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文章目录
- 1、数仓开发之ADS层
- 1.1 流量主题
- 1.1.1 各渠道流量统计
- 1.1.2 路径分析
- 1.2 用户主题
- 1.2.1 用户变动统计
- 1.2.2 用户留存率
- 1.2.3 用户新增活跃统计
- 1.2.4 用户行为漏斗分析
- 1.2.5 新增交易用户统计
- 1.3 商品主题
- 1.3.1 最近7/30日各品牌复购率
- 1.3.2 各品牌商品交易统计
- 1.3.3 各品类商品交易统计
- 1.3.4 各分类商品购物车存量Top10
- 1.4 交易主题
- 1.4.1 交易综合统计
- 1.4.2 各省份交易统计
- 1.5 优惠券主题
- 1.5.1 最近30天发布的优惠券的补贴率
- 1.6 活动主题
- 1.7 数据装载脚本
1、数仓开发之ADS层
1.1 流量主题
1.1.1 各渠道流量统计
需求说明如下
1、建表语句
DROP TABLE IF EXISTS ads_traffic_stats_by_channel;
CREATE EXTERNAL TABLE ads_traffic_stats_by_channel
(
`dt` STRING COMMENT '统计日期',
`recent_days` BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
`channel` STRING COMMENT '渠道',
`uv_count` BIGINT COMMENT '访客人数',
`avg_duration_sec` BIGINT COMMENT '会话平均停留时长，单位为秒',
`avg_page_count` BIGINT COMMENT '会话平均浏览页面数',
`sv_count` BIGINT COMMENT '会话数',
`bounce_rate` DECIMAL(16, 2) COMMENT '跳出率'
) COMMENT '各渠道流量统计'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/ads/ads_traffic_stats_by_channel/';
2、数据装载
insert overwrite table ads_traffic_stats_by_channel
select * from ads_traffic_stats_by_channel
union
select
'2020-06-14' dt,
recent_days,
channel,
cast(count(distinct(mid_id)) as bigint) uv_count,
cast(avg(during_time_1d)/1000 as bigint) avg_duration_sec,
cast(avg(page_count_1d) as bigint) avg_page_count,
cast(count(*) as bigint) sv_count,
cast(sum(if(page_count_1d=1,1,0))/count(*) as decimal(16,2)) bounce_rate
from dws_traffic_session_page_view_1d lateral view explode(array(1,7,30)) tmp as recent_days
where dt>=date_add('2020-06-14',-recent_days+1)
group by recent_days,channel;
1.1.2 路径分析
用户路径分析，顾名思义，就是指用户在APP或网站中的访问路径。为了衡量网站优化的效果或营销推广的效果，以及了解用户行为偏好，时常要对访问路径进行分析。
用户访问路径的可视化通常使用桑基图。如下图所示，该图可真实还原用户的访问路径，包括页面跳转和页面访问次序。
桑基图需要我们提供每种页面跳转的次数，每个跳转由source/target表示，source指跳转起始页面，target表示跳转终到页面。
1、建表语句
DROP TABLE IF EXISTS ads_page_path;
CREATE EXTERNAL TABLE ads_page_path
(
`dt` STRING COMMENT '统计日期',
`recent_days` BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
`source` STRING COMMENT '跳转起始页面ID',
`target` STRING COMMENT '跳转终到页面ID',
`path_count` BIGINT COMMENT '跳转次数'
) COMMENT '页面浏览路径分析'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/ads/ads_page_path/';
2、数据装载
insert overwrite table ads_page_path
select * from ads_page_path
union
select
'2020-06-14' dt,
recent_days,
source,
nvl(target,'null'),
count(*) path_count
from
(
select
recent_days,
concat('step-',rn,':',page_id) source,
concat('step-',rn+1,':',next_page_id) target
from
(
select
recent_days,
page_id,
lead(page_id,1,null) over(partition by session_id,recent_days) next_page_id,
row_number() over (partition by session_id,recent_days order by view_time) rn
from dwd_traffic_page_view_inc lateral view explode(array(1,7,30)) tmp as recent_days
where dt>=date_add('2020-06-14',-recent_days+1)
)t1
)t2
group by recent_days,source,target;
1.2 用户主题
1.2.1 用户变动统计
该需求包括两个指标，分别为流失用户数和回流用户数，以下为对两个指标的解释说明。
1、建表语句
DROP TABLE IF EXISTS ads_user_change;
CREATE EXTERNAL TABLE ads_user_change
(
`dt` STRING COMMENT '统计日期',
`user_churn_count` BIGINT COMMENT '流失用户数',
`user_back_count` BIGINT COMMENT '回流用户数'
) COMMENT '用户变动统计'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/ads/ads_user_change/';
2、数据装载
insert overwrite table ads_user_change
select * from ads_user_change
union
select
churn.dt,
user_churn_count,
user_back_count
from
(
select
'2020-06-14' dt,
count(*) user_churn_count
from dws_user_user_login_td
where dt='2020-06-14'
and login_date_last=date_add('2020-06-14',-7)
)churn
join
(
select
'2020-06-14' dt,
count(*) user_back_count
from
(
select
user_id,
login_date_last
from dws_user_user_login_td
where dt='2020-06-14'
)t1
join
(
select
user_id,
login_date_last login_date_previous
from dws_user_user_login_td
where dt=date_add('2020-06-14',-1)
)t2
on t1.user_id=t2.user_id
where datediff(login_date_last,login_date_previous)>=8
)back
on churn.dt=back.dt;
1.2.2 用户留存率
留存分析一般包含新增留存和活跃留存分析。
新增留存分析是分析某天的新增用户中，有多少人有后续的活跃行为。活跃留存分析是分析某天的活跃用户中，有多少人有后续的活跃行为。
留存分析是衡量产品对用户价值高低的重要指标。
此处要求统计新增留存率，新增留存率具体是指留存用户数与新增用户数的比值，例如2020-06-14新增100个用户，1日之后（2020-06-15）这100人中有80个人活跃了，那2020-06-14的1日留存数则为80，2020-06-14的1日留存率则为80%。
要求统计每天的1至7日留存率，如下图所示。
1、建表语句
DROP TABLE IF EXISTS ads_user_retention;
CREATE EXTERNAL TABLE ads_user_retention
(
`dt` STRING COMMENT '统计日期',
`create_date` STRING COMMENT '用户新增日期',
`retention_day` INT COMMENT '截至当前日期留存天数',
`retention_count` BIGINT COMMENT '留存用户数量',
`new_user_count` BIGINT COMMENT '新增用户数量',
`retention_rate` DECIMAL(16, 2) COMMENT '留存率'
) COMMENT '用户留存率'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/ads/ads_user_retention/';
2、数据装载
insert overwrite table ads_user_retention
select * from ads_user_retention
union
select
'2020-06-14' dt,
login_date_first create_date,
datediff('2020-06-14',login_date_first) retention_day,
sum(if(login_date_last='2020-06-14',1,0)) retention_count,
count(*) new_user_count,
cast(sum(if(login_date_last='2020-06-14',1,0))/count(*)*100 as decimal(16,2)) retention_rate
from
(
select
user_id,
