当前位置： 首页 > article >正文

数仓搭建(hive):DWS层(服务数据层)

article 2025/2/21 18:49:55

DWS层示例: 搭建日主题宽表

需求

维度

步骤

在hive中建数据库dws >>建表
CREATE DATABASE if NOT EXISTS DWS;
建表sql 
CREATE TABLE yp_dws.dws_sale_daycount(
--维度
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',
group_type string COMMENT '分组类型：store，trade_area，city，brand，
min_class，mid_class，max_class，all',
-- =======日统计=======
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 '销售主题日统计宽表'
PARTITIONED BY(dt STRING)
ROW format delimited fields terminated BY '\t'
stored AS orc tblproperties ('orc.compress' = 'SNAPPY');
查询数据sql 
set hive.exec.mode.local.auto=true;
WITH TEMP AS (
    SELECT
        -- 先抽取维度字段
        O.dt
        -- 城市
        ,S.city_id
        ,S.city_name
         -- 商圈
        ,S.trade_area_id
        ,S.trade_area_name
         -- 店铺
        ,S.id
        ,S.store_name
        -- 品牌
        ,G.brand_id
        ,G.brand_name
        -- 大类
        ,G.max_class_id
        ,G.max_class_name
         -- 中
        ,G.mid_class_id
        ,G.mid_class_name
         -- 小
        ,G.min_class_id
        ,G.min_class_name
        -- 抽取字段字段
        -- 订单量指标
        ,O.order_id
        -- 金额指标
        ,O.order_amount
        ,O.goods_price
        ,O.plat_fee
        ,O.settlement_amount
        ,O.dispatcher_money
        ,O.order_from
        ,O.evaluation_state
        ,O.geval_scores
        ,O.is_delivery  -- 是否配送
        ,O.refund_id  -- 退款单号
        -- 去重
        ,ROW_NUMBER()OVER(PARTITION BY O.order_id ORDER BY order_id) RN
    FROM DWB.DWB_ORDER_DETAIL1 O
    LEFT JOIN DWB.DWB_SHOP_DETAIL S
    ON O.store_id = S.id
    LEFT JOIN DWB.dwb_goods_detail G
    ON G.store_id = S.id
)
SELECT
    T.city_id
    ,T.city_name
    ,T.trade_area_id
    ,T.trade_area_name
    ,T.ID AS STORE_ID
    ,T.store_name
    ,T.brand_id
    ,T.brand_name
    ,T.max_class_id
    ,T.max_class_name
    ,T.mid_class_id
    ,T.mid_class_name
    ,T.min_class_id
    ,T.min_class_name
    ,(CASE WHEN T.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
    ,SUM(CASE WHEN RN = 1 THEN T.order_amount END) AS SALE_AMT
    ,SUM(CASE WHEN RN = 1 THEN T.plat_fee END) AS PLAT_AMT
    ,SUM(CASE WHEN RN = 1 AND T.is_delivery = 1 THEN T.order_amount END) AS DELIVER_SALE_AMT
    ,SUM(CASE WHEN RN = 1 AND T.order_from = 'miniapp' THEN T.order_amount END) AS MINI_APP_SALE_AMT
    ,SUM(CASE WHEN RN = 1 AND T.order_from = 'android' THEN T.order_amount END) AS android_SALE_AMT
    ,SUM(CASE WHEN RN = 1 AND T.order_from = 'ios' THEN T.order_amount END) AS ios_SALE_AMT
    ,SUM(CASE WHEN RN = 1 AND T.order_from = 'pcweb' THEN T.order_amount END) AS PCWEB_SALE_AMT
    ,COUNT(CASE WHEN RN = 1 THEN T.order_id END ) AS ORDER_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.evaluation_state = 1 THEN 1 END) AS EVA_ORDER_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.geval_scores < 3 THEN 1 END) AS BAD_EVA_ORDER_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.is_delivery = 1 THEN 1 END) AS DELIVER_ORDER_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.refund_id IS NOT NULL THEN 1 END) AS  REFUND_ORDER_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.order_from = 'miniapp' THEN 1 END) AS MINI_APP_SALE_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.order_from = 'android' THEN 1 END) AS android_SALE_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.order_from = 'ios' THEN 1 END) AS ios_SALE_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.order_from = 'pcweb' THEN 1 END) AS PCWEB_SALE_CNT
    ,T.dt

