数仓项目（九）数仓搭建 - DWS 层

问题导读

1.DWS 层有哪些专业术语？
2.系统函数有哪些？
3.nvl 函数基本语法是什么？
4.DWS 层（业务）包含哪些内容？


上一篇
企业级数据仓库构建（八）：搭建DWD 层-业务数据
https://www.aboutyun.com/forum.php?mod=viewthread&tid=29557



一、数仓搭建 - DWS 层
1.1 业务术语
1）用户
用户以设备为判断标准，在移动统计中，每个独立设备认为是一个独立用户。Android
系统根据 IMEI 号，IOS 系统根据 OpenUDID 来标识一个独立用户，每部手机一个用户

2）新增用户
首次联网使用应用的用户。如果一个用户首次打开某 APP，那这个用户定义为新增用
户；卸载再安装的设备，不会被算作一次新增。新增用户包括日新增用户、周新增用户、月
新增用户

3）活跃用户
打开应用的用户即为活跃用户，不考虑用户的使用情况。每天一台设备打开多次会被计
为一个活跃用户

4）周（月）活跃用户
某个自然周（月）内启动过应用的用户，该周（月）内的多次启动只记一个活跃用户

5）月活跃率
月活跃用户与截止到该月累计的用户总和之间的比例

6）沉默用户
用户仅在安装当天（次日）启动一次，后续时间无再启动行为。该指标可以反映新增用
户质量和用户与 APP 的匹配程度

7）版本分布
不同版本的周内各天新增用户数，活跃用户数和启动次数。利于判断 APP 各个版本之
间的优劣和用户行为习惯

8）本周回流用户
上周未启动过应用，本周启动了应用的用户

9）连续 n 周活跃用户
连续 n 周，每周至少启动一次

10）忠诚用户
连续活跃 5 周以上的用户

11）连续活跃用户
连续 2 周及以上活跃的用户

12）近期流失用户
连续 n（2<= n <= 4）周没有启动应用的用户。（第 n+1 周没有启动过）

13）留存用户
某段时间内的新增用户，经过一段时间后，仍然使用应用的被认作是留存用户；这部分
用户占当时新增用户的比例即是留存率
例如，5 月份新增用户 200，这 200 人在 6 月份启动过应用的有 100 人，7 月份启动过应用的有 80 人，8 月份启动过应用的有 50 人；则 5 月份新增用户一个月后的留存率是 50%，二个月后的留存率是 40%，三个月后的留存率是 25%

14）用户新鲜度
每天启动应用的新老用户比例，即新增用户数占活跃用户数的比例

15）单次使用时长
每次启动使用的时间长度

16）日使用时长
累计一天内的使用时间长度

17）启动次数计算标准
IOS 平台应用退到后台就算一次独立的启动；Android 平台我们规定，两次启动之间的间隔小于 30 秒，被计算一次启动。用户在使用过程中，若因收发短信或接电话等退出应用30 秒又再次返回应用中，那这两次行为应该是延续而非独立的，所以可以被算作一次使用行为，即一次启动。业内大多使用 30 秒这个标准，但用户还是可以自定义此时间间隔


1.2 系统函数
1.2.1 collect_set 函数
1）创建原数据表

drop table if exists stud;
create table stud (name string, area string, course string, score int);
复制代码

2）向原数据表中插入数据

insert into table stud values('zhang3','bj','math',88);
insert into table stud values('li4','bj','math',99);
insert into table stud values('wang5','sh','chinese',92);
insert into table stud values('zhao6','sh','chinese',54);
insert into table stud values('tian7','bj','chinese',91);
复制代码

3）查询表中数据

select * from stud;

stud.name stud.area stud.course stud.score
zhang3 bj math 88
li4 bj math 99
wang5 sh chinese 92
zhao6 sh chinese 54
tian7 bj chinese 91
复制代码

4）把同一分组的不同行的数据聚合成一个集合

select course, collect_set(area), avg(score) from stud group by course;

chinese ["sh","bj"] 79.0
math ["bj"] 93.5
复制代码

5） 用下标可以取某一个

select course, collect_set(area)[0], avg(score) from

stud group by course;
chinese sh 79.0
math bj 93.5
复制代码

1.2.2 nvl 函数
1）基本语法

NVL（表达式 1，表达式 2）

如果表达式 1 为空值，NVL 返回值为表达式 2 的值，否则返回表达式 1 的值。 该函数的目的是把一个空值（null）转换成一个实际的值。其表达式的值可以是数字型、字符型和日期型。但是表达式 1 和表达式 2 的数据类型必须为同一个类型

1.2.3 日期处理函数
1）date_format 函数（根据格式整理日期）

hive (gmall)> select date_format('2020-03-10','yyyy-MM');
2020-03
复制代码

2）date_add 函数（加减日期）

hive (gmall)> select date_add('2020-03-10',-1);
2020-03-09
hive (gmall)> select date_add('2020-03-10',1);
2020-03-11
复制代码

3）next_day 函数
（1）取当前天的下一个周一

hive (gmall)> select next_day('2020-03-12','MO');
2020-03-16

说明：星期一到星期日的英文（Monday，Tuesday、Wednesday、Thursday、Friday、Saturday、Sunday）
复制代码

（2）取当前周的周一

hive (gmall)> select date_add(next_day('2020-03-12','MO'),-7);
2020-03-11
复制代码

4）last_day 函数（求当月最后一天日期）

hive (gmall)> select last_day('2020-03-10');
2020-03-31
复制代码

1.3 DWS 层（用户行为）
1.3.1 每日设备行为
每日设备行为，主要按照 设备 id 统计




1）建表语句

drop table if exists dws_uv_detail_daycount;
create external table dws_uv_detail_daycount
(
`mid_id` string COMMENT '设备唯一标识',
`user_id` string COMMENT '用户标识',
`version_code` string COMMENT '程序版本号',
`version_name` string COMMENT '程序版本名',
`lang` string COMMENT '系统语言',
`source` string COMMENT '渠道号',
`os` string COMMENT '安卓系统版本',
`area` string COMMENT '区域',
`model` string COMMENT '手机型号',
`brand` string COMMENT '手机品牌',
`sdk_version` string COMMENT 'sdkVersion',
`gmail` string COMMENT 'gmail',
`height_width` string COMMENT '屏幕宽高',
`app_time` string COMMENT '客户端日志产生时的时间',
`network` string COMMENT '网络模式',
`lng` string COMMENT '经度',
`lat` string COMMENT '纬度',
`login_count` bigint COMMENT '活跃次数'
)
partitioned by(dt string)
stored as parquet
location '/warehouse/gmall/dws/dws_uv_detail_daycount';
复制代码