date_id login_date_first
from dwd_user_register_inc
where dt>=date_add('2020-06-14',-7)
and dt<'2020-06-14'
)t1
join
(
select
user_id,
login_date_last
from dws_user_user_login_td
where dt='2020-06-14'
)t2
on t1.user_id=t2.user_id
group by login_date_first;
1.2.3 用户新增活跃统计
1、建表语句
DROP TABLE IF EXISTS ads_user_stats;
CREATE EXTERNAL TABLE ads_user_stats
(
`dt` STRING COMMENT '统计日期',
`recent_days` BIGINT COMMENT '最近n日,1:最近1日,7:最近7日,30:最近30日',
`new_user_count` BIGINT COMMENT '新增用户数',
`active_user_count` BIGINT COMMENT '活跃用户数'
) COMMENT '用户新增活跃统计'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/ads/ads_user_stats/';
2、数据装载
insert overwrite table ads_user_stats
select * from ads_user_stats
union
select
'2020-06-14' dt,
t1.recent_days,
new_user_count,
active_user_count
from
(
select
recent_days,
sum(if(login_date_last>=date_add('2020-06-14',-recent_days+1),1,0)) new_user_count
from dws_user_user_login_td lateral view explode(array(1,7,30)) tmp as recent_days
where dt='2020-06-14'
group by recent_days
)t1
join
(
select
recent_days,
sum(if(date_id>=date_add('2020-06-14',-recent_days+1),1,0)) active_user_count
from dwd_user_register_inc lateral view explode(array(1,7,30)) tmp as recent_days
group by recent_days
)t2
on t1.recent_days=t2.recent_days;
1.2.4 用户行为漏斗分析
漏斗分析是一个数据分析模型，它能够科学反映一个业务过程从起点到终点各阶段用户转化情况。由于其能将各阶段环节都展示出来，故哪个阶段存在问题，就能一目了然。
1、建表语句
DROP TABLE IF EXISTS ads_user_action;
CREATE EXTERNAL TABLE ads_user_action
(
`dt` STRING COMMENT '统计日期',
`recent_days` BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
`home_count` BIGINT COMMENT '浏览首页人数',
`good_detail_count` BIGINT COMMENT '浏览商品详情页人数',
`cart_count` BIGINT COMMENT '加入购物车人数',
`order_count` BIGINT COMMENT '下单人数',
`payment_count` BIGINT COMMENT '支付人数'
) COMMENT '漏斗分析'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/ads/ads_user_action/';
2、数据装载
insert overwrite table ads_user_action
select * from ads_user_action
union
select
'2020-06-14' dt,
page.recent_days,
home_count,
good_detail_count,
cart_count,
order_count,
payment_count
from
(
select
1 recent_days,
sum(if(page_id='home',1,0)) home_count,
sum(if(page_id='good_detail',1,0)) good_detail_count
from dws_traffic_page_visitor_page_view_1d
where dt='2020-06-14'
and page_id in ('home','good_detail')
union all
select
recent_days,
sum(if(page_id='home' and view_count>0,1,0)),
sum(if(page_id='good_detail' and view_count>0,1,0))
from
(
select
recent_days,
page_id,
case recent_days
when 7 then view_count_7d
when 30 then view_count_30d
end view_count
from dws_traffic_page_visitor_page_view_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='2020-06-14'
and page_id in ('home','good_detail')
)t1
group by recent_days
)page
join
(
select
1 recent_days,
count(*) cart_count
from dws_trade_user_cart_add_1d
where dt='2020-06-14'
union all
select
recent_days,
sum(if(cart_count>0,1,0))
from
(
select
recent_days,
case recent_days
when 7 then cart_add_count_7d
when 30 then cart_add_count_30d
end cart_count
from dws_trade_user_cart_add_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='2020-06-14'
)t1
group by recent_days
)cart
on page.recent_days=cart.recent_days
join
(
select
1 recent_days,
count(*) order_count
from dws_trade_user_order_1d
where dt='2020-06-14'
union all
select
recent_days,
sum(if(order_count>0,1,0))
from
(
select
recent_days,
case recent_days
when 7 then order_count_7d
when 30 then order_count_30d
end order_count
from dws_trade_user_order_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='2020-06-14'
)t1
group by recent_days
)ord
on page.recent_days=ord.recent_days
join
(
select
1 recent_days,
count(*) payment_count
from dws_trade_user_payment_1d
where dt='2020-06-14'
union all
select
recent_days,
sum(if(order_count>0,1,0))
from
(
select
recent_days,
case recent_days
when 7 then payment_count_7d
when 30 then payment_count_30d
end order_count
from dws_trade_user_payment_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='2020-06-14'
)t1
group by recent_days
)pay
on page.recent_days=pay.recent_days;
1.2.5 新增交易用户统计
1、建表语句
DROP TABLE IF EXISTS ads_new_buyer_stats;
CREATE EXTERNAL TABLE ads_new_buyer_stats
(
`dt` STRING COMMENT '统计日期',
`recent_days` BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
`new_order_user_count` BIGINT COMMENT '新增下单人数',
`new_payment_user_count` BIGINT COMMENT '新增支付人数'
) COMMENT '新增交易用户统计'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/ads/ads_new_buyer_stats/';
2、数据装载
insert overwrite table ads_new_buyer_stats
select * from ads_new_buyer_stats
union
select
'2020-06-14',
odr.recent_days,
new_order_user_count,
new_payment_user_count
from
(
select
recent_days,
sum(if(order_date_first>=date_add('2020-06-14',-recent_days+1),1,0)) new_order_user_count
from dws_trade_user_order_td lateral view explode(array(1,7,30)) tmp as recent_days
where dt='2020-06-14'
group by recent_days
)odr
join
(
select
recent_days,
sum(if(payment_date_first>=date_add('2020-06-14',-recent_days+1),1,0)) new_payment_user_count
