本文详细介绍了如何使用Hive进行大数据分析，包括设备主题中的活跃设备数、每日新增设备、留存率、沉默用户数、本周回流用户数、流失用户数以及连续活跃用户数的统计。此外，还涉及到了会员、商品主题的相关数据统计，如会员信息、商品销量、收藏和购物车排名，以及退款率和差评率。最后提到了营销主题的下单、支付信息统计和品牌复购率，以及地区主题的相关信息分析。
7.2 设备主题
7.2.1 活跃设备数（日、周、月）
需求定义：
日活：当日活跃的设备数
周活：当周活跃的设备数
月活：当月活跃的设备数
1）建表语句
hive (gmall)>
drop table if exists ads_uv_count;
create external table ads_uv_count(
`dt` string COMMENT '统计日期',
`day_count` bigint COMMENT '当日用户数量',
`wk_count` bigint COMMENT '当周用户数量',
`mn_count` bigint COMMENT '当月用户数量',
`is_weekend` string COMMENT 'Y,N是否是周末,用于得到本周最终结果',
`is_monthend` string COMMENT 'Y,N是否是月末,用于得到本月最终结果'
) COMMENT '活跃设备数'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_uv_count/';
2）导入数据
hive (gmall)>
insert into table ads_uv_count
select
'2021-06-20' dt,
daycount.ct,
wkcount.ct,
mncount.ct,
if(date_add(next_day('2021-06-20','MO'),-1)='2021-06-20','Y','N') ,
if(last_day('2021-06-20')='2021-06-20','Y','N')
from
(
select
'2021-06-20' dt,
count(*) ct
from dwt_uv_topic
where login_date_last='2021-06-20'
)daycount join
(
select
'2021-06-20' dt,
count (*) ct
from dwt_uv_topic
where login_date_last>=date_add(next_day('2021-06-20','MO'),-7)
and login_date_last<= date_add(next_day('2021-06-20','MO'),-1)
) wkcount on daycount.dt=wkcount.dt
join
(
select
'2021-06-20' dt,
count (*) ct
from dwt_uv_topic
where date_format(login_date_last,'yyyy-MM')=date_format('2021-06-20','yyyy-MM')
)mncount on daycount.dt=mncount.dt;
3）查询导入结果
hive (gmall)> select * from ads_uv_count;
7.2.2 每日新增设备
1）建表语句
hive (gmall)>
drop table if exists ads_new_mid_count;
create external table ads_new_mid_count
(
`create_date` string comment '创建时间' ,
`new_mid_count` BIGINT comment '新增设备数量'
) COMMENT '每日新增设备数量'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_new_mid_count/';
2）导入数据
hive (gmall)>
insert into table ads_new_mid_count
select
'2021-06-20',
count(*)
from dwt_uv_topic
where login_date_first='2021-06-20';
3）查询导入数据
hive (gmall)> select * from ads_new_mid_count;
7.2.3 留存率
1）建表语句
hive (gmall)>
drop table if exists ads_user_retention_day_rate;
create external table ads_user_retention_day_rate
(
`stat_date` string comment '统计日期',
`create_date` string comment '设备新增日期',
`retention_day` int comment '截止当前日期留存天数',
`retention_count` bigint comment '留存数量',
`new_mid_count` bigint comment '设备新增数量',
`retention_ratio` decimal(16,2) comment '留存率'
) COMMENT '留存率'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_user_retention_day_rate/';
2）导入数据
hive (gmall)>
insert into table ads_user_retention_day_rate
select
'2021-06-21',
date_add('2021-06-21',-1),
1,--留存天数
sum(if(login_date_first=date_add('2021-06-21',-1) and login_date_last='2021-06-21',1,0)),
sum(if(login_date_first=date_add('2021-06-21',-1),1,0)),
sum(if(login_date_first=date_add('2021-06-21',-1) and login_date_last='2021-06-21',1,0))/sum(if(login_date_first=date_add('2021-06-21',-1),1,0))*100
from dwt_uv_topic
union all
select
'2021-06-21',
date_add('2021-06-21',-2),
2,
sum(if(login_date_first=date_add('2021-06-21',-2) and login_date_last='2021-06-21',1,0)),
sum(if(login_date_first=date_add('2021-06-21',-2),1,0)),
sum(if(login_date_first=date_add('2021-06-21',-2) and login_date_last='2021-06-21',1,0))/sum(if(login_date_first=date_add('2021-06-21',-2),1,0))*100
from dwt_uv_topic
union all
select
