 和 **escheduler_env.py** 两个环境变量文件复制到 **common/common.properties**配置的**escheduler.env.path** 和 **escheduler.env.py** 的目录下，并将 **owner** 修改为**部署用户**
|
||||
|
||||
### 5，修改 install.sh
|
||||
|
||||
修改 install.sh 中变量的值，替换成自身业务所需的值
|
||||
|
||||
### 6，一键部署
|
||||
|
||||
- 安装 pip install kazoo
|
||||
- 安装目录如下：
|
||||
|
||||
```
|
||||
bin
|
||||
conf
|
||||
escheduler-1.0.0-SNAPSHOT.tar.gz
|
||||
install.sh
|
||||
lib
|
||||
monitor_server.py
|
||||
script
|
||||
sql
|
||||
|
||||
```
|
||||
|
||||
- 使用部署用户 sh install.sh 一键部署
|
||||
|
||||
- 注意：scp_hosts.sh 里 `tar -zxvf $workDir/../escheduler-1.0.0.tar.gz -C $installPath` 中的版本号(1.0.0)需要执行前手动替换成对应的版本号
|
||||
|
||||
## 服务监控
|
||||
|
||||
monitor_server.py 脚本是监听，master和worker服务挂掉重启的脚本
|
||||
|
||||
注意：在全部服务都启动之后启动
|
||||
|
||||
nohup python -u monitor_server.py > nohup.out 2>&1 &
|
||||
|
||||
## 日志查看
|
||||
日志统一存放于指定文件夹内
|
||||
|
||||
```日志路径
|
||||
logs/
|
||||
├── escheduler-alert-server.log
|
||||
├── escheduler-master-server.log
|
||||
|—— escheduler-worker-server.log
|
||||
|—— escheduler-api-server.log
|
||||
|—— escheduler-logger-server.log
|
||||
```
|
||||
115
install.sh
115
install.sh
@ -47,8 +47,57 @@ mysqlUserName="xx"
|
||||
# mysql 密码
|
||||
mysqlPassword="xx"
|
||||
|
||||
# conf/config/install_config.conf配置
|
||||
# 安装路径,不要当前路径(pwd)一样
|
||||
installPath="/data1_1T/escheduler"
|
||||
|
||||
# 部署用户
|
||||
deployUser="escheduler"
|
||||
|
||||
# zk集群
|
||||
zkQuorum="192.168.xx.xx:2181,192.168.xx.xx:2181,192.168.xx.xx:2181"
|
||||
|
||||
# 安装hosts
|
||||
ips="ark0,ark1,ark2,ark3,ark4"
|
||||
|
||||
# conf/config/run_config.conf配置
|
||||
# 运行Master的机器
|
||||
masters="ark0,ark1"
|
||||
|
||||
# 运行Worker的机器
|
||||
workers="ark2,ark3,ark4"
|
||||
|
||||
# 运行Alert的机器
|
||||
alertServer="ark3"
|
||||
|
||||
# 运行Api的机器
|
||||
apiServers="ark1"
|
||||
|
||||
# alert配置
|
||||
# 邮件协议
|
||||
mailProtocol="SMTP"
|
||||
|
||||
# 邮件服务host
|
||||
mailServerHost="smtp.exmail.qq.com"
|
||||
|
||||
# 邮件服务端口
|
||||
mailServerPort="25"
|
||||
|
||||
# 发送人
|
||||
mailSender="xxxxxxxxxx"
|
||||
|
||||
# 发送人密码
|
||||
mailPassword="xxxxxxxxxx"
|
||||
|
||||
# 下载Excel路径
|
||||
xlsFilePath="/tmp/xls"
|
||||
|
||||
|
||||
# hadoop 配置
|
||||
# 是否启动hdfs,如果启动则为true,需要配置以下hadoop相关参数;
|
||||
# 不启动设置为false,如果为false,以下配置不需要修改
|
||||
hdfsStartupSate="false"
|
||||
|
||||
# namenode地址,支持HA,需要将core-site.xml和hdfs-site.xml放到conf目录下
|
||||
namenodeFs="hdfs://mycluster:8020"
|
||||
|
||||
@ -58,6 +107,8 @@ yarnHaIps="192.168.xx.xx,192.168.xx.xx"
|
||||
# 如果是单 resourcemanager,只需要配置一个主机名称,如果是resourcemanager HA,则默认配置就好
|
||||
singleYarnIp="ark1"
|
||||
|
||||
# hdfs根路径,根路径的owner必须是部署用户
|
||||
hdfsPath="/escheduler"
|
||||
|
||||
# common 配置
|
||||
# 程序路径
|
||||
@ -69,17 +120,11 @@ downloadPath="/tmp/escheduler/download"
|
||||
# 任务执行路径
|
||||
execPath="/tmp/escheduler/exec"
|
||||
|
||||
# hdfs根路径
|