from dws_trade_user_payment_td lateral view explode(array(1,7,30)) tmp as recent_days
where dt='2020-06-14'
group by recent_days
)pay
on odr.recent_days=pay.recent_days;
1.3 商品主题
1.3.1 最近7/30日各品牌复购率
1、建表语句
DROP TABLE IF EXISTS ads_repeat_purchase_by_tm;
CREATE EXTERNAL TABLE ads_repeat_purchase_by_tm
(
`dt` STRING COMMENT '统计日期',
`recent_days` BIGINT COMMENT '最近天数,7:最近7天,30:最近30天',
`tm_id` STRING COMMENT '品牌ID',
`tm_name` STRING COMMENT '品牌名称',
`order_repeat_rate` DECIMAL(16, 2) COMMENT '复购率'
) COMMENT '各品牌复购率统计'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/ads/ads_repeat_purchase_by_tm/';
2、数据装载
insert overwrite table ads_repeat_purchase_by_tm
select * from ads_repeat_purchase_by_tm
union
select
'2020-06-14' dt,
recent_days,
tm_id,
tm_name,
cast(sum(if(order_count>=2,1,0))/sum(if(order_count>=1,1,0)) as decimal(16,2))
from
(
select
'2020-06-14' dt,
recent_days,
user_id,
tm_id,
tm_name,
sum(order_count) order_count
from
(
select
recent_days,
user_id,
tm_id,
tm_name,
case recent_days
when 7 then order_count_7d
when 30 then order_count_30d
end order_count
from dws_trade_user_sku_order_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='2020-06-14'
)t1
group by recent_days,user_id,tm_id,tm_name
)t2
group by recent_days,tm_id,tm_name;
1.3.2 各品牌商品交易统计
1、建表语句
DROP TABLE IF EXISTS ads_trade_stats_by_tm;
CREATE EXTERNAL TABLE ads_trade_stats_by_tm
(
`dt` STRING COMMENT '统计日期',
`recent_days` BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
`tm_id` STRING COMMENT '品牌ID',
`tm_name` STRING COMMENT '品牌名称',
`order_count` BIGINT COMMENT '订单数',
`order_user_count` BIGINT COMMENT '订单人数',
`order_refund_count` BIGINT COMMENT '退单数',
`order_refund_user_count` BIGINT COMMENT '退单人数'
) COMMENT '各品牌商品交易统计'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/ads/ads_trade_stats_by_tm/';
2、数据装载
insert overwrite table ads_trade_stats_by_tm
select * from ads_trade_stats_by_tm
union
select
'2020-06-14' dt,
nvl(odr.recent_days,refund.recent_days),
nvl(odr.tm_id,refund.tm_id),
nvl(odr.tm_name,refund.tm_name),
nvl(order_count,0),
nvl(order_user_count,0),
nvl(order_refund_count,0),
nvl(order_refund_user_count,0)
from
(
select
1 recent_days,
tm_id,
tm_name,
sum(order_count_1d) order_count,
count(distinct(user_id)) order_user_count
from dws_trade_user_sku_order_1d
where dt='2020-06-14'
group by tm_id,tm_name
union all
select
recent_days,
tm_id,
tm_name,
sum(order_count),
count(distinct(if(order_count>0,user_id,null)))
from
(
select
recent_days,
user_id,
tm_id,
tm_name,
case recent_days
when 7 then order_count_7d
when 30 then order_count_30d
end order_count
from dws_trade_user_sku_order_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='2020-06-14'
)t1
group by recent_days,tm_id,tm_name
)odr
full outer join
(
select
1 recent_days,
tm_id,
tm_name,
sum(order_refund_count_1d) order_refund_count,
count(distinct(user_id)) order_refund_user_count
from dws_trade_user_sku_order_refund_1d
where dt='2020-06-14'
group by tm_id,tm_name
union all
select
recent_days,
tm_id,
tm_name,
sum(order_refund_count),
count(if(order_refund_count>0,user_id,null))
from
(
select
recent_days,
user_id,
tm_id,
tm_name,
case recent_days
when 7 then order_refund_count_7d
when 30 then order_refund_count_30d
end order_refund_count
from dws_trade_user_sku_order_refund_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='2020-06-14'
)t1
group by recent_days,tm_id,tm_name
)refund
on odr.recent_days=refund.recent_days
and odr.tm_id=refund.tm_id
and odr.tm_name=refund.tm_name;
1.3.3 各品类商品交易统计
1、建表语句
DROP TABLE IF EXISTS ads_trade_stats_by_cate;
CREATE EXTERNAL TABLE ads_trade_stats_by_cate
(
`dt` STRING COMMENT '统计日期',
`recent_days` BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
`category1_id` STRING COMMENT '一级分类id',
`category1_name` STRING COMMENT '一级分类名称',
`category2_id` STRING COMMENT '二级分类id',
`category2_name` STRING COMMENT '二级分类名称',
`category3_id` STRING COMMENT '三级分类id',
`category3_name` STRING COMMENT '三级分类名称',
`order_count` BIGINT COMMENT '订单数',
`order_user_count` BIGINT COMMENT '订单人数',
`order_refund_count` BIGINT COMMENT '退单数',
`order_refund_user_count` BIGINT COMMENT '退单人数'
) COMMENT '各分类商品交易统计'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/ads/ads_trade_stats_by_cate/';
2、数据装载·
insert overwrite table ads_trade_stats_by_cate
select * from ads_trade_stats_by_cate
union
select
'2020-06-14' dt,
nvl(odr.recent_days,refund.recent_days),
nvl(odr.category1_id,refund.category1_id),
nvl(odr.category1_name,refund.category1_name),
nvl(odr.category2_id,refund.category2_id),
nvl(odr.category2_name,refund.category2_name),
nvl(odr.category3_id,refund.category3_id),
nvl(odr.category3_name,refund.category3_name),
nvl(order_count,0),
nvl(order_user_count,0),
nvl(order_refund_count,0),
nvl(order_refund_user_count,0)
from
(
select
1 recent_days,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
sum(order_count_1d) order_count,
count(distinct(user_id)) order_user_count
from dws_trade_user_sku_order_1d
where dt='2020-06-14'
