DM层 数据集市层 (Data Mart)
粒度上卷(Roll-up):
指的是沿着维度层次向上聚合汇总数据,从细粒度到粗粒度观察数据的操作。
示例
数仓的上一层DWS的是按日汇总
DM层基于DWS层主题日宽表上卷统计出按年,月,周的数据 >>用DWS层的宽表连接DWD层的时间维度表
创建DM层 : 建数据库>>建表
CREATE DATABASE if NOT EXISTS DM;
建表: 表结构和DWS层的表结构几乎一致, 只多了关于日期的维度字段
建表sql
CREATE TABLE dm.dm_sale(
date_time string COMMENT '统计日期,不能用来分组统计' ,--记录哪一天干活
time_type string COMMENT '统计时间维度:year、month、week、date(就是天day)',
year_code string COMMENT '年code',
year_month string COMMENT '年月',
month_code string COMMENT '月份编码',
day_month_num string COMMENT '一月第几天',
dim_date_id string COMMENT '日期',
year_week_name_cn string COMMENT '年中第几周',
group_type string COMMENT '分组类型:store,trade_area,city,brand,min_class,mid_class,max_class,all',
city_id string COMMENT '城市id',
city_name string COMMENT '城市name',
trade_area_id string COMMENT '商圈id',
trade_area_name string COMMENT '商圈名称',
store_id string COMMENT '店铺的id',
store_name string COMMENT '店铺名称',
brand_id string COMMENT '品牌id',
brand_name string COMMENT '品牌名称',
max_class_id string COMMENT '商品大类id',
max_class_name string COMMENT '大类名称',
mid_class_id string COMMENT '中类id',
mid_class_name string COMMENT '中类名称',
min_class_id string COMMENT '小类id',
min_class_name string COMMENT '小类名称',
-- =======统计=======
sale_amt DECIMAL(38,2) COMMENT '销售收入',
plat_amt DECIMAL(38,2) COMMENT '平台收入',
deliver_sale_amt DECIMAL(38,2) COMMENT '配送成交额',
mini_app_sale_amt DECIMAL(38,2) COMMENT '小程序成交额',
android_sale_amt DECIMAL(38,2) COMMENT '安卓APP成交额',
ios_sale_amt DECIMAL(38,2) COMMENT '苹果APP成交额',
pcweb_sale_amt DECIMAL(38,2) COMMENT 'PC商城成交额',
order_cnt BIGINT COMMENT '成交单量',
eva_order_cnt BIGINT COMMENT '参评单量comment=>cmt',
bad_eva_order_cnt BIGINT COMMENT '差评单量negtive-comment=>ncmt',
deliver_order_cnt BIGINT COMMENT '配送单量',
refund_order_cnt BIGINT COMMENT '退款单量',
miniapp_order_cnt BIGINT COMMENT '小程序成交单量',
android_order_cnt BIGINT COMMENT '安卓APP订单量',
ios_order_cnt BIGINT COMMENT '苹果APP订单量',
pcweb_order_cnt BIGINT COMMENT 'PC商城成交单量'
)
COMMENT '销售主题宽表'
ROW format delimited fields terminated BY '\t'
stored AS orc tblproperties ('orc.compress' = 'SNAPPY');
插入数据sql
WITH TEMP AS (
SELECT
D.year_code,
D.year_month,
D.month_code,
D.day_month_num,
D.dim_date_id,
D.year_week_name_cn,
city_id, city_name, trade_area_id, trade_area_name, store_id, store_name, brand_id, brand_name, max_class_id, max_class_name, mid_class_id, mid_class_name, min_class_id, min_class_name, sale_amt, plat_amt, deliver_sale_amt, mini_app_sale_amt, android_sale_amt, ios_sale_amt, pcweb_sale_amt, order_cnt, eva_order_cnt, bad_eva_order_cnt, deliver_order_cnt, refund_order_cnt, miniapp_order_cnt, android_order_cnt, ios_order_cnt, pcweb_order_cnt, dt
FROM DWS.DWS_SALE_DAYCOUNT S
INNER JOIN DWD.DIM_DATE D
ON S.dt = D.date_code
)
INSERT overwrite table dm.dm_sale
SELECT
CURRENT_DATE AS DATE_TIME,
CASE
WHEN dim_date_id IS NOT NULL THEN 'DATE'
WHEN year_week_name_cn IS NOT NULL THEN 'WEEK'
WHEN month_code IS NOT NULL THEN 'MONTH'
WHEN year_code IS NOT NULL THEN 'YEAR'
END AS TIME_TYPE,
year_code,
year_month,
month_code,
day_month_num,
dim_date_id,
year_week_name_cn,
CASE
WHEN T.store_id IS NOT NULL THEN '店铺'
WHEN T.trade_area_id IS NOT NULL THEN '商圈'
WHEN T.city_id IS NOT NULL THEN '城市'
WHEN T.min_class_id IS NOT NULL THEN '小类'
WHEN T.mid_class_id IS NOT NULL THEN '中类'
WHEN T.max_class_id IS NOT NULL THEN '大类'
WHEN T.brand_id IS NOT NULL THEN '品牌'
ELSE '日期'
END AS GROUP_TYPE,
city_id,
city_name,
trade_area_ID,
