离线数仓 (十三) --------- DWD 层搭建

深碍√TFBOYSˉ_ 2024-04-01 11:30 95阅读 0赞

#### 目录 ####

*  前言
 *  一、DWD 层 (用户行为日志)
 *   *  1. 日志解析思路
     *  2. get\_json\_object 函数使用
     *  3. 启动日志表
     *  4. 页面日志表
     *  5. 动作日志表
     *  6. 曝光日志表
     *  7. 错误日志表
     *  8. DWD 层用户行为数据加载脚本
 *  二、DWD层 (业务数据)
 *   *  1. 评价事实表 (事务型事实表)
     *  2. 订单明细事实表 (事务型事实表)
     *  3. 退单事实表 (事务型事实表)
     *  4. 加购事实表 (周期型快照事实表，每日快照)
     *  5. 收藏事实表 (周期型快照事实表，每日快照)
     *  6. 优惠券领用事实表 (累积型快照事实表)
     *  7. 支付事实表 (累积型快照事实表)
     *  8. 退款事实表 (累积型快照事实表)
     *  9. 订单事实表 (累积型快照事实表)
     *  10. DWD 层业务数据首日装载脚本
     *  11. DWD层业务数据每日装载脚本

--------------------

## 前言 ##

DWD 层主要完成的功能 ：

*  对用户行为数据解析。
 *  对业务数据采用维度模型重新建模。

--------------------

## 一、DWD 层 (用户行为日志) ##

### 1. 日志解析思路 ###

**A、日志结构回顾**

页面埋点日志

![在这里插入图片描述][9b0db79c0f4f40de9a4f92d983d84c30.png]  
启动日志![在这里插入图片描述][70d94ba38bd0410f9a715e37b02abc39.png]  
**B、日志解析思路**

![在这里插入图片描述][47d76a6305f6480899917f21b01d8407.png]

### 2. get\_json\_object 函数使用 ###

**A、数据**

[{
        "name":"大郎","sex":"男","age":"25"},{
        "name":"西门庆","sex":"男","age":"47"}]

**B、取出第一个 json 对象**

hive (gmall)>
    select get_json_object('[{"name":"大郎","sex":"男","age":"25"},{"name":"西门庆","sex":"男","age":"47"}]','$[0]');

结果是：`{"name":"大郎","sex":"男","age":"25"}`

**C、取出第一个 json 的 age 字段的值**

hive (gmall)>
    SELECT get_json_object('[{"name":"大郎","sex":"男","age":"25"},{"name":"西门庆","sex":"男","age":"47"}]',"$[0].age");

结果是：25

### 3. 启动日志表 ###

启动日志解析思路：启动日志表中每行数据对应一个启动记录，一个启动记录应该包含日志中的公共信息和启动信息。先将所有包含start字段的日志过滤出来，然后使用get\_json\_object函数解析每个字段。

![在这里插入图片描述][ec65ae3188004bc29bf38d188c1a07c8.png]

**A、建表语句**

DROP TABLE IF EXISTS dwd_start_log;
    CREATE EXTERNAL TABLE dwd_start_log(
        `area_code` STRING COMMENT '地区编码',
        `brand` STRING COMMENT '手机品牌',
        `channel` STRING COMMENT '渠道',
        `is_new` STRING COMMENT '是否首次启动',
        `model` STRING COMMENT '手机型号',
        `mid_id` STRING COMMENT '设备id',
        `os` STRING COMMENT '操作系统',
        `user_id` STRING COMMENT '会员id',
        `version_code` STRING COMMENT 'app版本号',
        `entry` STRING COMMENT 'icon手机图标 notice 通知 install 安装后启动',
        `loading_time` BIGINT COMMENT '启动加载时间',
        `open_ad_id` STRING COMMENT '广告页ID ',
        `open_ad_ms` BIGINT COMMENT '广告总共播放时间',
        `open_ad_skip_ms` BIGINT COMMENT '用户跳过广告时点',
        `ts` BIGINT COMMENT '时间'
    ) COMMENT '启动日志表'
    PARTITIONED BY (`dt` STRING) -- 按照时间创建分区
    STORED AS PARQUET -- 采用parquet列式存储
    LOCATION '/warehouse/gmall/dwd/dwd_start_log' -- 指定在HDFS上存储位置
    TBLPROPERTIES('parquet.compression'='lzo') -- 采用LZO压缩
    ;

**B、数据导入**

![在这里插入图片描述][b4433e3dde8f4838b083e235af18075d.png]

hive (gmall)> 
    insert overwrite table dwd_start_log partition(dt='2020-06-14')
    select
        get_json_object(line,'$.common.ar'),
        get_json_object(line,'$.common.ba'),
        get_json_object(line,'$.common.ch'),
        get_json_object(line,'$.common.is_new'),
        get_json_object(line,'$.common.md'),
        get_json_object(line,'$.common.mid'),
        get_json_object(line,'$.common.os'),
        get_json_object(line,'$.common.uid'),
        get_json_object(line,'$.common.vc'),
        get_json_object(line,'$.start.entry'),
        get_json_object(line,'$.start.loading_time'),
        get_json_object(line,'$.start.open_ad_id'),
        get_json_object(line,'$.start.open_ad_ms'),
        get_json_object(line,'$.start.open_ad_skip_ms'),
        get_json_object(line,'$.ts')
    from ods_log
    where dt='2020-06-14'
    and get_json_object(line,'$.start') is not null;

**C、查看数据**

hive (gmall)> 
    select * from dwd_start_log where dt='2020-06-14' limit 2;

### 4. 页面日志表 ###

页面日志解析思路：页面日志表中每行数据对应一个页面访问记录，一个页面访问记录应该包含日志中的公共信息和页面信息。先将所有包含 page 字段的日志过滤出来，然后使用 get\_json\_object 函数解析每个字段。

页面日志表解析：

![在这里插入图片描述][c9104b749a944e1b8588ac2f16fcd40e.png]

**A、建表语句**

DROP TABLE IF EXISTS dwd_page_log;
    CREATE EXTERNAL TABLE dwd_page_log(
        `area_code` STRING COMMENT '地区编码',
        `brand` STRING COMMENT '手机品牌',
        `channel` STRING COMMENT '渠道',
        `is_new` STRING COMMENT '是否首次启动',
        `model` STRING COMMENT '手机型号',
        `mid_id` STRING COMMENT '设备id',
        `os` STRING COMMENT '操作系统',
        `user_id` STRING COMMENT '会员id',
        `version_code` STRING COMMENT 'app版本号',
        `during_time` BIGINT COMMENT '持续时间毫秒',
        `page_item` STRING COMMENT '目标id ',
        `page_item_type` STRING COMMENT '目标类型',
        `last_page_id` STRING COMMENT '上页类型',
        `page_id` STRING COMMENT '页面ID ',
        `source_type` STRING COMMENT '来源类型',
        `ts` bigint
    ) COMMENT '页面日志表'
    PARTITIONED BY (`dt` STRING)
    STORED AS PARQUET
    LOCATION '/warehouse/gmall/dwd/dwd_page_log'
    TBLPROPERTIES('parquet.compression'='lzo');

**B、数据导入**

hive (gmall)>
    insert overwrite table dwd_page_log partition(dt='2020-06-14')
    select
        get_json_object(line,'$.common.ar'),
        get_json_object(line,'$.common.ba'),
        get_json_object(line,'$.common.ch'),
        get_json_object(line,'$.common.is_new'),
        get_json_object(line,'$.common.md'),
        get_json_object(line,'$.common.mid'),
        get_json_object(line,'$.common.os'),
        get_json_object(line,'$.common.uid'),
        get_json_object(line,'$.common.vc'),
        get_json_object(line,'$.page.during_time'),
        get_json_object(line,'$.page.item'),
        get_json_object(line,'$.page.item_type'),
        get_json_object(line,'$.page.last_page_id'),
        get_json_object(line,'$.page.page_id'),
        get_json_object(line,'$.page.source_type'),
        get_json_object(line,'$.ts')
    from ods_log
    where dt='2020-06-14'
    and get_json_object(line,'$.page') is not null;

**C、查看数据**

hive (gmall)> 
    select * from dwd_page_log where dt='2020-06-14' limit 2;

### 5. 动作日志表 ###

动作日志解析思路：动作日志表中每行数据对应用户的一个动作记录，一个动作记录应当包含公共信息、页面信息以及动作信息。先将包含action字段的日志过滤出来，然后通过 UDTF 函数，将action数组 “炸开”（类似于explode函数的效果），然后使用 get\_json\_object 函数解析每个字段。

动作日志表解析：

![在这里插入图片描述][6a2849bb223c459ba97e61e450e84561.png]

**A、建表语句**

DROP TABLE IF EXISTS dwd_action_log;
    CREATE EXTERNAL TABLE dwd_action_log(
        `area_code` STRING COMMENT '地区编码',
        `brand` STRING COMMENT '手机品牌',
        `channel` STRING COMMENT '渠道',
        `is_new` STRING COMMENT '是否首次启动',
        `model` STRING COMMENT '手机型号',
        `mid_id` STRING COMMENT '设备id',
        `os` STRING COMMENT '操作系统',
        `user_id` STRING COMMENT '会员id',
        `version_code` STRING COMMENT 'app版本号',
        `during_time` BIGINT COMMENT '持续时间毫秒',
        `page_item` STRING COMMENT '目标id ',
        `page_item_type` STRING COMMENT '目标类型',
        `last_page_id` STRING COMMENT '上页类型',
        `page_id` STRING COMMENT '页面id ',
        `source_type` STRING COMMENT '来源类型',
        `action_id` STRING COMMENT '动作id',
        `item` STRING COMMENT '目标id ',
        `item_type` STRING COMMENT '目标类型',
        `ts` BIGINT COMMENT '时间'
    ) COMMENT '动作日志表'
    PARTITIONED BY (`dt` STRING)
    STORED AS PARQUET
    LOCATION '/warehouse/gmall/dwd/dwd_action_log'
    TBLPROPERTIES('parquet.compression'='lzo');

**B、创建 UDTF 函数 —— 设计思路**

UDTF 思想：

![在这里插入图片描述][05c6b9a7bb3540eabab6642243153154.png]  
UDTF 设计思路：

![在这里插入图片描述][50d4ca3bfff7410280e674f3c7fed00a.png]

