2023年4月第一周，他们在SQLintern辛勤写代码

*排序依据为查询次数，非会员显示ip，会员显示脱敏账号；已添加代码复杂度检测功能，恶意刷榜将被拉黑；站长会关注当月辛勤学习的同学，并主动提供必要的答疑和帮助

排名 用户 查询次数
🥇 NoG*No* 1727
🥈 she*yu* 1518
🥉 dav*ds* 969
4 103.57.12.82 523
5 shi*ei* 479
6 fan*pe* 442
7 sql*nt* 354
8 43.252.112.73 317
9 111.206.214.37 290
10 siy*n_* 278

2023年4月第一周小蜜蜂查询记录

小蜜蜂可联系站长导出所有查询记录存档，回顾学习

usr_name visit_ip visit_time visit_page
she*yu* 183.247.177.35 2023-04-08 21:18:51 scene3_3
she*yu* 183.247.177.35 2023-04-08 21:08:03 scene6_6
she*yu* 183.247.177.35 2023-04-08 21:05:25 select gp,count(gp)
from(
select gp, prd_nm
from tb_clk_rcd r
join (
select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null)a using(cust_uid)
join tb_prd_map m using(prd_id)
where if_snd = 1) b
group by gp
order by gp
she*yu* 183.247.177.35 2023-04-08 21:05:15 select gp,sum(gp)
from(
select gp, prd_nm
from tb_clk_rcd r
join (
select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null)a using(cust_uid)
join tb_prd_map m using(prd_id)
where if_snd = 1) b
group by gp
order by gp
she*yu* 183.247.177.35 2023-04-08 21:02:35 select gp, prd_nm,count(1)
from(
select gp, prd_nm
from tb_clk_rcd r
join (
select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null)a using(cust_uid)
join tb_prd_map m using(prd_id)
where if_snd = 1) b
group by gp, prd_nm
order by gp,count(1)
she*yu* 183.247.177.35 2023-04-08 21:02:27 select gp, prd_nm,count(1),sum(count(1))
from(
select gp, prd_nm
from tb_clk_rcd r
join (
select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null)a using(cust_uid)
join tb_prd_map m using(prd_id)
where if_snd = 1) b
group by gp, prd_nm
order by gp,count(1)
she*yu* 183.247.177.35 2023-04-08 21:01:46 select gp, prd_nm,count(1)
from(
select gp, prd_nm
from tb_clk_rcd r
join (
select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null)a using(cust_uid)
join tb_prd_map m using(prd_id)
where if_snd = 1) b
group by gp, prd_nm
order by gp,count(1)
she*yu* 183.247.177.35 2023-04-08 21:01:34 select gp, prd_nm,count(1)
from(
select gp, prd_nm
from tb_clk_rcd r
join (
select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null)a using(cust_uid)
join tb_prd_map m using(prd_id)
where if_snd = 1) b
group by gp, prd_nm
order by gp
she*yu* 183.247.177.35 2023-04-08 21:01:22 select gp, prd_nm,count(1)
from(
select gp, prd_nm
from tb_clk_rcd r
join (
select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null)a using(cust_uid)
join tb_prd_map m using(prd_id)
where if_snd = 1) b
group by gp, prd_nm
she*yu* 183.247.177.35 2023-04-08 20:59:53 select gp, prd_nm
from tb_clk_rcd r
join (
select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null)a using(cust_uid)
join tb_prd_map m using(prd_id)
where if_snd = 1
she*yu* 183.247.177.35 2023-04-08 20:59:36 select gp, r.prd_id, prd_nm
from tb_clk_rcd r
join (
select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null)a using(cust_uid)
join tb_prd_map m using(prd_id)
where if_snd = 1
she*yu* 183.247.177.35 2023-04-08 20:58:09 select r.cust_uid, gp, r.prd_id, prd_nm
from tb_clk_rcd r
join (
select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null)a using(cust_uid)
join tb_prd_map m using(prd_id)
where if_snd = 1
she*yu* 183.247.177.35 2023-04-08 20:57:58 select r.cust_uid, gp, if_snd, r.prd_id, prd_nm
from tb_clk_rcd r
join (
select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null)a using(cust_uid)
join tb_prd_map m using(prd_id)
where if_snd = 1
she*yu* 183.247.177.35 2023-04-08 20:57:28 select r.cust_uid, gp, if_snd, r.prd_id, prd_nm
from tb_clk_rcd r
join (
select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null)a using(cust_uid)
join tb_prd_map m using(prd_id)
she*yu* 183.247.177.35 2023-04-08 20:54:02 select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null
she*yu* 183.247.177.35 2023-04-08 20:52:29 select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null
order by gp
she*yu* 183.247.177.35 2023-04-08 20:52:22 select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null
she*yu* 183.247.177.35 2023-04-08 20:52:11 select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
order by gp
having gp is not null
she*yu* 183.247.177.35 2023-04-08 20:51:51 select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null
she*yu* 183.247.177.35 2023-04-08 20:51:46 select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by gp,cust_uid
having gp is not null
she*yu* 183.247.177.35 2023-04-08 20:51:26 select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by cust_uid,gp
having gp is not null
she*yu* 183.247.177.35 2023-04-08 20:51:20 select cust_uid,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
group by cust_uid
having gp is not null
she*yu* 183.247.177.35 2023-04-08 20:50:13 select *,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
having gp is not null
she*yu* 183.247.177.35 2023-04-08 20:49:47 select *,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when gdr = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
she*yu* 183.247.177.35 2023-04-08 20:49:37 select *,
case
when gdr = 'M' and (age between 20 and 35) then 'A组'
when grd = 'F' and (age between 45 and 55) then 'B组'
end as 'gp'
from tb_cst_bas_inf
she*yu* 183.247.177.35 2023-04-08 20:46:32 select *
from tb_cst_bas_inf
she*yu* 183.247.177.35 2023-04-08 20:41:16 select la, count(1) as amount
from(
select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as 'la'
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1) t1
group by la
she*yu* 183.247.177.35 2023-04-08 20:40:43 select la
from(
select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as 'la'
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1) t1
she*yu* 183.247.177.35 2023-04-08 20:40:09 select lag
from(
select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as lag
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1) t1
she*yu* 183.247.177.35 2023-04-08 20:39:56 select 'lag'
from(
select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as 'lag'
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1) t1
she*yu* 183.247.177.35 2023-04-08 20:39:36 select lag
from(
select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as 'lag'
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1) t1
she*yu* 183.247.177.35 2023-04-08 20:38:35
select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as 'lag'
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1
she*yu* 183.247.177.35 2023-04-08 20:38:29
select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as 'lag'
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1) a
she*yu* 183.247.177.35 2023-04-08 20:38:08 select lag
from(
select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as 'lag'
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1) a
group by lag
order by amount desc
she*yu* 183.247.177.35 2023-04-08 20:36:55 select lag, count(1) as amount
from(
select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as 'lag'
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1) a
group by lag
order by amount desc
she*yu* 183.247.177.35 2023-04-08 20:35:50 select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as 'lag'
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1
she*yu* 183.247.177.35 2023-04-08 20:35:37 scene3_3
she*yu* 183.247.177.35 2023-04-08 20:34:52 select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as 'lag'
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1
she*yu* 183.247.177.35 2023-04-08 20:33:51 select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as lag
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1
she*yu* 183.247.177.35 2023-04-08 20:33:50 select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as lag
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1
she*yu* 183.247.177.35 2023-04-08 20:33:49 select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as lag
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1
she*yu* 183.247.177.35 2023-04-08 20:32:07 select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as lag
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1
she*yu* 183.247.177.35 2023-04-08 20:31:34 select r.prd_id, price
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1
she*yu* 183.247.177.35 2023-04-08 20:30:37 select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as lag
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1
she*yu* 183.247.177.35 2023-04-08 20:29:23 select *,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as lag
from(
select r.prd_id, price
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1) a
she*yu* 183.247.177.35 2023-04-08 20:29:02 select r.prd_id, price
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1
she*yu* 183.247.177.35 2023-04-08 20:28:16 select r.prd_id, price,
case
when price < 100 then '100元及以下'
when price between 100 and 500 then '100-500元'
when price > 500 then '500元以上'
end as lag
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1
she*yu* 183.247.177.35 2023-04-08 20:24:52 select r.prd_id, price
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
where if_buy = 1
she*yu* 183.247.177.35 2023-04-08 20:22:13 select r.prd_id,prd_nm,avg(age) as av
from tb_clk_rcd r
join tb_cst_bas_inf i using(cust_uid)
join tb_prd_map m using(prd_id)
where if_buy = 1
group by r.prd_id,prd_nm
order by av desc
she*yu* 183.247.177.35 2023-04-08 20:21:53 select r.prd_id,prd_nm,avg(age) as av
from tb_clk_rcd r
join tb_cst_bas_inf i using(cust_uid)
join tb_prd_map m using(prd_id)
where if_buy = 1
group by r.prd_id,prd_nm
order by r.prd_id
she*yu* 183.247.177.35 2023-04-08 20:21:46 select r.prd_id,prd_nm,avg(age) as av
from tb_clk_rcd r
join tb_cst_bas_inf i using(cust_uid)
join tb_prd_map m using(prd_id)
where if_buy = 1
group by r.prd_id
order by r.prd_id
she*yu* 183.247.177.35 2023-04-08 20:21:33 select r.prd_id,avg(age) as av
from tb_clk_rcd r
join tb_cst_bas_inf i using(cust_uid)
join tb_prd_map m using(prd_id)
where if_buy = 1
group by r.prd_id
order by r.prd_id
she*yu* 183.247.177.35 2023-04-08 20:19:46 select prd_id,r.cust_uid,age
from tb_clk_rcd r
join tb_cst_bas_inf i using(cust_uid)
where if_buy = 1
group by prd_id,r.cust_uid,age
order by prd_id
she*yu* 183.247.177.35 2023-04-08 20:19:23 select prd_id,distinct r.cust_uid,age
from tb_clk_rcd r
join tb_cst_bas_inf i using(cust_uid)
where if_buy = 1
group by prd_id,r.cust_uid,age
order by prd_id
she*yu* 183.247.177.35 2023-04-08 20:18:14 select prd_id,r.cust_uid,age
from tb_clk_rcd r
join tb_cst_bas_inf i using(cust_uid)
where if_buy = 1
group by prd_id,r.cust_uid,age
order by prd_id
she*yu* 183.247.177.35 2023-04-08 20:17:56 select prd_id,r.cust_uid,age
from tb_clk_rcd r
join tb_cst_bas_inf i using(cust_uid)
where if_buy = 1
group by prd_id,r.cust_uid,age
she*yu* 183.247.177.35 2023-04-08 20:17:45 select prd_id,r.cust_uid,age
from tb_clk_rcd r
join tb_cst_bas_inf i using(cust_uid)
where if_buy = 1
group by prd_id,r.cust_uid
she*yu* 183.247.177.35 2023-04-08 20:14:03 select r.prd_id, prd_nm, sum(if_cart) / count(1) as cart
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
group by r.prd_id, prd_nm
order by cart desc
she*yu* 183.247.177.35 2023-04-08 20:13:47 select r.prd_id, prd_mn,sum(if_cart) / count(1) as cart
from tb_clk_rcd r
join tb_prd_map m using(prd_id)
group by r.prd_id, prd_mn
order by cart desc
she*yu* 183.247.177.35 2023-04-08 20:11:39 select prd_id, sum(if_cart) / count(1) as cart
from tb_clk_rcd
group by prd_id
order by cart desc
she*yu* 183.247.177.35 2023-04-08 20:11:10 select prd_id, sum(if_cart) / count(1) as cart
from tb_clk_rcd
group by prd_id
she*yu* 183.247.177.35 2023-04-08 19:51:06 scene3_3
she*yu* 183.247.177.35 2023-04-08 16:50:36 meta_base
she*yu* 183.247.177.35 2023-04-08 16:29:40 select t1.cart/t2.vw
from(
select count(distinct cust_uid) cart
from(
select cust_uid ,if_cart
from tb_clk_rcd
where if_cart = 1 and if_vw = 1) a)t1
,
(select count( distinct cust_uid) vw
from(
select cust_uid,if_vw
from tb_clk_rcd
where if_vw = 1 )b)t2
she*yu* 183.247.177.35 2023-04-08 16:27:27 select
count(cust_uid) pv,
sum(if_snd) as snd,
sum(if_vw) as vw,
sum(if_cart) as cart,
sum(if_buy) as buy,
sum(if_vw) / sum(if_snd) as a1,
sum(if_cart) / sum(if_vw) as a2,
sum(if_buy) / sum(if_cart) as a3
from(
select distinct cust_uid,if_snd,if_vw,if_cart,if_buy
from tb_clk_rcd) t1
she*yu* 183.247.177.35 2023-04-08 16:25:27 select
count(cust_uid) pv,
sum(if_snd) as snd,
sum(if_vw) as vw,
sum(if_cart) as cart,
sum(if_buy) as buy
from(
select distinct cust_uid,if_snd,if_vw,if_cart,if_buy
from tb_clk_rcd) t1
she*yu* 183.247.177.35 2023-04-08 16:25:12 select
count(distinct cust_uid) pv,
sum(if_snd) as snd,
sum(if_vw) as vw,
sum(if_cart) as cart,
sum(if_buy) as buy
from(
select distinct cust_uid,if_snd,if_vw,if_cart,if_buy
from tb_clk_rcd) t1
she*yu* 183.247.177.35 2023-04-08 16:24:23 select distinct cust_uid,if_snd,if_vw,if_cart,if_buy
from tb_clk_rcd
she*yu* 183.247.177.35 2023-04-08 16:19:49 select
count(distinct cust_uid) pv,
sum(if_snd) as snd,
sum(if_vw) as vw,
sum(if_cart) as cart,
sum(if_buy) as buy
from tb_clk_rcd
she*yu* 183.247.177.35 2023-04-08 16:05:39 select t1.buy/t2.cart
from(
select count(distinct cust_uid) buy
from(
select cust_uid ,if_buy
from tb_clk_rcd
where if_cart = 1 and if_buy = 1) a)t1
,
(select count( distinct cust_uid) cart
from(
select cust_uid,if_cart
from tb_clk_rcd
where if_cart = 1 )b)t2
she*yu* 183.247.177.35 2023-04-08 16:05:34 scene3_3
she*yu* 183.247.177.35 2023-04-08 16:01:53 select t1.buy/t2.cart
from(
select count(distinct cust_uid) buy
from(
select cust_uid ,if_buy
from tb_clk_rcd
where if_cart = 1 and if_buy = 1) a)t1
,
(select count( distinct cust_uid) cart
from(
select cust_uid,if_cart
from tb_clk_rcd
where if_cart = 1 )b)t2
she*yu* 183.247.177.35 2023-04-08 16:00:47 select t1.cart/t2.vw
from(
select count(distinct cust_uid) cart
from(
select cust_uid ,if_cart
from tb_clk_rcd
where if_cart = 1 and if_vw = 1) a)t1
,
(select count( distinct cust_uid) vw
from(
select cust_uid,if_vw
from tb_clk_rcd
where if_vw = 1 )b)t2
she*yu* 183.247.177.35 2023-04-08 15:59:23 select t1.cart/t2.vw
from(
select count(cust_uid) cart
from(
select cust_uid ,if_cart
from tb_clk_rcd
where if_cart = 1 and if_vw = 1) a)t1
,
(select count(cust_uid) vw
from(
select cust_uid,if_vw
from tb_clk_rcd
where if_vw = 1 )b)t2
she*yu* 183.247.177.35 2023-04-08 15:59:12 select t1.cart/t2.vw
from(
select count(cust_uid) cart
from(
select cust_uid ,if_cart
from tb_clk_rcd
where if_cart = 1 and if_vw = 1) a)t1
,
(select count(cust_uid) snd
from(
select cust_uid,if_vw
from tb_clk_rcd
where if_vw = 1 )b)t2
she*yu* 183.247.177.35 2023-04-08 15:59:09 select t1.cart/t2.vw
from(
select count(cust_uid) cart
from(
select cust_uid ,if_cart
from tb_clk_rcd
where if_cart = 1 and if_vw = 1) a)t1
,
(select count(cust_uid) snd
from(
select cust_uid,if_vw
from tb_clk_rcd
where if_vw = 1 )b)t2
she*yu* 183.247.177.35 2023-04-08 15:58:57 select t1.vw/t2.snd
from(
select count(cust_uid) cart
from(
select cust_uid ,if_cart
from tb_clk_rcd
where if_cart = 1 and if_vw = 1) a)t1
,
(select count(cust_uid) snd
from(
select cust_uid,if_vw
from tb_clk_rcd
where if_vw = 1 )b)t2
she*yu* 183.247.177.35 2023-04-08 15:54:10 select t1.vw/t2.snd
from(
select count(cust_uid) vw
from(
select cust_uid,if_snd,if_vw
from tb_clk_rcd
where if_snd = 1 and if_vw = 1) a)t1
,
(select count(cust_uid) snd
from(
select cust_uid,if_snd
from tb_clk_rcd
where if_snd = 1 )b)t2
she*yu* 183.247.177.35 2023-04-08 15:49:01
select count(cust_uid) vw
from(
select cust_uid,if_snd,if_vw
from tb_clk_rcd
where if_snd = 1 and if_vw = 1) a
she*yu* 183.247.177.35 2023-04-08 15:41:45 select cust_uid,if_snd,if_vw
from tb_clk_rcd
where if_snd = 1 and if_vw = 1
she*yu* 183.247.177.35 2023-04-08 15:40:42 select cust_uid,if_snd
from tb_clk_rcd
where if_snd = 1
she*yu* 183.247.177.35 2023-04-08 15:35:05 select count(if_snd = 1) snd, count(if_vw = 1) vw
from tb_clk_rcd
she*yu* 183.247.177.35 2023-04-08 15:34:45 select count(if_snd = 1) snd
from tb_clk_rcd
she*yu* 183.247.177.35 2023-04-08 15:33:39 select count(cust_uid) snd
from tb_clk_rcd
where if_snd = 1
she*yu* 183.247.177.35 2023-04-08 15:33:03 select count(cust_uid)
from tb_clk_rcd
where if_snd = 1
she*yu* 183.247.177.35 2023-04-08 15:32:16 select cust_uid,count(1)
from tb_clk_rcd
where if_snd = 1
group by cust_uid
she*yu* 183.247.177.35 2023-04-08 15:32:03 select cust_uid,count(1)
from tb_clk_rcd
where if_snd = 1
she*yu* 183.247.177.35 2023-04-08 15:31:07 select cust_uid,count(if_snd = 1)
from tb_clk_rcd
group by cust_uid
she*yu* 183.247.177.35 2023-04-08 15:30:51 select cust_uid,count(if_snd = 1)
from tb_clk_rcd
she*yu* 183.247.177.35 2023-04-08 15:23:19 select * from tb_clk_rcd limit 5
she*yu* 183.247.177.35 2023-04-08 15:23:01 select * from tb_prd_map limit 5
she*yu* 183.247.177.35 2023-04-08 15:21:59 scene3_3
she*yu* 183.247.177.35 2023-04-08 15:21:50 member_enter2
she*yu* 183.247.177.35 2023-04-08 15:21:48 ser1
she*yu* 183.247.177.35 2023-04-08 15:21:46 会员专享
she*yu* 183.247.177.35 2023-04-08 15:19:14 scene3
she*yu* 183.247.177.35 2023-04-08 11:58:02 meta_base
she*yu* 183.247.177.35 2023-04-08 11:13:00 scene3
she*yu* 183.247.177.35 2023-04-08 11:12:35 scene3
she*yu* 183.247.177.35 2023-04-08 11:12:17 changjing_computer
she*yu* 183.247.177.35 2023-04-08 11:12:14 a1
she*yu* 183.247.177.35 2023-04-08 11:11:09 scene16
she*yu* 183.247.177.35 2023-04-08 11:11:08 changjing_computer
she*yu* 183.247.177.35 2023-04-08 11:09:25 select count(distinct usr_id)
from (
select usr_id,base,count(1)
from(
select *, date_sub(load_dt, interval rnk day) as base
from(
select *,row_number() over(partition by a.usr_id order by a.load_dt) as rnk
from(
select
usr_id,load_dt
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a)b)c
group by usr_id,base
having count(1) >= 7)c
she*yu* 183.247.177.35 2023-04-08 11:07:19 select count(distinct usr_id)
from (
select usr_id,base,count(1)
from(
select *, date_sub(load_dt, interval rnk day) as base
from(
select *,row_number() over(partition by a.usr_id order by a.load_dt) as rnk
from(
select
usr_id,load_dt
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a)b)c
group by usr_id,base
having count(1) >= 5)c
she*yu* 183.247.177.35 2023-04-08 11:07:09 select count(distinct usr_id)
from (
select usr_id,base,count(1)
from(
select *, date_sub(load_dt, interval rnk day) as base
from(
select *,row_number() over(partition by a.usr_id order by a.load_dt) as rnk
from(
select
usr_id,load_dt
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a)b)c
group by b.usr_id,b.base
having count(1) >= 5)c
she*yu* 183.247.177.35 2023-04-08 11:07:07 select count(distinct usr_id)
from (
select usr_id,base,count(1)
from(
select *, date_sub(load_dt, interval rnk day) as base
from(
select *,row_number() over(partition by a.usr_id order by a.load_dt) as rnk
from(
select
usr_id,load_dt
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a)b)c
group by b.usr_id,b.base
having count(1) >= 5)c
she*yu* 183.247.177.35 2023-04-08 11:06:55 select count(distinct usr_id)
from (
select b.usr_id,b.base,count(1)
from(
select *, date_sub(b.load_dt, interval rnk day) as base
from(
select *,row_number() over(partition by a.usr_id order by a.load_dt) as rnk
from(
select
usr_id,load_dt
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a)b)c
group by b.usr_id,b.base
having count(1) >= 5)c
she*yu* 183.247.177.35 2023-04-08 11:06:38 select count(distinct usr_id)
from (
select b.usr_id,b.base,count(1)
from(
select *, date_sub(a.load_dt, interval rnk day) as base
from(
select *,row_number() over(partition by a.usr_id order by a.load_dt) as rnk
from(
select
usr_id,load_dt
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a)b)c
group by b.usr_id,b.base
having count(1) >= 5)c
she*yu* 183.247.177.35 2023-04-08 11:04:31 select count(distinct usr_id)
from (
select b.usr_id,b.base,count(1)
from(
select *, date_sub(a.load_dt, interval rnk day) as base
from(
select
usr_id,load_dt,
row_number() over(partition by usr_id order by load_dt) as rnk
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a)b
group by b.usr_id,b.base
having count(1) >= 5)c
she*yu* 183.247.177.35 2023-04-08 11:04:22 select distinct usr_id
from (
select b.usr_id,b.base,count(1)
from(
select *, date_sub(a.load_dt, interval rnk day) as base
from(
select
usr_id,load_dt,
row_number() over(partition by usr_id order by load_dt) as rnk
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a)b
group by b.usr_id,b.base
having count(1) >= 5)c
she*yu* 183.247.177.35 2023-04-08 11:03:39 select b.usr_id,b.base,count(1)
from(
select *, date_sub(a.load_dt, interval rnk day) as base
from(
select
usr_id,load_dt,
row_number() over(partition by usr_id order by load_dt) as rnk
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a)b
group by b.usr_id,b.base
having count(1) >= 5
she*yu* 183.247.177.35 2023-04-08 11:03:37 select b.usr_id,b.base,count(1)
from(
select *, date_sub(a.load_dt, interval rnk day) as base
from(
select
usr_id,load_dt,
row_number() over(partition by usr_id order by load_dt) as rnk
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a)b
group by b.usr_id,b.base
having count(1) >= 5
she*yu* 183.247.177.35 2023-04-08 11:03:00 select b.usr_id,b.base,count(b.base)
from(
select *, date_sub(a.load_dt, interval rnk day) as base
from(
select
usr_id,load_dt,
row_number() over(partition by usr_id order by load_dt) as rnk
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a)b
group by b.usr_id,b.base
having count(b.base) >= 5
she*yu* 183.247.177.35 2023-04-08 11:01:41 select count(distinct c.usr_id)
from(
select b.usr_id,b.base,count(b.base)
from(
select *, date_sub(a.load_dt, interval rnk day) as base
from(
select
usr_id,load_dt,
row_number() over(partition by usr_id order by load_dt) as rnk
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a)b
group by b.usr_id,b.base
having count(b.base) >= 5) c
she*yu* 183.247.177.35 2023-04-08 11:01:29 select count(c.usr_id)
from(
select b.usr_id,b.base,count(b.base)
from(
select *, date_sub(a.load_dt, interval rnk day) as base
from(
select
usr_id,load_dt,
row_number() over(partition by usr_id order by load_dt) as rnk
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a)b
group by b.usr_id,b.base
having count(b.base) >= 5) c
she*yu* 183.247.177.35 2023-04-08 11:00:18 select b.usr_id,b.base,count(b.base)
from(
select *, date_sub(a.load_dt, interval rnk day) as base
from(
select
usr_id,load_dt,
row_number() over(partition by usr_id order by load_dt) as rnk
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a)b
group by b.usr_id,b.base
having count(b.base) >= 5
she*yu* 183.247.177.35 2023-04-08 10:59:46 select b.usr_id,b.base
from(
select *, date_sub(a.load_dt, interval rnk day) as base
from(
select
usr_id,load_dt,
row_number() over(partition by usr_id order by load_dt) as rnk
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a)b
where count(b.base) >= 5
group by b.usr_id,b.base
she*yu* 183.247.177.35 2023-04-08 10:55:37 select *, date_sub(a.load_dt, interval rnk day) as base
from(
select
usr_id,load_dt,
row_number() over(partition by usr_id order by load_dt) as rnk
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a
she*yu* 183.247.177.35 2023-04-08 10:54:14 select *, (a.load_dt - a.ran) as base
from(
select
usr_id,load_dt,
row_number() over(partition by usr_id order by load_dt) as ran
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) a
she*yu* 183.247.177.35 2023-04-08 10:47:19 select
usr_id,load_dt,row_number() over(partition by usr_id order by load_dt) as ran
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt
she*yu* 183.247.177.35 2023-04-08 10:47:12 select
usr_id,load_dt,row_number() over(partition by a.usr_id order by a.load_dt) as ran
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt
she*yu* 183.247.177.35 2023-04-08 10:46:16 select a.usr_id, a.load_dt,
row_number() over(partition by a.usr_id order by a.load_dt) as ran
from(
select
usr_id,load_dt
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt)a
she*yu* 183.247.177.35 2023-04-08 10:45:38 select a.usr_id, a.load_dt,
row_number() over(partition by a.usr_id order by a.load_dt) as rank
from(
select
usr_id,load_dt
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt)a
she*yu* 183.247.177.35 2023-04-08 10:45:36 select a.usr_id, a.load_dt,
row_number() over(partition by a.usr_id order by a.load_dt) as rank
from(
select
usr_id,load_dt
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt)a
she*yu* 183.247.177.35 2023-04-08 10:45:02 select t1.usr_id, t2.load_dt,
row_number() over(partition by t1.usr_id order by t1.load_dt) as rank
from(
select
usr_id,load_dt
from td_load_rcd
where substr(load_dt,1,7) = '2020-06'
group by usr_id,load_dt) t1
she*yu* 183.247.177.35 2023-04-08 10:43:33 select t1.usr_id, t2.load_dt,
row_number() over(partition by t1.usr_id order by t1.load_dt) as rank
from(
select
usr_id,load_dt
from td_load_rcd
where load_dt between '2020-06-01' and '2020-06-30'
group by usr_id,load_dt) t1
she*yu* 183.247.177.35 2023-04-08 10:42:25 select
usr_id,load_dt
from td_load_rcd
where load_dt between '2020-06-01' and '2020-06-30'
group by usr_id,load_dt
she*yu* 183.247.177.35 2023-04-08 10:42:20 select
usr_id,load_dt,
from td_load_rcd
where load_dt between '2020-06-01' and '2020-06-30'
group by usr_id,load_dt
she*yu* 183.247.177.35 2023-04-08 10:42:00 select
usr_id,load_dt,
row_number() over(partition by usr_id order by load_dt) as rank
from td_load_rcd
where load_dt between '2020-06-01' and '2020-06-30'
group by usr_id,load_dt
she*yu* 183.247.177.35 2023-04-08 10:41:59 select
usr_id,load_dt,
row_number() over(partition by usr_id order by load_dt) as rank
from td_load_rcd
where load_dt between '2020-06-01' and '2020-06-30'
group by usr_id,load_dt
she*yu* 183.247.177.35 2023-04-08 10:41:57 select
usr_id,load_dt,
row_number() over(partition by usr_id order by load_dt) as rank
from td_load_rcd
where load_dt between '2020-06-01' and '2020-06-30'
group by usr_id,load_dt
she*yu* 183.247.177.35 2023-04-08 10:39:14 select
usr_id,load_dt,
row_number() over(order by load_dt) as rank
from td_load_rcd
where load_dt between '2020-06-01' and '2020-06-30'
group by usr_id,load_dt
order by usr_id,load_dt
she*yu* 183.247.177.35 2023-04-08 10:38:10 select
usr_id,load_dt,
row_number() over(order by load_dt) as rank
from
td_load_rcd
where load_dt between '2020-06-01' and '2020-06-30'
group by usr_id,load_dt
order by usr_id, load_dt
she*yu* 183.247.177.35 2023-04-08 10:36:43 select
usr_id,load_dt
from
td_load_rcd
where load_dt between '2020-06-01' and '2020-06-30'
group by usr_id,load_dt
order by usr_id, load_dt
she*yu* 183.247.177.35 2023-04-08 10:34:26 select
usr_id,load_dt
from
td_load_rcd
group by usr_id,load_dt
order by usr_id, load_dt
she*yu* 183.247.177.35 2023-04-08 10:33:50 select
usr_id,load_dt
from
td_load_rcd
order by usr_is, load_dt
she*yu* 183.247.177.35 2023-04-08 10:32:18 scene1
she*yu* 183.247.177.35 2023-04-07 21:52:18 select
load_dt, usr_id
from
td_load_rcd
where load_dt between '2020-06-01' and '2020-06-30'
group by load_dt, usr_id
she*yu* 183.247.177.35 2023-04-07 21:51:35 select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id
she*yu* 183.247.177.35 2023-04-07 21:47:47 select
load_dt, usr_id
from
td_load_rcd
where load_dt between '2020-06-01' and '2020-06-30'
group by load_dt, usr_id
she*yu* 183.247.177.35 2023-04-07 21:47:36 select
load_dt, usr_id
from
td_load_rcd
where load_dt between '2020-06-01' and '2020-06-30'
group by load_dt, usr_id
having count(usr_id) = 7
she*yu* 183.247.177.35 2023-04-07 21:46:37 select
load_dt, usr_id
from
td_load_rcd
where load_dt between '2020-06-01' and '2020-06-30'
group by load_dt, usr_id
she*yu* 183.247.177.35 2023-04-07 21:42:59 select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id
she*yu* 183.247.177.35 2023-04-07 21:42:46 select
usr_id,load_dt
from
td_load_rcd
group by usr_id,load_dt
she*yu* 183.247.177.35 2023-04-07 21:42:42 select
usr_id,load_dt
from
td_load_rcd
group by usr_id,load_dt
she*yu* 183.247.177.35 2023-04-07 21:42:02 select
usr_id, min(load_dt) as load_dt
from
td_load_rcd
group by usr_id
she*yu* 183.247.177.35 2023-04-07 21:38:15 scene1
she*yu* 183.247.177.35 2023-04-07 21:38:07 meta_base
she*yu* 183.247.177.35 2023-04-07 21:35:23 select t0.load_dt
,count(t0.usr_id) as cst_dt_0
,count(t1.usr_id) as cst_dt_1
,count(t1.usr_id) / count(t0.usr_id) as cst_dt_pct_1
,count(t2.usr_id) as cst_dt_2
,count(t2.usr_id) / count(t0.usr_id) as cst_dt_pct_3
,count(t3.usr_id) as cst_dt_3
,count(t3.usr_id) / count(t0.usr_id) as cst_dt_pct_7
from(
select
usr_id, min(load_dt) as load_dt
from
td_load_rcd
group by usr_id)t0
left join(
select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id)t1
on t0.usr_id = t1.usr_id and t0.load_dt = date_sub(t1.load_dt,interval 1 day)
left join(
select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id)t2
on t0.usr_id = t2.usr_id and t0.load_dt = date_sub(t2.load_dt,interval 3 day)
left join(
select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id)t3
on t0.usr_id = t3.usr_id and t0.load_dt = date_sub(t3.load_dt,interval 7 day)
group by t0.load_dt
order by t0.load_dt
she*yu* 183.247.177.35 2023-04-07 21:30:19 select t0.load_dt
,count(t0.usr_id) as cst_dt_0
,count(t1.usr_id) as cst_dt_1
,count(t1.usr_id) / count(t0.usr_id) as cst_dt_pct_1
from(
select
usr_id, min(load_dt) as load_dt
from
td_load_rcd
group by usr_id)t0
left join(
select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id)t1
on t0.usr_id = t1.usr_id and t0.load_dt = date_sub(t1.load_dt,interval 1 day)
group by t0.load_dt
order by t0.load_dt
she*yu* 183.247.177.35 2023-04-07 21:29:18 select *
from(
select
usr_id, min(load_dt) as load_dt
from
td_load_rcd
group by usr_id)t0
left join(
select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id)t1
on t0.usr_id = t1.usr_id and t0.load_dt = date_sub(t1.load_dt,interval 1 day)
group by t0.load_dt
order by t0.load_dt
she*yu* 183.247.177.35 2023-04-07 21:27:25 select t0.load_dt
,count(t0.usr_id) as cst_dt_0
,count(t1.usr_id) as cst_dt_1
,count(t1.usr_id) / count(t0.usr_id) as cst_dt_pct_1
from(
select
usr_id, min(load_dt) as load_dt
from
td_load_rcd
group by usr_id)t0
left join(
select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id)t1
on t0.usr_id = t1.usr_id and t0.load_dt = date_sub(t1.load_dt,interval 1 day)
group by t0.load_dt
order by t0.load_dt
she*yu* 183.247.177.35 2023-04-07 21:25:59 select t0.load_dt
,count(t0.usr_id) as cst_dt_0
,count(t1.usr_id) as cst_dt_1
,count(t1.usr_id) / count(t0.usr_id) as cst_dt_pct_1
from(
select
usr_id, min(load_dt) as load_dt
from
td_load_rcd
group by usr_id) t0
left join(
select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id) t1
on t0.usr_id = t1.usr_id and t0.load_dt = date_sub(t1.load_dt,interval 1 day)
she*yu* 183.247.177.35 2023-04-07 21:23:39 create view td_distinct_load_rcd_min as
select
usr_id, min(load_dt) as load_dt
from
td_load_rcd
group by usr_id
;
create view td_distinct_load_rcd as
select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id
;

