update queue
gongzijian
parent
2f8acc5b7e
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@ -430,22 +430,12 @@
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<section class="normal markdown-section">
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<h1 id="前端部署文档">前端部署文档</h1>
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<ul>
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<li><h5 id="1-开发环境搭建">1. 开发环境搭建</h5>
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</li>
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<li><h5 id="2-自动化部署">2. 自动化部署</h5>
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</li>
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<li><h5 id="3-手动部署">3. 手动部署</h5>
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</li>
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<li><h5 id="4-liunx下使用node启动并且守护进程">4. Liunx下使用node启动并且守护进程</h5>
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</li>
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</ul>
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<h3 id="1开发环境搭建">1.开发环境搭建</h3>
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<ul>
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<li><h4 id="node安装">node安装</h4>
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<li><h4 id="安装node">安装node</h4>
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<p>Node包下载 (注意版本 8.9.4) <code>https://nodejs.org/download/release/v8.9.4/</code> </p>
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</li>
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<li><h4 id="前端项目构建">前端项目构建</h4>
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<li><h4 id="构建项目">构建项目</h4>
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<p>用命令行模式 <code>cd</code> 进入 <code>escheduler-ui</code>项目目录并执行 <code>npm install</code> 拉取项目依赖包</p>
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</li>
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</ul>
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@ -469,6 +459,7 @@ API_BASE = http://192.168.220.204:12345
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<li><p><code>npm run build</code> 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)</p>
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</li>
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</ul>
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<h3 id="2自动部署方式">2.自动部署方式</h3>
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<h3 id="2自动化部署">2.自动化部署`</h3>
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<p>在项目<code>escheduler-ui</code>根目录编辑安装文件<code>vi install(线上环境).sh</code></p>
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<p>更改前端访问端口和后端代理接口地址</p>
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@ -478,6 +469,8 @@ esc_proxy="8888"
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# 配置代理后端接口
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esc_proxy_port="http://192.168.220.154:12345"
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</code></pre><p>前端自动部署基于<code>yum</code>操作，部署之前请先安装更新`yum</p>
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<p>在项目<code>escheduler-ui</code>根目录下，修改install.sh中的参数，执行<code>./install(线上环境).sh</code> </p>
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<h3 id="3手动部署方式">3.手动部署方式</h3>
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<p>在项目<code>escheduler-ui</code>根目录执行<code>./install(线上环境).sh</code> </p>
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<h3 id="3手动部署">3.手动部署</h3>
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<p>安装epel源 <code>yum install epel-release -y</code></p>
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@ -560,12 +553,14 @@ esc_proxy_port="http://192.168.220.154:12345"
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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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</code></pre><h2 id="问题">问题</h2>
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<h4 id="1-上传文件大小限制">1. 上传文件大小限制</h4>
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<p>编辑配置文件 <code>vi /etc/nginx/nginx.conf</code></p>
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<pre><code># 更改上传大小
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client_max_body_size 1024m
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</code></pre>
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## FAQ
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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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</code></pre><h1 id="更改上传大小">更改上传大小</h1>