'2021-06-21',
date_add('2021-06-21',-3),
3,
sum(if(login_date_first=date_add('2021-06-21',-3) and login_date_last='2021-06-21',1,0)),
sum(if(login_date_first=date_add('2021-06-21',-3),1,0)),
sum(if(login_date_first=date_add('2021-06-21',-3) and login_date_last='2021-06-21',1,0))/sum(if(login_date_first=date_add('2021-06-21',-3),1,0))*100
from dwt_uv_topic;
3）查询导入数据
hive (gmall)>select * from ads_user_retention_day_rate;
7.2.4 沉默用户数
需求定义：
沉默用户：只在安装当天启动过，且启动时间是在7天前
1）建表语句
hive (gmall)>
drop table if exists ads_silent_count;
create external table ads_silent_count(
`dt` string COMMENT '统计日期',
`silent_count` bigint COMMENT '沉默设备数'
) COMMENT '沉默用户数'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_silent_count';
2）导入2020-06-25数据
hive (gmall)>
insert into table ads_silent_count
select
'2020-06-25',
count(*)
from dwt_uv_topic
where login_date_first=login_date_last
and login_date_last<=date_add('2020-06-25',-7);
3）查询导入数据
hive (gmall)> select * from ads_silent_count;
7.2.5 本周回流用户数
需求定义：
本周回流用户：上周未活跃，本周活跃的设备，且不是本周新增设备
1）建表语句
hive (gmall)>
drop table if exists ads_back_count;
create external table ads_back_count(
`dt` string COMMENT '统计日期',
`wk_dt` string COMMENT '统计日期所在周',
`wastage_count` bigint COMMENT '回流设备数'
) COMMENT '本周回流用户数'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_back_count';
2）导入数据：
hive (gmall)>
insert into table ads_back_count
select
'2020-06-25',
concat(date_add(next_day('2020-06-25','MO'),-7),'_', date_add(next_day('2020-06-25','MO'),-1)),
count(*)
from
(
select
mid_id
from dwt_uv_topic
where login_date_last>=date_add(next_day('2020-06-25','MO'),-7)
and login_date_last<= date_add(next_day('2020-06-25','MO'),-1)
and login_date_first<date_add(next_day('2020-06-25','MO'),-7)
)current_wk
left join
(
select
mid_id
from dws_uv_detail_daycount
where dt>=date_add(next_day('2020-06-25','MO'),-7*2)
and dt<= date_add(next_day('2020-06-25','MO'),-7-1)
group by mid_id
)last_wk
on current_wk.mid_id=last_wk.mid_id
where last_wk.mid_id is null;
3）查询结果
hive (gmall)> select * from ads_back_count;
7.2.6 流失用户数
需求定义：
流失用户：最近7天未活跃的设备
1）建表语句
hive (gmall)>
drop table if exists ads_wastage_count;
create external table ads_wastage_count(
`dt` string COMMENT '统计日期',
`wastage_count` bigint COMMENT '流失设备数'
) COMMENT '流失用户数'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_wastage_count';
2）导入2020-06-25数据
hive (gmall)>
insert into table ads_wastage_count
select
'2020-06-25',
count(*)
from
(
select
mid_id
from dwt_uv_topic
where login_date_last<=date_add('2020-06-25',-7)
group by mid_id
)t1;
3）查询结果
hive (gmall)> select * from ads_wastage_count;
7.2.7 最近连续三周活跃用户数
1）建表语句
hive (gmall)>
drop table if exists ads_continuity_wk_count;
create external table ads_continuity_wk_count(
`dt` string COMMENT '统计日期,一般用结束周周日日期,如果每天计算一次,可用当天日期',
`wk_dt` string COMMENT '持续时间',
`continuity_count` bigint COMMENT '活跃次数'
) COMMENT '最近连续三周活跃用户数'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_continuity_wk_count';
2）导入2020-06-25所在周的数据
hive (gmall)>
insert into table ads_continuity_wk_count
select
'2020-06-25',
concat(date_add(next_day('2020-06-25','MO'),-7*3),'_',date_add(next_day('2020-06-25','MO'),-1)),
count(*)
from
(
select
mid_id
from
(
select
mid_id
from dws_uv_detail_daycount
where dt>=date_add(next_day('2020-06-25','monday'),-7)