group by category1_id,category1_name,category2_id,category2_name,category3_id,category3_name
union all
select
recent_days,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
sum(order_count),
count(distinct(if(order_count>0,user_id,null)))
from
(
select
recent_days,
user_id,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
case recent_days
when 7 then order_count_7d
when 30 then order_count_30d
end order_count
from dws_trade_user_sku_order_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='2020-06-14'
)t1
group by recent_days,category1_id,category1_name,category2_id,category2_name,category3_id,category3_name
)odr
full outer join
(
select
1 recent_days,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
sum(order_refund_count_1d) order_refund_count,
count(distinct(user_id)) order_refund_user_count
from dws_trade_user_sku_order_refund_1d
where dt='2020-06-14'
group by category1_id,category1_name,category2_id,category2_name,category3_id,category3_name
union all
select
recent_days,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
sum(order_refund_count),
count(distinct(if(order_refund_count>0,user_id,null)))
from
(
select
recent_days,
user_id,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
case recent_days
when 7 then order_refund_count_7d
when 30 then order_refund_count_30d
end order_refund_count
from dws_trade_user_sku_order_refund_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='2020-06-14'
)t1
group by recent_days,category1_id,category1_name,category2_id,category2_name,category3_id,category3_name
)refund
on odr.recent_days=refund.recent_days
and odr.category1_id=refund.category1_id
and odr.category1_name=refund.category1_name
and odr.category2_id=refund.category2_id
and odr.category2_name=refund.category2_name
and odr.category3_id=refund.category3_id
and odr.category3_name=refund.category3_name;
1.3.4 各分类商品购物车存量Top10
1、建表语句
DROP TABLE IF EXISTS ads_sku_cart_num_top3_by_cate;
CREATE EXTERNAL TABLE ads_sku_cart_num_top3_by_cate
(
`dt` STRING COMMENT '统计日期',
`category1_id` STRING COMMENT '一级分类ID',
`category1_name` STRING COMMENT '一级分类名称',
`category2_id` STRING COMMENT '二级分类ID',
`category2_name` STRING COMMENT '二级分类名称',
`category3_id` STRING COMMENT '三级分类ID',
`category3_name` STRING COMMENT '三级分类名称',
`sku_id` STRING COMMENT '商品id',
`sku_name` STRING COMMENT '商品名称',
`cart_num` BIGINT COMMENT '购物车中商品数量',
`rk` BIGINT COMMENT '排名'
) COMMENT '各分类商品购物车存量Top10'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/ads/ads_sku_cart_num_top3_by_cate/';
2、数据装载
insert overwrite table ads_sku_cart_num_top3_by_cate
select * from ads_sku_cart_num_top3_by_cate
union
select
'2020-06-14' dt,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
sku_id,
sku_name,
cart_num,
rk
from
(
select
sku_id,
sku_name,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
cart_num,
rank() over (partition by category1_id,category2_id,category3_id order by cart_num desc) rk
from
(
select
sku_id,
sum(sku_num) cart_num
from dwd_trade_cart_full
where dt='2020-06-14'
group by sku_id
)cart
left join
(
select
id,
sku_name,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name
from dim_sku_full
where dt='2020-06-14'
)sku
on cart.sku_id=sku.id
)t1
where rk<=3;
1.4 交易主题
1.4.1 交易综合统计
1、建表语句
DROP TABLE IF EXISTS ads_trade_stats;
CREATE EXTERNAL TABLE ads_trade_stats
(
`dt` STRING COMMENT '统计日期',
`recent_days` BIGINT COMMENT '最近天数,1:最近1日,7:最近7天,30:最近30天',
`order_total_amount` DECIMAL(16, 2) COMMENT '订单总额,GMV',
`order_count` BIGINT COMMENT '订单数',
`order_user_count` BIGINT COMMENT '下单人数',
`order_refund_count` BIGINT COMMENT '退单数',
`order_refund_user_count` BIGINT COMMENT '退单人数'
) COMMENT '交易统计'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/ads/ads_trade_stats/';
2、数据装载
insert overwrite table ads_trade_stats
select * from ads_trade_stats
union
select
'2020-06-14',
odr.recent_days,
order_total_amount,
order_count,
order_user_count,
order_refund_count,
order_refund_user_count
from
(
select
1 recent_days,
sum(order_total_amount_1d) order_total_amount,
sum(order_count_1d) order_count,
count(*) order_user_count
from dws_trade_user_order_1d
where dt='2020-06-14'
union all
select
recent_days,
sum(order_total_amount),
sum(order_count),
sum(if(order_count>0,1,0))
from
(
select
recent_days,
case recent_days
when 7 then order_total_amount_7d
when 30 then order_total_amount_30d
end order_total_amount,
case recent_days
when 7 then order_count_7d
when 30 then order_count_30d
end order_count
from dws_trade_user_order_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='2020-06-14'
)t1
group by recent_days
)odr
join
(
select
1 recent_days,
sum(order_refund_count_1d) order_refund_count,
count(*) order_refund_user_count
from dws_trade_user_order_refund_1d
where dt='2020-06-14'
union all
select
recent_days,
sum(order_refund_count),
sum(if(order_refund_count>0,1,0))
from
(
select
recent_days,
case recent_days
when 7 then order_refund_count_7d
when 30 then order_refund_count_30d
end order_refund_count