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<p>client_max_body_size 1024m
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```</p>
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</section>
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@ -604,7 +599,7 @@ client_max_body_size 1024m
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<script>
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});
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</script>
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</div>
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@ -435,7 +435,7 @@
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<li><a href="https://blog.csdn.net/u011886447/article/details/79796802" target="_blank">Mysql</a> (5.5+) : 必装</li>
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<li><a href="https://www.oracle.com/technetwork/java/javase/downloads/index.html" target="_blank">JDK</a> (1.8+) : 必装</li>
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<li><a href="https://www.jianshu.com/p/de90172ea680" target="_blank">ZooKeeper</a>(3.4.6) ：必装 </li>
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<li><a href="https://blog.csdn.net/Evankaka/article/details/51612437" target="_blank">Hadoop</a>(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)</li>
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<li><a href="https://blog.csdn.net/Evankaka/article/details/51612437" target="_blank">Hadoop</a>(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)</li>
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<li><a href="https://staroon.pro/2017/12/09/HiveInstall/" target="_blank">Hive</a>(1.2.1) : 选装，hive任务提交需要安装</li>
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<li>Spark(1.x,2.x) : 选装，Spark任务提交需要安装</li>
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<li>PostgreSQL(8.2.15+) : 选装，PostgreSQL PostgreSQL存储过程需要安装</li>
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@ -450,13 +450,7 @@
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<li>查看目录</li>
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</ul>
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<p>正常编译完后，会在当前目录生成 target/escheduler-{version}/</p>
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<pre><code> 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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</code></pre><ul>
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<ul>
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<li>说明</li>
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</ul>
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<pre><code>bin : 基础服务启动脚本
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@ -483,7 +477,9 @@ mysql -h {host} -u {user} -p{password} -D {db} < escheduler.sql
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mysql -h {host} -u {user} -p{password} -D {db} < quartz.sql
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</code></pre><h2 id="创建部署用户">创建部署用户</h2>
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<p>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。</p>
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<ul>
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<li>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。</li>
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</ul>
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<pre><code class="lang-部署账号">vi /etc/sudoers
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# 部署用户是 escheduler 账号
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@ -492,301 +488,65 @@ escheduler ALL=(ALL) NOPASSWD: NOPASSWD: ALL
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# 并且需要注释掉 Default requiretty 一行
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#Default requiretty
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</code></pre>
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<h2 id="配置文件说明">配置文件说明</h2>
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<pre><code>说明：配置文件位于 target/escheduler-{version}/conf 下面
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</code></pre><h3 id="escheduler-alert">escheduler-alert</h3>
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<p>配置邮件告警信息</p>
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<h2 id="ssh免密配置">ssh免密配置</h2>
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<p> 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</p>
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<ul>
