Node包下载 (注意版本 8.9.4) https://nodejs.org/download/release/v8.9.4/
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
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)
在项目escheduler-ui根目录编辑安装文件vi install(线上环境).sh
更改前端访问端口和后端代理接口地址
@@ -478,6 +469,8 @@ esc_proxy="8888" # 配置代理后端接口 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
在项目escheduler-ui根目录执行./install(线上环境).sh
安装epel源 yum install epel-release -y
编辑配置文件 vi /etc/nginx/nginx.conf
# 更改上传大小
-client_max_body_size 1024m
-client_max_body_size 1024m +```
+正常编译完后，会在当前目录生成 target/escheduler-{version}/
- bin
- conf
- lib
- script
- sql
- install.sh
-bin : 基础服务启动脚本
@@ -483,7 +477,9 @@ mysql -h {host} -u {user} -p{password} -D {db} < escheduler.sql
mysql -h {host} -u {user} -p{password} -D {db} < quartz.sql
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。
+vi /etc/sudoers
# 部署用户是 escheduler 账号
@@ -492,301 +488,65 @@ escheduler ALL=(ALL) NOPASSWD: NOPASSWD: ALL
# 并且需要注释掉 Default requiretty 一行
#Default requiretty
说明：配置文件位于 target/escheduler-{version}/conf 下面
-配置邮件告警信息
+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
#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
-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
+#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
-# 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
-#============================================================================
-# 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 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
-dao数据源配置
-# 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
-master配置文件
-# 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 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
-web配置文件
-# 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
- 如上 创建部署用户
- 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
- 如上进行 项目编译
- 根据 配置文件说明 修改配置文件和 环境变量 文件
-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
+ bin
+ conf
+ install.sh
+ lib
+ script
+ sql
+修改权限(deployUser修改为对应部署用户)
+ sudo chown -R deployUser:deployUser *
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
+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
+ MasterServer ----- master服务
+ WorkerServer ----- worker服务
+ LoggerServer ----- logger服务
+ ApiApplicationServer ----- api服务
+ AlertServer ----- alert服务
+日志统一存放于指定文件夹内
+ logs/
+ ├── escheduler-alert-server.log
+ ├── escheduler-master-server.log
+ |—— escheduler-worker-server.log
+ |—— escheduler-api-server.log
+ |—— escheduler-logger-server.log
+sh ./bin/escheduler-daemon.sh start alert-server
sh ./bin/escheduler-daemon.sh stop alert-server
- 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
- 如上进行 项目编译
- 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
- 修改 install.sh 中变量的值，替换成自身业务所需的值
- 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
-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 &
-日志统一存放于指定文件夹内
- logs/
- ├── escheduler-alert-server.log
- ├── escheduler-master-server.log
- |—— escheduler-worker-server.log
- |—— escheduler-api-server.log
- |—— escheduler-logger-server.log