大数据之CDH数仓(9) | 数仓之业务数仓搭建

约定不等于承诺〃 2022-12-11 15:11 320阅读 0赞

### 目录 ###

*  业务数据生成
 *   *  建表语句
     *  生成业务数据
 *  业务数据导入数仓
 *   *  Sqoop定时导入脚本
 *  ODS层
 *   *  创建订单表
     *  创建订单详情表
     *  创建商品表
     *  创建用户表
     *  创建商品一级分类表
     *  创建商品二级分类表
     *  创建商品三级分类表
     *  创建支付流水表
     *  ODS层数据导入脚本
 *  DWD层
 *   *  创建订单表
     *  创建订单详情表
     *  创建用户表
     *  创建支付流水表
     *  创建商品表（增加分类）
     *  DWD层数据导入脚本1）
 *  DWS层之用户行为宽表
 *   *  创建用户行为宽表
     *  用户行为数据宽表导入脚本
 *  ADS层（需求：GMV成交总额）
 *   *  建表语句
     *  数据导入脚本
     *  数据导出脚本

# 业务数据生成 #

数据库生成脚本

链接：https://pan.baidu.com/s/1f-yAUqfte-T0Yk2mEBeAUw 
    提取码：cwwx

## 建表语句 ##

1）通过Navicat创建数据库gmall  
![在这里插入图片描述][20201003172249697.png_pic_center]  
2）设置数据库编码  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center]

3）导入建表语句（1建表脚本）  
选择->1建表脚本.sql  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center 1]

4）重复步骤3的导入方式，依次导入：2商品分类数据插入脚本、3函数脚本、4存储过程脚本。

## 生成业务数据 ##

1）生成业务数据函数说明  
init\_data ( do\_date\_string VARCHAR(20) , order\_incr\_num INT, user\_incr\_num INT , sku\_num INT , if\_truncate BOOLEAN )：  
参数一：do\_date\_string生成数据日期  
参数二：order\_incr\_num订单id个数  
参数三：user\_incr\_num用户id个数  
参数四：sku\_num商品sku个数  
参数五：if\_truncate是否删除数据  
2）案例测试：  
（1）需求：生成日期2019年2月10日数据、订单1000个、用户200个、商品sku300个、删除原始数据。  
（2）查询生成数据结果

CALL init_data('2019-02-10',1000,200,300,TRUE);

# 业务数据导入数仓 #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center 2]

## Sqoop定时导入脚本 ##

1）在/root/bin目录下创建脚本sqoop\_import.sh

[root@hadoop102 bin]$ vim sqoop_import.sh

在脚本中填写如下内容

#!/bin/bash
    export HADOOP_USER_NAME=hive
    db_date=$2
    echo $db_date
    db_name=gmall
    
    import_data() { 
    sqoop import \
    --connect jdbc:mysql://hadoop102:3306/$db_name \
    --username root \
    --password Yy8266603@ \
    --target-dir  /origin_data/$db_name/db/$1/$db_date \
    --delete-target-dir \
    --num-mappers 1 \
    --fields-terminated-by "\t" \
    --query "$2"' and $CONDITIONS;'
    }
    
    import_sku_info(){ 
      import_data  "sku_info"  "select id, spu_id, price, sku_name, sku_desc, weight, tm_id, category3_id, create_time from sku_info where 1=1"
    }
    
    import_user_info(){ 
      import_data "user_info" "select id, name, birthday, gender, email, user_level, create_time from user_info where 1=1"
    }
    
    import_base_category1(){ 
      import_data "base_category1" "select id, name from base_category1 where 1=1"
    }
    
    import_base_category2(){ 
      import_data "base_category2" "select id, name, category1_id from base_category2 where 1=1"
    }
    
    import_base_category3(){ 
      import_data "base_category3" "select id, name, category2_id from base_category3 where 1=1"
    }
    