2）数据装载

insert overwrite table dws_uv_detail_daycount partition(dt='2020-03-10')
select
mid_id,
concat_ws('|', collect_set(user_id)) user_id,
concat_ws('|', collect_set(version_code)) version_code,
concat_ws('|', collect_set(version_name)) version_name,
concat_ws('|', collect_set(lang))lang,
concat_ws('|', collect_set(source)) source,
concat_ws('|', collect_set(os)) os,
concat_ws('|', collect_set(area)) area,
concat_ws('|', collect_set(model)) model,
concat_ws('|', collect_set(brand)) brand,
concat_ws('|', collect_set(sdk_version)) sdk_version,
concat_ws('|', collect_set(gmail)) gmail,
concat_ws('|', collect_set(height_width)) height_width,
concat_ws('|', collect_set(app_time)) app_time,
concat_ws('|', collect_set(network)) network,
concat_ws('|', collect_set(lng)) lng,
concat_ws('|', collect_set(lat)) lat,
count(*) login_count
from dwd_start_log
where dt='2020-03-10'
group by mid_id;
复制代码

3）查询加载结果

select * from dws_uv_detail_daycount where dt='2020-03-10';
复制代码

1.4 DWS 层（业务）
DWS 层的宽表字段，是站在不同维度的视角去看事实表，重点关注事实表的度量值




1.4.1 每日会员行为
1）建表语句

drop table if exists dws_user_action_daycount;
create external table dws_user_action_daycount
(
user_id string comment '用户 id',
login_count bigint comment '登录次数',
cart_count bigint comment '加入购物车次数',
cart_amount double comment '加入购物车金额',
order_count bigint comment '下单次数',
order_amount decimal(16,2) comment '下单金额',
payment_count bigint comment '支付次数',
payment_amount decimal(16,2) comment '支付金额'
) COMMENT '每日用户行为'
PARTITIONED BY (`dt` string)
stored as parquet
location '/warehouse/gmall/dws/dws_user_action_daycount/'
tblproperties ("parquet.compression"="lzo");
复制代码

2）数据装载

with
tmp_login as
(
select
user_id,
count(*) login_count
from dwd_start_log
where dt='2020-03-10'
and user_id is not null
group by user_id
),
tmp_cart as
(
select
user_id,
count(*) cart_count,
sum(cart_price*sku_num) cart_amount
from dwd_fact_cart_info
where dt='2020-03-10'
and user_id is not null
and date_format(create_time,'yyyy-MM-dd')='2020-03-10'
group by user_id
),
tmp_order as
(
select
user_id,
count(*) order_count,
sum(final_total_amount) order_amount
from dwd_fact_order_info
where dt='2020-03-10'
group by user_id
) ,
tmp_payment as
(
select
user_id,
count(*) payment_count,
sum(payment_amount) payment_amount
from dwd_fact_payment_info
where dt='2020-03-10'
group by user_id
)
insert overwrite table dws_user_action_daycount partition(dt='2020-03-10')
select
user_actions.user_id,
sum(user_actions.login_count),
sum(user_actions.cart_count),
sum(user_actions.cart_amount),
sum(user_actions.order_count),
sum(user_actions.order_amount),
sum(user_actions.payment_count),
sum(user_actions.payment_amount)
from
(
select
user_id,
login_count,
0 cart_count,
0 cart_amount,
0 order_count,
0 order_amount,
0 payment_count,
0 payment_amount
from
tmp_login
union all
select
user_id,
0 login_count,
cart_count,
cart_amount,
0 order_count,
0 order_amount,
0 payment_count,
0 payment_amount
from
tmp_cart
union all
select
user_id,
0 login_count,
0 cart_count,
0 cart_amount,
order_count,
order_amount,
0 payment_count,
0 payment_amount
from tmp_order
union all
select
user_id,
0 login_count,
0 cart_count,
0 cart_amount,
0 order_count,
0 order_amount,
payment_count,
payment_amount
from tmp_payment
) user_actions
group by user_id;
复制代码

3）查询加载结果
hive (gmall)> select * from dws_user_action_daycount where dt=‘2020-03-10’;

1.4.2 每日商品行为

1）建表语句

drop table if exists dws_sku_action_daycount;
create external table dws_sku_action_daycount
(
sku_id string comment 'sku_id',
order_count bigint comment '被下单次数',
order_num bigint comment '被下单件数',
order_amount decimal(16,2) comment '被下单金额',
payment_count bigint comment '被支付次数',
payment_num bigint comment '被支付件数',
payment_amount decimal(16,2) comment '被支付金额',
refund_count bigint comment '被退款次数',
refund_num bigint comment '被退款件数',
refund_amount decimal(16,2) comment '被退款金额',
cart_count bigint comment '被加入购物车次数',
cart_num bigint comment '被加入购物车件数',
favor_count bigint comment '被收藏次数',
appraise_good_count bigint comment '好评数',
appraise_mid_count bigint comment '中评数',
appraise_bad_count bigint comment '差评数',
appraise_default_count bigint comment '默认评价数'
) COMMENT '每日商品行为'
PARTITIONED BY (`dt` string)
stored as parquet
location '/warehouse/gmall/dws/dws_sku_action_daycount/'
tblproperties ("parquet.compression"="lzo");
复制代码