and dt<=date_add(next_day('2020-06-25','monday'),-1)
group by mid_id
union all
select
mid_id
from dws_uv_detail_daycount
where dt>=date_add(next_day('2020-06-25','monday'),-7*2)
and dt<=date_add(next_day('2020-06-25','monday'),-7-1)
group by mid_id
union all
select
mid_id
from dws_uv_detail_daycount
where dt>=date_add(next_day('2020-06-25','monday'),-7*3)
and dt<=date_add(next_day('2020-06-25','monday'),-7*2-1)
group by mid_id
)t1
group by mid_id
having count(*)=3
)t2;
3）查询
hive (gmall)> select * from ads_continuity_wk_count;
7.2.8 最近七天内连续三天活跃用户数
1）建表语句
hive (gmall)>
drop table if exists ads_continuity_uv_count;
create external table ads_continuity_uv_count(
`dt` string COMMENT '统计日期',
`wk_dt` string COMMENT '最近7天日期',
`continuity_count` bigint
) COMMENT '最近七天内连续三天活跃用户数'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_continuity_uv_count';
2）写出导入数据的SQL语句
hive (gmall)>
insert into table ads_continuity_uv_count
select
'2021-06-22',
concat(date_add('2021-06-22',-6),'_','2021-06-22'),
count(*)
from
(
select mid_id
from
(
select mid_id
from
(
select
mid_id,
date_sub(dt,rank) date_dif
from
(
select
mid_id,
dt,
rank() over(partition by mid_id order by dt) rank
from dws_uv_detail_daycount
where dt>=date_add('2021-06-22',-6) and dt<='2021-06-22'
)t1
)t2
group by mid_id,date_dif
having count(*)>=3
)t3
group by mid_id
)t4;
3）查询
hive (gmall)> select * from ads_continuity_uv_count;
7.3 会员主题
7.3.1 会员信息
1）建表
hive (gmall)>
drop table if exists ads_user_topic;
create external table ads_user_topic(
`dt` string COMMENT '统计日期',
`day_users` string COMMENT '活跃会员数',
`day_new_users` string COMMENT '新增会员数',
`day_new_payment_users` string COMMENT '新增消费会员数',
`payment_users` string COMMENT '总付费会员数',
`users` string COMMENT '总会员数',
`day_users2users` decimal(16,2) COMMENT '会员活跃率',
`payment_users2users` decimal(16,2) COMMENT '会员付费率',
`day_new_users2users` decimal(16,2) COMMENT '会员新鲜度'
) COMMENT '会员信息表'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_user_topic';
2）导入数据
hive (gmall)>
insert into table ads_user_topic
select
'2021-06-20',
sum(if(login_date_last='2021-06-20',1,0)),
sum(if(login_date_first='2021-06-20',1,0)),
sum(if(payment_date_first='2021-06-20',1,0)),
sum(if(payment_count>0,1,0)),
count(*),
sum(if(login_date_last='2021-06-20',1,0))/count(*),
sum(if(payment_count>0,1,0))/count(*),
sum(if(login_date_first='2021-06-20',1,0))/sum(if(login_date_last='2021-06-20',1,0))
from dwt_user_topic;
3）查询数据
hive (gmall)> select * from ads_user_topic;
7.3.2 漏斗分析
统计“浏览首页->浏览商品详情页->加入购物车->下单->支付”的转化率
思路：统计各个行为的人数，然后计算比值。
1）建表语句
hive (gmall)>
drop table if exists ads_user_action_convert_day;
create external table ads_user_action_convert_day(
`dt` string COMMENT '统计日期',
`home_count` bigint COMMENT '浏览首页人数',
`good_detail_count` bigint COMMENT '浏览商品详情页人数',
`home2good_detail_convert_ratio` decimal(16,2) COMMENT '首页到商品详情转化率',
`cart_count` bigint COMMENT '加入购物车的人数',
`good_detail2cart_convert_ratio` decimal(16,2) COMMENT '商品详情页到加入购物车转化率',
`order_count` bigint COMMENT '下单人数',
`cart2order_convert_ratio` decimal(16,2) COMMENT '加入购物车到下单转化率',
`payment_amount` bigint COMMENT '支付人数',
`order2payment_convert_ratio` decimal(16,2) COMMENT '下单到支付的转化率'
) COMMENT '漏斗分析'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_user_action_convert_day/';
