基于Spark的电信客户流失数据分析系统_毕设
舟率率 3/28/2026 springbootredissqoopjavavue
# 项目概况
# 数据类型
电信客户流失数据
# 开发环境
centos7
# 软件版本
hadoop3.2.0、hive3.1.2、spark3.1.2、mysql5.7.38、jdk8
# 开发语言
Java、shell、SQL
# 可视化图表
# 操作步骤
# 启动MySQL
# 查看mysql是否启动 启动命令: systemctl start mysqld.service
systemctl status mysqld.service
# 进入mysql终端
# MySQL的用户名:root 密码:123456
# MySQL的用户名:root 密码:123456
# MySQL的用户名:root 密码:123456
mysql -uroot -p123456
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# 启动Hadoop
# 离开安全模式: hdfs dfsadmin -safemode leave
# 启动hadoop
bash /export/software/hadoop-3.2.0/sbin/start-hadoop.sh
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# 启动hive
# 在第一个窗口中,执行后等待10-20秒
/export/software/apache-hive-3.1.2-bin/bin/hive --service metastore
# 在第二个窗口中,执行后等待10-20秒
/export/software/apache-hive-3.1.2-bin/bin/hive --service hiveserver2
# 连接进入hive终端命令如下:
# /export/software/apache-hive-3.1.2-bin/bin/beeline -u jdbc:hive2://master:10000 -n root
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# 准备目录
mkdir -p /data/jobs/project/
cd /data/jobs/project/
# 解压 "project-spark-telecom-customer-churn-data-analysis-system/" 目录下的 "data.7z" 到当前目录下
# 上传 "project-spark-telecom-customer-churn-data-analysis-system" 整个文件夹 到 "/data/jobs/project/" 目录
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# 初始化MySQL表
cd /data/jobs/project/project-spark-telecom-customer-churn-data-analysis-system/mysql/
mysql -uroot -p123456 < mysql.sql
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# 启动前端
cd /data/jobs/project/project-spark-telecom-customer-churn-data-analysis-system/前端/front_ui/
# 安装node
npm install --registry=https://registry.npmmirror.com
chmod -R 755 node_modules/.bin
npm run dev
# http://master:8080/login
# 登录用户: admin
# 登录密码: 123456
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# 启动后端
cd /data/jobs/project/project-spark-telecom-customer-churn-data-analysis-system/后端/springboot-demo/
mvn clean package -DskipTests
java -jar target/springboot-demo-1.0-SNAPSHOT.jar --model.path=/data/output/model --app.fileBaseDir=/data/jobs/project/project-spark-telecom-customer-churn-data-analysis-system
# 启动项目后,在"数据清洗"完成后,可以选择手动训练模型
cd /data/jobs/project/project-spark-telecom-customer-churn-data-analysis-system/spark_ml/spark-job/
# mvn clean package -DskipTests
# bash /export/software/spark-3.1.2-bin-hadoop3.2/sbin/start-all.sh
# spark-submit --master yarn --deploy-mode cluster --class org.example.ChurnModelTrain target/spark-job.jar /data/input/ /data/output/model/
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