2）数据装载
注意：如果是 23 点 59 下单，支付日期跨天。需要从订单详情里面取出支付时间是今天，订单时间是昨天或者今天的订单

with
tmp_order as
(
select
sku_id,
count(*) order_count,
sum(sku_num) order_num,
sum(total_amount) order_amount
from dwd_fact_order_detail
where dt='2020-03-10'
group by sku_id
),
tmp_payment as
(
select
sku_id,
count(*) payment_count,
sum(sku_num) payment_num,
sum(total_amount) payment_amount
from dwd_fact_order_detail
where dt='2020-03-10'
and order_id in
(
select
id
from dwd_fact_order_info
where (dt='2020-03-10' or dt=date_add('2020-03-10',-1))
and date_format(payment_time,'yyyy-MM-dd')='2020-03-10'
)
group by sku_id
),
tmp_refund as
(
select
sku_id,
count(*) refund_count,
sum(refund_num) refund_num,
sum(refund_amount) refund_amount
from dwd_fact_order_refund_info
where dt='2020-03-10'
group by sku_id
),
tmp_cart as
(
select
sku_id,
count(*) cart_count,
sum(sku_num) cart_num
from dwd_fact_cart_info
where dt='2020-03-10'
and date_format(create_time,'yyyy-MM-dd')='2020-03-10'
group by sku_id
),
tmp_favor as
(
select
sku_id,
count(*) favor_count
from dwd_fact_favor_info
where dt='2020-03-10'
and date_format(create_time,'yyyy-MM-dd')='2020-03-10'
group by sku_id
),
tmp_appraise as
(
select
sku_id,
sum(if(appraise='1201',1,0)) appraise_good_count,
sum(if(appraise='1202',1,0)) appraise_mid_count,
sum(if(appraise='1203',1,0)) appraise_bad_count,
sum(if(appraise='1204',1,0)) appraise_default_count
from dwd_fact_comment_info
where dt='2020-03-10'
group by sku_id
)
insert overwrite table dws_sku_action_daycount partition(dt='2020-03-10')
select
sku_id,
sum(order_count),
sum(order_num),
sum(order_amount),
sum(payment_count),
sum(payment_num),
sum(payment_amount),
sum(refund_count),
sum(refund_num),
sum(refund_amount),
sum(cart_count),
sum(cart_num),
sum(favor_count),
sum(appraise_good_count),
sum(appraise_mid_count),
sum(appraise_bad_count),
sum(appraise_default_count)
from
(
select
sku_id,
order_count,
order_num,
order_amount,
0 payment_count,
0 payment_num,
0 payment_amount,
0 refund_count,
0 refund_num,
0 refund_amount,
0 cart_count,
0 cart_num,
0 favor_count,
0 appraise_good_count,
0 appraise_mid_count,
0 appraise_bad_count,
0 appraise_default_count
from tmp_order
union all
select
sku_id,
0 order_count,
0 order_num,
0 order_amount,
payment_count,
payment_num,
payment_amount,
0 refund_count,
0 refund_num,
0 refund_amount,
0 cart_count,
0 cart_num,
0 favor_count,
0 appraise_good_count,
0 appraise_mid_count,
0 appraise_bad_count,
0 appraise_default_count
from tmp_payment
union all
select
sku_id,
0 order_count,
0 order_num,
0 order_amount,
0 payment_count,
0 payment_num,
0 payment_amount,
refund_count,
refund_num,
refund_amount,
0 cart_count,
0 cart_num,
0 favor_count,
0 appraise_good_count,
0 appraise_mid_count,
0 appraise_bad_count,
0 appraise_default_count
from tmp_refund
union all
select
sku_id,
0 order_count,
0 order_num,
0 order_amount,
0 payment_count,
0 payment_num,
0 payment_amount,
0 refund_count,
0 refund_num,
0 refund_amount,
cart_count,
cart_num,
0 favor_count,
0 appraise_good_count,
0 appraise_mid_count,
0 appraise_bad_count,
0 appraise_default_count
from tmp_cart
union all
select
sku_id,
0 order_count,
0 order_num,
0 order_amount,
0 payment_count,
0 payment_num,
0 payment_amount,
0 refund_count,
0 refund_num,
0 refund_amount,
0 cart_count,
0 cart_num,
favor_count,
0 appraise_good_count,
0 appraise_mid_count,
0 appraise_bad_count,
0 appraise_default_count
from tmp_favor
union all
select
sku_id,
0 order_count,
0 order_num,
0 order_amount,
0 payment_count,
0 payment_num,
0 payment_amount,
0 refund_count,
0 refund_num,
0 refund_amount,
0 cart_count,
0 cart_num,
0 favor_count,
appraise_good_count,
appraise_mid_count,
appraise_bad_count,
appraise_default_count
from tmp_appraise
)tmp
group by sku_id;
复制代码

3）查询加载结果

select * from dws_sku_action_daycount where dt='2020-03-10';
复制代码

1.4.5 每日购买行为




1）建表语句

drop table if exists dws_sale_detail_daycount;
create external table dws_sale_detail_daycount
(
user_id string comment '用户 id',
sku_id string comment '商品 id',
user_gender string comment '用户性别',
user_age string comment '用户年龄',
user_level string comment '用户等级',
order_price decimal(10,2) comment '商品价格',
sku_name string comment '商品名称',
sku_tm_id string comment '品牌 id',
sku_category3_id string comment '商品三级品类 id',
sku_category2_id string comment '商品二级品类 id',
sku_category1_id string comment '商品一级品类 id',
sku_category3_name string comment '商品三级品类名称',
sku_category2_name string comment '商品二级品类名称',
sku_category1_name string comment '商品一级品类名称',
spu_id string comment '商品 spu',
sku_num int comment '购买个数',
order_count bigint comment '当日下单单数',
order_amount decimal(16,2) comment '当日下单金额'
) COMMENT '每日购买行为'
PARTITIONED BY (`dt` string)
stored as parquet
location '/warehouse/gmall/dws/dws_sale_detail_daycount/'
tblproperties ("parquet.compression"="lzo");
复制代码

2）数据装载

insert overwrite table dws_sale_detail_daycount partition(dt='2020-03-10')
select
op.user_id,
op.sku_id,
ui.gender,
months_between('2020-03-10', ui.birthday)/12 age,
ui.user_level,
si.price,
si.sku_name,
si.tm_id,
si.category3_id,
si.category2_id,
si.category1_id,
si.category3_name,
si.category2_name,
si.category1_name,
si.spu_id,
op.sku_num,
op.order_count,
op.order_amount
from
(
select
user_id,
sku_id,
sum(sku_num) sku_num,
count(*) order_count,
sum(total_amount) order_amount
from dwd_fact_order_detail
where dt='2020-03-10'
group by user_id, sku_id
)op
join
(
select
*
from dwd_dim_user_info_his
where end_date='9999-99-99'
)ui on op.user_id = ui.id
join
(
select
*
from dwd_dim_sku_info
where dt='2020-03-10'
)si on op.sku_id = si.id;
复制代码