select t0.load_dt
,count(t0.usr_id) as cst_dt_0
,count(t1.usr_id) as cst_dt_1
,count(t1.usr_id) / count(t0.usr_id) as cst_dt_pct_1
from td_distinct_load_rcd_min t0
left join td_distinct_load_rcd t1
on t0.usr_id = t1.usr_id and t0.load_dt = date_sub(t1.load_dt,interval 1 day)
she*yu* 183.247.177.35 2023-04-07 21:22:17 create view td_distinct_load_rcd_min as
select
usr_id, min(load_dt) as load_dt
from
td_load_rcd
group by usr_id
;
create view td_distinct_load_rcd as
select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id
;

select t0.load_dt
,count(t0.usr_id) as cst_dt_0
,count(t1.usr_id) as cst_dt_1
,count(t1.usr_id) / count(t0.usr_id) as cst_dt_pct_1
from td_distinct_load_rcd_min t0
left join td_distinct_load_rcd t1
on t0.usr_id = t1.usr_id and t0.load_dt = date_sub(t1.load_dt,interval 1 day)
she*yu* 183.247.177.35 2023-04-07 21:19:08 create view td_distinct_load_rcd_min as
select
usr_id, min(load_dt) as load_dt
from
td_load_rcd
group by usr_id
;
create view td_distinct_load_rcd as
select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id
;

select *
from td_distinct_load_rcd_min t0
left join td_distinct_load_rcd t1
on t0.usr_id = t1.usr_id and t0.load_dt = date_sub(t1.load_dt,interval 1 day)
she*yu* 183.247.177.35 2023-04-07 21:17:13 create view td_distinct_load_rcd_min as
select
usr_id, min(load_dt) as load_dt
from
td_load_rcd
group by usr_id
;
create view td_distinct_load_rcd as
select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id
;

select *
from td_distinct_load_rcd_min t0
left join td_distinct_load_rcd t1
on t0.usr_id = t1.usr_id
she*yu* 183.247.177.35 2023-04-07 21:17:11 create view td_distinct_load_rcd_min as
select
usr_id, min(load_dt) as load_dt
from
td_load_rcd
group by usr_id
;
create view td_distinct_load_rcd as
select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id
;

select *
from td_distinct_load_rcd_min t0
left join td_distinct_load_rcd t1
on t0.usr_id = t1.usr_id
she*yu* 183.247.177.35 2023-04-07 21:16:21 create view td_distinct_load_rcd_min as
select
usr_id, min(load_dt) as load_dt
from
td_load_rcd
group by usr_id
;
create view td_distinct_load_rcd as
select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id
;