**C、创建UDTF函数 —— 编写代码**

（1）创建一个 maven 工程：hivefunction  
（2）创建包名：`com.fancy.hive.udtf`  
（3）引入如下依赖

<dependencies>
        
        <dependency>
            <groupId>org.apache.hive</groupId>
            <artifactId>hive-exec</artifactId>
            <version>3.1.2</version>
        </dependency>
    </dependencies>

（4）编码

package com.fancy.hive.udtf;
    
    import org.apache.hadoop.hive.ql.exec.UDFArgumentException;
    import org.apache.hadoop.hive.ql.metadata.HiveException;
    import org.apache.hadoop.hive.ql.udf.generic.GenericUDTF;
    import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
    import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory;
    import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector;
    import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
    import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory;
    import org.json.JSONArray;
    
    import java.util.ArrayList;
    import java.util.List;
    
    public class ExplodeJSONArray extends GenericUDTF {
        
    
        @Override
        public StructObjectInspector initialize(ObjectInspector[] argOIs) throws UDFArgumentException {
        
    
            // 1 参数合法性检查
            if (argOIs.length != 1) {
        
                throw new UDFArgumentException("explode_json_array 只需要一个参数");
            }
    
            // 2 第一个参数必须为string
            //判断参数是否为基础数据类型
            if (argOIs[0].getCategory() != ObjectInspector.Category.PRIMITIVE) {
        
                throw new UDFArgumentException("explode_json_array 只接受基础类型参数");
            }
    
            //将参数对象检查器强转为基础类型对象检查器
            PrimitiveObjectInspector argumentOI = (PrimitiveObjectInspector) argOIs[0];
    
            //判断参数是否为String类型
            if (argumentOI.getPrimitiveCategory() != PrimitiveObjectInspector.PrimitiveCategory.STRING) {
        
                throw new UDFArgumentException("explode_json_array 只接受string类型的参数");
            }
    
            // 3 定义返回值名称和类型
            List<String> fieldNames = new ArrayList<String>();
            List<ObjectInspector> fieldOIs = new ArrayList<ObjectInspector>();
    
            fieldNames.add("items");
            fieldOIs.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector);
    
            return ObjectInspectorFactory.getStandardStructObjectInspector(fieldNames, fieldOIs);
        }
    
        public void process(Object[] objects) throws HiveException {
        
    
            // 1 获取传入的数据
            String jsonArray = objects[0].toString();
    
            // 2 将string转换为json数组
            JSONArray actions = new JSONArray(jsonArray);
    
            // 3 循环一次，取出数组中的一个json，并写出
            for (int i = 0; i < actions.length(); i++) {
        
    
                String[] result = new String[1];
                result[0] = actions.getString(i);
                forward(result);
            }
        }
    
        public void close() throws HiveException {
        
    
        }
    
    }

**D、创建函数**

（1）打包

（2）将 hivefunction-1.0-SNAPSHOT.jar 上传到 node101 的 /opt/module，然后再将该 jar 包上传到 HDFS 的 /user/hive/jars 路径下

[fancy@node101 module]$ hadoop fs -mkdir -p /user/hive/jars
    [fancy@node101 module]$ hadoop fs -put hivefunction-1.0-SNAPSHOT.jar /user/hive/jars

（3）创建永久函数与开发好的java class关联

create function explode_json_array as 'com.fancy.hive.udtf.ExplodeJSONArray' using jar 'hdfs://hadoop102:8020/user/hive/jars/hivefunction-1.0-SNAPSHOT.jar';

**注意：**  
如果修改了自定义函数重新生成 jar 包怎么处理？只需要替换HDFS 路径上的旧 jar 包，然后重启 Hive 客户端即可。

**E、数据导入**

insert overwrite table dwd_action_log partition(dt='2020-06-14')
    select
        get_json_object(line,'$.common.ar'),
        get_json_object(line,'$.common.ba'),
        get_json_object(line,'$.common.ch'),
        get_json_object(line,'$.common.is_new'),
        get_json_object(line,'$.common.md'),
        get_json_object(line,'$.common.mid'),
        get_json_object(line,'$.common.os'),
        get_json_object(line,'$.common.uid'),
        get_json_object(line,'$.common.vc'),
        get_json_object(line,'$.page.during_time'),
        get_json_object(line,'$.page.item'),
        get_json_object(line,'$.page.item_type'),
        get_json_object(line,'$.page.last_page_id'),
        get_json_object(line,'$.page.page_id'),
        get_json_object(line,'$.page.source_type'),
        get_json_object(action,'$.action_id'),
        get_json_object(action,'$.item'),
        get_json_object(action,'$.item_type'),
        get_json_object(action,'$.ts')
    from ods_log lateral view explode_json_array(get_json_object(line,'$.actions')) tmp as action
    where dt='2020-06-14'
    and get_json_object(line,'$.actions') is not null;

**F、查看数据**

select * from dwd_action_log where dt='2020-06-14' limit 2;

### 6. 曝光日志表 ###

曝光日志解析思路：曝光日志表中每行数据对应一个曝光记录，一个曝光记录应当包含公共信息、页面信息以及曝光信息。先将包含 display 字段的日志过滤出来，然后通过 UDTF 函数，将 display 数组 “炸开”（类似于 explode 函数的效果），然后使用 get\_json\_object 函数解析每个字段。

**曝光日志表解析**

![在这里插入图片描述][e9a768441f8047db8926b1822d0c337f.png]

**A、建表语句**

DROP TABLE IF EXISTS dwd_display_log;
    CREATE EXTERNAL TABLE dwd_display_log(
        `area_code` STRING COMMENT '地区编码',
        `brand` STRING COMMENT '手机品牌',
        `channel` STRING COMMENT '渠道',
        `is_new` STRING COMMENT '是否首次启动',
        `model` STRING COMMENT '手机型号',
        `mid_id` STRING COMMENT '设备id',
        `os` STRING COMMENT '操作系统',
        `user_id` STRING COMMENT '会员id',
        `version_code` STRING COMMENT 'app版本号',
        `during_time` BIGINT COMMENT 'app版本号',
        `page_item` STRING COMMENT '目标id ',
        `page_item_type` STRING COMMENT '目标类型',
        `last_page_id` STRING COMMENT '上页类型',
        `page_id` STRING COMMENT '页面ID ',
        `source_type` STRING COMMENT '来源类型',
        `ts` BIGINT COMMENT 'app版本号',
        `display_type` STRING COMMENT '曝光类型',
        `item` STRING COMMENT '曝光对象id ',
        `item_type` STRING COMMENT 'app版本号',
        `order` BIGINT COMMENT '曝光顺序',
        `pos_id` BIGINT COMMENT '曝光位置'
    ) COMMENT '曝光日志表'
    PARTITIONED BY (`dt` STRING)
    STORED AS PARQUET
    LOCATION '/warehouse/gmall/dwd/dwd_display_log'
    TBLPROPERTIES('parquet.compression'='lzo');

**B、数据导入**

insert overwrite table dwd_display_log partition(dt='2020-06-14')
    select
        get_json_object(line,'$.common.ar'),
        get_json_object(line,'$.common.ba'),
        get_json_object(line,'$.common.ch'),
        get_json_object(line,'$.common.is_new'),
        get_json_object(line,'$.common.md'),
        get_json_object(line,'$.common.mid'),
        get_json_object(line,'$.common.os'),
        get_json_object(line,'$.common.uid'),
        get_json_object(line,'$.common.vc'),
        get_json_object(line,'$.page.during_time'),
        get_json_object(line,'$.page.item'),
        get_json_object(line,'$.page.item_type'),
        get_json_object(line,'$.page.last_page_id'),
        get_json_object(line,'$.page.page_id'),
        get_json_object(line,'$.page.source_type'),
        get_json_object(line,'$.ts'),
        get_json_object(display,'$.display_type'),
        get_json_object(display,'$.item'),
        get_json_object(display,'$.item_type'),
        get_json_object(display,'$.order'),
        get_json_object(display,'$.pos_id')
    from ods_log lateral view explode_json_array(get_json_object(line,'$.displays')) tmp as display
    where dt='2020-06-14'
    and get_json_object(line,'$.displays') is not null;

**C、查看数据**

select * from dwd_display_log where dt='2020-06-14' limit 2;

### 7. 错误日志表 ###

错误日志解析思路：错误日志表中每行数据对应一个错误记录，为方便定位错误，一个错误记录应当包含与之对应的公共信息、页面信息、曝光信息、动作信息、启动信息以及错误信息。先将包含err字段的日志过滤出来，然后使用 get\_json\_object 函数解析所有字段。

![在这里插入图片描述][d8f93b84339643a7b3a5a863bf86716b.png]

**A、建表语句**

DROP TABLE IF EXISTS dwd_error_log;
    CREATE EXTERNAL TABLE dwd_error_log(
        `area_code` STRING COMMENT '地区编码',
        `brand` STRING COMMENT '手机品牌',
        `channel` STRING COMMENT '渠道',
        `is_new` STRING COMMENT '是否首次启动',
        `model` STRING COMMENT '手机型号',
        `mid_id` STRING COMMENT '设备id',
        `os` STRING COMMENT '操作系统',
        `user_id` STRING COMMENT '会员id',
        `version_code` STRING COMMENT 'app版本号',
        `page_item` STRING COMMENT '目标id ',
        `page_item_type` STRING COMMENT '目标类型',
        `last_page_id` STRING COMMENT '上页类型',
        `page_id` STRING COMMENT '页面ID ',
        `source_type` STRING COMMENT '来源类型',
        `entry` STRING COMMENT ' icon手机图标  notice 通知 install 安装后启动',
        `loading_time` STRING COMMENT '启动加载时间',
        `open_ad_id` STRING COMMENT '广告页ID ',
        `open_ad_ms` STRING COMMENT '广告总共播放时间',
        `open_ad_skip_ms` STRING COMMENT '用户跳过广告时点',
        `actions` STRING COMMENT '动作',
        `displays` STRING COMMENT '曝光',
        `ts` STRING COMMENT '时间',
        `error_code` STRING COMMENT '错误码',
        `msg` STRING COMMENT '错误信息'
    ) COMMENT '错误日志表'
    PARTITIONED BY (`dt` STRING)
    STORED AS PARQUET
    LOCATION '/warehouse/gmall/dwd/dwd_error_log'
    TBLPROPERTIES('parquet.compression'='lzo');