    import_order_detail(){ 
      import_data   "order_detail"   "select od.id, order_id, user_id, sku_id, sku_name, order_price, sku_num, o.create_time from order_info o , order_detail od where o.id=od.order_id and DATE_FORMAT(create_time,'%Y-%m-%d')='$db_date'"
    }
    
    import_payment_info(){ 
      import_data "payment_info"   "select id, out_trade_no, order_id, user_id, alipay_trade_no, total_amount, subject, payment_type, payment_time from payment_info where DATE_FORMAT(payment_time,'%Y-%m-%d')='$db_date'"
    }
    
    import_order_info(){ 
      import_data   "order_info"   "select id, total_amount, order_status, user_id, payment_way, out_trade_no, create_time, operate_time from order_info where (DATE_FORMAT(create_time,'%Y-%m-%d')='$db_date' or DATE_FORMAT(operate_time,'%Y-%m-%d')='$db_date')"
    }
    
    case $1 in
      "base_category1")
         import_base_category1
    ;;
      "base_category2")
         import_base_category2
    ;;
      "base_category3")
         import_base_category3
    ;;
      "order_info")
         import_order_info
    ;;
      "order_detail")
         import_order_detail
    ;;
      "sku_info")
         import_sku_info
    ;;
      "user_info")
         import_user_info
    ;;
      "payment_info")
         import_payment_info
    ;;
       "all")
       import_base_category1
       import_base_category2
       import_base_category3
       import_order_info
       import_order_detail
       import_sku_info
       import_user_info
       import_payment_info
    ;;
    esac

2）增加脚本执行权限

[root@hadoop102 bin]$ chmod 777 sqoop_import.sh

3）执行脚本导入数据

[root@hadoop102 bin]# sqoop_import.sh all 2019-02-10

4）修改/orgin\_data/gmall/db路径的访问权限

[root@hadoop102 bin]# sudo -u hdfs hadoop fs -chmod -R 777 /origin_data/gmall/db

# ODS层 #

完全仿照业务数据库中的表字段，一模一样的创建ODS层对应表。

## 创建订单表 ##

drop table if exists ods_order_info;
    create external table ods_order_info ( 
        `id` string COMMENT '订单编号',
        `total_amount` decimal(10,2) COMMENT '订单金额', 
        `order_status` string COMMENT '订单状态', 
        `user_id` string COMMENT '用户id' ,
        `payment_way` string COMMENT '支付方式',  
        `out_trade_no` string COMMENT '支付流水号',  
        `create_time` string COMMENT '创建时间',  
        `operate_time` string COMMENT '操作时间' 
    ) COMMENT '订单表'
    PARTITIONED BY ( `dt` string)
    row format delimited  fields terminated by '\t' 
    location '/warehouse/gmall/ods/ods_order_info/'
    ;

## 创建订单详情表 ##

drop table if exists ods_order_detail;
    create external table ods_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 '商品id',  
        `sku_name` string COMMENT '商品名称',  
        `order_price` string COMMENT '商品价格',  
        `sku_num` string COMMENT '商品数量',  
        `create_time` string COMMENT '创建时间'
    ) COMMENT '订单明细表'
    PARTITIONED BY ( `dt` string)
    row format delimited  fields terminated by '\t' 
    location '/warehouse/gmall/ods/ods_order_detail/'
    ;

## 创建商品表 ##

drop table if exists ods_sku_info;
    create external table ods_sku_info( 
        `id` string COMMENT 'skuId',
        `spu_id` string   COMMENT 'spuid', 
        `price` decimal(10,2) COMMENT '价格' ,
        `sku_name` string COMMENT '商品名称',  
        `sku_desc` string COMMENT '商品描述',  
        `weight` string COMMENT '重量',  
        `tm_id` string COMMENT '品牌id',  
        `category3_id` string COMMENT '品类id',  
        `create_time` string COMMENT '创建时间'
    ) COMMENT '商品表'
    PARTITIONED BY ( `dt` string)
    row format delimited  fields terminated by '\t' 
    location '/warehouse/gmall/ods/ods_sku_info/'
    ;