select *
from td_distinct_load_rcd_min t0
left join td_distinct_load_rcd t1
she*yu* 183.247.177.35 2023-04-07 21:10:34 select
load_dt, usr_id
from
td_load_rcd
group by load_dt, usr_id
she*yu* 183.247.177.35 2023-04-07 21:10:17 select
usr_id, load_dt
from
td_load_rcd
group by load_dt, usr_id
she*yu* 183.247.177.35 2023-04-07 21:10:01 select
usr_id, load_dt
from
td_load_rcd
group by usr_id, load_dt
she*yu* 183.247.177.35 2023-04-07 21:03:29 select
usr_id, min(load_dt) as load_dt
from
td_load_rcd
group by usr_id
she*yu* 183.247.177.35 2023-04-07 21:02:50 select
distinct usr_id, min(load_dt) as load_dt
from
td_load_rcd
group by usr_id
she*yu* 183.247.177.35 2023-04-07 21:02:40 select
distinct usr_id, min(load_dt）as load_dt
from
td_load_rcd
group by usr_id
she*yu* 183.247.177.35 2023-04-07 21:02:24 select
distinct usr_id, mix(load_dt）as load_dt
from
td_load_rcd
group by usr_id
she*yu* 183.247.177.35 2023-04-07 20:58:37 select
distinct usr_id, load_dt
from
td_load_rcd
where load_dt = '2020-06-12'
order by usr_id desc
she*yu* 183.247.177.35 2023-04-07 20:58:06 select
distinct usr_id, load_dt
from
td_load_rcd
where load_dt = '2020-06-11'
order by usr_id desc
she*yu* 183.247.177.35 2023-04-07 20:57:54 select
distinct usr_id, load_dt
from
td_load_rcd
where load_dt = '2020-06-11'
order by load_dt desc
she*yu* 183.247.177.35 2023-04-07 20:57:40 select
distinct usr_id, load_dt
from
td_load_rcd
where load_dt = '2020-06-11'
order by load_dt
she*yu* 183.247.177.35 2023-04-07 20:57:07 select
usr_id, load_dt
from
td_load_rcd
where load_dt in ('2020-06-11','2020-06-12')
order by load_dt
she*yu* 183.247.177.35 2023-04-07 20:56:52 select
usr_id, load_dt
from
td_load_rcd
where load_dt in ('2020-06-11','2020-06-12')
she*yu* 183.247.177.35 2023-04-07 20:53:38 select
substr(load_dt,1,7) as '月份'
, count(1) as '登录人次'
, count(distinct usr_id) as '月活'
from
td_load_rcd
group by 1
she*yu* 183.247.177.35 2023-04-07 20:53:06 scene1
she*yu* 183.247.177.35 2023-04-07 20:43:09 select count(distinct usr_id)
from(
select
usr_id, load_dt,
count(usr_id) as 'amount'
from
td_load_rcd
group by usr_id, load_dt
having amount >= 5) t1
she*yu* 183.247.177.35 2023-04-07 20:42:10
select
usr_id, load_dt,
count(usr_id) as 'amount'
from
td_load_rcd
group by usr_id, load_dt
having amount >= 5
she*yu* 183.247.177.35 2023-04-07 20:40:47 select
usr_id,
count(usr_id) as 'amount'
from
td_load_rcd
group by usr_id
having amount >= 5
she*yu* 183.247.177.35 2023-04-07 20:36:17 select
case
when hour(load_tm) between 7 and 8 or hour(load_tm) between 18 and 19 then '通勤'
when hour(load_tm) between 11 and 12 then '午休'
when hour(load_tm) in (22,23,0) then '临睡'
end as tag,
count(distinct usr_id) as '月活'
from
td_load_rcd
where load_dt in ('2020-07-01','2020-07-02','2020-07-03','2020-07-06',
'2020-07-07','2020-07-08','2020-07-09','2020-07-10')
group by
case
when hour(load_tm) between 7 and 8 or hour(load_tm) between 18 and 19 then '通勤'
when hour(load_tm) between 11 and 12 then '午休'
when hour(load_tm) in (22,23,0) then '临睡'
end
she*yu* 183.247.177.35 2023-04-07 20:36:01 select
case
when hour(load_tm) between 7 and 8 or hour(load_tm) between 18 and 19 then '通勤'
when hour(load_tm) between 11 and 12 then '午休'
when hour(load_tm) in (22,23,0) then '临睡'
end as tag,
count(distinct usr_id) as '月活'
from
td_load_rcd
where load_dt in ('2020-07-01'，'2020-07-02'，'2020-07-03'，'2020-07-06'，
'2020-07-07'，'2020-07-08'，'2020-07-09'，'2020-07-10')
group by
case
when hour(load_tm) between 7 and 8 or hour(load_tm) between 18 and 19 then '通勤'
when hour(load_tm) between 11 and 12 then '午休'
when hour(load_tm) in (22,23,0) then '临睡'
end
she*yu* 183.247.177.35 2023-04-07 20:34:34 select
case
when hour(load_tm) between 7 and 8 or hour(load_tm) between 18 and 19 then '通勤'
when hour(load_tm) between 11 and 12 then '午休'
when hour(load_tm) in (22,23,0) then '临睡'
end as tag,
count(distinct usr_id) as '月活'
from
td_load_rcd
where load_dt in (select load_dt from (select load_dt, weekday(load_dt) as week from td_load_rcd
where load_dt between '2020-07-01' and '2020-07-10'
having week not in (5,6)) t1)
group by
case
when hour(load_tm) between 7 and 8 or hour(load_tm) between 18 and 19 then '通勤'
when hour(load_tm) between 11 and 12 then '午休'
when hour(load_tm) in (22,23,0) then '临睡'
end
she*yu* 183.247.177.35 2023-04-07 20:34:03 select
case
when hour(load_tm) between 7 and 8 or hour(load_tm) between 18 and 19 then '通勤'
when hour(load_tm) between 11 and 12 then '午休'
when hour(load_tm) in (22,23,0) then '临睡'
end as tag,
count(distinct usr_id) as '月活'
from
td_load_rcd
where load_dt in (select load_dt from (select load_dt, weekday(load_dt) as week from td_load_rcd
where load_dt between '2020-07-01' and '2020-07-10'
having week not in (5,6)) t1)
group by
case
when hour(load_tm) between 7 and 8 or hour(load_tm) between 18 and 19 then '通勤'
when hour(load_tm) between 11 and 12 then '午休'
when hour(load_tm) in (22,23,0) then '午休'
end
she*yu* 183.247.177.35 2023-04-07 20:33:42 select
case
when hour(load_tm) between 7 and 8 or hour(load_tm) between 18 and 19 then '通勤'
when hour(load_tm) between 11 and 12 then '午休'
when hour(load_tm) in (22,23,0) then '午休'
end as tag,
count(distinct usr_id) as '月活'
from
td_load_rcd
where load_dt in (select load_dt from (select load_dt, weekday(load_dt) as week from td_load_rcd
where load_dt between '2020-07-01' and '2020-07-10'
having week not in (5,6)) t1)
group by
case
when hour(load_tm) between 7 and 8 or hour(load_tm) between 18 and 19 then '通勤'
when hour(load_tm) between 11 and 12 then '午休'
when hour(load_tm) in (22,23,0) then '午休'
end
she*yu* 183.247.177.35 2023-04-07 20:32:40 select load_dt from (select load_dt, weekday(load_dt) as week from td_load_rcd
where load_dt between '2020-07-01' and '2020-07-10'
having week not in (5,6)) t1
she*yu* 183.247.177.35 2023-04-07 20:32:03 select load_dt, weekday(load_dt) as week from td_load_rcd
where load_dt between '2020-07-01' and '2020-07-10'
having week not in (5,6)
she*yu* 183.247.177.35 2023-04-07 20:30:58 select weekday(load_dt) as week from td_load_rcd
where load_dt between '2020-07-01' and '2020-07-10'
having week not in (5,6)
she*yu* 183.247.177.35 2023-04-07 20:30:44 select weekday(load_dt) as week from td_load_rcd
where load_dt between '2020-07-01' and '2020-07-10
having week not in (5,6)
she*yu* 183.247.177.35 2023-04-07 20:28:43 select weekday(load_dt) as week from td_load_rcd
where load_dt between '2020-07-01' and '2020-07-31' having week not in (5,6)
she*yu* 183.247.177.35 2023-04-07 20:28:05 select
case
when hour(load_tm) between 7 and 8 or hour(load_tm) between 18 and 19 then '通勤'
when hour(load_tm) between 11 and 12 then '午休'
when hour(load_tm) in (22,23,0) then '午休'
end as tag,
count(distinct usr_id) as '月活'
from
td_load_rcd
where load_dt in (select weekday(load_dt) as week from td_load_rcd
where load_dt between '2020-07-01' and '2020-07-31' having week not in (5,6))
group by
case
when hour(load_tm) between 7 and 8 or hour(load_tm) between 18 and 19 then '通勤'
when hour(load_tm) between 11 and 12 then '午休'
when hour(load_tm) in (22,23,0) then '午休'
end
she*yu* 183.247.177.35 2023-04-07 14:57:11 scene1
she*yu* 183.247.177.35 2023-04-07 12:21:57 meta_base
she*yu* 183.247.177.35 2023-04-07 11:33:12 select
substr(load_dt,1,7) as '月份'
, count(1) as '登录人次'
, count(distinct usr_id) as '月活'
from
td_load_rcd
group by 1
she*yu* 183.247.177.35 2023-04-07 11:33:09 scene1
she*yu* 183.247.177.35 2023-04-07 10:32:31 scene1
she*yu* 183.247.177.35 2023-04-07 10:32:16 meta_base
she*yu* 183.247.177.35 2023-04-06 22:33:45 meta_base
she*yu* 183.247.177.35 2023-04-06 22:23:47 select
case
when hour(load_tm) between 7 and 8 or hour(load_tm) between 18 and 19 then '通勤'
when hour(load_tm) between 11 and 12 then '午休'
when hour(load_tm) in (22,23,0) then '临睡'
end as tag,
count(distinct usr_id) as '月活'
from
td_load_rcd
group by
case
when hour(load_tm) between 7 and 8 or hour(load_tm) between 18 and 19 then '通勤'
when hour(load_tm) between 11 and 12 then '午休'
when hour(load_tm) in (22,23,0) then '临睡'
end
she*yu* 183.247.177.35 2023-04-06 22:21:08 select
case
when load_tm between '07:00:00' and '09:00:00' or load_tm between '18:00:00' and '20:00:00' then '通勤'
when load_tm between '11:00:00' and '13:00:00' then '午休'
when load_tm between '22:00:00' and '01:00:00' then '临睡'
end as tag,
count(distinct usr_id) as '月活'
from
td_load_rcd
group by
case
when load_tm between '07:00:00' and '09:00:00' or load_tm between '18:00:00' and '20:00:00' then '通勤'
when load_tm between '11:00:00' and '13:00:00' then '午休'
when load_tm between '22:00:00' and '01:00:00' then '临睡'
end
she*yu* 183.247.177.35 2023-04-06 22:17:46 select
load_dt,
case
when load_tm between '07:00:00' and '09:00:00' or load_tm between '18:00:00' and '20:00:00' then '通勤'
when load_tm between '11:00:00' and '13:00:00' then '午休'
when load_tm between '22:00:00' and '01:00:00' then '临睡'
end as tag,
count(distinct usr_id) as '月活'
from
td_load_rcd
group by load_dt,

she*yu* 183.247.177.35 2023-04-06 22:17:20 select
load_dt,
case
when load_tm between '07:00:00' and '09:00:00' or load_tm between '18:00:00' and '20:00:00' then '通勤'
when load_tm between '11:00:00' and '13:00:00' then '午休'
when load_tm between '22:00:00' and '01:00:00' then '临睡'
end as tag,
count(2) as '月活'
from
td_load_rcd
group by load_dt
she*yu* 183.247.177.35 2023-04-06 22:17:04 select
load_dt,
case
when load_tm between '07:00:00' and '09:00:00' or load_tm between '18:00:00' and '20:00:00' then '通勤'
when load_tm between '11:00:00' and '13:00:00' then '午休'
when load_tm between '22:00:00' and '01:00:00' then '临睡'
end as tag,
count(2) as '月活'
from
td_load_rcd
group by load_dt,tag
she*yu* 183.247.177.35 2023-04-06 22:17:03 select
load_dt,
case
when load_tm between '07:00:00' and '09:00:00' or load_tm between '18:00:00' and '20:00:00' then '通勤'
when load_tm between '11:00:00' and '13:00:00' then '午休'
when load_tm between '22:00:00' and '01:00:00' then '临睡'
end as tag,
count(2) as '月活'
from
td_load_rcd
group by load_dt,tag
she*yu* 183.247.177.35 2023-04-06 22:17:00 select
load_dt,
case
when load_tm between '07:00:00' and '09:00:00' or load_tm between '18:00:00' and '20:00:00' then '通勤'
when load_tm between '11:00:00' and '13:00:00' then '午休'
when load_tm between '22:00:00' and '01:00:00' then '临睡'
end as tag,
count(2) as '月活'
from
td_load_rcd
group by load_dt,tag
she*yu* 183.247.177.35 2023-04-06 22:16:10 select
load_dt,
case
when load_tm between '07:00:00' and '09:00:00' or load_tm between '18:00:00' and '20:00:00' then '通勤'
when load_tm between '11:00:00' and '13:00:00' then '午休'
when load_tm between '22:00:00' and '01:00:00' then '临睡'
else null
end as tag,
count(2) as '月活'
from
td_load_rcd
group by load_dt,tag
she*yu* 183.247.177.35 2023-04-06 22:16:06 select
load_dt,
case
when load_tm between '07:00:00' and '09:00:00' or load_tm between '18:00:00' and '20:00:00' then '通勤'
when load_tm between '11:00:00' and '13:00:00' then '午休'
when load_tm between '22:00:00' and '01:00:00' then '临睡'
else null
end as tag,
count(2) as '月活'
from
td_load_rcd
group by load_dt,tag
having left(load_dt,7) = 7
she*yu* 183.247.177.35 2023-04-06 22:16:05 select
load_dt,
case
when load_tm between '07:00:00' and '09:00:00' or load_tm between '18:00:00' and '20:00:00' then '通勤'
when load_tm between '11:00:00' and '13:00:00' then '午休'
when load_tm between '22:00:00' and '01:00:00' then '临睡'
else null
end as tag,
count(2) as '月活'
from
td_load_rcd
group by load_dt,tag
having left(load_dt,7) = 7
she*yu* 183.247.177.35 2023-04-06 22:16:02 select
load_dt,
case
when load_tm between '07:00:00' and '09:00:00' or load_tm between '18:00:00' and '20:00:00' then '通勤'
when load_tm between '11:00:00' and '13:00:00' then '午休'
when load_tm between '22:00:00' and '01:00:00' then '临睡'
else null
end as tag,
count(2) as '月活'
from
td_load_rcd
group by load_dt,tag
having left(load_dt,7) = 7
she*yu* 183.247.177.35 2023-04-06 22:15:48 select
load_dt,
case
when load_tm between '07:00:00' and '09:00:00' or load_tm between '18:00:00' and '20:00:00' then '通勤'
when load_tm between '11:00:00' and '13:00:00' then '午休'
when load_tm between '22:00:00' and '01:00:00' then '临睡'
else null
end as tag,
count(2) as '月活'
from
td_load_rcd
group by tag
having left(load_dt,7) = 7
she*yu* 183.247.177.35 2023-04-06 22:12:03 select
load_dt,
case
when load_tm between '07:00:00' and '09:00:00' or load_tm between '18:00:00' and '20:00:00' then '通勤'
when load_tm between '11:00:00' and '13:00:00' then '午休'
when load_tm between '22:00:00' and '01:00:00' then '临睡'
else null
end as tag
from
td_load_rcd
she*yu* 183.247.177.35 2023-04-06 22:10:18 select
load_dt,
case
when load_tm (between '07:00:00' and '09:00:00') or (between '18:00:00' and '20:00:00') then '通勤'
when load_tm between '11:00:00' and '13:00:00' then '午休'
when load_tm between '22:00:00' and '01:00:00' then '临睡'
else null
end as tag
from
td_load_rcd
she*yu* 183.247.177.35 2023-04-06 22:01:57 select
substr(load_dt,1,7) as '月份'

from
td_load_rcd
she*yu* 183.247.177.35 2023-04-06 22:01:54 select
substr(load_dt,1,7) as '月份'

from
td_load_rcd
she*yu* 183.247.177.35 2023-04-06 22:01:52 select
substr(load_dt,1,7) as '月份'

from
td_load_rcd
she*yu* 183.247.177.35 2023-04-06 22:01:48 select
substr(load_dt,1,7) as '月份'，

from
td_load_rcd
she*yu* 183.247.177.35 2023-04-06 21:58:53 select *
from
td_load_rcd
she*yu* 183.247.177.35 2023-04-06 21:58:38 select
load_dt
, count(1) as '登录人次'
, count(distinct usr_id) as '月活'
from
td_load_rcd
she*yu* 183.247.177.35 2023-04-06 21:57:58 select
load_dt
, count(1) as '登录人次'
, count(distinct usr_id) as '月活'
from
td_load_rcd
group by 1
she*yu* 183.247.177.35 2023-04-06 21:57:45 select
load_dt,
, count(1) as '登录人次'
, count(distinct usr_id) as '月活'
from
td_load_rcd
group by 1
she*yu* 183.247.177.35 2023-04-06 21:53:45 select
substr(load_dt,1,7) as '月份'
, count(1) as '登录人次'
, count(distinct usr_id) as '月活'
from
td_load_rcd
group by 1
she*yu* 183.247.177.35 2023-04-06 21:48:43 scene1
she*yu* 183.247.177.35 2023-04-06 21:48:15 scene3
she*yu* 183.247.177.35 2023-04-06 21:48:06 changjing_computer
she*yu* 183.247.177.35 2023-04-06 21:48:02 a29R
she*yu* 183.247.177.35 2023-04-06 21:46:51 a29
she*yu* 183.247.177.35 2023-04-06 21:46:31 a29R
she*yu* 183.247.177.35 2023-04-06 21:46:15 a29WA
she*yu* 183.247.177.35 2023-04-06 21:45:43 select mch_nm, time1,time2, time2 - time1 as diff
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
where time2 != 0
she*yu* 183.247.177.35 2023-04-06 21:45:40 select mch_nm, time1,time2, time2 - time1 as diff
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
where time2 != 0
having diff = 1
she*yu* 183.247.177.35 2023-04-06 21:45:35 select mch_nm, time1,time2, time2 - time1 as diff
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
where time2 != 0
having diff = 89 or diff = 1
she*yu* 183.247.177.35 2023-04-06 21:45:29 select mch_nm, time1,time2, time2 - time1 as diff
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
where time2 != 0
having diff = 1 or diff = 1
she*yu* 183.247.177.35 2023-04-06 21:45:14 select mch_nm, time1,time2, time2 - time1 as diff
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
where time2 != 0 and (diff = 1 or diff = 1)
she*yu* 183.247.177.35 2023-04-06 21:44:52 select mch_nm, time1,time2, time2 - time1 as diff
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
where time2 != 0
she*yu* 183.247.177.35 2023-04-06 21:44:46 select mch_nm, time1,time2, time2 - time1 as diff
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
where time2 != 0 and
she*yu* 183.247.177.35 2023-04-06 21:44:09 select mch_nm, time1,time2, time2 - time1
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
where time2 != 0
she*yu* 183.247.177.35 2023-04-06 21:43:54 select mch_nm, time1,time2, time2 - time1
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
she*yu* 183.247.177.35 2023-04-06 21:43:34 select mch_nm, time1,time2
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
she*yu* 183.247.177.35 2023-04-06 21:43:21 select mch_nm, time1,time2
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
where time2 != 0
she*yu* 183.247.177.35 2023-04-06 21:40:25 select mch_nm, time1,time2
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
where time2 != 0
she*yu* 183.247.177.35 2023-04-06 21:40:22 select mch_nm, time1,time2
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
where time 2 != 0
she*yu* 183.247.177.35 2023-04-06 21:39:05 select mch_nm, time1,time2, datediff(time1,time2)
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
she*yu* 183.247.177.35 2023-04-06 21:38:55 select mch_nm, time1,time2, datediff(month,time1,time2)
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
she*yu* 183.247.177.35 2023-04-06 21:38:32 select mch_nm, time1,time2, timestampdiff(month,time1,time2)
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
she*yu* 183.247.177.35 2023-04-06 21:36:32 select mch_nm, time1,time2, datediff(time1,time2)
from(
select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1)t3
she*yu* 183.247.177.35 2023-04-06 21:34:50 select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1
she*yu* 183.247.177.35 2023-04-06 21:34:11 select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1 and time2 != 0
she*yu* 183.247.177.35 2023-04-06 21:33:39 select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1,1,0) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1
she*yu* 183.247.177.35 2023-04-06 21:33:22 select mch_nm, time1 ,(num1-num2) / num2 as ad,
lead(time1) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1
she*yu* 183.247.177.35 2023-04-06 21:32:20 select mch_nm, time1 ,(num1-num2) / num2 as ad,
row_number() over(partition by mch_nm order by time1) as ranking1,
lead(time1) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1
she*yu* 183.247.177.35 2023-04-06 21:31:56 select mch_nm, time1 ,(num1-num2) / num2 as ad,
row_number() over(partition by mch_nm order by time1) as ranking1,
lead(time1) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1 and time2 is not null
she*yu* 183.247.177.35 2023-04-06 21:31:45 select mch_nm, time1 ,(num1-num2) / num2 as ad,
row_number() over(partition by mch_nm order by time1) as ranking1,
lead(time1) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1and time2 is not null
she*yu* 183.247.177.35 2023-04-06 21:30:55 select mch_nm, time1 ,(num1-num2) / num2 as ad,
row_number() over(partition by mch_nm order by time1) as ranking1,
lead(time1) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1
she*yu* 183.247.177.35 2023-04-06 21:30:46 select mch_nm, time,(num1-num2) / num2 as ad,
row_number() over(partition by mch_nm order by time1) as ranking1,
lead(time1) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time1) t1)t2
having ad >1
she*yu* 183.247.177.35 2023-04-06 21:30:35 select mch_nm, time,(num1-num2) / num2 as ad,
row_number() over(partition by mch_nm order by time1) as ranking1,
lead(time1) over(partition by mch_nm) as time2
from(
select *, lag(num1) over(partition by mch_nm order by time1) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time1, count(*) as num1
from trx_rcd
group by mch_nm, time) t1)t2
having ad >1
she*yu* 183.247.177.35 2023-04-06 21:28:32 select mch_nm, time,(num1-num2) / num2 as ad,
row_number() over(partition by mch_nm order by time) as ranking1
from(
select *, lag(num1) over(partition by mch_nm order by time) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1)t2
having ad >1
she*yu* 183.247.177.35 2023-04-06 21:28:15 select mch_nm, time,(num1-num2) / num2 as ad,
row_number() over(partition by mch_nm order by time) as ranking1,

