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
# 配置前端访问端口
@@ -604,7 +604,7 @@ client_max_body_size 1024m
diff --git "a/docs/zh_CN/_book/\345\220\216\347\253\257\351\203\250\347\275\262\346\226\207\346\241\243.html" "b/docs/zh_CN/_book/\345\220\216\347\253\257\351\203\250\347\275\262\346\226\207\346\241\243.html"
index ab7bf3765e402d8d46d01e96df3870636838036c..68cdcf9ca2777b8f08e003a8add5c36657603764 100644
--- "a/docs/zh_CN/_book/\345\220\216\347\253\257\351\203\250\347\275\262\346\226\207\346\241\243.html"
+++ "b/docs/zh_CN/_book/\345\220\216\347\253\257\351\203\250\347\275\262\346\226\207\346\241\243.html"
@@ -435,7 +435,7 @@
Mysql (5.5+) : 必装
JDK (1.8+) : 必装
ZooKeeper(3.4.6) ：必装
-Hadoop(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(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(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
@@ -450,13 +450,7 @@
查看目录
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
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
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。
+
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
-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
+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
-- zookeeper.properties
+- 将 主机器 和各个其它机器SSH打通
-#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
+部署
+1. 修改安装目录权限
-- dao/data_source.properties
+- 安装目录如下：
-# 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
-
-- master.properties
+
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 *
+
-# 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
+2. 修改环境变量文件
-- worker.properties
+- 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 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
-
escheduler-api
-web配置文件
+3. 修改部署参数
-- application.properties
+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
+
-# 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
-
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
+4. 一键部署
-创建 common/common.properties 下的data.basedir.path、data.download.basedir.path和process.exec.basepath路径
+安装zookeeper工具
+ pip install kazoo
+切换到部署用户，一键部署
+ sh install.sh
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
+jps查看服务是否启动
-6，启停服务
+ MasterServer ----- master服务
+ WorkerServer ----- worker服务
+ LoggerServer ----- logger服务
+ ApiApplicationServer ----- api服务
+ AlertServer ----- alert服务
+
+日志查看
+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
+
+启停服务
- 启停Master
@@ -813,54 +573,7 @@ sh ./bin/escheduler-daemon.sh stop logger-server
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
-
-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
- logs/
- ├── escheduler-alert-server.log
- ├── escheduler-master-server.log
- |—— escheduler-worker-server.log
- |—— escheduler-api-server.log
- |—— escheduler-logger-server.log
-
@@ -899,7 +612,7 @@ sh ./bin/escheduler-daemon.sh stop alert-server
diff --git "a/docs/zh_CN/\345\211\215\347\253\257\351\203\250\347\275\262\346\226\207\346\241\243.md" "b/docs/zh_CN/\345\211\215\347\253\257\351\203\250\347\275\262\346\226\207\346\241\243.md"
index e639c737b4947ab0f76ffd57a807f0800e031fcd..7f22a5ddaa2080aa2d334576fe159e10fac650eb 100644
--- "a/docs/zh_CN/\345\211\215\347\253\257\351\203\250\347\275\262\346\226\207\346\241\243.md"
+++ "b/docs/zh_CN/\345\211\215\347\253\257\351\203\250\347\275\262\346\226\207\346\241\243.md"
@@ -45,7 +45,7 @@ API_BASE = http://192.168.220.204:12345
-### 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`
diff --git "a/docs/zh_CN/\345\220\216\347\253\257\351\203\250\347\275\262\346\226\207\346\241\243.md" "b/docs/zh_CN/\345\220\216\347\253\257\351\203\250\347\275\262\346\226\207\346\241\243.md"
index 4559e4b8176bf7ae51e83e5a80477569814957b3..bba0b0f172f3a90a23c5f057d86a9c69348aa1ed 100644
--- "a/docs/zh_CN/\345\220\216\347\253\257\351\203\250\347\275\262\346\226\207\346\241\243.md"
+++ "b/docs/zh_CN/\345\220\216\347\253\257\351\203\250\347\275\262\346\226\207\346\241\243.md"
@@ -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
@@ -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
-#============================================================================
+## 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
-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
+- [将 **主机器** 和各个其它机器SSH打通](http://geek.analysys.cn/topic/113)
-#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
-```
+## 部署
+### 1. 修改安装目录权限
-
-### escheduler-server
-
-master配置文件
-
-- 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
-
-# only larger than reserved memory, master server can work. default value : physical memory * 1/10, unit is G.
