diff --git a/docs/zh_CN/前端部署文档.md b/docs/zh_CN/前端部署文档.md index e639c737b4..77ade9680d 100644 --- a/docs/zh_CN/前端部署文档.md +++ b/docs/zh_CN/前端部署文档.md @@ -1,20 +1,11 @@ # 前端部署文档 -- ##### 1. 开发环境搭建 - -- ##### 2. 自动化部署 - -- ##### 3. 手动部署 - -- ##### 4. Liunx下使用node启动并且守护进程 - - ### 1.开发环境搭建 -- #### node安装 +- #### 安装node Node包下载 (注意版本 8.9.4) `https://nodejs.org/download/release/v8.9.4/` -- #### 前端项目构建 +- #### 构建项目 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 > 如果 `npm install` 速度非常慢 @@ -23,8 +14,6 @@ Node包下载 (注意版本 8.9.4) `https://nodejs.org/download/release/v8.9.4/` > 运行 `cnpm install` - - > ##### ！！！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 ``` npm install node-sass --unsafe-perm //单独安装node-sass依赖 @@ -44,6 +33,7 @@ API_BASE = http://192.168.220.204:12345 - `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) +### 2.自动部署方式 ### 2.自动化部署` @@ -61,6 +51,11 @@ esc_proxy_port="http://192.168.220.154:12345" 前端自动部署基于`yum`操作，部署之前请先安装更新`yum +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` + + + +### 3.手动部署方式 在项目`escheduler-ui`根目录执行`./install(线上环境).sh` @@ -167,11 +162,8 @@ systemctl restart nginx │ npm │ 0 │ N/A │ fork │ 6168 │ online │ 31 │ 0s │ 0% │ 5.6 MB │ root │ disabled │ └──────────┴────┴─────────┴──────┴──────┴────────┴─────────┴────────┴─────┴──────────┴──────┴──────────┘ Use `pm2 show ` to get more details about an app +## FAQ -``` - - -## 问题 #### 1. 上传文件大小限制 编辑配置文件 `vi /etc/nginx/nginx.conf` ``` diff --git a/docs/zh_CN/后端部署文档.md b/docs/zh_CN/后端部署文档.md index 4559e4b817..bba0b0f172 100644 --- a/docs/zh_CN/后端部署文档.md +++ b/docs/zh_CN/后端部署文档.md @@ -6,7 +6,7 @@ * [Mysql](https://blog.csdn.net/u011886447/article/details/79796802) (5.5+) : 必装 * [JDK](https://www.oracle.com/technetwork/java/javase/downloads/index.html) (1.8+) : 必装 * [ZooKeeper](https://www.jianshu.com/p/de90172ea680)(3.4.6) ：必装 - * [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) + * [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) * [Hive](https://staroon.pro/2017/12/09/HiveInstall/)(1.2.1) : 选装，hive任务提交需要安装 * Spark(1.x,2.x) : 选装，Spark任务提交需要安装 * PostgreSQL(8.2.15+) : 选装，PostgreSQL PostgreSQL存储过程需要安装 @@ -27,15 +27,6 @@ 正常编译完后，会在当前目录生成 target/escheduler-{version}/ -``` - bin - conf - lib - script - sql - install.sh -``` - - 说明 ``` @@ -74,7 +65,7 @@ mysql -h {host} -u {user} -p{password} -D {db} < quartz.sql ## 创建部署用户 -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。 +- 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。 ```部署账号 vi /etc/sudoers @@ -86,386 +77,73 @@ escheduler ALL=(ALL) NOPASSWD: NOPASSWD: ALL #Default requiretty ``` -## 配置文件说明 - -``` -说明：配置文件位于 target/escheduler-{version}/conf 下面 -``` - -### escheduler-alert - -配置邮件告警信息 - - -* alert.properties - -``` -#以qq邮箱为例，如果是别的邮箱，请更改对应配置 -#alert type is EMAIL/SMS -alert.type=EMAIL - -# mail server configuration -mail.protocol=SMTP -mail.server.host=smtp.exmail.qq.com -mail.server.port=25 -mail.sender=xxxxxxx@qq.com -mail.passwd=xxxxxxx - -# xls file path, need manually create it before use if not exist -xls.file.path=/opt/xls -``` - - - - -### escheduler-common - -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 - -- common/common.properties - -``` -#task queue implementation, default "zookeeper" -escheduler.queue.impl=zookeeper - -# user data directory path, self configuration, please make sure the directory exists and have read write permissions -data.basedir.path=/tmp/escheduler - -# directory path for user data download. self configuration, please make sure the directory exists and have read write permissions -data.download.basedir.path=/tmp/escheduler/download - -# process execute directory. self configuration, please make sure the directory exists and have read write permissions -process.exec.basepath=/tmp/escheduler/exec - -# 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 -data.store2hdfs.basepath=/escheduler - -# whether hdfs starts -hdfs.startup.state=true - -# system env path. self configuration, please make sure the directory and file exists