from(
select *, lag(num1) over(partition by mch_nm order by time) as num2
from(
select mch_nm, date_format(trx_time,'%Y-%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1)t2
having ad >1
she*yu* 183.247.177.35 2023-04-06 21:22:20 select mch_nm, time,(num1-num2) / num2 as ad,
row_number() over(partition by mch_nm order by time) as ranking1
from(
select *, lag(num1) over(partition by mch_nm order by time) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1)t2
having ad >1
she*yu* 183.247.177.35 2023-04-06 21:19:23 select mch_nm, time,(num1-num2) / num2 as ad
from(
select *, lag(num1) over(partition by mch_nm order by time) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1)t2
having ad >1
she*yu* 183.247.177.35 2023-04-06 21:19:16 select mch_nm, time,(num1-num2) / num2 as ad
from(
select *, lag(num1) over(partition by mch_nm order by time) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1)t2
group by mch_nm
having ad >1 and count(*) >1
she*yu* 183.247.177.35 2023-04-06 21:18:07 select mch_nm, time,(num1-num2) / num2 as ad
from(
select *, lag(num1) over(partition by mch_nm order by time) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1)t2
having ad >1
she*yu* 183.247.177.35 2023-04-06 21:18:02 select mch_nm, time,(num1-num2) / num2 as ad
from(
select *, lag(num1) over(partition by mch_nm order by time) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1)t2
having ad >1 and count(mch_nm) >1
she*yu* 183.247.177.35 2023-04-06 21:17:44 select mch_nm, time,(num1-num2) / num2 as ad
from(
select *, lag(num1) over(partition by mch_nm order by time) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1)t2
having ad >1,count(mch_nm) >1
she*yu* 183.247.177.35 2023-04-06 21:16:18 select mch_nm, time,(num1-num2) / num2 as ad
from(
select *, lag(num1) over(partition by mch_nm order by time) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1)t2
having ad >1
she*yu* 183.247.177.35 2023-04-06 21:16:07 select mch_nm, time,(num1-num2) / num2 as ad
from(
select *, lag(num1) over(partition by mch_nm order by time) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1)t2
where ad >1
she*yu* 183.247.177.35 2023-04-06 21:11:21 select mch_nm, time,(num1-num2) / num2 as ad
from(
select *, lag(num1) over(partition by mch_nm order by time) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1)t2
she*yu* 183.247.177.35 2023-04-06 21:11:08 select mch_nm, time,(num1-num2) / num2 as ad
from(
select *, lag(num1,1,0) over(partition by mch_nm order by time) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1)t2
she*yu* 183.247.177.35 2023-04-06 21:11:03 select mch_nm, time,(num1-num2) / num2 as ad
from(
select *, lag(num1,1,0.00) over(partition by mch_nm order by time) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1)t2
she*yu* 183.247.177.35 2023-04-06 21:10:45 select mch_nm, time,(num1-num2) / num2 as ad
from(
select *, lag(num1) over(partition by mch_nm order by time) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1)t2
she*yu* 183.247.177.35 2023-04-06 21:08:16 select *, lag(num1) over(partition by mch_nm order by time) as num2
from(
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1
she*yu* 183.247.177.35 2023-04-06 21:08:04 select *, lag(num1) over(partition by mch_nm order by time)
from(
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num1
from trx_rcd
group by mch_nm, time) t1
she*yu* 183.247.177.35 2023-04-06 21:05:51
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num
from trx_rcd
group by mch_nm, time
order by mch_nm, time,num desc
she*yu* 183.247.177.35 2023-04-06 21:05:33
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num
from trx_rcd
group by mch_nm, time
order by mch_nm, num desc
she*yu* 183.247.177.35 2023-04-06 21:05:06
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num
from trx_rcd
group by mch_nm, time
order by num desc
she*yu* 183.247.177.35 2023-04-06 21:04:33
select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num
from trx_rcd
group by mch_nm, time
order by time, num desc
she*yu* 183.247.177.35 2023-04-06 21:03:23 select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num
from trx_rcd
group by mch_nm, time
order by num desc
she*yu* 183.247.177.35 2023-04-06 21:03:02 select mch_nm, date_format(trx_time,'%Y%m') as time, count(*) as num
from trx_rcd
group by mch_nm, time
she*yu* 183.247.177.35 2023-04-06 21:02:20 select mch_nm, date_format(trx_time,'%Y%m') as time, count(*)
from trx_rcd
group by mch_nm, time
she*yu* 183.247.177.35 2023-04-06 21:01:40 select mch_nm, date_format(trx_time,'%Y%m') as time
from trx_rcd
group by mch_nm, time
she*yu* 183.247.177.35 2023-04-06 20:59:49 select mch_nm, date_format(trx_time,'%Y%m') as time
from trx_rcd
she*yu* 183.247.177.35 2023-04-06 20:57:48 select *
from trx_rcd
she*yu* 183.247.177.35 2023-04-06 20:55:35 a28R
she*yu* 183.247.177.35 2023-04-06 20:55:03 select mch_nm, mch_typ,dense_rank() over(partition by mch_typ order by avg desc) as ranking
from(
select mch_nm, mch_typ, sum(trx_amt) / count(*) as avg
from trx_rcd
where mch_typ = '珠宝首饰'
group by mch_nm,mch_typ) t1
she*yu* 183.247.177.35 2023-04-06 20:54:13 select mch_nm, mch_typ,dense_rank() over(partition by mch_typ order by avg desc) as ranking
from(
select mch_nm, mch_typ, sum(trx_amt) / count(*) as avg
from trx_rcd
where mch_typ = '珠宝首饰' or mch_typ = '服饰美容'
group by mch_nm,mch_typ) t1
she*yu* 183.247.177.35 2023-04-06 20:54:06 select mch_nm, mch_type,dense_rank() over(partition by mch_typ order by avg desc) as ranking
from(
select mch_nm, mch_typ, sum(trx_amt) / count(*) as avg
from trx_rcd
where mch_typ = '珠宝首饰' or mch_typ = '服饰美容'
group by mch_nm,mch_typ) t1
she*yu* 183.247.177.35 2023-04-06 20:53:31 select dense_rank() over(partition by mch_typ order by avg desc) as ranking
from(
select mch_nm, mch_typ, sum(trx_amt) / count(*) as avg
from trx_rcd
where mch_typ = '珠宝首饰' or mch_typ = '服饰美容'
group by mch_nm,mch_typ) t1
she*yu* 183.247.177.35 2023-04-06 20:51:41
select mch_nm, mch_typ, sum(trx_amt) / count(*) as avg
from trx_rcd
where mch_typ = '珠宝首饰' or mch_typ = '服饰美容'
group by mch_nm,mch_typ
she*yu* 183.247.177.35 2023-04-06 20:50:51 select mch_nm, sum(trx_amt) / count(*) as avg
from trx_rcd
where mch_typ = '珠宝首饰' or mch_typ = '服饰美容'
group by mch_nm
she*yu* 183.247.177.35 2023-04-06 20:46:26 select *
from trx_rcd
where mch_typ = '珠宝首饰' or mch_typ = '服饰美容'
she*yu* 183.247.177.35 2023-04-06 20:46:20 select *
from trx_rcd
where mch_typ = '珠宝首饰' or mach_typ = '服饰美容'
she*yu* 183.247.177.35 2023-04-06 19:40:29 a27R
she*yu* 183.247.177.35 2023-04-06 19:39:54 select month , sum(s1) over(order by year) as sum
from(
select date_format(trx_time,'%M-%y') as month, date_format(trx_time,'%y%m') as year,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '医疗健康' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,year) t1
she*yu* 183.247.177.35 2023-04-06 19:39:39 select month , sum(s1) over(order by year) as sum
from(
select date_format(trx_time,'%M-%y') as month, date_format(trx_time,'%y%m') as year,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,year) t1
she*yu* 183.247.177.35 2023-04-06 19:39:05 select month ,sum(trx_amt) over(order by year) as sum
from(
select date_format(trx_time,'%M-%y') as month, date_format(trx_time,'%y%m') as year,
trx_amt
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,year) t1
she*yu* 183.247.177.35 2023-04-06 19:38:57 select month ,sum(trx_time) over(order by year) as sum
from(
select date_format(trx_time,'%M-%y') as month, date_format(trx_time,'%y%m') as year,
trx_amt
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,year) t1
she*yu* 183.247.177.35 2023-04-06 19:38:16 select month ,sum(s1) over(order by year) as sum
from(
select date_format(trx_time,'%M-%y') as month, date_format(trx_time,'%y%m') as year,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,year) t1
she*yu* 183.247.177.35 2023-04-06 19:36:51
select date_format(trx_time,'%M-%y') as month, date_format(trx_time,'%y%m') as year,
sum(trx_amt) over(order by date_format(trx_time,'%y%m')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,year
she*yu* 183.247.177.35 2023-04-06 19:36:36
select date_format(trx_time,'%M-%y') as month,order by date_format(trx_time,'%y%m') as year,
sum(trx_amt) over(order by date_format(trx_time,'%y%m')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,year
she*yu* 183.247.177.35 2023-04-06 19:36:28
select date_format(trx_time,'%M-%y') as month,order by date_format(trx_time,'%y%m') as year
sum(trx_amt) over(order by date_format(trx_time,'%y%m')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,year
she*yu* 183.247.177.35 2023-04-06 19:35:49
select date_format(trx_time,'%M-%y') as month,
sum(trx_amt) over(order by date_format(trx_time,'%y%m')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
she*yu* 183.247.177.35 2023-04-06 19:35:43
select date_format(trx_time,'%M-%y') as month,
sum(trx_amt) over(order by date_format(trx_time,'%y%m')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,sum
she*yu* 183.247.177.35 2023-04-06 19:35:32
select date_format(trx_time,'%M-%y') as month,
sum(trx_amt) over(order by date_format(trx_time,'%y%m')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
she*yu* 183.247.177.35 2023-04-06 19:33:42 select month, sum(s1) over(order by year) as sum
from(
select date_format(trx_time,'%M-%y') as month,date_format(trx_time,'%y%m') as year,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,year) t1
she*yu* 183.247.177.35 2023-04-06 19:33:25 select month, sum(s1) over(order by year desc) as sum
from(
select date_format(trx_time,'%M-%y') as month,date_format(trx_time,'%y%m') as year,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,year) t1
she*yu* 183.247.177.35 2023-04-06 19:31:00
select date_format(trx_time,'%M-%y') as Month,date_format(trx_time,'%y%m') as year,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by Month,year
order by year desc
she*yu* 183.247.177.35 2023-04-06 19:30:25
select date_format(trx_time,'%M-%y') as Month,date_format(trx_time,'%y%m') as year,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by Month,year
order by year
she*yu* 183.247.177.35 2023-04-06 19:29:40
select date_format(trx_time,'%M-%y') as Month,date_format(trx_time,'%y') as year,
date_format(trx_time,'%m') as month, sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by Month,year
order by year
she*yu* 183.247.177.35 2023-04-06 19:29:36
select date_format(trx_time,'%M-%y') as Month,date_format(trx_time,'%y') as year,
date_format(trx_time,'%m') as month, sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by Month,year
order by year, month desc
she*yu* 183.247.177.35 2023-04-06 19:29:19
select date_format(trx_time,'%M-%y') as Month,date_format(trx_time,'%y') as year,
date_format(trx_time,'%m') as month, sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by Month,year,month
order by year, month desc
she*yu* 183.247.177.35 2023-04-06 19:29:05
select date_format(trx_time,'%M-%y') as Month,date_format(trx_time,'%y') as year,
date_format(trx_time,'%m') as month, sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by Month,year,month,s1
order by year, month desc
she*yu* 183.247.177.35 2023-04-06 19:28:47
select date_format(trx_time,'%M-%y') as Month,date_format(trx_time,'%y') as year,
date_format(trx_time,'%m') as month, sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by Month,year,month
order by year, month desc
she*yu* 183.247.177.35 2023-04-06 19:28:37
select date_format(trx_time,'%M-%y') as Month,date_format(trx_time,'%y') as year,
date_format(trx_time,'%m') as month, sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,year
order by year, month desc
she*yu* 183.247.177.35 2023-04-06 19:27:14
select date_format(trx_time,'%m-%y') as month,date_format(trx_time,'%y') as year,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,year
order by year
she*yu* 183.247.177.35 2023-04-06 19:27:09
select date_format(trx_time,'%m-%y') as month,date_format(trx_time,'%y') as year,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
order by year
she*yu* 183.247.177.35 2023-04-06 19:26:52
select date_format(trx_time,'%m-%y') as month,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
order by date_format(trx_time,'%y')
she*yu* 183.247.177.35 2023-04-06 19:23:53
select date_format(trx_time,'%m-%y') as month,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
order by month desc
she*yu* 183.247.177.35 2023-04-06 19:23:22
select date_format(trx_time,'%m-%y') as month,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
order by month
she*yu* 183.247.177.35 2023-04-06 19:22:45
select date_format(trx_time,'%m-%y') as month,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
she*yu* 183.247.177.35 2023-04-06 19:22:25
select date_format(trx_time,'%M-%y') as month,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
she*yu* 183.247.177.35 2023-04-06 19:22:04
select date_format(trx_time,'%M-%y') as month,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,s1
order by YEAR(trx_time), month(trx_time)
she*yu* 183.247.177.35 2023-04-06 19:21:49
select date_format(trx_time,'%M-%y') as month,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
order by YEAR(trx_time), month(trx_time)
she*yu* 183.247.177.35 2023-04-06 19:21:08
select date_format(trx_time,'%M-%y') as mo ,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by mo
order by YEAR(trx_time), month(trx_time)
she*yu* 183.247.177.35 2023-04-06 19:20:58
select date_format(trx_time,'%M-%y') as mo ,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
order by YEAR(trx_time), month(trx_time)
she*yu* 183.247.177.35 2023-04-06 19:20:42
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
order by YEAR(trx_time), month(trx_time)
she*yu* 183.247.177.35 2023-04-06 19:17:40
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
order by month
she*yu* 183.247.177.35 2023-04-06 19:16:45
select date_format(trx_time,'%m-%y') as month ,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
order by month
she*yu* 183.247.177.35 2023-04-06 19:16:35
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
order by month
she*yu* 183.247.177.35 2023-04-06 19:12:14
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
she*yu* 183.247.177.35 2023-04-06 19:12:03
select date_format(trx_time,'%M-%y') as month ,trx_time,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
she*yu* 183.247.177.35 2023-04-06 19:11:43
select date_format(trx_time,'%M-%y') as month ,trx_time,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,trx_time
she*yu* 183.247.177.35 2023-04-06 19:11:35
select date_format(trx_time,'%M-%y') as month ,trx_time
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,trx_time,s1
she*yu* 183.247.177.35 2023-04-06 19:11:29
select date_format(trx_time,'%M-%y') as month ,trx_time
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month,trx_time
she*yu* 183.247.177.35 2023-04-06 19:11:22
select date_format(trx_time,'%M-%y') as month ,trx_time
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
she*yu* 183.247.177.35 2023-04-06 19:11:02
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
she*yu* 183.247.177.35 2023-04-06 19:10:29
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 19:06:08
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 19:05:54
select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 19:05:45 select month, sum(s1) over(order by s1)
from(
select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 19:01:58 select month, sum(s1) over(order by s1)
from(
select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 19:01:49 select month, sum(s1) over(partition by month order by s1)
from(
select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:58:56 select month, sum(s1) over(partition by month)
from(
select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:58:11 select month, s1
from(
select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:57:48 select month, s1
from(
select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by trx_time) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:55:20 select month, s1
from(
select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:55:07 select month, sum(s1) over(order by s1) as sum
from(
select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:54:59 select month, sum(s1) over(order by month) as sum
from(
select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:54:37 select distinct month, sum(s1) over(order by trx_time) as sum
from(
select distinct date_format(trx_time,'%M-%y') as month ,trx_time,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:54:36 select distinct month, sum(s1) over(order by trx_time) as sum
from(
select distinct date_format(trx_time,'%M-%y') as month ,trx_time,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:54:25 select month, sum(s1) over(order by trx_time) as sum
from(
select distinct date_format(trx_time,'%M-%y') as month ,trx_time,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:53:54 select month, sum(s1) over(order by month) as sum
from(
select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:52:38 select month, sum(s1) over(order by s1) as sum
from(
select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:52:12 select month, sum(s1) over(order by month) as sum
from(
select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:51:00 select month, sum(s1) over(order by s1) as sum
from(
select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:50:38 select month, sum(s1) over(order by sum) as sum
from(
select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as s1
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:49:21 select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
order by sum
she*yu* 183.247.177.35 2023-04-06 18:46:48 select distinct date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 18:46:14 select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 18:45:49 select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by date_format(trx_time,'%M-%y')
she*yu* 183.247.177.35 2023-04-06 18:45:45 select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
she*yu* 183.247.177.35 2023-04-06 18:45:25 select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 18:45:14
select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 18:44:57 select month, sum
from(
select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
group by month,sum
she*yu* 183.247.177.35 2023-04-06 18:44:50 select month, sum
from(
select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
group by month
she*yu* 183.247.177.35 2023-04-06 18:44:44 select month
from(
select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
group by month
she*yu* 183.247.177.35 2023-04-06 18:44:33 select month, max(sum)
from(
select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
group by month
order by month
she*yu* 183.247.177.35 2023-04-06 18:43:53 select month, max(sum)
from(
select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by trx_time) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
group by month
order by month
she*yu* 183.247.177.35 2023-04-06 18:43:37 select month, max(sum)
from(
select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by trx_time) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
group by month
order by month desc
she*yu* 183.247.177.35 2023-04-06 18:43:32 select month, max(sum), sum()
from(
select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by trx_time) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
group by month
order by month desc
she*yu* 183.247.177.35 2023-04-06 18:42:01 select month, max(sum)
from(
select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by trx_time) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
group by month
order by max(sum) desc
she*yu* 183.247.177.35 2023-04-06 18:41:41 select month, max(sum)
from(
select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by trx_time) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
group by month
she*yu* 183.247.177.35 2023-04-06 18:41:29 select month, max(sum)
from(
select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by trx_time) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 18:40:44 select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by trx_time) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 18:37:55 select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 18:37:37 select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by trx_time ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 18:35:32 select date_format(trx_time,'%M-%y') as month , trx_time, trx_amt,
sum(trx_amt) over(partition by trx_time ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 18:35:31 select date_format(trx_time,'%M-%y') as month , trx_time, trx_amt,
sum(trx_amt) over(partition by trx_time ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 18:35:10 select date_format(trx_time,'%M-%y') as month , trx_time, trx_amt,
sum(trx_amt) over(partition by trx_time) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 18:35:00 select date_format(trx_time,'%M-%y') as month , trx_time,trx_amt,
sum(trx_amt) over(partition by trx_time) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 18:34:42 select date_format(trx_time,'%M-%y') as month , trx_time,trx_amt,
sum(trx_amt) over(partition by trx_time order by trx_time) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 18:33:58 select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by trx_time order by trx_time) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 18:33:18 select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 18:32:56 select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')
order by sum desc
she*yu* 183.247.177.35 2023-04-06 18:32:17 select distinct t1.month, sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
order by sum desc
she*yu* 183.247.177.35 2023-04-06 18:31:39 select distinct t1.month, sum
from(
select date_format(trx_time,'%M-%y') as month ,
max(sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by date_format(trx_time,'%M-%y'))) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
order by sum desc
she*yu* 183.247.177.35 2023-04-06 18:30:43 select distinct month, max(sum)
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
group by t1.month
order by sum desc
she*yu* 183.247.177.35 2023-04-06 18:30:38 select distinct month, max(sum)
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
group by month
order by sum desc
she*yu* 183.247.177.35 2023-04-06 18:29:56 select distinct month, max(sum)
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
order by sum desc
she*yu* 183.247.177.35 2023-04-06 18:29:42 select distinct month,sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by date_format(trx_time,'%M-%y')) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
order by sum desc
she*yu* 183.247.177.35 2023-04-06 18:28:31 select distinct month,sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
order by sum desc
she*yu* 183.247.177.35 2023-04-06 18:28:30 select distinct month,sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
order by sum desc
she*yu* 183.247.177.35 2023-04-06 18:28:28 select distinct month,sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
order by sum desc
she*yu* 183.247.177.35 2023-04-06 17:17:52 select distinct month,sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-07-01')) t1
order by sum desc
she*yu* 183.247.177.35 2023-04-06 17:17:41 select distinct month,sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-07-21')) t1
order by sum desc
she*yu* 183.247.177.35 2023-04-06 17:17:33 select distinct month,sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-06-31')) t1
order by sum desc
she*yu* 183.247.177.35 2023-04-06 17:17:27 select distinct month,sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-07-31')) t1
order by sum desc
she*yu* 183.247.177.35 2023-04-06 17:17:21 select distinct month,sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-07-01')) t1
order by sum desc
she*yu* 183.247.177.35 2023-04-06 17:17:09 select distinct month,sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-07-01')) t1
order by sum desc
she*yu* 183.247.177.35 2023-04-06 17:17:05 select distinct month,sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-07-01')) t1
order by sum desc
she*yu* 183.247.177.35 2023-04-06 17:16:22 select distinct month,sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
order by sum desc
she*yu* 183.247.177.35 2023-04-06 17:16:17 select distinct month,sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
order by sum
she*yu* 183.247.177.35 2023-04-06 17:15:53 select distinct month,sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '休闲娱乐' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 17:14:44 select distinct month,sum
from(
select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')) t1
she*yu* 183.247.177.35 2023-04-06 17:13:33 select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') )
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 17:11:47 select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') order by trx_time)
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 17:11:05 select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') )
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
she*yu* 183.247.177.35 2023-04-06 17:10:53 select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') )
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
group by trx_time
she*yu* 183.247.177.35 2023-04-06 17:09:11 select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') )
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 17:09:07 select date_format(trx_time,'%Mm-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') )
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 17:08:43 select date_format(trx_time,'%M-%y') as month , trx_amt,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') )
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 17:08:32 select date_format(trx_time,'%M-%y') as month , trx_amt
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 17:06:11 select t1.month, sum(trx_amt) over(partition by month ) as sum
from(
select date_format(trx_time,'%M-%y') as month , trx_amt
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')) t1
group by t1.month
she*yu* 183.247.177.35 2023-04-06 17:05:53 select month, sum(trx_amt) over(partition by month ) as sum
from(
select date_format(trx_time,'%M-%y') as month , trx_amt
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')) t1
group by month
she*yu* 183.247.177.35 2023-04-06 17:05:44 select month, sum(trx_amt) over(partition by month ) as sum
from(
select date_format(trx_time,'%M-%y') as month , trx_amt
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')) t1
group by month, sum
she*yu* 183.247.177.35 2023-04-06 17:05:24 select month, sum(trx_amt) over(partition by month )
from(
select date_format(trx_time,'%M-%y') as month , trx_amt
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')) t1
group by month
she*yu* 183.247.177.35 2023-04-06 17:05:14 select month, sum(trx_amt) over(partition by month )
from(
select date_format(trx_time,'%M-%y') as month , trx_amt
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01'))
group by month
she*yu* 183.247.177.35 2023-04-06 17:04:20 select date_format(trx_time,'%M-%y') as month , trx_amt
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 17:02:44 select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') )
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
she*yu* 183.247.177.35 2023-04-06 17:02:31 select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as su
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
group by month, su
she*yu* 183.247.177.35 2023-04-06 17:02:21 select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') ) as sum
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
group by month, sum
she*yu* 183.247.177.35 2023-04-06 17:02:06 select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') )
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
group by month
she*yu* 183.247.177.35 2023-04-06 17:01:26 select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by date_format(trx_time,'%M-%y') )
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 17:00:54 select date_format(trx_time,'%M-%y') as month ,
sum(trx_amt) over(partition by month )
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 16:53:10 select date_format(trx_time,'%M-%y') as month，
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 16:53:06 select date_format(trx_time,'%m-%y') as month，
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 16:52:59 select date_format(trx_time,'%M-%y') as month，
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 16:52:53 select date_format(trx_time,'%M-%yy') as month，
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 16:43:29 select date_format(trx_time,'%M-%Y') as month，
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 16:42:39 select date_format(trx_time,'%M-%Y') as month，
sum(trx_amt) over(partition by month order by)
from trx_rcd
where mch_typ = '餐饮' and (trx_time between '2021-07-01' and '2022-08-01')
she*yu* 183.247.177.35 2023-04-06 16:42:28 select date_format(trx_time,'%M-%Y') as month，
sum(trx_amt) over(partition by month order by)
from trx_rcd
where mch_typ = '餐饮' and trx_time between '2021-07-01' and '2022-08-01'
she*yu* 183.247.177.35 2023-04-06 16:11:22 select date_format(trx_time,'%M-%d')
from trx_rcd
where mch_typ = '餐饮'
she*yu* 183.247.177.35 2023-04-06 16:11:16 select date_format(trx_time,'%M-%d')，
from trx_rcd
where mch_typ = '餐饮'
she*yu* 183.247.177.35 2023-04-06 16:10:41 select date_format(trx_time,'%M %d')
from trx_rcd
