​需求一：用户活跃主题

DWS 层--（用户行为宽表层） 目标：统计当日、当周、当月活动的每个设备明细

1 每日活跃设备明细 dwd_start_log--->dws_uv_detail_day

--把相同的字段 collect_set 到一个数组, 按 mid_id 分组(便于后边统计)

collect_set 将某字段的值进行去重汇总，产生 array 类型字段。如: concat_ws('|', collect_set(user_id)) user_id,

建分区表 dws_uv_detail_day：partitioned by ('dt' string)

drop table if exists dws_uv_detail_day;

create table dws_uv_detail_day(

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'

) COMMENT '活跃用户按天明细'

PARTITIONED BY ( dt string)

stored as parquet

location '/warehouse/gmall/dws/dws_uv_detail_day/'

;

数据导入

按周分区；过滤出一周内的数据；按设备 id 分组；===>count（*）得到最终结果；

partition(dt='2019-02-10') from dwd_start_log where dt='2019-02-10' group by mid_id ( mid_id 设备唯一标示 )

以用户单日访问为 key 进行聚合，如果某个用户在一天中使用了两种操作系统、两个系统版本、多个地区，登录不同账号，只取其中之一

hive (gmall)>

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table dws_uv_detail_day partition(dt='2019-02-10')

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

from dwd_start_log

where dt='2019-02-10'

group by mid_id;

查询导入结果；

hive (gmall)> select * from dws_uv_detail_day limit 1;

###最后 count(*)即是每日活跃设备的个数；

hive (gmall)> select count(*) from dws_uv_detail_day;

2 每周（dws_uv_detail_wk）活跃设备明细 partition(wk_dt)

周一到周日 concat(date_add(next_day('2019-02-10', 'MO'), -7), '_', date_add(next_day('2019-02-10', 'MO'), -1))即 2019-02-04_2019-02-10

创建分区表：partitioned by('wk_dt' string)

hive (gmall)>

drop table if exists dws_uv_detail_wk;

create table dws_uv_detail_wk(

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',

monday_date string COMMENT '周一日期',

sunday_date string COMMENT '周日日期'

) COMMENT '活跃用户按周明细'

PARTITIONED BY (wk_dt string)

stored as parquet

location '/warehouse/gmall/dws/dws_uv_detail_wk/'

;

导入数据：以周为分区；过滤出一个月内的数据，按设备 id 分组；

周一：date_add(next_day('2019-05-16','MO'),-7);

周日：date_add(next_day('2019-05-16','MO'),-1);

周一---周日：concat(date_add(next_day('2019-05-16', 'MO'), -7), "_", date_add(next_day('2019-05-16', 'MO'), -1));

insert overwrite table dws_uv_detail_wk partition(wk_dt)

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,

date_add(next_day('2019-02-10', 'MO'), -7),

date_add(next_day('2019-02-10', 'MO'), -1),

concat(date_add(next_day('2019-02-10', 'MO'), -7), '_', date_add(next_day('2019-02-10', 'MO'), -1))

from dws_uv_detail_day

where dt >= date_add(next_day('2019-02-10', 'MO'), -7) and dt <= date_add(next_day('2019-02-10', 'MO'), -1)

group by mid_id;

查询导入结果

hive (gmall)> select * from dws_uv_detail_wk limit 1;

hive (gmall)> select count(*) from dws_uv_detail_wk;

3 每月活跃设备明细 dws_uv_detail_mn partition(mn) - 把每日的数据插入进去

DWS 层创建分区表 partitioned by(mn string)

hive (gmall)>

drop table if exists dws_uv_detail_mn;

create external table dws_uv_detail_mn(

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'

) COMMENT '活跃用户按月明细'

PARTITIONED BY (mn string)

stored as parquet

location '/warehouse/gmall/dws/dws_uv_detail_mn/'

;

数据导入 按月分区；过滤出一个月内的数据，按照设备 id 分组；

data_format('2019-03-10', 'yyyy-MM') ---> 2019-03

where date_format('dt', 'yyyy-MM') = date_format('2019-02-10', 'yyyy-MM') group by mid_id;

hive (gmall)>

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table dws_uv_detail_mn partition(mn)

