本地数仓项目(二)——搭建系统业务数仓详细流程

1 说明

本文基于《本地数据仓库项目(一)——本地数仓搭建详细流程》业务数据，在本地搭建系统业务数仓。
根据模拟sql脚本生成业务数据，依次执行生成业务数据即可。

sql脚本提供如下

链接：https://pan.baidu.com/s/1AhLIuTNIyJ_GBD7M0b2RoA 
提取码：1lm8

生成的数据如下：

2 业务数据导入数仓

数仓整体框架如下，在前面的《本地数据仓库项目(一)——本地数仓搭建详细流程》已完成对数据采集及分析整体流程。这里的业务数仓数据需要用到sqoop完成从mysql导入数据到HDFS中。

2.1 安装sqoop

2.1.1 解压并重命名

tar -zxvf sqoop-1.4.6.bin__hadoop-2.0.4-alpha.tar.gz
mv sqoop-1.4.6.bin__hadoop-2.0.4-alpha sqoop-1.4.6

2.1.2 配置SQOOP_HOME环境变量

SQOOP_HOME=/root/soft/sqoop-1.4.6
PATH=$PATH:$JAVA_HOME/bin:$SHELL_HOME:$FLUME_HOME/bin:$HIVE_HOME/bin:$KAFKA_HOME/bin:$ZOOKEEPER_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$SQOOP_HOME/bin

2.1.3 配置sqoop-env.sh

mv sqoop-env-template.sh sqoop-env.sh
export HADOOP_COMMON_HOME=/root/soft/hadoop-2.7.2
export HADOOP_MAPRED_HOME=/root/soft/hadoop-2.7.2
export HIVE_HOME=/root/soft/hive
export ZOOKEEPER_HOME=/root/soft/zookeeper-3.4.10
export ZOOCFGDIR=/root/soft/zookeeper-3.4.10

2.1.4拷贝mysql的jdbc驱动到sqoop的lib目录下

2.1.5 测试链接

bin/sqoop list-databases --connect jdbc:mysql://192.168.2.100:3306/ --username root --password 123456

出现如下页面表示sqoop安装成功

2.2 sqoop导入数据到HDFS

如下sqoop脚本，可实现定时自动导入数据到HDFS

#!/bin/bash
db_date=$2
echo $db_date
db_name=gmall
import_data() {
/root/soft/sqoop-1.4.6/bin/sqoop import \
--connect jdbc:mysql://192.168.2.100:3306/$db_name \
--username root \
--password 123456 \
--target-dir /origin_data/$db_name/db/$1/$db_date \
--delete-target-dir \
--num-mappers 1 \
--fields-terminated-by "\t" \
--query "$2"' and $CONDITIONS;' \
--null-string '\\N' \
--null-non-string '\\N'
}
import_sku_info(){
  import_data "sku_info" "select 
id, spu_id, price, sku_name, sku_desc, weight, tm_id,
category3_id, create_time
  from sku_info where 1=1"
}
import_user_info(){
  import_data "user_info" "select 
id, name, birthday, gender, email, user_level, 
create_time 
from user_info where 1=1"
}
import_base_category1(){
  import_data "base_category1" "select 
id, name from base_category1 where 1=1"
}
import_base_category2(){
  import_data "base_category2" "select 
id, name, category1_id from base_category2 where 1=1"
}
import_base_category3(){
  import_data "base_category3" "select id, name, category2_id from base_category3 where 1=1"
}
import_order_detail(){
  import_data   "order_detail"   "select 
    od.id, 
    order_id, 
    user_id, 
    sku_id, 
    sku_name, 
    order_price, 
    sku_num, 
    o.create_time  
  from order_info o, order_detail od
  where o.id=od.order_id
  and DATE_FORMAT(create_time,'%Y-%m-%d')='$db_date'"
}
import_payment_info(){
  import_data "payment_info"   "select 
    id,  
    out_trade_no, 
    order_id, 
    user_id, 
    alipay_trade_no, 
    total_amount,  
    subject, 
    payment_type, 
    payment_time 
  from payment_info 
  where DATE_FORMAT(payment_time,'%Y-%m-%d')='$db_date'"
}
import_order_info(){
  import_data   "order_info"   "select 
    id, 
    total_amount, 
    order_status, 
    user_id, 
    payment_way, 
    out_trade_no, 
    create_time, 
    operate_time  
  from order_info 
  where (DATE_FORMAT(create_time,'%Y-%m-%d')='$db_date' or DATE_FORMAT(operate_time,'%Y-%m-%d')='$db_date')"
}
case $1 in
  "base_category1")
     import_base_category1
;;
  "base_category2")
     import_base_category2
;;
  "base_category3")
     import_base_category3
;;
  "order_info")
     import_order_info
;;
  "order_detail")
     import_order_detail
;;
  "sku_info")
     import_sku_info
;;
  "user_info")
     import_user_info
;;
  "payment_info")
     import_payment_info
;;
   "all")
   import_base_category1
   import_base_category2
   import_base_category3
   import_order_info
   import_order_detail
   import_sku_info
   import_user_info
   import_payment_info
;;
esac