3）查询加载结果

select * from dws_sale_detail_daycount where dt='2020-03-10';
复制代码

1.5 DWS 层数据导入脚本
1）vim dwd_to_dws.sh
在脚本中填写如下内容

#!/bin/bash
APP=gmall
hive=/opt/modules/hive/bin/hive
# 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 "$1" ] ;then
do_date=$1
else
do_date=`date -d "-1 day" +%F`
fi
sql="
insert overwrite table ${APP}.dws_uv_detail_daycount partition(dt='$do_date')
select
mid_id,
concat_ws('|', collect_set(user_id)) user_id,
concat_ws('|', collect_set(version_code)) version_code,
concat_ws('|', collect_set(version_name)) version_name,
concat_ws('|', collect_set(lang))lang,
concat_ws('|', collect_set(source)) source,
concat_ws('|', collect_set(os)) os,
concat_ws('|', collect_set(area)) area,
concat_ws('|', collect_set(model)) model,
concat_ws('|', collect_set(brand)) brand,
concat_ws('|', collect_set(sdk_version)) sdk_version,
concat_ws('|', collect_set(gmail)) gmail,
concat_ws('|', collect_set(height_width)) height_width,
concat_ws('|', collect_set(app_time)) app_time,
concat_ws('|', collect_set(network)) network,
concat_ws('|', collect_set(lng)) lng,
concat_ws('|', collect_set(lat)) lat,
count(*) login_count
from ${APP}.dwd_start_log
where dt='$do_date'
group by mid_id;
with
tmp_login as
(
select
user_id,
count(*) login_count
from ${APP}.dwd_start_log
where dt='$do_date'
and user_id is not null
group by user_id
),
tmp_cart as
(
select
user_id,
count(*) cart_count,
sum(cart_price*sku_num) cart_amount
from ${APP}.dwd_fact_cart_info
where dt='$do_date'
and user_id is not null
and date_format(create_time,'yyyy-MM-dd')='$do_date'
group by user_id
),
tmp_order as
(
select
user_id,
count(*) order_count,
sum(final_total_amount) order_amount
from ${APP}.dwd_fact_order_info
where dt='$do_date'
group by user_id
) ,
tmp_payment as
(
select
user_id,
count(*) payment_count,
sum(payment_amount) payment_amount
from ${APP}.dwd_fact_payment_info
where dt='$do_date'
group by user_id
)


insert overwrite table ${APP}.dws_user_action_daycount partition(dt='$do_date')
select
user_actions.user_id,
sum(user_actions.login_count),
sum(user_actions.cart_count),
sum(user_actions.cart_amount),
sum(user_actions.order_count),
sum(user_actions.order_amount),
sum(user_actions.payment_count),
sum(user_actions.payment_amount)
from
(
select
user_id,
login_count,
0 cart_count,
0 cart_amount,
0 order_count,
0 order_amount,
0 payment_count,
0 payment_amount
from
tmp_login
union all
select
user_id,
0 login_count,
cart_count,
cart_amount,
0 order_count,
0 order_amount,
0 payment_count,
0 payment_amount
from
tmp_cart
union all
select
user_id,
0 login_count,
0 cart_count,
0 cart_amount,
order_count,
order_amount,
0 payment_count,
0 payment_amount
from tmp_order
union all
select
user_id,
0 login_count,
0 cart_count,
0 cart_amount,
0 order_count,
0 order_amount,
payment_count,
payment_amount
from tmp_payment
) user_actions
group by user_id;
with
tmp_order as
(
select
sku_id,
count(*) order_count,
sum(sku_num) order_num,
sum(total_amount) order_amount
from ${APP}.dwd_fact_order_detail
where dt='$do_date'
group by sku_id
),
tmp_payment as
(
select
sku_id,
count(*) payment_count,
sum(sku_num) payment_num,
sum(total_amount) payment_amount
from ${APP}.dwd_fact_order_detail
where dt='$do_date'
and order_id in
(
select
id
from ${APP}.dwd_fact_order_info
where (dt='$do_date' or dt=date_add('$do_date',-1))
and date_format(payment_time,'yyyy-MM-dd')='$do_date'
)
group by sku_id
),
tmp_refund as
(
select
sku_id,
count(*) refund_count,
sum(refund_num) refund_num,
sum(refund_amount) refund_amount
from ${APP}.dwd_fact_order_refund_info
where dt='$do_date'
group by sku_id
),
tmp_cart as
(
select
sku_id,
count(*) cart_count,
sum(sku_num) cart_num
from ${APP}.dwd_fact_cart_info
where dt='$do_date'
and date_format(create_time,'yyyy-MM-dd')='$do_date'
group by sku_id
),
tmp_favor as
(
select
sku_id,
count(*) favor_count
from ${APP}.dwd_fact_favor_info
where dt='$do_date'
and date_format(create_time,'yyyy-MM-dd')='$do_date'
group by sku_id
),
tmp_appraise as
(
select
sku_id,
sum(if(appraise='1201',1,0)) appraise_good_count,
sum(if(appraise='1202',1,0)) appraise_mid_count,
sum(if(appraise='1203',1,0)) appraise_bad_count,
sum(if(appraise='1204',1,0)) appraise_default_count
from ${APP}.dwd_fact_comment_info
where dt='$do_date'
group by sku_id
)


insert overwrite table ${APP}.dws_sku_action_daycount partition(dt='$do_date')
select
sku_id,
sum(order_count),
sum(order_num),
sum(order_amount),
sum(payment_count),
sum(payment_num),
sum(payment_amount),
sum(refund_count),
sum(refund_num),
sum(refund_amount),
sum(cart_count),
sum(cart_num),
sum(favor_count),
sum(appraise_good_count),
sum(appraise_mid_count),
sum(appraise_bad_count),
sum(appraise_default_count)
from
(
select
sku_id,
order_count,
order_num,
order_amount,
0 payment_count,
0 payment_num,
0 payment_amount,
0 refund_count,
0 refund_num,
0 refund_amount,
0 cart_count,
0 cart_num,
0 favor_count,
0 appraise_good_count,
0 appraise_mid_count,
0 appraise_bad_count,
0 appraise_default_count
from tmp_order
union all
select
sku_id,
0 order_count,
0 order_num,
0 order_amount,
payment_count,
payment_num,
payment_amount,
0 refund_count,
0 refund_num,
0 refund_amount,
0 cart_count,
0 cart_num,
0 favor_count,
0 appraise_good_count,
0 appraise_mid_count,
0 appraise_bad_count,
0 appraise_default_count
from tmp_payment
union all
select
sku_id,
0 order_count,
0 order_num,
0 order_amount,
0 payment_count,
0 payment_num,
0 payment_amount,
refund_count,
refund_num,
refund_amount,
0 cart_count,
0 cart_num,
0 favor_count,
0 appraise_good_count,
0 appraise_mid_count,
0 appraise_bad_count,
0 appraise_default_count
from tmp_refund
union all
select
sku_id,
0 order_count,
0 order_num,
0 order_amount,
0 payment_count,
0 payment_num,
0 payment_amount,
0 refund_count,
0 refund_num,
0 refund_amount,
cart_count,
cart_num,
0 favor_count,
0 appraise_good_count,
0 appraise_mid_count,
0 appraise_bad_count,
0 appraise_default_count
from tmp_cart
union all
select
sku_id,
0 order_count,
0 order_num,
0 order_amount,
0 payment_count,
0 payment_num,
0 payment_amount,
0 refund_count,
0 refund_num,
0 refund_amount,
0 cart_count,
0 cart_num,
favor_count,
0 appraise_good_count,
0 appraise_mid_count,
0 appraise_bad_count,
0 appraise_default_count
from tmp_favor
union all
select
sku_id,
0 order_count,
0 order_num,
0 order_amount,
0 payment_count,
0 payment_num,
0 payment_amount,
0 refund_count,
0 refund_num,
0 refund_amount,
0 cart_count,
0 cart_num,
0 favor_count,
appraise_good_count,
appraise_mid_count,
appraise_bad_count,
appraise_default_count
from tmp_appraise
)tmp
group by sku_id;