2）数据装载
hive (gmall)>
with
tmp_uv as
(
select
'2021-06-20' dt,
sum(if(array_contains(pages,'home'),1,0)) home_count,
sum(if(array_contains(pages,'good_detail'),1,0)) good_detail_count
from
(
select
mid_id,
collect_set(page_id) pages
from dwd_page_log
where dt='2021-06-20'
and page_id in ('home','good_detail')
group by mid_id
)tmp
),
tmp_cop as
(
select
'2021-06-20' dt,
sum(if(cart_count>0,1,0)) cart_count,
sum(if(order_count>0,1,0)) order_count,
sum(if(payment_count>0,1,0)) payment_count
from dws_user_action_daycount
where dt='2021-06-20'
)
insert into table ads_user_action_convert_day
select
tmp_uv.dt,
tmp_uv.home_count,
tmp_uv.good_detail_count,
tmp_uv.good_detail_count/tmp_uv.home_count*100,
tmp_cop.cart_count,
tmp_cop.cart_count/tmp_uv.good_detail_count*100,
tmp_cop.order_count,
tmp_cop.order_count/tmp_cop.cart_count*100,
tmp_cop.payment_count,
tmp_cop.payment_count/tmp_cop.order_count*100
from tmp_uv
join tmp_cop
on tmp_uv.dt=tmp_cop.dt;
3）查询加载数据
hive (gmall)> select * from ads_user_action_convert_day;
7.4 商品主题
7.4.1 商品个数信息
1）建表语句
hive (gmall)>
drop table if exists ads_product_info;
create external table ads_product_info(
`dt` string COMMENT '统计日期',
`sku_num` string COMMENT 'sku个数',
`spu_num` string COMMENT 'spu个数'
) COMMENT '商品个数信息'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_product_info';
2）导入数据
hive (gmall)>
insert into table ads_product_info
select
'2021-06-20' dt,
sku_num,
spu_num
from
(
select
'2021-06-20' dt,
count(*) sku_num
from
dwt_sku_topic
) tmp_sku_num
join
(
select
'2021-06-20' dt,
count(*) spu_num
from
(
select
spu_id
from
dwt_sku_topic
group by
spu_id
) tmp_spu_id
) tmp_spu_num
on tmp_sku_num.dt=tmp_spu_num.dt;
3）查询结果数据
hive (gmall)> select * from ads_product_info;
7.4.2 商品销量排名
1）建表语句
hive (gmall)>
drop table if exists ads_product_sale_topN;
create external table ads_product_sale_topN(
`dt` string COMMENT '统计日期',
`sku_id` string COMMENT '商品ID',
`payment_amount` bigint COMMENT '销量'
) COMMENT '商品销量排名'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_product_sale_topN';
2）导入数据
hive (gmall)>
insert into table ads_product_sale_topN
select
'2021-06-20' dt,
sku_id,
payment_amount
from
dws_sku_action_daycount
where
dt='2021-06-20'
order by payment_amount desc
limit 10;
3）查询结果数据
hive (gmall)> select * from ads_product_sale_topN;
7.4.3 商品收藏排名
1）建表语句
hive (gmall)>
drop table if exists ads_product_favor_topN;
create external table ads_product_favor_topN(
`dt` string COMMENT '统计日期',
`sku_id` string COMMENT '商品ID',
`favor_count` bigint COMMENT '收藏量'
) COMMENT '商品收藏排名'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_product_favor_topN';
2）导入数据
hive (gmall)>
insert into table ads_product_favor_topN
select
'2021-06-20' dt,
sku_id,
favor_count
from
dws_sku_action_daycount
where
dt='2021-06-20'
order by favor_count desc
limit 10;
3）查询数据
hive (gmall)> select * from ads_product_favor_topN;
7.4.4 商品加入购物车排名
1）建表语句
hive (gmall)>
drop table if exists ads_product_cart_topN;
create external table ads_product_cart_topN(
`dt` string COMMENT '统计日期',
`sku_id` string COMMENT '商品ID',
`cart_count` bigint COMMENT '加入购物车次数'
) COMMENT '商品加入购物车排名'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_product_cart_topN';
2）导入数据
hive (gmall)>