注意：
①默认sqoop到import数据时，将Mysql的Null类型，转为’null’
②hive中使用\N代表NULL类型
③如果希望在import时，讲将Mysql的Null类型，转为自己期望的类型，
需要使用–null-string and —null-non-string

–null-string： 当mysql的string类型列为null时，导入到hive时，使用什么来代替！
–null-string a: 如果mysql中，当前列是字符串类型(varchar,char)，假如这列值为NULL，导入到hive时，使用a来代替！
–null-non-string： 当mysql的非string类型列为null时，导入到hive时，使用什么来代替！
–null-non-string b: 如果mysql中，当前列不是字符串类型(varchar,char)，假如这列值为NULL，导入到hive时，使用b来代替！

④如果到导出时，希望将指定的参数，导出为mysql的NULL类型，需要使用

–input-null-string and —input-null-non-string —input-null-string a： 在hive导出到mysql时，如果hive中string类型的列的值为a,导出到mysql中，使用NULL代替！
–input-null-non-string b:
在hive导出到mysql时，如果hive中非string类型的列的值为b,导出到mysql中，使用NULL代替！

执行脚本，导入数据

3 ODS层

3.1 创建ods表

3.1.1 创建订单表

drop table if exists ods_order_info;
create external table ods_order_info (
    `id` string COMMENT '订单编号',
    `total_amount` decimal(10,2) COMMENT '订单金额',
    `order_status` string COMMENT '订单状态',
    `user_id` string COMMENT '用户id',
    `payment_way` string COMMENT '支付方式',
    `out_trade_no` string COMMENT '支付流水号',
    `create_time` string COMMENT '创建时间',
    `operate_time` string COMMENT '操作时间'
) COMMENT '订单表'
PARTITIONED BY (`dt` string)
row format delimited fields terminated by '\t'
location '/wavehouse/gmall/ods/ods_order_info/';

3.1.2 创建订单明细表

drop table if exists ods_order_detail;
create external table ods_order_detail( 
    `id` string COMMENT '订单详情编号',
    `order_id` string  COMMENT '订单号', 
    `user_id` string COMMENT '用户id',
    `sku_id` string COMMENT '商品id',
    `sku_name` string COMMENT '商品名称',
    `order_price` string COMMENT '商品单价',
    `sku_num` string COMMENT '商品数量',
    `create_time` string COMMENT '创建时间'
) COMMENT '订单明细表'
PARTITIONED BY (`dt` string)
row format delimited fields terminated by '\t' 
location '/wavehouse/gmall/ods/ods_order_detail/';

3.1.3 创建商品信息表

drop table if exists ods_sku_info;
create external table ods_sku_info( 
    `id` string COMMENT 'skuId',
    `spu_id` string   COMMENT 'spuid', 
    `price` decimal(10,2) COMMENT '价格',
    `sku_name` string COMMENT '商品名称',
    `sku_desc` string COMMENT '商品描述',
    `weight` string COMMENT '重量',
    `tm_id` string COMMENT '品牌id',
    `category3_id` string COMMENT '品类id',
    `create_time` string COMMENT '创建时间'
) COMMENT '商品表'
PARTITIONED BY (`dt` string)
row format delimited fields terminated by '\t'
location '/wavehouse/gmall/ods/ods_sku_info/';

3.1.4 创建用户表

drop table if exists ods_user_info;
create external table ods_user_info( 
    `id` string COMMENT '用户id',
    `name`  string COMMENT '姓名',
    `birthday` string COMMENT '生日',
    `gender` string COMMENT '性别',
    `email` string COMMENT '邮箱',
    `user_level` string COMMENT '用户等级',
    `create_time` string COMMENT '创建时间'
) COMMENT '用户信息'
PARTITIONED BY (`dt` string)
row format delimited fields terminated by '\t'
location '/wavehouse/gmall/ods/ods_user_info/';

