SparkSQL电商用户画像之电商用户画像数据可视化

Posted 2021-05-18 大码王

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8.1 数据可视化方案

技术框架：maven+spring+phoenix

核心点：采用phoenix与hbase整合，通过我们熟知的sql语句来操作NoSql数据库。

8.2 Phoenix建立hbase表的映射表

CREATE TABLE IF NOT EXISTS "itcast_adm_personas_hbase_2017_01_01"(

rw varchar(100) primary key,

"basicInfo"."user_name" VARCHAR(100),

"basicInfo"."user_sex"  VARCHAR(100),

"basicInfo"."user_birthday" VARCHAR(100),

"basicInfo"."user_age"  VARCHAR(100),

"basicInfo"."constellation" VARCHAR(100),

"basicInfo"."province" VARCHAR(100),

"basicInfo"."city" VARCHAR(100),

"basicInfo"."city_level" VARCHAR(100),

"basicInfo"."hex_mail" VARCHAR(100),

"basicInfo"."op_mail" VARCHAR(100),

"basicInfo"."hex_phone" VARCHAR(100),

"basicInfo"."fore_phone" VARCHAR(100),

"basicInfo"."op_phone" VARCHAR(100),

"basicInfo"."add_time" VARCHAR(100),

"basicInfo"."login_ip" VARCHAR(100),

"basicInfo"."login_source" VARCHAR(100),

"basicInfo"."request_user" VARCHAR(100),

"basicInfo"."total_mark" VARCHAR(100),

"basicInfo"."used_mark" VARCHAR(100),

"basicInfo"."level_name" VARCHAR(100),

"basicInfo"."blacklist" VARCHAR(100),

"basicInfo"."is_married" VARCHAR(100),

"basicInfo"."education" VARCHAR(100),

"basicInfo"."monthly_money" VARCHAR(100),

"basicInfo"."profession" VARCHAR(100),

"basicInfo"."sex_model" VARCHAR(100),

"basicInfo"."is_pregnant_woman" VARCHAR(100),

"basicInfo"."is_have_children" VARCHAR(100),

"basicInfo"."children_sex_rate" VARCHAR(100),

"basicInfo"."children_age_rate" VARCHAR(100),

"basicInfo"."is_have_car" VARCHAR(100),

"basicInfo"."potential_car_user_rate" VARCHAR(100),

"basicInfo"."phone_brand" VARCHAR(100),

"basicInfo"."phone_brand_level" VARCHAR(100),

"basicInfo"."phone_cnt" VARCHAR(100),

"basicInfo"."change_phone_rate" VARCHAR(100),

"basicInfo"."majia_flag" VARCHAR(100),

"basicInfo"."majie_account_cnt" VARCHAR(100),

"basicInfo"."loyal_model" VARCHAR(100),

"basicInfo"."shopping_type_model" VARCHAR(100),

"basicInfo"."figure_model" VARCHAR(100),

"basicInfo"."stature_model" VARCHAR(100),

"order"."first_order_time" VARCHAR(100),

"order"."last_order_time" VARCHAR(100),

"order"."first_order_ago" VARCHAR(100),

"order"."last_order_ago" VARCHAR(100),

"order"."month1_hg_order_cnt" VARCHAR(100),

"order"."month1_hg_order_amt" VARCHAR(100),

"order"."month2_hg_order_cnt" VARCHAR(100),

"order"."month2_hg_order_amt" VARCHAR(100),

"order"."month3_hg_order_cnt" VARCHAR(100),

"order"."month3_hg_order_amt" VARCHAR(100),

"order"."month1_order_cnt" VARCHAR(100),

"order"."month1_order_amt" VARCHAR(100),

"order"."month2_order_cnt" VARCHAR(100),

"order"."month2_order_amt" VARCHAR(100),

"order"."month3_order_cnt" VARCHAR(100),

"order"."month3_order_amt" VARCHAR(100),

"order"."max_order_amt" VARCHAR(100),

"order"."min_order_amt" VARCHAR(100),

"order"."total_order_cnt" VARCHAR(100),

"order"."total_order_amt" VARCHAR(100),

"order"."user_avg_amt" VARCHAR(100),

"order"."month3_user_avg_amt" VARCHAR(100),

"order"."common_address" VARCHAR(100),

"order"."common_paytype" VARCHAR(100),

"order"."month1_cart_cnt" VARCHAR(100),

"order"."month1_cart_goods_cnt" VARCHAR(100),

"order"."month1_cart_submit_cnt" VARCHAR(100),

"order"."month1_cart_rate" VARCHAR(100),

"order"."month1_cart_cancle_cnt" VARCHAR(100),

"order"."return_cnt" VARCHAR(100),

"order"."return_amt" VARCHAR(100),

"order"."reject_cnt" VARCHAR(100),

"order"."reject_amt" VARCHAR(100),

"order"."last_return_time" VARCHAR(100),

"order"."school_order_cnt" VARCHAR(100),

"order"."company_order_cnt" VARCHAR(100),

"order"."home_order_cnt" VARCHAR(100),

"order"."forenoon_order_cnt" VARCHAR(100),

"order"."afternoon_order_cnt" VARCHAR(100),

