数仓搭建-DIM层

目录

数仓搭建-DIM层

商品维度表（全量）

优惠券维度表（全量）

活动维度表（全量）

地区维度表（特殊）

时间维度表（特殊）

用户维度表（拉链表）

 DIM层首日数据装载脚本

DIM层每日数据装载脚本

---

数仓搭建-DIM层

商品维度表（全量）

--DIM
--商品维度表
DROP TABLE IF EXISTS dim_sku_info;
CREATE EXTERNAL TABLE dim_sku_info (
    `id` STRING COMMENT '商品id',
    `price` DECIMAL(16,2) COMMENT '商品价格',
    `sku_name` STRING COMMENT '商品名称',
    `sku_desc` STRING COMMENT '商品描述',
    `weight` DECIMAL(16,2) COMMENT '重量',
    `is_sale` BOOLEAN COMMENT '是否在售',
    `spu_id` STRING COMMENT 'spu编号',
    `spu_name` STRING COMMENT 'spu名称',
    `category3_id` STRING COMMENT '三级分类id',
    `category3_name` STRING COMMENT '三级分类名称',
    `category2_id` STRING COMMENT '二级分类id',
    `category2_name` STRING COMMENT '二级分类名称',
    `category1_id` STRING COMMENT '一级分类id',
    `category1_name` STRING COMMENT '一级分类名称',
    `tm_id` STRING COMMENT '品牌id',
    `tm_name` STRING COMMENT '品牌名称',
    `sku_attr_values` ARRAY> COMMENT '平台属性',
    `sku_sale_attr_values` ARRAY> COMMENT '销售属性',
    `create_time` STRING COMMENT '创建时间'
) COMMENT '商品维度表'
PARTITIONED BY (`dt` STRING)
STORED AS PARQUET
LOCATION '/warehouse/gmall/dim/dim_sku_info/'
TBLPROPERTIES ("parquet.compression"="lzo");

首日装载

with
sku as
(
    select
        id,
        price,
        sku_name,
        sku_desc,
        weight,
        is_sale,
        spu_id,
        category3_id,
        tm_id,
        create_time
    from ods_sku_info
    where dt='2020-06-14'
),
spu as
(
    select
        id,
        spu_name
    from ods_spu_info
    where dt='2020-06-14'
),
c3 as
(
    select
        id,
        name,
        category2_id
    from ods_base_category3
    where dt='2020-06-14'
),
c2 as
(
    select
        id,
        name,
        category1_id
    from ods_base_category2
    where dt='2020-06-14'
),
c1 as
(
    select
        id,
        name
    from ods_base_category1
    where dt='2020-06-14'
),
tm as
(
    select
        id,
        tm_name
    from ods_base_trademark
    where dt='2020-06-14'
),
attr as
(
    select
        sku_id,
        collect_set(named_struct('attr_id',attr_id,'value_id',value_id,'attr_name',attr_name,'value_name',value_name)) attrs
    from ods_sku_attr_value
    where dt='2020-06-14'
    group by sku_id
),
sale_attr as
(
    select
        sku_id,
        collect_set(named_struct('sale_attr_id',sale_attr_id,'sale_attr_value_id',sale_attr_value_id,'sale_attr_name',sale_attr_name,'sale_attr_value_name',sale_attr_value_name)) sale_attrs
    from ods_sku_sale_attr_value
    where dt='2020-06-14'
    group by sku_id
)
insert overwrite table dim_sku_info partition(dt='2020-06-14')
select
    sku.id,
    sku.price,
    sku.sku_name,
    sku.sku_desc,
    sku.weight,
    sku.is_sale,
    sku.spu_id,
    spu.spu_name,
    sku.category3_id,
    c3.name,
    c3.category2_id,
    c2.name,
    c2.category1_id,
    c1.name,
    sku.tm_id,
    tm.tm_name,
    attr.attrs,
    sale_attr.sale_attrs,
    sku.create_time
from sku
left join spu on sku.spu_id=spu.id
left join c3 on sku.category3_id=c3.id
left join c2 on c3.category2_id=c2.id
left join c1 on c2.category1_id=c1.id
left join tm on sku.tm_id=tm.id
left join attr on sku.id=attr.sku_id
left join sale_attr on sku.id=sale_attr.sku_id;
３）每日装载
with
sku as
(
    select
        id,
        price,
        sku_name,
        sku_desc,
        weight,
        is_sale,
        spu_id,
        category3_id,
        tm_id,
        create_time
    from ods_sku_info
    where dt='2020-06-15'
),
spu as
(
    select
        id,
        spu_name
    from ods_spu_info
    where dt='2020-06-15'
),
c3 as
(
    select
        id,
        name,
        category2_id
    from ods_base_category3
    where dt='2020-06-15'
),
c2 as
(
    select
        id,
        name,
        category1_id
    from ods_base_category2
    where dt='2020-06-15'
),
c1 as
(
    select
        id,
        name
    from ods_base_category1
    where dt='2020-06-15'
),
tm as
(
    select
        id,
        tm_name
    from ods_base_trademark
    where dt='2020-06-15'
),
attr as
(
    select
        sku_id,
        collect_set(named_struct('attr_id',attr_id,'value_id',value_id,'attr_name',attr_name,'value_name',value_name)) attrs
    from ods_sku_attr_value
    where dt='2020-06-15'
    group by sku_id
),
sale_attr as
(
    select
        sku_id,
        collect_set(named_struct('sale_attr_id',sale_attr_id,'sale_attr_value_id',sale_attr_value_id,'sale_attr_name',sale_attr_name,'sale_attr_value_name',sale_attr_value_name)) sale_attrs
    from ods_sku_sale_attr_value
    where dt='2020-06-15'
    group by sku_id
)
insert overwrite table dim_sku_info partition(dt='2020-06-15')
select
    sku.id,
    sku.price,
    sku.sku_name,
    sku.sku_desc,
    sku.weight,
    sku.is_sale,
    sku.spu_id,
    spu.spu_name,
    sku.category3_id,
    c3.name,
    c3.category2_id,
    c2.name,
    c2.category1_id,
    c1.name,
    sku.tm_id,
    tm.tm_name,
    attr.attrs,
    sale_attr.sale_attrs,
    sku.create_time
from sku
left join spu on sku.spu_id=spu.id
left join c3 on sku.category3_id=c3.id
left join c2 on c3.category2_id=c2.id
left join c1 on c2.category1_id=c1.id
left join tm on sku.tm_id=tm.id
left join attr on sku.id=attr.sku_id
left join sale_attr on sku.id=sale_attr.sku_id;