说明：此处为对动作数组和曝光数组做处理，如需分析错误与单个动作或曝光的关联，可先使用 explode\_json\_array 函数将数组“炸开”，再使用get\_json\_object 函数获取具体字段。

**D、数据导入**

insert overwrite table dwd_error_log partition(dt='2020-06-14')
    select
        get_json_object(line,'$.common.ar'),
        get_json_object(line,'$.common.ba'),
        get_json_object(line,'$.common.ch'),
        get_json_object(line,'$.common.is_new'),
        get_json_object(line,'$.common.md'),
        get_json_object(line,'$.common.mid'),
        get_json_object(line,'$.common.os'),
        get_json_object(line,'$.common.uid'),
        get_json_object(line,'$.common.vc'),
        get_json_object(line,'$.page.item'),
        get_json_object(line,'$.page.item_type'),
        get_json_object(line,'$.page.last_page_id'),
        get_json_object(line,'$.page.page_id'),
        get_json_object(line,'$.page.source_type'),
        get_json_object(line,'$.start.entry'),
        get_json_object(line,'$.start.loading_time'),
        get_json_object(line,'$.start.open_ad_id'),
        get_json_object(line,'$.start.open_ad_ms'),
        get_json_object(line,'$.start.open_ad_skip_ms'),
        get_json_object(line,'$.actions'),
        get_json_object(line,'$.displays'),
        get_json_object(line,'$.ts'),
        get_json_object(line,'$.err.error_code'),
        get_json_object(line,'$.err.msg')
    from ods_log
    where dt='2020-06-14'
    and get_json_object(line,'$.err') is not null;

**D、查看数据**

hive (gmall)> 
    select * from dwd_error_log where dt='2020-06-14' limit 2;

### 8. DWD 层用户行为数据加载脚本 ###

**A、编写脚本**

在 node101 的 /home/fancy/bin 目录下创建脚本

[fancy@node101 bin]$ vim ods_to_dwd_log.sh

在脚本中编写如下内容

#!/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
    
    dwd_start_log="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_start_log partition(dt='$do_date')
    select
        get_json_object(line,'$.common.ar'),
        get_json_object(line,'$.common.ba'),
        get_json_object(line,'$.common.ch'),
        get_json_object(line,'$.common.is_new'),
        get_json_object(line,'$.common.md'),
        get_json_object(line,'$.common.mid'),
        get_json_object(line,'$.common.os'),
        get_json_object(line,'$.common.uid'),
        get_json_object(line,'$.common.vc'),
        get_json_object(line,'$.start.entry'),
        get_json_object(line,'$.start.loading_time'),
        get_json_object(line,'$.start.open_ad_id'),
        get_json_object(line,'$.start.open_ad_ms'),
        get_json_object(line,'$.start.open_ad_skip_ms'),
        get_json_object(line,'$.ts')
    from ${APP}.ods_log
    where dt='$do_date'
    and get_json_object(line,'$.start') is not null;"
    
    dwd_page_log="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_page_log partition(dt='$do_date')
    select
        get_json_object(line,'$.common.ar'),
        get_json_object(line,'$.common.ba'),
        get_json_object(line,'$.common.ch'),
        get_json_object(line,'$.common.is_new'),
        get_json_object(line,'$.common.md'),
        get_json_object(line,'$.common.mid'),
        get_json_object(line,'$.common.os'),
        get_json_object(line,'$.common.uid'),
        get_json_object(line,'$.common.vc'),
        get_json_object(line,'$.page.during_time'),
        get_json_object(line,'$.page.item'),
        get_json_object(line,'$.page.item_type'),
        get_json_object(line,'$.page.last_page_id'),
        get_json_object(line,'$.page.page_id'),
        get_json_object(line,'$.page.source_type'),
        get_json_object(line,'$.ts')
    from ${APP}.ods_log
    where dt='$do_date'
    and get_json_object(line,'$.page') is not null;"
    
    dwd_action_log="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_action_log partition(dt='$do_date')
    select
        get_json_object(line,'$.common.ar'),
        get_json_object(line,'$.common.ba'),
        get_json_object(line,'$.common.ch'),
        get_json_object(line,'$.common.is_new'),
        get_json_object(line,'$.common.md'),
        get_json_object(line,'$.common.mid'),
        get_json_object(line,'$.common.os'),
        get_json_object(line,'$.common.uid'),
        get_json_object(line,'$.common.vc'),
        get_json_object(line,'$.page.during_time'),
        get_json_object(line,'$.page.item'),
        get_json_object(line,'$.page.item_type'),
        get_json_object(line,'$.page.last_page_id'),
        get_json_object(line,'$.page.page_id'),
        get_json_object(line,'$.page.source_type'),
        get_json_object(action,'$.action_id'),
        get_json_object(action,'$.item'),
        get_json_object(action,'$.item_type'),
        get_json_object(action,'$.ts')
    from ${APP}.ods_log lateral view ${APP}.explode_json_array(get_json_object(line,'$.actions')) tmp as action
    where dt='$do_date'
    and get_json_object(line,'$.actions') is not null;"
    
    
    dwd_display_log="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_display_log partition(dt='$do_date')
    select
        get_json_object(line,'$.common.ar'),
        get_json_object(line,'$.common.ba'),
        get_json_object(line,'$.common.ch'),
        get_json_object(line,'$.common.is_new'),
        get_json_object(line,'$.common.md'),
        get_json_object(line,'$.common.mid'),
        get_json_object(line,'$.common.os'),
        get_json_object(line,'$.common.uid'),
        get_json_object(line,'$.common.vc'),
        get_json_object(line,'$.page.during_time'),
        get_json_object(line,'$.page.item'),
        get_json_object(line,'$.page.item_type'),
        get_json_object(line,'$.page.last_page_id'),
        get_json_object(line,'$.page.page_id'),
        get_json_object(line,'$.page.source_type'),
        get_json_object(line,'$.ts'),
        get_json_object(display,'$.display_type'),
        get_json_object(display,'$.item'),
        get_json_object(display,'$.item_type'),
        get_json_object(display,'$.order'),
        get_json_object(display,'$.pos_id')
    from ${APP}.ods_log lateral view ${APP}.explode_json_array(get_json_object(line,'$.displays')) tmp as display
    where dt='$do_date'
    and get_json_object(line,'$.displays') is not null;"
    
    
    dwd_error_log="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_error_log partition(dt='$do_date')
    select
        get_json_object(line,'$.common.ar'),
        get_json_object(line,'$.common.ba'),
        get_json_object(line,'$.common.ch'),
        get_json_object(line,'$.common.is_new'),
        get_json_object(line,'$.common.md'),
        get_json_object(line,'$.common.mid'),
        get_json_object(line,'$.common.os'),
        get_json_object(line,'$.common.uid'),
        get_json_object(line,'$.common.vc'),
        get_json_object(line,'$.page.item'),
        get_json_object(line,'$.page.item_type'),
        get_json_object(line,'$.page.last_page_id'),
        get_json_object(line,'$.page.page_id'),
        get_json_object(line,'$.page.source_type'),
        get_json_object(line,'$.start.entry'),
        get_json_object(line,'$.start.loading_time'),
        get_json_object(line,'$.start.open_ad_id'),
        get_json_object(line,'$.start.open_ad_ms'),
        get_json_object(line,'$.start.open_ad_skip_ms'),
        get_json_object(line,'$.actions'),
        get_json_object(line,'$.displays'),
        get_json_object(line,'$.ts'),
        get_json_object(line,'$.err.error_code'),
        get_json_object(line,'$.err.msg')
    from ${APP}.ods_log
    where dt='$do_date'
    and get_json_object(line,'$.err') is not null;"
    
    
    case $1 in
        dwd_start_log )
            hive -e "$dwd_start_log"
        ;;
        dwd_page_log )
            hive -e "$dwd_page_log"
        ;;
        dwd_action_log )
            hive -e "$dwd_action_log"
        ;;
        dwd_display_log )
            hive -e "$dwd_display_log"
        ;;
        dwd_error_log )
            hive -e "$dwd_error_log"
        ;;
        all )
            hive -e "$dwd_start_log$dwd_page_log$dwd_action_log$dwd_display_log$dwd_error_log"
        ;;
    esac

增加脚本执行权限

[fancy@node101 bin]$ chmod 777 ods_to_dwd_log.sh

**B、脚本使用**

执行脚本

[fancy@node101 module]$ ods_to_dwd_log.sh all 2020-06-14

查询导入结果

## 二、DWD层 (业务数据) ##

业务数据方面DWD层的搭建主要注意点在于维度建模。

### 1. 评价事实表 (事务型事实表) ###

**A、建表语句**

DROP TABLE IF EXISTS dwd_comment_info;
    CREATE EXTERNAL TABLE dwd_comment_info(
        `id` STRING COMMENT '编号',
        `user_id` STRING COMMENT '用户ID',
        `sku_id` STRING COMMENT '商品sku',
        `spu_id` STRING COMMENT '商品spu',
        `order_id` STRING COMMENT '订单ID',
        `appraise` STRING COMMENT '评价(好评、中评、差评、默认评价)',
        `create_time` STRING COMMENT '评价时间'
    ) COMMENT '评价事实表'
    PARTITIONED BY (`dt` STRING)
    STORED AS PARQUET
    LOCATION '/warehouse/gmall/dwd/dwd_comment_info/'
    TBLPROPERTIES ("parquet.compression"="lzo");

**B、分区规划**

![在这里插入图片描述][a84cffd5b1df40bda647157838a22342.png]

**C、数据装载**

![在这里插入图片描述][8c0befee781c4081a1ad9c04badb547c.png]  
1.首日装载

insert overwrite table dwd_comment_info partition (dt)
    select
        id,
        user_id,
        sku_id,
        spu_id,
        order_id,
        appraise,
        create_time,
        date_format(create_time,'yyyy-MM-dd')
    from ods_comment_info
    where dt='2020-06-14';

2.每日装载

insert overwrite table dwd_comment_info partition(dt='2020-06-15')
    select
        id,
        user_id,
        sku_id,
        spu_id,
        order_id,
        appraise,
        create_time
    from ods_comment_info where dt='2020-06-15';

