数据仓库 业务数仓 DWD层

Posted 2020-12-14 noyouth

业务数仓的DWD层一般有两个典型操作：

①因为是DWD层，所以要进行数据清洗。

②因为数据来源于web项目的数据库，标的设计遵循三范式，因此在数仓里需要进行降维，以减少join次数。

在示例的8张表中，订单表，订单详情表，用户表，支付流水表字段与ODS层一致。对商品表的分类进行降维。增加二级分类，一级分类的id和名称。

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

DWD层数据导入脚本，降维时，只需对相关表进行join即可。

#!/bin/bash

# 定义变量方便修改
APP=gmall
hive=/opt/module/hive/bin/hive

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

sql="

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

insert overwrite table "$APP".dwd_order_info partition(dt)
select * from "$APP".ods_order_info 
where dt=‘$do_date‘ and id is not null;
 
insert overwrite table "$APP".dwd_order_detail partition(dt)
select * from "$APP".ods_order_detail 
where dt=‘$do_date‘   and id is not null;

insert overwrite table "$APP".dwd_user_info partition(dt)
select * from "$APP".ods_user_info
where dt=‘$do_date‘ and id is not null;
 
insert overwrite table "$APP".dwd_payment_info partition(dt)
select * from "$APP".ods_payment_info
where dt=‘$do_date‘ and id is not null;

insert overwrite table "$APP".dwd_sku_info partition(dt)
select  
    sku.id,
    sku.spu_id,
    sku.price,
    sku.sku_name,
    sku.sku_desc,
    sku.weight,
    sku.tm_id,
    sku.category3_id,
    c2.id category2_id,
    c1.id category1_id,
    c3.name category3_name,
    c2.name category2_name,
    c1.name category1_name,
    sku.create_time,
    sku.dt
from
    "$APP".ods_sku_info sku
join "$APP".ods_base_category3 c3 on sku.category3_id=c3.id 
    join "$APP".ods_base_category2 c2 on c3.category2_id=c2.id 
    join "$APP".ods_base_category1 c1 on c2.category1_id=c1.id 
where sku.dt=‘$do_date‘  and c2.dt=‘$do_date‘
and c3.dt=‘$do_date‘ and c1.dt=‘$do_date‘
and sku.id is not null;
"

$hive -e "$sql"