优惠券维度表（全量）

DROP TABLE IF EXISTS dim_coupon_info;
CREATE EXTERNAL TABLE dim_coupon_info(
    `id` STRING COMMENT '购物券编号',
    `coupon_name` STRING COMMENT '购物券名称',
    `coupon_type` STRING COMMENT '购物券类型 1 现金券 2 折扣券 3 满减券 4 满件打折券',
    `condition_amount` DECIMAL(16,2) COMMENT '满额数',
    `condition_num` BIGINT COMMENT '满件数',
    `activity_id` STRING COMMENT '活动编号',
    `benefit_amount` DECIMAL(16,2) COMMENT '减金额',
    `benefit_discount` DECIMAL(16,2) COMMENT '折扣',
    `create_time` STRING COMMENT '创建时间',
    `range_type` STRING COMMENT '范围类型 1、商品 2、品类 3、品牌',
    `limit_num` BIGINT COMMENT '最多领取次数',
    `taken_count` BIGINT COMMENT '已领取次数',
    `start_time` STRING COMMENT '可以领取的开始日期',
    `end_time` STRING COMMENT '可以领取的结束日期',
    `operate_time` STRING COMMENT '修改时间',
    `expire_time` STRING COMMENT '过期时间'
) COMMENT '优惠券维度表'
PARTITIONED BY (`dt` STRING)
STORED AS PARQUET
LOCATION '/warehouse/gmall/dim/dim_coupon_info/'
TBLPROPERTIES ("parquet.compression"="lzo");

首日装载

--数据装载
insert overwrite table dim_coupon_info partition(dt='2022-09-03')
select
    id,
    coupon_name,
    coupon_type,
    condition_amount,
    condition_num,
    activity_id,
    benefit_amount,
    benefit_discount,
    create_time,
    range_type,
    limit_num,
    taken_count,
    start_time,
    end_time,
    operate_time,
    expire_time
from ods_coupon_info
where dt='2022-09-03';