trade_area_name,
store_id,
store_name,
brand_id,
brand_name,
max_class_id,
max_class_name,
mid_class_id,
mid_class_name,
min_class_id,
min_class_name,
SUM(sale_amt),
SUM(plat_amt),
SUM(deliver_sale_amt),
SUM(mini_app_sale_amt),
SUM(android_sale_amt),
SUM(ios_sale_amt),
SUM(pcweb_sale_amt),
SUM(order_cnt),
SUM(eva_order_cnt),
SUM(bad_eva_order_cnt),
SUM(deliver_order_cnt),
SUM(refund_order_cnt),
SUM(miniapp_order_cnt),
SUM(android_order_cnt),
SUM(ios_order_cnt),
SUM(pcweb_order_cnt)
FROM TEMP T
GROUP BY
-- 所有 GROUPING SETS 中出现的列都要包含在 GROUP BY 中
day_month_num,
dim_date_id,
city_id, city_name, trade_area_id, trade_area_name, store_id, store_name, brand_id, brand_name, max_class_id, max_class_name, mid_class_id, mid_class_name, min_class_id, min_class_name,
year_code,
year_month,
month_code,
year_week_name_cn
GROUPING SETS (
(day_month_num, dim_date_id),
(day_month_num, dim_date_id, city_id, city_name),
(day_month_num, dim_date_id, city_id, city_name, trade_area_id, trade_area_name),
(day_month_num, dim_date_id, city_id, city_name, trade_area_id, trade_area_name, store_id, store_name),
(day_month_num, dim_date_id, brand_id, brand_name),
(day_month_num, dim_date_id, max_class_id, max_class_name),
(day_month_num, dim_date_id, max_class_id, max_class_name, mid_class_name, mid_class_id),
(day_month_num, dim_date_id, max_class_id, max_class_name, mid_class_name, mid_class_id, min_class_id, min_class_name),
(year_week_name_cn),
(year_week_name_cn, city_id, city_name),
(year_week_name_cn, city_id, city_name, trade_area_id, trade_area_name),
(year_week_name_cn, city_id, city_name, trade_area_id, trade_area_name, store_id, store_name),
(year_week_name_cn, brand_id, brand_name),
(year_week_name_cn, max_class_id, max_class_name),
(year_week_name_cn, max_class_id, max_class_name, mid_class_name, mid_class_id),
(year_week_name_cn, max_class_id, max_class_name, mid_class_name, mid_class_id, min_class_id, min_class_name),
(year_month, month_code),
(year_month, month_code, city_id, city_name),
(year_month, month_code, city_id, city_name, trade_area_id, trade_area_name),
(year_month, month_code, city_id, city_name, trade_area_id, trade_area_name, store_id, store_name),
(year_month, month_code, brand_id, brand_name),
(year_month, month_code, max_class_id, max_class_name),
(year_month, month_code, max_class_id, max_class_name, mid_class_name, mid_class_id),
(year_month, month_code, max_class_id, max_class_name, mid_class_name, mid_class_id, min_class_id, min_class_name),
(year_code),
(year_code, city_id, city_name),
(year_code, city_id, city_name, trade_area_id, trade_area_name),
(year_code, city_id, city_name, trade_area_id, trade_area_name, store_id, store_name),
(year_code, brand_id, brand_name),
(year_code, max_class_id, max_class_name),
(year_code, max_class_id, max_class_name, mid_class_name, mid_class_id),
(year_code, max_class_id, max_class_name, mid_class_name, mid_class_id, min_class_id, min_class_name)
);
插入sql分析
查询DWS层的宽表>>确认连接字段dt的数据格式
查询时间维度表>>找到和DWS层的宽表的连接字段数据格式一样的字段>>查找新维度的相应字段
select * from DWD.DIM_DATE
在with as临时表里面把用DWS层的宽表连接DWD层的时间维度表, 内连接,连接字段dt(日)
在临时表查询语句中把目标表新增的时间维度的字段添加进去
目标表
临时表
INSERT overwrite table dm.dm_sale 是hive中全量插入的语法
在查询语句中把目标表新增的列实现
group_type string COMMENT '分组类型:store,trade_area,city,brand,min_class,mid_class,max_class,all',
枚举类型>>case when
group by 分组后面跟除了指标字段及 group_type 的所有字段(维度字段)
用grouping sets 写出需要的维度组合