FROM TEMP T
GROUP BY
     T.dt
    ,T.city_id,T.city_name
    ,T.trade_area_id,T.trade_area_name
    ,T.id,T.store_name
    ,T.brand_id,T.brand_name
    ,T.max_class_id,T.max_class_name
    ,T.mid_class_id,T.mid_class_name
    ,T.min_class_id,T.min_class_name
GROUPING SETS (
     (T.dt)
    ,(T.dt,T.city_id,T.city_name)
    ,(T.dt,T.city_id,T.city_name,T.trade_area_id,T.trade_area_name)
    ,(T.dt,T.city_id,T.city_name,T.trade_area_id,T.trade_area_name,T.id,T.store_name)
    ,(T.dt,T.brand_id,T.brand_name)
    ,(T.dt,T.max_class_id,T.max_class_name)
    ,(T.dt,T.max_class_id,T.max_class_name,T.mid_class_id,T.mid_class_name)
    ,(T.dt,T.max_class_id,T.max_class_name,T.mid_class_id,T.mid_class_name,T.min_class_id,T.min_class_name)
);

在hive中查询数据很慢

方法一:

打开hive的本地模式 (默认是false关闭状态) 
set hive.exec.mode.local.auto=true;

方法二: Hue上执行

插入数据

查看表结构

要插入的目标表是分区表 >> 开启动态插入模式/非严格模式

在hive中,insert into 要紧跟select

set hive.exec.mode.local.auto=true; -- 本地模式
SET hive.exec.dynamic.partition = true; -- 动态分区
SET hive.exec.dynamic.partition.mode=nonstrict; -- 非严格模式
WITH TEMP AS (
    SELECT
        -- 先抽取维度字段
        O.dt
        -- 城市
        ,S.city_id
        ,S.city_name
         -- 商圈
        ,S.trade_area_id
        ,S.trade_area_name
         -- 店铺
        ,S.id
        ,S.store_name
        -- 品牌
        ,G.brand_id
        ,G.brand_name
        -- 大类
        ,G.max_class_id
        ,G.max_class_name
         -- 中
        ,G.mid_class_id
        ,G.mid_class_name
         -- 小
        ,G.min_class_id
        ,G.min_class_name
        -- 抽取字段字段
        -- 订单量指标
        ,O.order_id
        -- 金额指标
        ,O.order_amount
        ,O.goods_price
        ,O.plat_fee
        ,O.settlement_amount
        ,O.dispatcher_money
        ,O.order_from
        ,O.evaluation_state
        ,O.geval_scores
        ,O.is_delivery  -- 是否配送
        ,O.refund_id  -- 退款单号
        -- 去重
        ,ROW_NUMBER()OVER(PARTITION BY O.order_id ORDER BY order_id) RN
    FROM DWB.DWB_ORDER_DETAIL1 O
    LEFT JOIN DWB.DWB_SHOP_DETAIL S
    ON O.store_id = S.id
    LEFT JOIN DWB.dwb_goods_detail G
    ON G.store_id = S.id
)

insert into dws.dws_sale_daycount(dt)

SELECT
    T.city_id
    ,T.city_name
    ,T.trade_area_id
    ,T.trade_area_name
    ,T.ID AS STORE_ID
    ,T.store_name
    ,T.brand_id
    ,T.brand_name
    ,T.max_class_id
    ,T.max_class_name
    ,T.mid_class_id
    ,T.mid_class_name
    ,T.min_class_id
    ,T.min_class_name
    ,(CASE WHEN T.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
    ,SUM(CASE WHEN RN = 1 THEN T.order_amount END) AS SALE_AMT
    ,SUM(CASE WHEN RN = 1 THEN T.plat_fee END) AS PLAT_AMT
    ,SUM(CASE WHEN RN = 1 AND T.is_delivery = 1 THEN T.order_amount END) AS DELIVER_SALE_AMT
    ,SUM(CASE WHEN RN = 1 AND T.order_from = 'miniapp' THEN T.order_amount END) AS MINI_APP_SALE_AMT
    ,SUM(CASE WHEN RN = 1 AND T.order_from = 'android' THEN T.order_amount END) AS android_SALE_AMT
    ,SUM(CASE WHEN RN = 1 AND T.order_from = 'ios' THEN T.order_amount END) AS ios_SALE_AMT
    ,SUM(CASE WHEN RN = 1 AND T.order_from = 'pcweb' THEN T.order_amount END) AS PCWEB_SALE_AMT
    ,COUNT(CASE WHEN RN = 1 THEN T.order_id END ) AS ORDER_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.evaluation_state = 1 THEN 1 END) AS EVA_ORDER_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.geval_scores < 3 THEN 1 END) AS BAD_EVA_ORDER_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.is_delivery = 1 THEN 1 END) AS DELIVER_ORDER_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.refund_id IS NOT NULL THEN 1 END) AS  REFUND_ORDER_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.order_from = 'miniapp' THEN 1 END) AS MINI_APP_SALE_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.order_from = 'android' THEN 1 END) AS android_SALE_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.order_from = 'ios' THEN 1 END) AS ios_SALE_CNT
    ,COUNT(CASE WHEN RN = 1 AND T.order_from = 'pcweb' THEN 1 END) AS PCWEB_SALE_CNT
    ,T.dt