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,

date_format('2019-02-10','yyyy-MM')

from dws_uv_detail_day

where date_format(dt,'yyyy-MM') = date_format('2019-02-10','yyyy-MM')

group by mid_id;

查询导入结果

hive (gmall)> select * from dws_uv_detail_mn limit 1;

hive (gmall)> select count(*) from dws_uv_detail_mn ;

DWS 层加载数据脚本

在 hadoop101 的/home/kris/bin 目录下创建脚本

[kris@hadoop101 bin]$ vim dws.sh

#!/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 overwrite table "$APP".dw{s}_u{v}_{d}etai{l}_{d}aypartition(dt{=}^{\prime }$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

from "$APP".dwd_start_log

where dt='$do_date'

group by mid_id;

insert overwrite table "$APP".dws_uv_detail_wk partition(wk_dt)

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,

date_add(next_day('$do_date','MO'),-7),

date_add(next_day('$do_date','SU'),-7),

concat(date_add( next_day('do_date','MO'),-7), '_' , date_add(next_day('do_date','MO'),-1)

)

from "$APP".dws_uv_detail_day

where dt>=date_add(next_day('$d{o}_{d}at{e}^{\prime }{,}^{\prime }M{O}^{\prime }),-7)anddt<=dat{e}_{a}dd(nex{t}_{d}ay{(}^{\prime }$do_date','MO'),-1)

group by mid_id;

insert overwrite table "$APP".dws_uv_detail_mn partition(mn)

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,

date_format('$do_date','yyyy-MM')

from "$APP".dws_uv_detail_day

where date_format(dt,'yyyy-MM') = date_format('$do_date','yyyy-MM')

group by mid_id;

"

$hive-e"$sql"

增加脚本执行权限 chmod 777 dws.sh

脚本使用[kris@hadoop101 module]$ dws.sh 2019-02-11

查询结果

hive (gmall)> select count(*) from dws_uv_detail_day;

hive (gmall)> select count(*) from dws_uv_detail_wk;

hive (gmall)> select count(*) from dws_uv_detail_mn ;

脚本执行时间；企业开发中一般在每日凌晨 30 分~1 点

ADS 层 目标：当日、当周、当月活跃设备数 使用 day_count 表 join wk_count join mn_count , 把 3 张表连接一起

建表 ads_uv_count 表：

字段有 day_count、wk_count、mn_count is_weekend if(date_add(next_day('2019-02-10', 'MO'), -1) = '2019-02-10', 'Y', 'N') is_monthend if(last_day('2019-02-10') = '2019-02-10', 'Y', 'N')

drop table if exists ads_uv_count;

create external table ads_uv_count(

dt string comment '统计日期',

day_count bigint comment '当日用户量',

wk_count bigint comment '当周用户量',

mn_count bigint comment '当月用户量',

is_weekend string comment 'Y,N 是否是周末,用于得到本周最终结果',

is_monthend string comment 'Y,N 是否是月末,用于得到本月最终结果'

) comment '每日活跃用户数量'

stored as parquet

location '/warehouse/gmall/ads/ads_uv_count/';

导入数据：

hive (gmall)>

insert overwrite table ads_uv_count

select

'2019-02-10' dt,

daycount.ct,

wkcount.ct,

mncount.ct,

if(date_add(next_day('2019-02-10','MO'),-1)='2019-02-10','Y','N') ,

if(last_day('2019-02-10')='2019-02-10','Y','N')

from

(

select

'2019-02-10' dt,

count(*) ct

from dws_uv_detail_day

where dt='2019-02-10'

)daycount join

(

select

'2019-02-10' dt,

count (*) ct

from dws_uv_detail_wk

where wk_dt=concat(date_add(next_day('2019-02-10','MO'),-7),'_' ,date_add(next_day('2019-02-10','MO'),-1) )

) wkcount on daycount.dt=wkcount.dt

join

(

select

'2019-02-10' dt,

count (*) ct

from dws_uv_detail_mn

where mn=date_format('2019-02-10','yyyy-MM')

)mncount on daycount.dt=mncount.dt

;

查询导入结果

hive (gmall)> select * from ads_uv_count ;

