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@@ -6,7 +6,7 @@
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* [Mysql](https://blog.csdn.net/u011886447/article/details/79796802) (5.5+) : 必装
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* [JDK](https://www.oracle.com/technetwork/java/javase/downloads/index.html) (1.8+) : 必装
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* [ZooKeeper](https://www.jianshu.com/p/de90172ea680)(3.4.6) ：必装
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- * [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)
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+ * [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)
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* [Hive](https://staroon.pro/2017/12/09/HiveInstall/)(1.2.1) : 选装，hive任务提交需要安装
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* Spark(1.x,2.x) : 选装，Spark任务提交需要安装
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* PostgreSQL(8.2.15+) : 选装，PostgreSQL PostgreSQL存储过程需要安装
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@@ -27,15 +27,6 @@
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正常编译完后，会在当前目录生成 target/escheduler-{version}/
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-```
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- bin
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- conf
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- lib
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- script
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- sql
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- install.sh
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-```
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-
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- 说明
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```
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@@ -74,7 +65,7 @@ mysql -h {host} -u {user} -p{password} -D {db} < quartz.sql
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## 创建部署用户
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-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。
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+- 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。
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```部署账号
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vi /etc/sudoers
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@@ -86,386 +77,73 @@ escheduler ALL=(ALL) NOPASSWD: NOPASSWD: ALL
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#Default requiretty
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```
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-## 配置文件说明
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-
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-```
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-说明：配置文件位于 target/escheduler-{version}/conf 下面
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-```
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-
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-### escheduler-alert
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-
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-配置邮件告警信息
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-
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-
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-* alert.properties
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-
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-```
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-#以qq邮箱为例，如果是别的邮箱，请更改对应配置
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-#alert type is EMAIL/SMS
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-alert.type=EMAIL
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-
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-# mail server configuration
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-mail.protocol=SMTP
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-mail.server.host=smtp.exmail.qq.com
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-mail.server.port=25
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-mail.sender=xxxxxxx@qq.com
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-mail.passwd=xxxxxxx
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-
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-# xls file path, need manually create it before use if not exist
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-xls.file.path=/opt/xls
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-```
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-
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-
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-
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-
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-### escheduler-common
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-
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-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
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-
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-- common/common.properties
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-
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-```
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-#task queue implementation, default "zookeeper"
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-escheduler.queue.impl=zookeeper
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-
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-# user data directory path, self configuration, please make sure the directory exists and have read write permissions
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-data.basedir.path=/tmp/escheduler
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-
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-# directory path for user data download. self configuration, please make sure the directory exists and have read write permissions
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-data.download.basedir.path=/tmp/escheduler/download
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-
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-# process execute directory. self configuration, please make sure the directory exists and have read write permissions
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-process.exec.basepath=/tmp/escheduler/exec
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-
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-# 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
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-data.store2hdfs.basepath=/escheduler
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-
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-# whether hdfs starts
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-hdfs.startup.state=true
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-
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-# system env path. self configuration, please make sure the directory and file exists and have read write execute permissions
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-escheduler.env.path=/opt/.escheduler_env.sh
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-escheduler.env.py=/opt/escheduler_env.py
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-
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-#resource.view.suffixs
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-resource.view.suffixs=txt,log,sh,conf,cfg,py,java,sql,hql,xml
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-
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-# is development state? default "false"
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-development.state=false
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-```
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-
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-
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-
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-SHELL任务 环境变量配置
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-
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-```
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-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
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-```
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-
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-.escheduler_env.sh
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-```
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-export HADOOP_HOME=/opt/soft/hadoop
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-export HADOOP_CONF_DIR=/opt/soft/hadoop/etc/hadoop
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-export SPARK_HOME1=/opt/soft/spark1
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-export SPARK_HOME2=/opt/soft/spark2
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-export PYTHON_HOME=/opt/soft/python
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-export JAVA_HOME=/opt/soft/java
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-export HIVE_HOME=/opt/soft/hive
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-
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-export PATH=$HADOOP_HOME/bin:$SPARK_HOME1/bin:$SPARK_HOME2/bin:$PYTHON_HOME/bin:$JAVA_HOME/bin:$HIVE_HOME/bin:$PATH
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-```
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-
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-
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-
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-
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-Python任务 环境变量配置
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-
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-```
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-说明：配置文件位于 target/escheduler-{version}/conf/env 下面
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-```
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-
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-escheduler_env.py
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-```
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-import os
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-
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-HADOOP_HOME="/opt/soft/hadoop"
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-SPARK_HOME1="/opt/soft/spark1"
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-SPARK_HOME2="/opt/soft/spark2"
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-PYTHON_HOME="/opt/soft/python"
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-JAVA_HOME="/opt/soft/java"
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-HIVE_HOME="/opt/soft/hive"
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-PATH=os.environ['PATH']
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-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)
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-