活动维度表（全量）

DROP TABLE IF EXISTS dim_activity_rule_info;
CREATE EXTERNAL TABLE dim_activity_rule_info(
    `activity_rule_id` STRING COMMENT '活动规则ID',
    `activity_id` STRING COMMENT '活动ID',
    `activity_name` STRING  COMMENT '活动名称',
    `activity_type` STRING  COMMENT '活动类型',
    `start_time` STRING  COMMENT '开始时间',
    `end_time` STRING  COMMENT '结束时间',
    `create_time` STRING  COMMENT '创建时间',
    `condition_amount` DECIMAL(16,2) COMMENT '满减金额',
    `condition_num` BIGINT COMMENT '满减件数',
    `benefit_amount` DECIMAL(16,2) COMMENT '优惠金额',
    `benefit_discount` DECIMAL(16,2) COMMENT '优惠折扣',
    `benefit_level` STRING COMMENT '优惠级别'
) COMMENT '活动信息表'
PARTITIONED BY (`dt` STRING)
STORED AS PARQUET
LOCATION '/warehouse/gmall/dim/dim_activity_rule_info/'
TBLPROPERTIES ("parquet.compression"="lzo");

首日装载
insert overwrite table dim_activity_rule_info partition(dt='2020-06-14')
select
    ar.id,
    ar.activity_id,
    ai.activity_name,
    ar.activity_type,
    ai.start_time,
    ai.end_time,
    ai.create_time,
    ar.condition_amount,
    ar.condition_num,
    ar.benefit_amount,
    ar.benefit_discount,
    ar.benefit_level
from
(
    select
        id,
        activity_id,
        activity_type,
        condition_amount,
        condition_num,
        benefit_amount,
        benefit_discount,
        benefit_level
    from ods_activity_rule
    where dt='2020-06-14'
)ar
left join
(
    select
        id,
        activity_name,
        start_time,
        end_time,
        create_time
    from ods_activity_info
    where dt='2020-06-14'
)ai
on ar.activity_id=ai.id;

地区维度表（特殊）

DROP TABLE IF EXISTS dim_base_province;
CREATE EXTERNAL TABLE dim_base_province (
    `id` STRING COMMENT 'id',
    `province_name` STRING COMMENT '省市名称',
    `area_code` STRING COMMENT '地区编码',
    `iso_code` STRING COMMENT 'ISO-3166编码，供可视化使用',
    `iso_3166_2` STRING COMMENT 'IOS-3166-2编码，供可视化使用',
    `region_id` STRING COMMENT '地区id',
    `region_name` STRING COMMENT '地区名称'
) COMMENT '地区维度表'
STORED AS PARQUET
LOCATION '/warehouse/gmall/dim/dim_base_province/'
TBLPROPERTIES ("parquet.compression"="lzo");

insert overwrite table dim_base_province
select
    bp.id,
    bp.name,
    bp.area_code,
    bp.iso_code,
    bp.iso_3166_2,
    bp.region_id,
    br.region_name
from ods_base_province bp
join ods_base_region br on bp.region_id = br.id;

时间维度表（特殊）

创建一个临时表

DROP TABLE IF EXISTS dim_date_info;
CREATE EXTERNAL TABLE dim_date_info(
    `date_id` STRING COMMENT '日',
    `week_id` STRING COMMENT '周ID',
    `week_day` STRING COMMENT '周几',
    `day` STRING COMMENT '每月的第几天',
    `month` STRING COMMENT '第几月',
    `quarter` STRING COMMENT '第几季度',
    `year` STRING COMMENT '年',
    `is_workday` STRING COMMENT '是否是工作日',
    `holiday_id` STRING COMMENT '节假日'
) COMMENT '时间维度表'
STORED AS PARQUET
LOCATION '/warehouse/gmall/dim/dim_date_info/'
TBLPROPERTIES ("parquet.compression"="lzo");

上传文件

创建真正的时间维度表

DROP TABLE IF EXISTS dim_date_info;
CREATE EXTERNAL TABLE dim_date_info(
    `date_id` STRING COMMENT '日',
    `week_id` STRING COMMENT '周ID',
    `week_day` STRING COMMENT '周几',
    `day` STRING COMMENT '每月的第几天',
    `month` STRING COMMENT '第几月',
    `quarter` STRING COMMENT '第几季度',
    `year` STRING COMMENT '年',
    `is_workday` STRING COMMENT '是否是工作日',
    `holiday_id` STRING COMMENT '节假日'
) COMMENT '时间维度表'
STORED AS PARQUET
LOCATION '/warehouse/gmall/dim/dim_date_info/'
TBLPROPERTIES ("parquet.compression"="lzo");