ADS 层加载数据脚本

1）在 hadoop101 的/home/kris/bin 目录下创建脚本

[kris@hadoop101 bin]$ vim ads.sh

#!/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_uv_count

select

'$do_date' dt,

daycount.ct,

wkcount.ct,

mncount.ct,

if(date_add(next_day('$d{o}_{d}at{e}^{\prime }{,}^{\prime }M{O}^{\prime }),-1){=}^{\prime }$do_date','Y','N') ,

if(last_day('$d{o}_{d}at{e}^{\prime }){=}^{\prime }$do_date','Y','N')

from

(

select

'$do_date' dt,

count(*) ct

from "$APP".dws_uv_detail_day

where dt='$do_date'

)daycount join

(

select

'$do_date' dt,

count (*) ct

from "$APP".dws_uv_detail_wk

where wk_dt=concat(date_add(next_day('do_date','MO'),-7),'_' ,date_add(next_day('do_date','MO'),-1) )

) wkcount on daycount.dt=wkcount.dt

join

(

select

'$do_date' dt,

count (*) ct

from "$APP".dws_uv_detail_mn

where mn=date_format('$do_date','yyyy-MM')

)mncount on daycount.dt=mncount.dt;

"

$hive-e"$sql"

增加脚本执行权限 chmod 777 ads.sh

脚本使用 ads.sh 2019-02-11

查询导入结果 hive (gmall)> select * from ads_uv_count ;

需求二：用户新增主题

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。

每日新增(老用户不算,之前没登陆过，今天是第一次登陆)设备--没有分区 -->以往的新增库里边没有他，但他今天活跃了即新增加的用户_大数据培训；

1 DWS 层（每日新增设备明细表） 创建每日新增设备明细表：dws_new_mid_day

hive (gmall)>

drop table if exists dws_new_mid_day;

create table dws_new_mid_day

(

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',

create_date string comment '创建时间'

) COMMENT '每日新增设备信息'

stored as parquet

location '/warehouse/gmall/dws/dws_new_mid_day/';

dws_uv_detail_day(每日活跃设备明细) left join dws_new_mid_day nm(以往的新增用户表, 新建字段 create_time2019-02-10) nm.mid_id is null;

导入数据

用每日活跃用户表 left join 每日新增设备表，关联的条件是 mid_id 相等。如果是每日新增的设备，则在每日新增设备表中为 null。

from dws_uv_detail_day ud left join dws_new_mid_day nm on ud.mid_id=nm.mid_id

where ud.dt='2019-02-10' and nm.mid_id is null;

hive (gmall)>

insert into table dws_new_mid_day

select

ud.mid_id,

ud.user_id ,

ud.version_code ,

ud.version_name ,

ud.lang ,

ud.source,

ud.os,

ud.area,

ud.model,

ud.brand,

ud.sdk_version,

ud.gmail,

ud.height_width,

ud.app_time,

ud.network,

ud.lng,

ud.lat,

'2019-02-10'

from dws_uv_detail_day ud left join dws_new_mid_day nm on ud.mid_id=nm.mid_id

where ud.dt='2019-02-10' and nm.mid_id is null;

查询导入数据

hive (gmall)> select count(*) from dws_new_mid_day ;

2 ADS 层（每日新增设备表） 创建每日新增设备表 ads_new_mid_count

hive (gmall)>

drop table if exists ads_new_mid_count;

create table ads_new_mid_count

(

create_date string comment '创建时间' ,

new_mid_count BIGINT comment '新增设备数量'

) COMMENT '每日新增设备信息数量'

row format delimited fields terminated by '\t'

location '/warehouse/gmall/ads/ads_new_mid_count/';

导入数据 count（*） dws_new_mid_day 表即可

加了 create_date 就必须 group by create_time，否则报错：not in GROUP BY key 'create_date'

hive (gmall)>

insert into table ads_new_mid_count

select create_date , count(*) from dws_new_mid_day

where create_date='2019-02-10'

group by create_date ;

查询导入数据

hive (gmall)> select * from ads_new_mid_count;

扩展每月新增：

--每月新增

drop table if exists dws_new_mid_mn;

create table dws_new_mid_mn(

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'