-master.reserved.memory=1
+ 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 *`
+### 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
-
-# worker heartbeat interval
-worker.heartbeat.interval=10
-
-# submit the number of tasks at a time
-worker.fetch.task.num = 10
+ - 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
-# 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
-```
+### 4. 一键部署
+- 安装zookeeper工具
+ `pip install kazoo`
-### escheduler-api
+- 切换到部署用户，一键部署
-web配置文件
+ `sh install.sh`
-- 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
@@ -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** 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
diff --git a/escheduler-ui/src/js/conf/home/pages/security/pages/queue/_source/createQueue.vue b/escheduler-ui/src/js/conf/home/pages/security/pages/queue/_source/createQueue.vue
index d3fb95e02c1dfcd03770a585ae60a70282fdd173..7061d02bf0c1a731c9c3894c2c3ad5708837e294 100644
--- a/escheduler-ui/src/js/conf/home/pages/security/pages/queue/_source/createQueue.vue
+++ b/escheduler-ui/src/js/conf/home/pages/security/pages/queue/_source/createQueue.vue
@@ -66,21 +66,39 @@
if (this.item) {
param.id = this.item.id
}
- this._verifyName(param).then(() => {
+
+ let $then = (res) => {
+ this.$emit('onUpdate')
+ this.$message.success(res.msg)
+ setTimeout(() => {
+ this.$refs['popup'].spinnerLoading = false
+ }, 800)
+ }
+
+ 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/${this.item ? 'updateQueueQ' : 'createQueueQ'}`, param).then(res => {
- this.$emit('onUpdate')
- this.$message.success(res.msg)
- setTimeout(() => {
- this.$refs['popup'].spinnerLoading = false
- }, 800)
+ 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 || '')
- this.$refs['popup'].spinnerLoading = false
})
- }).catch(e => {
- this.$message.error(e.msg || '')
- })
+ }
},
_verification(){
diff --git a/install.sh b/install.sh
index 81772729de7ba862cb583f03a79c4dc446eacae4..c6c734078f0036984a3bce5e354905dd370e0166 100644
--- a/install.sh
+++ b/install.sh
@@ -47,8 +47,57 @@ mysqlUserName="xx"
# 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"
@@ -58,6 +107,8 @@ yarnHaIps="192.168.xx.xx,192.168.xx.xx"
# 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
@@ -69,17 +120,11 @@ downloadPath="/tmp/escheduler/download"
# 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"
@@ -87,11 +132,7 @@ 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"
@@ -168,7 +209,6 @@ workerMaxCupLoadAvg="10"
# 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"
@@ -188,53 +228,6 @@ springMaxRequestSize="1024MB"
# 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
@@ -317,8 +310,6 @@ sed -i ${txt} "s#alertServer.*#alertServer=${alertServer}#g" conf/config/run_con
sed -i ${txt} "s#apiServers.*#apiServers=${apiServers}#g" conf/config/run_config.conf
-
-
# 2,创建目录
echo "2,创建目录"
diff --git a/monitor_server.py b/script/monitor_server.py
similarity index 100%
rename from monitor_server.py
rename to script/monitor_server.py
diff --git a/script/scp_hosts.sh b/script/scp_hosts.sh
index 3884a72d8088fbff2879438833fa42c6bf006711..74db0c14d9b47aab4a4d8f2cf1a8ae0e4a9acb1a 100755
--- a/script/scp_hosts.sh
+++ b/script/scp_hosts.sh
@@ -5,8 +5,6 @@ workDir=`cd ${workDir};pwd`
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