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Title: GitHub - pkeropen/BigData-News: 基于Spark2.2新闻网大数据实时系统项目 · GitHub

Open Graph Title: GitHub - pkeropen/BigData-News: 基于Spark2.2新闻网大数据实时系统项目

X Title: GitHub - pkeropen/BigData-News: 基于Spark2.2新闻网大数据实时系统项目

Description: 基于Spark2.2新闻网大数据实时系统项目. Contribute to pkeropen/BigData-News development by creating an account on GitHub.

Open Graph Description: 基于Spark2.2新闻网大数据实时系统项目. Contribute to pkeropen/BigData-News development by creating an account on GitHub.

X Description: 基于Spark2.2新闻网大数据实时系统项目. Contribute to pkeropen/BigData-News development by creating an account on GitHub.

Opengraph URL: https://github.com/pkeropen/BigData-News

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https://github.com/pkeropen/BigData-News#基于spark22新闻网大数据实时系统项目
https://github.com/pkeropen/BigData-News#1-说明
项目代码https://github.com/pkeropen/BigData-News
基于Spark2.x新闻网大数据实时分析可视化系统项目https://blog.csdn.net/u011254180/article/details/80172452
大数据项目实战之新闻话题的实时统计分析http://www.raincent.com/content-10-11077-1.html
https://github.com/pkeropen/BigData-News#2环境配置
地址https://blog.51cto.com/kaliarch/2122467
https://github.com/pkeropen/BigData-News#21-cdh-5142-安装步骤可参考地址关于版本是按实际操作-cdh的版本兼容性很好
https://github.com/pkeropen/BigData-News#22-主机配置
https://github.com/pkeropen/BigData-News#23-项目架构
https://github.com/pkeropen/BigData-News/blob/master/pic/Architecture.png
https://github.com/pkeropen/BigData-News#24-安装依赖包
https://github.com/pkeropen/BigData-News#3--编写数据生成模拟程序
下载https://www.sogou.com/labs/resource/q.php
https://github.com/pkeropen/BigData-News#31-模拟从nginx生成日志的log数据来源搜狗实验室下载用户查询日志搜索引擎查询日志库设计为包括约1个月2008年6月sogou搜索引擎部分网页查询需求及用户点击情况的网页查询日志数据集合
https://github.com/pkeropen/BigData-News#32-数据清洗
https://github.com/pkeropen/BigData-News#数据格式为访问时间t用户idt查询词t该url在返回结果中的排名t用户点击的顺序号t用户点击的url其中用户id是根据用户使用浏览器访问搜索引擎时的cookie信息自动赋值即同一次使用浏览器输入的不同查询对应同一个用户id
https://github.com/pkeropen/BigData-News#33-主要代码段
打包步骤https://blog.csdn.net/xuemengrui12/article/details/74984731
https://github.com/pkeropen/BigData-News#34-打包成weblogsjar打包步骤--写shell脚本weblog-shellsh
https://github.com/pkeropen/BigData-News#35-修改weblog-shellsh可执行权限
https://github.com/pkeropen/BigData-News#4--flume数据采集配置
https://github.com/pkeropen/BigData-News#41-将hadoop02-hadoop03中flume数据采集到hadoop01中而且hadoop02和hadoop03的flume配置文件大致相同
https://github.com/pkeropen/BigData-News#42-hadoop01通过flume接收hadoop02与hadoop03中flume传来的数据并将其分别发送至hbase与kafka中配置内容如下
https://github.com/pkeropen/BigData-News#43--配置flume执行shell脚本
https://github.com/pkeropen/BigData-News#44--flume分发到hbase集成
https://github.com/pkeropen/BigData-News#45--将项目打包成jarvita-flume-ng-hbase-sinkjar分发到cdh的flumelibs下
https://github.com/pkeropen/BigData-News#5--kafka配置测试环境kafka部署hadoop01不做高可用
https://github.com/pkeropen/BigData-News#51-配置
https://github.com/pkeropen/BigData-News#52-测试生产消费是否成功
https://github.com/pkeropen/BigData-News#53--编写kafka-consumer执行脚本kfk-test-consumersh分发到optshell
https://github.com/pkeropen/BigData-News#6--hbase配置
https://github.com/pkeropen/BigData-News#61-创建业务表
https://github.com/pkeropen/BigData-News#7-hive配置
https://github.com/pkeropen/BigData-News#71-cdh配置-hive与hbase集成或者配置
https://github.com/pkeropen/BigData-News#72-在hive中创建与hbase集成的外部表
https://github.com/pkeropen/BigData-News#8-structured-streaming配置
https://github.com/pkeropen/BigData-News#81-测试spark与mysql
https://github.com/pkeropen/BigData-News#82-structured-streaming与mysql集成
https://github.com/pkeropen/BigData-News#83-structured-streaming关键代码
https://github.com/pkeropen/BigData-News#84-项目打包spark-weblogsjar
https://github.com/pkeropen/BigData-News#9-启动流程
https://github.com/pkeropen/BigData-News#91-cdh启动zookeeperhadoophbasemysqlyarnflumekafka
https://github.com/pkeropen/BigData-News#92-先在hadoop01-执行optshellflume-kfk-hb-startsh-将数据分别传到hbase和kafka中
https://github.com/pkeropen/BigData-News#93-在hadoop02hadoop03-执行optshellflume-collect-startsh--将数据发送到hadoop01中
https://github.com/pkeropen/BigData-News#94-在hadoop01--执行提交spark任务
地址https://blog.csdn.net/yiluohan0307/article/details/80048765
https://github.com/pkeropen/BigData-News#95-在hadoop02hadoop03-执行optweblog-shellsh--启动-structuredstreamingkafka来从kafka中取得数据处理后存到mysql中
https://github.com/pkeropen/BigData-News#96-登录mysql-查看webcount表
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