电商数仓笔记6_数据仓库系统（数仓搭建-ODS层，数仓搭建-DIM层）
电商数仓
- 一、数仓搭建-ODS层
- 1、ODS层（用户行为数据）
- （1）创建日志表ods_log
- （2）Shell中单引号和双引号区别
- （3）ODS层日志表加载数据脚本
- 2、ODS层（业务数据）
- （1）ODS层业务表首日数据装载脚本
- （2）ODS层业务表每日数据装载脚本
- 二、数仓搭建-DIM层
- 1、商品维度表（全量）
- 2、优惠券维度表（全量）
- 3、活动维度表（全量）
- 4、地区维度表（特殊）
- 5、时间维度表（特殊）
- 6、用户维度表（拉链表）
- （1）拉链表概述
- （2）制作拉链表
- 7、DIM层首日数据装载脚本
- 8、DIM层每日数据装载脚本
一、数仓搭建-ODS层
1）保持数据原貌不做任何修改，起到备份数据的作用。
2）数据采用LZO压缩，减少磁盘存储空间。100G数据可以压缩到10G以内。
3）创建分区表，防止后续的全表扫描，在企业开发中大量使用分区表。
4）创建外部表。在企业开发中，除了自己用的临时表，创建内部表外，绝大多数场景都是创建外部表。
1、ODS层（用户行为数据）
（1）创建日志表ods_log
1）创建支持lzo压缩的分区表
（1）建表语句
hive (gmall)>
drop table if exists ods_log;
CREATE EXTERNAL TABLE ods_log (`line` string)
PARTITIONED BY (`dt` string) -- 按照时间创建分区
STORED AS -- 指定存储方式，读数据采用LzoTextInputFormat；
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_log' -- 指定数据在hdfs上的存储位置
;
说明Hive的LZO压缩：https://cwiki.apache.org/confluence/display/Hive/LanguageManual+LZO
（2）分区规划
2）加载数据
hive (gmall)>
load data inpath '/origin_data/gmall/log/topic_log/2020-06-14' into table ods_log partition(dt='2020-06-14');
注意：时间格式都配置成YYYY-MM-DD格式，这是Hive默认支持的时间格式
3）为lzo压缩文件创建索引
[lyh@hadoop102 bin]$ hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/common/hadoop-lzo-0.4.20.jar com.hadoop.compression.lzo.DistributedLzoIndexer /warehouse/gmall/ods/ods_log/dt=2020-06-14
（2）Shell中单引号和双引号区别
1）在/home/lyh/bin创建一个test.sh文件
[lyh@hadoop102 bin]$ vim test.sh
在文件中添加如下内容
#!/bin/bash
do_date=$1
echo '$do_date'
echo "$do_date"
echo "'$do_date'"
echo '"$do_date"'
echo `date`
2）查看执行结果
[lyh@hadoop102 bin]$ test.sh 2020-06-14
$do_date
2020-06-14
'2020-06-14'
"$do_date"
2020年 06月 18日 星期四 21:02:08 CST
3）总结：
（1）单引号不取变量值
（2）双引号取变量值
（3）反引号`，执行引号中命令
（4）双引号内部嵌套单引号，取出变量值
（5）单引号内部嵌套双引号，不取出变量值
（3）ODS层日志表加载数据脚本
1）编写脚本
（1）在hadoop102的/home/lyh/bin目录下创建脚本
[lyh@hadoop102 bin]$ vim hdfs_to_ods_log.sh
在脚本中编写如下内容
#!/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
echo ================== 日志日期为 $do_date ==================
sql="
load data inpath '/origin_data/$APP/log/topic_log/$do_date' into table ${APP}.ods_log partition(dt='$do_date');
"
hive -e "$sql"
hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/common/hadoop-lzo-0.4.20.jar com.hadoop.compression.lzo.DistributedLzoIndexer /warehouse/$APP/ods/ods_log/dt=$do_date
（1）说明1：
[ -n 变量值 ] 判断变量的值，是否为空
-- 变量的值，非空，返回true
-- 变量的值，为空，返回false
注意：[ -n 变量值 ]不会解析数据，使用[ -n 变量值 ]时，需要对变量加上双引号(" ")
（2）说明2：
查看date命令的使用，date --help
（2）增加脚本执行权限
[lyh@hadoop102 bin]$ chmod 777 hdfs_to_ods_log.sh
2）脚本使用
（1）执行脚本
[lyh@hadoop102 module]$ hdfs_to_ods_log.sh 2020-06-14
（2）查看导入数据
2、ODS层（业务数据）
- ODS层业务表分区规划如下
- ODS层业务表数据装载思路如下
4.2.1 活动信息表
DROP TABLE IF EXISTS ods_activity_info;
CREATE EXTERNAL TABLE ods_activity_info(
`id` STRING COMMENT '编号',
`activity_name` STRING COMMENT '活动名称',
`activity_type` STRING COMMENT '活动类型',
`start_time` STRING COMMENT '开始时间',
`end_time` STRING COMMENT '结束时间',
`create_time` STRING COMMENT '创建时间'
) COMMENT '活动信息表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_activity_info/';
4.2.2 活动规则表
DROP TABLE IF EXISTS ods_activity_rule;
CREATE EXTERNAL TABLE ods_activity_rule(
`id` STRING COMMENT '编号',
`activity_id` STRING COMMENT '活动ID',
`activity_type` STRING COMMENT '活动类型',
`condition_amount` DECIMAL(16,2) COMMENT '满减金额',
`condition_num` BIGINT COMMENT '满减件数',
`benefit_amount` DECIMAL(16,2) COMMENT '优惠金额',
`benefit_discount` DECIMAL(16,2) COMMENT '优惠折扣',
`benefit_level` STRING COMMENT '优惠级别'
) COMMENT '活动规则表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_activity_rule/';
4.2.3 一级品类表
DROP TABLE IF EXISTS ods_base_category1;
CREATE EXTERNAL TABLE ods_base_category1(
`id` STRING COMMENT 'id',
`name` STRING COMMENT '名称'
) COMMENT '商品一级分类表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_base_category1/';
4.2.4 二级品类表
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 '商品二级分类表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_base_category2/';
4.2.5 三级品类表
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 '商品三级分类表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_base_category3/';
4.2.6 编码字典表
DROP TABLE IF EXISTS ods_base_dic;
CREATE EXTERNAL TABLE ods_base_dic(
`dic_code` STRING COMMENT '编号',
`dic_name` STRING COMMENT '编码名称',
`parent_code` STRING COMMENT '父编码',
`create_time` STRING COMMENT '创建日期',
`operate_time` STRING COMMENT '操作日期'
) COMMENT '编码字典表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_base_dic/';
4.2.7 省份表
DROP TABLE IF EXISTS ods_base_province;
CREATE EXTERNAL TABLE ods_base_province (
`id` STRING COMMENT '编号',
`name` STRING COMMENT '省份名称',
`region_id` STRING COMMENT '地区ID',
`area_code` STRING COMMENT '地区编码',
`iso_code` STRING COMMENT 'ISO-3166编码，供可视化使用',
`iso_3166_2` STRING COMMENT 'IOS-3166-2编码，供可视化使用'
) COMMENT '省份表'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_base_province/';
4.2.8 地区表
DROP TABLE IF EXISTS ods_base_region;
CREATE EXTERNAL TABLE ods_base_region (
`id` STRING COMMENT '编号',
`region_name` STRING COMMENT '地区名称'
) COMMENT '地区表'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_base_region/';
4.2.9 品牌表
DROP TABLE IF EXISTS ods_base_trademark;
CREATE EXTERNAL TABLE ods_base_trademark (
`id` STRING COMMENT '编号',
`tm_name` STRING COMMENT '品牌名称'
) COMMENT '品牌表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_base_trademark/';
4.2.10 购物车表
DROP TABLE IF EXISTS ods_cart_info;
CREATE EXTERNAL TABLE ods_cart_info(
`id` STRING COMMENT '编号',
`user_id` STRING COMMENT '用户id',
`sku_id` STRING COMMENT 'skuid',
`cart_price` DECIMAL(16,2) COMMENT '放入购物车时价格',
`sku_num` BIGINT COMMENT '数量',
`sku_name` STRING COMMENT 'sku名称 (冗余)',
`create_time` STRING COMMENT '创建时间',
`operate_time` STRING COMMENT '修改时间',
`is_ordered` STRING COMMENT '是否已经下单',
`order_time` STRING COMMENT '下单时间',
`source_type` STRING COMMENT '来源类型',
`source_id` STRING COMMENT '来源编号'
) COMMENT '加购表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_cart_info/';
4.2.11 评论表
DROP TABLE IF EXISTS ods_comment_info;
CREATE EXTERNAL TABLE ods_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',
`create_time` STRING COMMENT '评价时间'
) COMMENT '商品评论表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_comment_info/';
4.2.12 优惠券信息表
DROP TABLE IF EXISTS ods_coupon_info;
CREATE EXTERNAL TABLE ods_coupon_info(
`id` STRING COMMENT '购物券编号',
`coupon_name` STRING COMMENT '购物券名称',