### 2. 订单明细事实表 (事务型事实表) ###

**A、建表语句**

DROP TABLE IF EXISTS dwd_order_detail;
    CREATE EXTERNAL TABLE dwd_order_detail (
        `id` STRING COMMENT '订单编号',
        `order_id` STRING COMMENT '订单号',
        `user_id` STRING COMMENT '用户id',
        `sku_id` STRING COMMENT 'sku商品id',
        `province_id` STRING COMMENT '省份ID',
        `activity_id` STRING COMMENT '活动ID',
        `activity_rule_id` STRING COMMENT '活动规则ID',
        `coupon_id` STRING COMMENT '优惠券ID',
        `create_time` STRING COMMENT '创建时间',
        `source_type` STRING COMMENT '来源类型',
        `source_id` STRING COMMENT '来源编号',
        `sku_num` BIGINT COMMENT '商品数量',
        `original_amount` DECIMAL(16,2) COMMENT '原始价格',
        `split_activity_amount` DECIMAL(16,2) COMMENT '活动优惠分摊',
        `split_coupon_amount` DECIMAL(16,2) COMMENT '优惠券优惠分摊',
        `split_final_amount` DECIMAL(16,2) COMMENT '最终价格分摊'
    ) COMMENT '订单明细事实表表'
    PARTITIONED BY (`dt` STRING)
    STORED AS PARQUET
    LOCATION '/warehouse/gmall/dwd/dwd_order_detail/'
    TBLPROPERTIES ("parquet.compression"="lzo");

**B、分区规划**

![在这里插入图片描述][e854e5c730154cdea315a6349d199a0b.png]

**C、数据装载**

![在这里插入图片描述][ea50d33f60c24a2c8f132925d655f3e4.png]

1.首日装载

insert overwrite table dwd_order_detail partition(dt)
    select
        od.id,
        od.order_id,
        oi.user_id,
        od.sku_id,
        oi.province_id,
        oda.activity_id,
        oda.activity_rule_id,
        odc.coupon_id,
        od.create_time,
        od.source_type,
        od.source_id,
        od.sku_num,
        od.order_price*od.sku_num,
        od.split_activity_amount,
        od.split_coupon_amount,
        od.split_final_amount,
        date_format(create_time,'yyyy-MM-dd')
    from
    (
        select
            *
        from ods_order_detail
        where dt='2020-06-14'
    )od
    left join
    (
        select
            id,
            user_id,
            province_id
        from ods_order_info
        where dt='2020-06-14'
    )oi
    on od.order_id=oi.id
    left join
    (
        select
            order_detail_id,
            activity_id,
            activity_rule_id
        from ods_order_detail_activity
        where dt='2020-06-14'
    )oda
    on od.id=oda.order_detail_id
    left join
    (
        select
            order_detail_id,
            coupon_id
        from ods_order_detail_coupon
        where dt='2020-06-14'
    )odc
    on od.id=odc.order_detail_id;

2.每日装载

insert overwrite table dwd_order_detail partition(dt='2020-06-15')
    select
        od.id,
        od.order_id,
        oi.user_id,
        od.sku_id,
        oi.province_id,
        oda.activity_id,
        oda.activity_rule_id,
        odc.coupon_id,
        od.create_time,
        od.source_type,
        od.source_id,
        od.sku_num,
        od.order_price*od.sku_num,
        od.split_activity_amount,
        od.split_coupon_amount,
        od.split_final_amount
    from
    (
        select
            *
        from ods_order_detail
        where dt='2020-06-15'
    )od
    left join
    (
        select
            id,
            user_id,
            province_id
        from ods_order_info
        where dt='2020-06-15'
    )oi
    on od.order_id=oi.id
    left join
    (
        select
            order_detail_id,
            activity_id,
            activity_rule_id
        from ods_order_detail_activity
        where dt='2020-06-15'
    )oda
    on od.id=oda.order_detail_id
    left join
    (
        select
            order_detail_id,
            coupon_id
        from ods_order_detail_coupon
        where dt='2020-06-15'
    )odc
    on od.id=odc.order_detail_id;

### 3. 退单事实表 (事务型事实表) ###

**A、建表语句**

DROP TABLE IF EXISTS dwd_order_refund_info;
    CREATE EXTERNAL TABLE dwd_order_refund_info(
        `id` STRING COMMENT '编号',
        `user_id` STRING COMMENT '用户ID',
        `order_id` STRING COMMENT '订单ID',
        `sku_id` STRING COMMENT '商品ID',
        `province_id` STRING COMMENT '地区ID',
        `refund_type` STRING COMMENT '退单类型',
        `refund_num` BIGINT COMMENT '退单件数',
        `refund_amount` DECIMAL(16,2) COMMENT '退单金额',
        `refund_reason_type` STRING COMMENT '退单原因类型',
        `create_time` STRING COMMENT '退单时间'
    ) COMMENT '退单事实表'
    PARTITIONED BY (`dt` STRING)
    STORED AS PARQUET
    LOCATION '/warehouse/gmall/dwd/dwd_order_refund_info/'
    TBLPROPERTIES ("parquet.compression"="lzo");

**B、分区规划**  
![在这里插入图片描述][ddc1179ca6724218a49a09a668c2e74c.png]

**C、数据装载**

![在这里插入图片描述][bd292efe3e9448ce8baaf9d586bc6d4b.png]

1.首日装载

insert overwrite table dwd_order_refund_info partition(dt)
    select
        ri.id,
        ri.user_id,
        ri.order_id,
        ri.sku_id,
        oi.province_id,
        ri.refund_type,
        ri.refund_num,
        ri.refund_amount,
        ri.refund_reason_type,
        ri.create_time,
        date_format(ri.create_time,'yyyy-MM-dd')
    from
    (
        select * from ods_order_refund_info where dt='2020-06-14'
    )ri
    left join
    (
        select id,province_id from ods_order_info where dt='2020-06-14'
    )oi
    on ri.order_id=oi.id;

2.每日装载

insert overwrite table dwd_order_refund_info partition(dt='2020-06-15')
    select
        ri.id,
        ri.user_id,
        ri.order_id,
        ri.sku_id,
        oi.province_id,
        ri.refund_type,
        ri.refund_num,
        ri.refund_amount,
        ri.refund_reason_type,
        ri.create_time
    from
    (
        select * from ods_order_refund_info where dt='2020-06-15'
    )ri
    left join
    (
        select id,province_id from ods_order_info where dt='2020-06-15'
    )oi
    on ri.order_id=oi.id;

3.查询加载结果

### 4. 加购事实表 (周期型快照事实表，每日快照) ###

**A、建表语句**

DROP TABLE IF EXISTS dwd_cart_info;
    CREATE EXTERNAL TABLE dwd_cart_info(
        `id` STRING COMMENT '编号',
        `user_id` STRING COMMENT '用户ID',
        `sku_id` STRING COMMENT '商品ID',
        `source_type` STRING COMMENT '来源类型',
        `source_id` STRING COMMENT '来源编号',
        `cart_price` DECIMAL(16,2) COMMENT '加入购物车时的价格',
        `is_ordered` STRING COMMENT '是否已下单',
        `create_time` STRING COMMENT '创建时间',
        `operate_time` STRING COMMENT '修改时间',
        `order_time` STRING COMMENT '下单时间',
        `sku_num` BIGINT COMMENT '加购数量'
    ) COMMENT '加购事实表'
    PARTITIONED BY (`dt` STRING)
    STORED AS PARQUET
    LOCATION '/warehouse/gmall/dwd/dwd_cart_info/'
    TBLPROPERTIES ("parquet.compression"="lzo");

**B、分区规划**

![在这里插入图片描述][426152ff0a0d4a27aa78a60abc2b19d9.png]

**C、数据装载**

![在这里插入图片描述][716ba0b34e0c49c5801d727ad22edf51.png]

1.首日装载

insert overwrite table dwd_cart_info partition(dt='2020-06-14')
    select
        id,
        user_id,
        sku_id,
        source_type,
        source_id,
        cart_price,
        is_ordered,
        create_time,
        operate_time,
        order_time,
        sku_num
    from ods_cart_info
    where dt='2020-06-14';

2.每日装载

insert overwrite table dwd_cart_info partition(dt='2020-06-15')
    select
        id,
        user_id,
        sku_id,
        source_type,
        source_id,
        cart_price,
        is_ordered,
        create_time,
        operate_time,
        order_time,
        sku_num
    from ods_cart_info
    where dt='2020-06-15';

### 5. 收藏事实表 (周期型快照事实表，每日快照) ###

**A、建表语句**

DROP TABLE IF EXISTS dwd_favor_info;
    CREATE EXTERNAL TABLE dwd_favor_info(
        `id` STRING COMMENT '编号',
        `user_id` STRING  COMMENT '用户id',
        `sku_id` STRING  COMMENT 'skuid',
        `spu_id` STRING  COMMENT 'spuid',
        `is_cancel` STRING  COMMENT '是否取消',
        `create_time` STRING  COMMENT '收藏时间',
        `cancel_time` STRING  COMMENT '取消时间'
    ) COMMENT '收藏事实表'
    PARTITIONED BY (`dt` STRING)
    STORED AS PARQUET
    LOCATION '/warehouse/gmall/dwd/dwd_favor_info/'
    TBLPROPERTIES ("parquet.compression"="lzo");

**B、分区规划**

![在这里插入图片描述][c06b29c92a3a46329951742cf3539a23.png]

**C、数据装载**

![在这里插入图片描述][a9c2648ba19a4571b8a2d3c0db61fce8.png]

1.首日装载

insert overwrite table dwd_favor_info partition(dt='2020-06-14')
    select
        id,
        user_id,
        sku_id,
        spu_id,
        is_cancel,
        create_time,
        cancel_time
    from ods_favor_info
    where dt='2020-06-14';

2.每日装载

insert overwrite table dwd_favor_info partition(dt='2020-06-15')
    select
        id,
        user_id,
        sku_id,
        spu_id,
        is_cancel,
        create_time,
        cancel_time
    from ods_favor_info
    where dt='2020-06-15';

