fix bug, update action would change the user of definition.

#Default requiretty

#Default requiretty

```

```

## 配置文件说明

## 伪分布式部署

```

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

### 2，根据实际需求来创建HDFS根路径

- master.properties

​	根据 **common/common.properties** 中 **hdf.startup.states** 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

# master execute thread num

master.exec.threads=100

# master execute task number in parallel

​	如上进行 **项目编译**

master.exec.task.number=20

# master heartbeat interval

###  4，修改配置文件

master.heartbeat.interval=10

# master commit task retry times

​	根据 **配置文件说明** 修改配置文件和 **环境变量** 文件

master.task.commit.retryTimes=5

# master commit task interval

### 5，创建目录并将环境变量文件复制到指定目录

master.task.commit.interval=100

- 创建 **common/common.properties** 下的data.basedir.path、data.download.basedir.path和process.exec.basepath路径

# only less than cpu avg load, master server can work. default value : the number of cpu cores * 2

- 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**

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

## 分布式部署

- worker.properties

### 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**

# worker execute thread num

- [将 **主机器** 和各个其它机器SSH打通](https://blog.csdn.net/thinkmore1314/article/details/22489203)

worker.exec.threads=100

# worker heartbeat interval

### 2，根据实际需求来创建HDFS根路径

worker.heartbeat.interval=10

# submit the number of tasks at a time

​	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

worker.fetch.task.num = 10

### 3，项目编译

# only less than cpu avg load, worker server can work. default value : the number of cpu cores * 2

​	如上进行 **项目编译**

worker.max.cpuload.avg=10

# only larger than reserved memory, worker server can work. default value : physical memory * 1/6, unit is G.

### 4，将环境变量文件复制到指定目录

worker.reserved.memory=1

```

​	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

### escheduler-api

​	修改 install.sh 中变量的值，替换成自身业务所需的值

web配置文件

### 6，一键部署

- application.properties

- 安装 pip install kazoo

- 安装目录如下：

```

```

# server port

    bin

server.port=12345

    conf

    escheduler-1.0.0-SNAPSHOT.tar.gz

# session config

    install.sh

server.session.timeout=7200

    lib

    monitor_server.py

server.context-path=/escheduler/

    script

    sql

# 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

```

```

- 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

## 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

### 7，启停服务

* 启停Master

* 启停Master

sh ./bin/escheduler-daemon.sh stop 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

```

- 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

## 服务监控

monitor_server.py 脚本是监听，master和worker服务挂掉重启的脚本

monitor_server.py 脚本是监听，master和worker服务挂掉重启的脚本

xlsFilePath="/opt/xls"

xlsFilePath="/opt/xls"

# conf/config/install_config.conf配置

# conf/config/install_config.conf配置

# 安装路径

# 安装路径,不要当前路径(pwd)一样

installPath="/data1_1T/escheduler"

installPath="/data1_1T/escheduler"

# 部署用户

# 部署用户