)comment "每月新增明细"

partitioned by(mn string)

stored as parquet

location "/warehouse/gmall/dws/dws_new_mid_mn";

insert overwrite table dws_new_mid_mn partition(mn)

select

um.mid_id,

um.user_id ,

um.version_code ,

um.version_name ,

um.lang ,

um.source,

um.os,

um.area,

um.model,

um.brand,

um.sdk_version,

um.gmail,

um.height_width,

um.app_time,

um.network,

um.lng,

um.lat,

date_format('2019-02-10', 'yyyy-MM')

from dws_uv_detail_mn um left join dws_new_mid_mn nm on um.mid_id = nm.mid_id

where um.mn =date_format('2019-02-10', 'yyyy-MM') and nm.mid_id = null; ----为什么加上它就是空的？？查不到数据了呢

--##注意这里不能写出 date_format(um.mn, 'yyyy-MM') =date_format('2019-02-10', 'yyyy-MM')

|

需求三：用户留存主题

如果不考虑 2019-02-11 和 2019-02-12 的新增用户：2019-02-10 新增 100 人，一天后它的留存率是 30%，2 天 12 号它的留存率是 25%，3 天后留存率 32%；

站在 2019-02-12 号看 02-11 的留存率：新增 200 人，12 号的留存率是 20%；

站在 2019-02-13 号看 02-12 的留存率：新增 100 人，13 号即一天后留存率是 25%；

用户留存率的分析：昨日的新增且今天是活跃的 / 昨日的新增用户量

如今天 11 日，要统计 10 日的 用户留存率---->10 日的新设备且是 11 日活跃的 / 10 日新增设备 　　分母：10 日的新增设备(每日活跃 left join 以往新增设备表(nm) nm.mid_id is null ) 　　分子：每日活跃表(ud) join 每日新增表(nm) where ud.dt='今天' and nm.create_date = '昨天'

① DWS 层（每日留存用户明细表 dws_user_retention_day） 用户 1 天留存的分析：===>>

留存用户=前一天新增 join 今天活跃

用户留存率=留存用户/前一天新增

创建表：dws_user_retention_day

hive (gmall)>

drop table if exists dws_user_retention_day;

create table dws_user_retention_day

(

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',

create_date string comment '设备新增时间',

retention_day int comment '截止当前日期留存天数'

) COMMENT '每日用户留存情况'

PARTITIONED BY ( dt string)

stored as parquet

location '/warehouse/gmall/dws/dws_user_retention_day/'

;

导入数据（每天计算前 1 天的新用户访问留存明细）

from dws_uv_detail_day 每日活跃设备 ud join dws_new_mid_day 每日新增设备 nm on ud.mid_id =nm.mid_id where ud.dt='2019-02-11' and nm.create_date=date_add('2019-02-11',-1);

hive (gmall)>

insert overwrite table dws_user_retention_day partition(dt="2019-02-11")

select

nm.mid_id,

nm.user_id ,

nm.version_code ,

nm.version_name ,

nm.lang ,

nm.source,

nm.os,

nm.area,

nm.model,

nm.brand,

nm.sdk_version,

nm.gmail,

nm.height_width,

nm.app_time,

nm.network,

nm.lng,

nm.lat,

nm.create_date,

1 retention_day

from dws_uv_detail_day ud join dws_new_mid_day nm on ud.mid_id =nm.mid_id

where ud.dt='2019-02-11' and nm.create_date=date_add('2019-02-11',-1);

查询导入数据（每天计算前 1 天的新用户访问留存明细）

hive (gmall)> select count(*) from dws_user_retention_day;

② DWS 层（1,2,3,n 天留存用户明细表）直接插入数据：dws_user_retention_day 用 union all 连接起来，汇总到一个表中；1）直接导入数据（每天计算前 1,2,3，n 天的新用户访问留存明细） 直接改变这个即可以，date_add('2019-02-11',-3); -1 是一天的留存率；-2 是两天的留存率、-3 是三天的留存率

hive (gmall)>

insert overwrite table dws_user_retention_day partition(dt="2019-02-11")