3.1.5 创建商品一级分类表

drop table if exists ods_base_category1;
create external table ods_base_category1( 
    `id` string COMMENT 'id',
    `name`  string COMMENT '名称'
) COMMENT '商品一级分类'
PARTITIONED BY (`dt` string)
row format delimited fields terminated by '\t'
location '/wavehouse/gmall/ods/ods_base_category1/';

3.1.6 创建商品二级分类表

drop table if exists ods_base_category2;
create external table ods_base_category2( 
    `id` string COMMENT ' id',
    `name` string COMMENT '名称',
    category1_id string COMMENT '一级品类id'
) COMMENT '商品二级分类'
PARTITIONED BY (`dt` string)
row format delimited fields terminated by '\t'
location '/wavehouse/gmall/ods/ods_base_category2/';

3.1.7 创建商品三级表

drop table if exists ods_base_category3;
create external table ods_base_category3(
    `id` string COMMENT ' id',
    `name`  string COMMENT '名称',
    category2_id string COMMENT '二级品类id'
) COMMENT '商品三级分类'
PARTITIONED BY (`dt` string)
row format delimited fields terminated by '\t'
location '/wavehouse/gmall/ods/ods_base_category3/';

3.1.8 创建支付流水表

drop table if exists ods_payment_info;
create external table ods_payment_info(
    `id`   bigint COMMENT '编号',
    `out_trade_no`    string COMMENT '对外业务编号',
    `order_id`        string COMMENT '订单编号',
    `user_id`         string COMMENT '用户编号',
    `alipay_trade_no` string COMMENT '支付宝交易流水编号',
    `total_amount`    decimal(16,2) COMMENT '支付金额',
    `subject`         string COMMENT '交易内容',
    `payment_type`    string COMMENT '支付类型',
    `payment_time`    string COMMENT '支付时间'
   )  COMMENT '支付流水表'
PARTITIONED BY (`dt` string)
row format delimited fields terminated by '\t'
location '/wavehouse/gmall/ods/ods_payment_info/';

3.2 导入数据

load data inpath '/origin_data/gmall/db/order_info/2023-01-04' OVERWRITE into table gmall.ods_order_info partition(dt='2023-01-04');
load data inpath '/origin_data/gmall/db/order_info/2023-01-05' OVERWRITE into table gmall.ods_order_info partition(dt='2023-01-05');
load data inpath '/origin_data/gmall/db/order_detail/2023-01-04' OVERWRITE into table gmall.ods_order_detail partition(dt='2023-01-04');
load data inpath '/origin_data/gmall/db/order_detail/2023-01-05' OVERWRITE into table gmall.ods_order_detail partition(dt='2023-01-05');
load data inpath '/origin_data/gmall/db/sku_info/2023-01-04' OVERWRITE into table gmall.ods_sku_info partition(dt='2023-01-04');
load data inpath '/origin_data/gmall/db/sku_info/2023-01-05' OVERWRITE into table gmall.ods_sku_info partition(dt='2023-01-05');
load data inpath '/origin_data/gmall/db/user_info/2023-01-04' OVERWRITE into table gmall.ods_user_info partition(dt='2023-01-04');
load data inpath '/origin_data/gmall/db/user_info/2023-01-05' OVERWRITE into table gmall.ods_user_info partition(dt='2023-01-05');
load data inpath '/origin_data/gmall/db/payment_info/2023-01-04' OVERWRITE into table gmall.ods_payment_info partition(dt='2023-01-04');
load data inpath '/origin_data/gmall/db/payment_info/2023-01-05' OVERWRITE into table gmall.ods_payment_info partition(dt='2023-01-05');
load data inpath '/origin_data/gmall/db/base_category1/2023-01-04' OVERWRITE into table gmall.ods_base_category1 partition(dt='2023-01-04');
load data inpath '/origin_data/gmall/db/base_category1/2023-01-05' OVERWRITE into table gmall.ods_base_category1 partition(dt='2023-01-05');
load data inpath '/origin_data/gmall/db/base_category2/2023-01-04' OVERWRITE into table gmall.ods_base_category2 partition(dt='2023-01-04');
load data inpath '/origin_data/gmall/db/base_category2/2023-01-05' OVERWRITE into table gmall.ods_base_category2 partition(dt='2023-01-05');
load data inpath '/origin_data/gmall/db/base_category3/2023-01-04' OVERWRITE into table gmall.ods_base_category3 partition(dt='2023-01-04');
load data inpath '/origin_data/gmall/db/base_category3/2023-01-05' OVERWRITE into table gmall.ods_base_category3 partition(dt='2023-01-05');

