目录
- 需求一：用户活跃主题
- 需求二：用户新增主题
- 需求三：用户留存主题
- 需求四：沉默用户数
- 需求五：本周回流用户数
- 需求六：流失用户数
- 需求七：最近连续3周活跃用户数
- 需求八：最近七天内连续三天活跃用户数
- 需求九：GMV（Gross Merchandise Volume）一段时间内的成交总额
- 需求十：转化率=新增用户/日活用户
- 需求十一：用户行为漏斗分析
- 需求十二：品牌复购率
- 需求十三：ADS层品牌复购率报表分析
- 需求十四：求每个等级的用户对应的复购率前十的商品排行
需求九：GMV一段时间内的成交总额
GMV拍下订单金额；包括付款和未付款；
建表ads_gmv_sum_day语句：
代码语言：javascript复制drop table if exists ads_gmv_sum_day;
create table ads_gmv_sum_day(
`dt` string comment '统计日期',
`gmv_count` bigint comment '当日GMV订单个数',
`gmv_amount` decimal(16, 2) comment '当日GMV订单总额',
`gmv_payment` decimal(16, 2) comment '当日支付金额'
) comment 'GMV'
row format delimited fields terminated by 't'
location '/warehouse/gmall/ads/ads_gmv_sum_day';
导入数据：from用户行为宽表dws_user_action
sum(order_count) gmv_count 、 sum(order_amount) gmv_amount 、sum(payment_amount) payment_amount 过滤日期，以dt分组；
代码语言：javascript复制insert into table ads_gmv_sum_day
select '2019-02-10' dt, sum(order_count) gmv_count, sum(order_amount) gmv_amount, sum(payment_amount) gmv_payment
from dws_user_action where dt='2019-02-10' group by dt;
编写脚本：
代码语言：javascript复制#/bin/bash
APP=gmall
hive=/opt/module/hive/bin/hive
if [ -n "$1" ]; then
do_date=$1
else
do_date=`date -d "-1 day" %F`
fi
sql="
insert into table "$APP".ads_gmv_sum_day
select '$do_date' dt, sum(order_count) gmv_count, sum(order_amount) gmv_amount, sum(payment_amount) gmv_payment
from "$APP".dws_user_action where dt='$do_date' group by dt;
"
$hive -e "$sql";
需求十：转化率=新增用户/日活用户
https://img2018.cnblogs.com/blog/1247221/201905/1247221-20190516210029788-275690435.png
ads_user_convert_day
dt
uv_m_count 当日活跃设备
new_m_count 当日新增设备
new_m_ratio 新增占日活比率
ads_uv_count 用户活跃数（在行为数仓中；） day_count dt
ads_new_mid_count 用户新增表（行为数仓中） new_mid_count create_date
建表ads_user_convert_day
代码语言：javascript复制drop table if exists ads_user_convert_day;
create table ads_user_convert_day(
`dt` string comment '统计日期',
`uv_m_count` bigint comment '当日活跃设备',
`new_m_count` bigint comment '当日新增设备',
`new_m_radio` decimal(10, 2) comment '当日新增占日活比率'
)comment '转化率'
row format delimited fields terminated by 't'
location '/warehouse/gmall/ads/ads_user_convert_day/';
数据导入 cast(sum( uc.nmc)/sum( uc.dc)*100 as decimal(10,2)) new_m_ratio ；使用union all
代码语言：javascript复制insert into table ads_user_convert_day select '2019-02-10', sum(uc.dc) sum_dc, sum(uc.nmc) sum_nmc,
cast(sum(uc.nmc)/sum(uc.dc) * 100 as decimal(10, 2)) new_m_radio
from(select day_count dc, 0 nmc from ads_uv_count where dt='2019-02-10'
union all select 0 dc, new_mid_count from ads_new_mid_count where create_date='2019-02-10'
)uc;
访问到下单转化率| 下单到支付转化率
代码语言：javascript复制ads_user_action_convert_day
dt
total_visitor_m_count 总访问人数
order_u_count 下单人数
visitor2order_convert_ratio 访问到下单转化率
payment_u_count 支付人数
order2payment_convert_ratio 下单到支付转化率
dws_user_action （宽表中）
user_id
order_count
order_amount
payment_count
payment_amount
comment_count
ads_uv_count 用户活跃数（行为数仓中）
dt
day_count
wk_count
mn_count
is_weekend
is_monthend
建表
代码语言：javascript复制drop table if exists ads_user_action_convert_day;
create table ads_user_action_convert_day(
`dt` string comment '统计日期',
`total_visitor_m_count` bigint comment '总访问人数',
`order_u_count` bigint comment '下单人数',