 执行以下语句将其导入时间维度表

insert overwrite table dim_date_info select * from tmp_dim_date_info;

用户维度表（拉链表）

建表语句
DROP TABLE IF EXISTS dim_user_info;
CREATE EXTERNAL TABLE dim_user_info(
    `id` STRING COMMENT '用户id',
    `login_name` STRING COMMENT '用户名称',
    `nick_name` STRING COMMENT '用户昵称',
    `name` STRING COMMENT '用户姓名',
    `phone_num` STRING COMMENT '手机号码',
    `email` STRING COMMENT '邮箱',
    `user_level` STRING COMMENT '用户等级',
    `birthday` STRING COMMENT '生日',
    `gender` STRING COMMENT '性别',
    `create_time` STRING COMMENT '创建时间',
    `operate_time` STRING COMMENT '操作时间',
    `start_date` STRING COMMENT '开始日期',
    `end_date` STRING COMMENT '结束日期'
) COMMENT '用户表'
PARTITIONED BY (`dt` STRING)
STORED AS PARQUET
LOCATION '/warehouse/gmall/dim/dim_user_info/'
TBLPROPERTIES ("parquet.compression"="lzo");

首日装载

insert overwrite table dim_user_info partition(dt='9999-99-99')
select
    id,
    login_name,
    nick_name,
    md5(name),
    md5(phone_num),
    md5(email),
    user_level,
    birthday,
    gender,
    create_time,
    operate_time,
    '2020-06-14',
    '9999-99-99'
from ods_user_info
where dt='2020-06-14';

sql编写

sql编写
with
tmp as
(
    select
        old.id old_id,
        old.login_name old_login_name,
        old.nick_name old_nick_name,
        old.name old_name,
        old.phone_num old_phone_num,
        old.email old_email,
        old.user_level old_user_level,
        old.birthday old_birthday,
        old.gender old_gender,
        old.create_time old_create_time,
        old.operate_time old_operate_time,
        old.start_date old_start_date,
        old.end_date old_end_date,
        new.id new_id,
        new.login_name new_login_name,
        new.nick_name new_nick_name,
        new.name new_name,
        new.phone_num new_phone_num,
        new.email new_email,
        new.user_level new_user_level,
        new.birthday new_birthday,
        new.gender new_gender,
        new.create_time new_create_time,
        new.operate_time new_operate_time,
        new.start_date new_start_date,
        new.end_date new_end_date
    from
    (
        select
            id,
            login_name,
            nick_name,
            name,
            phone_num,
            email,
            user_level,
            birthday,
            gender,
            create_time,
            operate_time,
            start_date,
            end_date
        from dim_user_info
        where dt='9999-99-99'
    )old
    full outer join
    (
        select
            id,
            login_name,
            nick_name,
            md5(name) name,
            md5(phone_num) phone_num,
            md5(email) email,
            user_level,
            birthday,
            gender,
            create_time,
            operate_time,
            '2020-06-15' start_date,
            '9999-99-99' end_date
        from ods_user_info
        where dt='2020-06-15'
    )new
    on old.id=new.id
)
insert overwrite table dim_user_info partition(dt)
select
    nvl(new_id,old_id),
    nvl(new_login_name,old_login_name),
    nvl(new_nick_name,old_nick_name),
    nvl(new_name,old_name),
    nvl(new_phone_num,old_phone_num),
    nvl(new_email,old_email),
    nvl(new_user_level,old_user_level),
    nvl(new_birthday,old_birthday),
    nvl(new_gender,old_gender),
    nvl(new_create_time,old_create_time),
    nvl(new_operate_time,old_operate_time),
    nvl(new_start_date,old_start_date),
    nvl(new_end_date,old_end_date),
    nvl(new_end_date,old_end_date) dt
from tmp
union all
select
    old_id,
    old_login_name,
    old_nick_name,
    old_name,
    old_phone_num,
    old_email,
    old_user_level,
    old_birthday,
    old_gender,
    old_create_time,
    old_operate_time,
    old_start_date,
    cast(date_add('2020-06-15',-1) as string),
    cast(date_add('2020-06-15',-1) as string) dt
from tmp
where new_id is not null and old_id is not null;