where mch_typ = '餐饮'
she*yu* 183.247.177.35 2023-04-06 16:02:29 a26R
she*yu* 183.247.177.35 2023-04-06 15:59:53 a26
she*yu* 183.247.177.35 2023-04-06 15:59:32 a26R
she*yu* 183.247.177.35 2023-04-06 15:59:15 a26
she*yu* 183.247.177.35 2023-04-06 15:59:13 a26R
she*yu* 183.247.177.35 2023-04-06 15:59:07 a26
she*yu* 183.247.177.35 2023-04-06 15:59:04 a1
she*yu* 183.247.177.35 2023-04-06 15:58:55 member_enter2
she*yu* 183.247.177.35 2023-04-06 15:58:45 a26R
she*yu* 183.247.177.35 2023-04-06 15:58:40 a26WAD
she*yu* 183.247.177.35 2023-04-06 15:58:24 a26WAE
she*yu* 183.247.177.35 2023-04-06 15:58:19 a26WAEF
she*yu* 183.247.177.35 2023-04-06 15:58:12 a26WAF
she*yu* 183.247.177.35 2023-04-06 15:56:53 a26WA
she*yu* 183.247.177.35 2023-04-06 15:49:02 a25R
she*yu* 183.247.177.35 2023-04-06 15:47:32 select usr_id, weekday(trx_time) as week
from trx_rcd
where mch_typ = '餐饮'
group by usr_id,week
order by week
she*yu* 183.247.177.35 2023-04-06 15:47:01 select usr_id, weekday(trx_time) as week,
from trx_rcd
where mch_typ = '餐饮'
group by usr_id,week
order by week
she*yu* 183.247.177.35 2023-04-06 15:45:31 select weekday(trx_time) as week, count(*) as count
from trx_rcd
where mch_typ = '餐饮'
group by week
order by count desc
she*yu* 183.247.177.35 2023-04-06 15:43:24 select dayofweek(trx_time) as week, count(*) as count
from trx_rcd
where mch_typ = '餐饮'
group by week
order by count desc
she*yu* 183.247.177.35 2023-04-06 15:43:13 select * from trx_rcd limit 5 select dayofweek(trx_time) as week, count(*) as count
from trx_rcd
where mch_typ = '餐饮'
group by week
order by count desc
she*yu* 183.247.177.35 2023-04-06 15:42:49 select dayofweek(trx_time) as week, count(*) as count
from trx_rcd
where mch_typ = '餐饮'
group by week
order by count desc
she*yu* 183.247.177.35 2023-04-06 15:41:35 select dayofweek(trx_time) as week, count(usr_id) as count
from trx_rcd
where mch_typ = '餐饮'
group by week
order by count desc
she*yu* 183.247.177.35 2023-04-06 15:40:29 select dayofweek(trx_time) as week, count(distinct usr_id) as count
from trx_rcd
where mch_typ = '餐饮'
group by week
order by count desc
she*yu* 183.247.177.35 2023-04-06 15:40:27 select dayofweek(trx_time) as week, count(distinct usr_id) as count
from trx_rcd
where mch_typ = '餐饮'
group by week
order by count desc
she*yu* 183.247.177.35 2023-04-06 15:40:14 select dayofweek(trx_time) as week, count(usr_id) as count
from trx_rcd
where mch_typ = '餐饮'
group by week
order by count desc
she*yu* 183.247.177.35 2023-04-06 15:39:50 select dayofweek(trx_time) as week, count(usr_id) as count
from trx_rcd
where trx_typ = '餐饮'
group by week
order by count desc
she*yu* 183.247.177.35 2023-04-06 15:39:48 select dayofweek(trx_time) as week, count(usr_id) as count
from trx_rcd
where trx_typ = '餐饮'
group by week
order by count desc
she*yu* 183.247.177.35 2023-04-06 15:39:41 select dayofweek(trx_time) as week, count(user_id) as count
from trx_rcd
where trx_typ = '餐饮'
group by week
order by count desc
she*yu* 183.247.177.35 2023-04-06 15:39:31 select dayofweek(trx_time) as week, count(user_id) as count
from trx_rcd
where trx_typ = '餐饮'
order by count desc
she*yu* 183.247.177.35 2023-04-06 15:38:00 a25R
she*yu* 183.247.177.35 2023-04-06 15:37:56 a25WA
she*yu* 183.247.177.35 2023-04-06 15:37:32 select dayofweek(trx_time) as week, count(usr_id) as count
from trx_rcd
where mch_typ = '餐饮'
group by week
order by count desc
she*yu* 183.247.177.35 2023-04-06 15:35:52 select dayofweek(trx_time) as week, count(distinct usr_id) as count
from trx_rcd
where mch_typ = '餐饮'
group by week
order by count desc
she*yu* 183.247.177.35 2023-04-06 15:35:31 select dayofweek(trx_time) as week, count(usr_id) as count
from trx_rcd
where mch_typ = '餐饮'
group by week
order by count desc
she*yu* 183.247.177.35 2023-04-06 15:35:23 select dayofweek(trx_time) as week, count(usr_id) as count
from trx_rcd
where mch_typ = '餐饮'
group by week
order by count
she*yu* 183.247.177.35 2023-04-06 15:35:10 select dayofweek(trx_time) as week, count(usr_id) as count
from trx_rcd
where mch_typ = '餐饮'
group by week
she*yu* 183.247.177.35 2023-04-06 15:34:57 select usr_id, dayofweek(trx_time) as week, count(usr_id) as count
from trx_rcd
where mch_typ = '餐饮'
group by week,usr_id
she*yu* 183.247.177.35 2023-04-06 15:34:36 select usr_id, dayofweek(trx_time) as week, count(dayofweek(trx_time)) as count
from trx_rcd
where mch_typ = '餐饮'
group by week,usr_id
she*yu* 183.247.177.35 2023-04-06 15:34:13 select usr_id, dayofweek(trx_time) as week, count(dayofweek(trx_time)) as count
from trx_rcd
where mch_typ = '餐饮'
group by week
she*yu* 183.247.177.35 2023-04-06 15:33:52 select usr_id, dayofweek(trx_time) as week, count(dayofweek(trx_time)) as count
from trx_rcd
where mch_typ = '餐饮'
group by week,usr_id
she*yu* 183.247.177.35 2023-04-06 15:33:35 select usr_id, dayofweek(trx_time) as week, count(dayofweek(trx_time)) as count
from trx_rcd
where mch_typ = '餐饮'
group by week,usr_id
order by week
she*yu* 183.247.177.35 2023-04-06 15:32:17 select usr_id, dayofweek(trx_time) as week
from trx_rcd
where mch_typ = '餐饮'
group by week,usr_id
order by week
she*yu* 183.247.177.35 2023-04-06 15:32:07 select usr_id, dayofweek(trx_time) as week
from trx_rcd
where mch_typ = '餐饮'
group by week,usr_id
she*yu* 183.247.177.35 2023-04-06 15:31:28 select usr_id, dayofweek(trx_time) as week
from trx_rcd
where mch_typ = '餐饮'
she*yu* 183.247.177.35 2023-04-06 15:30:31 select usr_id, mch_typ, dayofweek(trx_time)
from trx_rcd
where mch_typ = '餐饮'
she*yu* 183.247.177.35 2023-04-06 15:30:00 select usr_id, mch_typ, dayofweek(trx_time)
from trx_rcd
she*yu* 183.247.177.35 2023-04-06 14:32:49 ser2
she*yu* 183.247.177.35 2023-04-06 11:35:32 a24R
she*yu* 183.247.177.35 2023-04-05 22:27:31 a24
she*yu* 183.247.177.35 2023-04-05 22:27:26 a24R
she*yu* 183.247.177.35 2023-04-05 22:26:56 a24WABCD
she*yu* 183.247.177.35 2023-04-05 22:26:43 a24WBCD
she*yu* 183.247.177.35 2023-04-05 22:24:40 a24WD
she*yu* 183.247.177.35 2023-04-05 22:24:26 a24
she*yu* 183.247.177.35 2023-04-05 22:24:21 a23
she*yu* 183.247.177.35 2023-04-05 22:24:18 a22
she*yu* 183.247.177.35 2023-04-05 22:23:08 a24WD
she*yu* 183.247.177.35 2023-04-05 22:23:02 a24WBD
she*yu* 183.247.177.35 2023-04-05 22:22:57 a24WABD
she*yu* 183.247.177.35 2023-04-05 22:22:48 a24WABCD
she*yu* 183.247.177.35 2023-04-05 22:22:43 a24WBCD
she*yu* 183.247.177.35 2023-04-05 22:22:38 a24WCD
she*yu* 183.247.177.35 2023-04-05 22:16:53 a23R
she*yu* 183.247.177.35 2023-04-05 22:16:50 a23W
she*yu* 183.247.177.35 2023-04-05 22:16:38 a23WA
she*yu* 183.247.177.35 2023-04-05 22:15:11 a22R
she*yu* 183.247.177.35 2023-04-05 22:14:33 a22
she*yu* 183.247.177.35 2023-04-05 22:14:29 a21
she*yu* 183.247.177.35 2023-04-05 22:14:27 a21R
she*yu* 183.247.177.35 2023-04-05 22:14:21 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '(\d)\1{3}'
she*yu* 183.247.177.35 2023-04-05 22:11:54 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '[0-9]{4}'
she*yu* 183.247.177.35 2023-04-05 22:08:25 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '*[0-9]{4}*'
she*yu* 183.247.177.35 2023-04-05 22:08:01 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '\d\1{3}'
she*yu* 183.247.177.35 2023-04-05 22:05:52 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num like '%3333%'
she*yu* 183.247.177.35 2023-04-05 22:05:40 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num like '%8888%'
she*yu* 183.247.177.35 2023-04-05 22:05:35 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num like '%6666%'
she*yu* 183.247.177.35 2023-04-05 22:05:27 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num like '%2222%'
she*yu* 183.247.177.35 2023-04-05 22:05:22 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num like '%22%'
she*yu* 183.247.177.35 2023-04-05 22:05:17 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '%22%'
she*yu* 183.247.177.35 2023-04-05 22:05:13 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '%222%'
she*yu* 183.247.177.35 2023-04-05 22:05:09 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '%2222%'
she*yu* 183.247.177.35 2023-04-05 22:05:05 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '%1111%'
she*yu* 183.247.177.35 2023-04-05 22:04:59 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '%6666%'
she*yu* 183.247.177.35 2023-04-05 22:04:38 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '%[0-9]{4}%'
she*yu* 183.247.177.35 2023-04-05 22:04:01 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '[0-9]{4}'
she*yu* 183.247.177.35 2023-04-05 22:04:00 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '[0-9]{4}'
she*yu* 183.247.177.35 2023-04-05 22:03:59 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '[0-9]{4}'
she*yu* 183.247.177.35 2023-04-05 22:03:53 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '[0-9]{4}'
she*yu* 183.247.177.35 2023-04-05 22:03:00 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '.{4}'
she*yu* 183.247.177.35 2023-04-05 22:02:54 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '%.{4}%'
she*yu* 183.247.177.35 2023-04-05 21:54:05 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '%.{4}%'
she*yu* 183.247.177.35 2023-04-05 21:52:27 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '(\d)\1{3}'
she*yu* 183.247.177.35 2023-04-05 21:51:02 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num regexp '(\d)\1{3}'
she*yu* 183.247.177.35 2023-04-05 21:48:18 select *
from trx_rcd t
join id_inf i using(usr_id)
where REGEXP_LIKE ('phone_num', '(\d)\1{3}')
she*yu* 183.247.177.35 2023-04-05 21:48:09 select *
from trx_rcd t
join id_inf i using(usr_id)
where REGEXP_LIKE (phone_num, '(\d)\1{3}')
she*yu* 183.247.177.35 2023-04-05 21:45:27 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '(\d)\1{3}'
she*yu* 183.247.177.35 2023-04-05 21:41:15 select *
from trx_rcd t
join id_inf i using(usr_id)
she*yu* 183.247.177.35 2023-04-05 21:37:47 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '(\d)\1{3}'
she*yu* 183.247.177.35 2023-04-05 21:37:36 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '(\d\1{3})'
she*yu* 183.247.177.35 2023-04-05 21:34:49 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num rlike '%[0-9]{4}%'
she*yu* 183.247.177.35 2023-04-05 21:32:48 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num like '%6666%'
she*yu* 183.247.177.35 2023-04-05 21:32:38 select *
from trx_rcd t
join id_inf i using(usr_id)
where phone_num like '%[0-9]{4}%'
she*yu* 183.247.177.35 2023-04-05 21:18:30 a20R
she*yu* 183.247.177.35 2023-04-05 21:18:28 select
left(ssn, 2) as 'address',
sum(trx_amt) / count(distinct usr_id) as avg
from trx_rcd t
join id_inf i using(usr_id)
where trx_time between '2021-08-01' and '2021-08-31'
group by left(ssn, 2)
order by avg desc
she*yu* 183.247.177.35 2023-04-05 21:17:59 select
left(ssn, 2) as 'address',
sum(trx_amt) / count(distinct usr_id) as avg
from trx_rcd t
join id_inf i using(usr_id)
where trx_time between '2021-08-01' and '2021-08-31'
group by left(ssn, 2)
order by avg desc
she*yu* 183.247.177.35 2023-04-05 21:17:39 select
left(ssn, 2) as 'address',
sum(trx_amt) / count(distinct usr_id) as avg
from trx_rcd t
join id_inf i using(usr_id)
where trx_time between '2021-08-01' and '2021-08-31'
group by left(ssn, 2)
order by avg desc
she*yu* 183.247.177.35 2023-04-05 21:17:27 select
left(ssn, 2) as 'address',
sum(trx_amt) / count(distinct usr_id) as avg
from trx_rcd t
join id_inf i using(usr_id)
where trx_time between '2021-08-01' and '2021-09-01'
group by left(ssn, 2)
order by avg desc
she*yu* 183.247.177.35 2023-04-05 21:17:20 select
left(ssn, 2) as 'address',
sum(trx_amt) / count(distinct usr_id) as avg
from trx_rcd t
join id_inf i using(usr_id)
where date(trx_time) between '2021-08-01' and '2021-09-01'
group by left(ssn, 2)
order by avg desc
she*yu* 183.247.177.35 2023-04-05 21:17:19 select
left(ssn, 2) as 'address',
sum(trx_amt) / count(distinct usr_id) as avg
from trx_rcd t
join id_inf i using(usr_id)
where date(trx_time) between '2021-08-01' and '2021-09-01'
group by left(ssn, 2)
order by avg desc
she*yu* 183.247.177.35 2023-04-05 21:17:18 select
left(ssn, 2) as 'address',
sum(trx_amt) / count(distinct usr_id) as avg
from trx_rcd t
join id_inf i using(usr_id)
where date(trx_time) between '2021-08-01' and '2021-09-01'
group by left(ssn, 2)
order by avg desc
she*yu* 183.247.177.35 2023-04-05 21:16:58 select
left(ssn, 2) as 'address',
sum(trx_amt) / count(distinct usr_id) as avg
from trx_rcd t
join id_inf i using(usr_id)
where date(trx_time) between '2021-08-01' and '2021-08-31'
group by left(ssn, 2)
order by avg desc
she*yu* 183.247.177.35 2023-04-05 21:16:00 select
left(ssn, 2) as 'address',
avg(trx_amt) as avg
from trx_rcd t
join id_inf i using(usr_id)
where date(trx_time) between '2021-08-01' and '2021-08-31'
group by left(ssn, 2)
order by avg desc
she*yu* 183.247.177.35 2023-04-05 21:15:33 select
left(ssn, 2) as 'address',
avg(trx_amt) as avg
from trx_rcd t
join id_inf i using(usr_id)
where trx_time between '2021-08-01' and '2021-08-31'
group by left(ssn, 2)
order by avg desc
she*yu* 183.247.177.35 2023-04-05 21:14:09 select
left(ssn, 2) as 'address',
avg(trx_amt) as avg
from trx_rcd t
join id_inf i using(usr_id)
where trx_time between '2021-08-01' and '2021-09-01'
group by left(ssn, 2)
order by avg desc
she*yu* 183.247.177.35 2023-04-05 21:11:41 select
left(ssn, 2) as 'address',
avg(trx_amt) as avg
from trx_rcd t
join id_inf i using(usr_id)
group by left(ssn, 2)
order by avg desc
she*yu* 183.247.177.35 2023-04-05 21:11:36 select
left(ssn, 2) as 'address',
avg(trx_amt) as avg
from trx_rcd t
join id_inf i using(usr_id)
group by left(ssn, 6)
order by avg desc
she*yu* 183.247.177.35 2023-04-05 21:10:42 select
left(ssn, 6) as 'address',
avg(trx_amt) as avg
from trx_rcd t
join id_inf i using(usr_id)
group by left(ssn, 6)
order by avg desc
she*yu* 183.247.177.35 2023-04-05 21:10:37 select
left(ssn, 6) as 'address',
avg(trx_amt) as avg
from trx_rcd t
join id_inf i using(usr_id)
group by left(ssn, 6)
order by avg
she*yu* 183.247.177.35 2023-04-05 21:10:19 select
left(ssn, 6) as 'address',
avg(trx_amt)
from trx_rcd t
join id_inf i using(usr_id)
group by left(ssn, 6)
she*yu* 183.247.177.35 2023-04-05 21:09:14 select
t.usr_id,
left(ssn, 6) as 'address',
t.trx_amt
from trx_rcd t
join id_inf i using(usr_id)
group by left(ssn, 6)
she*yu* 183.247.177.35 2023-04-05 21:08:04 select
t.usr_id,
left(ssn, 6) as 'address',
t.trx_amt
from trx_rcd t
join id_inf i using(usr_id)
she*yu* 183.247.177.35 2023-04-05 21:07:56 a20
she*yu* 183.247.177.35 2023-04-05 21:07:55 a21
she*yu* 183.247.177.35 2023-04-05 21:07:51 a17
she*yu* 183.247.177.35 2023-04-05 21:07:51 a18
she*yu* 183.247.177.35 2023-04-05 21:07:49 a20
she*yu* 183.247.177.35 2023-04-05 21:07:47 a21
she*yu* 183.247.177.35 2023-04-05 21:07:28 a18
she*yu* 183.247.177.35 2023-04-05 21:06:52 select
t.usr_id,
left(ssn, 1, 6) as 'address',
t.trx_amt
from trx_rcd t
join id_inf i using(usr_id)
she*yu* 183.247.177.35 2023-04-05 21:05:46 select
t.usr_id,
left(ssn, 1, 6) as address,
trx_amt
from trx_rcd t
join id_inf i using(usr_id)
she*yu* 183.247.177.35 2023-04-05 21:04:37 select
usr_id,
left(ssn, 1, 6) as address,
trx_amt
from trx_rcd
join id_inf using(usr_id)
she*yu* 183.247.177.35 2023-04-05 21:02:07 select usr_id, substr(ssn,6) as add, trx_amt
from trx_rcd
join id_inf using(usr_id)
she*yu* 183.247.177.35 2023-04-05 20:54:27 select *
from trx_rcd
join id_inf using(usr_id)
she*yu* 183.247.177.35 2023-04-05 20:50:48 select * from ssn_addr_map
she*yu* 183.247.177.35 2023-04-05 20:50:42 select * from ssn_addr_map limit 5
she*yu* 183.247.177.35 2023-04-05 20:49:12 select * from trx_rcd limit 5
she*yu* 183.247.177.35 2023-04-05 20:48:53 a20
she*yu* 183.247.177.35 2023-04-05 20:48:51 a20R
she*yu* 183.247.177.35 2023-04-05 20:48:47 a20WA
she*yu* 183.247.177.35 2023-04-05 20:48:12 df22
she*yu* 183.247.177.35 2023-04-05 20:46:37 a19R
she*yu* 183.247.177.35 2023-04-05 20:46:29 a19
she*yu* 183.247.177.35 2023-04-05 20:46:27 a19R
she*yu* 183.247.177.35 2023-04-05 20:46:16 a19WBCE
she*yu* 183.247.177.35 2023-04-05 20:46:07 a19WCE
she*yu* 183.247.177.35 2023-04-05 17:55:11 a19
she*yu* 183.247.177.35 2023-04-05 17:55:10 a19R
she*yu* 183.247.177.35 2023-04-05 17:52:56 a19WBCD
she*yu* 183.247.177.35 2023-04-05 17:52:48 a19WBD
she*yu* 183.247.177.35 2023-04-05 17:52:44 a19WBDE
she*yu* 183.247.177.35 2023-04-05 17:51:46 a18R
she*yu* 183.247.177.35 2023-04-05 17:51:40 a18
she*yu* 183.247.177.35 2023-04-05 17:51:38 a18R
she*yu* 183.247.177.35 2023-04-05 17:51:30 a18WAB
she*yu* 183.247.177.35 2023-04-05 17:51:27 a18WABC
she*yu* 183.247.177.35 2023-04-05 17:48:12 a18WABC
she*yu* 183.247.177.35 2023-04-05 17:47:06 a17R
she*yu* 183.247.177.35 2023-04-05 17:46:24 a17
she*yu* 183.247.177.35 2023-04-05 17:46:22 a17R
she*yu* 183.247.177.35 2023-04-05 17:46:13 a17WC
she*yu* 183.247.177.35 2023-04-05 17:45:17 a17
she*yu* 183.247.177.35 2023-04-05 17:45:13 a12
she*yu* 183.247.177.35 2023-04-05 17:45:11 a13
she*yu* 183.247.177.35 2023-04-05 17:45:09 a14
she*yu* 183.247.177.35 2023-04-05 17:45:08 a15
she*yu* 183.247.177.35 2023-04-05 17:45:07 a15
she*yu* 183.247.177.35 2023-04-05 17:45:05 a16
she*yu* 183.247.177.35 2023-04-05 17:37:33 a16R
she*yu* 183.247.177.35 2023-04-05 17:35:15 SELECT
a.usr_id,
b.mch_nm,
( 6371* acos( cos( a.lon ) * cos( b.lat ) * cos( b.lon - a.lat) )
+ sin( a.lon ) * sin( b.lat )) AS distance
FROM log_loc a, mch_loc b
where b.mch_nm = '屈臣氏东门中路店'
order by distance
she*yu* 183.247.177.35 2023-04-05 17:34:56 SELECT
a.usr_id,
b.mch_nm,
( 6371* acos( cos( a.lon ) * cos( b.lat ) * cos( b.lon - a.lat) )
+ sin( a.lon ) * sin( b.lat )) AS distance
FROM log_loc a, mch_loc b
where b.mch_nm = '屈臣氏东门中路店'
she*yu* 183.247.177.35 2023-04-05 17:34:44 SELECT
a.usr_id,
b.mch_nm,
( 6371* acos( cos( a.lon ) * cos( b.lat ) * cos( b.lon - a.lat) )
+ sin( a.lon ) * sin( b.lat )) AS distance
FROM log_loc a, mch_loc b
where b.mch_nm = '屈臣氏东门中路店'
she*yu* 183.247.177.35 2023-04-05 17:32:07 SELECT
a.usr_id,
b.mch_nm,
( 6371* acos( cos( radians(a.lon) ) * cos( radians( b.lat ) ) * cos( radians( b.lon ) - radians(a.lat) )
+ sin( radians(a.lon) ) * sin( radians( b.lat ) ) ) ) AS distance
FROM log_loc a, mch_loc b
where b.mch_nm = '屈臣氏东门中路店'
she*yu* 183.247.177.35 2023-04-05 17:32:01 SELECT
a.usr_id,
b.mch_nm,
( 6371* acos( cos( radians(a.lon) ) * cos( radians( b.lat ) ) * cos( radians( b.lon ) - radians(a.lat) )
+ sin( radians(a.lon) ) * sin( radians( b.lat ) ) ) ) AS distance
FROM log_loc a, mch_loc b
where b.mch_nm = '屈臣氏东门中路店'
HAVING distance < 500
she*yu* 183.247.177.35 2023-04-05 17:26:23 a16
she*yu* 183.247.177.35 2023-04-05 17:25:20 a9
she*yu* 183.247.177.35 2023-04-05 17:25:12 a16
she*yu* 183.247.177.35 2023-04-05 17:25:10 a16R
she*yu* 183.247.177.35 2023-04-05 17:23:10 select
L.usr_id,
M.mch_nm,
( 6371* acos( cos( radians(L.lon) ) * cos( radians( M.lat ) ) * cos( radians( M.lon ) - radians(L.lat) )
+ sin( radians(L.lon) ) * sin( radians( M.lat ) ) ) ) AS distance
from log_loc L, mch_loc M
where mch_nm = '屈臣氏东门中路店'
GROUP BY L.usr_id, M.mch_nm, distance
order by distance
she*yu* 183.247.177.35 2023-04-05 17:22:58 select
L.usr_id,
M.mch_nm,
( 6371* acos( cos( radians(L.lon) ) * cos( radians( M.lat ) ) * cos( radians( M.lon ) - radians(L.lat) )
+ sin( radians(L.lon) ) * sin( radians( M.lat ) ) ) ) AS distance
from log_loc L, mch_loc M
where mch_nm = '屈臣氏东门中路店'
GROUP BY L.usr_id, M.mch_nm, distance
she*yu* 183.247.177.35 2023-04-05 17:22:44 select
L.usr_id,
M.mch_nm,
( 6371* acos( cos( radians(L.lon) ) * cos( radians( M.lat ) ) * cos( radians( M.lon ) - radians(L.lat) )
+ sin( radians(L.lon) ) * sin( radians( M.lat ) ) ) ) AS distance
from log_loc L, mch_loc M
where mch_nm = '屈臣氏东门中路店'
GROUP BY L.usr_id, M.mch_nm, distance
having distance < 500
she*yu* 183.247.177.35 2023-04-05 17:22:27 select
L.usr_id,
M.mch_nm,
( 6371* acos( cos( radians(L.lon) ) * cos( radians( M.lat ) ) * cos( radians( M.lon ) - radians(L.lat) )
+ sin( radians(L.lon) ) * sin( radians( M.lat ) ) ) ) AS distance
from log_loc L, mch_loc M
where mch_nm = '屈臣氏东门中路店'
GROUP BY L.usr_id, M.mch_nm, distance
she*yu* 183.247.177.35 2023-04-05 17:20:42 select
L.usr_id,
M.mch_nm,
( 6371* acos( cos( radians(L.lon) ) * cos( radians( M.lat ) ) * cos( radians( M.lon ) - radians(L.lat) )
+ sin( radians(L.lon) ) * sin( radians( M.lat ) ) ) ) AS distance
from log_loc L, mch_loc M
where mch_nm = '屈臣氏东门中路店'
GROUP BY L.usr_id, M.mch_nm
she*yu* 183.247.177.35 2023-04-05 17:17:11 select
usr_id,
mch_nm,
( 6371* acos( cos( radians(L.lon) ) * cos( radians( M.lat ) ) * cos( radians( M.lon ) - radians(L.lat) )
+ sin( radians(L.lon) ) * sin( radians( M.lat ) ) ) ) AS distance
from log_loc L, mch_loc M
where mch_nm = '屈臣氏东门中路店'
having distance < 500
she*yu* 183.247.177.35 2023-04-05 17:16:48 select
usr_id,
mch_nm,
( 6371* acos( cos( radians(L.lon) ) * cos( radians( M.lat ) ) * cos( radians( M.lon ) - radians(L.lat) )
+ sin( radians(L.lon) ) * sin( radians( M.lat ) ) ) ) AS distance
from log_loc L, mch_loc M
where mch_nm = '屈臣氏东门中路店'
she*yu* 183.247.177.35 2023-04-05 17:16:43 select
usr_id,
mch_nm,
( 6371* acos( cos( radians(L.lon) ) * cos( radians( M.lat ) ) * cos( radians( M.lon ) - radians(L.lat) )
+ sin( radians(L.lon) ) * sin( radians( M.lat ) ) ) ) AS distance,
from log_loc L, mch_loc M
where mch_nm = '屈臣氏东门中路店'
she*yu* 183.247.177.35 2023-04-05 17:16:27 select
usr_id,
mch_nm,
( 6371* acos( cos( radians(L.lon) ) * cos( radians( M.lat ) ) * cos( radians( M.lon ) - radians(L.lat) )
+ sin( radians(L.lon) ) * sin( radians( M.lat ) ) ) ) AS distance,
count(distinct usr_id)
from log_loc L, mch_loc M
where mch_nm = '屈臣氏东门中路店'
group by usr_id,mch_nm
she*yu* 183.247.177.35 2023-04-05 17:15:53 select
usr_id,
mch_nm,
( 6371* acos( cos( radians(L.lon) ) * cos( radians( M.lat ) ) * cos( radians( M.lon ) - radians(L.lat) )
+ sin( radians(L.lon) ) * sin( radians( M.lat ) ) ) ) AS distance,
count(distinct usr_id)
from log_loc L, mch_loc M
where mch_nm = '屈臣氏东门中路店'
she*yu* 183.247.177.35 2023-04-05 17:15:44 select
usr_id,
mch_nm,
( 6371* acos( cos( radians(L.lon) ) * cos( radians( M.lat ) ) * cos( radians( M.lon ) - radians(L.lat) )
+ sin( radians(L.lon) ) * sin( radians( M.lat ) ) ) ) AS distance,
count(distinct urs_id)
from log_loc L, mch_loc M
where mch_nm = '屈臣氏东门中路店'
she*yu* 183.247.177.35 2023-04-05 17:14:54 select
usr_id,
mch_nm,
( 6371* acos( cos( radians(L.lon) ) * cos( radians( M.lat ) ) * cos( radians( M.lon ) - radians(L.lat) )
+ sin( radians(L.lon) ) * sin( radians( M.lat ) ) ) ) AS distance
from log_loc L, mch_loc M
where mch_nm = '屈臣氏东门中路店'
she*yu* 183.247.177.35 2023-04-05 17:14:17 select
usr_id,
mch_nm,
( 6371* acos( cos( radians(L.lon) ) * cos( radians( M.lat ) ) * cos( radians( M.lon ) - radians(L.lat) )
+ sin( radians(L.lon) ) * sin( radians( M.lat ) ) ) ) AS distance
from log_loc L, mch_loc M
she*yu* 183.247.177.35 2023-04-05 17:11:45 a16
she*yu* 183.247.177.35 2023-04-05 17:09:20 a9
she*yu* 183.247.177.35 2023-04-05 17:08:36 select * from log_loc, mch_loc
she*yu* 183.247.177.35 2023-04-05 17:07:50 a16
she*yu* 183.247.177.35 2023-04-05 17:07:48 a16R
she*yu* 183.247.177.35 2023-04-05 17:07:44 a16WBC
she*yu* 183.247.177.35 2023-04-05 17:04:27 a16
she*yu* 183.247.177.35 2023-04-05 17:03:47 a9
she*yu* 183.247.177.35 2023-04-05 16:59:25 a15R
she*yu* 183.247.177.35 2023-04-05 16:59:15 select distinct mch_nm
from
(select*
from trx_rcd
where usr_id = '3581980399641129' )A
inner join
(select*
from trx_rcd
where usr_id = '3581980399641129' )B
using(mch_nm)
order by mch_nm
she*yu* 183.247.177.35 2023-04-05 16:58:54 select mch_nm
from
(select*
from trx_rcd
where usr_id = '3581980399641129' )A
inner join
(select*
from trx_rcd
where usr_id = '3581980399641129' )B
using(mch_nm)
order by mch_nm
she*yu* 183.247.177.35 2023-04-05 16:58:14 select mch_nm
from
(select*
from trx_rcd
where usr_id = '3581980399641129' )A
inner join
(select*
from trx_rcd
where usr_id = '3581980399641129' )B
using(mch_nm)
she*yu* 183.247.177.35 2023-04-05 16:57:29 select mch_nm
from
(select*
from trx_rcd
where usr_id = '3581980399641129' )A
join
(select*
from trx_rcd
where usr_id = '3581980399641129' )B
using(mch_nm)
she*yu* 183.247.177.35 2023-04-05 16:56:53 select mch_nm
from
(select*
from trx_rcd
where usr_id = '3581980399641129' )A
join
(select*
from trx_rcd
where usr_id = '3581980399641129' )B
on A.mch_nm = B.mch_nm
she*yu* 183.247.177.35 2023-04-05 16:55:17 select usr_id, mch_nm
from trx_rcd
where usr_id = '3581980399641129' or usr_id = '4066802156346859215'
group by mch_nm,usr_id
order by mch_nm
she*yu* 183.247.177.35 2023-04-05 16:54:58 select usr_id, mch_nm
from trx_rcd
where usr_id = '3581980399641129' or usr_id = '4066802156346859215'
group by mch_nm,usr_id
she*yu* 183.247.177.35 2023-04-05 16:53:30 select *
from trx_rcd
where usr_id = '3581980399641129' or usr_id = '4066802156346859215'
she*yu* 183.247.177.35 2023-04-05 16:53:22 select *
from trx_rcd
where usr_id = '3581980399641129' and usr_id = '4066802156346859215'
she*yu* 183.247.177.35 2023-04-05 16:51:34 a15
she*yu* 183.247.177.35 2023-04-05 16:51:32 a15R
she*yu* 183.247.177.35 2023-04-05 16:51:28 a15WA
she*yu* 183.247.177.35 2023-04-05 16:51:24 a15WC
she*yu* 183.247.177.35 2023-04-05 16:51:19 a15WB
she*yu* 183.247.177.35 2023-04-05 16:51:14 a15WBC
she*yu* 183.247.177.35 2023-04-05 16:50:36 select mch_nm
from
(select *
from trx_rcd
where usr_id = '3581980399641129' ) A
right join
(select *
from trx_rcd
where usr_id = '4066802156346859215' ) B using(mch_nm)
where A.mch_nm is null
she*yu* 183.247.177.35 2023-04-05 16:50:04 select mch_nm
from
(select *
from trx_rcd
where usr_id = '3581980399641129' ) A
left join
(select *
from trx_rcd
where usr_id = '4066802156346859215' ) B using(mch_nm)
where B.mch_nm is null
she*yu* 183.247.177.35 2023-04-05 16:49:32 select mch_nm
from
(select *
from trx_rcd
where usr_id = '3581980399641129' ) A
left join
(select *
from trx_rcd
where usr_id = '4066802156346859215' ) B using(mch_nm)
she*yu* 183.247.177.35 2023-04-05 16:49:02 select mch_nm
from
(select *
from trx_rcd
where usr_id = '3581980399641129' ) A
left join
(select *
from trx_rcd
where usr_id = '4066802156346859215' ) B on A.mch_nm = B.mch_nm
she*yu* 183.247.177.35 2023-04-05 16:48:35 select mch_nm
from
(select *
from trx_rcd
where usr_id = '3581980399641129' ) A
left join
(select *
from trx_rcd
where usr_id = '4066802156346859215' ) B on A.mch_nm = B.mch_nm
where B.mch_nm is null
she*yu* 183.247.177.35 2023-04-05 16:46:38 select mch_nm
from
(select *
from trx_rcd
where usr_id = '3581980399641129' ) A
left join
(select *
from trx_rcd
where usr_id = '4066802156346859215' ) B on A.mch_nm != B.mch_nm
she*yu* 183.247.177.35 2023-04-05 16:45:30 select mch_nm
from
(select *
from trx_rcd
where usr_id = '3581980399641129' ) A
join
(select *
from trx_rcd
where usr_id = '4066802156346859215' ) B using(mch_nm)
she*yu* 183.247.177.35 2023-04-05 16:42:30 select *
from trx_rcd
where usr_id = '3581980399641129'
she*yu* 183.247.177.35 2023-04-05 16:41:53 select *
from trx_rcd
where usr_id = '3581980399641129' or usr_id = '4066802156346859215'
she*yu* 183.247.177.35 2023-04-05 16:40:52 a14R
she*yu* 183.247.177.35 2023-04-05 16:40:00 a14WACE
she*yu* 183.247.177.35 2023-04-05 16:38:50 select
*
from
test_join_a a
join test_join_b b
on a.a+b.b<7
she*yu* 183.247.177.35 2023-04-05 16:31:37 a14WAC
she*yu* 183.247.177.35 2023-04-05 16:28:32 a13R
she*yu* 183.247.177.35 2023-04-05 16:28:28 a13
she*yu* 183.247.177.35 2023-04-05 16:28:25 a13R
she*yu* 183.247.177.35 2023-04-05 16:26:47 a13
she*yu* 183.247.177.35 2023-04-05 16:24:18 select category, count(distinct usr_id)
from(
select
usr_id,
trx_time,
case
when ((mch_typ = '餐饮' or mch_typ = '休闲娱乐') and trx_amt > 500) then '享受生活'
when (mch_typ = '医疗健康' and trx_amt > 20000) or mch_nm = '中国铁路-硬座' and trx_amt >200 then '颠沛流离'
else '其他'
end as category
from trx_rcd
where trx_time between '2021-09-19' and '2021-09-22') t1
group by category
she*yu* 183.247.177.35 2023-04-05 16:22:28 select
usr_id,
trx_time,
case
when ((mch_typ = '餐饮' or mch_typ = '休闲娱乐') and trx_amt > 500) then '享受生活'
when (mch_typ = '医疗健康' and trx_amt > 20000) or mch_nm = '中国铁路-硬座' and trx_amt >200 then '颠沛流离'
else '其他'
end as category
from trx_rcd
where trx_time between '2021-09-19' and '2021-09-22'
she*yu* 183.247.177.35 2023-04-05 16:20:59 select usr_id, trx_time
case
when ((mch_typ = '餐饮' or mch_typ = '休闲娱乐') and trx_amt > 500) then '享受生活'
when (mch_typ = '医疗健康' and trx_amt > 20000) or mch_nm = '中国铁路-硬座' and trx_amt >200 then '颠沛流离'
else '其他'
end as category
from trx_rcd
where trx_time between '2021-09-19' and '2021-09-22'
she*yu* 183.247.177.35 2023-04-05 16:20:33 select usr_id, trx_time
case
when ((mch_nm = '餐饮' or mch_nm = '休闲娱乐') and trx_amt > 500) then '享受生活'
when (mch_nm = '医疗健康' and trx_amt > 20000) or mch_typ = '中国铁路-硬座' and trx_amt >200 then '颠沛流离'
else '其他'
end as category
from trx_rcd
where trx_time between '2021-09-19' and '2021-09-22'
she*yu* 183.247.177.35 2023-04-05 16:19:27 select *
from trx_rcd
where (mch_typ = '餐饮' or mch_typ = '休闲娱乐') and trx_amt > 500
she*yu* 183.247.177.35 2023-04-05 16:19:02 select *
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 16:18:52 select *
from trx_rcd
where mch_nm = '休闲娱乐'
she*yu* 183.247.177.35 2023-04-05 16:18:22 select *
from trx_rcd
where mch_nm = '餐饮'
she*yu* 183.247.177.35 2023-04-05 16:18:21 select *
from trx_rcd
where mch_nm = '餐饮'
she*yu* 183.247.177.35 2023-04-05 16:18:16 select *
from trx_rcd
where mch_nm = '餐饮' or mch_nm = '休闲娱乐'
she*yu* 183.247.177.35 2023-04-05 16:18:06 select *
from trx_rcd
where(mch_nm = '餐饮' or mch_nm = '休闲娱乐') and trx_amt > 500
she*yu* 183.247.177.35 2023-04-05 16:17:50 select *
from trx_rcd
where trx_time between '2021-09-19' and '2021-09-22'