`visitor2order_convert_radio` decimal(10, 2) comment '访问到下单转化率',
`payment_u_count` bigint comment '支付人数',
`order2payment_convert_radio` decimal(10, 2) comment '下单到支付的转化率'
)COMMENT '用户行为漏斗分析'
row format delimited fields terminated by 't'
location '/warehouse/gmall/ads/ads_user_convert_day/'
;
插入数据
代码语言：javascript复制insert into table ads_user_action_convert_day
select '2019-02-10', uv.day_count, ua.order_count,
cast(ua.order_count/uv.day_count * 100 as decimal(10, 2)) visitor2order_convert_radio,
ua.payment_count,
cast(ua.payment_count/ua.order_count * 100 as decimal(10, 2)) order2payment_convert_radio
from(
select sum(if(order_count>0, 1, 0)) order_count,
sum(if(payment_count>0, 1, 0)) payment_count
from dws_user_action where dt='2019-02-10'
)ua, ads_uv_count uv where uv.dt='2019-02-10';
需求十一：品牌复购率
需求：以月为单位统计，购买2次以上商品的用户，用户购买商品明细表 dws_sale_detail_daycount：（宽表）建表dws_sale_detail_daycount
代码语言：javascript复制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 string comment '当日下单单数',
order_amount string comment '当日下单金额'
) comment '用户购买商品明细表'
partitioned by(`dt` string)
stored as parquet
location '/warehouse/gmall/dws/dws_sale_detail_daycount'
tblproperties("parquet.compression"="snappy");
数据导入
ods_order_detail订单详情表、dwd_user_info用户表、dwd_sku_info商品表
代码语言：javascript复制with tmp_detail as(
select user_id, sku_id, sum(sku_num) sku_num, count(*) order_count, sum(od.order_price*sku_num) order_amount
from ods_order_detail od where od.dt='2019-02-10' and user_id is not null group by user_id, sku_id
)
insert overwrite table dws_sale_detail_daycount partition(dt='2019-02-10')
select
tmp_detail.user_id,
tmp_detail.sku_id,
u.gender,
months_between('2019-02-10', u.birthday)/12 age,
u.user_level,
price,
sku_name,
tm_id,
category3_id ,
category2_id ,
category1_id ,
category3_name ,
category2_name ,
category1_name ,
spu_id,
tmp_detail.sku_num,
tmp_detail.order_count,
tmp_detail.order_amount
from tmp_detail
left join dwd_user_info u on u.id=tmp_detail.user_id and u.dt='2019-02-10'
left join dwd_sku_info s on s.id=tmp_detail.sku_id and s.dt='2019-02-10';
ADS层 品牌复购率报表分析 建表ads_sale_tm_category1_stat_mn
buycount 购买人数、buy_twice_last两次以上购买人数、
buy_twice_last_ratio '单次复购率'、
buy_3times_last '三次以上购买人数',
buy_3times_last_ratio 多次复购率'
代码语言：javascript复制drop table ads_sale_tm_category1_stat_mn;
create 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(10,2) comment '单次复购率',
buy_3times_last bigint comment '三次以上购买人数',
buy_3times_last_ratio decimal(10,2) comment '多次复购率' ,
stat_mn string comment '统计月份',
stat_date string comment '统计日期'
) COMMENT '复购率统计'
row format delimited fields terminated by 't'
location '/warehouse/gmall/ads/ads_sale_tm_category1_stat_mn/'
;
插入数据
代码语言：javascript复制insert into table ads_sale_tm_category1_stat_mn
select mn.sku_tm_id,
mn.sku_category1_id,
mn.sku_category1_name,
sum(if(mn.order_count >= 1, 1, 0)) buycount,
sum(if(mn.order_count >= 2, 1, 0)) buyTwiceLast,
sum(if(mn.order_count >= 2, 1, 0)) / sum(if(mn.order_count >= 1, 1, 0)) buyTwiceLastRatio,
sum(if(mn.order_count >= 3, 1, 0)) buy3timeLast,
sum(if(mn.order_count >= 3, 1, 0)) / sum(if(mn.order_count >= 1, 1, 0)) buy3timeLastRadio,
date_format ('2019-02-10' ,'yyyy-MM') stat_mn,
'2019-02-10' stat_date