### 6. 优惠券领用事实表 (累积型快照事实表) ###

**A、建表语句**

DROP TABLE IF EXISTS dwd_coupon_use;
    CREATE EXTERNAL TABLE dwd_coupon_use(
        `id` STRING COMMENT '编号',
        `coupon_id` STRING  COMMENT '优惠券ID',
        `user_id` STRING  COMMENT 'userid',
        `order_id` STRING  COMMENT '订单id',
        `coupon_status` STRING  COMMENT '优惠券状态',
        `get_time` STRING  COMMENT '领取时间',
        `using_time` STRING  COMMENT '使用时间(下单)',
        `used_time` STRING  COMMENT '使用时间(支付)',
        `expire_time` STRING COMMENT '过期时间'
    ) COMMENT '优惠券领用事实表'
    PARTITIONED BY (`dt` STRING)
    STORED AS PARQUET
    LOCATION '/warehouse/gmall/dwd/dwd_coupon_use/'
    TBLPROPERTIES ("parquet.compression"="lzo");

**B、分区规划**  
![在这里插入图片描述][2975546fe2d84849b097c62c0fa1f3e8.png]

**C、数据装载**

![在这里插入图片描述][9026a650e72b4779969206ea42d0704f.png]

1.首日装载

insert overwrite table dwd_coupon_use partition(dt)
    select
        id,
        coupon_id,
        user_id,
        order_id,
        coupon_status,
        get_time,
        using_time,
        used_time,
        expire_time,
        coalesce(date_format(used_time,'yyyy-MM-dd'),date_format(expire_time,'yyyy-MM-dd'),'9999-99-99')
    from ods_coupon_use
    where dt='2020-06-14';

2.每日装载

a.装载逻辑  
![在这里插入图片描述][9cf6a48d76c6440e81f5e75006477c25.png]  
b.转载语句

insert overwrite table dwd_coupon_use partition(dt)
    select
        nvl(new.id,old.id),
        nvl(new.coupon_id,old.coupon_id),
        nvl(new.user_id,old.user_id),
        nvl(new.order_id,old.order_id),
        nvl(new.coupon_status,old.coupon_status),
        nvl(new.get_time,old.get_time),
        nvl(new.using_time,old.using_time),
        nvl(new.used_time,old.used_time),
        nvl(new.expire_time,old.expire_time),
        coalesce(date_format(nvl(new.used_time,old.used_time),'yyyy-MM-dd'),date_format(nvl(new.expire_time,old.expire_time),'yyyy-MM-dd'),'9999-99-99')
    from
    (
        select
            id,
            coupon_id,
            user_id,
            order_id,
            coupon_status,
            get_time,
            using_time,
            used_time,
            expire_time
        from dwd_coupon_use
        where dt='9999-99-99'
    )old
    full outer join
    (
        select
            id,
            coupon_id,
            user_id,
            order_id,
            coupon_status,
            get_time,
            using_time,
            used_time,
            expire_time
        from ods_coupon_use
        where dt='2020-06-15'
    )new
    on old.id=new.id;

### 7. 支付事实表 (累积型快照事实表) ###

**A、建表语句**

DROP TABLE IF EXISTS dwd_payment_info;
    CREATE EXTERNAL TABLE dwd_payment_info (
        `id` STRING COMMENT '编号',
        `order_id` STRING COMMENT '订单编号',
        `user_id` STRING COMMENT '用户编号',
        `province_id` STRING COMMENT '地区ID',
        `trade_no` STRING COMMENT '交易编号',
        `out_trade_no` STRING COMMENT '对外交易编号',
        `payment_type` STRING COMMENT '支付类型',
        `payment_amount` DECIMAL(16,2) COMMENT '支付金额',
        `payment_status` STRING COMMENT '支付状态',
        `create_time` STRING COMMENT '创建时间',--调用第三方支付接口的时间
        `callback_time` STRING COMMENT '完成时间'--支付完成时间，即支付成功回调时间
    ) COMMENT '支付事实表表'
    PARTITIONED BY (`dt` STRING)
    STORED AS PARQUET
    LOCATION '/warehouse/gmall/dwd/dwd_payment_info/'
    TBLPROPERTIES ("parquet.compression"="lzo");

**B、分区规划**  
![在这里插入图片描述][b39c777655ec47ea89971ed9525d0094.png]

**C、数据装载**

![在这里插入图片描述][389931d797b741229d8c4c456cab6bf9.png]

1.首日装载

insert overwrite table dwd_payment_info partition(dt)
    select
        pi.id,
        pi.order_id,
        pi.user_id,
        oi.province_id,
        pi.trade_no,
        pi.out_trade_no,
        pi.payment_type,
        pi.payment_amount,
        pi.payment_status,
        pi.create_time,
        pi.callback_time,
        nvl(date_format(pi.callback_time,'yyyy-MM-dd'),'9999-99-99')
    from
    (
        select * from ods_payment_info where dt='2020-06-14'
    )pi
    left join
    (
        select id,province_id from ods_order_info where dt='2020-06-14'
    )oi
    on pi.order_id=oi.id;

2.每日装载

insert overwrite table dwd_payment_info partition(dt)
    select
        nvl(new.id,old.id),
        nvl(new.order_id,old.order_id),
        nvl(new.user_id,old.user_id),
        nvl(new.province_id,old.province_id),
        nvl(new.trade_no,old.trade_no),
        nvl(new.out_trade_no,old.out_trade_no),
        nvl(new.payment_type,old.payment_type),
        nvl(new.payment_amount,old.payment_amount),
        nvl(new.payment_status,old.payment_status),
        nvl(new.create_time,old.create_time),
        nvl(new.callback_time,old.callback_time),
        nvl(date_format(nvl(new.callback_time,old.callback_time),'yyyy-MM-dd'),'9999-99-99')
    from
    (
        select id,
           order_id,
           user_id,
           province_id,
           trade_no,
           out_trade_no,
           payment_type,
           payment_amount,
           payment_status,
           create_time,
           callback_time
        from dwd_payment_info
        where dt = '9999-99-99'
    )old
    full outer join
    (
        select
            pi.id,
            pi.out_trade_no,
            pi.order_id,
            pi.user_id,
            oi.province_id,
            pi.payment_type,
            pi.trade_no,
            pi.payment_amount,
            pi.payment_status,
            pi.create_time,
            pi.callback_time
        from
        (
            select * from ods_payment_info where dt='2020-06-15'
        )pi
        left join
        (
            select id,province_id from ods_order_info where dt='2020-06-15'
        )oi
        on pi.order_id=oi.id
    )new
    on old.id=new.id;

### 8. 退款事实表 (累积型快照事实表) ###

**A、建表语句**

DROP TABLE IF EXISTS dwd_refund_payment;
    CREATE EXTERNAL TABLE dwd_refund_payment (
        `id` STRING COMMENT '编号',
        `user_id` STRING COMMENT '用户ID',
        `order_id` STRING COMMENT '订单编号',
        `sku_id` STRING COMMENT 'SKU编号',
        `province_id` STRING COMMENT '地区ID',
        `trade_no` STRING COMMENT '交易编号',
        `out_trade_no` STRING COMMENT '对外交易编号',
        `payment_type` STRING COMMENT '支付类型',
        `refund_amount` DECIMAL(16,2) COMMENT '退款金额',
        `refund_status` STRING COMMENT '退款状态',
        `create_time` STRING COMMENT '创建时间',--调用第三方支付接口的时间
        `callback_time` STRING COMMENT '回调时间'--支付接口回调时间，即支付成功时间
    ) COMMENT '退款事实表'
    PARTITIONED BY (`dt` STRING)
    STORED AS PARQUET
    LOCATION '/warehouse/gmall/dwd/dwd_refund_payment/'
    TBLPROPERTIES ("parquet.compression"="lzo");

**B、分区规划**  
![在这里插入图片描述][1c57d745530a417bb78553893717b670.png]

**C、数据装载**

![在这里插入图片描述][efbff032703d49d9a65d99597442c3d5.png]

1.首日装载

insert overwrite table dwd_refund_payment partition(dt)
    select
        rp.id,
        user_id,
        order_id,
        sku_id,
        province_id,
        trade_no,
        out_trade_no,
        payment_type,
        refund_amount,
        refund_status,
        create_time,
        callback_time,
        nvl(date_format(callback_time,'yyyy-MM-dd'),'9999-99-99')
    from
    (
        select
            id,
            out_trade_no,
            order_id,
            sku_id,
            payment_type,
            trade_no,
            refund_amount,
            refund_status,
            create_time,
            callback_time
        from ods_refund_payment
        where dt='2020-06-14'
    )rp
    left join
    (
        select
            id,
            user_id,
            province_id
        from ods_order_info
        where dt='2020-06-14'
    )oi
    on rp.order_id=oi.id;

2.每日装载

insert overwrite table dwd_refund_payment partition(dt)
    select
        nvl(new.id,old.id),
        nvl(new.user_id,old.user_id),
        nvl(new.order_id,old.order_id),
        nvl(new.sku_id,old.sku_id),
        nvl(new.province_id,old.province_id),
        nvl(new.trade_no,old.trade_no),
        nvl(new.out_trade_no,old.out_trade_no),
        nvl(new.payment_type,old.payment_type),
        nvl(new.refund_amount,old.refund_amount),
        nvl(new.refund_status,old.refund_status),
        nvl(new.create_time,old.create_time),
        nvl(new.callback_time,old.callback_time),
        nvl(date_format(nvl(new.callback_time,old.callback_time),'yyyy-MM-dd'),'9999-99-99')
    from
    (
        select
            id,
            user_id,
            order_id,
            sku_id,
            province_id,
            trade_no,
            out_trade_no,
            payment_type,
            refund_amount,
            refund_status,
            create_time,
            callback_time
        from dwd_refund_payment
        where dt='9999-99-99'
    )old
    full outer join
    (
        select
            rp.id,
            user_id,
            order_id,
            sku_id,
            province_id,
            trade_no,
            out_trade_no,
            payment_type,
            refund_amount,
            refund_status,
            create_time,
            callback_time
        from
        (
            select
                id,
                out_trade_no,
                order_id,
                sku_id,
                payment_type,
                trade_no,
                refund_amount,
                refund_status,
                create_time,
                callback_time
            from ods_refund_payment
            where dt='2020-06-15'
        )rp
        left join
        (
            select
                id,
                user_id,
                province_id
            from ods_order_info
            where dt='2020-06-15'
        )oi
        on rp.order_id=oi.id
    )new
    on old.id=new.id;