she*yu* 183.247.177.35 2023-04-05 16:17:41 select *
from trx_rcd
where (trx_time between '2021-09-19' and '2021-09-22' ) and


she*yu* 183.247.177.35 2023-04-05 16:17:37 select *
from trx_rcd
where (trx_time between '2021-09-19' and '2021-09-22' ) and
mch_nm = '餐饮'
she*yu* 183.247.177.35 2023-04-05 16:17:28 select *
from trx_rcd
where (trx_time between '2021-09-19' and '2021-09-22' ) and
(mch_nm = '餐饮' or mch_nm = '休闲娱乐')
she*yu* 183.247.177.35 2023-04-05 16:17:17 select *
from trx_rcd
where (trx_time between '2021-09-19' and '2021-09-22' ) and
(mch_nm = '餐饮' or mch_nm = '休闲娱乐') and trx_amt > 500
she*yu* 183.247.177.35 2023-04-05 16:17:12 select *
from trx_rcd
where (trx_time between '2021-09-19' and '2021-09-22' ) and
(mch_nm = '餐饮' or mch_nm = '休闲娱乐') and trx_amt > 500
she*yu* 183.247.177.35 2023-04-05 16:13:58 select category, count(distinct usr_id)
from(
select
usr_id, trx_time,
case
when ((mch_nm = '餐饮' or mch_nm = '休闲娱乐') and trx_amt > 500) then '享受生活'
when (mch_nm = '医疗健康' and trx_amt > 20000) or mch_typ = '中国铁路-硬座' and trx_amt >200 then '颠沛流离'
else '其他'
end as category
from trx_rcd
where trx_time between '2021-09-19' and '2021-09-22') t1
group by category
she*yu* 183.247.177.35 2023-04-05 16:13:50 select category, count(distinct usr_id)
from(
select
usr_id, trx_time,
case
when ((mch_nm = '餐饮' or mch_nm = '休闲娱乐') and trx_amt > 500) then '享受生活'
when (mch_nm = '医疗健康' and trx_amt > 20000) or mch_typ = '中国铁路-硬座' and trx_amt >200 then '颠沛流离'
else '其他'
end as category
from trx_rcd
where trx_time between '2021-09-19' and '2021-09-22')
group by category
she*yu* 183.247.177.35 2023-04-05 16:11:28 select
usr_id, trx_time,
case
when ((mch_nm = '餐饮' or mch_nm = '休闲娱乐') and trx_amt > 500) then '享受生活'
when (mch_nm = '医疗健康' and trx_amt > 20000) or mch_typ = '中国铁路-硬座' and trx_amt >200 then '颠沛流离'
else '其他'
end as category
from trx_rcd
where trx_time between '2021-09-19' and '2021-09-22'
she*yu* 183.247.177.35 2023-04-05 16:10:12 select
usr_id, trx_time,
case
when ((mch_nm = '餐饮' or mch_nm = '休闲娱乐') and trx_amt > 500) then '享受生活'
when (mch_nm = '医疗健康' and trx_amt > 20000) or mch_typ = '中国铁路-硬座' and trx_amt >200 then '颠沛流离'
else '其他'
end as category,
from trx_rcd
where trx_time between '2021-09-19' and '2021-09-22'
she*yu* 183.247.177.35 2023-04-05 16:08:14 select
usr_id, trx_time,
case
when ((mch_nm = '餐饮' or mch_nm = '休闲娱乐') and trx_amt > 500) then '享受生活'
when (mch_nm = '医疗健康' and trx_amt > 20000) or mch_typ = '中国铁路-硬座' and trx_amt >200 then '颠沛流离'
else '其他'
end as category,
from trx_rcd
where trx_time between '2021-09-19' and '2021-09-22'
she*yu* 183.247.177.35 2023-04-05 16:07:01 select
usr_id, trx_time,
case
when ((mch_nm = '餐饮' or mch_nm = '休闲娱乐') and trx_amt > 500) then '享受生活'
when (mch_nm = '医疗健康' and trx_amt > 20000) or mch_typ = '中国铁路-硬座' and trx_amt >200 then '颠沛流离'
else '其他'
end as category,
from trx_rcd
where trx_time (between '2021-09-19' and '2021-09-22')
she*yu* 183.247.177.35 2023-04-05 16:06:36 select
usr_id, trx_time,
case
when ((mch_nm = '餐饮' or mch_nm = '休闲娱乐') and trx_amt > 500) then '享受生活'
when (mch_nm = '医疗健康' and trx_amt > 20000) or mch_typ = '中国铁路-硬座' and trx_amt >200 then '颠沛流离'
else '其他'
end as category,
from trx_rcd
where trx_time between '2021-09-19' and '2021-09-22'
she*yu* 183.247.177.35 2023-04-05 16:06:21 select
usr_id, trx_time,
case
when ((mch_nm = '餐饮' or mch_nm = '休闲娱乐') and trx_amt > 500) then '享受生活'
when (mch_nm = '医疗健康' and trx_amt > 20000) or mch_typ = '中国铁路-硬座' and trx_amt >200 then '颠沛流离'
else '其他'
end as category,
from trx_rcd
where trx_time between '2022-09-19' and '2022-09-22'
she*yu* 183.247.177.35 2023-04-05 16:02:26 select
usr_id, trx_time,
case
when ((mch_nm = '餐饮' or mch_nm = '休闲娱乐') and trx_amt > 500) then '享受生活'
when (mch_nm = '医疗健康' and trx_amt > 20000) or mch_typ = '中国铁路-硬座' and trx_amt >200 then '颠沛流离'
else '其他'
end as category
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 16:00:48 select
usr_id, trx_time,
[case
when ((mch_nm = '餐饮' or mch_nm = '休闲娱乐') and trx_amt > 500) then '享受生活'
when (mch_nm = '医疗健康' and trx_amt > 20000) or mch_typ = '中国铁路-硬座' and trx_amt >200 then '颠沛流离'
else '其他'
end as category]
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:59:00 select
usr_id, trx_time,
[case
when (mch_nm = '餐饮' or mch_nm = '休闲娱乐') and trx_amt > 500 then '享受生活'
when (mch_nm = '医疗健康' and trx_amt > 20000) or mch_typ = '中国铁路-硬座' and trx_amt >200 then '颠沛流离'
else '其他'
end as category]
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:51:51 select *
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:51:42 select *,
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:51:33 select *,
[case when (mch_typ = '医疗健康' and trx_amt > 200000)
or (mch_nm = '中国铁路-硬座' and trx_amt > 200) then 'dpll'
[else 'others' ]
end as 'category']
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:51:16 select *,
[case when mch_typ = ('餐饮' or '休闲娱乐' ) and trx_amt > 500 then 'xssh'
case when (mch_typ = '医疗健康' and trx_amt > 200000)
or (mch_nm = '中国铁路-硬座' and trx_amt > 200) then 'dpll'
[else 'others' ]
end as 'category']
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:49:12 select *,
[case
when mch_typ = ('餐饮' or '休闲娱乐' ) and trx_amt > 500 then 'xssh'
when (mch_typ = '医疗健康' and trx_amt > 200000)
or (mch_nm = '中国铁路-硬座' and trx_amt > 200) then 'dpll'
[else 'others' ]
end as 'category']
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:44:45 select *,
[case
when (mch_typ = '餐饮' and trx_amt > 500)
or (mch_typ = '休闲娱乐' and trx_amt > 500) then 'xssh'
when (mch_typ = '医疗健康' and trx_amt > 200000)
or (mch_nm = '中国铁路-硬座' and trx_amt > 200) then 'dpll'
[else 'others' ]
end as 'category']
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:35:36 select *,
[case
when (mch_typ = '餐饮' or '休闲娱乐' ) and trx_amt > 500 then 'xssh'
when (mch_typ = '医疗健康' and trx_amt > 200000)
or (mch_nm = '中国铁路-硬座' and trx_amt > 200) then 'dpll'
[else 'others' ]
end as 'category']
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:33:55 select *,
[case
when (mch_typ = '餐饮' or mch_typ = '休闲娱乐' ) and trx_amt > 500 then 'xssh'
when (mch_typ = '医疗健康' and trx_amt > 200000)
or (mch_nm = '中国铁路-硬座' and trx_amt > 200) then 'dpll'
[else 'others' ]
end as 'category']
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:33:54 select *,
[case
when (mch_typ = '餐饮' or mch_typ = '休闲娱乐' ) and trx_amt > 500 then 'xssh'
when (mch_typ = '医疗健康' and trx_amt > 200000)
or (mch_nm = '中国铁路-硬座' and trx_amt > 200) then 'dpll'
[else 'others' ]
end as 'category']
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:33:53 select *,
[case
when (mch_typ = '餐饮' or mch_typ = '休闲娱乐' ) and trx_amt > 500 then 'xssh'
when (mch_typ = '医疗健康' and trx_amt > 200000)
or (mch_nm = '中国铁路-硬座' and trx_amt > 200) then 'dpll'
[else 'others' ]
end as 'category']
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:33:52 select *,
[case
when (mch_typ = '餐饮' or mch_typ = '休闲娱乐' ) and trx_amt > 500 then 'xssh'
when (mch_typ = '医疗健康' and trx_amt > 200000)
or (mch_nm = '中国铁路-硬座' and trx_amt > 200) then 'dpll'
[else 'others' ]
end as 'category']
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:33:50 select *,
[case
when (mch_typ = '餐饮' or mch_typ = '休闲娱乐' ) and trx_amt > 500 then 'xssh'
when (mch_typ = '医疗健康' and trx_amt > 200000)
or (mch_nm = '中国铁路-硬座' and trx_amt > 200) then 'dpll'
[else 'others' ]
end as 'category']
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:32:49 select *,
[case
when (mch_typ = '餐饮' or mch_typ = '休闲娱乐' ) and trx_amt > 500 then '享受生活'
when (mch_typ = '医疗健康' and trx_amt > 200000)
or (mch_nm = '中国铁路-硬座' and trx_amt > 200) then '颠沛流离'
[else '其他' ]
end as 'category']
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:30:58 select *,
[case
when (mch_typ = '餐饮' or mch_typ = '休闲娱乐' ) and trx_amt > 500 then '享受生活'
when (mch_typ = '医疗健康' and trx_amt > 200000）
or (mch_nm = '中国铁路-硬座' and trx_amt > 200) then '颠沛流离'
[else '其他' ]
end as 'category']
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:29:35 select *,
case
when (mch_typ = '餐饮' or mch_typ = '休闲娱乐' ) and trx_amt > 500 then '享受生活'
when (mch_typ = '医疗健康' and trx_amt > 200000）
or (mch_nm = '中国铁路-硬座' and trx_amt > 200) then '颠沛流离'
[else '其他' ]
end as 'category'
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:28:45 select *,
case
when (mch_typ = '餐饮' or mch_typ = '休闲娱乐' ) and trx_amt > 500 then '享受生活'
when (mch_typ = '医疗健康' and trx_amt > 200000）
or (mch_nm = '中国铁路-硬座' and trx_amt > 200) then '颠沛流离'
else '其他'
end as 'category'
from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:22:21 select *