可以将以上写成脚本，以日期为传参参数，每天定时执行即可。

4 DWD层

4.1 创建dwd明细表

4.1.1 创建订单表

drop table if exists dwd_order_info;
create external table dwd_order_info (
    `id` string COMMENT '',
    `total_amount` decimal(10,2) COMMENT '',
    `order_status` string COMMENT ' 1 2 3 4 5',
    `user_id` string COMMENT 'id',
    `payment_way` string COMMENT '',
    `out_trade_no` string COMMENT '',
    `create_time` string COMMENT '',
    `operate_time` string COMMENT ''
) 
PARTITIONED BY (`dt` string)
stored as parquet
location '/wavehouse/gmall/dwd/dwd_order_info/'
tblproperties ("parquet.compression"="snappy");

4.1.2 创建订单详情表

drop table if exists dwd_order_detail;
create external table dwd_order_detail( 
    `id` string COMMENT '',
    `order_id` decimal(10,2) COMMENT '', 
    `user_id` string COMMENT 'id',
    `sku_id` string COMMENT 'id',
    `sku_name` string COMMENT '',
    `order_price` string COMMENT '',
    `sku_num` string COMMENT '',
    `create_time` string COMMENT ''
)
PARTITIONED BY (`dt` string)
stored as parquet
location '/wavehouse/gmall/dwd/dwd_order_detail/'
tblproperties ("parquet.compression"="snappy");

4.1.3 创建用户表

drop table if exists dwd_user_info;
create external table dwd_user_info( 
    `id` string COMMENT 'id',
    `name` string COMMENT '', 
    `birthday` string COMMENT '',
    `gender` string COMMENT '',
    `email` string COMMENT '',
    `user_level` string COMMENT '',
    `create_time` string COMMENT ''
) 
PARTITIONED BY (`dt` string)
stored as parquet
location '/wavehouse/gmall/dwd/dwd_user_info/'
tblproperties ("parquet.compression"="snappy");

4.1.4 创建支付流水表

drop table if exists dwd_payment_info;
create external table dwd_payment_info(
    `id`   bigint COMMENT '',
    `out_trade_no`    string COMMENT '',
    `order_id`        string COMMENT '',
    `user_id`         string COMMENT '',
    `alipay_trade_no` string COMMENT '',
    `total_amount`    decimal(16,2) COMMENT '',
    `subject`         string COMMENT '',
    `payment_tpe`    string COMMENT '',
    `payment_time`    string COMMENT ''
   )  
PARTITIONED BY (`dt` string)
stored as parquet
location '/wavehouse/gmall/dwd/dwd_payment_info/'
tblproperties ("parquet.compression"="snappy");

4.1.5 创建商品分类表

drop table if exists dwd_sku_info;
create external table dwd_sku_info(
    `id` string COMMENT 'skuId',
    `spu_id` string COMMENT 'spuid',
    `price` decimal(10,2) COMMENT '',
    `sku_name` string COMMENT '',
    `sku_desc` string COMMENT '',
    `weight` string COMMENT '',
    `tm_id` string COMMENT 'id',
    `category3_id` string COMMENT '1id',
    `category2_id` string COMMENT '2id',
    `category1_id` string COMMENT '3id',
    `category3_name` string COMMENT '3',
    `category2_name` string COMMENT '2',
    `category1_name` string COMMENT '1',
    `create_time` string COMMENT ''
) 
PARTITIONED BY (`dt` string)
stored as parquet
location '/wavehouse/gmall/dwd/dwd_sku_info/'
tblproperties ("parquet.compression"="snappy");