## 创建用户表 ##

drop table if exists ods_user_info;
    create external table ods_user_info( 
        `id` string COMMENT '用户id',
        `name`  string COMMENT '姓名', 
        `birthday` string COMMENT '生日' ,
        `gender` string COMMENT '性别',  
        `email` string COMMENT '邮箱',  
        `user_level` string COMMENT '用户等级',  
        `create_time` string COMMENT '创建时间'
    ) COMMENT '用户信息'
    PARTITIONED BY ( `dt` string)
    row format delimited  fields terminated by '\t' 
    location '/warehouse/gmall/ods/ods_user_info/'
    ;

## 创建商品一级分类表 ##

drop table if exists ods_base_category1;
    create external table ods_base_category1( 
        `id` string COMMENT 'id',
        `name`  string COMMENT '名称'
    ) COMMENT '商品一级分类'
    PARTITIONED BY ( `dt` string)
    row format delimited  fields terminated by '\t' 
    location '/warehouse/gmall/ods/ods_base_category1/'
    ;

## 创建商品二级分类表 ##

drop table if exists ods_base_category2;
    create external table ods_base_category2( 
        `id` string COMMENT ' id',
        `name`  string COMMENT '名称',
        category1_id string COMMENT '一级品类id'
    ) COMMENT '商品二级分类'
    PARTITIONED BY ( `dt` string)
    row format delimited  fields terminated by '\t' 
    location '/warehouse/gmall/ods/ods_base_category2/'
    ;

## 创建商品三级分类表 ##

drop table if exists ods_base_category3;
    create external table ods_base_category3( 
        `id` string COMMENT ' id',
        `name`  string COMMENT '名称',
        category2_id string COMMENT '二级品类id'
    ) COMMENT '商品三级分类'
    PARTITIONED BY ( `dt` string)
    row format delimited  fields terminated by '\t' 
    location '/warehouse/gmall/ods/ods_base_category3/'
    ;

## 创建支付流水表 ##

drop table if exists `ods_payment_info`;
    create external table  `ods_payment_info`(
        `id`   bigint COMMENT '编号',
        `out_trade_no`    string COMMENT '对外业务编号',
        `order_id`        string COMMENT '订单编号',
        `user_id`         string COMMENT '用户编号',
        `alipay_trade_no` string COMMENT '支付宝交易流水编号',
        `total_amount`    decimal(16,2) COMMENT '支付金额',
        `subject`         string COMMENT '交易内容',
        `payment_type` string COMMENT '支付类型',
        `payment_time`   string COMMENT '支付时间'
       )  COMMENT '支付流水表'
    PARTITIONED BY ( `dt` string)
    row format delimited  fields terminated by '\t' 
    location '/warehouse/gmall/ods/ods_payment_info/'
    ;

## ODS层数据导入脚本 ##

1）在/root/bin目录下创建脚本ods\_db.sh

[root@hadoop102 bin]$ vim ods_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 "$1" ] ;then
    	do_date=$1
    else 
    	do_date=`date -d "-1 day" +%F`  
    fi
    
    sql=" load data inpath '/origin_data/$APP/db/order_info/$do_date' OVERWRITE into table "$APP".ods_order_info partition(dt='$do_date'); load data inpath '/origin_data/$APP/db/order_detail/$do_date' OVERWRITE into table "$APP".ods_order_detail partition(dt='$do_date'); load data inpath '/origin_data/$APP/db/sku_info/$do_date' OVERWRITE into table "$APP".ods_sku_info partition(dt='$do_date'); load data inpath '/origin_data/$APP/db/user_info/$do_date' OVERWRITE into table "$APP".ods_user_info partition(dt='$do_date'); load data inpath '/origin_data/$APP/db/payment_info/$do_date' OVERWRITE into table "$APP".ods_payment_info partition(dt='$do_date'); load data inpath '/origin_data/$APP/db/base_category1/$do_date' OVERWRITE into table "$APP".ods_base_category1 partition(dt='$do_date'); load data inpath '/origin_data/$APP/db/base_category2/$do_date' OVERWRITE into table "$APP".ods_base_category2 partition(dt='$do_date'); load data inpath '/origin_data/$APP/db/base_category3/$do_date' OVERWRITE into table "$APP".ods_base_category3 partition(dt='$do_date'); "
    beeline -u "jdbc:hive2://hadoop102:10000/" -n hive -e "$sql"

