# 后端部署文档 ## 基础软件安装 * [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) * [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 ``` 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。 ``` ## 项目编译 * 执行编译命令： ``` mvn -U clean package assembly:assembly -Dmaven.test.skip=true ``` * 查看目录 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 ``` bin : 基础服务启动脚本 conf : 项目配置文件 lib : 项目依赖jar包，包括各个模块jar和第三方jar script : 集群启动、停止和服务监控启停脚本 sql : 项目依赖sql文件 install.sh : 一键部署脚本 ``` ## 数据库初始化 * 创建database和账号 ``` mysql -h {host} -u {user} -p{password} mysql> CREATE DATABASE escheduler DEFAULT CHARACTER SET utf8 DEFAULT COLLATE utf8_general_ci; mysql> GRANT ALL PRIVILEGES ON escheduler.* TO '{user}'@'%' IDENTIFIED BY '{password}'; mysql> GRANT ALL PRIVILEGES ON escheduler.* TO '{user}'@'localhost' IDENTIFIED BY '{password}'; mysql> flush privileges; ``` * 创建表和导入基础数据 ``` 说明：在 target/escheduler-{version}/sql/escheduler.sql和quartz.sql mysql -h {host} -u {user} -p{password} -D {db} < escheduler.sql 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。 ```部署账号 vi /etc/sudoers # 部署用户是 escheduler 账号 escheduler ALL=(ALL) NOPASSWD: NOPASSWD: ALL # 并且需要注释掉 Default requiretty 一行 #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 #============================================================================ 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 ``` zookeeper 配置文件 - zookeeper.properties ``` #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 ``` ### 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 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Master ```启动master sh ./bin/escheduler-daemon.sh start master-server sh ./bin/escheduler-daemon.sh stop master-server ``` * 启停Worker ``` sh ./bin/escheduler-daemon.sh start worker-server sh ./bin/escheduler-daemon.sh stop worker-server ``` * 启停Api ``` sh ./bin/escheduler-daemon.sh start api-server sh ./bin/escheduler-daemon.sh stop api-server ``` * 启停Logger ``` sh ./bin/escheduler-daemon.sh start logger-server sh ./bin/escheduler-daemon.sh stop logger-server ``` * 启停Alert ``` 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 ``` - 一键部署 - 将编译完成的escheduler-1.0.0.tar.gz包放在install.sh同级目录下 - 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 - 使用部署用户 sh install.sh ## 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 ```