from dws_trade_user_order_refund_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='2020-06-14'
)t1
group by recent_days
)refund
on odr.recent_days=refund.recent_days;
1.4.2 各省份交易统计
1、建表语句
DROP TABLE IF EXISTS ads_order_by_province;
CREATE EXTERNAL TABLE ads_order_by_province
(
`dt` STRING COMMENT '统计日期',
`recent_days` BIGINT COMMENT '最近天数,1:最近1天,7:最近7天,30:最近30天',
`province_id` STRING COMMENT '省份ID',
`province_name` STRING COMMENT '省份名称',
`area_code` STRING COMMENT '地区编码',
`iso_code` STRING COMMENT '国际标准地区编码',
`iso_code_3166_2` STRING COMMENT '国际标准地区编码',
`order_count` BIGINT COMMENT '订单数',
`order_total_amount` DECIMAL(16, 2) COMMENT '订单金额'
) COMMENT '各地区订单统计'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/ads/ads_order_by_province/';
2、数据装载
insert overwrite table ads_order_by_province
select * from ads_order_by_province
union
select
'2020-06-14' dt,
1 recent_days,
province_id,
province_name,
area_code,
iso_code,
iso_3166_2,
order_count_1d,
order_total_amount_1d
from dws_trade_province_order_1d
where dt='2020-06-14'
union
select
'2020-06-14' dt,
recent_days,
province_id,
province_name,
area_code,
iso_code,
iso_3166_2,
sum(order_count),
sum(order_total_amount)
from
(
select
recent_days,
province_id,
province_name,
area_code,
iso_code,
iso_3166_2,
case recent_days
when 7 then order_count_7d
when 30 then order_count_30d
end order_count,
case recent_days
when 7 then order_total_amount_7d
when 30 then order_total_amount_30d
end order_total_amount
from dws_trade_province_order_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='2020-06-14'
)t1
group by recent_days,province_id,province_name,area_code,iso_code,iso_3166_2;
1.5 优惠券主题
1.5.1 最近30天发布的优惠券的补贴率
1、建表语句
DROP TABLE IF EXISTS ads_coupon_stats;
CREATE EXTERNAL TABLE ads_coupon_stats
(
`dt` STRING COMMENT '统计日期',
`coupon_id` STRING COMMENT '优惠券ID',
`coupon_name` STRING COMMENT '优惠券名称',
`start_date` STRING COMMENT '发布日期',
`rule_name` STRING COMMENT '优惠规则，例如满100元减10元',
`reduce_rate` DECIMAL(16, 2) COMMENT '补贴率'
) COMMENT '优惠券统计'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/ads/ads_coupon_stats/';
2、数据装载
insert overwrite table ads_coupon_stats
select * from ads_coupon_stats
union
select
'2020-06-14' dt,
coupon_id,
coupon_name,
start_date,
coupon_rule,
cast(coupon_reduce_amount_30d/original_amount_30d as decimal(16,2))
from dws_trade_coupon_order_nd
where dt='2020-06-14';
1.6 活动主题
1、建表语句
DROP TABLE IF EXISTS ads_activity_stats;
CREATE EXTERNAL TABLE ads_activity_stats
(
`dt` STRING COMMENT '统计日期',
`activity_id` STRING COMMENT '活动ID',
`activity_name` STRING COMMENT '活动名称',
`start_date` STRING COMMENT '活动开始日期',
`reduce_rate` DECIMAL(16, 2) COMMENT '补贴率'
) COMMENT '活动统计'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/ads/ads_activity_stats/';
2、数据装载
insert overwrite table ads_activity_stats
select * from ads_activity_stats
union
select
'2020-06-14' dt,
activity_id,
activity_name,
start_date,
cast(activity_reduce_amount_30d/original_amount_30d as decimal(16,2))
from dws_trade_activity_order_nd
where dt='2020-06-14';
1.7 数据装载脚本
1、每日数据装载脚本
（1）在hadoop102的/home/zhm/bin目录下创建dws_to_ads.sh
（2）编写如下内容
#!/bin/bash
APP=gmall
# 如果是输入的日期按照取输入日期；如果没输入日期取当前时间的前一天
if [ -n "$2" ] ;then
do_date=$2
else
do_date=`date -d "-1 day" +%F`
fi
ads_activity_stats="
insert overwrite table ${APP}.ads_activity_stats
select * from ${APP}.ads_activity_stats
union
select
'$do_date' dt,
activity_id,
activity_name,
start_date,
cast(activity_reduce_amount_30d/original_amount_30d as decimal(16,2))
from ${APP}.dws_trade_activity_order_nd
where dt='$do_date';
"
ads_coupon_stats="
insert overwrite table ${APP}.ads_coupon_stats
select * from ${APP}.ads_coupon_stats
union
select
'$do_date' dt,
coupon_id,
coupon_name,
start_date,
coupon_rule,
cast(coupon_reduce_amount_30d/original_amount_30d as decimal(16,2))
from ${APP}.dws_trade_coupon_order_nd
where dt='$do_date';
"
ads_new_buyer_stats="
insert overwrite table ${APP}.ads_new_buyer_stats
select * from ${APP}.ads_new_buyer_stats
union
select
'$do_date',
odr.recent_days,
new_order_user_count,
new_payment_user_count
from
(
select
recent_days,
sum(if(order_date_first>=date_add('$do_date',-recent_days+1),1,0)) new_order_user_count
from ${APP}.dws_trade_user_order_td lateral view explode(array(1,7,30)) tmp as recent_days
where dt='$do_date'
group by recent_days
)odr
join
(
select
recent_days,
sum(if(payment_date_first>=date_add('$do_date',-recent_days+1),1,0)) new_payment_user_count
from ${APP}.dws_trade_user_payment_td lateral view explode(array(1,7,30)) tmp as recent_days
where dt='$do_date'
group by recent_days
)pay
on odr.recent_days=pay.recent_days;
"
ads_order_by_province="
insert overwrite table ${APP}.ads_order_by_province
select * from ${APP}.ads_order_by_province
union
select
'$do_date' dt,
1 recent_days,
province_id,
province_name,
area_code,
iso_code,
iso_3166_2,
order_count_1d,
order_total_amount_1d
from ${APP}.dws_trade_province_order_1d
where dt='$do_date'
union
select
