9.数据仓库搭建之DIM层搭建

数据仓库搭建之DIM层搭建

在开发数据仓库的DIM层时，我们需要注意以下几点：

1）DIM层的设计依据是维度建模理论，该层存储维度模型的维度表。

2）在我们该项目当中，DIM层的数据存储格式为orc列式存储+snappy压缩。

3）DIM层表名的命名规范为dim_表名_全量表或者拉链表标识（full/zip）。

1.维度确定

我们根据之前构建的业务总线矩阵，来确定我们当前需要构建的维度表。

我们可以看到，我们所有的业务过程所涉及到的维度有时间、用户、商品、地区、活动、优惠券、支付方式、退单类型、退单原因类型、渠道以及设备。

虽然有这么多的维度，但是我们并不会将这些维度都构建成维度表。但是我们考虑到维度退化，一些维度中字段比较少，较为简单，因此我们将该维度中的字段退化到与之对应的事实表当中。

因此，我们最终选择的维度有时间、用户、商品、地区、活动和优惠券共六个维度。

2.维度表设计

2.1商品维度表

2.1.1确定维度

这里的维度已经确定，是商品维度。

2.1.2确定主维表和相关维表

此处的主维表和相关维表均指电商业务系统中与某维度相关的表。

由于我们表中的字段有部分冗余（为了提高查询的速度），因此我们最终的主维表和相关维表如下图所示（图中有颜色的是最终的主维表和相关维表）：

2.1.3确定维度属性

DROP TABLE IF EXISTS dim_sku_full;
CREATE EXTERNAL TABLE dim_sku_full
(
    `id`                   STRING COMMENT 'sku_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 ORC
    LOCATION '/warehouse/gmall/dim/dim_sku_full/'
    TBLPROPERTIES ('orc.compress' = 'snappy');

2.1.4数据的装载逻辑（以2022-05-01为例）

我们需要从每一个表当中取出需要的数据，之后再通过join连接起来。

（1）sku_info表

select
        id,
        price,
        sku_name,
        sku_desc,
        weight,
        is_sale,
        spu_id,
        category3_id,
        tm_id,
        create_time
    from ods_sku_info_full
    where dt='2022-05-01'

（2）spu_info表

select
	id,
	spu_name
from ods_spu_info_full
where dt='2022-05-01'

（3）base_caregory3表

select
	id,
	name,
	category2_id
from ods_base_category3_full
where dt='2022-05-01'

（3）base_caregory2表

select
	id,
	name,	
	category1_id
from ods_base_category2_full
where dt='2022-05-01'

（4）base_caregory1表

select
	id,
	name
from ods_base_category1_full
where dt='2022-05-01'

（5）base_trademark表

select
	id,
	tm_name
from ods_base_trademark_full
where dt='2022-05-01'

（6）sku_attr_value表

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_full
where dt='2022-05-01'
group by sku_id

（7）sku_sale_attr_value表

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_full
where dt='2022-05-01'
group by sku_id

最终，我们将上述从表中取到的数据进行join，然后装载到该商品维度表当中：

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_full
    where dt='2022-05-01'
),
spu as
(
    select
        id,
        spu_name
    from ods_spu_info_full
    where dt='2022-05-01'
),
c3 as
(
    select
        id,
        name,
        category2_id
    from ods_base_category3_full
    where dt='2022-05-01'
),
c2 as
(
    select
        id,
        name,
        category1_id
    from ods_base_category2_full
    where dt='2022-05-01'
),
c1 as
(
    select
        id,
        name
    from ods_base_category1_full
    where dt='2022-05-01'
),
tm as
(
    select
        id,
        tm_name
    from ods_base_trademark_full
    where dt='2022-05-01'
),
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_full
    where dt='2022-05-01'
    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_full
    where dt='2022-05-01'
    group by sku_id
)
insert overwrite table dim_sku_full partition(dt='2022-05-01')
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;

2.2用户维度表

2.2.1确定维度

这里的维度已经确定，是用户维度。

2.2.2确定主维表和相关维表

此处的主维表和相关维表均指电商业务系统中与某维度相关的表。

我们最终只选择user_info一张表作为用户维度的主维表，因为我们对用户的地址表不经常使用，因此此处不添加用户地址表进行join。

2.2.3确定维度属性

DROP TABLE IF EXISTS dim_user_zip;
CREATE EXTERNAL TABLE dim_user_zip
(
    `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 ORC
    LOCATION '/warehouse/gmall/dim/dim_user_zip/'
    TBLPROPERTIES ('orc.compress' = 'snappy');