from trx_rcd
she*yu* 183.247.177.35 2023-04-05 15:01:26 a11R
she*yu* 183.247.177.35 2023-04-05 15:01:17 select mch_nm, count(usr_id) / count(distinct usr_id) as avg
from trx_rcd
where mch_typ = '酒店'
group by mch_nm
order by avg desc
she*yu* 183.247.177.35 2023-04-05 14:34:32 select mch_nm, sum(trx_amt) / count(usr_id) as avg
from trx_rcd
where mch_typ = '酒店'
group by mch_nm
order by avg desc
she*yu* 183.247.177.35 2023-04-05 14:34:25 select mch_nm, sum(trx_amt) / count(usr_id) as avg
from trx_rcd
where mch_typ = '酒店'
group by mch_nm
order by avg
she*yu* 183.247.177.35 2023-04-05 14:34:00 select mch_nm, sum(trx_amt) / count(usr_id)
from trx_rcd
where mch_typ = '酒店'
group by mch_nm
she*yu* 183.247.177.35 2023-04-05 14:33:54 select mch_nm, sum(trx_amt) / count(usr_id)
from trx_rcd
where mch_typ = '酒店'
group by mch_nm,
she*yu* 183.247.177.35 2023-04-05 14:33:41 select mch_nm, usr_id, trx_amt, sum(trx_amt) / count(usr_id)
from trx_rcd
where mch_typ = '酒店'
group by mch_nm, usr_id, trx_amt
she*yu* 183.247.177.35 2023-04-05 14:32:23 select mch_nm, usr_id, trx_amt
from trx_rcd
where mch_typ = '酒店'
group by mch_nm, usr_id, trx_amt
she*yu* 183.247.177.35 2023-04-05 14:30:34 df22
she*yu* 183.247.177.35 2023-04-05 11:06:00 select usr_id,mch_nm
from trx_rcd
where mch_typ = '餐饮'
group by mch_nm,usr_id
order by mch_nm,usr_id
she*yu* 183.247.177.35 2023-04-05 11:05:17 select usr_id,mch_nm
from trx_rcd
where mch_typ = '餐饮'
group by mch_nm,usr_id
order by usr_id
she*yu* 183.247.177.35 2023-04-05 11:04:58 select usr_id,mch_nm
from trx_rcd
where mch_typ = '餐饮'
group by mch_nm,usr_id
she*yu* 183.247.177.35 2023-04-05 11:01:47 df22
she*yu* 183.247.177.35 2023-04-05 10:59:53 select *
from trx_rcd
where mch_typ = '餐饮'
group by mch_nm
she*yu* 183.247.177.35 2023-04-05 10:57:28 select *
from trx_rcd
where mch_typ = '餐饮'
she*yu* 183.247.177.35 2023-04-05 10:56:45 select *
from trx_rcd
where mch_typ = '餐饮' or mch_typ = '酒店'
she*yu* 183.247.177.35 2023-04-05 10:55:28 a11
she*yu* 183.247.177.35 2023-04-05 10:55:26 a11R
she*yu* 183.247.177.35 2023-04-05 10:54:37 select *
from trx_rcd
where mch_typ = '酒店'
she*yu* 183.247.177.35 2023-04-05 10:53:22 select mch_nm,usr_id,trx_amt
from trx_rcd
where mch_typ = '酒店'
group by mch_nm,usr_id,trx_amt
order by mch_nm
she*yu* 183.247.177.35 2023-04-05 10:52:54 select mch_nm,usr_id
from trx_rcd
where mch_typ = '酒店'
group by mch_nm,usr_id
order by mch_nm
she*yu* 183.247.177.35 2023-04-05 10:52:35 select mch_nm
from trx_rcd
where mch_typ = '酒店'
group by mch_nm
order by mch_nm
she*yu* 183.247.177.35 2023-04-05 10:51:37 select mch_nm,usr_id
from trx_rcd
where mch_typ = '酒店'
group by mch_nm, usr_id
order by mch_nm
she*yu* 183.247.177.35 2023-04-05 10:50:36 select mch_nm,usr_id
from trx_rcd
where mch_typ = '酒店'
group by mch_nm,usr_id
she*yu* 183.247.177.35 2023-04-05 10:50:27 select mch_nm,usr_id
from trx_rcd
where mch_typ = '酒店'
group by mch_nm
she*yu* 183.247.177.35 2023-04-05 10:50:03 select *
from trx_rcd
where mch_typ = '酒店'
group by mch_nm
she*yu* 183.247.177.35 2023-04-05 10:48:22 select *
from trx_rcd
where mch_typ = '酒店'
she*yu* 183.247.177.35 2023-04-05 10:48:13 select *
from trx_rcd
where mch_typ = '酒店'
group by mch_nm
order by count(mch_nm) desc
she*yu* 183.247.177.35 2023-04-04 22:28:34 a11WA
she*yu* 183.247.177.35 2023-04-04 22:28:10 a11WC
she*yu* 183.247.177.35 2023-04-04 22:27:50 select mch_nm, count(mch_nm)
from trx_rcd
where mch_typ = '酒店'
group by mch_nm
order by count(mch_nm) desc
she*yu* 183.247.177.35 2023-04-04 22:27:12 select mch_nm, count(mch_nm)
from trx_rcd
group by mch_nm
order by count(mch_nm) desc
she*yu* 183.247.177.35 2023-04-04 22:27:07 select mch_nm, count(mch_nm)
from trx_rcd
group by mch_nm
order by count(mch_nm)
she*yu* 183.247.177.35 2023-04-04 22:26:48 select mch_nm, count(mch_nm)
from trx_rcd
group by mch_nm
she*yu* 183.247.177.35 2023-04-04 22:24:32 select * from trx_rcd limit 5
she*yu* 183.247.177.35 2023-04-04 22:20:37 a11
she*yu* 183.247.177.35 2023-04-04 22:20:20 a10WBCEH
she*yu* 183.247.177.35 2023-04-04 22:20:13 a10WBCEH
she*yu* 183.247.177.35 2023-04-04 22:20:07 a10WABCEH
she*yu* 183.247.177.35 2023-04-04 22:19:19 a10WABCEHI
she*yu* 183.247.177.35 2023-04-04 22:19:14 a10WBCEHI
she*yu* 183.247.177.35 2023-04-04 22:17:41 a10WBCFHI
she*yu* 183.247.177.35 2023-04-04 22:17:12 a10WBCHI
she*yu* 183.247.177.35 2023-04-04 22:15:28 a10WABCHI
she*yu* 183.247.177.35 2023-04-04 22:15:22 a10WABCEHI
she*yu* 183.247.177.35 2023-04-04 22:15:05 a10WBCHI
she*yu* 183.247.177.35 2023-04-04 22:13:30 a10
she*yu* 183.247.177.35 2023-04-04 22:13:28 a11
she*yu* 183.247.177.35 2023-04-04 22:11:31 a10WBCDH
she*yu* 183.247.177.35 2023-04-04 22:11:19 a10WBCEH
she*yu* 183.247.177.35 2023-04-04 22:11:05 a10WBCEFHI
she*yu* 183.247.177.35 2023-04-04 22:10:58 a10WBEHI
she*yu* 183.247.177.35 2023-04-04 22:10:54 a10WCEHI
she*yu* 183.247.177.35 2023-04-04 22:10:34 a10WBCEHI
she*yu* 183.247.177.35 2023-04-04 22:09:15 a10WBCEHI
she*yu* 183.247.177.35 2023-04-04 22:09:10 a10WBCDHI
she*yu* 183.247.177.35 2023-04-04 22:02:56 a10WDHI
she*yu* 183.247.177.35 2023-04-04 22:02:23 a10WBCEHI
she*yu* 183.247.177.35 2023-04-04 22:01:54 a10WEH
she*yu* 183.247.177.35 2023-04-04 22:01:30 a10WEHI
she*yu* 183.247.177.35 2023-04-04 21:56:07 a9R
she*yu* 183.247.177.35 2023-04-04 21:55:22 SELECT
(
6371 * acos(
cos(radians(39.916527)) * cos(radians(22.539762)) * cos(
radians(116.397128) - radians(114.057383)
) + sin(radians(39.916527)) * sin(radians(22.539762))
)
)*1000 AS distance
she*yu* 183.247.177.35 2023-04-04 21:48:14 delimiter //
CREATE FUNCTION `FUN_JW_DIST`(lng1 double(15,9), lat1 double(15, 9), lng2 double(15,9), lat2 double(15,9)) RETURNS int(11)
BEGIN
DECLARE dist int;