'$do_date' dt,
recent_days,
province_id,
province_name,
area_code,
iso_code,
iso_3166_2,
sum(order_count),
sum(order_total_amount)
from
(
select
recent_days,
province_id,
province_name,
area_code,
iso_code,
iso_3166_2,
case recent_days
when 7 then order_count_7d
when 30 then order_count_30d
end order_count,
case recent_days
when 7 then order_total_amount_7d
when 30 then order_total_amount_30d
end order_total_amount
from ${APP}.dws_trade_province_order_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='$do_date'
)t1
group by recent_days,province_id,province_name,area_code,iso_code,iso_3166_2;
"
ads_page_path="
insert overwrite table ${APP}.ads_page_path
select * from ${APP}.ads_page_path
union
select
'$do_date' dt,
recent_days,
source,
nvl(target,'null'),
count(*) path_count
from
(
select
recent_days,
concat('step-',rn,':',page_id) source,
concat('step-',rn+1,':',next_page_id) target
from
(
select
recent_days,
page_id,
lead(page_id,1,null) over(partition by session_id,recent_days) next_page_id,
row_number() over (partition by session_id,recent_days order by view_time) rn
from ${APP}.dwd_traffic_page_view_inc lateral view explode(array(1,7,30)) tmp as recent_days
where dt>=date_add('$do_date',-recent_days+1)
)t1
)t2
group by recent_days,source,target;
"
ads_repeat_purchase_by_tm="
insert overwrite table ${APP}.ads_repeat_purchase_by_tm
select * from ${APP}.ads_repeat_purchase_by_tm
union
select
'$do_date' dt,
recent_days,
tm_id,
tm_name,
cast(sum(if(order_count>=2,1,0))/sum(if(order_count>=1,1,0)) as decimal(16,2))
from
(
select
'$do_date' dt,
recent_days,
user_id,
tm_id,
tm_name,
sum(order_count) order_count
from
(
select
recent_days,
user_id,
tm_id,
tm_name,
case recent_days
when 7 then order_count_7d
when 30 then order_count_30d
end order_count
from ${APP}.dws_trade_user_sku_order_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='$do_date'
)t1
group by recent_days,user_id,tm_id,tm_name
)t2
group by recent_days,tm_id,tm_name;
"
ads_sku_cart_num_top3_by_cate="
insert overwrite table ${APP}.ads_sku_cart_num_top3_by_cate
select * from ${APP}.ads_sku_cart_num_top3_by_cate
union
select
'$do_date' dt,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
sku_id,
sku_name,
cart_num,
rk
from
(
select
sku_id,
sku_name,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
cart_num,
rank() over (partition by category1_id,category2_id,category3_id order by cart_num desc) rk
from
(
select
sku_id,
sum(sku_num) cart_num
from ${APP}.dwd_trade_cart_full
where dt='$do_date'
group by sku_id
)cart
left join
(
select
id,
sku_name,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name
from ${APP}.dim_sku_full
where dt='$do_date'
)sku
on cart.sku_id=sku.id
)t1
where rk<=3;
"
ads_trade_stats="
insert overwrite table ${APP}.ads_trade_stats
select * from ${APP}.ads_trade_stats
union
select
'$do_date',
odr.recent_days,
order_total_amount,
order_count,
order_user_count,
order_refund_count,
order_refund_user_count
from
(
select
1 recent_days,
sum(order_total_amount_1d) order_total_amount,
sum(order_count_1d) order_count,
count(*) order_user_count
from ${APP}.dws_trade_user_order_1d
where dt='$do_date'
union all
select
recent_days,
sum(order_total_amount),
sum(order_count),
sum(if(order_count>0,1,0))
from
(
select
recent_days,
case recent_days
when 7 then order_total_amount_7d
when 30 then order_total_amount_30d
end order_total_amount,
case recent_days
when 7 then order_count_7d
when 30 then order_count_30d
end order_count
from ${APP}.dws_trade_user_order_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='$do_date'
)t1
group by recent_days
)odr
join
(
select
1 recent_days,
sum(order_refund_count_1d) order_refund_count,
count(*) order_refund_user_count
from ${APP}.dws_trade_user_order_refund_1d
where dt='$do_date'
union all
select
recent_days,
sum(order_refund_count),
sum(if(order_refund_count>0,1,0))
from
(
select
recent_days,
case recent_days
when 7 then order_refund_count_7d
when 30 then order_refund_count_30d
end order_refund_count
from ${APP}.dws_trade_user_order_refund_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='$do_date'
)t1
group by recent_days
)refund
on odr.recent_days=refund.recent_days;
"
ads_trade_stats_by_cate="
insert overwrite table ${APP}.ads_trade_stats_by_cate
select * from ${APP}.ads_trade_stats_by_cate
union
select
'$do_date' dt,
nvl(odr.recent_days,refund.recent_days),
nvl(odr.category1_id,refund.category1_id),
nvl(odr.category1_name,refund.category1_name),
nvl(odr.category2_id,refund.category2_id),
nvl(odr.category2_name,refund.category2_name),
nvl(odr.category3_id,refund.category3_id),
nvl(odr.category3_name,refund.category3_name),
nvl(order_count,0),
nvl(order_user_count,0),
nvl(order_refund_count,0),
nvl(order_refund_user_count,0)
from
(
select
1 recent_days,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
sum(order_count_1d) order_count,
count(distinct(user_id)) order_user_count
from ${APP}.dws_trade_user_sku_order_1d
where dt='$do_date'