**D、查询加载结果**

### 9. 订单事实表 (累积型快照事实表) ###

**A、建表语句**

DROP TABLE IF EXISTS dwd_order_info;
    CREATE EXTERNAL TABLE dwd_order_info(
        `id` STRING COMMENT '编号',
        `order_status` STRING COMMENT '订单状态',
        `user_id` STRING COMMENT '用户ID',
        `province_id` STRING COMMENT '地区ID',
        `payment_way` STRING COMMENT '支付方式',
        `delivery_address` STRING COMMENT '邮寄地址',
        `out_trade_no` STRING COMMENT '对外交易编号',
        `tracking_no` STRING COMMENT '物流单号',
        `create_time` STRING COMMENT '创建时间(未支付状态)',
        `payment_time` STRING COMMENT '支付时间(已支付状态)',
        `cancel_time` STRING COMMENT '取消时间(已取消状态)',
        `finish_time` STRING COMMENT '完成时间(已完成状态)',
        `refund_time` STRING COMMENT '退款时间(退款中状态)',
        `refund_finish_time` STRING COMMENT '退款完成时间(退款完成状态)',
        `expire_time` STRING COMMENT '过期时间',
        `feight_fee` DECIMAL(16,2) COMMENT '运费',
        `feight_fee_reduce` DECIMAL(16,2) COMMENT '运费减免',
        `activity_reduce_amount` DECIMAL(16,2) COMMENT '活动减免',
        `coupon_reduce_amount` DECIMAL(16,2) COMMENT '优惠券减免',
        `original_amount` DECIMAL(16,2) COMMENT '订单原始价格',
        `final_amount` DECIMAL(16,2) COMMENT '订单最终价格'
    ) COMMENT '订单事实表'
    PARTITIONED BY (`dt` STRING)
    STORED AS PARQUET
    LOCATION '/warehouse/gmall/dwd/dwd_order_info/'
    TBLPROPERTIES ("parquet.compression"="lzo");

**B、分区规划**  
![在这里插入图片描述][936236d5c32d457ea07032fcd1b7e147.png]

**C、数据装载**

![在这里插入图片描述][a2579f2da2024fc4a388035a30d8c5f8.png]

1.首日装载

insert overwrite table dwd_order_info partition(dt)
    select
        oi.id,
        oi.order_status,
        oi.user_id,
        oi.province_id,
        oi.payment_way,
        oi.delivery_address,
        oi.out_trade_no,
        oi.tracking_no,
        oi.create_time,
        times.ts['1002'] payment_time,
        times.ts['1003'] cancel_time,
        times.ts['1004'] finish_time,
        times.ts['1005'] refund_time,
        times.ts['1006'] refund_finish_time,
        oi.expire_time,
        feight_fee,
        feight_fee_reduce,
        activity_reduce_amount,
        coupon_reduce_amount,
        original_amount,
        final_amount,
        case
            when times.ts['1003'] is not null then date_format(times.ts['1003'],'yyyy-MM-dd')
            when times.ts['1004'] is not null and date_add(date_format(times.ts['1004'],'yyyy-MM-dd'),7)<='2020-06-14' and times.ts['1005'] is null then date_add(date_format(times.ts['1004'],'yyyy-MM-dd'),7)
            when times.ts['1006'] is not null then date_format(times.ts['1006'],'yyyy-MM-dd')
            when oi.expire_time is not null then date_format(oi.expire_time,'yyyy-MM-dd')
            else '9999-99-99'
        end
    from
    (
        select
            *
        from ods_order_info
        where dt='2020-06-14'
    )oi
    left join
    (
        select
            order_id,
            str_to_map(concat_ws(',',collect_set(concat(order_status,'=',operate_time))),',','=') ts
        from ods_order_status_log
        where dt='2020-06-14'
        group by order_id
    )times
    on oi.id=times.order_id;

2.每日装载

insert overwrite table dwd_order_info partition(dt)
    select
        nvl(new.id,old.id),
        nvl(new.order_status,old.order_status),
        nvl(new.user_id,old.user_id),
        nvl(new.province_id,old.province_id),
        nvl(new.payment_way,old.payment_way),
        nvl(new.delivery_address,old.delivery_address),
        nvl(new.out_trade_no,old.out_trade_no),
        nvl(new.tracking_no,old.tracking_no),
        nvl(new.create_time,old.create_time),
        nvl(new.payment_time,old.payment_time),
        nvl(new.cancel_time,old.cancel_time),
        nvl(new.finish_time,old.finish_time),
        nvl(new.refund_time,old.refund_time),
        nvl(new.refund_finish_time,old.refund_finish_time),
        nvl(new.expire_time,old.expire_time),
        nvl(new.feight_fee,old.feight_fee),
        nvl(new.feight_fee_reduce,old.feight_fee_reduce),
        nvl(new.activity_reduce_amount,old.activity_reduce_amount),
        nvl(new.coupon_reduce_amount,old.coupon_reduce_amount),
        nvl(new.original_amount,old.original_amount),
        nvl(new.final_amount,old.final_amount),
        case
            when new.cancel_time is not null then date_format(new.cancel_time,'yyyy-MM-dd')
            when new.finish_time is not null and date_add(date_format(new.finish_time,'yyyy-MM-dd'),7)='2020-06-15' and new.refund_time is null then '2020-06-15'
            when new.refund_finish_time is not null then date_format(new.refund_finish_time,'yyyy-MM-dd')
            when new.expire_time is not null then date_format(new.expire_time,'yyyy-MM-dd')
            else '9999-99-99'
        end
    from
    (
        select
            id,
            order_status,
            user_id,
            province_id,
            payment_way,
            delivery_address,
            out_trade_no,
            tracking_no,
            create_time,
            payment_time,
            cancel_time,
            finish_time,
            refund_time,
            refund_finish_time,
            expire_time,
            feight_fee,
            feight_fee_reduce,
            activity_reduce_amount,
            coupon_reduce_amount,
            original_amount,
            final_amount
        from dwd_order_info
        where dt='9999-99-99'
    )old
    full outer join
    (
        select
            oi.id,
            oi.order_status,
            oi.user_id,
            oi.province_id,
            oi.payment_way,
            oi.delivery_address,
            oi.out_trade_no,
            oi.tracking_no,
            oi.create_time,
            times.ts['1002'] payment_time,
            times.ts['1003'] cancel_time,
            times.ts['1004'] finish_time,
            times.ts['1005'] refund_time,
            times.ts['1006'] refund_finish_time,
            oi.expire_time,
            feight_fee,
            feight_fee_reduce,
            activity_reduce_amount,
            coupon_reduce_amount,
            original_amount,
            final_amount
        from
        (
            select
                *
            from ods_order_info
            where dt='2020-06-15'
        )oi
        left join
        (
            select
                order_id,
                str_to_map(concat_ws(',',collect_set(concat(order_status,'=',operate_time))),',','=') ts
            from ods_order_status_log
            where dt='2020-06-15'
            group by order_id
        )times
        on oi.id=times.order_id
    )new
    on old.id=new.id;

### 10. DWD 层业务数据首日装载脚本 ###

**A、编写脚本**

在 /home/fancy/bin 目录下创建脚本 ods\_to\_dwd\_db\_init.sh

[fancy@node101 bin]$ vim ods_to_dwd_db_init.sh

在脚本中填写如下内容

#!/bin/bash
    APP=gmall
    
    if [ -n "$2" ] ;then
       do_date=$2
    else 
       echo "请传入日期参数"
       exit
    fi 
    
    dwd_order_info="
    set hive.exec.dynamic.partition.mode=nonstrict;
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_order_info partition(dt)
    select
        oi.id,
        oi.order_status,
        oi.user_id,
        oi.province_id,
        oi.payment_way,
        oi.delivery_address,
        oi.out_trade_no,
        oi.tracking_no,
        oi.create_time,
        times.ts['1002'] payment_time,
        times.ts['1003'] cancel_time,
        times.ts['1004'] finish_time,
        times.ts['1005'] refund_time,
        times.ts['1006'] refund_finish_time,
        oi.expire_time,
        feight_fee,
        feight_fee_reduce,
        activity_reduce_amount,
        coupon_reduce_amount,
        original_amount,
        final_amount,
        case
            when times.ts['1003'] is not null then date_format(times.ts['1003'],'yyyy-MM-dd')
            when times.ts['1004'] is not null and date_add(date_format(times.ts['1004'],'yyyy-MM-dd'),7)<='$do_date' and times.ts['1005'] is null then date_add(date_format(times.ts['1004'],'yyyy-MM-dd'),7)
            when times.ts['1006'] is not null then date_format(times.ts['1006'],'yyyy-MM-dd')
            when oi.expire_time is not null then date_format(oi.expire_time,'yyyy-MM-dd')
            else '9999-99-99'
        end
    from
    (
        select
            *
        from ${APP}.ods_order_info
        where dt='$do_date'
    )oi
    left join
    (
        select
            order_id,
            str_to_map(concat_ws(',',collect_set(concat(order_status,'=',operate_time))),',','=') ts
        from ${APP}.ods_order_status_log
        where dt='$do_date'
        group by order_id
    )times
    on oi.id=times.order_id;"
    
    dwd_order_detail="
    set hive.exec.dynamic.partition.mode=nonstrict;
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_order_detail partition(dt)
    select
        od.id,
        od.order_id,
        oi.user_id,
        od.sku_id,
        oi.province_id,
        oda.activity_id,
        oda.activity_rule_id,
        odc.coupon_id,
        od.create_time,
        od.source_type,
        od.source_id,
        od.sku_num,
        od.order_price*od.sku_num,
        od.split_activity_amount,
        od.split_coupon_amount,
        od.split_final_amount,
        date_format(create_time,'yyyy-MM-dd')
    from
    (
        select
            *
        from ${APP}.ods_order_detail
        where dt='$do_date'
    )od
    left join
    (
        select
            id,
            user_id,
            province_id
        from ${APP}.ods_order_info
        where dt='$do_date'
    )oi
    on od.order_id=oi.id
    left join
    (
        select
            order_detail_id,
            activity_id,
            activity_rule_id
        from ${APP}.ods_order_detail_activity
        where dt='$do_date'
    )oda
    on od.id=oda.order_detail_id
    left join
    (
        select
            order_detail_id,
            coupon_id
        from ${APP}.ods_order_detail_coupon
        where dt='$do_date'
    )odc
    on od.id=odc.order_detail_id;"
    