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<li>alert.properties </li>
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<li><a href="http://geek.analysys.cn/topic/113" target="_blank">将 <strong>主机器</strong> 和各个其它机器SSH打通</a></li>
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</ul>
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<pre><code>#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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# 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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# 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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</code></pre><h3 id="escheduler-common">escheduler-common</h3>
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<p>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</p>
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<h2 id="部署">部署</h2>
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<h3 id="1-修改安装目录权限">1. 修改安装目录权限</h3>
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<ul>
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<li>common/common.properties</li>
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<li>安装目录如下：</li>
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</ul>
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<pre><code>#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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</code></pre><p>SHELL任务 环境变量配置</p>
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<pre><code>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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</code></pre><p>.escheduler_env.sh </p>
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<pre><code>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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</code></pre><p>​ </p>
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<p>Python任务 环境变量配置</p>
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<pre><code>说明：配置文件位于 target/escheduler-{version}/conf/env 下面
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</code></pre><p>escheduler_env.py</p>
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<pre><code>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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</code></pre><p>hadoop 配置文件</p>
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<ul>
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<li>common/hadoop/hadoop.properties</li>
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</ul>
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<pre><code># 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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</code></pre><p>定时器配置文件</p>
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<ul>
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<li>quartz.properties</li>
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</ul>
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<pre><code>#============================================================================
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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
|
||||
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
|
||||
|
||||
#============================================================================
|
||||
# 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
|
||||
</code></pre><p>zookeeper 配置文件</p>
|
||||
<ul>
|
||||
<li>zookeeper.properties</li>
|
||||
</ul>
|
||||
<pre><code>#zookeeper cluster
|
||||
zookeeper.quorum=192.168.xx.xx:2181,192.168.xx.xx:2181,192.168.xx.xx:2181
|
||||
|
||||
#escheduler root directory
|
||||
zookeeper.escheduler.root=/escheduler
|
||||
|
||||
#zookeeper server dirctory
|
||||
zookeeper.escheduler.dead.servers=/escheduler/dead-servers
|
||||
zookeeper.escheduler.masters=/escheduler/masters
|
||||
zookeeper.escheduler.workers=/escheduler/workers
|
||||
|
||||
#zookeeper lock dirctory
|
||||
zookeeper.escheduler.lock.masters=/escheduler/lock/masters
|
||||
zookeeper.escheduler.lock.workers=/escheduler/lock/workers
|
||||
|
||||
#escheduler failover directory
|
||||
zookeeper.escheduler.lock.masters.failover=/escheduler/lock/failover/masters
|
||||
zookeeper.escheduler.lock.workers.failover=/escheduler/lock/failover/workers
|
||||
|
||||
#escheduler failover directory
|
||||
zookeeper.session.timeout=300
|
||||
zookeeper.connection.timeout=300
|
||||
zookeeper.retry.sleep=1000
|
||||
zookeeper.retry.maxtime=5
