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<h1 id="后端部署文档">后端部署文档</h1> |
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<h2 id="基础软件安装">基础软件安装</h2> |
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<ul> |
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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://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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</ul> |
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<pre><code> 注意：EasyScheduler本身不依赖Hadoop、Hive、Spark、PostgreSQL,仅是会调用他们的Client，用于对应任务的运行。 |
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</code></pre><h2 id="项目编译">项目编译</h2> |
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<ul> |
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<li>执行编译命令：</li> |
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</ul> |
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<pre><code> mvn -U clean package assembly:assembly -Dmaven.test.skip=true |
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</code></pre><ul> |
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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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<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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conf : 项目配置文件 |
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lib : 项目依赖jar包，包括各个模块jar和第三方jar |
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script : 集群启动、停止和服务监控启停脚本 |
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sql : 项目依赖sql文件 |
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install.sh : 一键部署脚本 |
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</code></pre><h2 id="数据库初始化">数据库初始化</h2> |
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<ul> |
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<li>创建database和账号</li> |
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</ul> |
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<pre><code>mysql -h {host} -u {user} -p{password} |
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mysql> CREATE DATABASE escheduler DEFAULT CHARACTER SET utf8 DEFAULT COLLATE utf8_general_ci; |
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mysql> GRANT ALL PRIVILEGES ON escheduler.* TO '{user}'@'%' IDENTIFIED BY '{password}'; |
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mysql> GRANT ALL PRIVILEGES ON escheduler.* TO '{user}'@'localhost' IDENTIFIED BY '{password}'; |
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mysql> flush privileges; |
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</code></pre><ul> |
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<li>创建表和导入基础数据</li> |
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</ul> |
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<pre><code>说明：在 target/escheduler-{version}/sql/escheduler.sql和quartz.sql |
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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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<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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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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<ul> |
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<li>alert.properties </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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<ul> |
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<li>common/common.properties</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 |
|
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 |
|
#============================================================================ |
|
|
|
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> |
|
</li> |
|
</ul> |
|
<h3 id="6，启停服务">6，启停服务</h3> |
|
<ul> |
|
<li>启停Master</li> |
|
</ul> |
|
<pre><code class="lang-启动master">sh ./bin/escheduler-daemon.sh start master-server |
|
sh ./bin/escheduler-daemon.sh stop master-server |
|
</code></pre> |
|
<ul> |
|
<li>启停Worker</li> |
|
</ul> |
|
<pre><code>sh ./bin/escheduler-daemon.sh start worker-server |
|
sh ./bin/escheduler-daemon.sh stop worker-server |
|
</code></pre><ul> |
|
<li>启停Api</li> |
|
</ul> |
|
<pre><code>sh ./bin/escheduler-daemon.sh start api-server |
|
sh ./bin/escheduler-daemon.sh stop api-server |
|
</code></pre><ul> |
|
<li>启停Logger</li> |
|
</ul> |
|
<pre><code>sh ./bin/escheduler-daemon.sh start logger-server |
|
sh ./bin/escheduler-daemon.sh stop logger-server |
|
</code></pre><ul> |
|
<li>启停Alert</li> |
|
</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> |
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