`coupon_type` STRING COMMENT '购物券类型 1 现金券 2 折扣券 3 满减券 4 满件打折券',
`condition_amount` DECIMAL(16,2) COMMENT '满额数',
`condition_num` BIGINT COMMENT '满件数',
`activity_id` STRING COMMENT '活动编号',
`benefit_amount` DECIMAL(16,2) COMMENT '减金额',
`benefit_discount` DECIMAL(16,2) COMMENT '折扣',
`create_time` STRING COMMENT '创建时间',
`range_type` STRING COMMENT '范围类型 1、商品 2、品类 3、品牌',
`limit_num` BIGINT COMMENT '最多领用次数',
`taken_count` BIGINT COMMENT '已领用次数',
`start_time` STRING COMMENT '开始领取时间',
`end_time` STRING COMMENT '结束领取时间',
`operate_time` STRING COMMENT '修改时间',
`expire_time` STRING COMMENT '过期时间'
) COMMENT '优惠券表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_coupon_info/';
4.2.13 优惠券领用表
DROP TABLE IF EXISTS ods_coupon_use;
CREATE EXTERNAL TABLE ods_coupon_use(
`id` STRING COMMENT '编号',
`coupon_id` STRING COMMENT '优惠券ID',
`user_id` STRING COMMENT 'skuid',
`order_id` STRING COMMENT 'spuid',
`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 '优惠券领用表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_coupon_use/';
4.2.14 收藏表
DROP TABLE IF EXISTS ods_favor_info;
CREATE EXTERNAL TABLE ods_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 '商品收藏表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_favor_info/';
4.2.15 订单明细表
DROP TABLE IF EXISTS ods_order_detail;
CREATE EXTERNAL TABLE ods_order_detail(
`id` STRING COMMENT '编号',
`order_id` STRING COMMENT '订单号',
`sku_id` STRING COMMENT '商品id',
`sku_name` STRING COMMENT '商品名称',
`order_price` DECIMAL(16,2) COMMENT '商品价格',
`sku_num` BIGINT COMMENT '商品数量',
`create_time` STRING COMMENT '创建时间',
`source_type` STRING COMMENT '来源类型',
`source_id` STRING COMMENT '来源编号',
`split_final_amount` DECIMAL(16,2) COMMENT '分摊最终金额',
`split_activity_amount` DECIMAL(16,2) COMMENT '分摊活动优惠',
`split_coupon_amount` DECIMAL(16,2) COMMENT '分摊优惠券优惠'
) COMMENT '订单详情表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_order_detail/';
4.2.16 订单明细活动关联表
DROP TABLE IF EXISTS ods_order_detail_activity;
CREATE EXTERNAL TABLE ods_order_detail_activity(
`id` STRING COMMENT '编号',
`order_id` STRING COMMENT '订单号',
`order_detail_id` STRING COMMENT '订单明细id',
`activity_id` STRING COMMENT '活动id',
`activity_rule_id` STRING COMMENT '活动规则id',
`sku_id` BIGINT COMMENT '商品id',
`create_time` STRING COMMENT '创建时间'
) COMMENT '订单详情活动关联表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_order_detail_activity/';
4.2.17 订单明细优惠券关联表
DROP TABLE IF EXISTS ods_order_detail_coupon;
CREATE EXTERNAL TABLE ods_order_detail_coupon(
`id` STRING COMMENT '编号',
`order_id` STRING COMMENT '订单号',
`order_detail_id` STRING COMMENT '订单明细id',
`coupon_id` STRING COMMENT '优惠券id',
`coupon_use_id` STRING COMMENT '优惠券领用记录id',
`sku_id` STRING COMMENT '商品id',
`create_time` STRING COMMENT '创建时间'
) COMMENT '订单详情活动关联表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_order_detail_coupon/';
4.2.18 订单表
DROP TABLE IF EXISTS ods_order_info;
CREATE EXTERNAL TABLE ods_order_info (
`id` STRING COMMENT '订单号',
`final_amount` DECIMAL(16,2) COMMENT '订单最终金额',
`order_status` STRING COMMENT '订单状态',
`user_id` STRING COMMENT '用户id',
`payment_way` STRING COMMENT '支付方式',
`delivery_address` STRING COMMENT '送货地址',
`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',
`expire_time` STRING COMMENT '过期时间',
`tracking_no` STRING COMMENT '物流单编号',
`province_id` STRING COMMENT '省份ID',
`activity_reduce_amount` DECIMAL(16,2) COMMENT '活动减免金额',
`coupon_reduce_amount` DECIMAL(16,2) COMMENT '优惠券减免金额',
`original_amount` DECIMAL(16,2) COMMENT '订单原价金额',
`feight_fee` DECIMAL(16,2) COMMENT '运费',
`feight_fee_reduce` DECIMAL(16,2) COMMENT '运费减免'
) COMMENT '订单表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_order_info/';
4.2.19 退单表
DROP TABLE IF EXISTS ods_order_refund_info;
CREATE EXTERNAL TABLE ods_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',
`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',
`refund_status` STRING COMMENT '退单状态',--退单状态应包含买家申请、卖家审核、卖家收货、退款完成等状态。此处未涉及到，故该表按增量处理
`create_time` STRING COMMENT '退单时间'
) COMMENT '退单表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_order_refund_info/';
4.2.20 订单状态日志表
DROP TABLE IF EXISTS ods_order_status_log;
CREATE EXTERNAL TABLE ods_order_status_log (
`id` STRING COMMENT '编号',
`order_id` STRING COMMENT '订单ID',
`order_status` STRING COMMENT '订单状态',
`operate_time` STRING COMMENT '修改时间'
) COMMENT '订单状态表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_order_status_log/';
4.2.21 支付表
DROP TABLE IF EXISTS ods_payment_info;
CREATE EXTERNAL TABLE ods_payment_info(
`id` STRING 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',
`payment_type` STRING COMMENT '支付类型',
`trade_no` STRING COMMENT '交易编号',
`payment_amount` DECIMAL(16,2) COMMENT '支付金额',
`subject` STRING COMMENT '交易内容',
`payment_status` STRING COMMENT '支付状态',
`create_time` STRING COMMENT '创建时间',
`callback_time` STRING COMMENT '回调时间'
) COMMENT '支付流水表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_payment_info/';
4.2.22 退款表
DROP TABLE IF EXISTS ods_refund_payment;
CREATE EXTERNAL TABLE ods_refund_payment(
`id` STRING COMMENT '编号',
`out_trade_no` STRING COMMENT '对外业务编号',
`order_id` STRING COMMENT '订单编号',
`sku_id` STRING COMMENT 'SKU编号',
`payment_type` STRING COMMENT '支付类型',
`trade_no` STRING COMMENT '交易编号',
`refund_amount` DECIMAL(16,2) COMMENT '支付金额',
`subject` STRING COMMENT '交易内容',
`refund_status` STRING COMMENT '支付状态',
`create_time` STRING COMMENT '创建时间',
`callback_time` STRING COMMENT '回调时间'
) COMMENT '支付流水表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_refund_payment/';
4.2.23 商品平台属性表
DROP TABLE IF EXISTS ods_sku_attr_value;
CREATE EXTERNAL TABLE ods_sku_attr_value(
`id` STRING COMMENT '编号',
`attr_id` STRING COMMENT '平台属性ID',
`value_id` STRING COMMENT '平台属性值ID',
`sku_id` STRING COMMENT '商品ID',
`attr_name` STRING COMMENT '平台属性名称',
`value_name` STRING COMMENT '平台属性值名称'
) COMMENT 'sku平台属性表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_sku_attr_value/';
4.2.24 商品（SKU）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(16,2) COMMENT '价格',
`sku_name` STRING COMMENT '商品名称',
`sku_desc` STRING COMMENT '商品描述',
`weight` DECIMAL(16,2) COMMENT '重量',
`tm_id` STRING COMMENT '品牌id',
`category3_id` STRING COMMENT '品类id',
`is_sale` STRING COMMENT '是否在售',
`create_time` STRING COMMENT '创建时间'
) COMMENT 'SKU商品表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_sku_info/';