2.2.4数据的分区规划

拉链表的意义就在于能够更加高效的保存维度信息的历史状态。拉链表适合于数据会发生变化，但是变化频率并不高的维度（缓慢变化维）。因此，我们的用户维度表设计为拉链表，因为每天变化的比例并不高。

我们的数据分区规划如下所示：

我们将全量最新的用户数据存储到dt=9999-12-31分区当中。

2.2.4数据装载

2.2.4.1拉链表数据装载过程

2.2.4.3拉链表数据流向

2.2.4.4用户维表首日装载（以2022-05-01作为首日）

用户的首日装载较为简单，即装载全量的数据：

insert overwrite table dim_user_zip partition (dt='9999-12-31')
select
    data.id,
    data.login_name,
    data.nick_name,
    md5(data.name),
    md5(data.phone_num),
    md5(data.email),
    data.user_level,
    data.birthday,
    data.gender,
    data.create_time,
    data.operate_time,
    '2022-05-01' start_date,
    '9999-12-31' end_date
from ods_user_info_inc
where dt='2022-05-01'
and type='bootstrap-insert';

2.2.4.5用户维表每日装载

（1）用户维度表每日装载思路

（2）具体装载语句

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_zip
        where dt='9999-12-31'
    )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,
            '2020-05-02' start_date,
            '9999-12-31' end_date
        from
        (
            select
                data.id,
                data.login_name,
                data.nick_name,
                data.name,
                data.phone_num,
                data.email,
                data.user_level,
                data.birthday,
                data.gender,
                data.create_time,
                data.operate_time,
                row_number() over (partition by data.id order by ts desc) rn
            from ods_user_info_inc
            where dt='2022-05-02'
        )t1
        where rn=1
    )new
    on old.id=new.id
)
insert overwrite table dim_user_zip partition(dt)
select
    if(new_id is not null,new_id,old_id),
    if(new_id is not null,new_login_name,old_login_name),
    if(new_id is not null,new_nick_name,old_nick_name),
    if(new_id is not null,new_name,old_name),
    if(new_id is not null,new_phone_num,old_phone_num),
    if(new_id is not null,new_email,old_email),
    if(new_id is not null,new_user_level,old_user_level),
    if(new_id is not null,new_birthday,old_birthday),
    if(new_id is not null,new_gender,old_gender),
    if(new_id is not null,new_create_time,old_create_time),
    if(new_id is not null,new_operate_time,old_operate_time),
    if(new_id is not null,new_start_date,old_start_date),
    if(new_id is not null,new_end_date,old_end_date),
    if(new_id is not null,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('2022-05-02',-1) as string) old_end_date,
    cast(date_add('2022-05-02',-1) as string) dt
from tmp
where old_id is not null
and new_id is not null;

2.3地区维度表

2.3.1确定维度

这里的维度已经确定，是地区维度。

2.3.2确定主维表和相关维表

我们选择的主维表是省份表，相关维表是地区表。

2.3.3确定维度属性

DROP TABLE IF EXISTS dim_province_full;
CREATE EXTERNAL TABLE dim_province_full
(
    `id`            STRING COMMENT 'id',
    `province_name` STRING COMMENT '省市名称',
    `area_code`     STRING COMMENT '地区编码',
    `iso_code`      STRING COMMENT '旧版ISO-3166-2编码，供可视化使用',
    `iso_3166_2`    STRING COMMENT '新版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'
    PARTITIONED BY (`dt` STRING)
    STORED AS ORC
    LOCATION '/warehouse/gmall/dim/dim_province_full/'
    TBLPROPERTIES ('orc.compress' = 'snappy');

2.3.4数据的装载逻辑（以2022-05-01为例）

我们需要从每一个表当中取出需要的数据，之后再通过join连接起来。

（1）base_province表

select
    id,
    name,
    region_id,
    area_code,
    iso_code,
    iso_3166_2
from ods_base_province_full
where dt='2022-05-01'

（2）base_region表

select
    id,
    region_name
from ods_base_region_full
where dt='2022-05-01'