and have read write execute permissions -escheduler.env.path=/opt/.escheduler_env.sh -escheduler.env.py=/opt/escheduler_env.py - -#resource.view.suffixs -resource.view.suffixs=txt,log,sh,conf,cfg,py,java,sql,hql,xml - -# is development state? default "false" -development.state=false -``` - - - -SHELL任务 环境变量配置 - -``` -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 -``` - -.escheduler_env.sh -``` -export HADOOP_HOME=/opt/soft/hadoop -export HADOOP_CONF_DIR=/opt/soft/hadoop/etc/hadoop -export SPARK_HOME1=/opt/soft/spark1 -export SPARK_HOME2=/opt/soft/spark2 -export PYTHON_HOME=/opt/soft/python -export JAVA_HOME=/opt/soft/java -export HIVE_HOME=/opt/soft/hive - -export PATH=$HADOOP_HOME/bin:$SPARK_HOME1/bin:$SPARK_HOME2/bin:$PYTHON_HOME/bin:$JAVA_HOME/bin:$HIVE_HOME/bin:$PATH -``` - - -​ - -Python任务 环境变量配置 - -``` -说明：配置文件位于 target/escheduler-{version}/conf/env 下面 -``` - -escheduler_env.py -``` -import os - -HADOOP_HOME="/opt/soft/hadoop" -SPARK_HOME1="/opt/soft/spark1" -SPARK_HOME2="/opt/soft/spark2" -PYTHON_HOME="/opt/soft/python" -JAVA_HOME="/opt/soft/java" -HIVE_HOME="/opt/soft/hive" -PATH=os.environ['PATH'] -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) - -os.putenv('PATH','%s'%PATH) -``` - - - -hadoop 配置文件 - -- common/hadoop/hadoop.properties - -``` -# ha or single namenode,If namenode ha needs to copy core-site.xml and hdfs-site.xml to the conf directory -fs.defaultFS=hdfs://mycluster:8020 - -#resourcemanager ha note this need ips , this empty if single -yarn.resourcemanager.ha.rm.ids=192.168.xx.xx,192.168.xx.xx - -# If it is a single resourcemanager, you only need to configure one host name. If it is resourcemanager HA, the default configuration is fine -yarn.application.status.address=http://ark1:8088/ws/v1/cluster/apps/%s - -``` - - - -定时器配置文件 - -- quartz.properties - -``` -#============================================================================ -# Configure Main Scheduler Properties -#============================================================================ -org.quartz.scheduler.instanceName = EasyScheduler -org.quartz.scheduler.instanceId = AUTO -org.quartz.scheduler.makeSchedulerThreadDaemon = true -org.quartz.jobStore.useProperties = false - -#============================================================================ -# Configure ThreadPool -#============================================================================ - -org.quartz.threadPool.class = org.quartz.simpl.SimpleThreadPool -org.quartz.threadPool.makeThreadsDaemons = true -org.quartz.threadPool.threadCount = 25 -org.quartz.threadPool.threadPriority = 5 - -#============================================================================ -# Configure JobStore -#============================================================================ +## ssh免密配置 + 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 -org.quartz.jobStore.class = org.quartz.impl.jdbcjobstore.JobStoreTX -org.quartz.jobStore.driverDelegateClass = org.quartz.impl.jdbcjobstore.StdJDBCDelegate -org.quartz.jobStore.tablePrefix = QRTZ_ -org.quartz.jobStore.isClustered = true -org.quartz.jobStore.misfireThreshold = 60000 -org.quartz.jobStore.clusterCheckinInterval = 5000 -org.quartz.jobStore.dataSource = myDs +- [将 **主机器** 和各个其它机器SSH打通](http://geek.analysys.cn/topic/113) -#============================================================================ -# Configure Datasources -#============================================================================ - -org.quartz.dataSource.myDs.driver = com.mysql.jdbc.Driver -org.quartz.dataSource.myDs.URL = jdbc:mysql://192.168.xx.xx:3306/escheduler?characterEncoding=utf8&useSSL=false -org.quartz.dataSource.myDs.user = xx -org.quartz.dataSource.myDs.password = xx -org.quartz.dataSource.myDs.maxConnections = 10 -org.quartz.dataSource.myDs.validationQuery = select 1 -``` +## 部署 +### 1. 