insert overwrite table ${APP}.dws_sale_detail_daycount partition(dt='$do_date')
select
op.user_id,
op.sku_id,
ui.gender,
months_between('$do_date', ui.birthday)/12 age,
ui.user_level,
si.price,
si.sku_name,
si.tm_id,
si.category3_id,
si.category2_id,
si.category1_id,
si.category3_name,
si.category2_name,
si.category1_name,
si.spu_id,
op.sku_num,
op.order_count,
op.order_amount
from
(
select
user_id,
sku_id,
sum(sku_num) sku_num,
count(*) order_count,
sum(total_amount) order_amount
from ${APP}.dwd_fact_order_detail
where dt='$do_date'
group by user_id, sku_id
)op
join
(
select
*
from ${APP}.dwd_dim_user_info_his
where end_date='9999-99-99'
)ui on op.user_id = ui.id
join
(
select
*
from ${APP}.dwd_dim_sku_info
where dt='$do_date'
)si on op.sku_id = si.id;
"
$hive -e "$sql"
复制代码

2）增加脚本执行权限

chmod 770 dwd_to_dws.sh
复制代码

3）执行脚本导入数据

dwd_to_dws.sh 2020-03-11
复制代码

4）查看导入数据

select * from dws_uv_detail_daycount where dt='2020-03-11';
select * from dws_user_action_daycount where dt='2020-03-11';
select * from dws_sku_action_daycount where dt='2020-03-11';
select * from dws_sale_detail_daycount where dt='2020-03-11';
复制代码

二、数仓搭建-DWT 层
2.1 设备主题宽表




1）建表语句

drop table if exists dwt_uv_topic;
create external table dwt_uv_topic
(
`mid_id` string COMMENT '设备唯一标识',
`user_id` string COMMENT '用户标识',
`version_code` string COMMENT '程序版本号',
`version_name` string COMMENT '程序版本名',
`lang` string COMMENT '系统语言',
`source` string COMMENT '渠道号',
`os` string COMMENT '安卓系统版本',
`area` string COMMENT '区域',
`model` string COMMENT '手机型号',
`brand` string COMMENT '手机品牌',
`sdk_version` string COMMENT 'sdkVersion',
`gmail` string COMMENT 'gmail',
`height_width` string COMMENT '屏幕宽高',
`app_time` string COMMENT '客户端日志产生时的时间',
`network` string COMMENT '网络模式',
`lng` string COMMENT '经度',
`lat` string COMMENT '纬度',
`login_date_first` string comment '首次活跃时间',
`login_date_last` string comment '末次活跃时间',
`login_day_count` bigint comment '当日活跃次数',
`login_count` bigint comment '累积活跃天数'
)
stored as parquet
location '/warehouse/gmall/dwt/dwt_uv_topic';
复制代码

2）数据装载

insert overwrite table dwt_uv_topic
select
nvl(new.mid_id,old.mid_id),
nvl(new.user_id,old.user_id),
nvl(new.version_code,old.version_code),
nvl(new.version_name,old.version_name),
nvl(new.lang,old.lang),
nvl(new.source,old.source),
nvl(new.os,old.os),
nvl(new.area,old.area),
nvl(new.model,old.model),
nvl(new.brand,old.brand),
nvl(new.sdk_version,old.sdk_version),
nvl(new.gmail,old.gmail),
nvl(new.height_width,old.height_width),
nvl(new.app_time,old.app_time),
nvl(new.network,old.network),
nvl(new.lng,old.lng),
nvl(new.lat,old.lat),
if(old.mid_id is null,'2020-03-10',old.login_date_first),
if(new.mid_id is not null,'2020-03-10',old.login_date_last),
if(new.mid_id is not null, new.login_count,0),
nvl(old.login_count,0)+if(new.login_count>0,1,0)
from
(
select
*
from dwt_uv_topic
)old
full outer join
(
select
*
from dws_uv_detail_daycount
where dt='2020-03-10'
)new
on old.mid_id=new.mid_id;
复制代码

3）查询加载结果

select * from dwt_uv_topic limit 5;
复制代码

2.2 会员主题宽表
宽表字段怎么来？维度关联的事实表度量值+开头、结尾+累积+累积一个时间段






1）建表语句

drop table if exists dwt_user_topic;
create external table dwt_user_topic
(
user_id string comment '用户 id',
login_date_first string comment '首次登录时间',
login_date_last string comment '末次登录时间',
login_count bigint comment '累积登录天数',
login_last_30d_count bigint comment '最近 30 日登录天数',
order_date_first string comment '首次下单时间',
order_date_last string comment '末次下单时间',
order_count bigint comment '累积下单次数',
order_amount decimal(16,2) comment '累积下单金额',
order_last_30d_count bigint comment '最近 30 日下单次数',
order_last_30d_amount bigint comment '最近 30 日下单金额',
payment_date_first string comment '首次支付时间',
payment_date_last string comment '末次支付时间',
payment_count decimal(16,2) comment '累积支付次数',
payment_amount decimal(16,2) comment '累积支付金额',
payment_last_30d_count decimal(16,2) comment '最近 30 日支付次数',
payment_last_30d_amount decimal(16,2) comment '最近 30 日支付金额'
)COMMENT '用户主题宽表'
stored as parquet
location '/warehouse/gmall/dwt/dwt_user_topic/'
tblproperties ("parquet.compression"="lzo");
复制代码

