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

6 years ago · cba13a1fee
2 changed files with 38 additions and 395 deletions
--- a/docs/zh_CN/后端部署文档.md
+++ b/docs/zh_CN/后端部署文档.md
@ -86,386 +86,75 @@ escheduler  ALL=(ALL)       NOPASSWD: NOPASSWD: ALL
 #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
@ -500,52 +189,6 @@ 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
 ```
 - 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
--- a/install.sh
+++ b/install.sh
@ -211,7 +211,7 @@ mailPassword="xxxxxxxxxx"
 xlsFilePath="/opt/xls"
 # 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"
 # 部署用户