最终，我们将上述从表中取到的数据进行join，然后装载到该地区维度表当中：

insert overwrite table dim_province_full partition(dt='2022-05-01')
select
    province.id,
    province.name,
    province.area_code,
    province.iso_code,
    province.iso_3166_2,
    region_id,
    region_name
from
(
    select
        id,
        name,
        region_id,
        area_code,
        iso_code,
        iso_3166_2
    from ods_base_province_full
    where dt='2022-05-01'
)province
left join
(
    select
        id,
        region_name
    from ods_base_region_full
    where dt='2022-05-01'
)region
on province.region_id=region.id;

2.4优惠券维度表

2.4.1确定维度

这里的维度是优惠券维度。

2.4.2确定主维表和相关维表

我们选择的主维表是优惠券表，相关维表是字典表（需要获取购物券类型和优惠券范围）。

2.4.3确定维度属性

DROP TABLE IF EXISTS dim_coupon_full;
CREATE EXTERNAL TABLE dim_coupon_full
(
    `id`               STRING COMMENT '购物券编号',
    `coupon_name`      STRING COMMENT '购物券名称',
    `coupon_type_code` STRING COMMENT '购物券类型编码',
    `coupon_type_name` STRING COMMENT '购物券类型名称',
    `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',
    `benefit_rule`     STRING COMMENT '优惠规则:满元*减*元，满*件打*折',
    `create_time`      STRING COMMENT '创建时间',
    `range_type_code`  STRING COMMENT '优惠范围类型编码',
    `range_type_name`  STRING COMMENT '优惠范围类型名称',
    `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 ORC
    LOCATION '/warehouse/gmall/dim/dim_coupon_full/'
    TBLPROPERTIES ('orc.compress' = 'snappy');

2.4.4数据的装载逻辑（以2022-05-01为例）

我们需要从每一个表当中取出需要的数据，之后再通过join连接起来。

（1）coupon_info表

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_full
where dt='2022-05-01'

（2）base_dic表（购物券类型）

select
    dic_code,
    dic_name
from ods_base_dic_full
where dt='2022-05-01'
and parent_code='32'

（3）base_region表（优惠券范围）

select
    dic_code,
    dic_name
from ods_base_dic_full
where dt='2022-05-01'
and parent_code='33'

最终，我们将上述从表中取到的数据进行join，然后装载到该优惠券维度表当中：

insert overwrite table dim_coupon_full partition(dt='2022-05-01')
select
    id,
    coupon_name,
    coupon_type,
    coupon_dic.dic_name,
    condition_amount,
    condition_num,
    activity_id,
    benefit_amount,
    benefit_discount,
    case coupon_type
        when '3201' then concat('满',condition_amount,'元减',benefit_amount,'元')
        when '3202' then concat('满',condition_num,'件打',10*(1-benefit_discount),'折')
        when '3203' then concat('减',benefit_amount,'元')
    end benefit_rule,
    create_time,
    range_type,
    range_dic.dic_name,
    limit_num,
    taken_count,
    start_time,
    end_time,
    operate_time,
    expire_time
from
(
    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_full
    where dt='2022-05-01'
)ci
left join
(
    select
        dic_code,
        dic_name
    from ods_base_dic_full
    where dt='2022-05-01'
    and parent_code='32'
)coupon_dic
on ci.coupon_type=coupon_dic.dic_code
left join
(
    select
        dic_code,
        dic_name
    from ods_base_dic_full
    where dt='2022-05-01'
    and parent_code='33'
)range_dic
on ci.range_type=range_dic.dic_code;

2.5活动维度表

2.5.1确定维度

这里的维度是活动维度。

2.5.2确定主维表和相关维表

我们选择的主维表是活动规则表，相关维表是活动信息表，字典表（活动类型）。

2.5.3确定维度属性

DROP TABLE IF EXISTS dim_activity_full;
CREATE EXTERNAL TABLE dim_activity_full
(
    `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_code` STRING COMMENT '活动类型编码',
    `activity_type_name` STRING COMMENT '活动类型名称',
    `activity_desc`      STRING COMMENT '活动描述',
    `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_rule`       STRING COMMENT '优惠规则',
    `benefit_level`      STRING COMMENT '优惠级别'
) COMMENT '活动信息表'
    PARTITIONED BY (`dt` STRING)
    STORED AS ORC
    LOCATION '/warehouse/gmall/dim/dim_activity_full/'
    TBLPROPERTIES ('orc.compress' = 'snappy');