4.2 导入数据

#!/bin/bash
# 定义变量方便修改
APP=gmall
hive=/root/soft/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".dwd_order_info partition(dt)
select * from "$APP".ods_order_info 
where dt='$do_date' and id is not null;
insert overwrite table "$APP".dwd_order_detail partition(dt)
select * from "$APP".ods_order_detail 
where dt='$do_date'   and id is not null;
insert overwrite table "$APP".dwd_user_info partition(dt)
select * from "$APP".ods_user_info
where dt='$do_date' and id is not null;
insert overwrite table "$APP".dwd_payment_info partition(dt)
select * from "$APP".ods_payment_info
where dt='$do_date' and id is not null;
insert overwrite table "$APP".dwd_sku_info partition(dt)
select  
    sku.id,
    sku.spu_id,
    sku.price,
    sku.sku_name,
    sku.sku_desc,
    sku.weight,
    sku.tm_id,
    sku.category3_id,
    c2.id category2_id,
    c1.id category1_id,
    c3.name category3_name,
    c2.name category2_name,
    c1.name category1_name,
    sku.create_time,
    sku.dt
from
    "$APP".ods_sku_info sku
join "$APP".ods_base_category3 c3 on sku.category3_id=c3.id 
    join "$APP".ods_base_category2 c2 on c3.category2_id=c2.id 
    join "$APP".ods_base_category1 c1 on c2.category1_id=c1.id 
where sku.dt='$do_date'  and c2.dt='$do_date'
and c3.dt='$do_date' and c1.dt='$do_date'
and sku.id is not null;
"
$hive -e "$sql"

5 dws层

5.1 用户行为宽表

需求目标，把每个用户单日的行为聚合起来组成一张多列宽表，以便之后关联用户维度信息后进行，不同角度的统计分析。

5.1.1 创建用户行为宽表

drop table if exists dws_user_action;
create external table dws_user_action 
(   
    user_id          string      comment '用户 id',
    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_count   bigint      comment '评论次数'
) COMMENT '每日用户行为宽表'
PARTITIONED BY (`dt` string)
stored as parquet
location '/wavehouse/gmall/dws/dws_user_action/';

5.1.2 导入数据

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')='2023-01-04'
    group by user_id
) ,
tmp_payment as
(
    select
        user_id, 
        sum(pi.total_amount) payment_amount, 
        count(*) payment_count 
    from dwd_payment_info pi 
    where date_format(pi.payment_time,'yyyy-MM-dd')='2023-01-04'
    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')='2023-01-04'
    group by user_id
)
insert overwrite table dws_user_action partition(dt='2023-01-04')
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;

6 需求

6.1 需求1

求GMV成交总额。GMV是指一定时间内的成交总额(如一天、一周、一个月)
建表

drop table if exists ads_gmv_sum_day;
create external 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订单总金额',
    `gmv_payment`  decimal(16,2) COMMENT '当日支付金额'
) COMMENT 'GMV'
row format delimited fields terminated by '\t'
location '/wavehouse/gmall/ads/ads_gmv_sum_day/';

插入数据

INSERT INTO TABLE ads_gmv_sum_day
SELECT 
'2023-01-04' dt, 
sum(order_count) gmv_count, 
sum(order_amount) gmv_amount, 
sum(payment_amount) gmv_payment
FROM 
dws_user_action
WHERE dt='2023-01-04'
GROUP BY dt;

6.2 需求2

求转换率之用户新鲜度及漏斗分析

6.2.1 ADS层之新增用户占日活跃用户比率（用户新鲜度）

建表

drop table if exists ads_user_convert_day;
create external 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_ratio`   decimal(10,2) COMMENT '当日新增占日活的比率'
) COMMENT '转化率'
row format delimited fields terminated by '\t'
location '/wavehouse/gmall/ads/ads_user_convert_day/';

数据导入

insert into table ads_user_convert_day
select
    '2023-01-04' dt,
    sum(uc.dc) sum_dc,
    sum(uc.nmc) sum_nmc,
    cast(sum( uc.nmc)/sum( uc.dc)*100 as decimal(10,2)) new_m_ratio
from 
(
    select
        day_count dc,
        0 nmc
    from ads_uv_count
    where dt='2023-01-04'
    union all
    select
        0 dc,
        new_mid_count nmc
    from ads_new_mid_count
    where create_date='2023-01-04'
)uc;

6.2.2 ADS层之用户行为漏斗分析

创建表

drop table if exists ads_user_action_convert_day;
create external  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_ratio`  decimal(10,2) COMMENT '访问到下单转化率',
    `payment_u_count` bigint     COMMENT '支付人数',
    `order2payment_convert_ratio` 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 '/wavehouse/gmall/ads/ads_user_action_convert_day/';

