电商数仓项目----笔记七(数仓DIM层)

2023-12-24 19:00:44

所谓的维度层其实就是分析数据的角度，维度层保存的表其实是分析数据的角度，比如：

????????--性别，年龄，品牌，品类

这层的表主要用于统计分析，因此DIM层的数据存储格式为orc列式存储+snappy压缩（时间短）

orc列式存储的好处：

查询的时候不需要扫描全部的数据，而只需要读取每次查询涉及的列，这样可以将I/O消耗降低N倍，另外可以保存每一列的统计信息(min、max、sum等)，实现部分的谓词下推。
由于每一列的成员都是同构的，可以针对不同的数据类型使用更高效的数据压缩算法，进一步减小I/O。
由于每一列的成员的同构性，可以使用更加适合CPU pipeline的编码方式，减小CPU的缓存失效。

维度表的设计

? ? ? ? 一个维度就是一张表，从实践的角度来讲，不同的维度就是这张表的字段，可以达到解耦的目的。如果维度特别简单，可以不用创建表，可以在事实表直接使用。

????????字段：只要能用来分析的维度，都是字段；

????????数据（字段）来源：参考业务数据的表字段：

? ? ? ? ????????-- 主维表：业务数据库主要用于分析维度字段的表；

????????????????-- 相关维表：业务数据库相关用于分析维度字段的表；

? ? ? ? 维度字段的确定：

? ? ? ? ? ? ? ? 尽可能生成丰富的维度属性：字段越多越好；

? ? ? ? ? ? ? ? 编码和文字共存（0男/1女）；

? ? ? ? ? ? ? ? 计算通用的维度属性；

下面举几个例子：

优惠券维度表?

从主维表和相关维表分析：

????????主维表：coupon_info，相关维表：coupon_range，coupon_use，但是coupon_use算是一种行为概念，并不属于状态，状态才是用来做分析的。但是在coupon_info里面也有range相关字段，因此发生了冗余，只需关注coupon_info即可。

coupon_info长这样：

我们这样设计：

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');

????????其中不一样的地方有我们将ODS层原表的coupon_type分解为了coupon_type_code和coupon_type_name。将range_type分解为了range_type_code，range_type_name，并且增加了benefit_rule字段（优惠规则）。这样做符合我们上面说的编码和文字共存规则。

数据装载

? ? ? ? 我们的表主要从coupon_info和base_dic(字典表)中取得：

? ? ? ? 记住这里的主维表是coupon_info，因此我们先select coupon_info这张表，select里面的字段依照我们建表语句里面的字段先写好，当然其中肯定会有几个字段会报红，没关系我们后面还要join 操作，其中coupon_type_code，coupon_type_name，range_type_code，range_type_name字段是找不到的，因此需要join操作。我们join base_dic字典表：

join base_dic两次分别得到coupon_type_code，coupon_type_name字段和range_type_code，range_type_name字段；

? ? ? ? 接下来是benefit_rule字段，这里需要我们自行拼接。拼接逻辑如下：

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,

?完整是这样：

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='2020-06-14'
)ci
left join
(
    select
        dic_code,
        dic_name
    from ods_base_dic_full
    where dt='2020-06-14'
    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='2020-06-14'
    and parent_code='33'
)range_dic
on ci.range_type=range_dic.dic_code;

数据装载我们只需要前面加上下面这一句即可：

insert overwrite table dim_coupon_full partition(dt='2020-06-14')

我们的Dim层优惠券维度表就设计完啦。

活动维度表

? ? ? ? 同样的，找到主维表和相关维表。

? ? ????activity_info ，activity_rule，activity_sku：我们分析的更多的是活动规则，而不是活动本身，所以主维表是activity_rule，相关维表是activity_info。

我们这样设计：

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');

数据装载：

insert overwrite table dim_activity_full partition(dt='2020-06-14')
select
    `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'
from
    (select
         id `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 `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',
         dt 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',
        case activity_type
            when '3101' then concat('满',condition_amount,'元减',benefit_amount,'元')
            when '3102' then concat('满',condition_num,'件打',benefit_discount,'折')
            when '3103' then concat('打',benefit_discount,'折')
        end `benefit_rule`       ,--STRING COMMENT '优惠规则',
        `benefit_level`      --STRING COMMENT '优惠级别'
    from ods_activity_rule_full
    where dt='2020-06-14')rule
left join
    (
    select
        id,
        activity_name,
        activity_desc,
        start_time,
        end_time
    from ods_activity_info_full
    where dt='2020-06-14') info
on rule.activity_id=info.id
left join (
    select
        dic_code,
        dic_name activity_type_name
    from ods_base_dic_full
    where dt='2020-06-14' and parent_code='31'
        )dic on rule.activity_type_code=dic.dic_code

????????整体思路就是先将create表中的字段复制到select 主维表的语句中，爆红的字段我们一一给他们join出来，或在join的那张表中给他们查询出来，这里就不详细分析了。

日期维度表

建表语句

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');

数据装载

????????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/';

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;

用户维度表

????????用户维度表这里我们使用拉链表，来记录用户姓名的变更或者用户的增加减少。

（1）数据装载过程

（2）数据流向?

首日装载?

我们的用户数据在进行首日装载和后续的变更都是insert overwrite到9999-12-31的分区，首日装载如下：

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,
    '2020-06-14' start_date,
    '9999-12-31' end_date
from ods_user_info_inc
where dt='2020-06-14'
and type='bootstrap-insert';

每日装载

????????装载思路：

?????????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,
            '2020-06-15' 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='2020-06-15'
        )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('2020-06-15',-1) as string) old_end_date,
    cast(date_add('2020-06-15',-1) as string) dt
from tmp
where old_id is not null
and new_id is not null;

数据装载脚本

首日装载脚本

#!/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

每日装载脚本

#!/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

文章来源:https://blog.csdn.net/zmx_messi/article/details/135167903
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