2）增加脚本执行权限

[root@hadoop102 bin]$ chmod +x ods_db.sh

3）采用脚本导入数据

[root@hadoop102 bin]$ ods_db.sh 2019-02-10

# DWD层 #

对ODS层数据进行判空过滤。对商品分类表进行维度退化(降维)。

## 创建订单表 ##

drop table if exists dwd_order_info;
    create external table dwd_order_info ( 
        `id` string COMMENT '',
        `total_amount` decimal(10,2) COMMENT '', 
        `order_status` string COMMENT ' 1 2 3 4 5', 
        `user_id` string COMMENT 'id' ,
        `payment_way` string COMMENT '',  
        `out_trade_no` string COMMENT '',  
        `create_time` string COMMENT '',  
        `operate_time` string COMMENT '' 
    ) 
    PARTITIONED BY ( `dt` string)
    stored as  parquet
    location '/warehouse/gmall/dwd/dwd_order_info/'
    ;

## 创建订单详情表 ##

drop table if exists dwd_order_detail;
    create external table dwd_order_detail( 
        `id` string COMMENT '',
        `order_id` decimal(10,2) COMMENT '', 
        `user_id` string COMMENT 'id' ,
        `sku_id` string COMMENT 'id',  
        `sku_name` string COMMENT '',  
        `order_price` string COMMENT '',  
        `sku_num` string COMMENT '', 
        `create_time` string COMMENT ''
    ) 
    PARTITIONED BY (`dt` string)
    stored as parquet
    location '/warehouse/gmall/dwd/dwd_order_detail/'
    ;

## 创建用户表 ##

drop table if exists dwd_user_info;
    create external table dwd_user_info( 
        `id` string COMMENT 'id',
        `name`  string COMMENT '', 
        `birthday` string COMMENT '' ,
        `gender` string COMMENT '',  
        `email` string COMMENT '',  
        `user_level` string COMMENT '',  
        `create_time` string COMMENT ''
    ) 
    PARTITIONED BY (`dt` string)
    stored as  parquet
    location '/warehouse/gmall/dwd/dwd_user_info/'
    ;

## 创建支付流水表 ##

drop table if exists `dwd_payment_info`;
    create external  table  `dwd_payment_info`(
        `id`   bigint COMMENT '',
        `out_trade_no`    string COMMENT '',
        `order_id`        string COMMENT '',
        `user_id`         string COMMENT '',
        `alipay_trade_no` string COMMENT '',
        `total_amount`    decimal(16,2) COMMENT '',
        `subject`         string COMMENT '',
        `payment_type`    string COMMENT '',
        `payment_time`    string COMMENT ''
       )  
    PARTITIONED BY ( `dt` string)
    stored as  parquet
    location '/warehouse/gmall/dwd/dwd_payment_info/'
    ;