select

nm.mid_id,

nm.user_id ,

nm.version_code ,

nm.version_name ,

nm.lang ,

nm.source,

nm.os,

nm.area,

nm.model,

nm.brand,

nm.sdk_version,

nm.gmail,

nm.height_width,

nm.app_time,

nm.network,

nm.lng,

nm.lat,

nm.create_date,

1 retention_day

from dws_uv_detail_day ud join dws_new_mid_day nm on ud.mid_id =nm.mid_id

where ud.dt='2019-02-11' and nm.create_date=date_add('2019-02-11',-1)

union all

select

nm.mid_id,

nm.user_id ,

nm.version_code ,

nm.version_name ,

nm.lang ,

nm.source,

nm.os,

nm.area,

nm.model,

nm.brand,

nm.sdk_version,

nm.gmail,

nm.height_width,

nm.app_time,

nm.network,

nm.lng,

nm.lat,

nm.create_date,

2 retention_day

from dws_uv_detail_day ud join dws_new_mid_day nm on ud.mid_id =nm.mid_id

where ud.dt='2019-02-11' and nm.create_date=date_add('2019-02-11',-2)

union all

select

nm.mid_id,

nm.user_id ,

nm.version_code ,

nm.version_name ,

nm.lang ,

nm.source,

nm.os,

nm.area,

nm.model,

nm.brand,

nm.sdk_version,

nm.gmail,

nm.height_width,

nm.app_time,

nm.network,

nm.lng,

nm.lat,

nm.create_date,

3 retention_day

from dws_uv_detail_day ud join dws_new_mid_day nm on ud.mid_id =nm.mid_id

where ud.dt='2019-02-11' and nm.create_date=date_add('2019-02-11',-3);

2）查询导入数据（每天计算前 1,2,3 天的新用户访问留存明细）

hive (gmall)> select retention_day , count(*) from dws_user_retention_day group by retention_day;

③ ADS 层 留存用户数 ads_user_retention_day_count 直接 count( * )即可 1）创建 ads_user_retention_day_count 表：

hive (gmall)>

drop table if exists ads_user_retention_day_count;

create table ads_user_retention_day_count

(

create_date string comment '设备新增日期',

retention_day int comment '截止当前日期留存天数',

retention_count bigint comment '留存数量'

) COMMENT '每日用户留存情况'

stored as parquet

location '/warehouse/gmall/ads/ads_user_retention_day_count/';

导入数据 按创建日期 create_date 和 留存天数 retention_day 进行分组 group by；

hive (gmall)>

insert into table ads_user_retention_day_count

select

create_date,

retention_day,

count(*) retention_count

from dws_user_retention_day

where dt='2019-02-11'

group by create_date,retention_day;

查询导入数据

hive (gmall)> select * from ads_user_retention_day_count;

---> 2019-02-10 1 112

④ 留存用户比率 retention_count / new_mid_count 即留存个数 / 新增个数 创建表 ads_user_retention_day_rate

hive (gmall)>

drop table if exists ads_user_retention_day_rate;

create table ads_user_retention_day_rate

(

stat_date string comment '统计日期',

create_date string comment '设备新增日期',

retention_day int comment '截止当前日期留存天数',

retention_count bigint comment '留存数量',

new_mid_count string comment '当日设备新增数量',

retention_ratio decimal(10,2) comment '留存率'

) COMMENT '每日用户留存情况'

stored as parquet

location '/warehouse/gmall/ads/ads_user_retention_day_rate/';

导入数据

join ads_new_mid_countt --->每日新增设备表

hive (gmall)>

insert into table ads_user_retention_day_rate

select

'2019-02-11' ,

ur.create_date,

ur.retention_day,

ur.retention_count ,

nc.new_mid_count,

ur.retention_count/nc.new_mid_count*100

from

(

select

create_date,

retention_day,

count(*) retention_count

from dws_user_retention_day

where dt='2019-02-11'

group by create_date,retention_day

) ur join ads_new_mid_count nc on nc.create_date=ur.create_date;

查询导入数据

hive (gmall)>select * from ads_user_retention_day_rate;

2019-02-11 2019-02-10 1 112 442 25.34

需求四：沉默用户数

沉默用户：指的是只在安装当天启动过，且启动时间是在一周前

使用日活明细表 dws_uv_detail_day 作为 DWS 层数据

建表语句

hive (gmall)>

drop table if exists ads_slient_count;

create external table ads_slient_count(

dt string COMMENT '统计日期',

slient_count bigint COMMENT '沉默设备数'