2.5.4数据的装载逻辑（以2022-05-01为例）

我们需要从每一个表当中取出需要的数据，之后再通过join连接起来。

（1）activity_rule表

select
    id,
    activity_id,
    activity_type,
    condition_amount,
    condition_num,
    benefit_amount,
    benefit_discount,
    benefit_level
from ods_activity_rule_full
where dt='2022-05-01'

（2）activity_info表

select
    id,
    activity_name,
    activity_type,
    activity_desc,
    start_time,
    end_time,
    create_time
from ods_activity_info_full
where dt='2022-05-01'

（3）base_dic表（活动类型）

select
    dic_code,
    dic_name
from ods_base_dic_full
where dt='2022-05-01'
and parent_code='31'

最终，我们将上述从表中取到的数据进行join，然后装载到该优惠券维度表当中：

insert overwrite table dim_activity_full partition(dt='2022-05-01')
select
    rule.id,
    info.id,
    activity_name,
    rule.activity_type,
    dic.dic_name,
    activity_desc,
    start_time,
    end_time,
    create_time,
    condition_amount,
    condition_num,
    benefit_amount,
    benefit_discount,
    case rule.activity_type
        when '3101' then concat('满',condition_amount,'元减',benefit_amount,'元')
        when '3102' then concat('满',condition_num,'件打',10*(1-benefit_discount),'折')
        when '3103' then concat('打',10*(1-benefit_discount),'折')
    end benefit_rule,
    benefit_level
from
(
    select
        id,
        activity_id,
        activity_type,
        condition_amount,
        condition_num,
        benefit_amount,
        benefit_discount,
        benefit_level
    from ods_activity_rule_full
    where dt='2022-05-01'
)rule
left join
(
    select
        id,
        activity_name,
        activity_type,
        activity_desc,
        start_time,
        end_time,
        create_time
    from ods_activity_info_full
    where dt='2022-05-01'
)info
on rule.activity_id=info.id
left join
(
    select
        dic_code,
        dic_name
    from ods_base_dic_full
    where dt='2022-05-01'
    and parent_code='31'
)dic
on rule.activity_type=dic.dic_code;

2.6日期维度表

2.6.1确定维度

这里的维度是日期维度。

2.6.2确定主维表和相关维表

该表比较特殊，因为日期是固定的，可以算出来的，因此我们的主维表就是日期表，通常会提前导入一到三年的时间数据。

2.5.3确定维度属性

DROP TABLE IF EXISTS dim_date;
CREATE EXTERNAL TABLE dim_date
(
    `date_id`    STRING COMMENT '日期ID',
    `week_id`    STRING COMMENT '周ID,一年中的第几周',
    `week_day`   STRING COMMENT '周几',
    `day`        STRING COMMENT '每月的第几天',
    `month`      STRING COMMENT '一年中的第几月',
    `quarter`    STRING COMMENT '一年中的第几季度',
    `year`       STRING COMMENT '年份',
    `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 ORC
    LOCATION '/warehouse/gmall/dim/dim_date/'
    TBLPROPERTIES ('orc.compress' = 'snappy');

2.5.4数据的装载逻辑

通常情况下，时间维度表的数据并不是来自于业务系统，而是手动写入，并且由于时间维度表数据的可预见性，无须每日导入，一般可一次性导入一年的数据。

（1）创建临时表

DROP TABLE IF EXISTS tmp_dim_date_info;
CREATE EXTERNAL TABLE tmp_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'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/tmp/tmp_dim_date_info/';

（2）将数据文件上传到HFDS上临时表路径/warehouse/gmall/tmp/tmp_dim_date_info

（3）执行以下语句将其导入时间维度表

insert overwrite table dim_date select * from tmp_dim_date_info;

（4）检查数据是否导入成功

select * from dim_date;

3.维度层数据装载脚本的编写

由于用户维表是拉链表，该表的首日装载和每日装载数据有区别，因此我们需要编写首日装载脚本和每日装载脚本。

3.1首日装载脚本

（1）在hadoop102的/home/root/bin目录下创建ods_to_dim_init.sh

[root@hadoop102 bin]$ vim ods_to_dim_init.sh

（2）具体的内容如下图所示：

#!/bin/bash

APP=gmall

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

dim_user_zip="
insert overwrite table ${APP}.dim_user_zip partition (dt='9999-12-31')
select
    data.id,
    data.login_name,
    data.nick_name,
    md5(data.name),
    md5(data.phone_num),
    md5(data.email),
    data.user_level,
    data.birthday,
    data.gender,
    data.create_time,
    data.operate_time,
    '$do_date' start_date,
    '9999-12-31' end_date
from ${APP}.ods_user_info_inc
where dt='$do_date'
and type='bootstrap-insert';
"