## 创建商品表（增加分类） ##

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

## DWD层数据导入脚本1） ##

在/root/bin目录下创建脚本dwd\_db.sh

[root@hadoop102 bin]$ vim dwd_db.sh

在脚本中填写如下内容

#!/bin/bash
    
    # 定义变量方便修改
    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 "$1" ] ;then
    	do_date=$1
    else 
    	do_date=`date -d "-1 day" +%F`  
    fi 
    
    sql=" set hive.exec.dynamic.partition.mode=nonstrict; insert overwrite table "$APP".dwd_order_info partition(dt) select * from "$APP".ods_order_info where dt='$do_date' and id is not null; insert overwrite table "$APP".dwd_order_detail partition(dt) select * from "$APP".ods_order_detail where dt='$do_date' and id is not null; insert overwrite table "$APP".dwd_user_info partition(dt) select * from "$APP".ods_user_info where dt='$do_date' and id is not null; insert overwrite table "$APP".dwd_payment_info partition(dt) select * from "$APP".ods_payment_info where dt='$do_date' and id is not null; insert overwrite table "$APP".dwd_sku_info partition(dt) select sku.id, sku.spu_id, sku.price, sku.sku_name, sku.sku_desc, sku.weight, sku.tm_id, sku.category3_id, c2.id category2_id , c1.id category1_id, c3.name category3_name, c2.name category2_name, c1.name category1_name, sku.create_time, sku.dt from "$APP".ods_sku_info sku join "$APP".ods_base_category3 c3 on sku.category3_id=c3.id join "$APP".ods_base_category2 c2 on c3.category2_id=c2.id join "$APP".ods_base_category1 c1 on c2.category1_id=c1.id where sku.dt='$do_date' and c2.dt='$do_date' and c3.dt='$do_date' and c1.dt='$do_date' and sku.id is not null; "
    beeline -u "jdbc:hive2://hadoop102:10000/" -n hive -e "$sql"

2）增加脚本执行权限

[root@hadoop102 bin]$ chmod 777 dwd_db.sh

3）采用脚本导入数据

[root@hadoop102 bin]$ dwd_db.sh 2019-02-10

# DWS层之用户行为宽表 #

1）为什么要建宽表  
需求目标，把每个用户单日的行为聚合起来组成一张多列宽表，以便之后关联用户维度信息后进行，不同角度的统计分析。

## 创建用户行为宽表 ##

drop table if exists dws_user_action;
    create external table dws_user_action 
    (   
        user_id          string      comment '用户 id',
        order_count     bigint      comment '下单次数 ',
        order_amount    decimal(16,2)  comment '下单金额 ',
        payment_count   bigint      comment '支付次数',
        payment_amount  decimal(16,2) comment '支付金额 '
    ) COMMENT '每日用户行为宽表'
    PARTITIONED BY (`dt` string)
    stored as parquet
    location '/warehouse/gmall/dws/dws_user_action/'
    tblproperties ("parquet.compression"="snappy");

## 用户行为数据宽表导入脚本 ##

1）在/root/bin目录下创建脚本dws\_db\_wide.sh

[root@hadoop102 bin]$ vim dws_db_wide.sh

在脚本中填写如下内容

#!/bin/bash
    
    # 定义变量方便修改
    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 "$1" ] ;then
    	do_date=$1
    else 
    	do_date=`date -d "-1 day" +%F`  
    fi 
    
    sql=" with tmp_order as ( select user_id, count(*) order_count, sum(oi.total_amount) order_amount from "$APP".dwd_order_info oi where date_format(oi.create_time,'yyyy-MM-dd')='$do_date' group by user_id ) , tmp_payment as ( select user_id, sum(pi.total_amount) payment_amount, count(*) payment_count from "$APP".dwd_payment_info pi where date_format(pi.payment_time,'yyyy-MM-dd')='$do_date' group by user_id ) insert overwrite table "$APP".dws_user_action partition(dt='$do_date') select user_actions.user_id, sum(user_actions.order_count), sum(user_actions.order_amount), sum(user_actions.payment_count), sum(user_actions.payment_amount) from ( select user_id, order_count, order_amount, 0 payment_count, 0 payment_amount from tmp_order union all select user_id, 0 order_count, 0 order_amount, payment_count, payment_amount from tmp_payment ) user_actions group by user_id; "
    
    beeline -u "jdbc:hive2://hadoop102:10000/" -n hive -e "$sql"