insert into table ads_product_cart_topN
select
'2021-06-20' dt,
sku_id,
cart_count
from
dws_sku_action_daycount
where
dt='2021-06-20'
order by cart_count desc
limit 10;
3）查询数据
hive (gmall)> select * from ads_product_cart_topN;
7.4.5 商品退款率排名（最近30天）
1）建表语句
hive (gmall)>
drop table if exists ads_product_refund_topN;
create external table ads_product_refund_topN(
`dt` string COMMENT '统计日期',
`sku_id` string COMMENT '商品ID',
`refund_ratio` decimal(16,2) COMMENT '退款率'
) COMMENT '商品退款率排名'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_product_refund_topN';
2）导入数据
hive (gmall)>
insert into table ads_product_refund_topN
select
'2021-06-20',
sku_id,
refund_last_30d_count/payment_last_30d_count*100 refund_ratio
from dwt_sku_topic
order by refund_ratio desc
limit 10;
3）查询数据
hive (gmall)> select * from ads_product_refund_topN;
7.4.6 商品差评率
1）建表语句
hive (gmall)>
drop table if exists ads_appraise_bad_topN;
create external table ads_appraise_bad_topN(
`dt` string COMMENT '统计日期',
`sku_id` string COMMENT '商品ID',
`appraise_bad_ratio` decimal(16,2) COMMENT '差评率'
) COMMENT '商品差评率'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_appraise_bad_topN';
2）导入数据
hive (gmall)>
insert into table ads_appraise_bad_topN
select
'2021-06-20' dt,
sku_id,
appraise_bad_count/(appraise_good_count+appraise_mid_count+appraise_bad_count+appraise_default_count) appraise_bad_ratio
from
dws_sku_action_daycount
where
dt='2021-06-20'
order by appraise_bad_ratio desc
limit 10;
3）查询数据
hive (gmall)> select * from ads_appraise_bad_topN;
7.5 营销主题（用户+商品+购买行为）
7.5.1 下单数目统计
需求分析：统计每日下单数，下单金额及下单用户数。
1）建表语句
hive (gmall)>
drop table if exists ads_order_daycount;
create external table ads_order_daycount(
dt string comment '统计日期',
order_count bigint comment '单日下单笔数',
order_amount bigint comment '单日下单金额',
order_users bigint comment '单日下单用户数'
) comment '下单数目统计'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_order_daycount';
2）导入数据
hive (gmall)>
insert into table ads_order_daycount
select
'2021-06-20',
sum(order_count),
sum(order_amount),
sum(if(order_count>0,1,0))
from dws_user_action_daycount
where dt='2021-06-20';
3）查询数据
hive (gmall)> select * from ads_order_daycount;
7.5.2 支付信息统计
每日支付金额、支付人数、支付商品数、支付笔数以及下单到支付的平均时长（取自DWD）
1）建表
hive (gmall)>
drop table if exists ads_payment_daycount;
create external table ads_payment_daycount(
dt string comment '统计日期',
order_count bigint comment '单日支付笔数',
order_amount bigint comment '单日支付金额',
payment_user_count bigint comment '单日支付人数',
payment_sku_count bigint comment '单日支付商品数',
payment_avg_time decimal(16,2) comment '下单到支付的平均时长，取分钟数'
) comment '支付信息统计'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_payment_daycount';
2）导入数据
hive (gmall)>
insert into table ads_payment_daycount
select
tmp_payment.dt,
tmp_payment.payment_count,
tmp_payment.payment_amount,
tmp_payment.payment_user_count,
tmp_skucount.payment_sku_count,
tmp_time.payment_avg_time
from
(
select
'2021-06-20' dt,
sum(payment_count) payment_count,
sum(payment_amount) payment_amount,
sum(if(payment_count>0,1,0)) payment_user_count
from dws_user_action_daycount
where dt='2021-06-20'
)tmp_payment
join
(
select
'2021-06-20' dt,
sum(if(payment_count>0,1,0)) payment_sku_count
from dws_sku_action_daycount
where dt='2021-06-20'