4.2.25 商品销售属性表
DROP TABLE IF EXISTS ods_sku_sale_attr_value;
CREATE EXTERNAL TABLE ods_sku_sale_attr_value(
`id` STRING COMMENT '编号',
`sku_id` STRING COMMENT 'sku_id',
`spu_id` STRING COMMENT 'spu_id',
`sale_attr_value_id` STRING COMMENT '销售属性值id',
`sale_attr_id` STRING COMMENT '销售属性id',
`sale_attr_name` STRING COMMENT '销售属性名称',
`sale_attr_value_name` STRING COMMENT '销售属性值名称'
) COMMENT 'sku销售属性名称'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_sku_sale_attr_value/';
4.2.26 商品（SPU）表
DROP TABLE IF EXISTS ods_spu_info;
CREATE EXTERNAL TABLE ods_spu_info(
`id` STRING COMMENT 'spuid',
`spu_name` STRING COMMENT 'spu名称',
`category3_id` STRING COMMENT '品类id',
`tm_id` STRING COMMENT '品牌id'
) COMMENT 'SPU商品表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_spu_info/';
4.2.27 用户表
DROP TABLE IF EXISTS ods_user_info;
CREATE EXTERNAL TABLE ods_user_info(
`id` STRING COMMENT '用户id',
`login_name` STRING COMMENT '用户名称',
`nick_name` STRING COMMENT '用户昵称',
`name` STRING COMMENT '用户姓名',
`phone_num` STRING COMMENT '手机号码',
`email` STRING COMMENT '邮箱',
`user_level` STRING COMMENT '用户等级',
`birthday` STRING COMMENT '生日',
`gender` STRING COMMENT '性别',
`create_time` STRING COMMENT '创建时间',
`operate_time` STRING COMMENT '操作时间'
) COMMENT '用户表'
PARTITIONED BY (`dt` STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS
INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION '/warehouse/gmall/ods/ods_user_info/';
（1）ODS层业务表首日数据装载脚本
1）编写脚本
（1）在/home/lyh/bin目录下创建脚本hdfs_to_ods_db_init.sh
[lyh@hadoop102 bin]$ vim hdfs_to_ods_db_init.sh
在脚本中填写如下内容
#!/bin/bash
APP=gmall
if [ -n "$2" ] ;then
do_date=$2
else
echo "请传入日期参数"
exit
fi
ods_order_info="
load data inpath '/origin_data/$APP/db/order_info/$do_date' OVERWRITE into table ${APP}.ods_order_info partition(dt='$do_date');"
ods_order_detail="
load data inpath '/origin_data/$APP/db/order_detail/$do_date' OVERWRITE into table ${APP}.ods_order_detail partition(dt='$do_date');"
ods_sku_info="
load data inpath '/origin_data/$APP/db/sku_info/$do_date' OVERWRITE into table ${APP}.ods_sku_info partition(dt='$do_date');"
ods_user_info="
load data inpath '/origin_data/$APP/db/user_info/$do_date' OVERWRITE into table ${APP}.ods_user_info partition(dt='$do_date');"
ods_payment_info="
load data inpath '/origin_data/$APP/db/payment_info/$do_date' OVERWRITE into table ${APP}.ods_payment_info partition(dt='$do_date');"
ods_base_category1="
load data inpath '/origin_data/$APP/db/base_category1/$do_date' OVERWRITE into table ${APP}.ods_base_category1 partition(dt='$do_date');"
ods_base_category2="
load data inpath '/origin_data/$APP/db/base_category2/$do_date' OVERWRITE into table ${APP}.ods_base_category2 partition(dt='$do_date');"
ods_base_category3="
load data inpath '/origin_data/$APP/db/base_category3/$do_date' OVERWRITE into table ${APP}.ods_base_category3 partition(dt='$do_date'); "
ods_base_trademark="
load data inpath '/origin_data/$APP/db/base_trademark/$do_date' OVERWRITE into table ${APP}.ods_base_trademark partition(dt='$do_date'); "
ods_activity_info="
load data inpath '/origin_data/$APP/db/activity_info/$do_date' OVERWRITE into table ${APP}.ods_activity_info partition(dt='$do_date'); "
ods_cart_info="
load data inpath '/origin_data/$APP/db/cart_info/$do_date' OVERWRITE into table ${APP}.ods_cart_info partition(dt='$do_date'); "
ods_comment_info="
load data inpath '/origin_data/$APP/db/comment_info/$do_date' OVERWRITE into table ${APP}.ods_comment_info partition(dt='$do_date'); "
ods_coupon_info="
load data inpath '/origin_data/$APP/db/coupon_info/$do_date' OVERWRITE into table ${APP}.ods_coupon_info partition(dt='$do_date'); "
ods_coupon_use="
load data inpath '/origin_data/$APP/db/coupon_use/$do_date' OVERWRITE into table ${APP}.ods_coupon_use partition(dt='$do_date'); "
ods_favor_info="
load data inpath '/origin_data/$APP/db/favor_info/$do_date' OVERWRITE into table ${APP}.ods_favor_info partition(dt='$do_date'); "
ods_order_refund_info="
load data inpath '/origin_data/$APP/db/order_refund_info/$do_date' OVERWRITE into table ${APP}.ods_order_refund_info partition(dt='$do_date'); "
ods_order_status_log="
load data inpath '/origin_data/$APP/db/order_status_log/$do_date' OVERWRITE into table ${APP}.ods_order_status_log partition(dt='$do_date'); "
ods_spu_info="
load data inpath '/origin_data/$APP/db/spu_info/$do_date' OVERWRITE into table ${APP}.ods_spu_info partition(dt='$do_date'); "
ods_activity_rule="
load data inpath '/origin_data/$APP/db/activity_rule/$do_date' OVERWRITE into table ${APP}.ods_activity_rule partition(dt='$do_date');"
ods_base_dic="
load data inpath '/origin_data/$APP/db/base_dic/$do_date' OVERWRITE into table ${APP}.ods_base_dic partition(dt='$do_date'); "
ods_order_detail_activity="
load data inpath '/origin_data/$APP/db/order_detail_activity/$do_date' OVERWRITE into table ${APP}.ods_order_detail_activity partition(dt='$do_date'); "
ods_order_detail_coupon="
load data inpath '/origin_data/$APP/db/order_detail_coupon/$do_date' OVERWRITE into table ${APP}.ods_order_detail_coupon partition(dt='$do_date'); "
ods_refund_payment="
load data inpath '/origin_data/$APP/db/refund_payment/$do_date' OVERWRITE into table ${APP}.ods_refund_payment partition(dt='$do_date'); "
ods_sku_attr_value="
load data inpath '/origin_data/$APP/db/sku_attr_value/$do_date' OVERWRITE into table ${APP}.ods_sku_attr_value partition(dt='$do_date'); "
ods_sku_sale_attr_value="
load data inpath '/origin_data/$APP/db/sku_sale_attr_value/$do_date' OVERWRITE into table ${APP}.ods_sku_sale_attr_value partition(dt='$do_date'); "
ods_base_province="
load data inpath '/origin_data/$APP/db/base_province/$do_date' OVERWRITE into table ${APP}.ods_base_province;"
ods_base_region="
load data inpath '/origin_data/$APP/db/base_region/$do_date' OVERWRITE into table ${APP}.ods_base_region;"
case $1 in
"ods_order_info"){
hive -e "$ods_order_info"
};;
"ods_order_detail"){
hive -e "$ods_order_detail"
};;
"ods_sku_info"){
hive -e "$ods_sku_info"
};;
"ods_user_info"){
hive -e "$ods_user_info"
};;
"ods_payment_info"){
hive -e "$ods_payment_info"
};;
"ods_base_category1"){
hive -e "$ods_base_category1"
};;
"ods_base_category2"){
hive -e "$ods_base_category2"
};;
"ods_base_category3"){
hive -e "$ods_base_category3"