group by category1_id,category1_name,category2_id,category2_name,category3_id,category3_name
union all
select
recent_days,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
sum(order_count),
count(distinct(if(order_count>0,user_id,null)))
from
(
select
recent_days,
user_id,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
case recent_days
when 7 then order_count_7d
when 30 then order_count_30d
end order_count
from ${APP}.dws_trade_user_sku_order_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='$do_date'
)t1
group by recent_days,category1_id,category1_name,category2_id,category2_name,category3_id,category3_name
)odr
full outer join
(
select
1 recent_days,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
sum(order_refund_count_1d) order_refund_count,
count(distinct(user_id)) order_refund_user_count
from ${APP}.dws_trade_user_sku_order_refund_1d
where dt='$do_date'
group by category1_id,category1_name,category2_id,category2_name,category3_id,category3_name
union all
select
recent_days,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
sum(order_refund_count),
count(distinct(if(order_refund_count>0,user_id,null)))
from
(
select
recent_days,
user_id,
category1_id,
category1_name,
category2_id,
category2_name,
category3_id,
category3_name,
case recent_days
when 7 then order_refund_count_7d
when 30 then order_refund_count_30d
end order_refund_count
from ${APP}.dws_trade_user_sku_order_refund_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='$do_date'
)t1
group by recent_days,category1_id,category1_name,category2_id,category2_name,category3_id,category3_name
)refund
on odr.recent_days=refund.recent_days
and odr.category1_id=refund.category1_id
and odr.category1_name=refund.category1_name
and odr.category2_id=refund.category2_id
and odr.category2_name=refund.category2_name
and odr.category3_id=refund.category3_id
and odr.category3_name=refund.category3_name;
"
ads_trade_stats_by_tm="
insert overwrite table ${APP}.ads_trade_stats_by_tm
select * from ${APP}.ads_trade_stats_by_tm
union
select
'$do_date' dt,
nvl(odr.recent_days,refund.recent_days),
nvl(odr.tm_id,refund.tm_id),
nvl(odr.tm_name,refund.tm_name),
nvl(order_count,0),
nvl(order_user_count,0),
nvl(order_refund_count,0),
nvl(order_refund_user_count,0)
from
(
select
1 recent_days,
tm_id,
tm_name,
sum(order_count_1d) order_count,
count(distinct(user_id)) order_user_count
from ${APP}.dws_trade_user_sku_order_1d
where dt='$do_date'
group by tm_id,tm_name
union all
select
recent_days,
tm_id,
tm_name,
sum(order_count),
count(distinct(if(order_count>0,user_id,null)))
from
(
select
recent_days,
user_id,
tm_id,
tm_name,
case recent_days
when 7 then order_count_7d
when 30 then order_count_30d
end order_count
from ${APP}.dws_trade_user_sku_order_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='$do_date'
)t1
group by recent_days,tm_id,tm_name
)odr
full outer join
(
select
1 recent_days,
tm_id,
tm_name,
sum(order_refund_count_1d) order_refund_count,
count(distinct(user_id)) order_refund_user_count
from ${APP}.dws_trade_user_sku_order_refund_1d
where dt='$do_date'
group by tm_id,tm_name
union all
select
recent_days,
tm_id,
tm_name,
sum(order_refund_count),
count(if(order_refund_count>0,user_id,null))
from
(
select
recent_days,
user_id,
tm_id,
tm_name,
case recent_days
when 7 then order_refund_count_7d
when 30 then order_refund_count_30d
end order_refund_count
from ${APP}.dws_trade_user_sku_order_refund_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='$do_date'
)t1
group by recent_days,tm_id,tm_name
)refund
on odr.recent_days=refund.recent_days
and odr.tm_id=refund.tm_id
and odr.tm_name=refund.tm_name;
"
ads_traffic_stats_by_channel="
insert overwrite table ${APP}.ads_traffic_stats_by_channel
select * from ${APP}.ads_traffic_stats_by_channel
union
select
'$do_date' dt,
recent_days,
channel,
cast(count(distinct(mid_id)) as bigint) uv_count,
cast(avg(during_time_1d)/1000 as bigint) avg_duration_sec,
cast(avg(page_count_1d) as bigint) avg_page_count,
cast(count(*) as bigint) sv_count,
cast(sum(if(page_count_1d=1,1,0))/count(*) as decimal(16,2)) bounce_rate
from ${APP}.dws_traffic_session_page_view_1d lateral view explode(array(1,7,30)) tmp as recent_days
where dt>=date_add('$do_date',-recent_days+1)
group by recent_days,channel;
"
ads_user_action="
insert overwrite table ${APP}.ads_user_action
select * from ${APP}.ads_user_action
union
select
'$do_date' dt,
page.recent_days,
home_count,
good_detail_count,
cart_count,
order_count,
payment_count
from
(
select
1 recent_days,
sum(if(page_id='home',1,0)) home_count,
sum(if(page_id='good_detail',1,0)) good_detail_count
from ${APP}.dws_traffic_page_visitor_page_view_1d
where dt='$do_date'
and page_id in ('home','good_detail')
union all
select
recent_days,
sum(if(page_id='home' and view_count>0,1,0)),
sum(if(page_id='good_detail' and view_count>0,1,0))
from
(
select
recent_days,
page_id,
case recent_days
when 7 then view_count_7d
when 30 then view_count_30d