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-os.putenv('PATH','%s'%PATH)
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-```
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-
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-
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-
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-hadoop 配置文件
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-
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-- common/hadoop/hadoop.properties
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-
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-```
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-# ha or single namenode,If namenode ha needs to copy core-site.xml and hdfs-site.xml to the conf directory
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-fs.defaultFS=hdfs://mycluster:8020
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-
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-#resourcemanager ha note this need ips , this empty if single
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-yarn.resourcemanager.ha.rm.ids=192.168.xx.xx,192.168.xx.xx
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-
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-# If it is a single resourcemanager, you only need to configure one host name. If it is resourcemanager HA, the default configuration is fine
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-yarn.application.status.address=http://ark1:8088/ws/v1/cluster/apps/%s
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-
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-```
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-
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-
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-
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-定时器配置文件
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-
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-- quartz.properties
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-
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-```
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-#============================================================================
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-# Configure Main Scheduler Properties
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-#============================================================================
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-org.quartz.scheduler.instanceName = EasyScheduler
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-org.quartz.scheduler.instanceId = AUTO
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-org.quartz.scheduler.makeSchedulerThreadDaemon = true
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-org.quartz.jobStore.useProperties = false
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-
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-#============================================================================
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-# Configure ThreadPool
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-#============================================================================
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-
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-org.quartz.threadPool.class = org.quartz.simpl.SimpleThreadPool
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-org.quartz.threadPool.makeThreadsDaemons = true
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-org.quartz.threadPool.threadCount = 25
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-org.quartz.threadPool.threadPriority = 5
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-
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-#============================================================================
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-# Configure JobStore
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-#============================================================================
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+## ssh免密配置
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+ 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
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-org.quartz.jobStore.class = org.quartz.impl.jdbcjobstore.JobStoreTX
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-org.quartz.jobStore.driverDelegateClass = org.quartz.impl.jdbcjobstore.StdJDBCDelegate
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-org.quartz.jobStore.tablePrefix = QRTZ_
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-org.quartz.jobStore.isClustered = true
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-org.quartz.jobStore.misfireThreshold = 60000
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-org.quartz.jobStore.clusterCheckinInterval = 5000
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-org.quartz.jobStore.dataSource = myDs
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-
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-#============================================================================
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-# Configure Datasources
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-#============================================================================
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-
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-org.quartz.dataSource.myDs.driver = com.mysql.jdbc.Driver
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-org.quartz.dataSource.myDs.URL = jdbc:mysql://192.168.xx.xx:3306/escheduler?characterEncoding=utf8&useSSL=false
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-org.quartz.dataSource.myDs.user = xx
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-org.quartz.dataSource.myDs.password = xx
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-org.quartz.dataSource.myDs.maxConnections = 10
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-org.quartz.dataSource.myDs.validationQuery = select 1
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-```
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-
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-
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-
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-zookeeper 配置文件
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-
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-
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-- zookeeper.properties
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-
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-```
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-#zookeeper cluster
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-zookeeper.quorum=192.168.xx.xx:2181,192.168.xx.xx:2181,192.168.xx.xx:2181
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-
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-#escheduler root directory
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-zookeeper.escheduler.root=/escheduler
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-
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-#zookeeper server dirctory
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-zookeeper.escheduler.dead.servers=/escheduler/dead-servers
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-zookeeper.escheduler.masters=/escheduler/masters
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-zookeeper.escheduler.workers=/escheduler/workers
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-
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-#zookeeper lock dirctory
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-zookeeper.escheduler.lock.masters=/escheduler/lock/masters
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-zookeeper.escheduler.lock.workers=/escheduler/lock/workers
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+- [将 **主机器** 和各个其它机器SSH打通](http://geek.analysys.cn/topic/113)
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-#escheduler failover directory
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-zookeeper.escheduler.lock.masters.failover=/escheduler/lock/failover/masters
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-zookeeper.escheduler.lock.workers.failover=/escheduler/lock/failover/workers
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-
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-#escheduler failover directory
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-zookeeper.session.timeout=300
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-zookeeper.connection.timeout=300
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-zookeeper.retry.sleep=1000
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-zookeeper.retry.maxtime=5
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-
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-```
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-
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-
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-
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-### escheduler-dao
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-
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-dao数据源配置
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-
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-- dao/data_source.properties
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-
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-```
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-# base spring data source configuration
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-spring.datasource.type=com.alibaba.druid.pool.DruidDataSource
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-spring.datasource.driver-class-name=com.mysql.jdbc.Driver
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-spring.datasource.url=jdbc:mysql://192.168.xx.xx:3306/escheduler?characterEncoding=UTF-8
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-spring.datasource.username=xx
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-spring.datasource.password=xx
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-
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-# connection configuration
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-spring.datasource.initialSize=5
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-# min connection number
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-spring.datasource.minIdle=5
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-# max connection number
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-spring.datasource.maxActive=50
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-
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-# max wait time for get a connection in milliseconds. if configuring maxWait, fair locks are enabled by default and concurrency efficiency decreases.