};;
"ods_base_trademark"){
hive -e "$ods_base_trademark"
};;
"ods_activity_info"){
hive -e "$ods_activity_info"
};;
"ods_cart_info"){
hive -e "$ods_cart_info"
};;
"ods_comment_info"){
hive -e "$ods_comment_info"
};;
"ods_coupon_info"){
hive -e "$ods_coupon_info"
};;
"ods_coupon_use"){
hive -e "$ods_coupon_use"
};;
"ods_favor_info"){
hive -e "$ods_favor_info"
};;
"ods_order_refund_info"){
hive -e "$ods_order_refund_info"
};;
"ods_order_status_log"){
hive -e "$ods_order_status_log"
};;
"ods_spu_info"){
hive -e "$ods_spu_info"
};;
"ods_activity_rule"){
hive -e "$ods_activity_rule"
};;
"ods_base_dic"){
hive -e "$ods_base_dic"
};;
"ods_order_detail_activity"){
hive -e "$ods_order_detail_activity"
};;
"ods_order_detail_coupon"){
hive -e "$ods_order_detail_coupon"
};;
"ods_refund_payment"){
hive -e "$ods_refund_payment"
};;
"ods_sku_attr_value"){
hive -e "$ods_sku_attr_value"
};;
"ods_sku_sale_attr_value"){
hive -e "$ods_sku_sale_attr_value"
};;
"ods_base_province"){
hive -e "$ods_base_province"
};;
"ods_base_region"){
hive -e "$ods_base_region"
};;
"all"){
hive -e "$ods_order_info$ods_order_detail$ods_sku_info$ods_user_info$ods_payment_info$ods_base_category1$ods_base_category2$ods_base_category3$ods_base_trademark$ods_activity_info$ods_cart_info$ods_comment_info$ods_coupon_info$ods_coupon_use$ods_favor_info$ods_order_refund_info$ods_order_status_log$ods_spu_info$ods_activity_rule$ods_base_dic$ods_order_detail_activity$ods_order_detail_coupon$ods_refund_payment$ods_sku_attr_value$ods_sku_sale_attr_value$ods_base_province$ods_base_region"
};;
esac
（2）增加执行权限
[lyh@hadoop102 bin]$ chmod +x hdfs_to_ods_db_init.sh
2）脚本使用
（1）执行脚本
[lyh@hadoop102 bin]$ hdfs_to_ods_db_init.sh all 2020-06-14
（2）查看数据是否导入成功
（2）ODS层业务表每日数据装载脚本
1）编写脚本
（1）在/home/lyh/bin目录下创建脚本hdfs_to_ods_db.sh
[lyh@hadoop102 bin]$ vim hdfs_to_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 "$2" ] ;then
do_date=$2
else
do_date=`date -d "-1 day" +%F`
fi
ods_order_info="
load data inpath '/origin_data/$APP/db/order_info/$do_date' OVERWRITE into table ${APP}.ods_order_info partition(dt='$do_date');"
ods_order_detail="
load data inpath '/origin_data/$APP/db/order_detail/$do_date' OVERWRITE into table ${APP}.ods_order_detail partition(dt='$do_date');"
ods_sku_info="
load data inpath '/origin_data/$APP/db/sku_info/$do_date' OVERWRITE into table ${APP}.ods_sku_info partition(dt='$do_date');"
ods_user_info="
load data inpath '/origin_data/$APP/db/user_info/$do_date' OVERWRITE into table ${APP}.ods_user_info partition(dt='$do_date');"
ods_payment_info="
load data inpath '/origin_data/$APP/db/payment_info/$do_date' OVERWRITE into table ${APP}.ods_payment_info partition(dt='$do_date');"
ods_base_category1="
load data inpath '/origin_data/$APP/db/base_category1/$do_date' OVERWRITE into table ${APP}.ods_base_category1 partition(dt='$do_date');"
ods_base_category2="
load data inpath '/origin_data/$APP/db/base_category2/$do_date' OVERWRITE into table ${APP}.ods_base_category2 partition(dt='$do_date');"
ods_base_category3="
load data inpath '/origin_data/$APP/db/base_category3/$do_date' OVERWRITE into table ${APP}.ods_base_category3 partition(dt='$do_date'); "
ods_base_trademark="
load data inpath '/origin_data/$APP/db/base_trademark/$do_date' OVERWRITE into table ${APP}.ods_base_trademark partition(dt='$do_date'); "
ods_activity_info="
load data inpath '/origin_data/$APP/db/activity_info/$do_date' OVERWRITE into table ${APP}.ods_activity_info partition(dt='$do_date'); "
ods_cart_info="
load data inpath '/origin_data/$APP/db/cart_info/$do_date' OVERWRITE into table ${APP}.ods_cart_info partition(dt='$do_date'); "
ods_comment_info="
load data inpath '/origin_data/$APP/db/comment_info/$do_date' OVERWRITE into table ${APP}.ods_comment_info partition(dt='$do_date'); "
ods_coupon_info="
load data inpath '/origin_data/$APP/db/coupon_info/$do_date' OVERWRITE into table ${APP}.ods_coupon_info partition(dt='$do_date'); "
ods_coupon_use="
load data inpath '/origin_data/$APP/db/coupon_use/$do_date' OVERWRITE into table ${APP}.ods_coupon_use partition(dt='$do_date'); "
ods_favor_info="
load data inpath '/origin_data/$APP/db/favor_info/$do_date' OVERWRITE into table ${APP}.ods_favor_info partition(dt='$do_date'); "
ods_order_refund_info="
load data inpath '/origin_data/$APP/db/order_refund_info/$do_date' OVERWRITE into table ${APP}.ods_order_refund_info partition(dt='$do_date'); "
ods_order_status_log="
load data inpath '/origin_data/$APP/db/order_status_log/$do_date' OVERWRITE into table ${APP}.ods_order_status_log partition(dt='$do_date'); "
ods_spu_info="
load data inpath '/origin_data/$APP/db/spu_info/$do_date' OVERWRITE into table ${APP}.ods_spu_info partition(dt='$do_date'); "
ods_activity_rule="
load data inpath '/origin_data/$APP/db/activity_rule/$do_date' OVERWRITE into table ${APP}.ods_activity_rule partition(dt='$do_date');"
ods_base_dic="
load data inpath '/origin_data/$APP/db/base_dic/$do_date' OVERWRITE into table ${APP}.ods_base_dic partition(dt='$do_date'); "
ods_order_detail_activity="
load data inpath '/origin_data/$APP/db/order_detail_activity/$do_date' OVERWRITE into table ${APP}.ods_order_detail_activity partition(dt='$do_date'); "
ods_order_detail_coupon="
load data inpath '/origin_data/$APP/db/order_detail_coupon/$do_date' OVERWRITE into table ${APP}.ods_order_detail_coupon partition(dt='$do_date'); "
ods_refund_payment="
load data inpath '/origin_data/$APP/db/refund_payment/$do_date' OVERWRITE into table ${APP}.ods_refund_payment partition(dt='$do_date'); "
ods_sku_attr_value="
load data inpath '/origin_data/$APP/db/sku_attr_value/$do_date' OVERWRITE into table ${APP}.ods_sku_attr_value partition(dt='$do_date'); "
ods_sku_sale_attr_value="
load data inpath '/origin_data/$APP/db/sku_sale_attr_value/$do_date' OVERWRITE into table ${APP}.ods_sku_sale_attr_value partition(dt='$do_date'); "
ods_base_province="
load data inpath '/origin_data/$APP/db/base_province/$do_date' OVERWRITE into table ${APP}.ods_base_province;"
ods_base_region="
load data inpath '/origin_data/$APP/db/base_region/$do_date' OVERWRITE into table ${APP}.ods_base_region;"
case $1 in
"ods_order_info"){
hive -e "$ods_order_info"
};;
"ods_order_detail"){
hive -e "$ods_order_detail"
};;