dim_sku_full="
with
sku as
(
    select
        id,
        price,
        sku_name,
        sku_desc,
        weight,
        is_sale,
        spu_id,
        category3_id,
        tm_id,
        create_time
    from ${APP}.ods_sku_info_full
    where dt='$do_date'
),
spu as
(
    select
        id,
        spu_name
    from ${APP}.ods_spu_info_full
    where dt='$do_date'
),
c3 as
(
    select
        id,
        name,
        category2_id
    from ${APP}.ods_base_category3_full
    where dt='$do_date'
),
c2 as
(
    select
        id,
        name,
        category1_id
    from ${APP}.ods_base_category2_full
    where dt='$do_date'
),
c1 as
(
    select
        id,
        name
    from ${APP}.ods_base_category1_full
    where dt='$do_date'
),
tm as
(
    select
        id,
        tm_name
    from ${APP}.ods_base_trademark_full
    where dt='$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 ${APP}.ods_sku_attr_value_full
    where dt='$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 ${APP}.ods_sku_sale_attr_value_full
    where dt='$do_date'
    group by sku_id
)
insert overwrite table ${APP}.dim_sku_full partition(dt='$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_province_full="
insert overwrite table ${APP}.dim_province_full partition(dt='$do_date')
select
    province.id,
    province.name,
    province.area_code,
    province.iso_code,
    province.iso_3166_2,
    region_id,
    region_name
from
(
    select
        id,
        name,
        region_id,
        area_code,
        iso_code,
        iso_3166_2
    from ${APP}.ods_base_province_full
    where dt='$do_date'
)province
left join
(
    select
        id,
        region_name
    from ${APP}.ods_base_region_full
    where dt='$do_date'
)region
on province.region_id=region.id;
"

dim_coupon_full="
insert overwrite table ${APP}.dim_coupon_full partition(dt='$do_date')
select
    id,
    coupon_name,
    coupon_type,
    coupon_dic.dic_name,
    condition_amount,
    condition_num,
    activity_id,
    benefit_amount,
    benefit_discount,
    case coupon_type
        when '3201' then concat('满',condition_amount,'元减',benefit_amount,'元')
        when '3202' then concat('满',condition_num,'件打',10*(1-benefit_discount),'折')
        when '3203' then concat('减',benefit_amount,'元')
    end benefit_rule,
    create_time,
    range_type,
    range_dic.dic_name,
    limit_num,
    taken_count,
    start_time,
    end_time,
    operate_time,
    expire_time
from
(
    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 ${APP}.ods_coupon_info_full
    where dt='$do_date'
)ci
left join
(
    select
        dic_code,
        dic_name
    from ${APP}.ods_base_dic_full
    where dt='$do_date'
    and parent_code='32'
)coupon_dic
on ci.coupon_type=coupon_dic.dic_code
left join
(
    select
        dic_code,
        dic_name
    from ${APP}.ods_base_dic_full
    where dt='$do_date'
    and parent_code='33'
)range_dic
on ci.range_type=range_dic.dic_code;
"

dim_activity_full="
insert overwrite table ${APP}.dim_activity_full partition(dt='$do_date')
select
    rule.id,
    info.id,
    activity_name,
    rule.activity_type,
    dic.dic_name,
    activity_desc,
    start_time,
    end_time,
    create_time,
    condition_amount,
    condition_num,
    benefit_amount,
    benefit_discount,
    case rule.activity_type
        when '3101' then concat('满',condition_amount,'元减',benefit_amount,'元')
        when '3102' then concat('满',condition_num,'件打',10*(1-benefit_discount),'折')
        when '3103' then concat('打',10*(1-benefit_discount),'折')
    end benefit_rule,
    benefit_level
from
(
    select
        id,
        activity_id,
        activity_type,
        condition_amount,
        condition_num,
        benefit_amount,
        benefit_discount,
        benefit_level
    from ${APP}.ods_activity_rule_full
    where dt='$do_date'
)rule
left join
(
    select
        id,
        activity_name,
        activity_type,
        activity_desc,
        start_time,
        end_time,
        create_time
    from ${APP}.ods_activity_info_full
    where dt='$do_date'
)info
on rule.activity_id=info.id
left join
(
    select
        dic_code,
        dic_name
    from ${APP}.ods_base_dic_full
    where dt='$do_date'
    and parent_code='31'
)dic
on rule.activity_type=dic.dic_code;
"

