Merge pull request #38 from lenboo/dev

# 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 

> 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

- `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`

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` 

│ npm      │ 0  │ N/A     │ fork │ 6168 │ online │ 31      │ 0s     │ 0%  │ 5.6 MB   │ root │ disabled │

└──────────┴────┴─────────┴──────┴──────┴────────┴─────────┴────────┴─────┴──────────┴──────┴──────────┘

 Use `pm2 show <id|name>` 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`

```

 * [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

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

```

## 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

#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

```

## 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

- [将 **主机器** 和各个其它机器SSH打通](http://geek.analysys.cn/topic/113)

### escheduler-server

## 部署

master配置文件

### 1. 修改安装目录权限

- master.properties

- 安装目录如下：

```

# 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

    bin

    conf

    install.sh

    lib

    script

    sql

# only larger than reserved memory, master server can work. default value : physical memory * 1/10, unit is G.

master.reserved.memory=1

```

- 修改权限(deployUser修改为对应部署用户)

    `sudo chown -R deployUser:deployUser *`

### 2. 修改环境变量文件

worker配置文件

- 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

- worker.properties

### 3. 修改部署参数

```

# worker execute thread num

worker.exec.threads=100

 - 修改 **install.sh**中的参数，替换成自身业务所需的值

# worker heartbeat interval

worker.heartbeat.interval=10

 -  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

# submit the number of tasks at a time

worker.fetch.task.num = 10

### 4. 一键部署

- 安装zookeeper工具 

# 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

```

   `pip install kazoo`

- 切换到部署用户，一键部署

### escheduler-api

    `sh install.sh` 

web配置文件

- application.properties

- 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

```

# 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

## 日志查看

日志统一存放于指定文件夹内

# post content

server.max-http-post-size=5000000

```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

```

## 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

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

# 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"

# 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

# 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"

# 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"

# 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"

# 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

sed -i ${txt} "s#apiServers.*#apiServers=${apiServers}#g" conf/config/run_config.conf

# 2,创建目录

echo "2,创建目录"

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