"ods_sku_info"){
hive -e "$ods_sku_info"
};;
"ods_user_info"){
hive -e "$ods_user_info"
};;
"ods_payment_info"){
hive -e "$ods_payment_info"
};;
"ods_base_category1"){
hive -e "$ods_base_category1"
};;
"ods_base_category2"){
hive -e "$ods_base_category2"
};;
"ods_base_category3"){
hive -e "$ods_base_category3"
};;
"ods_base_trademark"){
hive -e "$ods_base_trademark"
};;
"ods_activity_info"){
hive -e "$ods_activity_info"
};;
"ods_cart_info"){
hive -e "$ods_cart_info"
};;
"ods_comment_info"){
hive -e "$ods_comment_info"
};;
"ods_coupon_info"){
hive -e "$ods_coupon_info"
};;
"ods_coupon_use"){
hive -e "$ods_coupon_use"
};;
"ods_favor_info"){
hive -e "$ods_favor_info"
};;
"ods_order_refund_info"){
hive -e "$ods_order_refund_info"
};;
"ods_order_status_log"){
hive -e "$ods_order_status_log"
};;
"ods_spu_info"){
hive -e "$ods_spu_info"
};;
"ods_activity_rule"){
hive -e "$ods_activity_rule"
};;
"ods_base_dic"){
hive -e "$ods_base_dic"
};;
"ods_order_detail_activity"){
hive -e "$ods_order_detail_activity"
};;
"ods_order_detail_coupon"){
hive -e "$ods_order_detail_coupon"
};;
"ods_refund_payment"){
hive -e "$ods_refund_payment"
};;
"ods_sku_attr_value"){
hive -e "$ods_sku_attr_value"
};;
"ods_sku_sale_attr_value"){
hive -e "$ods_sku_sale_attr_value"
};;
"all"){
hive -e "$ods_order_info$ods_order_detail$ods_sku_info$ods_user_info$ods_payment_info$ods_base_category1$ods_base_category2$ods_base_category3$ods_base_trademark$ods_activity_info$ods_cart_info$ods_comment_info$ods_coupon_info$ods_coupon_use$ods_favor_info$ods_order_refund_info$ods_order_status_log$ods_spu_info$ods_activity_rule$ods_base_dic$ods_order_detail_activity$ods_order_detail_coupon$ods_refund_payment$ods_sku_attr_value$ods_sku_sale_attr_value"
};;
esac
（2）修改权限
[lyh@hadoop102 bin]$ chmod +x hdfs_to_ods_db.sh
2）脚本使用
（1）执行脚本
[lyh@hadoop102 bin]$ hdfs_to_ods_db.sh all 2020-06-14
（2）查看数据是否导入成功
二、数仓搭建-DIM层
1、商品维度表（全量）
1.建表语句
DROP TABLE IF EXISTS dim_sku_info;
CREATE EXTERNAL TABLE dim_sku_info (
`id` STRING COMMENT '商品id',
`price` DECIMAL(16,2) COMMENT '商品价格',
`sku_name` STRING COMMENT '商品名称',
`sku_desc` STRING COMMENT '商品描述',
`weight` DECIMAL(16,2) COMMENT '重量',
`is_sale` BOOLEAN COMMENT '是否在售',
`spu_id` STRING COMMENT 'spu编号',
`spu_name` STRING COMMENT 'spu名称',
`category3_id` STRING COMMENT '三级分类id',
`category3_name` STRING COMMENT '三级分类名称',
`category2_id` STRING COMMENT '二级分类id',
`category2_name` STRING COMMENT '二级分类名称',
`category1_id` STRING COMMENT '一级分类id',
`category1_name` STRING COMMENT '一级分类名称',
`tm_id` STRING COMMENT '品牌id',
`tm_name` STRING COMMENT '品牌名称',
`sku_attr_values` ARRAY<STRUCT<attr_id:STRING,value_id:STRING,attr_name:STRING,value_name:STRING>> COMMENT '平台属性',
`sku_sale_attr_values` ARRAY<STRUCT<sale_attr_id:STRING,sale_attr_value_id:STRING,sale_attr_name:STRING,sale_attr_value_name:STRING>> COMMENT '销售属性',
`create_time` STRING COMMENT '创建时间'
) COMMENT '商品维度表'
PARTITIONED BY (`dt` STRING)
STORED AS PARQUET
LOCATION '/warehouse/gmall/dim/dim_sku_info/'
TBLPROPERTIES ("parquet.compression"="lzo");
2.分区规划
3.数据装载
１）Hive读取索引文件问题
（1）两种方式，分别查询数据有多少行
hive (gmall)> select * from ods_log;
Time taken: 0.706 seconds, Fetched: 2955 row(s)
hive (gmall)> select count(*) from ods_log;
2959
（2）两次查询结果不一致。
原因是select * from ods_log不执行MR操作，直接采用的是ods_log建表语句中指定的DeprecatedLzoTextInputFormat，能够识别lzo.index为索引文件。
select count(*) from ods_log执行MR操作，会先经过hive.input.format，其默认值为CombineHiveInputFormat，其会先将索引文件当成小文件合并，将其当做普通文件处理。更严重的是，这会导致LZO文件无法切片。
hive (gmall)>
hive.input.format=org.apache.hadoop.hive.ql.io.CombineHiveInputFormat;
解决办法：修改CombineHiveInputFormat为HiveInputFormat
hive (gmall)>
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
２）首日装载
with
sku as
(
select
id,
price,
sku_name,
sku_desc,
weight,
is_sale,
spu_id,
category3_id,
tm_id,
create_time
from ods_sku_info
where dt='2020-06-14'
),
spu as
(
select
id,
spu_name
from ods_spu_info
where dt='2020-06-14'
),
c3 as
(
select
id,
name,
category2_id
from ods_base_category3
where dt='2020-06-14'
),
c2 as
(
select
id,
name,
category1_id
from ods_base_category2
where dt='2020-06-14'
),
c1 as
(
select
id,
name
from ods_base_category1
where dt='2020-06-14'
),
tm as
(
select
id,
tm_name
from ods_base_trademark
where dt='2020-06-14'
),
attr as
(
select
sku_id,
collect_set(named_struct('attr_id',attr_id,'value_id',value_id,'attr_name',attr_name,'value_name',value_name)) attrs
from ods_sku_attr_value
where dt='2020-06-14'
group by sku_id
),
sale_attr as
(
select
sku_id,
collect_set(named_struct('sale_attr_id',sale_attr_id,'sale_attr_value_id',sale_attr_value_id,'sale_attr_name',sale_attr_name,'sale_attr_value_name',sale_attr_value_name)) sale_attrs
from ods_sku_sale_attr_value
where dt='2020-06-14'
group by sku_id
)
insert overwrite table dim_sku_info partition(dt='2020-06-14')
select
sku.id,
sku.price,
sku.sku_name,
sku.sku_desc,
sku.weight,
sku.is_sale,
sku.spu_id,
spu.spu_name,
sku.category3_id,
c3.name,
c3.category2_id,
c2.name,
c2.category1_id,
c1.name,
sku.tm_id,
tm.tm_name,
attr.attrs,
sale_attr.sale_attrs,
sku.create_time
from sku
left join spu on sku.spu_id=spu.id
left join c3 on sku.category3_id=c3.id
left join c2 on c3.category2_id=c2.id
left join c1 on c2.category1_id=c1.id
left join tm on sku.tm_id=tm.id
left join attr on sku.id=attr.sku_id
left join sale_attr on sku.id=sale_attr.sku_id;
３）每日装载
with
sku as
(
select
id,
price,
sku_name,
sku_desc,
weight,
is_sale,
spu_id,
category3_id,
tm_id,
create_time
from ods_sku_info
where dt='2020-06-15'
),
spu as
(
select
id,
spu_name
from ods_spu_info
where dt='2020-06-15'
),
c3 as
(
select
id,
name,
category2_id
from ods_base_category3
where dt='2020-06-15'
),
c2 as
(
select
id,
name,
category1_id
from ods_base_category2
where dt='2020-06-15'
),
c1 as
(
select
id,
name
from ods_base_category1
where dt='2020-06-15'
),
tm as
(
select
id,
tm_name
from ods_base_trademark
where dt='2020-06-15'
),
attr as
(
select
sku_id,
collect_set(named_struct('attr_id',attr_id,'value_id',value_id,'attr_name',attr_name,'value_name',value_name)) attrs
from ods_sku_attr_value
where dt='2020-06-15'
group by sku_id
),
sale_attr as
(
select
sku_id,
collect_set(named_struct('sale_attr_id',sale_attr_id,'sale_attr_value_id',sale_attr_value_id,'sale_attr_name',sale_attr_name,'sale_attr_value_name',sale_attr_value_name)) sale_attrs