    dwd_payment_info="
    set hive.exec.dynamic.partition.mode=nonstrict;
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_payment_info partition(dt)
    select
        pi.id,
        pi.order_id,
        pi.user_id,
        oi.province_id,
        pi.trade_no,
        pi.out_trade_no,
        pi.payment_type,
        pi.payment_amount,
        pi.payment_status,
        pi.create_time,
        pi.callback_time,
        nvl(date_format(pi.callback_time,'yyyy-MM-dd'),'9999-99-99')
    from
    (
        select * from ${APP}.ods_payment_info where dt='$do_date'
    )pi
    left join
    (
        select id,province_id from ${APP}.ods_order_info where dt='$do_date'
    )oi
    on pi.order_id=oi.id;"
    
    dwd_cart_info="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_cart_info partition(dt='$do_date')
    select
        id,
        user_id,
        sku_id,
        source_type,
        source_id,
        cart_price,
        is_ordered,
        create_time,
        operate_time,
        order_time,
        sku_num
    from ${APP}.ods_cart_info
    where dt='$do_date';"
    
    dwd_comment_info="
    set hive.exec.dynamic.partition.mode=nonstrict;
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_comment_info partition(dt)
    select
        id,
        user_id,
        sku_id,
        spu_id,
        order_id,
        appraise,
        create_time,
        date_format(create_time,'yyyy-MM-dd')
    from ${APP}.ods_comment_info
    where dt='$do_date';
    "
    
    dwd_favor_info="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_favor_info partition(dt='$do_date')
    select
        id,
        user_id,
        sku_id,
        spu_id,
        is_cancel,
        create_time,
        cancel_time
    from ${APP}.ods_favor_info
    where dt='$do_date';"
    
    dwd_coupon_use="
    set hive.exec.dynamic.partition.mode=nonstrict;
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_coupon_use partition(dt)
    select
        id,
        coupon_id,
        user_id,
        order_id,
        coupon_status,
        get_time,
        using_time,
        used_time,
        expire_time,
        coalesce(date_format(used_time,'yyyy-MM-dd'),date_format(expire_time,'yyyy-MM-dd'),'9999-99-99')
    from ${APP}.ods_coupon_use
    where dt='$do_date';"
    
    dwd_order_refund_info="
    set hive.exec.dynamic.partition.mode=nonstrict;
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_order_refund_info partition(dt)
    select
        ri.id,
        ri.user_id,
        ri.order_id,
        ri.sku_id,
        oi.province_id,
        ri.refund_type,
        ri.refund_num,
        ri.refund_amount,
        ri.refund_reason_type,
        ri.create_time,
        date_format(ri.create_time,'yyyy-MM-dd')
    from
    (
        select * from ${APP}.ods_order_refund_info where dt='$do_date'
    )ri
    left join
    (
        select id,province_id from ${APP}.ods_order_info where dt='$do_date'
    )oi
    on ri.order_id=oi.id;"
    
    dwd_refund_payment="
    set hive.exec.dynamic.partition.mode=nonstrict;
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_refund_payment partition(dt)
    select
        rp.id,
        user_id,
        order_id,
        sku_id,
        province_id,
        trade_no,
        out_trade_no,
        payment_type,
        refund_amount,
        refund_status,
        create_time,
        callback_time,
        nvl(date_format(callback_time,'yyyy-MM-dd'),'9999-99-99')
    from
    (
        select
            id,
            out_trade_no,
            order_id,
            sku_id,
            payment_type,
            trade_no,
            refund_amount,
            refund_status,
            create_time,
            callback_time
        from ${APP}.ods_refund_payment
        where dt='$do_date'
    )rp
    left join
    (
        select
            id,
            user_id,
            province_id
        from ${APP}.ods_order_info
        where dt='$do_date'
    )oi
    on rp.order_id=oi.id;"
    
    case $1 in
        dwd_order_info )
            hive -e "$dwd_order_info"
        ;;
        dwd_order_detail )
            hive -e "$dwd_order_detail"
        ;;
        dwd_payment_info )
            hive -e "$dwd_payment_info"
        ;;
        dwd_cart_info )
            hive -e "$dwd_cart_info"
        ;;
        dwd_comment_info )
            hive -e "$dwd_comment_info"
        ;;
        dwd_favor_info )
            hive -e "$dwd_favor_info"
        ;;
        dwd_coupon_use )
            hive -e "$dwd_coupon_use"
        ;;
        dwd_order_refund_info )
            hive -e "$dwd_order_refund_info"
        ;;
        dwd_refund_payment )
            hive -e "$dwd_refund_payment"
        ;;
        all )
            hive -e "$dwd_order_info$dwd_order_detail$dwd_payment_info$dwd_cart_info$dwd_comment_info$dwd_favor_info$dwd_coupon_use$dwd_order_refund_info$dwd_refund_payment"
        ;;
    esac

增加执行权限

[fancy@node101 bin]$ chmod +x ods_to_dwd_db_init.sh

**B、脚本使用**

执行脚本

[fancy@node101 bin]$ ods_to_dwd_db_init.sh all 2020-06-14

查看数据是否导入成功

### 11. DWD层业务数据每日装载脚本 ###

**A、编写脚本**

在 /home/fancy/bin 目录下创建脚本ods\_to\_dwd\_db.sh

[fancy@node101 bin]$ vim ods_to_dwd_db.sh

在脚本中填写如下内容

#!/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
    
    
    # 假设某累积型快照事实表，某天所有的业务记录全部完成，则会导致9999-99-99分区的数据未被覆盖，从而导致数据重复，该函数根据9999-99-99分区的数据的末次修改时间判断其是否被覆盖了，如果未被覆盖，就手动清理
    clear_data(){
        current_date=`date +%F`
        current_date_timestamp=`date -d "$current_date" +%s`
    
        last_modified_date=`hadoop fs -ls /warehouse/gmall/dwd/$1 | grep '9999-99-99' | awk '{print $6}'`
        last_modified_date_timestamp=`date -d "$last_modified_date" +%s`
    
        if [[ $last_modified_date_timestamp -lt $current_date_timestamp ]]; then
            echo "clear table $1 partition(dt=9999-99-99)"
            hadoop fs -rm -r -f /warehouse/gmall/dwd/$1/dt=9999-99-99/*
        fi
    }
    
    dwd_order_info="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    set hive.exec.dynamic.partition.mode=nonstrict;
    insert overwrite table ${APP}.dwd_order_info partition(dt)
    select
        nvl(new.id,old.id),
        nvl(new.order_status,old.order_status),
        nvl(new.user_id,old.user_id),
        nvl(new.province_id,old.province_id),
        nvl(new.payment_way,old.payment_way),
        nvl(new.delivery_address,old.delivery_address),
        nvl(new.out_trade_no,old.out_trade_no),
        nvl(new.tracking_no,old.tracking_no),
        nvl(new.create_time,old.create_time),
        nvl(new.payment_time,old.payment_time),
        nvl(new.cancel_time,old.cancel_time),
        nvl(new.finish_time,old.finish_time),
        nvl(new.refund_time,old.refund_time),
        nvl(new.refund_finish_time,old.refund_finish_time),
        nvl(new.expire_time,old.expire_time),
        nvl(new.feight_fee,old.feight_fee),
        nvl(new.feight_fee_reduce,old.feight_fee_reduce),
        nvl(new.activity_reduce_amount,old.activity_reduce_amount),
        nvl(new.coupon_reduce_amount,old.coupon_reduce_amount),
        nvl(new.original_amount,old.original_amount),
        nvl(new.final_amount,old.final_amount),
        case
            when new.cancel_time is not null then date_format(new.cancel_time,'yyyy-MM-dd')
            when new.finish_time is not null and date_add(date_format(new.finish_time,'yyyy-MM-dd'),7)='$do_date' and new.refund_time is null then '$do_date'
            when new.refund_finish_time is not null then date_format(new.refund_finish_time,'yyyy-MM-dd')
            when new.expire_time is not null then date_format(new.expire_time,'yyyy-MM-dd')
            else '9999-99-99'
        end
    from
    (
        select
            id,
            order_status,
            user_id,
            province_id,
            payment_way,
            delivery_address,
            out_trade_no,
            tracking_no,
            create_time,
            payment_time,
            cancel_time,
            finish_time,
            refund_time,
            refund_finish_time,
            expire_time,
            feight_fee,
            feight_fee_reduce,
            activity_reduce_amount,
            coupon_reduce_amount,
            original_amount,
            final_amount
        from ${APP}.dwd_order_info
        where dt='9999-99-99'
    )old
    full outer join
    (
        select
            oi.id,
            oi.order_status,
            oi.user_id,
            oi.province_id,
            oi.payment_way,
            oi.delivery_address,
            oi.out_trade_no,
            oi.tracking_no,
            oi.create_time,
            times.ts['1002'] payment_time,
            times.ts['1003'] cancel_time,
            times.ts['1004'] finish_time,
            times.ts['1005'] refund_time,
            times.ts['1006'] refund_finish_time,
            oi.expire_time,
            feight_fee,
            feight_fee_reduce,
            activity_reduce_amount,
            coupon_reduce_amount,
            original_amount,
            final_amount
        from
        (
            select
                *
            from ${APP}.ods_order_info
            where dt='$do_date'
        )oi
        left join
        (
            select
                order_id,
                str_to_map(concat_ws(',',collect_set(concat(order_status,'=',operate_time))),',','=') ts
            from ${APP}.ods_order_status_log
            where dt='$do_date'
            group by order_id
        )times
        on oi.id=times.order_id
    )new
    on old.id=new.id;"
    