FROM TEMP T
GROUP BY
     T.dt
    ,T.city_id,T.city_name
    ,T.trade_area_id,T.trade_area_name
    ,T.id,T.store_name
    ,T.brand_id,T.brand_name
    ,T.max_class_id,T.max_class_name
    ,T.mid_class_id,T.mid_class_name
    ,T.min_class_id,T.min_class_name
GROUPING SETS (
     (T.dt)
    ,(T.dt,T.city_id,T.city_name)
    ,(T.dt,T.city_id,T.city_name,T.trade_area_id,T.trade_area_name)
    ,(T.dt,T.city_id,T.city_name,T.trade_area_id,T.trade_area_name,T.id,T.store_name)
    ,(T.dt,T.brand_id,T.brand_name)
    ,(T.dt,T.max_class_id,T.max_class_name)
    ,(T.dt,T.max_class_id,T.max_class_name,T.mid_class_id,T.mid_class_name)
    ,(T.dt,T.max_class_id,T.max_class_name,T.mid_class_id,T.mid_class_name,T.min_class_id,T.min_class_name)
);

​

查询数据sql分析

数据来源>>DWB层的数据表

下面分析维度/指标需要哪些数据信息(字段)以及来自哪些表

维度: 

日期(dt)>>DWB_ORDER_DETAIL1

城市(city_id, city_name) >>DWB_SHOP_DETAIL

商圈(trade_area_id, trade_area_name)>>DWB_SHOP_DETAIL

店铺(id, store_name)>>DWB_SHOP_DETAIL

品牌(brand_id, brand_name)>>dwb_goods_detail

大类(max_class_id, max_class_name)>>dwb_goods_detail

中类(mid_class_id, mid_class_name)>> dwb_goods_detail

小类(min_class_id, min_class_name) >>dwb_goods_detail

指标

订单量(order_id)>>DWB_ORDER_DETAIL1

金额(order_amount, goods_price, plat_fee, settlement_amount, dispatcher_money, order_from, evaluation_state, geval_scores, is_delivery, refund_id)>>DWB_ORDER_DETAIL1

维度/指标的数据来源于3张表, 且订单表(DWB_ORDER_DETAIL1)最多>>表连接时把订单表作为主表

使用with  as 做一个公共表达式先把指标/维度相关的字段数据抽取出来>>再用select 语句对数据进行分组汇总>>插入数据

抽取数据注意: 订单表的重复数据>>去重>>ROW_NUMBER()

重复数据原因:  

订单表是一个宽表, 由多张事实表连接到一起, 容易产生重复数据

比如一张订单里面有多家商铺的商品,那么就会产生多条同样的订单号

select 语句的字段及数据类型, 注意和目标表的字段相对应

目标表

目标表的group_type字段,用case when 实现

字段信息:   group_type    string COMMENT '分组类型：store，trade_area，city，brand，
min_class，mid_class，max_class，all',

注意: '分组类型'字段在用case when进行条件判断时, 只有false不满足条件才进行下一步判断>>先判断小维度,再到大维度>>减少sql 量

比如: 在维度中, 店铺 < 商圈 < 城市 ;  小类 < 中类 < 大类  ;    

目标表的指标字段

select 语句中的指标数据汇总实现

(目标表)收入/成交额>>sum() 汇总

(目标表)单量>>count() 汇总

注意: case when 去重/过滤条件

目标表的分区字段  dt >>T.dt


http://www.kler.cn/a/555036.html

相关文章：

  • Ubuntu 20.04源码安装opencv 4.5.0
  • 视频图像质量评价开源算法介绍【持续更新】
  • 有向图的强连通分量: Kosaraju算法和Tarjan算法详解
  • MapReduce理论知识与实践
  • ESXI 8.0 linux vSphere Client service has stopped working.手动启动服务
  • BS架构网络安全 网络安全架构分析
  • UI学习备忘
  • 第1章 快速认识线程
  • 用deepseek学大模型05逻辑回归
  • 在 Python 中操作 Excel 文件
  • 将RocketMQ集成到了Spring Boot项目中，实现站内信功能
  • Java中Map循环安全的删除数据的4中方法
  • 循环神经网络RNN原理与优化
  • uniapp 使用v-html在微信小程序中渲染成rich-text如何显示文本溢出省略
  • 关于房间传感器监测数据集的探索
  • 矩阵加减乘除的意义与应用
  • 云服务器和物理服务器该如何选择
  • C# 中DevExpress的GridView中Appearance 属性
  • Oracle EBS 12.1和APEX 集成时 Apache的配置代理
  • C语言中的链表封装