from ods_sku_sale_attr_value
where dt='2020-06-15'
group by sku_id
)
insert overwrite table dim_sku_info partition(dt='2020-06-15')
select
sku.id,
sku.price,
sku.sku_name,
sku.sku_desc,
sku.weight,
sku.is_sale,
sku.spu_id,
spu.spu_name,
sku.category3_id,
c3.name,
c3.category2_id,
c2.name,
c2.category1_id,
c1.name,
sku.tm_id,
tm.tm_name,
attr.attrs,
sale_attr.sale_attrs,
sku.create_time
from sku
left join spu on sku.spu_id=spu.id
left join c3 on sku.category3_id=c3.id
left join c2 on c3.category2_id=c2.id
left join c1 on c2.category1_id=c1.id
left join tm on sku.tm_id=tm.id
left join attr on sku.id=attr.sku_id
left join sale_attr on sku.id=sale_attr.sku_id;
2、优惠券维度表（全量）
1.建表语句
DROP TABLE IF EXISTS dim_coupon_info;
CREATE EXTERNAL TABLE dim_coupon_info(
`id` STRING COMMENT '购物券编号',
`coupon_name` STRING COMMENT '购物券名称',
`coupon_type` STRING COMMENT '购物券类型 1 现金券 2 折扣券 3 满减券 4 满件打折券',
`condition_amount` DECIMAL(16,2) COMMENT '满额数',
`condition_num` BIGINT COMMENT '满件数',
`activity_id` STRING COMMENT '活动编号',
`benefit_amount` DECIMAL(16,2) COMMENT '减金额',
`benefit_discount` DECIMAL(16,2) COMMENT '折扣',
`create_time` STRING COMMENT '创建时间',
`range_type` STRING COMMENT '范围类型 1、商品 2、品类 3、品牌',
`limit_num` BIGINT COMMENT '最多领取次数',
`taken_count` BIGINT COMMENT '已领取次数',
`start_time` STRING COMMENT '可以领取的开始日期',
`end_time` STRING COMMENT '可以领取的结束日期',
`operate_time` STRING COMMENT '修改时间',
`expire_time` STRING COMMENT '过期时间'
) COMMENT '优惠券维度表'
PARTITIONED BY (`dt` STRING)
STORED AS PARQUET
LOCATION '/warehouse/gmall/dim/dim_coupon_info/'
TBLPROPERTIES ("parquet.compression"="lzo");
2.分区规划
3.数据装载
1）首日装载
insert overwrite table dim_coupon_info partition(dt='2020-06-14')
select
id,
coupon_name,
coupon_type,
condition_amount,
condition_num,
activity_id,
benefit_amount,
benefit_discount,
create_time,
range_type,
limit_num,
taken_count,
start_time,
end_time,
operate_time,
expire_time
from ods_coupon_info
where dt='2020-06-14';
2）每日装载
insert overwrite table dim_coupon_info partition(dt='2020-06-15')
select
id,
coupon_name,
coupon_type,
condition_amount,
condition_num,
activity_id,
benefit_amount,
benefit_discount,
create_time,
range_type,
limit_num,
taken_count,
start_time,
end_time,
operate_time,
expire_time
from ods_coupon_info
where dt='2020-06-15';
3、活动维度表（全量）
1.建表语句
DROP TABLE IF EXISTS dim_activity_rule_info;
CREATE EXTERNAL TABLE dim_activity_rule_info(
`activity_rule_id` STRING COMMENT '活动规则ID',
`activity_id` STRING COMMENT '活动ID',
`activity_name` STRING COMMENT '活动名称',
`activity_type` STRING COMMENT '活动类型',
`start_time` STRING COMMENT '开始时间',
`end_time` STRING COMMENT '结束时间',
`create_time` STRING COMMENT '创建时间',
`condition_amount` DECIMAL(16,2) COMMENT '满减金额',
`condition_num` BIGINT COMMENT '满减件数',
`benefit_amount` DECIMAL(16,2) COMMENT '优惠金额',
`benefit_discount` DECIMAL(16,2) COMMENT '优惠折扣',
`benefit_level` STRING COMMENT '优惠级别'
) COMMENT '活动信息表'
PARTITIONED BY (`dt` STRING)
STORED AS PARQUET
LOCATION '/warehouse/gmall/dim/dim_activity_rule_info/'
TBLPROPERTIES ("parquet.compression"="lzo");
2.分区规划
3.数据装载
1）首日装载
insert overwrite table dim_activity_rule_info partition(dt='2020-06-14')
select
ar.id,
ar.activity_id,
ai.activity_name,
ar.activity_type,
ai.start_time,
ai.end_time,
ai.create_time,
ar.condition_amount,
ar.condition_num,
ar.benefit_amount,
ar.benefit_discount,
ar.benefit_level
from
(
select
id,
activity_id,
activity_type,
condition_amount,
condition_num,
benefit_amount,
benefit_discount,
benefit_level
from ods_activity_rule
where dt='2020-06-14'
)ar
left join
(
select
id,
activity_name,
start_time,
end_time,
create_time
from ods_activity_info
where dt='2020-06-14'
)ai
on ar.activity_id=ai.id;
2）每日装载
insert overwrite table dim_activity_rule_info partition(dt='2020-06-15')
select
ar.id,
ar.activity_id,
ai.activity_name,
ar.activity_type,
ai.start_time,
ai.end_time,
ai.create_time,
ar.condition_amount,
ar.condition_num,
ar.benefit_amount,
ar.benefit_discount,
ar.benefit_level
from
(
select
id,
activity_id,
activity_type,
condition_amount,
condition_num,
benefit_amount,
benefit_discount,
benefit_level
from ods_activity_rule
where dt='2020-06-15'
)ar
left join
(
select
id,
activity_name,
start_time,
end_time,
create_time
from ods_activity_info
where dt='2020-06-15'
)ai
on ar.activity_id=ai.id;
4、地区维度表（特殊）
1.建表语句
DROP TABLE IF EXISTS dim_base_province;
CREATE EXTERNAL TABLE dim_base_province (
`id` STRING COMMENT 'id',
`province_name` STRING COMMENT '省市名称',
`area_code` STRING COMMENT '地区编码',
`iso_code` STRING COMMENT 'ISO-3166编码，供可视化使用',
`iso_3166_2` STRING COMMENT 'IOS-3166-2编码，供可视化使用',
`region_id` STRING COMMENT '地区id',
`region_name` STRING COMMENT '地区名称'
) COMMENT '地区维度表'
STORED AS PARQUET
LOCATION '/warehouse/gmall/dim/dim_base_province/'
TBLPROPERTIES ("parquet.compression"="lzo");
2.数据装载
- 地区维度表数据相对稳定，变化概率较低，故无需每日装载。
insert overwrite table dim_base_province
select
bp.id,
bp.name,
bp.area_code,
bp.iso_code,
bp.iso_3166_2,
bp.region_id,
br.region_name
from ods_base_province bp
join ods_base_region br on bp.region_id = br.id;
5、时间维度表（特殊）
1.建表语句
DROP TABLE IF EXISTS dim_date_info;
CREATE EXTERNAL TABLE dim_date_info(
`date_id` STRING COMMENT '日',
`week_id` STRING COMMENT '周ID',
`week_day` STRING COMMENT '周几',
`day` STRING COMMENT '每月的第几天',
`month` STRING COMMENT '第几月',
`quarter` STRING COMMENT '第几季度',
`year` STRING COMMENT '年',
`is_workday` STRING COMMENT '是否是工作日',
`holiday_id` STRING COMMENT '节假日'
) COMMENT '时间维度表'
STORED AS PARQUET
LOCATION '/warehouse/gmall/dim/dim_date_info/'
TBLPROPERTIES ("parquet.compression"="lzo");
2.数据装载
- 通常情况下，时间维度表的数据并不是来自于业务系统，而是手动写入，并且由于时间维度表数据的可预见性，无须每日导入，一般可一次性导入一年的数据。
1）创建临时表
DROP TABLE IF EXISTS tmp_dim_date_info;
CREATE EXTERNAL TABLE tmp_dim_date_info (
`date_id` STRING COMMENT '日',
`week_id` STRING COMMENT '周ID',
`week_day` STRING COMMENT '周几',
`day` STRING COMMENT '每月的第几天',
`month` STRING COMMENT '第几月',
`quarter` STRING COMMENT '第几季度',
`year` STRING COMMENT '年',
`is_workday` STRING COMMENT '是否是工作日',
`holiday_id` STRING COMMENT '节假日'
) COMMENT '时间维度表'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LOCATION '/warehouse/gmall/tmp/tmp_dim_date_info/';
2）将数据文件上传到HFDS上临时表指定路径/warehouse/gmall/tmp/tmp_dim_date_info/
2020-01-01 1 3 1 1 1 2020 0 元旦