case $1 in
"dim_user_zip")
    hive -e "$dim_user_zip"
;;
"dim_sku_full")
    hive -e "$dim_sku_full"
;;
"dim_province_full")
    hive -e "$dim_province_full"
;;
"dim_coupon_full")
    hive -e "$dim_coupon_full"
;;
"dim_activity_full")
    hive -e "$dim_activity_full"
;;
"all")
    hive -e "$dim_user_zip$dim_sku_full$dim_province_full$dim_coupon_full$dim_activity_full"
;;
esac

（3）我们为该脚本增加执行权限

[root@hadoop102 bin]$ chmod +x ods_to_dim_init.sh 

（4）我们需要在数仓的首日执行该脚本（我们数仓的首日定在2022-05-01）

[root@hadoop102 bin]$ ods_to_dim_init.sh all 2022-05-01

3.2每日装载脚本

（1）在hadoop102的/home/root/bin目录下创建ods_to_dim.sh

[root@hadoop102 bin]$ vim ods_to_dim.sh 

（2）脚本中的内容如下所示：

#!/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_zip="
set hive.exec.dynamic.partition.mode=nonstrict;
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 ${APP}.dim_user_zip
        where dt='9999-12-31'
    )old
    full outer join
    (
        select
            id,
            login_name,
            nick_name,
            md5(name) name,
            md5(phone_num) phone_num,
            md5(email) email,
            user_level,
            birthday,
            gender,
            create_time,
            operate_time,
            '$do_date' start_date,
            '9999-12-31' end_date
        from
        (
            select
                data.id,
                data.login_name,
                data.nick_name,
                data.name,
                data.phone_num,
                data.email,
                data.user_level,
                data.birthday,
                data.gender,
                data.create_time,
                data.operate_time,
                row_number() over (partition by data.id order by ts desc) rn
            from ${APP}.ods_user_info_inc
            where dt='$do_date'
        )t1
        where rn=1
    )new
    on old.id=new.id
)
insert overwrite table ${APP}.dim_user_zip partition(dt)
select
    if(new_id is not null,new_id,old_id),
    if(new_id is not null,new_login_name,old_login_name),
    if(new_id is not null,new_nick_name,old_nick_name),
    if(new_id is not null,new_name,old_name),
    if(new_id is not null,new_phone_num,old_phone_num),
    if(new_id is not null,new_email,old_email),
    if(new_id is not null,new_user_level,old_user_level),
    if(new_id is not null,new_birthday,old_birthday),
    if(new_id is not null,new_gender,old_gender),
    if(new_id is not null,new_create_time,old_create_time),
    if(new_id is not null,new_operate_time,old_operate_time),
    if(new_id is not null,new_start_date,old_start_date),
    if(new_id is not null,new_end_date,old_end_date),
    if(new_id is not null,new_end_date,old_end_date) dt
from tmp
union all
select
    old_id,
    old_login_name,
    old_nick_name,
    old_name,
    old_phone_num,
    old_email,
    old_user_level,
    old_birthday,
    old_gender,
    old_create_time,
    old_operate_time,
    old_start_date,
    cast(date_add('$do_date',-1) as string) old_end_date,
    cast(date_add('$do_date',-1) as string) dt
from tmp
where old_id is not null
and new_id is not null;
"

