hive对电商用户订单行为特征分析(二)

今天用hive查询用户日志表.这是日志表的格式:

user_id,item_id,cat_id,merchant_id,brand_id,month,day,action,age_range,gender,province
328862,323294,833,2882,2661,8,29,0,0,1,内蒙古
328862,844400,1271,2882,2661,8,29,0,1,1,山西
328862,575153,1271,2882,2661,8,29,0,2,1,山西
328862,996875,1271,2882,2661,8,29,0,1,1,内蒙古
328862,1086186,1271,1253,1049,8,29,0,0,2,浙江
328862,623866,1271,2882,2661,8,29,0,0,2,黑龙江
328862,542871,1467,2882,2661,8,29,0,5,2,四川
328862,536347,1095,883,1647,8,29,0,7,1,吉林
328862,364513,1271,2882,2661,8,29,0,1,2,贵州
328862,575153,1271,2882,2661,8,29,0,0,0,陕西

日志数据以及元数据的上传,详见 本人这篇博客: http://blog.csdn.net/cafebar123/article/details/74371463
下面是对日志记录行为查询:
创建数据库名:

create database hive;

创建表名:

CREATE TABLE hive.user_log(user_id INT,item_id INT,cat_id INT,merchant_id INT,brand_id INT,month STRING,day STRING,action INT,age_range INT,gender INT,province STRING) COMMENT 'Welcome to xmu dblab,Now create hive.user_log!' ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS TEXTFILE LOCATION '/user/hive/user_log/user_log';

(1)查询10个交易记录:

select * from user_log limit 10;

(2)对于复杂的列名,可以使用别名:

select merchant_id as meri from user_log;

(3)使用嵌套语句

select ul.meri from (select merchant_id as meri from user_log) as ul limit 10;

(4)统计有多少条行数据

select count(*) from user_log;

(5)统计不重复的数据

select count(distinct user_id) from user_log;

(6)使用group by 查询不重复的数据

select count(*) from (select user_id,item_id,cat_id,merchant_id,brand_id,action on from user_log group by user_id,item_id,cat_id,merchant_id,brand_id,action having count(*)=1)a;

(7)查询某一天多少人购买了产品

select count(distinct user_id) from user_log where action='2' and month='11' and day='11';

action=’2’ 表示支付,action=’1’表加入购物车

(8)查询某一天男女购买的比例

select count(*) from user_log where gender=0 and month='11' and day='11';

select count(*) from user_log where gender=1 and month='11' and day='11';

(9)查询某天某商品的购买用户,且某用户购买2次以上

select user_id from user_log where action='2' group by user_id having count(action='2')>1;

(10)查询某品牌商品的浏览次数

select brand_id,count(action) from user_log where action='2' group by brand_id;

未完待续

参考:http://dblab.xmu.edu.cn/blog/1363-2/