2020-01-02 1 4 2 1 1 2020 1 \N
2020-01-03 1 5 3 1 1 2020 1 \N
2020-01-04 1 6 4 1 1 2020 0 \N
2020-01-05 1 7 5 1 1 2020 0 \N
2020-01-06 2 1 6 1 1 2020 1 \N
2020-01-07 2 2 7 1 1 2020 1 \N
2020-01-08 2 3 8 1 1 2020 1 \N
2020-01-09 2 4 9 1 1 2020 1 \N
2020-01-10 2 5 10 1 1 2020 1 \N
2020-01-11 2 6 11 1 1 2020 0 \N
2020-01-12 2 7 12 1 1 2020 0 \N
2020-01-13 3 1 13 1 1 2020 1 \N
2020-01-14 3 2 14 1 1 2020 1 \N
2020-01-15 3 3 15 1 1 2020 1 \N
2020-01-16 3 4 16 1 1 2020 1 \N
2020-01-17 3 5 17 1 1 2020 1 \N
2020-01-18 3 6 18 1 1 2020 0 \N
2020-01-19 3 7 19 1 1 2020 1 \N
2020-01-20 4 1 20 1 1 2020 1 \N
2020-01-21 4 2 21 1 1 2020 1 \N
2020-01-22 4 3 22 1 1 2020 1 \N
2020-01-23 4 4 23 1 1 2020 1 \N
2020-01-24 4 5 24 1 1 2020 0 春节
2020-01-25 4 6 25 1 1 2020 0 春节
2020-01-26 4 7 26 1 1 2020 0 春节
2020-01-27 5 1 27 1 1 2020 0 春节
2020-01-28 5 2 28 1 1 2020 0 春节
2020-01-29 5 3 29 1 1 2020 0 春节
2020-01-30 5 4 30 1 1 2020 0 春节
2020-01-31 5 5 31 1 1 2020 0 春节
2020-02-01 5 6 1 2 1 2020 0 春节
2020-02-02 5 7 2 2 1 2020 0 春节
2020-02-03 6 1 3 2 1 2020 1 \N
2020-02-04 6 2 4 2 1 2020 1 \N
2020-02-05 6 3 5 2 1 2020 1 \N
2020-02-06 6 4 6 2 1 2020 1 \N
2020-02-07 6 5 7 2 1 2020 1 \N
2020-02-08 6 6 8 2 1 2020 0 \N
2020-02-09 6 7 9 2 1 2020 0 \N
2020-02-10 7 1 10 2 1 2020 1 \N
2020-02-11 7 2 11 2 1 2020 1 \N
2020-02-12 7 3 12 2 1 2020 1 \N
2020-02-13 7 4 13 2 1 2020 1 \N
2020-02-14 7 5 14 2 1 2020 1 \N
2020-02-15 7 6 15 2 1 2020 0 \N
2020-02-16 7 7 16 2 1 2020 0 \N
2020-02-17 8 1 17 2 1 2020 1 \N
2020-02-18 8 2 18 2 1 2020 1 \N
2020-02-19 8 3 19 2 1 2020 1 \N
2020-02-20 8 4 20 2 1 2020 1 \N
2020-02-21 8 5 21 2 1 2020 1 \N
2020-02-22 8 6 22 2 1 2020 0 \N
2020-02-23 8 7 23 2 1 2020 0 \N
2020-02-24 9 1 24 2 1 2020 1 \N
2020-02-25 9 2 25 2 1 2020 1 \N
2020-02-26 9 3 26 2 1 2020 1 \N
2020-02-27 9 4 27 2 1 2020 1 \N
2020-02-28 9 5 28 2 1 2020 1 \N
2020-02-29 9 6 29 2 1 2020 0 \N
2020-03-01 9 7 1 3 1 2020 0 \N
2020-03-02 10 1 2 3 1 2020 1 \N
2020-03-03 10 2 3 3 1 2020 1 \N
2020-03-04 10 3 4 3 1 2020 1 \N
2020-03-05 10 4 5 3 1 2020 1 \N
2020-03-06 10 5 6 3 1 2020 1 \N
2020-03-07 10 6 7 3 1 2020 0 \N
2020-03-08 10 7 8 3 1 2020 0 \N
2020-03-09 11 1 9 3 1 2020 1 \N
2020-03-10 11 2 10 3 1 2020 1 \N
2020-03-11 11 3 11 3 1 2020 1 \N
2020-03-12 11 4 12 3 1 2020 1 \N
2020-03-13 11 5 13 3 1 2020 1 \N
2020-03-14 11 6 14 3 1 2020 0 \N
2020-03-15 11 7 15 3 1 2020 0 \N
2020-03-16 12 1 16 3 1 2020 1 \N
2020-03-17 12 2 17 3 1 2020 1 \N
2020-03-18 12 3 18 3 1 2020 1 \N
2020-03-19 12 4 19 3 1 2020 1 \N
2020-03-20 12 5 20 3 1 2020 1 \N
2020-03-21 12 6 21 3 1 2020 0 \N
2020-03-22 12 7 22 3 1 2020 0 \N
2020-03-23 13 1 23 3 1 2020 1 \N
2020-03-24 13 2 24 3 1 2020 1 \N
2020-03-25 13 3 25 3 1 2020 1 \N
2020-03-26 13 4 26 3 1 2020 1 \N
2020-03-27 13 5 27 3 1 2020 1 \N
2020-03-28 13 6 28 3 1 2020 0 \N
2020-03-29 13 7 29 3 1 2020 0 \N
2020-03-30 14 1 30 3 1 2020 1 \N
2020-03-31 14 2 31 3 1 2020 1 \N
2020-04-01 14 3 1 4 2 2020 1 \N
2020-04-02 14 4 2 4 2 2020 1 \N
2020-04-03 14 5 3 4 2 2020 1 \N
2020-04-04 14 6 4 4 2 2020 0 清明节
2020-04-05 14 7 5 4 2 2020 0 清明节
2020-04-06 15 1 6 4 2 2020 0 清明节
2020-04-07 15 2 7 4 2 2020 1 \N
2020-04-08 15 3 8 4 2 2020 1 \N
2020-04-09 15 4 9 4 2 2020 1 \N
2020-04-10 15 5 10 4 2 2020 1 \N
2020-04-11 15 6 11 4 2 2020 0 \N
2020-04-12 15 7 12 4 2 2020 0 \N
2020-04-13 16 1 13 4 2 2020 1 \N
2020-04-14 16 2 14 4 2 2020 1 \N
2020-04-15 16 3 15 4 2 2020 1 \N
2020-04-16 16 4 16 4 2 2020 1 \N
2020-04-17 16 5 17 4 2 2020 1 \N
2020-04-18 16 6 18 4 2 2020 0 \N
2020-04-19 16 7 19 4 2 2020 0 \N
2020-04-20 17 1 20 4 2 2020 1 \N
2020-04-21 17 2 21 4 2 2020 1 \N
2020-04-22 17 3 22 4 2 2020 1 \N
2020-04-23 17 4 23 4 2 2020 1 \N
2020-04-24 17 5 24 4 2 2020 1 \N
2020-04-25 17 6 25 4 2 2020 0 \N
2020-04-26 17 7 26 4 2 2020 1 \N
2020-04-27 18 1 27 4 2 2020 1 \N
2020-04-28 18 2 28 4 2 2020 1 \N
2020-04-29 18 3 29 4 2 2020 1 \N
2020-04-30 18 4 30 4 2 2020 1 \N
2020-05-01 18 5 1 5 2 2020 0 劳动节
2020-05-02 18 6 2 5 2 2020 0 劳动节
2020-05-03 18 7 3 5 2 2020 0 劳动节
2020-05-04 19 1 4 5 2 2020 0 劳动节
2020-05-05 19 2 5 5 2 2020 0 劳动节
2020-05-06 19 3 6 5 2 2020 1 \N
2020-05-07 19 4 7 5 2 2020 1 \N
2020-05-08 19 5 8 5 2 2020 1 \N
2020-05-09 19 6 9 5 2 2020 1 \N
2020-05-10 19 7 10 5 2 2020 0 \N
2020-05-11 20 1 11 5 2 2020 1 \N
2020-05-12 20 2 12 5 2 2020 1 \N
2020-05-13 20 3 13 5 2 2020 1 \N
2020-05-14 20 4 14 5 2 2020 1 \N
2020-05-15 20 5 15 5 2 2020 1 \N
2020-05-16 20 6 16 5 2 2020 0 \N
2020-05-17 20 7 17 5 2 2020 0 \N
2020-05-18 21 1 18 5 2 2020 1 \N
2020-05-19 21 2 19 5 2 2020 1 \N
2020-05-20 21 3 20 5 2 2020 1 \N
2020-05-21 21 4 21 5 2 2020 1 \N
2020-05-22 21 5 22 5 2 2020 1 \N
2020-05-23 21 6 23 5 2 2020 0 \N
2020-05-24 21 7 24 5 2 2020 0 \N
2020-05-25 22 1 25 5 2 2020 1 \N
2020-05-26 22 2 26 5 2 2020 1 \N
2020-05-27 22 3 27 5 2 2020 1 \N
2020-05-28 22 4 28 5 2 2020 1 \N
2020-05-29 22 5 29 5 2 2020 1 \N
2020-05-30 22 6 30 5 2 2020 0 \N
2020-05-31 22 7 31 5 2 2020 0 \N
2020-06-01 23 1 1 6 2 2020 1 \N
2020-06-02 23 2 2 6 2 2020 1 \N
2020-06-03 23 3 3 6 2 2020 1 \N
2020-06-04 23 4 4 6 2 2020 1 \N
2020-06-05 23 5 5 6 2 2020 1 \N
2020-06-06 23 6 6 6 2 2020 0 \N
2020-06-07 23 7 7 6 2 2020 0 \N
2020-06-08 24 1 8 6 2 2020 1 \N
2020-06-09 24 2 9 6 2 2020 1 \N
2020-06-10 24 3 10 6 2 2020 1 \N
2020-06-11 24 4 11 6 2 2020 1 \N
2020-06-12 24 5 12 6 2 2020 1 \N
2020-06-13 24 6 13 6 2 2020 0 \N
2020-06-14 24 7 14 6 2 2020 0 \N
2020-06-15 25 1 15 6 2 2020 1 \N
2020-06-16 25 2 16 6 2 2020 1 \N
2020-06-17 25 3 17 6 2 2020 1 \N
2020-06-18 25 4 18 6 2 2020 1 \N
2020-06-19 25 5 19 6 2 2020 1 \N
2020-06-20 25 6 20 6 2 2020 0 \N
2020-06-21 25 7 21 6 2 2020 0 \N
2020-06-22 26 1 22 6 2 2020 1 \N
2020-06-23 26 2 23 6 2 2020 1 \N
2020-06-24 26 3 24 6 2 2020 1 \N
2020-06-25 26 4 25 6 2 2020 0 端午节
2020-06-26 26 5 26 6 2 2020 0 端午节
2020-06-27 26 6 27 6 2 2020 0 端午节
2020-06-28 26 7 28 6 2 2020 1 \N
2020-06-29 27 1 29 6 2 2020 1 \N
2020-06-30 27 2 30 6 2 2020 1 \N
2020-07-01 27 3 1 7 3 2020 1 \N
2020-07-02 27 4 2 7 3 2020 1 \N
2020-07-03 27 5 3 7 3 2020 1 \N
2020-07-04 27 6 4 7 3 2020 0 \N
2020-07-05 27 7 5 7 3 2020 0 \N
2020-07-06 28 1 6 7 3 2020 1 \N
2020-07-07 28 2 7 7 3 2020 1 \N
2020-07-08 28 3 8 7 3 2020 1 \N
2020-07-09 28 4 9 7 3 2020 1 \N
2020-07-10 28 5 10 7 3 2020 1 \N
2020-07-11 28 6 11 7 3 2020 0 \N
