基于pyspark的双十一美妆数据分析及可视化
舟率率 7/13/2025 pythonflask
# 项目概况
# 数据类型
双十一淘宝美妆订单数据
# 开发环境
centos7
# 软件版本
python3.8.18、hadoop3.2.0、spark3.1.2、mysql5.7.38、scala2.12.18、jdk8
# 开发语言
python
# 开发流程
数据清洗(python)->数据上传(hdfs)->数据清洗(mapreduce)->数据分析(hive)->数据分析(spark)->数据存储(mysql)->后端(flask)->前端(html+js+css)
# 可视化图表
# 操作步骤
# python安装包
pip3 install pandas==2.0.3 -i https://mirrors.aliyun.com/pypi/simple/
pip3 install flask==3.0.0 -i https://mirrors.aliyun.com/pypi/simple/
pip3 install flask-cors==4.0.1 -i https://mirrors.aliyun.com/pypi/simple/
pip3 install pymysql==1.1.0 -i https://mirrors.aliyun.com/pypi/simple/
pip3 install pyecharts==2.0.4 -i https://pypi.tuna.tsinghua.edu.cn/simple
pip3 install openpyxl==3.1.5 -i https://mirrors.aliyun.com/pypi/simple/
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# 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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# 创建MySQL库
CREATE DATABASE IF NOT EXISTS echarts CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;
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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/
# 上传 "project-cosmetics-analysis" 整个文件夹 到 "/data/jobs/project/" 目录
cd /data/jobs/project/project-cosmetics-analysis/
python3 data_clean.py
ls -l output/
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# 上传文件到hdfs
cd /data/jobs/project/project-cosmetics-analysis/
hdfs dfs -mkdir -p /data/input/
hdfs dfs -rm -r /data/input/*
hdfs dfs -put output/dim_product.csv /data/input/
hdfs dfs -put output/fact_order.csv /data/input/
hdfs dfs -put output/result.csv /data/input/
hdfs dfs -put output/result_order.csv /data/input/
hdfs dfs -ls /data/input/
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# spark数据分析
cd /data/jobs/project/project-cosmetics-analysis/
spark-submit \
--master local[*] \
--jars /export/software/spark-3.1.2-bin-hadoop3.2/jars/mysql-connector-j-8.0.33.jar \
--driver-class-path /export/software/spark-3.1.2-bin-hadoop3.2/jars/mysql-connector-j-8.0.33.jar \
pyspark/pyspark_app.py /data/input
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# 启动可视化
mkdir -p /data/jobs/project/myapp/
cd /data/jobs/project/myapp/
# 上传 "可视化" 目录下的 "所有" 文件和文件夹 到 "/data/jobs/project/" 目录
# windows本地运行: python app.py
python3 app.py pro
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