Commit 81c39f10 authored by ligang's avatar ligang
Browse files

Merge remote-tracking branch 'upstream/dev' into dev

parents 68d0c974 8aeb1b46
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@@ -469,7 +469,7 @@ API_BASE = http://192.168.220.204:12345
<li><p><code>npm run build</code> &#x9879;&#x76EE;&#x6253;&#x5305; (&#x6253;&#x5305;&#x540E;&#x6839;&#x76EE;&#x5F55;&#x4F1A;&#x521B;&#x5EFA;&#x4E00;&#x4E2A;&#x540D;&#x4E3A;dist&#x6587;&#x4EF6;&#x5939;&#xFF0C;&#x7528;&#x4E8E;&#x53D1;&#x5E03;&#x7EBF;&#x4E0A;Nginx)</p>
</li>
</ul>
<h3 id="2&#x81EA;&#x52A8;&#x5316;&#x90E8;&#x7F72;">2.&#x81EA;&#x52A8;&#x5316;&#x90E8;&#x7F72;`</h3>
<h3 id="2&#x81EA;&#x52A8;&#x5316;&#x90E8;&#x7F72;">2.&#x81EA;&#x52A8;&#x5316;&#x90E8;&#x7F72;</h3>
<p>&#x5728;&#x9879;&#x76EE;<code>escheduler-ui</code>&#x6839;&#x76EE;&#x5F55;&#x7F16;&#x8F91;&#x5B89;&#x88C5;&#x6587;&#x4EF6;<code>vi install(&#x7EBF;&#x4E0A;&#x73AF;&#x5883;).sh</code></p>
<p>&#x66F4;&#x6539;&#x524D;&#x7AEF;&#x8BBF;&#x95EE;&#x7AEF;&#x53E3;&#x548C;&#x540E;&#x7AEF;&#x4EE3;&#x7406;&#x63A5;&#x53E3;&#x5730;&#x5740;</p>
<pre><code># &#x914D;&#x7F6E;&#x524D;&#x7AEF;&#x8BBF;&#x95EE;&#x7AEF;&#x53E3;
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### 2.自动化部署`
### 2.自动化部署

在项目`escheduler-ui`根目录编辑安装文件`vi install(线上环境).sh`

+43 −431
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@@ -6,7 +6,7 @@
 * [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
@@ -27,15 +27,6 @@

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

```
@@ -74,7 +65,7 @@ mysql -h {host} -u {user} -p{password} -D {db} < quartz.sql

## 创建部署用户

因为escheduler worker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
@@ -86,386 +77,73 @@ escheduler ALL=(ALL) NOPASSWD: NOPASSWD: ALL
#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****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****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

-**.escheduler_env.sh****escheduler_env.py** 两个环境变量文件复制到 **common/common.properties**配置的**escheduler.env.path****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

@@ -500,68 +178,3 @@ 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****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****escheduler_env.py** 两个环境变量文件复制到 **common/common.properties**配置的**escheduler.env.path****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
+29 −11
Original line number Diff line number Diff line
@@ -66,21 +66,39 @@
        if (this.item) {
          param.id = this.item.id
        }
        this._verifyName(param).then(() => {
          this.$refs['popup'].spinnerLoading = true
          this.store.dispatch(`security/${this.item ? 'updateQueueQ' : 'createQueueQ'}`, param).then(res => {

        let $then = (res) => {
          this.$emit('onUpdate')
          this.$message.success(res.msg)
          setTimeout(() => {
            this.$refs['popup'].spinnerLoading = false
          }, 800)
          }).catch(e => {
        }

        let $catch = (e) => {
          this.$message.error(e.msg || '')
          this.$refs['popup'].spinnerLoading = false
        }

        if (this.item) {
          this.$refs['popup'].spinnerLoading = true
          this.store.dispatch(`security/updateQueueQ`, param).then(res => {
            $then(res)
          }).catch(e => {
            $catch(e)
          })
        }else{
          this._verifyName(param).then(() => {
            this.$refs['popup'].spinnerLoading = true
            this.store.dispatch(`security/createQueueQ`, param).then(res => {
              $then(res)
            }).catch(e => {
              $catch(e)
            })
          }).catch(e => {
            this.$message.error(e.msg || '')
          })
        }

      },
      _verification(){
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