    dwd_order_detail="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_order_detail partition(dt='$do_date')
    select
        od.id,
        od.order_id,
        oi.user_id,
        od.sku_id,
        oi.province_id,
        oda.activity_id,
        oda.activity_rule_id,
        odc.coupon_id,
        od.create_time,
        od.source_type,
        od.source_id,
        od.sku_num,
        od.order_price*od.sku_num,
        od.split_activity_amount,
        od.split_coupon_amount,
        od.split_final_amount
    from
    (
        select
            *
        from ${APP}.ods_order_detail
        where dt='$do_date'
    )od
    left join
    (
        select
            id,
            user_id,
            province_id
        from ${APP}.ods_order_info
        where dt='$do_date'
    )oi
    on od.order_id=oi.id
    left join
    (
        select
            order_detail_id,
            activity_id,
            activity_rule_id
        from ${APP}.ods_order_detail_activity
        where dt='$do_date'
    )oda
    on od.id=oda.order_detail_id
    left join
    (
        select
            order_detail_id,
            coupon_id
        from ${APP}.ods_order_detail_coupon
        where dt='$do_date'
    )odc
    on od.id=odc.order_detail_id;"
    
    
    dwd_payment_info="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    set hive.exec.dynamic.partition.mode=nonstrict;
    insert overwrite table ${APP}.dwd_payment_info partition(dt)
    select
        nvl(new.id,old.id),
        nvl(new.order_id,old.order_id),
        nvl(new.user_id,old.user_id),
        nvl(new.province_id,old.province_id),
        nvl(new.trade_no,old.trade_no),
        nvl(new.out_trade_no,old.out_trade_no),
        nvl(new.payment_type,old.payment_type),
        nvl(new.payment_amount,old.payment_amount),
        nvl(new.payment_status,old.payment_status),
        nvl(new.create_time,old.create_time),
        nvl(new.callback_time,old.callback_time),
        nvl(date_format(nvl(new.callback_time,old.callback_time),'yyyy-MM-dd'),'9999-99-99')
    from
    (
        select id,
           order_id,
           user_id,
           province_id,
           trade_no,
           out_trade_no,
           payment_type,
           payment_amount,
           payment_status,
           create_time,
           callback_time
        from ${APP}.dwd_payment_info
        where dt = '9999-99-99'
    )old
    full outer join
    (
        select
            pi.id,
            pi.out_trade_no,
            pi.order_id,
            pi.user_id,
            oi.province_id,
            pi.payment_type,
            pi.trade_no,
            pi.payment_amount,
            pi.payment_status,
            pi.create_time,
            pi.callback_time
        from
        (
            select * from ${APP}.ods_payment_info where dt='$do_date'
        )pi
        left join
        (
            select id,province_id from ${APP}.ods_order_info where dt='$do_date'
        )oi
        on pi.order_id=oi.id
    )new
    on old.id=new.id;"
    
    dwd_cart_info="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_cart_info partition(dt='$do_date')
    select
        id,
        user_id,
        sku_id,
        source_type,
        source_id,
        cart_price,
        is_ordered,
        create_time,
        operate_time,
        order_time,
        sku_num
    from ${APP}.ods_cart_info
    where dt='$do_date';"
    
    
    dwd_comment_info="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_comment_info partition(dt='$do_date')
    select
        id,
        user_id,
        sku_id,
        spu_id,
        order_id,
        appraise,
        create_time
    from ${APP}.ods_comment_info where dt='$do_date';"
    
    
    dwd_favor_info="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_favor_info partition(dt='$do_date')
    select
        id,
        user_id,
        sku_id,
        spu_id,
        is_cancel,
        create_time,
        cancel_time
    from ${APP}.ods_favor_info
    where dt='$do_date';"
    
    
    dwd_coupon_use="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    set hive.exec.dynamic.partition.mode=nonstrict;
    insert overwrite table ${APP}.dwd_coupon_use partition(dt)
    select
        nvl(new.id,old.id),
        nvl(new.coupon_id,old.coupon_id),
        nvl(new.user_id,old.user_id),
        nvl(new.order_id,old.order_id),
        nvl(new.coupon_status,old.coupon_status),
        nvl(new.get_time,old.get_time),
        nvl(new.using_time,old.using_time),
        nvl(new.used_time,old.used_time),
        nvl(new.expire_time,old.expire_time),
        coalesce(date_format(nvl(new.used_time,old.used_time),'yyyy-MM-dd'),date_format(nvl(new.expire_time,old.expire_time),'yyyy-MM-dd'),'9999-99-99')
    from
    (
        select
            id,
            coupon_id,
            user_id,
            order_id,
            coupon_status,
            get_time,
            using_time,
            used_time,
            expire_time
        from ${APP}.dwd_coupon_use
        where dt='9999-99-99'
    )old
    full outer join
    (
        select
            id,
            coupon_id,
            user_id,
            order_id,
            coupon_status,
            get_time,
            using_time,
            used_time,
            expire_time
        from ${APP}.ods_coupon_use
        where dt='$do_date'
    )new
    on old.id=new.id;"
    
    dwd_order_refund_info="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    insert overwrite table ${APP}.dwd_order_refund_info partition(dt='$do_date')
    select
        ri.id,
        ri.user_id,
        ri.order_id,
        ri.sku_id,
        oi.province_id,
        ri.refund_type,
        ri.refund_num,
        ri.refund_amount,
        ri.refund_reason_type,
        ri.create_time
    from
    (
        select * from ${APP}.ods_order_refund_info where dt='$do_date'
    )ri
    left join
    (
        select id,province_id from ${APP}.ods_order_info where dt='$do_date'
    )oi
    on ri.order_id=oi.id;"
    
    
    dwd_refund_payment="
    set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
    set hive.exec.dynamic.partition.mode=nonstrict;
    insert overwrite table ${APP}.dwd_refund_payment partition(dt)
    select
        nvl(new.id,old.id),
        nvl(new.user_id,old.user_id),
        nvl(new.order_id,old.order_id),
        nvl(new.sku_id,old.sku_id),
        nvl(new.province_id,old.province_id),
        nvl(new.trade_no,old.trade_no),
        nvl(new.out_trade_no,old.out_trade_no),
        nvl(new.payment_type,old.payment_type),
        nvl(new.refund_amount,old.refund_amount),
        nvl(new.refund_status,old.refund_status),
        nvl(new.create_time,old.create_time),
        nvl(new.callback_time,old.callback_time),
        nvl(date_format(nvl(new.callback_time,old.callback_time),'yyyy-MM-dd'),'9999-99-99')
    from
    (
        select
            id,
            user_id,
            order_id,
            sku_id,
            province_id,
            trade_no,
            out_trade_no,
            payment_type,
            refund_amount,
            refund_status,
            create_time,
            callback_time
        from ${APP}.dwd_refund_payment
        where dt='9999-99-99'
    )old
    full outer join
    (
        select
            rp.id,
            user_id,
            order_id,
            sku_id,
            province_id,
            trade_no,
            out_trade_no,
            payment_type,
            refund_amount,
            refund_status,
            create_time,
            callback_time
        from
        (
            select
                id,
                out_trade_no,
                order_id,
                sku_id,
                payment_type,
                trade_no,
                refund_amount,
                refund_status,
                create_time,
                callback_time
            from ${APP}.ods_refund_payment
            where dt='$do_date'
        )rp
        left join
        (
            select
                id,
                user_id,
                province_id
            from ${APP}.ods_order_info
            where dt='$do_date'
        )oi
        on rp.order_id=oi.id
    )new
    on old.id=new.id;"
    
    case $1 in
        dwd_order_info )
            hive -e "$dwd_order_info"
            clear_data dwd_order_info
        ;;
        dwd_order_detail )
            hive -e "$dwd_order_detail"
        ;;
        dwd_payment_info )
            hive -e "$dwd_payment_info"
            clear_data dwd_payment_info
        ;;
        dwd_cart_info )
            hive -e "$dwd_cart_info"
        ;;
        dwd_comment_info )
            hive -e "$dwd_comment_info"
        ;;
        dwd_favor_info )
            hive -e "$dwd_favor_info"
        ;;
        dwd_coupon_use )
            hive -e "$dwd_coupon_use"
            clear_data dwd_coupon_use
        ;;
        dwd_order_refund_info )
            hive -e "$dwd_order_refund_info"
        ;;
        dwd_refund_payment )
            hive -e "$dwd_refund_payment"
            clear_data dwd_refund_payment
        ;;
        all )
            hive -e "$dwd_order_info$dwd_order_detail$dwd_payment_info$dwd_cart_info$dwd_comment_info$dwd_favor_info$dwd_coupon_use$dwd_order_refund_info$dwd_refund_payment"
            clear_data dwd_order_info
            clear_data dwd_payment_info
            clear_data dwd_coupon_use
            clear_data dwd_refund_payment
        ;;
    esac

增加脚本执行权限

[fancy@node101 bin]$ chmod 777 ods_to_dwd_db.sh

**B、脚本使用**

执行脚本

[fancy@node101 bin]$ ods_to_dwd_db.sh all 2020-06-14

查看数据是否导入成功

[9b0db79c0f4f40de9a4f92d983d84c30.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/e7bbe42694e44ecf88163af0603c9183.png
[70d94ba38bd0410f9a715e37b02abc39.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/4d8fb63745954021b4bf372c5df17cec.png
[47d76a6305f6480899917f21b01d8407.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/22d0fe0a72fb4b0387e66fa72c82cb6b.png
[ec65ae3188004bc29bf38d188c1a07c8.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/e277b61a1c124e9d8629e4457d217b84.png
[b4433e3dde8f4838b083e235af18075d.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/541024fef64d499d8d3dafe4e1f88bae.png
[c9104b749a944e1b8588ac2f16fcd40e.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/2e30f7f295ac44eea40a299eb1d2bea0.png
[6a2849bb223c459ba97e61e450e84561.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/454254c4f0a74b228838e4c77feb5c72.png
[05c6b9a7bb3540eabab6642243153154.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/9bc16274a7384cd0a45393ddd474b77d.png
[50d4ca3bfff7410280e674f3c7fed00a.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/50e560a7442b40b2a3f039098e3b83ef.png
[e9a768441f8047db8926b1822d0c337f.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/216acb677b674d14867a33b5d9e20e1c.png
[d8f93b84339643a7b3a5a863bf86716b.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/d8418dd825b8438a8279595ff10b1a18.png
[a84cffd5b1df40bda647157838a22342.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/b3d7372f5e6a48e0a4f3b7a5031c4f42.png
[8c0befee781c4081a1ad9c04badb547c.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/01/9076a02643ed4163b126a5849d3b4241.png
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