插入数据

insert into table ads_user_action_convert_day
select 
    '2023-01-04',
    uv.day_count,
    ua.order_count,
    cast(ua.order_count/uv.day_count as  decimal(10,2)) visitor2order_convert_ratio,
    ua.payment_count,
    cast(ua.payment_count/ua.order_count as  decimal(10,2)) order2payment_convert_ratio
from  
(
    select 
        dt,
        sum(if(order_count>0,1,0)) order_count,
        sum(if(payment_count>0,1,0)) payment_count
    from dws_user_action
    where dt='2023-01-04'
    group by dt
)ua join ads_uv_count  uv on uv.dt=ua.dt;

6.3 需求3

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

6.3.1 DWS层建表

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 '/wavehouse/gmall/dws/dws_user_sale_detail_daycount/'
tblproperties ("parquet.compression"="snappy");

数据导入

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 dwd_order_detail od
    where od.dt='2023-01-05'
    group by user_id, sku_id
)  
insert overwrite table dws_sale_detail_daycount partition(dt='2023-01-05')
select 
    tmp_detail.user_id,
    tmp_detail.sku_id,
    u.gender,
    months_between('2023-01-05', 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 tmp_detail.user_id =u.id and u.dt='2023-01-05'
left join dwd_sku_info s on tmp_detail.sku_id =s.id and s.dt='2023-01-05';

6.3.2 ods层

建表

drop table ads_sale_tm_category1_stat_mn;
create external table ads_sale_tm_category1_stat_mn
(   
    tm_id string comment '品牌id',
    category1_id string comment '1级品类id ',
    category1_name string comment '1级品类名称 ',
    buycount   bigint comment  '购买人数',
    buy_twice_last bigint  comment '两次以上购买人数',
    buy_twice_last_ratio decimal(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 '/wavehouse/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)) buy3timeLastRatio ,
    date_format('2023-01-04' ,'yyyy-MM') stat_mn,
    '2023-01-04' stat_date
from 
(
select 
        user_id, 
sd.sku_tm_id,
        sd.sku_category1_id,
        sd.sku_category1_name,
        sum(order_count) order_count
    from dws_sale_detail_daycount sd 
    where date_format(dt,'yyyy-MM')=date_format('2023-01-04' ,'yyyy-MM')
    group by user_id, sd.sku_tm_id, sd.sku_category1_id, sd.sku_category1_name
) mn
group by mn.sku_tm_id, mn.sku_category1_id, mn.sku_category1_name;

6.4 需求4

各用户等级对应的复购率前十的商品排行
建表

drop  table ads_ul_rep_ratio;
create  table ads_ul_rep_ratio(   
    user_level string comment '用户等级' ,
    sku_id string comment '商品id',
buy_count bigint  comment '购买总人数',
buy_twice_count bigint comment  '两次购买总数',
    buy_twice_rate decimal(10,2)  comment  '二次复购率', 
rank string comment  '排名' ,
    state_date string comment '统计日期'
)   COMMENT '复购率统计'
row format delimited  fields terminated by '\t' 
location '/wavehouse/gmall/ads/ads_ul_rep_ratio/';

插入数据

with 
tmp_count as(
  select -- 每个等级内每个用户对每个产品的下单次数    
user_level,
user_id,
    sku_id,
    sum(order_count) order_count
  from dws_sale_detail_daycount
  where dt<='2023-01-04'
  group by user_level, user_id, sku_id
)
insert overwrite table ads_ul_rep_ratio
select
  *
from(
  select
    user_level,
    sku_id,
    sum(if(order_count >=1, 1, 0)) buy_count,
    sum(if(order_count >=2, 1, 0)) buy_twice_count,
    sum(if(order_count >=2, 1, 0)) / sum(if(order_count >=1, 1, 0)) * 100  buy_twice_rate,
    row_number() over(partition by user_level order by sum(if(order_count >=2, 1, 0)) / sum(if(order_count >=1, 1, 0)) desc) rn,
    '2023-01-04'
  from tmp_count
  group by user_level, sku_id
) t1
where rn<=10

接下来是本地数仓项目数据可视化和任务调度，详见《本地数仓项目(三）—— 数据可视化和任务调度》