||||
hdfsPath="/escheduler"
|
||||
|
||||
# 是否启动hdfs,如果启动则为true,不启动设置为false
|
||||
hdfsStartupSate="true"
|
||||
|
||||
# SHELL环境变量路径
|
||||
shellEnvPath="/opt/.escheduler_env.sh"
|
||||
shellEnvPath="$installPath/conf/env/.escheduler_env.sh"
|
||||
|
||||
# Python换将变量路径
|
||||
pythonEnvPath="/opt/escheduler_env.py"
|
||||
pythonEnvPath="$installPath/conf/env/escheduler_env.py"
|
||||
|
||||
# 资源文件的后缀
|
||||
resSuffixs="txt,log,sh,conf,cfg,py,java,sql,hql,xml"
|
||||
@ -87,11 +132,7 @@ resSuffixs="txt,log,sh,conf,cfg,py,java,sql,hql,xml"
|
||||
# 开发状态,如果是true,对于SHELL脚本可以在execPath目录下查看封装后的SHELL脚本,如果是false则执行完成直接删除
|
||||
devState="true"
|
||||
|
||||
|
||||
# zk 配置
|
||||
# zk集群
|
||||
zkQuorum="192.168.xx.xx:2181,192.168.xx.xx:2181,192.168.xx.xx:2181"
|
||||
|
||||
# zk根目录
|
||||
zkRoot="/escheduler"
|
||||
|
||||
@ -168,7 +209,6 @@ workerMaxCupLoadAvg="10"
|
||||
# worker预留内存,用来判断master是否还有执行能力
|
||||
workerReservedMemory="1"
|
||||
|
||||
|
||||
# api 配置
|
||||
# api 服务端口
|
||||
apiServerPort="12345"
|
||||
@ -188,53 +228,6 @@ springMaxRequestSize="1024MB"
|
||||
# api 最大post请求大小
|
||||
apiMaxHttpPostSize="5000000"
|
||||
|
||||
|
||||
|
||||
# alert配置
|
||||
|
||||
# 邮件协议
|
||||
mailProtocol="SMTP"
|
||||
|
||||
# 邮件服务host
|
||||
mailServerHost="smtp.exmail.qq.com"
|
||||
|
||||
# 邮件服务端口
|
||||
mailServerPort="25"
|
||||
|
||||
# 发送人
|
||||
mailSender="xxxxxxxxxx"
|
||||
|
||||
# 发送人密码
|
||||
mailPassword="xxxxxxxxxx"
|
||||
|
||||
# 下载Excel路径
|
||||
xlsFilePath="/opt/xls"
|
||||
|
||||
# conf/config/install_config.conf配置
|
||||
# 安装路径
|
||||
installPath="/data1_1T/escheduler"
|
||||
|
||||
# 部署用户
|
||||
deployUser="escheduler"
|
||||
|
||||
# 安装hosts
|
||||
ips="ark0,ark1,ark2,ark3,ark4"
|
||||
|
||||
|
||||
# conf/config/run_config.conf配置
|
||||
# 运行Master的机器
|
||||
masters="ark0,ark1"
|
||||
|
||||
# 运行Worker的机器
|
||||
workers="ark2,ark3,ark4"
|
||||
|
||||
# 运行Alert的机器
|
||||
alertServer="ark3"
|
||||
|
||||
# 运行Api的机器
|
||||
apiServers="ark1"
|
||||
|
||||
|
||||
# 1,替换文件
|
||||
echo "1,替换文件"
|
||||
sed -i ${txt} "s#spring.datasource.url.*#spring.datasource.url=jdbc:mysql://${mysqlHost}/${mysqlDb}?characterEncoding=UTF-8#g" conf/dao/data_source.properties
|
||||
@ -317,8 +310,6 @@ sed -i ${txt} "s#alertServer.*#alertServer=${alertServer}#g" conf/config/run_con
|
||||
sed -i ${txt} "s#apiServers.*#apiServers=${apiServers}#g" conf/config/run_config.conf
|
||||
|
||||
|
||||
|
||||
|
||||
# 2,创建目录
|
||||
echo "2,创建目录"
|
||||
|
||||
|
||||
@ -5,8 +5,6 @@ workDir=`cd ${workDir};pwd`
|
||||
source $workDir/../conf/config/run_config.conf
|
||||
source $workDir/../conf/config/install_config.conf
|
||||
|
||||
tar -zxvf $workDir/../EasyScheduler-1.0.0.tar.gz -C $installPath
|
||||
|
||||
hostsArr=(${ips//,/ })
|
||||
for host in ${hostsArr[@]}
|
||||
do
|
||||
|
||||
Loading…
Reference in New Issue
Block a user