"order"."night_order_cnt" VARCHAR(100),

"order"."morning_order_cnt" VARCHAR(100),

"category"."first_category_id" VARCHAR(100),

"category"."first_category_name" VARCHAR(100),

"category"."second_category_id" VARCHAR(100),

"category"."second_catery_name" VARCHAR(100),

"category"."third_category_id" VARCHAR(100),

"category"."third_category_name" VARCHAR(100),

"category"."month1_category_cnt" VARCHAR(100),

"category"."month1_category_amt" VARCHAR(100),

"category"."month3_category_cnt" VARCHAR(100),

"category"."month3_category_amt" VARCHAR(100),

"category"."month6_category_cnt" VARCHAR(100),

"category"."month6_category_amt" VARCHAR(100),

"category"."total_category_cnt" VARCHAR(100),

"category"."total_category_amt" VARCHAR(100),

"category"."month1_cart_category_cnt" VARCHAR(100),

"category"."month3_cart_category_cnt" VARCHAR(100),

"category"."month6_cart_category_cnt" VARCHAR(100),

"category"."total_cart_category_cnt" VARCHAR(100),

"category"."last_category_time" VARCHAR(100),

"category"."last_category_ago" VARCHAR(100),

"visit"."latest_pc_visit_date" VARCHAR(100),

"visit"."latest_app_visit_date" VARCHAR(100),

"visit"."latest_pc_visit_session" VARCHAR(100),

"visit"."latest_pc_cookies" VARCHAR(100),

"visit"."latest_pc_pv" VARCHAR(100),

"visit"."latest_pc_browser_name" VARCHAR(100),

"visit"."latest_pc_visit_os" VARCHAR(100),

"visit"."latest_app_name" VARCHAR(100),

"visit"."latest_app_visit_os" VARCHAR(100),

"visit"."latest_visit_ip" VARCHAR(100),

"visit"."latest_city" VARCHAR(100),

"visit"."latest_province" VARCHAR(100),

"visit"."first_pc_visit_date" VARCHAR(100),

"visit"."first_app_visit_date" VARCHAR(100),

"visit"."first_pc_visit_session" VARCHAR(100),

"visit"."first_pc_cookies" VARCHAR(100),

"visit"."first_pc_pv" VARCHAR(100),

"visit"."first_pc_browser_name" VARCHAR(100),

"visit"."first_pc_visit_os" VARCHAR(100),

"visit"."first_app_name" VARCHAR(100),

"visit"."first_app_visit_os" VARCHAR(100),

"visit"."first_visit_ip" VARCHAR(100),

"visit"."first_city" VARCHAR(100),

"visit"."first_province" VARCHAR(100),

"visit"."day7_app_cnt" VARCHAR(100),

"visit"."day15_app_cnt" VARCHAR(100),

"visit"."month1_app_cnt" VARCHAR(100),

"visit"."month2_app_cnt" VARCHAR(100),

"visit"."month3_app_cnt" VARCHAR(100),

"visit"."day7_pc_cnt" VARCHAR(100),

"visit"."day15_pc_cnt" VARCHAR(100),

"visit"."month1_pc_cnt" VARCHAR(100),

"visit"."month2_pc_cnt" VARCHAR(100),

"visit"."month3_pc_cnt" VARCHAR(100),

"visit"."month1_pc_days" VARCHAR(100),

"visit"."month1_pc_pv" VARCHAR(100),

"visit"."month1_pc_avg_pv" VARCHAR(100),

"visit"."month1_pc_diff_ip_cnt" VARCHAR(100),

"visit"."month1_pc_diff_cookie_cnt" VARCHAR(100),

"visit"."month1_pc_common_ip" VARCHAR(100),

"visit"."month1_pc_common_cookie" VARCHAR(100),

"visit"."month1_pc_common_browser_name" VARCHAR(100),

"visit"."month1_pc_common_os" VARCHAR(100),

"visit"."month1_hour025_cnt" VARCHAR(100),

"visit"."month1_hour627_cnt" VARCHAR(100),

"visit"."month1_hour829_cnt" VARCHAR(100),

"visit"."month1_hour10212_cnt" VARCHAR(100),

"visit"."month1_hour13214_cnt" VARCHAR(100),

"visit"."month1_hour15217_cnt" VARCHAR(100),

"visit"."month1_hour18219_cnt" VARCHAR(100),

"visit"."month1_hour20221_cnt" VARCHAR(100),

"visit"."month1_hour22223_cnt" VARCHAR(100)

);

这个语句有几个注意点

在建立映射表之前要说明的是，Phoenix是大小写敏感的，并且所有命令都是大写，如果你建的表名没有用双引号括起来，那么无论你输入的是大写还是小写，建立出来的表名都是大写的，如果你需要建立出同时包含大写和小写的表名和字段名，请把表名或者字段名用双引号括起来。你可以建立读写的表或者只读的表，他们的区别如下：

读写表：如果你定义的列簇不存在，会被自动建立出来，并且赋以空值
只读表：你定义的列簇必须事先存在
IF NOT EXISTS可以保证如果已经有建立过这个表，配置不会被覆盖
作为rowkey的字段用 PRIMARY KEY标定
列簇用 columnFamily.columnName 来表

8.3 构建maven工程

9.4 用户画像查询展现

根据不同的维度进行组合查询，筛选出满足条件的用户。

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