2）数据装载

insert overwrite table dwt_user_topic
select
nvl(new.user_id,old.user_id),
if(old.login_date_first is null and
new.login_count>0,'2020-03-10',old.login_date_first),
if(new.login_count>0,'2020-03-10',old.login_date_last),
nvl(old.login_count,0)+if(new.login_count>0,1,0),
nvl(new.login_last_30d_count,0),
if(old.order_date_first is null and
new.order_count>0,'2020-03-10',old.order_date_first),
if(new.order_count>0,'2020-03-10',old.order_date_last),
nvl(old.order_count,0)+nvl(new.order_count,0),
nvl(old.order_amount,0)+nvl(new.order_amount,0),
nvl(new.order_last_30d_count,0),
nvl(new.order_last_30d_amount,0),
if(old.payment_date_first is null and
new.payment_count>0,'2020-03-10',old.payment_date_first),
if(new.payment_count>0,'2020-03-10',old.payment_date_last),
nvl(old.payment_count,0)+nvl(new.payment_count,0),
nvl(old.payment_amount,0)+nvl(new.payment_amount,0),
nvl(new.payment_last_30d_count,0),
nvl(new.payment_last_30d_amount,0)
from
dwt_user_topic old
full outer join
(
select
user_id,
sum(if(dt='2020-03-10',login_count,0)) login_count,
sum(if(dt='2020-03-10',order_count,0)) order_count,
sum(if(dt='2020-03-10',order_amount,0)) order_amount,
sum(if(dt='2020-03-10',payment_count,0)) payment_count,
sum(if(dt='2020-03-10',payment_amount,0)) payment_amount,
sum(if(login_count>0,1,0)) login_last_30d_count,
sum(order_count) order_last_30d_count,
sum(order_amount) order_last_30d_amount,
sum(payment_count) payment_last_30d_count,
sum(payment_amount) payment_last_30d_amount
from dws_user_action_daycount
where dt>=date_add( '2020-03-10',-30)
group by user_id
)new
on old.user_id=new.user_id;
复制代码

3）查询加载结果

select * from dwt_user_topic limit 5;
复制代码

2.3 商品主题宽表
1）建表语句

create external table dwt_sku_topic
(
sku_id string comment 'sku_id',
spu_id string comment 'spu_id',
order_last_30d_count bigint comment '最近 30 日被下单次数',
order_last_30d_num bigint comment '最近 30 日被下单件数',
order_last_30d_amount decimal(16,2) comment '最近 30 日被下单金额',
order_count bigint comment '累积被下单次数',
order_num bigint comment '累积被下单件数',
order_amount decimal(16,2) comment '累积被下单金额',
payment_last_30d_count bigint comment '最近 30 日被支付次数',
payment_last_30d_num bigint comment '最近 30 日被支付件数',
payment_last_30d_amount decimal(16,2) comment '最近 30 日被支付金额',
payment_count bigint comment '累积被支付次数',
payment_num bigint comment '累积被支付件数',
payment_amount decimal(16,2) comment '累积被支付金额',
refund_last_30d_count bigint comment '最近三十日退款次数',
refund_last_30d_num bigint comment '最近三十日退款件数',
refund_last_30d_amount decimal(10,2) comment '最近三十日退款金额',
refund_count bigint comment '累积退款次数',
refund_num bigint comment '累积退款件数',
refund_amount decimal(10,2) comment '累积退款金额',
cart_last_30d_count bigint comment '最近 30 日被加入购物车次数',
cart_last_30d_num bigint comment '最近 30 日被加入购物车件数',
cart_count bigint comment '累积被加入购物车次数',
cart_num bigint comment '累积被加入购物车件数',
favor_last_30d_count bigint comment '最近 30 日被收藏次数',
favor_count bigint comment '累积被收藏次数',
appraise_last_30d_good_count bigint comment '最近 30 日好评数',
appraise_last_30d_mid_count bigint comment '最近 30 日中评数',
appraise_last_30d_bad_count bigint comment '最近 30 日差评数',
appraise_last_30d_default_count bigint comment '最近 30 日默认评价数',
appraise_good_count bigint comment '累积好评数',
appraise_mid_count bigint comment '累积中评数',
appraise_bad_count bigint comment '累积差评数',
appraise_default_count bigint comment '累积默认评价数'
)COMMENT '商品主题宽表'
stored as parquet
location '/warehouse/gmall/dwt/dwt_sku_topic/'
tblproperties ("parquet.compression"="lzo");
复制代码