from (
select sd.sku_tm_id, sd.sku_category1_id, sd.sku_category1_name, user_id, sum(order_count) order_count
from dws_sale_detail_daycount sd where date_format(dt, 'yyyy-MM') <= date_format('2019-02-10', 'yyyy-MM')
group by sd.sku_tm_id, sd.sku_category1_id, user_id, sd.sku_category1_name
) mn
group by mn.sku_tm_id, mn.sku_category1_id, mn.sku_category1_name
;
数据导入脚本
1）在/home/kris/bin目录下创建脚本ads_sale.sh
[kris@hadoop101 bin]$ vim ads_sale.sh
代码语言：javascript复制#!/bin/bash
# 定义变量方便修改
APP=gmall
hive=/opt/module/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=$1
else
do_date=`date -d "-1 day" %F`
fi
sql="
set hive.exec.dynamic.partition.mode=nonstrict;
insert into table "$APP".ads_sale_tm_category1_stat_mn
select
mn.sku_tm_id,
mn.sku_category1_id,
mn.sku_category1_name,
sum(if(mn.order_count>=1,1,0)) buycount,
sum(if(mn.order_count>=2,1,0)) buyTwiceLast,
sum(if(mn.order_count>=2,1,0))/sum( if(mn.order_count>=1,1,0)) buyTwiceLastRatio,
sum(if(mn.order_count>=3,1,0)) buy3timeLast ,
sum(if(mn.order_count>=3,1,0))/sum( if(mn.order_count>=1,1,0)) buy3timeLastRatio ,
date_format('$do_date' ,'yyyy-MM') stat_mn,
'$do_date' stat_date
from
(
select od.sku_tm_id,
od.sku_category1_id,
od.sku_category1_name,
user_id ,
sum(order_count) order_count
from "$APP".dws_sale_detail_daycount od
where date_format(dt,'yyyy-MM')<=date_format('$do_date' ,'yyyy-MM')
group by od.sku_tm_id, od.sku_category1_id, user_id, od.sku_category1_name
) mn
group by mn.sku_tm_id, mn.sku_category1_id, mn.sku_category1_name;
"
$hive -e "$sql"
增加脚本执行权限
[kris@hadoop101 bin]$ chmod 777 ads_sale.sh
执行脚本导入数据
[kris@hadoop101 bin]$ ads_sale.sh 2019-02-11
查看导入数据
hive (gmall)>select * from ads_sale_tm_category1_stat_mn limit 2;
品牌复购率结果输出到MySQL
1）在MySQL中创建ads_sale_tm_category1_stat_mn表
代码语言：javascript复制create table ads_sale_tm_category1_stat_mn
(
tm_id varchar(200) comment '品牌id ' ,
category1_id varchar(200) comment '1级品类id ',
category1_name varchar(200) comment '1级品类名称 ',
buycount varchar(200) comment '购买人数',
buy_twice_last varchar(200) comment '两次以上购买人数',
buy_twice_last_ratio varchar(200) comment '单次复购率',
buy_3times_last varchar(200) comment '三次以上购买人数',
buy_3times_last_ratio varchar(200) comment '多次复购率' ,
stat_mn varchar(200) comment '统计月份',
stat_date varchar(200) comment '统计日期'
)
2）编写Sqoop导出脚本
在/home/kris/bin目录下创建脚本sqoop_export.sh
[kris@hadoop101 bin]$ vim sqoop_export.sh
代码语言：javascript复制#!/bin/bash
db_name=gmall
export_data() {
/opt/module/sqoop/bin/sqoop export
--connect "jdbc:mysql://hadoop101:3306/${db_name}?useUnicode=true&characterEncoding=utf-8"
--username root
--password 123456
--table $1
--num-mappers 1
--export-dir /warehouse/$db_name/ads/$1
--input-fields-terminated-by "t"
--update-key "tm_id,category1_id,stat_mn,stat_date"
--update-mode allowinsert
--input-null-string '\N'
--input-null-non-string '\N'
}
case $1 in
"ads_sale_tm_category1_stat_mn")
export_data "ads_sale_tm_category1_stat_mn"
;;
"all")
export_data "ads_sale_tm_category1_stat_mn"
;;
esac
3）执行Sqoop导出脚本
[kris@hadoop101 bin]$ chmod 777 sqoop_export.sh
[kris@hadoop101 bin]$ sqoop_export.sh all
4）在MySQL中查看结果
SELECT * FROM ads_sale_tm_category1_stat_mn;
需求十二：求每个等级的用户对应的复购率前十的商品排行