)

row format delimited fields terminated by '\t'

location '/warehouse/gmall/ads/ads_slient_count';

导入数据

hive (gmall)>

insert into table ads_slient_count

select

'2019-02-20' dt,

count(*) slient_count

from

(

select mid_id

from dws_uv_detail_day

where dt<='2019-02-20'

group by mid_id

having count(*)=1 and min(dt)<date_add('2019-02-20',-7)

) t1;

需求五：本周回流用户数

本周回流=本周活跃-本周新增-上周活跃

使用日活明细表 dws_uv_detail_day 作为 DWS 层数据

本周回流(上周以前活跃过，上周没活跃，本周活跃了)=本周活跃-本周新增-上周活跃 本周回流=本周活跃 left join 本周新增 left join 上周活跃，且本周新增 id 为 null，上周活跃 id 为 null_大数据视频；

建表：

hive (gmall)>

drop table if exists ads_back_count;

create external table ads_back_count(

dt string COMMENT '统计日期',

wk_dt string COMMENT '统计日期所在周',

wastage_count bigint COMMENT '回流设备数'

)

row format delimited fields terminated by '\t'

location '/warehouse/gmall/ads/ads_back_count';

导入数据

hive (gmall)>

insert into table ads_back_count

select

'2019-02-20' dt,

concat(date_add(next_day('2019-02-20','MO'),-7),'_',date_add(next_day('2019-02-20','MO'),-1)) wk_dt,

count(*)

from

(

select t1.mid_id

from

(

select mid_id

from dws_uv_detail_wk

where wk_dt=concat(date_add(next_day('2019-02-20','MO'),-7),'_',date_add(next_day('2019-02-20','MO'),-1))

)t1

left join

(

select mid_id

from dws_new_mid_day

where create_date<=date_add(next_day('2019-02-20','MO'),-1) and create_date>=date_add(next_day('2019-02-20','MO'),-7)

)t2

on t1.mid_id=t2.mid_id

left join

(

select mid_id

from dws_uv_detail_wk

where wk_dt=concat(date_add(next_day('2019-02-20','MO'),-7*2),'_',date_add(next_day('2019-02-20','MO'),-7-1))

)t3

on t1.mid_id=t3.mid_id

where t2.mid_id is null and t3.mid_id is null

)t4;

需求六：流失用户数

流失用户：最近 7 天未登录我们称之为流失用户

使用日活明细表 dws_uv_detail_day 作为 DWS 层数据

建表语句

hive (gmall)>

drop table if exists ads_wastage_count;

create external table ads_wastage_count(

dt string COMMENT '统计日期',

wastage_count bigint COMMENT '流失设备数'

)

row format delimited fields terminated by '\t'

location '/warehouse/gmall/ads/ads_wastage_count';

导入数据

hive (gmall)>

insert into table ads_wastage_count

select

'2019-02-20',

count(*)

from

(

select mid_id

from dws_uv_detail_day

group by mid_id

having max(dt)<=date_add('2019-02-20',-7)

)t1;

需求七：最近连续 3 周活跃用户数

最近 3 周连续活跃的用户：通常是周一对前 3 周的数据做统计，该数据一周计算一次。

使用周活明细表 dws_uv_detail_wk 作为 DWS 层数据

建表语句

hive (gmall)>

drop table if exists ads_continuity_wk_count;

create external table ads_continuity_wk_count(

dt string COMMENT '统计日期,一般用结束周周日日期,如果每天计算一次,可用当天日期',

wk_dt string COMMENT '持续时间',

continuity_count bigint

)

row format delimited fields terminated by '\t'

location '/warehouse/gmall/ads/ads_continuity_wk_count';

导入数据

hive (gmall)>

insert into table ads_continuity_wk_count

select

'2019-02-20',

concat(date_add(next_day('2019-02-20','MO'),-7*3),'_',date_add(next_day('2019-02-20','MO'),-1)),

count(*)

from

(

select mid_id

from dws_uv_detail_wk

where wk_dt>=concat(date_add(next_day('2019-02-20','MO'),-73),'_',date_add(next_day('2019-02-20','MO'),-72-1))

and wk_dt<=concat(date_add(next_day('2019-02-20','MO'),-7),'_',date_add(next_day('2019-02-20','MO'),-1))

group by mid_id

having count(*)=3

)t1;