end view_count
from ${APP}.dws_traffic_page_visitor_page_view_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='$do_date'
and page_id in ('home','good_detail')
)t1
group by recent_days
)page
join
(
select
1 recent_days,
count(*) cart_count
from ${APP}.dws_trade_user_cart_add_1d
where dt='$do_date'
union all
select
recent_days,
sum(if(cart_count>0,1,0))
from
(
select
recent_days,
case recent_days
when 7 then cart_add_count_7d
when 30 then cart_add_count_30d
end cart_count
from ${APP}.dws_trade_user_cart_add_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='$do_date'
)t1
group by recent_days
)cart
on page.recent_days=cart.recent_days
join
(
select
1 recent_days,
count(*) order_count
from ${APP}.dws_trade_user_order_1d
where dt='$do_date'
union all
select
recent_days,
sum(if(order_count>0,1,0))
from
(
select
recent_days,
case recent_days
when 7 then order_count_7d
when 30 then order_count_30d
end order_count
from ${APP}.dws_trade_user_order_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='$do_date'
)t1
group by recent_days
)ord
on page.recent_days=ord.recent_days
join
(
select
1 recent_days,
count(*) payment_count
from ${APP}.dws_trade_user_payment_1d
where dt='$do_date'
union all
select
recent_days,
sum(if(order_count>0,1,0))
from
(
select
recent_days,
case recent_days
when 7 then payment_count_7d
when 30 then payment_count_30d
end order_count
from ${APP}.dws_trade_user_payment_nd lateral view explode(array(7,30)) tmp as recent_days
where dt='$do_date'
)t1
group by recent_days
)pay
on page.recent_days=pay.recent_days;
"
ads_user_change="
insert overwrite table ${APP}.ads_user_change
select * from ${APP}.ads_user_change
union
select
churn.dt,
user_churn_count,
user_back_count
from
(
select
'$do_date' dt,
count(*) user_churn_count
from ${APP}.dws_user_user_login_td
where dt='$do_date'
and login_date_last=date_add('$do_date',-7)
)churn
join
(
select
'$do_date' dt,
count(*) user_back_count
from
(
select
user_id,
login_date_last
from ${APP}.dws_user_user_login_td
where dt='$do_date'
)t1
join
(
select
user_id,
login_date_last login_date_previous
from ${APP}.dws_user_user_login_td
where dt=date_add('$do_date',-1)
)t2
on t1.user_id=t2.user_id
where datediff(login_date_last,login_date_previous)>=8
)back
on churn.dt=back.dt;
"
ads_user_retention="
insert overwrite table ${APP}.ads_user_retention
select * from ${APP}.ads_user_retention
union
select
'$do_date' dt,
login_date_first create_date,
datediff('$do_date',login_date_first) retention_day,
sum(if(login_date_last='$do_date',1,0)) retention_count,
count(*) new_user_count,
cast(sum(if(login_date_last='$do_date',1,0))/count(*)*100 as decimal(16,2)) retention_rate
from
(
select
user_id,
date_id login_date_first
from ${APP}.dwd_user_register_inc
where dt>=date_add('$do_date',-7)
and dt<'$do_date'
)t1
join
(
select
user_id,
login_date_last
from ${APP}.dws_user_user_login_td
where dt='$do_date'
)t2
on t1.user_id=t2.user_id
group by login_date_first;
"
ads_user_stats="
insert overwrite table ${APP}.ads_user_stats
select * from ${APP}.ads_user_stats
union
select
'$do_date' dt,
t1.recent_days,
new_user_count,
active_user_count
from
(
select
recent_days,
sum(if(login_date_last>=date_add('$do_date',-recent_days+1),1,0)) new_user_count
from ${APP}.dws_user_user_login_td lateral view explode(array(1,7,30)) tmp as recent_days
where dt='$do_date'
group by recent_days
)t1
join
(
select
recent_days,
sum(if(date_id>=date_add('$do_date',-recent_days+1),1,0)) active_user_count
from ${APP}.dwd_user_register_inc lateral view explode(array(1,7,30)) tmp as recent_days
group by recent_days
)t2
on t1.recent_days=t2.recent_days;
"
case $1 in
"ads_activity_stats" )
hive -e "$ads_activity_stats"
;;
"ads_coupon_stats" )
hive -e "$ads_coupon_stats"
;;
"ads_new_buyer_stats" )
hive -e "$ads_new_buyer_stats"
;;
"ads_order_by_province" )
hive -e "$ads_order_by_province"
;;
"ads_page_path" )
hive -e "$ads_page_path"
;;
"ads_repeat_purchase_by_tm" )
hive -e "$ads_repeat_purchase_by_tm"
;;
"ads_sku_cart_num_top3_by_cate" )
hive -e "$ads_sku_cart_num_top3_by_cate"
;;
"ads_trade_stats" )
hive -e "$ads_trade_stats"
;;
"ads_trade_stats_by_cate" )
hive -e "$ads_trade_stats_by_cate"
;;
"ads_trade_stats_by_tm" )
hive -e "$ads_trade_stats_by_tm"
;;
"ads_traffic_stats_by_channel" )
hive -e "$ads_traffic_stats_by_channel"
;;
"ads_user_action" )
hive -e "$ads_user_action"
;;
"ads_user_change" )
hive -e "$ads_user_change"
;;
"ads_user_retention" )
hive -e "$ads_user_retention"
;;
"ads_user_stats" )
hive -e "$ads_user_stats"
;;
"all" )
hive -e "$ads_activity_stats$ads_coupon_stats$ads_new_buyer_stats$ads_order_by_province$ads_page_path$ads_repeat_purchase_by_tm$ads_sku_cart_num_top3_by_cate$ads_trade_stats$ads_trade_stats_by_cate$ads_trade_stats_by_tm$ads_traffic_stats_by_channel$ads_user_action$ads_user_change$ads_user_retention$ads_user_stats"
;;
esac
（3）增加脚本执行权限
（4）脚本用法
dws_to_ads.sh all 2020-06-14
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