2）数据装载

insert overwrite table dwt_sku_topic
select
nvl(new.sku_id,old.sku_id), sku_info.spu_id,
nvl(new.order_count30,0),
nvl(new.order_num30,0),
nvl(new.order_amount30,0),
nvl(old.order_count,0) + nvl(new.order_count,0),
nvl(old.order_num,0) + nvl(new.order_num,0),
nvl(old.order_amount,0) + nvl(new.order_amount,0),
nvl(new.payment_count30,0),
nvl(new.payment_num30,0),
nvl(new.payment_amount30,0),
nvl(old.payment_count,0) + nvl(new.payment_count,0),
nvl(old.payment_num,0) + nvl(new.payment_count,0),
nvl(old.payment_amount,0) + nvl(new.payment_count,0),
nvl(new.refund_count30,0),
nvl(new.refund_num30,0),
nvl(new.refund_amount30,0),
nvl(old.refund_count,0) + nvl(new.refund_count,0),
nvl(old.refund_num,0) + nvl(new.refund_num,0),
nvl(old.refund_amount,0) + nvl(new.refund_amount,0),
nvl(new.cart_count30,0),
nvl(new.cart_num30,0),
nvl(old.cart_count,0) + nvl(new.cart_count,0),
nvl(old.cart_num,0) + nvl(new.cart_num,0),
nvl(new.favor_count30,0),
nvl(old.favor_count,0) + nvl(new.favor_count,0),
nvl(new.appraise_good_count30,0),
nvl(new.appraise_mid_count30,0),
nvl(new.appraise_bad_count30,0),
nvl(new.appraise_default_count30,0) ,
nvl(old.appraise_good_count,0) + nvl(new.appraise_good_count,0),
nvl(old.appraise_mid_count,0) + nvl(new.appraise_mid_count,0),
nvl(old.appraise_bad_count,0) + nvl(new.appraise_bad_count,0),
nvl(old.appraise_default_count,0) + nvl(new.appraise_default_count,0)
from
(
select
sku_id,
spu_id,
order_last_30d_count,
order_last_30d_num,
order_last_30d_amount,
order_count,
order_num,
order_amount ,
payment_last_30d_count,
payment_last_30d_num,
payment_last_30d_amount,
payment_count,
payment_num,
payment_amount,
refund_last_30d_count,
refund_last_30d_num,
refund_last_30d_amount,
refund_count,
refund_num,
refund_amount,
cart_last_30d_count,
cart_last_30d_num,
cart_count,
cart_num,
favor_last_30d_count,
favor_count,
appraise_last_30d_good_count,
appraise_last_30d_mid_count,
appraise_last_30d_bad_count,
appraise_last_30d_default_count,
appraise_good_count,
appraise_mid_count,
appraise_bad_count,
appraise_default_count
from dwt_sku_topic
)old
full outer join
(
select
sku_id,
sum(if(dt='2020-03-10', order_count,0 )) order_count,
sum(if(dt='2020-03-10',order_num ,0 )) order_num,
sum(if(dt='2020-03-10',order_amount,0 )) order_amount ,
sum(if(dt='2020-03-10',payment_count,0 )) payment_count,
sum(if(dt='2020-03-10',payment_num,0 )) payment_num,
sum(if(dt='2020-03-10',payment_amount,0 )) payment_amount,
sum(if(dt='2020-03-10',refund_count,0 )) refund_count,
sum(if(dt='2020-03-10',refund_num,0 )) refund_num,
sum(if(dt='2020-03-10',refund_amount,0 )) refund_amount,
sum(if(dt='2020-03-10',cart_count,0 )) cart_count,
sum(if(dt='2020-03-10',cart_num,0 )) cart_num,
sum(if(dt='2020-03-10',favor_count,0 )) favor_count,
sum(if(dt='2020-03-10',appraise_good_count,0 )) appraise_good_count,
sum(if(dt='2020-03-10',appraise_mid_count,0 ) ) appraise_mid_count ,
sum(if(dt='2020-03-10',appraise_bad_count,0 )) appraise_bad_count,
sum(if(dt='2020-03-10',appraise_default_count,0 )) appraise_default_count,
sum(order_count) order_count30 ,
sum(order_num) order_num30,
sum(order_amount) order_amount30,
sum(payment_count) payment_count30,
sum(payment_num) payment_num30,
sum(payment_amount) payment_amount30,
sum(refund_count) refund_count30,
sum(refund_num) refund_num30,
sum(refund_amount) refund_amount30,
sum(cart_count) cart_count30,
sum(cart_num) cart_num30,
sum(favor_count) favor_count30,
sum(appraise_good_count) appraise_good_count30,
sum(appraise_mid_count) appraise_mid_count30,
sum(appraise_bad_count) appraise_bad_count30,
sum(appraise_default_count) appraise_default_count30
from dws_sku_action_daycount
where dt >= date_add ('2020-03-10', -30)
group by sku_id
)new
on new.sku_id = old.sku_id
left join
(select * from dwd_dim_sku_info where dt='2020-03-10') sku_info
on nvl(new.sku_id,old.sku_id)= sku_info.id;
复制代码

3）查询加载结果

select * from dwt_sku_topic limit 5;
复制代码

2.4 DWT 层数据导入脚本
1）vim dws_to_dwt.sh
在脚本中填写如下内容

#!/bin/bash
APP=gmall
hive=/opt/modules/hive/bin/hive
# 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 "$1" ] ;then
do_date=$1
else
do_date=`date -d "-1 day" +%F`
fi
sql="
insert overwrite table ${APP}.dwt_uv_topic
select
nvl(new.mid_id,old.mid_id),
nvl(new.user_id,old.user_id),
nvl(new.version_code,old.version_code),
nvl(new.version_name,old.version_name),
nvl(new.lang,old.lang),
nvl(new.source,old.source),
nvl(new.os,old.os),
nvl(new.area,old.area),
nvl(new.model,old.model),
nvl(new.brand,old.brand),
nvl(new.sdk_version,old.sdk_version),
nvl(new.gmail,old.gmail),
nvl(new.height_width,old.height_width),
nvl(new.app_time,old.app_time),
nvl(new.network,old.network),
nvl(new.lng,old.lng),
nvl(new.lat,old.lat),
nvl(old.login_date_first,'$do_date'),
if(new.login_count>0,'$do_date',old.login_date_last),
nvl(new.login_count,0),
nvl(new.login_count,0)+nvl(old.login_count,0)
from
(
select
*
from ${APP}.dwt_uv_topic
)old
full outer join
(
select
*
from ${APP}.dws_uv_detail_daycount
where dt='$do_date'
)new
on old.mid_id=new.mid_id;