dim_sku_full="
with
sku as
(
    select
        id,
        price,
        sku_name,
        sku_desc,
        weight,
        is_sale,
        spu_id,
        category3_id,
        tm_id,
        create_time
    from ${APP}.ods_sku_info_full
    where dt='$do_date'
),
spu as
(
    select
        id,
        spu_name
    from ${APP}.ods_spu_info_full
    where dt='$do_date'
),
c3 as
(
    select
        id,
        name,
        category2_id
    from ${APP}.ods_base_category3_full
    where dt='$do_date'
),
c2 as
(
    select
        id,
        name,
        category1_id
    from ${APP}.ods_base_category2_full
    where dt='$do_date'
),
c1 as
(
    select
        id,
        name
    from ${APP}.ods_base_category1_full
    where dt='$do_date'
),
tm as
(
    select
        id,
        tm_name
    from ${APP}.ods_base_trademark_full
    where dt='$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 ${APP}.ods_sku_attr_value_full
    where dt='$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 ${APP}.ods_sku_sale_attr_value_full
    where dt='$do_date'
    group by sku_id
)
insert overwrite table ${APP}.dim_sku_full partition(dt='$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_province_full="
insert overwrite table ${APP}.dim_province_full partition(dt='$do_date')
select
    province.id,
    province.name,
    province.area_code,
    province.iso_code,
    province.iso_3166_2,
    region_id,
    region_name
from
(
    select
        id,
        name,
        region_id,
        area_code,
        iso_code,
        iso_3166_2
    from ${APP}.ods_base_province_full
    where dt='$do_date'
)province
left join
(
    select
        id,
        region_name
    from ${APP}.ods_base_region_full
    where dt='$do_date'
)region
on province.region_id=region.id;
"

dim_coupon_full="
insert overwrite table ${APP}.dim_coupon_full partition(dt='$do_date')
select
    id,
    coupon_name,
    coupon_type,
    coupon_dic.dic_name,
    condition_amount,
    condition_num,
    activity_id,
    benefit_amount,
    benefit_discount,
    case coupon_type
        when '3201' then concat('满',condition_amount,'元减',benefit_amount,'元')
        when '3202' then concat('满',condition_num,'件打',10*(1-benefit_discount),'折')
        when '3203' then concat('减',benefit_amount,'元')
    end benefit_rule,
    create_time,
    range_type,
    range_dic.dic_name,
    limit_num,
    taken_count,
    start_time,
    end_time,
    operate_time,
    expire_time
from
(
    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 ${APP}.ods_coupon_info_full
    where dt='$do_date'
)ci
left join
(
    select
        dic_code,
        dic_name
    from ${APP}.ods_base_dic_full
    where dt='$do_date'
    and parent_code='32'
)coupon_dic
on ci.coupon_type=coupon_dic.dic_code
left join
(
    select
        dic_code,
        dic_name
    from ${APP}.ods_base_dic_full
    where dt='$do_date'
    and parent_code='33'
)range_dic
on ci.range_type=range_dic.dic_code;
"

dim_activity_full="
insert overwrite table ${APP}.dim_activity_full partition(dt='$do_date')
select
    rule.id,
    info.id,
    activity_name,
    rule.activity_type,
    dic.dic_name,
    activity_desc,
    start_time,
    end_time,
    create_time,
    condition_amount,
    condition_num,
    benefit_amount,
    benefit_discount,
    case rule.activity_type
        when '3101' then concat('满',condition_amount,'元减',benefit_amount,'元')
        when '3102' then concat('满',condition_num,'件打',10*(1-benefit_discount),'折')
        when '3103' then concat('打',10*(1-benefit_discount),'折')
    end benefit_rule,
    benefit_level
from
(
    select
        id,
        activity_id,
        activity_type,
        condition_amount,
        condition_num,
        benefit_amount,
        benefit_discount,
        benefit_level
    from ${APP}.ods_activity_rule_full
    where dt='$do_date'
)rule
left join
(
    select
        id,
        activity_name,
        activity_type,
        activity_desc,
        start_time,
        end_time,
        create_time
    from ${APP}.ods_activity_info_full
    where dt='$do_date'
)info
on rule.activity_id=info.id
left join
(
    select
        dic_code,
        dic_name
    from ${APP}.ods_base_dic_full
    where dt='$do_date'
    and parent_code='31'
)dic
on rule.activity_type=dic.dic_code;
"

case $1 in
"dim_user_zip")
    hive -e "$dim_user_zip"
;;
"dim_sku_full")
    hive -e "$dim_sku_full"
;;
"dim_province_full")
    hive -e "$dim_province_full"
;;
"dim_coupon_full")
    hive -e "$dim_coupon_full"
;;
"dim_activity_full")
    hive -e "$dim_activity_full"
;;
"all")
    hive -e "$dim_user_zip$dim_sku_full$dim_province_full$dim_coupon_full$dim_activity_full"
;;
esac

（3）增加该脚本执行权限

[root@hadoop102 bin]$ chmod +x ods_to_dim.sh 

（4）我们每日调用该脚本即可（以2022-05-02为例）

[root@hadoop102 bin]$ ods_to_dim.sh all 2022-05-02