|
||||
</code></pre><h3 id="escheduler-dao">escheduler-dao</h3>
|
||||
<p>dao数据源配置</p>
|
||||
<ul>
|
||||
<li>dao/data_source.properties</li>
|
||||
</ul>
|
||||
<pre><code># 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
|
||||
</code></pre><h3 id="escheduler-server">escheduler-server</h3>
|
||||
<p>master配置文件</p>
|
||||
<ul>
|
||||
<li>master.properties</li>
|
||||
</ul>
|
||||
<pre><code># 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
|
||||
</code></pre><p>worker配置文件</p>
|
||||
<ul>
|
||||
<li>worker.properties</li>
|
||||
</ul>
|
||||
<pre><code># 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
|
||||
</code></pre><h3 id="escheduler-api">escheduler-api</h3>
|
||||
<p>web配置文件</p>
|
||||
<ul>
|
||||
<li>application.properties</li>
|
||||
</ul>
|
||||
<pre><code># 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
|
||||
</code></pre><h2 id="伪分布式部署">伪分布式部署</h2>
|
||||
<h3 id="1，创建部署用户">1，创建部署用户</h3>
|
||||
<p>​ 如上 <strong>创建部署用户</strong></p>
|
||||
<h3 id="2，根据实际需求来创建hdfs根路径">2，根据实际需求来创建HDFS根路径</h3>
|
||||
<p>​ 根据 <strong>common/common.properties</strong> 中 <strong>hdfs.startup.state</strong> 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 <strong>owner</strong> 修改为<strong>部署用户</strong>，否则忽略此步骤</p>
|
||||
<h3 id="3，项目编译">3，项目编译</h3>
|
||||
<p>​ 如上进行 <strong>项目编译</strong></p>
|
||||
<h3 id="4，修改配置文件">4，修改配置文件</h3>
|
||||
<p>​ 根据 <strong>配置文件说明</strong> 修改配置文件和 <strong>环境变量</strong> 文件</p>
|
||||
<h3 id="5，创建目录并将环境变量文件复制到指定目录">5，创建目录并将环境变量文件复制到指定目录</h3>
|
||||
<ul>
|
||||
<li><p>创建 <strong>common/common.properties</strong> 下的data.basedir.path、data.download.basedir.path和process.exec.basepath路径</p>
|
||||
</li>
|
||||
<li><p>将<strong>.escheduler_env.sh</strong> 和 <strong>escheduler_env.py</strong> 两个环境变量文件复制到 <strong>common/common.properties</strong>配置的<strong>escheduler.env.path</strong> 和 <strong>escheduler.env.py</strong> 的目录下，并将 <strong>owner</strong> 修改为<strong>部署用户</strong></p>
|
||||
<pre><code> bin
|
||||
conf
|
||||
install.sh
|
||||
lib
|
||||
script
|
||||
sql
|
||||
</code></pre><ul>
|
||||
<li><p>修改权限(deployUser修改为对应部署用户)</p>
|
||||
<p> <code>sudo chown -R deployUser:deployUser *</code></p>
|
||||
</li>
|
||||
</ul>
|
||||
<h3 id="6，启停服务">6，启停服务</h3>
|
||||
<h3 id="2-修改环境变量文件">2. 修改环境变量文件</h3>
|
||||
<ul>
|
||||
<li>根据业务需求，修改conf/env/目录下的<strong>escheduler_env.py</strong>，<strong>.escheduler_env.sh</strong>两个文件中的环境变量</li>
|
||||
</ul>
|
||||
<h3 id="3-修改部署参数">3. 修改部署参数</h3>
|
||||
<ul>
|
||||
<li><p>修改 <strong>install.sh</strong>中的参数，替换成自身业务所需的值</p>
|
||||
</li>
|
||||
<li><p>如果使用hdfs相关功能，需要拷贝<strong>hdfs-site.xml</strong>和<strong>core-site.xml</strong>到conf目录下</p>
|
||||
</li>
|
||||
</ul>
|
||||
<h3 id="4-一键部署">4. 一键部署</h3>
|
||||
<ul>
|
||||
<li><p>安装zookeeper工具 </p>
|
||||
<p> <code>pip install kazoo</code></p>
|
||||
</li>
|
||||
<li><p>切换到部署用户，一键部署</p>
|
||||
<p> <code>sh install.sh</code> </p>
|
||||
</li>
|
||||
<li><p>jps查看服务是否启动</p>
|
||||
</li>
|
||||
</ul>
|
||||
<pre><code class="lang-aidl"> MasterServer ----- master服务
|
||||
WorkerServer ----- worker服务
|
||||
LoggerServer ----- logger服务
|
||||
ApiApplicationServer ----- api服务
|
||||
AlertServer ----- alert服务
|
||||
</code></pre>
|
||||
<h2 id="日志查看">日志查看</h2>
|
||||
<p>日志统一存放于指定文件夹内</p>
|
||||
<pre><code class="lang-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
|
||||
</code></pre>
|
||||
<h2 id="启停服务">启停服务</h2>
|
||||
<ul>
|
||||
<li>启停Master</li>
|
||||
</ul>
|
||||
|
|
@ -813,54 +573,7 @@ sh ./bin/escheduler-daemon.sh stop logger-server
|
|||
</ul>
|
||||
<pre><code>sh ./bin/escheduler-daemon.sh start alert-server
|
||||
sh ./bin/escheduler-daemon.sh stop alert-server
|
||||
</code></pre><h2 id="分布式部署">分布式部署</h2>
|
||||
<h3 id="1，创建部署用户">1，创建部署用户</h3>
|
||||
<ul>
|
||||
<li>在需要部署调度的机器上如上 <strong>创建部署用户</strong></li>
|
||||
<li><a href="https://blog.csdn.net/thinkmore1314/article/details/22489203" target="_blank">将 <strong>主机器</strong> 和各个其它机器SSH打通</a></li>
|
||||
</ul>
|
||||
<h3 id="2，根据实际需求来创建hdfs根路径">2，根据实际需求来创建HDFS根路径</h3>
|
||||
<p>​ 根据 <strong>common/common.properties</strong> 中 <strong>hdfs.startup.state</strong> 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 <strong>owner</strong> 修改为<strong>部署用户</strong>，否则忽略此步骤</p>
|
||||
<h3 id="3，项目编译">3，项目编译</h3>
|
||||
<p>​ 如上进行 <strong>项目编译</strong></p>
|
||||
<h3 id="4，将环境变量文件复制到指定目录">4，将环境变量文件复制到指定目录</h3>
|
||||
<p>​ 将<strong>.escheduler_env.sh</strong> 和 <strong>escheduler_env.py</strong> 两个环境变量文件复制到 <strong>common/common.properties</strong>配置的<strong>escheduler.env.path</strong> 和 <strong>escheduler.env.py</strong> 的目录下，并将 <strong>owner</strong> 修改为<strong>部署用户</strong></p>