2）增加脚本执行权限

[root@hadoop102 bin]# chmod 777 dws_db_wide.sh

3）增加脚本执行权限

[root@hadoop102 bin]# dws_db_wide.sh 2019-02-10

# ADS层（需求：GMV成交总额） #

## 建表语句 ##

drop table if exists ads_gmv_sum_day;
    create external table ads_gmv_sum_day(
        `dt` string COMMENT '统计日期',
        `gmv_count`  bigint COMMENT '当日gmv订单个数',
        `gmv_amount`  decimal(16,2) COMMENT '当日gmv订单总金额',
        `gmv_payment`  decimal(16,2) COMMENT '当日支付金额'
    ) COMMENT 'GMV'
    row format delimited fields terminated by '\t'
    location '/warehouse/gmall/ads/ads_gmv_sum_day/'
    ;

## 数据导入脚本 ##

1）在/root/bin目录下创建脚本ads\_db\_gmv.sh

[root@hadoop102 bin]$ vim ads_db_gmv.sh

在脚本中填写如下内容

#!/bin/bash
    
    # 定义变量方便修改
    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 "$1" ] ;then
    	do_date=$1
    else 
    	do_date=`date -d "-1 day" +%F`
    fi 
    
    sql=" insert into table "$APP".ads_gmv_sum_day select '$do_date' dt, sum(order_count) gmv_count, sum(order_amount) gmv_amount, sum(payment_amount) payment_amount from "$APP".dws_user_action where dt ='$do_date' group by dt; "
    
    beeline -u "jdbc:hive2://hadoop102:10000/" -n hive -e "$sql"

2）增加脚本执行权限

[root@hadoop102 bin]$ chmod 777 ads_db_gmv.sh

3）执行脚本

[root@hadoop102 bin]$ ads_db_gmv.sh 2019-02-10

## 数据导出脚本 ##

1）在MySQL中创建ads\_gmv\_sum\_day表

DROP TABLE IF EXISTS ads_gmv_sum_day;
    CREATE TABLE ads_gmv_sum_day(
      `dt` varchar(200) DEFAULT NULL COMMENT '统计日期',
      `gmv_count` bigint(20) DEFAULT NULL COMMENT '当日gmv订单个数',
      `gmv_amount` decimal(16, 2) DEFAULT NULL COMMENT '当日gmv订单总金额',
      `gmv_payment` decimal(16, 2) DEFAULT NULL COMMENT '当日支付金额'
    ) ENGINE = InnoDB CHARACTER SET = utf8 COLLATE = utf8_general_ci COMMENT = '每日活跃用户数量' ROW_FORMAT = Dynamic;

2）在/root/bin目录下创建脚本sqoop\_export.sh

[root@hadoop102 bin]$ vim sqoop_export.sh

在脚本中填写如下内容

#!/bin/bash
    
    export HADOOP_USER_NAME=hive
    
    db_name=gmall
    
    export_data() { 
    sqoop export \
    --connect "jdbc:mysql://hadoop102:3306/${db_name}?useUnicode=true&characterEncoding=utf-8"  \
    --username root \
    --password Atguigu.123456 \
    --table $1 \
    --num-mappers 1 \
    --export-dir /warehouse/$db_name/ads/$1 \
    --input-fields-terminated-by "\t" \
    --update-mode allowinsert \
    --update-key $2 \
    --input-null-string '\\N'    \
    --input-null-non-string '\\N'
    }
    
    case $1 in
      "ads_gmv_sum_day")
         export_data "ads_gmv_sum_day" "dt"
    ;;
       "all")
         export_data "ads_gmv_sum_day" "dt"
    ;;
    esac

3）增加脚本执行权限

[root@hadoop102 bin]$ chmod 777 sqoop_export.sh

4）执行脚本导入数据

[root@hadoop102 bin]$ sqoop_export.sh all

5）在SQLyog查看导出数据

select * from ads_gmv_sum_day

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