需求八：最近七天内连续三天活跃用户数

说明：最近 7 天内连续 3 天活跃用户数

使用日活明细表 dws_uv_detail_day 作为 DWS 层数据

建表

hive (gmall)>

drop table if exists ads_continuity_uv_count;

create external table ads_continuity_uv_count(

dt string COMMENT '统计日期',

wk_dt string COMMENT '最近 7 天日期',

continuity_count bigint

) COMMENT '连续活跃设备数'

row format delimited fields terminated by '\t'

location '/warehouse/gmall/ads/ads_continuity_uv_count';

导入数据

hive (gmall)>

insert into table ads_continuity_uv_count

select

'2019-02-12',

concat(date_add('2019-02-12',-6),'_','2019-02-12'),

count(*)

from

(

select mid_id

from

(

select mid_id

from

(

select

mid_id,

date_sub(dt,rank) date_dif

from

(

select

mid_id,

dt,

rank() over(partition by mid_id order by dt) rank

from dws_uv_detail_day

where dt>=date_add('2019-02-12',-6) and dt<='2019-02-12'

)t1

)t2

group by mid_id,date_dif

having count(*)>=3

)t3

group by mid_id

)t4;

ODS 层跟原始字段要一模一样；

DWD 层 　　dwd_order_info 订单表 　　dwd_order_detail 订单详情(订单和商品) 　　dwd_user_info 用户表 　　dwd_payment_info 支付流水 　　dwd_sku_info 商品表(增加分类)

每日用户行为宽表 dws_user_action

字段：user_id、order_count、order_amount、payment_count、payment_amount 、comment_count

drop table if exists dws_user_action;

create external table dws_user_action(

user_id string comment '用户 id',

order_count bigint comment '用户下单数',

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_count bigint comment '评论次数'

)comment '每日用户行为宽表'

partitioned by(dt string)

stored as parquet

location '/warehouse/gmall/dws/dws_user_action/'

tblproperties("parquet.compression"="snappy");

导入数据

0 占位符，第一个字段要有别名

with tmp_order as(

select user_id, count(*) order_count, sum(oi.total_amount) order_amount from dwd_order_info oi

where date_format(oi.create_time, 'yyyy-MM-dd')='2019-02-10' group by user_id

),

tmp_payment as(

select user_id, count(*) payment_count, sum(pi.total_amount) payment_amount from dwd_payment_info pi

where date_format(pi.payment_time, 'yyyy-MM-dd')='2019-02-10' group by user_id

),

tmp_comment as(

select user_id, count(*) comment_count from dwd_comment_log c

where date_format(c.dt, 'yyyy-MM-dd')='2019-02-10' group by user_id

)

insert overwrite table dws_user_action partition(dt='2019-02-10')

select user_actions.user_id, sum(user_actions.order_count), sum(user_actions.order_amount),

sum(user_actions.payment_count),

sum(user_actions.payment_amount),

sum(user_actions.comment_count) from(

select user_id, order_count, order_amount, 0 payment_count, 0 payment_amount, 0 comment_count from tmp_order

union all select user_id, 0, 0, payment_count, payment_amount, 0 from tmp_payment

union all select user_id, 0, 0, 0, 0, comment_count from tmp_comment

) user_actions group by user_id;

GMV 拍下订单金额；包括付款和未付款；

建表 ads_gmv_sum_day 语句：

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；

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：

#/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 "$APP".dw{s}_{u}se{r}_{a}ctionwheredt{=}^{\prime }$do_date' group by dt;

"

$hive-e"$sql";

需求十：转化率=新增用户/日活用户

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

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

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

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

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

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

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

with tmp_detail as(

select user_id, sku_id, sum(sku_num) sku_num, count() order_count, sum(od.order_pricesku_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'

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

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

#!/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

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

#!/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/$d{b}_{n}ame/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

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

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

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()

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

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;

文章转载来源于大数据学习与分享

​