insert overwrite table ${APP}.dwt_user_topic
select
nvl(new.user_id,old.user_id),
if(old.login_date_first is null and
new.login_count>0,'$do_date',old.login_date_first),
if(new.login_count>0,'$do_date',old.login_date_last),
nvl(old.login_count,0)+if(new.login_count>0,1,0),
nvl(new.login_last_30d_count,0),
if(old.order_date_first is null and
new.order_count>0,'$do_date',old.order_date_first),
if(new.order_count>0,'$do_date',old.order_date_last),
nvl(old.order_count,0)+nvl(new.order_count,0),
nvl(old.order_amount,0)+nvl(new.order_amount,0),
nvl(new.order_last_30d_count,0),
nvl(new.order_last_30d_amount,0),
if(old.payment_date_first is null and
new.payment_count>0,'$do_date',old.payment_date_first),
if(new.payment_count>0,'$do_date',old.payment_date_last),
nvl(old.payment_count,0)+nvl(new.payment_count,0),
nvl(old.payment_amount,0)+nvl(new.payment_amount,0),
nvl(new.payment_last_30d_count,0),
nvl(new.payment_last_30d_amount,0)
from
(
select
*
from ${APP}.dwt_user_topic
)old
full outer join
(
select
user_id,
sum(if(dt='$do_date',login_count,0)) login_count,
sum(if(dt='$do_date',order_count,0)) order_count,
sum(if(dt='$do_date',order_amount,0)) order_amount,
sum(if(dt='$do_date',payment_count,0)) payment_count,
sum(if(dt='$do_date',payment_amount,0)) payment_amount,
sum(if(order_count>0,1,0)) login_last_30d_count,
sum(order_count) order_last_30d_count,
sum(order_amount) order_last_30d_amount,
sum(payment_count) payment_last_30d_count,
sum(payment_amount) payment_last_30d_amount
from ${APP}.dws_user_action_daycount
where dt>=date_add( '$do_date',-30)
group by user_id
)new
on old.user_id=new.user_id;
with
sku_act as
(
select
sku_id,
sum(if(dt='$do_date', order_count,0 )) order_count,
sum(if(dt='$do_date',order_num ,0 )) order_num,
sum(if(dt='$do_date',order_amount,0 )) order_amount ,
sum(if(dt='$do_date',payment_count,0 )) payment_count,
sum(if(dt='$do_date',payment_num,0 )) payment_num,
sum(if(dt='$do_date',payment_amount,0 )) payment_amount,
sum(if(dt='$do_date',refund_count,0 )) refund_count,
sum(if(dt='$do_date',refund_num,0 )) refund_num,
sum(if(dt='$do_date',refund_amount,0 )) refund_amount,
sum(if(dt='$do_date',cart_count,0 )) cart_count,
sum(if(dt='$do_date',cart_num,0 )) cart_num,
sum(if(dt='$do_date',favor_count,0 )) favor_count,
sum(if(dt='$do_date',appraise_good_count,0 )) appraise_good_count,
sum(if(dt='$do_date',appraise_mid_count,0 ) ) appraise_mid_count ,
sum(if(dt='$do_date',appraise_bad_count,0 )) appraise_bad_count,
sum(if(dt='$do_date',appraise_default_count,0 )) appraise_default_count,
sum( order_count ) order_count30 ,
sum( order_num ) order_num30,
sum(order_amount ) order_amount30,
sum(payment_count ) payment_count30,
sum(payment_num ) payment_num30,
sum(payment_amount ) payment_amount30,
sum(refund_count ) refund_count30,
sum(refund_num ) refund_num30,
sum(refund_amount ) refund_amount30,
sum(cart_count ) cart_count30,
sum(cart_num ) cart_num30,
sum(favor_count ) favor_count30,
sum(appraise_good_count ) appraise_good_count30,
sum(appraise_mid_count ) appraise_mid_count30,
sum(appraise_bad_count ) appraise_bad_count30,
sum(appraise_default_count ) appraise_default_count30
from ${APP}.dws_sku_action_daycount
where dt>=date_add ( '$do_date',-30)
group by sku_id
),
sku_topic
as
(
select
sku_id,
spu_id,
order_last_30d_count,
order_last_30d_num,
order_last_30d_amount,
order_count,
order_num,
order_amount ,
payment_last_30d_count,
payment_last_30d_num,
payment_last_30d_amount,
payment_count,
payment_num,
payment_amount,
refund_last_30d_count,
refund_last_30d_num,
refund_last_30d_amount ,
refund_count ,
refund_num ,
refund_amount ,
cart_last_30d_count ,
cart_last_30d_num ,
cart_count ,
cart_num ,
favor_last_30d_count ,
favor_count ,
appraise_last_30d_good_count ,
appraise_last_30d_mid_count ,
appraise_last_30d_bad_count ,
appraise_last_30d_default_count ,
appraise_good_count ,
appraise_mid_count ,
appraise_bad_count ,
appraise_default_count
from ${APP}.dwt_sku_topic
)


insert overwrite table ${APP}.dwt_sku_topic
select
nvl(sku_act.sku_id,sku_topic.sku_id) ,
sku_info.spu_id,
nvl (sku_act.order_count30,0) ,
nvl (sku_act.order_num30,0) ,
nvl (sku_act.order_amount30,0) ,
nvl(sku_topic.order_count,0)+ nvl (sku_act.order_count,0) ,
nvl(sku_topic.order_num,0)+ nvl (sku_act.order_num,0) ,
nvl(sku_topic.order_amount,0)+ nvl (sku_act.order_amount,0),
nvl (sku_act.payment_count30,0),
nvl (sku_act.payment_num30,0),
nvl (sku_act.payment_amount30,0),
nvl(sku_topic.payment_count,0)+ nvl (sku_act.payment_count,0) ,
nvl(sku_topic.payment_num,0)+ nvl (sku_act.payment_count,0) ,
nvl(sku_topic.payment_amount,0)+ nvl (sku_act.payment_count,0) ,
nvl (refund_count30,0),
nvl (sku_act.refund_num30,0),
nvl (sku_act.refund_amount30,0),
nvl(sku_topic.refund_count,0)+ nvl (sku_act.refund_count,0),
nvl(sku_topic.refund_num,0)+ nvl (sku_act.refund_num,0),
nvl(sku_topic.refund_amount,0)+ nvl (sku_act.refund_amount,0),
nvl(sku_act.cart_count30,0) ,
nvl(sku_act.cart_num30,0) ,
nvl(sku_topic.cart_count ,0)+ nvl (sku_act.cart_count,0),
nvl( sku_topic.cart_num ,0)+ nvl (sku_act.cart_num,0),
nvl(sku_act.favor_count30 ,0) ,
nvl (sku_topic.favor_count ,0)+ nvl (sku_act.favor_count,0),
nvl (sku_act.appraise_good_count30 ,0) ,
nvl (sku_act.appraise_mid_count30 ,0) ,
nvl (sku_act.appraise_bad_count30 ,0) ,
nvl (sku_act.appraise_default_count30 ,0) ,
nvl (sku_topic.appraise_good_count ,0)+ nvl
(sku_act.appraise_good_count,0) ,
nvl (sku_topic.appraise_mid_count ,0)+ nvl
(sku_act.appraise_mid_count,0) ,
nvl (sku_topic.appraise_bad_count ,0)+ nvl
(sku_act.appraise_bad_count,0) ,
nvl (sku_topic.appraise_default_count ,0)+ nvl
(sku_act.appraise_default_count,0)
from sku_act
full outer join sku_topic
on sku_act.sku_id =sku_topic.sku_id
left join
(select * from ${APP}.dwd_dim_sku_info where dt='$do_date') sku_info
on nvl(sku_topic.sku_id,sku_act.sku_id)= sku_info.id;
"
$hive -e "$sql"
复制代码

2）增加脚本执行权限

chmod 770 dws_to_dwt.sh
复制代码

3）执行脚本导入数据

dws_to_dwt.sh 2020-03-11
复制代码

4）查看导入数据

select * from dwt_uv_topic limit 5;
select * from dwt_user_topic limit 5;
select * from dwt_sku_topic limit 5;
复制代码

获取更多资源：
领取100本书+1T资源
http://www.aboutyun.com/forum.php?mod=viewthread&tid=26480

大数据5个项目视频
http://www.aboutyun.com/forum.php?mod=viewthread&tid=25235

名企资源、名企面试题、最新BAT面试题、专题面试题等资源汇总
https://www.aboutyun.com/forum.php?mod=viewthread&tid=27732


名企资源、名企面试题、最新BAT面试题、专题面试题等资源汇总
https://www.aboutyun.com/forum.php?mod=viewthread&tid=27732


原文链接
https://blog.csdn.net/qq_43733123/article/details/105943032

博主码字不易，谢谢分享。

谢谢分享