1）每个等级，每种商品，买一次的用户数，买两次的用户数=》得出复购率
2）利用开窗函数，取每个等级的前十
3）形成脚本
用户购买明细宽表 dws_sale_detail_daycount
① t1--按user_leval, sku_id, user_id统计下单次数
代码语言：javascript复制select
user_level,
sku_id,
user_id,
sum(order_count) order_count_sum
from dws_sale_detail_daycount
where date_format(dt, 'yyyy-MM') = date_format('2019-02-13', 'yyyy-MM')
group by user_level, sku_id, user_id limit 10;
② t2 --求出每个等级，每种商品，买一次的用户数，买两次的用户数 得出复购率
代码语言：javascript复制select
t1.user_level,
t1.sku_id,
sum(if(t1.order_count_sum > 0, 1, 0)) buyOneCount,
sum(if(t1.order_count_sum > 1, 1, 0)) buyTwiceCount,
sum(if(t1.order_count_sum > 1, 1, 0)) / sum(if(t1.order_count_sum > 0, 1, 0)) * 100 buyTwiceCountRatio,
'2019-02-13' stat_date
from(
select
user_level,
sku_id,
user_id,
sum(order_count) order_count_sum
from dws_sale_detail_daycount
where date_format(dt, 'yyyy-MM') = date_format('2019-02-13', 'yyyy-MM')
group by user_level, sku_id, user_id
) t1
group by t1.user_level, t1.sku_id;
③ t3 --按用户等级分区，复购率排序
代码语言：javascript复制select
t2.user_level,
t2.sku_id,
t2.buyOneCount,
t2.buyTwiceCount,
t2.buyTwiceCountRatio,
t2.stat_date
from(
select
t1.user_level,
t1.sku_id,
sum(if(t1.order_count_sum > 0, 1, 0)) buyOneCount,
sum(if(t1.order_count_sum > 1, 1, 0)) buyTwiceCount,
sum(if(t1.order_count_sum > 1, 1, 0)) / sum(if(t1.order_count_sum > 0, 1, 0)) * 100 buyTwiceCountRatio,
'2019-02-13' stat_date
from(
select
user_level,
sku_id,
user_id,
sum(order_count) order_count_sum
from dws_sale_detail_daycount
where date_format(dt, 'yyyy-MM') = date_format('2019-02-13', 'yyyy-MM')
group by user_level, sku_id, user_id
) t1
group by t1.user_level, t1.sku_id
)t2
④ -分区排序 rank()
代码语言：javascript复制select
t2.user_level,
t2.sku_id,
t2.buyOneCount,
t2.buyTwiceCount,
t2.buyTwiceCountRatio,
rank() over(partition by t2.sku_id order by t2.buyTwiceCount) rankNo
from(
select
t1.user_level,
t1.sku_id,
sum(if(t1.order_count_sum > 0, 1, 0)) buyOneCount,
sum(if(t1.order_count_sum > 1, 1, 0)) buyTwiceCount,
sum(if(t1.order_count_sum > 1, 1, 0)) / sum(if(t1.order_count_sum > 0, 1, 0)) * 100 buyTwiceCountRatio,
'2019-02-13' stat_date
from(
select
user_level,
sku_id,
user_id,
sum(order_count) order_count_sum
from dws_sale_detail_daycount
where date_format(dt, 'yyyy-MM') = date_format('2019-02-13', 'yyyy-MM')
group by user_level, sku_id, user_id
) t1
group by t1.user_level, t1.sku_id
)t2
⑤ 作为子查询取前10
代码语言：javascript复制select t3.user_level, t3.sku_id, t3.buyOneCount, t3.buyTwiceCount, t3.buyTwiceCountRatio, t3.rankNo
from(
select
t2.user_level,
t2.sku_id,
t2.buyOneCount,
t2.buyTwiceCount,
t2.buyTwiceCountRatio,
rank() over(partition by t2.sku_id order by t2.buyTwiceCount) rankNo
from(
select
t1.user_level,
t1.sku_id,
sum(if(t1.order_count_sum > 0, 1, 0)) buyOneCount,
sum(if(t1.order_count_sum > 1, 1, 0)) buyTwiceCount,
sum(if(t1.order_count_sum > 1, 1, 0)) / sum(if(t1.order_count_sum > 0, 1, 0)) * 100 buyTwiceCountRatio,
'2019-02-13' stat_date
from(
select
user_level,
sku_id,
user_id,
sum(order_count) order_count_sum
from dws_sale_detail_daycount
where date_format(dt, 'yyyy-MM') = date_format('2019-02-13', 'yyyy-MM')
group by user_level, sku_id, user_id
) t1
group by t1.user_level, t1.sku_id
)t2
) t3 where rankNo <= 10;