 DIM层首日数据装载脚本

[doudou@hadoop102 bin]$ vim ods_to_dim_db_init.sh

#!/bin/bash

APP=gmall

if [ -n "$2" ] ;then
   do_date=$2
else 
   echo "请传入日期参数"
   exit
fi 

dim_user_info="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
insert overwrite table ${APP}.dim_user_info partition(dt='9999-99-99')
select
    id,
    login_name,
    nick_name,
    md5(name),
    md5(phone_num),
    md5(email),
    user_level,
    birthday,
    gender,
    create_time,
    operate_time,
    '$do_date',
    '9999-99-99'
from ${APP}.ods_user_info
where dt='$do_date';
"

dim_sku_info="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
with
sku as
(
    select
        id,
        price,
        sku_name,
        sku_desc,
        weight,
        is_sale,
        spu_id,
        category3_id,
        tm_id,
        create_time
    from ${APP}.ods_sku_info
    where dt='$do_date'
),
spu as
(
    select
        id,
        spu_name
    from ${APP}.ods_spu_info
    where dt='$do_date'
),
c3 as
(
    select
        id,
        name,
        category2_id
    from ${APP}.ods_base_category3
    where dt='$do_date'
),
c2 as
(
    select
        id,
        name,
        category1_id
    from ${APP}.ods_base_category2
    where dt='$do_date'
),
c1 as
(
    select
        id,
        name
    from ${APP}.ods_base_category1
    where dt='$do_date'
),
tm as
(
    select
        id,
        tm_name
    from ${APP}.ods_base_trademark
    where dt='$do_date'
),
attr as
(
    select
        sku_id,
        collect_set(named_struct('attr_id',attr_id,'value_id',value_id,'attr_name',attr_name,'value_name',value_name)) attrs
    from ${APP}.ods_sku_attr_value
    where dt='$do_date'
    group by sku_id
),
sale_attr as
(
    select
        sku_id,
        collect_set(named_struct('sale_attr_id',sale_attr_id,'sale_attr_value_id',sale_attr_value_id,'sale_attr_name',sale_attr_name,'sale_attr_value_name',sale_attr_value_name)) sale_attrs
    from ${APP}.ods_sku_sale_attr_value
    where dt='$do_date'
    group by sku_id
)

insert overwrite table ${APP}.dim_sku_info partition(dt='$do_date')
select
    sku.id,
    sku.price,
    sku.sku_name,
    sku.sku_desc,
    sku.weight,
    sku.is_sale,
    sku.spu_id,
    spu.spu_name,
    sku.category3_id,
    c3.name,
    c3.category2_id,
    c2.name,
    c2.category1_id,
    c1.name,
    sku.tm_id,
    tm.tm_name,
    attr.attrs,
    sale_attr.sale_attrs,
    sku.create_time
from sku
left join spu on sku.spu_id=spu.id
left join c3 on sku.category3_id=c3.id
left join c2 on c3.category2_id=c2.id
left join c1 on c2.category1_id=c1.id
left join tm on sku.tm_id=tm.id
left join attr on sku.id=attr.sku_id
left join sale_attr on sku.id=sale_attr.sku_id;
"

dim_base_province="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
insert overwrite table ${APP}.dim_base_province
select
    bp.id,
    bp.name,
    bp.area_code,
    bp.iso_code,
    bp.iso_3166_2,
    bp.region_id,
    br.region_name
from ${APP}.ods_base_province bp
join ${APP}.ods_base_region br on bp.region_id = br.id;
"

dim_coupon_info="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
insert overwrite table ${APP}.dim_coupon_info partition(dt='$do_date')
select
    id,
    coupon_name,
    coupon_type,
    condition_amount,
    condition_num,
    activity_id,
    benefit_amount,
    benefit_discount,
    create_time,
    range_type,
    limit_num,
    taken_count,
    start_time,
    end_time,
    operate_time,
    expire_time
from ${APP}.ods_coupon_info
where dt='$do_date';
"

dim_activity_rule_info="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
insert overwrite table ${APP}.dim_activity_rule_info partition(dt='$do_date')
select
    ar.id,
    ar.activity_id,
    ai.activity_name,
    ar.activity_type,
    ai.start_time,
    ai.end_time,
    ai.create_time,
    ar.condition_amount,
    ar.condition_num,
    ar.benefit_amount,
    ar.benefit_discount,
    ar.benefit_level
from
(
    select
        id,
        activity_id,
        activity_type,
        condition_amount,
        condition_num,
        benefit_amount,
        benefit_discount,
        benefit_level
    from ${APP}.ods_activity_rule
    where dt='$do_date'
)ar
left join
(
    select
        id,
        activity_name,
        start_time,
        end_time,
        create_time
    from ${APP}.ods_activity_info
    where dt='$do_date'
)ai
on ar.activity_id=ai.id;
"