|
||||
<h3 id="5，修改-installsh">5，修改 install.sh</h3>
|
||||
<p>​ 修改 install.sh 中变量的值，替换成自身业务所需的值</p>
|
||||
<h3 id="6，一键部署">6，一键部署</h3>
|
||||
<ul>
|
||||
<li>安装 pip install kazoo</li>
|
||||
<li>安装目录如下：</li>
|
||||
</ul>
|
||||
<pre><code> bin
|
||||
conf
|
||||
escheduler-1.0.0-SNAPSHOT.tar.gz
|
||||
install.sh
|
||||
lib
|
||||
monitor_server.py
|
||||
script
|
||||
sql
|
||||
</code></pre><ul>
|
||||
<li><p>使用部署用户 sh install.sh 一键部署</p>
|
||||
<ul>
|
||||
<li>注意：scp_hosts.sh 里 <code>tar -zxvf $workDir/../escheduler-1.0.0.tar.gz -C $installPath</code> 中的版本号(1.0.0)需要执行前手动替换成对应的版本号</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
<h2 id="服务监控">服务监控</h2>
|
||||
<p>monitor_server.py 脚本是监听，master和worker服务挂掉重启的脚本</p>
|
||||
<p>注意：在全部服务都启动之后启动</p>
|
||||
<p>nohup python -u monitor_server.py > nohup.out 2>&1 &</p>
|
||||
<h2 id="日志查看">日志查看</h2>
|
||||
<p>日志统一存放于指定文件夹内</p>
|
||||
<pre><code class="lang-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
|
||||
</code></pre>
|
||||
|
||||
|
||||
</section>
|
||||
|
||||
|
|
@ -899,7 +612,7 @@ sh ./bin/escheduler-daemon.sh stop alert-server
|
|||
<script>
|
||||
var gitbook = gitbook || [];
|
||||
gitbook.push(function() {
|
||||
gitbook.page.hasChanged({"page":{"title":"后端部署文档","level":"1.3.1","depth":2,"next":{"title":"系统使用手册","level":"1.4","depth":1,"anchor":"#使用手册","path":"系统使用手册.md","ref":"系统使用手册.md#使用手册","articles":[]},"previous":{"title":"后端部署文档","level":"1.3","depth":1,"ref":"","articles":[{"title":"后端部署文档","level":"1.3.1","depth":2,"anchor":"#部署文档","path":"后端部署文档.md","ref":"后端部署文档.md#部署文档","articles":[]}]},"dir":"ltr"},"config":{"plugins":["expandable-chapters","insert-logo-link","livereload"],"styles":{"website":"./styles/website.css"},"pluginsConfig":{"livereload":{},"insert-logo-link":{"src":"http://geek.analysys.cn/static/upload/236/2019-03-29/379450b4-7919-4707-877c-4d33300377d4.png","url":"https://github.com/analysys/EasyScheduler"},"search":{},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"fontsettings":{"theme":"white","family":"sans","size":2},"highlight":{},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"theme-default":{"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"showLevel":false},"expandable-chapters":{}},"theme":"default","author":"YIGUAN","pdf":{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"top":56,"bottom":56}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"variables":{},"title":"调度系统-EasyScheduler","language":"zh-hans","gitbook":"3.2.3","description":"调度系统"},"file":{"path":"后端部署文档.md","mtime":"2019-04-08T08:09:31.074Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2019-04-10T07:14:01.407Z"},"basePath":".","book":{"language":""}});
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|
||||
});
|
||||
</script>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -65,26 +65,40 @@
|
|||
// edit
|
||||
if (this.item) {
|
||||
param.id = this.item.id
|
||||
if (this.item.queueName === this.queueName && this.item.queue === this.queue) {
|
||||
this.$message.warning(`名称和队列值未做更改`)
|
||||
return
|
||||
}
|
||||
}
|
||||
this._verifyName(param).then(() => {
|
||||
|
||||
let $then = (res) => {
|
||||
this.$emit('onUpdate')
|
||||
this.$message.success(res.msg)
|
||||
setTimeout(() => {
|
||||
this.$refs['popup'].spinnerLoading = false
|
||||
}, 800)
|
||||
}
|
||||
|
||||
let $catch = (e) => {
|
||||
this.$message.error(e.msg || '')
|
||||
this.$refs['popup'].spinnerLoading = false
|
||||
}
|
||||
|
||||
if (this.item) {
|
||||
this.$refs['popup'].spinnerLoading = true
|
||||
this.store.dispatch(`security/${this.item ? 'updateQueueQ' : 'createQueueQ'}`, param).then(res => {
|
||||
this.$emit('onUpdate')
|
||||
this.$message.success(res.msg)
|
||||
setTimeout(() => {
|
||||
this.$refs['popup'].spinnerLoading = false
|
||||
}, 800)
|
||||
this.store.dispatch(`security/updateQueueQ`, param).then(res => {
|
||||
$then(res)
|
||||
}).catch(e => {
|
||||
$catch(e)
|
||||
})
|
||||
}else{
|
||||
this._verifyName(param).then(() => {
|
||||
this.$refs['popup'].spinnerLoading = true
|
||||
this.store.dispatch(`security/createQueueQ`, param).then(res => {
|
||||
$then(res)
|
||||
}).catch(e => {
|
||||
$catch(e)
|
||||
})
|
||||
}).catch(e => {
|
||||
this.$message.error(e.msg || '')
|
||||
this.$refs['popup'].spinnerLoading = false
|
||||
})
|
||||
}).catch(e => {
|
||||
this.$message.error(e.msg || '')
|
||||
})
|
||||
}
|
||||
|
||||
},
|
||||
_verification(){
|
||||
|
|
|
|||
Loading…
Reference in New Issue