SET dist = round((
6371 * acos(
cos(radians(lat1)) * cos(radians(lat2)) * cos(
radians(lng1) - radians(lng2)
) + sin(radians(lat1)) * sin(radians(lat2))
)
)*1000);

RETURN (dist);
END;

SELECT FUN_JW_DIST(116.397128,39.916527 ,114.057383,22.539762);
she*yu* 183.247.177.35 2023-04-04 21:46:53 delimiter //
drop function if exists FUN_JW_DIST;
CREATE FUNCTION `FUN_JW_DIST`(lng1 double(15,9), lat1 double(15, 9), lng2 double(15,9), lat2 double(15,9)) RETURNS int(11)
BEGIN
DECLARE dist int;

SET dist = round((
6371 * acos(
cos(radians(lat1)) * cos(radians(lat2)) * cos(
radians(lng1) - radians(lng2)
) + sin(radians(lat1)) * sin(radians(lat2))
)
)*1000);

RETURN (dist);
END;

SELECT FUN_JW_DIST(116.397128,39.916527 ,114.057383,22.539762);
she*yu* 183.247.177.35 2023-04-04 20:39:43 select * from log_loc
she*yu* 183.247.177.35 2023-04-04 15:52:11 select * from log_loc limit 5
she*yu* 183.247.177.35 2023-04-04 15:51:59 select * from trx_rcd limit 5
she*yu* 183.247.177.35 2023-04-04 15:51:42 a8R
she*yu* 183.247.177.35 2023-04-04 15:51:38 select sum(trx_amt) / count(distinct usr_id)
from trx_rcd
where mch_typ = '酒店'
she*yu* 183.247.177.35 2023-04-04 15:45:48 a8R
she*yu* 183.247.177.35 2023-04-04 15:45:29 a8WBC
she*yu* 183.247.177.35 2023-04-04 15:45:18 a8WBCD
she*yu* 183.247.177.35 2023-04-04 15:44:59 select count(usr_id)/count(distinct usr_id)
from trx_rcd
where mch_typ = '酒店'
she*yu* 183.247.177.35 2023-04-04 15:42:57 select usr_id, count(usr_id)
from trx_rcd
where mch_typ = '酒店'
group by usr_id
she*yu* 183.247.177.35 2023-04-04 15:40:53 select count(usr_id)
from trx_rcd
where mch_typ = '酒店'
she*yu* 183.247.177.35 2023-04-04 15:40:15 select avg(trx_amt)
from trx_rcd
where mch_typ = '酒店'
she*yu* 183.247.177.35 2023-04-04 15:39:45 select count(distinct usr_id)
from trx_rcd
where mch_typ = '酒店'
she*yu* 183.247.177.35 2023-04-04 15:38:57 select *
from trx_rcd
where mch_typ = '酒店'
she*yu* 183.247.177.35 2023-04-04 15:38:11 select * from trx_rcd limit 5
she*yu* 183.247.177.35 2023-04-04 15:37:53 a7R
she*yu* 183.247.177.35 2023-04-04 15:37:45 select *
from trx_rcd
where mch_nm = '惠州金爵大酒店'
order by trx_time
she*yu* 183.247.177.35 2023-04-04 15:37:35 select *
from trx_rcd
where mch_nm = '惠州金爵大酒店'
order by trx_time desc
she*yu* 183.247.177.35 2023-04-04 15:37:06 select *
from trx_rcd
where mac_nm = '惠州金爵大酒店'
order by trx_time desc
she*yu* 183.247.177.35 2023-04-04 15:36:52 select *
where mac_nm = '惠州金爵大酒店'
from trx_rcd
order by trx_time desc
she*yu* 183.247.177.35 2023-04-04 15:32:18 select * from trx_rcd limit 5
she*yu* 183.247.177.35 2023-04-04 15:32:05 a6R
she*yu* 183.247.177.35 2023-04-04 15:25:14 a6
she*yu* 183.247.177.35 2023-04-04 15:25:09 a6R
she*yu* 183.247.177.35 2023-04-04 15:22:26 a5R
she*yu* 183.247.177.35 2023-04-04 15:21:47 select *

from trx_rcd
where trx_amt > 100000
and (trx_time between '2021-10-01' and '2021-10-08'
or trx_time between '2022-01-01' and '2021-01-04' )
she*yu* 183.247.177.35 2023-04-04 15:21:26 select *

from trx_rcd
where trx_amt > 100000
and (trx_time between '2021-10-01' and '2021-10-08'
or trx_time between '2022-01-01' and '2021-01-04' )
she*yu* 183.247.177.35 2023-04-04 15:21:10 select distinct usr_id
from trx_rcd
where trx_amt > 100000
and (trx_time between '2021-10-01' and '2021-10-08'
or trx_time between '2022-01-01' and '2021-01-04' )
she*yu* 183.247.177.35 2023-04-04 15:16:40 a5
she*yu* 183.247.177.35 2023-04-04 15:16:38 a5R
she*yu* 183.247.177.35 2023-04-04 15:16:32 a5W
she*yu* 183.247.177.35 2023-04-04 15:15:56 a5W
she*yu* 183.247.177.35 2023-04-04 15:15:20 select distinct usr_id from trx_rcd
where trx_time between '2021-10-01' and '2021-10-08'
or trx_time between '2022-01-01' and '2022-01-03'
she*yu* 183.247.177.35 2023-04-04 15:14:35 select * from trx_rcd
where trx_time between '2021-10-01' and '2021-10-08'
or trx_time between '2022-01-01' and '2022-01-03'
she*yu* 183.247.177.35 2023-04-04 15:14:31 select * from trx_rcd
where trx_time between '2021-10-01' and '2021-10-08'
or trx_time between '2022-01-01' and '2022-01-03
she*yu* 183.247.177.35 2023-04-04 15:10:28 a4R
she*yu* 183.247.177.35 2023-04-04 15:02:31 a4
she*yu* 183.247.177.35 2023-04-04 15:00:13 a5
she*yu* 183.247.177.35 2023-04-04 14:59:49 a4
she*yu* 183.247.177.35 2023-04-04 14:59:33 select * from trx_rcd limit 5
she*yu* 183.247.177.35 2023-04-04 14:59:26 a5W
she*yu* 183.247.177.35 2023-04-04 14:59:17 a5W
she*yu* 183.247.177.35 2023-04-04 14:58:53 select * from trx_rcd
where trx_amt > 100000
and trx_time between '2021-10-01' and '2021-10-07'
or trx_time between '2022-01-01' and '2022-01-03'
she*yu* 183.247.177.35 2023-04-04 14:58:41 select * from trx_rcd
where trx_amt > 100000
and trx_time between '2021-10-01' and '2021-10-07'
or trx_time between '2022-01-01' and '2022-01-03'
limit 5
she*yu* 183.247.177.35 2023-04-04 14:58:26 select * from trx_rcd
where trx_amt > 100000
and trx_time between '2021-10-01' and '2021-10-07'
or trx_time between '2021-01-01' and '2021-01-03'
limit 5
she*yu* 183.247.177.35 2023-04-04 14:57:02 select * from trx_rcd
where trx_amt > 100000
and trx_time between '2021-10-01' and '2021-10-07'
limit 5
she*yu* 183.247.177.35 2023-04-04 14:56:55 select * from trx_rcd
where trx_amt > 100000
and trx_time between '2021-10-01' and '2021-10-07'
or datetime(trx_time) = '2022-01-01'
limit 5
she*yu* 183.247.177.35 2023-04-04 14:52:23 select * from trx_rcd limit 5
she*yu* 183.247.177.35 2023-04-04 14:51:26 df22
she*yu* 183.247.177.35 2023-04-04 14:51:23 a4R
she*yu* 183.247.177.35 2023-04-04 14:45:38 a4WBCD
she*yu* 183.247.177.35 2023-04-04 14:45:27 a4WBCDE
she*yu* 183.247.177.35 2023-04-04 14:45:09 a4WCDE
she*yu* 183.247.177.35 2023-04-04 14:44:58 a4WCE
she*yu* 183.247.177.35 2023-04-04 14:43:49 a3
she*yu* 183.247.177.35 2023-04-04 14:41:45 a2R
she*yu* 183.247.177.35 2023-04-04 14:41:23 a2WB
she*yu* 183.247.177.35 2023-04-04 14:41:20 select count(distinct usr_id)
from trx_rcd
where mch_typ='休闲娱乐' or mch_typ='餐饮'
and trx_amt >= 900
and month(trx_time) = 8
she*yu* 183.247.177.35 2023-04-04 14:40:54 select count(distinct usr_id)
from trx_rcd
where mch_typ='休闲娱乐' or mch_typ='餐饮'
and month(trx_time) = 8
she*yu* 183.247.177.35 2023-04-04 14:40:09 select count(distinct usr_id)
from trx_rcd
where mch_typ='休闲娱乐' or mch_typ='餐饮'
and trx_amt>=900
and month(trx_time) = 8
she*yu* 183.247.177.35 2023-04-04 14:39:21 a2
she*yu* 183.247.177.35 2023-04-04 14:39:15 a1
she*yu* 183.247.177.35 2023-04-04 14:39:07 select count(distinct usr_id),
substr(trx_time,1,7) as '月份'
from trx_rcd
where mch_typ='休闲娱乐' or mch_typ='餐饮'
and trx_amt>=900
she*yu* 183.247.177.35 2023-04-04 14:37:53 select count(distinct usr_id)
from trx_rcd
where mch_typ='休闲娱乐' or mch_typ='餐饮'
and trx_amt>=900
and trx_time between '2021-08-01' and '2021-08-31'
she*yu* 183.247.177.35 2023-04-04 14:35:34 a2
she*yu* 183.247.177.35 2023-04-04 14:35:11 select
substr(load_dt,1,7) as '月份'
, count(1)
, count(distinct usr_id)
from
td_load_rcd
group by 1
she*yu* 183.247.177.35 2023-04-04 14:34:52 a1
she*yu* 183.247.177.35 2023-04-04 13:46:56 select count(distinct usr_id)
from trx_rcd
where mch_typ='休闲娱乐' or mch_typ='餐饮'
and trx_amt>=900
she*yu* 183.247.177.35 2023-04-04 13:46:50 a2
she*yu* 183.247.177.35 2023-04-04 13:46:44 a2R
she*yu* 183.247.177.35 2023-04-04 13:46:41 a2
she*yu* 183.247.177.35 2023-04-04 13:46:40 a1
she*yu* 183.247.177.35 2023-04-04 13:46:35 member_enter2
she*yu* 183.247.177.35 2023-04-04 13:46:31 a2WB
she*yu* 183.247.177.35 2023-04-04 13:45:58 select count(usr_id)
from trx_rcd
where mch_typ='休闲娱乐' or mch_typ='餐饮'
and trx_amt>=900
she*yu* 183.247.177.35 2023-04-04 13:45:44 a1R
she*yu* 183.247.177.35 2023-04-04 13:45:38 a1
she*yu* 183.247.177.35 2023-04-04 13:45:20 ser1
she*yu* 183.247.177.35 2023-04-04 13:45:19 a2WC
she*yu* 183.247.177.35 2023-04-04 13:45:02 select count(distinct usr_id)
from trx_rcd
where mch_typ='休闲娱乐' or mch_typ='餐饮'
and trx_amt>=900
she*yu* 183.247.177.35 2023-04-04 13:44:48 a2WC
she*yu* 183.247.177.35 2023-04-04 13:44:15 select count(distinct usr_id)
from trx_rcd
where mch_typ='休闲娱乐' or mch_typ='餐饮'
and trx_amt>=900
she*yu* 183.247.177.35 2023-04-04 13:42:40 a1R
she*yu* 183.247.177.35 2023-04-04 13:42:28 select
substr(load_dt,1,7) as '月份'
, count(1)
, count(distinct usr_id)
from
td_load_rcd
group by 1
she*yu* 183.247.177.35 2023-04-04 13:42:23 a1
she*yu* 183.247.177.35 2023-04-04 13:42:02 member_enter2
she*yu* 183.247.177.35 2023-04-04 13:41:59 ser1
she*yu* 183.247.177.35 2023-04-04 13:41:46 a1WB
she*yu* 183.247.177.35 2023-04-04 13:33:46 select
substr(load_dt,1,7) as '月份'
, count(1)
, count(distinct usr_id)
from
td_load_rcd
group by 1
she*yu* 183.247.177.35 2023-04-04 13:33:41 a1
she*yu* 183.247.177.35 2023-04-04 13:32:42 ser2
she*yu* 183.247.177.35 2023-04-04 13:32:38 changjing_computer
she*yu* 183.247.177.35 2023-04-04 13:31:11 ser2
she*yu* 183.247.177.35 2023-04-04 13:29:33 a1
she*yu* 183.247.177.35 2023-04-04 13:29:32 ser2
she*yu* 183.247.177.35 2023-04-04 13:29:31 a1
she*yu* 183.247.177.35 2023-04-04 13:29:30 changjing_computer
she*yu* 183.247.177.35 2023-04-04 13:29:04 a1
she*yu* 183.247.177.35 2023-04-04 13:29:01 changjing_computer
she*yu* 183.247.177.35 2023-04-04 13:28:49 ser2
she*yu* 183.247.177.35 2023-04-04 13:28:46 a1
she*yu* 183.247.177.35 2023-04-04 13:28:44 changjing_computer
she*yu* 183.247.177.35 2023-04-04 13:28:28 ser2
she*yu* 183.247.177.35 2023-04-04 13:28:25 a1
she*yu* 183.247.177.35 2023-04-04 13:27:48 member_enter2
she*yu* 183.247.177.35 2023-04-04 13:27:33 select
substr(load_dt,1,7) as '月份'
, count(1)
, count(distinct usr_id)
from
td_load_rcd
group by 1
she*yu* 183.247.177.35 2023-04-04 13:25:05 a1
she*yu* 183.247.177.35 2023-04-04 13:25:04 a2
she*yu* 183.247.177.35 2023-04-04 13:24:59 a1
she*yu* 183.247.177.35 2023-04-04 13:23:28 member_enter2