case $1 in
"dim_user_info"){
    hive -e "$dim_user_info"
};;
"dim_sku_info"){
    hive -e "$dim_sku_info"
};;
"dim_base_province"){
    hive -e "$dim_base_province"
};;
"dim_coupon_info"){
    hive -e "$dim_coupon_info"
};;
"dim_activity_rule_info"){
    hive -e "$dim_activity_rule_info"
};;
"all"){
    hive -e "$dim_user_info$dim_sku_info$dim_coupon_info$dim_activity_rule_info$dim_base_province"
};;
esac

[doudou@hadoop102 bin]$ chmod 777 ods_to_dim_db_init.sh 

DIM层每日数据装载脚本

[doudou@hadoop102 bin]$ vim ods_to_dim_db.sh

#!/bin/bash

APP=gmall

# 如果是输入的日期按照取输入日期；如果没输入日期取当前时间的前一天
if [ -n "$2" ] ;then
    do_date=$2
else 
    do_date=`date -d "-1 day" +%F`
fi

dim_user_info="
set hive.exec.dynamic.partition.mode=nonstrict;
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
with
tmp as
(
    select
        old.id old_id,
        old.login_name old_login_name,
        old.nick_name old_nick_name,
        old.name old_name,
        old.phone_num old_phone_num,
        old.email old_email,
        old.user_level old_user_level,
        old.birthday old_birthday,
        old.gender old_gender,
        old.create_time old_create_time,
        old.operate_time old_operate_time,
        old.start_date old_start_date,
        old.end_date old_end_date,
        new.id new_id,
        new.login_name new_login_name,
        new.nick_name new_nick_name,
        new.name new_name,
        new.phone_num new_phone_num,
        new.email new_email,
        new.user_level new_user_level,
        new.birthday new_birthday,
        new.gender new_gender,
        new.create_time new_create_time,
        new.operate_time new_operate_time,
        new.start_date new_start_date,
        new.end_date new_end_date
    from
    (
        select
            id,
            login_name,
            nick_name,
            name,
            phone_num,
            email,
            user_level,
            birthday,
            gender,
            create_time,
            operate_time,
            start_date,
            end_date
        from ${APP}.dim_user_info
        where dt='9999-99-99'
        and start_date<'$do_date'
    )old
    full outer join
    (
        select
            id,
            login_name,
            nick_name,
            md5(name) name,
            md5(phone_num) phone_num,
            md5(email) email,
            user_level,
            birthday,
            gender,
            create_time,
            operate_time,
            '$do_date' start_date,
            '9999-99-99' end_date
        from ${APP}.ods_user_info
        where dt='$do_date'
    )new
    on old.id=new.id
)
insert overwrite table ${APP}.dim_user_info partition(dt)
select
    nvl(new_id,old_id),
    nvl(new_login_name,old_login_name),
    nvl(new_nick_name,old_nick_name),
    nvl(new_name,old_name),
    nvl(new_phone_num,old_phone_num),
    nvl(new_email,old_email),
    nvl(new_user_level,old_user_level),
    nvl(new_birthday,old_birthday),
    nvl(new_gender,old_gender),
    nvl(new_create_time,old_create_time),
    nvl(new_operate_time,old_operate_time),
    nvl(new_start_date,old_start_date),
    nvl(new_end_date,old_end_date),
    nvl(new_end_date,old_end_date) dt
from tmp
union all
select
    old_id,
    old_login_name,
    old_nick_name,
    old_name,
    old_phone_num,
    old_email,
    old_user_level,
    old_birthday,
    old_gender,
    old_create_time,
    old_operate_time,
    old_start_date,
    cast(date_add('$do_date',-1) as string),
    cast(date_add('$do_date',-1) as string) dt
from tmp
where new_id is not null and old_id is not null;
"