)tmp_skucount on tmp_payment.dt=tmp_skucount.dt
join
(
select
'2021-06-20' dt,
sum(unix_timestamp(payment_time)-unix_timestamp(create_time))/count(*)/60 payment_avg_time
from dwd_fact_order_info
where dt='2021-06-20'
and payment_time is not null
)tmp_time on tmp_payment.dt=tmp_time.dt;
3）查询数据
hive (gmall)> select * from ads_payment_daycount;
7.5.3 品牌复购率
1）建表语句
hive (gmall)>
drop table ads_sale_tm_category1_stat_mn;
create external table ads_sale_tm_category1_stat_mn
(
tm_id string comment '品牌id',
category1_id string comment '1级品类id ',
category1_name string comment '1级品类名称 ',
buycount bigint comment '购买人数',
buy_twice_last bigint comment '两次以上购买人数',
buy_twice_last_ratio decimal(16,2) comment '单次复购率',
buy_3times_last bigint comment '三次以上购买人数',
buy_3times_last_ratio decimal(16,2) comment '多次复购率',
stat_mn string comment '统计月份',
stat_date string comment '统计日期'
) COMMENT '品牌复购率统计'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_sale_tm_category1_stat_mn/';
2）数据导入
hive (gmall)>
with
tmp_order as
(
select
user_id,
order_stats_struct.sku_id sku_id,
order_stats_struct.order_count order_count
from dws_user_action_daycount lateral view explode(order_detail_stats) tmp as order_stats_struct
where date_format(dt,'yyyy-MM')=date_format('2021-06-20','yyyy-MM')
),
tmp_sku as
(
select
id,
tm_id,
category1_id,
category1_name
from dwd_dim_sku_info
where dt='2021-06-20'
)
insert into table ads_sale_tm_category1_stat_mn
select
tm_id,
category1_id,
category1_name,
sum(if(order_count>=1,1,0)) buycount,
sum(if(order_count>=2,1,0)) buyTwiceLast,
sum(if(order_count>=2,1,0))/sum( if(order_count>=1,1,0)) buyTwiceLastRatio,
sum(if(order_count>=3,1,0)) buy3timeLast ,
sum(if(order_count>=3,1,0))/sum( if(order_count>=1,1,0)) buy3timeLastRatio ,
date_format('2021-06-20' ,'yyyy-MM') stat_mn,
'2021-06-20' stat_date
from
(
select
tmp_order.user_id,
tmp_sku.category1_id,
tmp_sku.category1_name,
tmp_sku.tm_id,
sum(order_count) order_count
from tmp_order
join tmp_sku
on tmp_order.sku_id=tmp_sku.id
group by tmp_order.user_id,tmp_sku.category1_id,tmp_sku.category1_name,tmp_sku.tm_id
)tmp
group by tm_id, category1_id, category1_name;
3）查询数据
hive (gmall)> select * from ads_sale_tm_category1_stat_mn;
7.6 地区主题
7.6.1 地区主题信息
1）建表语句
hive (gmall)>
drop table if exists ads_area_topic;
create external table ads_area_topic(
`dt` string COMMENT '统计日期',
`id` bigint COMMENT '编号',
`province_name` string COMMENT '省份名称',
`area_code` string COMMENT '地区编码',
`iso_code` string COMMENT 'iso编码',
`region_id` string COMMENT '地区ID',
`region_name` string COMMENT '地区名称',
`login_day_count` bigint COMMENT '当天活跃设备数',
`order_day_count` bigint COMMENT '当天下单次数',
`order_day_amount` decimal(16,2) COMMENT '当天下单金额',
`payment_day_count` bigint COMMENT '当天支付次数',
`payment_day_amount` decimal(16,2) COMMENT '当天支付金额'
) COMMENT '地区主题信息'
row format delimited fields terminated by '\t'
location '/warehouse/gmall/ads/ads_area_topic/';
2）数据装载
hive (gmall)>
insert into table ads_area_topic
select
'2021-06-20',
id,
province_name,
area_code,
iso_code,
region_id,
region_name,
login_day_count,
order_day_count,
order_day_amount,
payment_day_count,
payment_day_amount
from dwt_area_topic;
3）查看结果
hive (gmall)> select * from ads_area_topic;
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