修改安装目录权限 - -zookeeper 配置文件 - - -- zookeeper.properties +- 安装目录如下： ``` -#zookeeper cluster -zookeeper.quorum=192.168.xx.xx:2181,192.168.xx.xx:2181,192.168.xx.xx:2181 + bin + conf + install.sh + lib + script + sql + +``` +- 修改权限(deployUser修改为对应部署用户) -#escheduler root directory -zookeeper.escheduler.root=/escheduler + `sudo chown -R deployUser:deployUser *` -#zookeeper server dirctory -zookeeper.escheduler.dead.servers=/escheduler/dead-servers -zookeeper.escheduler.masters=/escheduler/masters -zookeeper.escheduler.workers=/escheduler/workers +### 2. 修改环境变量文件 -#zookeeper lock dirctory -zookeeper.escheduler.lock.masters=/escheduler/lock/masters -zookeeper.escheduler.lock.workers=/escheduler/lock/workers +- 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 -#escheduler failover directory -zookeeper.escheduler.lock.masters.failover=/escheduler/lock/failover/masters -zookeeper.escheduler.lock.workers.failover=/escheduler/lock/failover/workers +### 3. 修改部署参数 -#escheduler failover directory -zookeeper.session.timeout=300 -zookeeper.connection.timeout=300 -zookeeper.retry.sleep=1000 -zookeeper.retry.maxtime=5 + - 修改 **install.sh**中的参数，替换成自身业务所需的值 + - 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 + +### 4. 一键部署 + +- 安装zookeeper工具 + + `pip install kazoo` + +- 切换到部署用户，一键部署 + + `sh install.sh` + +- jps查看服务是否启动 + +```aidl + MasterServer ----- master服务 + WorkerServer ----- worker服务 + LoggerServer ----- logger服务 + ApiApplicationServer ----- api服务 + AlertServer ----- alert服务 ``` +## 日志查看 +日志统一存放于指定文件夹内 - -### escheduler-dao - -dao数据源配置 - -- dao/data_source.properties - +```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 ``` -# 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 -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 $installPath` 中的版本号(1.0.0)需要执行前手动替换成对应的版本号 - -## 服务监控 - -monitor_server.py 脚本是监听，master和worker服务挂掉重启的脚本 - -注意：在全部服务都启动之后启动 - -nohup python -u monitor_server.py > nohup.out 2>&1 & - -## 日志查看 -日志统一存放于指定文件夹内 - -```日志路径 - logs/ - ├── escheduler-alert-server.log - ├── escheduler-master-server.log - |—— escheduler-worker-server.log - |—— escheduler-api-server.log - |—— escheduler-logger-server.log -``` \ No newline at end of file diff --git a/install.sh b/install.sh index 81772729de..c6c734078f 100644 --- a/install.sh +++ b/install.sh @@ -47,8 +47,57 @@ mysqlUserName="xx" # mysql 密码 mysqlPassword="xx" +# conf/config/install_config.conf配置 +# 安装路径,不要当前路径(pwd)一样 +installPath="/data1_1T/escheduler" + +# 部署用户 +deployUser="escheduler" + +# zk集群 +zkQuorum="192.168.xx.xx:2181,192.168.xx.xx:2181,192.168.xx.xx:2181" + +# 安装hosts +ips="ark0,ark1,ark2,ark3,ark4" + +# conf/config/run_config.conf配置 +# 运行Master的机器 +masters="ark0,ark1" + +# 运行Worker的机器 +workers="ark2,ark3,ark4" + +# 运行Alert的机器 +alertServer="ark3" + +# 运行Api的机器 +apiServers="ark1" + +# alert配置 +# 邮件协议 +mailProtocol="SMTP" + +# 邮件服务host +mailServerHost="smtp.exmail.qq.com" + +# 邮件服务端口 +mailServerPort="25" + +# 发送人 +mailSender="xxxxxxxxxx" + +# 发送人密码 +mailPassword="xxxxxxxxxx" + +# 下载Excel路径 +xlsFilePath="/tmp/xls" + # hadoop 配置 +# 是否启动hdfs,如果启动则为true,需要配置以下hadoop相关参数; +# 不启动设置为false,如果为false,以下配置不需要修改 +hdfsStartupSate="false" + # namenode地址,支持HA,需要将core-site.xml和hdfs-site.xml放到conf目录下 namenodeFs="hdfs://mycluster:8020" @@ -58,6 +107,8 @@ yarnHaIps="192.168.xx.xx,192.168.xx.xx" # 如果是单 resourcemanager,只需要配置一个主机名称,如果是resourcemanager HA,则默认配置就好 singleYarnIp="ark1" +# hdfs根路径,根路径的owner必须是部署用户 +hdfsPath="/escheduler" # common 配置 # 程序路径 @@ -69,17 +120,11 @@ downloadPath="/tmp/escheduler/download" # 任务执行路径 execPath="/tmp/escheduler/exec" -# hdfs根路径 -hdfsPath="/escheduler" - -# 是否启动hdfs,如果启动则为true,不启动设置为false -hdfsStartupSate="true" - # SHELL环境变量路径 -shellEnvPath="/opt/.escheduler_env.sh" +shellEnvPath="$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" diff --git a/monitor_server.py b/script/monitor_server.py similarity index 100% rename from monitor_server.py rename to script/monitor_server.py diff --git a/script/scp_hosts.sh b/script/scp_hosts.sh index 3884a72d80..74db0c14d9 100755 --- a/script/scp_hosts.sh +++ b/script/scp_hosts.sh @@ -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