dim_sku_info="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
with
sku as
(
    select
        id,
        price,
        sku_name,
        sku_desc,
        weight,
        is_sale,
        spu_id,
        category3_id,
        tm_id,
        create_time
    from ${APP}.ods_sku_info
    where dt='$do_date'
),
spu as
(
    select
        id,
        spu_name
    from ${APP}.ods_spu_info
    where dt='$do_date'
),
c3 as
(
    select
        id,
        name,
        category2_id
    from ${APP}.ods_base_category3
    where dt='$do_date'
),
c2 as
(
    select
        id,
        name,
        category1_id
    from ${APP}.ods_base_category2
    where dt='$do_date'
),
c1 as
(
    select
        id,
        name
    from ${APP}.ods_base_category1
    where dt='$do_date'
),
tm as
(
    select
        id,
        tm_name
    from ${APP}.ods_base_trademark
    where dt='$do_date'
),
attr as
(
    select
        sku_id,
        collect_set(named_struct('attr_id',attr_id,'value_id',value_id,'attr_name',attr_name,'value_name',value_name)) attrs
    from ${APP}.ods_sku_attr_value
    where dt='$do_date'
    group by sku_id
),
sale_attr as
(
    select
        sku_id,
        collect_set(named_struct('sale_attr_id',sale_attr_id,'sale_attr_value_id',sale_attr_value_id,'sale_attr_name',sale_attr_name,'sale_attr_value_name',sale_attr_value_name)) sale_attrs
    from ${APP}.ods_sku_sale_attr_value
    where dt='$do_date'
    group by sku_id
)

insert overwrite table ${APP}.dim_sku_info partition(dt='$do_date')
select
    sku.id,
    sku.price,
    sku.sku_name,
    sku.sku_desc,
    sku.weight,
    sku.is_sale,
    sku.spu_id,
    spu.spu_name,
    sku.category3_id,
    c3.name,
    c3.category2_id,
    c2.name,
    c2.category1_id,
    c1.name,
    sku.tm_id,
    tm.tm_name,
    attr.attrs,
    sale_attr.sale_attrs,
    sku.create_time
from sku
left join spu on sku.spu_id=spu.id
left join c3 on sku.category3_id=c3.id
left join c2 on c3.category2_id=c2.id
left join c1 on c2.category1_id=c1.id
left join tm on sku.tm_id=tm.id
left join attr on sku.id=attr.sku_id
left join sale_attr on sku.id=sale_attr.sku_id;
"

dim_base_province="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
insert overwrite table ${APP}.dim_base_province
select
    bp.id,
    bp.name,
    bp.area_code,
    bp.iso_code,
    bp.iso_3166_2,
    bp.region_id,
    bp.name
from ${APP}.ods_base_province bp
join ${APP}.ods_base_region br on bp.region_id = br.id;
"

dim_coupon_info="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
insert overwrite table ${APP}.dim_coupon_info partition(dt='$do_date')
select
    id,
    coupon_name,
    coupon_type,
    condition_amount,
    condition_num,
    activity_id,
    benefit_amount,
    benefit_discount,
    create_time,
    range_type,
    limit_num,
    taken_count,
    start_time,
    end_time,
    operate_time,
    expire_time
from ${APP}.ods_coupon_info
where dt='$do_date';
"

dim_activity_rule_info="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
insert overwrite table ${APP}.dim_activity_rule_info partition(dt='$do_date')
select
    ar.id,
    ar.activity_id,
    ai.activity_name,
    ar.activity_type,
    ai.start_time,
    ai.end_time,
    ai.create_time,
    ar.condition_amount,
    ar.condition_num,
    ar.benefit_amount,
    ar.benefit_discount,
    ar.benefit_level
from
(
    select
        id,
        activity_id,
        activity_type,
        condition_amount,
        condition_num,
        benefit_amount,
        benefit_discount,
        benefit_level
    from ${APP}.ods_activity_rule
    where dt='$do_date'
)ar
left join
(
    select
        id,
        activity_name,
        start_time,
        end_time,
        create_time
    from ${APP}.ods_activity_info
    where dt='$do_date'
)ai
on ar.activity_id=ai.id;
"

case $1 in
"dim_user_info"){
    hive -e "$dim_user_info"
};;
"dim_sku_info"){
    hive -e "$dim_sku_info"
};;
"dim_base_province"){
    hive -e "$dim_base_province"
};;
"dim_coupon_info"){
    hive -e "$dim_coupon_info"
};;
"dim_activity_rule_info"){
    hive -e "$dim_activity_rule_info"
};;
"all"){
    hive -e "$dim_user_info$dim_sku_info$dim_coupon_info$dim_activity_rule_info"
};;
esac

[doudou@hadoop102 bin]$ chmod 777 ods_to_dim_db.sh