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-# If necessary, unfair locks can be used by configuring the useUnfairLock attribute to true.
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-spring.datasource.maxWait=60000
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-
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-# milliseconds for check to close free connections
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-spring.datasource.timeBetweenEvictionRunsMillis=60000
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-
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-# 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.
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-spring.datasource.timeBetweenConnectErrorMillis=60000
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-
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-# the longest time a connection remains idle without being evicted, in milliseconds
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-spring.datasource.minEvictableIdleTimeMillis=300000
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-
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-#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.
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-spring.datasource.validationQuery=SELECT 1
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-#check whether the connection is valid for timeout, in seconds
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-spring.datasource.validationQueryTimeout=3
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-
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-# when applying for a connection, if it is detected that the connection is idle longer than time Between Eviction Runs Millis,
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-# validation Query is performed to check whether the connection is valid
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-spring.datasource.testWhileIdle=true
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-
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-#execute validation to check if the connection is valid when applying for a connection
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-spring.datasource.testOnBorrow=true
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-#execute validation to check if the connection is valid when the connection is returned
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-spring.datasource.testOnReturn=false
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-spring.datasource.defaultAutoCommit=true
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-spring.datasource.keepAlive=true
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-
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-# open PSCache, specify count PSCache for every connection
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-spring.datasource.poolPreparedStatements=true
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-spring.datasource.maxPoolPreparedStatementPerConnectionSize=20
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-```
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+## 部署
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+### 1. 修改安装目录权限
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-
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-### escheduler-server
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-
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-master配置文件
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-
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-- master.properties
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+- 安装目录如下：
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```
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-# master execute thread num
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-master.exec.threads=100
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-
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-# master execute task number in parallel
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-master.exec.task.number=20
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-
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-# master heartbeat interval
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-master.heartbeat.interval=10
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-
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-# master commit task retry times
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-master.task.commit.retryTimes=5
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-
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-# master commit task interval
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-master.task.commit.interval=100
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-
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-
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-# only less than cpu avg load, master server can work. default value : the number of cpu cores * 2
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-master.max.cpuload.avg=10
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-
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-# only larger than reserved memory, master server can work. default value : physical memory * 1/10, unit is G.
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-master.reserved.memory=1
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+ bin
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+ conf
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+ install.sh
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+ lib
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+ script
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+ sql
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+
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```
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+- 修改权限(deployUser修改为对应部署用户)
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+ `sudo chown -R deployUser:deployUser *`
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+### 2. 修改环境变量文件
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-worker配置文件
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+- 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
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-- worker.properties
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+### 3. 修改部署参数
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-```
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-# worker execute thread num
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-worker.exec.threads=100
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-
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-# worker heartbeat interval
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-worker.heartbeat.interval=10
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-
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-# submit the number of tasks at a time
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-worker.fetch.task.num = 10
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+ - 修改 **install.sh**中的参数，替换成自身业务所需的值
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+ - 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
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-# only less than cpu avg load, worker server can work. default value : the number of cpu cores * 2
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-worker.max.cpuload.avg=10
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-
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-# only larger than reserved memory, worker server can work. default value : physical memory * 1/6, unit is G.
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-worker.reserved.memory=1
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-```
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+### 4. 一键部署
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+- 安装zookeeper工具
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+ `pip install kazoo`
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-### escheduler-api
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|
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+- 切换到部署用户，一键部署
|
|
|
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-web配置文件
|
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+ `sh install.sh`
|
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|
|
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-- application.properties
|
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+- jps查看服务是否启动
|
|
|
|
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|
+```aidl
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+ MasterServer ----- master服务
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+ WorkerServer ----- worker服务
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+ LoggerServer ----- logger服务
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+ ApiApplicationServer ----- api服务
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+ AlertServer ----- alert服务
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```
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|
|
-# server port
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-server.port=12345
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|
|
|
|
-# session config
|
|
|
-server.session.timeout=7200
|
|
|
-
|
|
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-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
|
|
|
-```
|
gongzijian