2020-07-12 28 7 12 7 3 2020 0 \N
2020-07-13 29 1 13 7 3 2020 1 \N
2020-07-14 29 2 14 7 3 2020 1 \N
2020-07-15 29 3 15 7 3 2020 1 \N
2020-07-16 29 4 16 7 3 2020 1 \N
2020-07-17 29 5 17 7 3 2020 1 \N
2020-07-18 29 6 18 7 3 2020 0 \N
2020-07-19 29 7 19 7 3 2020 0 \N
2020-07-20 30 1 20 7 3 2020 1 \N
2020-07-21 30 2 21 7 3 2020 1 \N
2020-07-22 30 3 22 7 3 2020 1 \N
2020-07-23 30 4 23 7 3 2020 1 \N
2020-07-24 30 5 24 7 3 2020 1 \N
2020-07-25 30 6 25 7 3 2020 0 \N
2020-07-26 30 7 26 7 3 2020 0 \N
2020-07-27 31 1 27 7 3 2020 1 \N
2020-07-28 31 2 28 7 3 2020 1 \N
2020-07-29 31 3 29 7 3 2020 1 \N
2020-07-30 31 4 30 7 3 2020 1 \N
2020-07-31 31 5 31 7 3 2020 1 \N
2020-08-01 31 6 1 8 3 2020 0 \N
2020-08-02 31 7 2 8 3 2020 0 \N
2020-08-03 32 1 3 8 3 2020 1 \N
2020-08-04 32 2 4 8 3 2020 1 \N
2020-08-05 32 3 5 8 3 2020 1 \N
2020-08-06 32 4 6 8 3 2020 1 \N
2020-08-07 32 5 7 8 3 2020 1 \N
2020-08-08 32 6 8 8 3 2020 0 \N
2020-08-09 32 7 9 8 3 2020 0 \N
2020-08-10 33 1 10 8 3 2020 1 \N
2020-08-11 33 2 11 8 3 2020 1 \N
2020-08-12 33 3 12 8 3 2020 1 \N
2020-08-13 33 4 13 8 3 2020 1 \N
2020-08-14 33 5 14 8 3 2020 1 \N
2020-08-15 33 6 15 8 3 2020 0 \N
2020-08-16 33 7 16 8 3 2020 0 \N
2020-08-17 34 1 17 8 3 2020 1 \N
2020-08-18 34 2 18 8 3 2020 1 \N
2020-08-19 34 3 19 8 3 2020 1 \N
2020-08-20 34 4 20 8 3 2020 1 \N
2020-08-21 34 5 21 8 3 2020 1 \N
2020-08-22 34 6 22 8 3 2020 0 \N
2020-08-23 34 7 23 8 3 2020 0 \N
2020-08-24 35 1 24 8 3 2020 1 \N
2020-08-25 35 2 25 8 3 2020 1 \N
2020-08-26 35 3 26 8 3 2020 1 \N
2020-08-27 35 4 27 8 3 2020 1 \N
2020-08-28 35 5 28 8 3 2020 1 \N
2020-08-29 35 6 29 8 3 2020 0 \N
2020-08-30 35 7 30 8 3 2020 0 \N
2020-08-31 36 1 31 8 3 2020 1 \N
2020-09-01 36 2 1 9 3 2020 1 \N
2020-09-02 36 3 2 9 3 2020 1 \N
2020-09-03 36 4 3 9 3 2020 1 \N
2020-09-04 36 5 4 9 3 2020 1 \N
2020-09-05 36 6 5 9 3 2020 0 \N
2020-09-06 36 7 6 9 3 2020 0 \N
2020-09-07 37 1 7 9 3 2020 1 \N
2020-09-08 37 2 8 9 3 2020 1 \N
2020-09-09 37 3 9 9 3 2020 1 \N
2020-09-10 37 4 10 9 3 2020 1 \N
2020-09-11 37 5 11 9 3 2020 1 \N
2020-09-12 37 6 12 9 3 2020 0 \N
2020-09-13 37 7 13 9 3 2020 0 \N
2020-09-14 38 1 14 9 3 2020 1 \N
2020-09-15 38 2 15 9 3 2020 1 \N
2020-09-16 38 3 16 9 3 2020 1 \N
2020-09-17 38 4 17 9 3 2020 1 \N
2020-09-18 38 5 18 9 3 2020 1 \N
2020-09-19 38 6 19 9 3 2020 0 \N
2020-09-20 38 7 20 9 3 2020 0 \N
2020-09-21 39 1 21 9 3 2020 1 \N
2020-09-22 39 2 22 9 3 2020 1 \N
2020-09-23 39 3 23 9 3 2020 1 \N
2020-09-24 39 4 24 9 3 2020 1 \N
2020-09-25 39 5 25 9 3 2020 1 \N
2020-09-26 39 6 26 9 3 2020 0 \N
2020-09-27 39 7 27 9 3 2020 1 \N
2020-09-28 40 1 28 9 3 2020 1 \N
2020-09-29 40 2 29 9 3 2020 1 \N
2020-09-30 40 3 30 9 3 2020 1 \N
2020-10-01 40 4 1 10 4 2020 0 国庆节、中秋节
2020-10-02 40 5 2 10 4 2020 0 国庆节、中秋节
2020-10-03 40 6 3 10 4 2020 0 国庆节、中秋节
2020-10-04 40 7 4 10 4 2020 0 国庆节、中秋节
2020-10-05 41 1 5 10 4 2020 0 国庆节、中秋节
2020-10-06 41 2 6 10 4 2020 0 国庆节、中秋节
2020-10-07 41 3 7 10 4 2020 0 国庆节、中秋节
2020-10-08 41 4 8 10 4 2020 0 国庆节、中秋节
2020-10-09 41 5 9 10 4 2020 1 \N
2020-10-10 41 6 10 10 4 2020 1 \N
2020-10-11 41 7 11 10 4 2020 0 \N
2020-10-12 42 1 12 10 4 2020 1 \N
2020-10-13 42 2 13 10 4 2020 1 \N
2020-10-14 42 3 14 10 4 2020 1 \N
2020-10-15 42 4 15 10 4 2020 1 \N
2020-10-16 42 5 16 10 4 2020 1 \N
2020-10-17 42 6 17 10 4 2020 0 \N
2020-10-18 42 7 18 10 4 2020 0 \N
2020-10-19 43 1 19 10 4 2020 1 \N
2020-10-20 43 2 20 10 4 2020 1 \N
2020-10-21 43 3 21 10 4 2020 1 \N
2020-10-22 43 4 22 10 4 2020 1 \N
2020-10-23 43 5 23 10 4 2020 1 \N
2020-10-24 43 6 24 10 4 2020 0 \N
2020-10-25 43 7 25 10 4 2020 0 \N
2020-10-26 44 1 26 10 4 2020 1 \N
2020-10-27 44 2 27 10 4 2020 1 \N
2020-10-28 44 3 28 10 4 2020 1 \N
2020-10-29 44 4 29 10 4 2020 1 \N
2020-10-30 44 5 30 10 4 2020 1 \N
2020-10-31 44 6 31 10 4 2020 0 \N
2020-11-01 44 7 1 11 4 2020 0 \N
2020-11-02 45 1 2 11 4 2020 1 \N
2020-11-03 45 2 3 11 4 2020 1 \N
2020-11-04 45 3 4 11 4 2020 1 \N
2020-11-05 45 4 5 11 4 2020 1 \N
2020-11-06 45 5 6 11 4 2020 1 \N
2020-11-07 45 6 7 11 4 2020 0 \N
2020-11-08 45 7 8 11 4 2020 0 \N
2020-11-09 46 1 9 11 4 2020 1 \N
2020-11-10 46 2 10 11 4 2020 1 \N
2020-11-11 46 3 11 11 4 2020 1 \N
2020-11-12 46 4 12 11 4 2020 1 \N
2020-11-13 46 5 13 11 4 2020 1 \N
2020-11-14 46 6 14 11 4 2020 0 \N
2020-11-15 46 7 15 11 4 2020 0 \N
2020-11-16 47 1 16 11 4 2020 1 \N
2020-11-17 47 2 17 11 4 2020 1 \N
2020-11-18 47 3 18 11 4 2020 1 \N
2020-11-19 47 4 19 11 4 2020 1 \N
2020-11-20 47 5 20 11 4 2020 1 \N
2020-11-21 47 6 21 11 4 2020 0 \N
2020-11-22 47 7 22 11 4 2020 0 \N
2020-11-23 48 1 23 11 4 2020 1 \N
2020-11-24 48 2 24 11 4 2020 1 \N
2020-11-25 48 3 25 11 4 2020 1 \N
2020-11-26 48 4 26 11 4 2020 1 \N
2020-11-27 48 5 27 11 4 2020 1 \N
2020-11-28 48 6 28 11 4 2020 0 \N
2020-11-29 48 7 29 11 4 2020 0 \N
2020-11-30 49 1 30 11 4 2020 1 \N
2020-12-01 49 2 1 12 4 2020 1 \N
2020-12-02 49 3 2 12 4 2020 1 \N
2020-12-03 49 4 3 12 4 2020 1 \N
2020-12-04 49 5 4 12 4 2020 1 \N
2020-12-05 49 6 5 12 4 2020 0 \N
2020-12-06 49 7 6 12 4 2020 0 \N
2020-12-07 50 1 7 12 4 2020 1 \N
2020-12-08 50 2 8 12 4 2020 1 \N
2020-12-09 50 3 9 12 4 2020 1 \N
2020-12-10 50 4 10 12 4 2020 1 \N
2020-12-11 50 5 11 12 4 2020 1 \N
2020-12-12 50 6 12 12 4 2020 0 \N
2020-12-13 50 7 13 12 4 2020 0 \N
2020-12-14 51 1 14 12 4 2020 1 \N
2020-12-15 51 2 15 12 4 2020 1 \N
2020-12-16 51 3 16 12 4 2020 1 \N
2020-12-17 51 4 17 12 4 2020 1 \N
2020-12-18 51 5 18 12 4 2020 1 \N
2020-12-19 51 6 19 12 4 2020 0 \N
2020-12-20 51 7 20 12 4 2020 0 \N
2020-12-21 52 1 21 12 4 2020 1 \N
2020-12-22 52 2 22 12 4 2020 1 \N
2020-12-23 52 3 23 12 4 2020 1 \N
2020-12-24 52 4 24 12 4 2020 1 \N
2020-12-25 52 5 25 12 4 2020 1 \N
2020-12-26 52 6 26 12 4 2020 0 \N
2020-12-27 52 7 27 12 4 2020 0 \N
2020-12-28 53 1 28 12 4 2020 1 \N
2020-12-29 53 2 29 12 4 2020 1 \N
2020-12-30 53 3 30 12 4 2020 1 \N
2020-12-31 53 4 31 12 4 2020 1 \N
2021-01-01 1 5 1 1 1 2021 0 元旦
2021-01-02 1 6 2 1 1 2021 0 元旦
2021-01-03 1 7 3 1 1 2021 0 元旦
2021-01-04 2 1 4 1 1 2021 1 \N
2021-01-05 2 2 5 1 1 2021 1 \N
2021-01-06 2 3 6 1 1 2021 1 \N
2021-01-07 2 4 7 1 1 2021 1 \N
2021-01-08 2 5 8 1 1 2021 1 \N
2021-01-09 2 6 9 1 1 2021 0 \N
2021-01-10 2 7 10 1 1 2021 0 \N
2021-01-11 3 1 11 1 1 2021 1 \N
2021-01-12 3 2 12 1 1 2021 1 \N
2021-01-13 3 3 13 1 1 2021 1 \N
2021-01-14 3 4 14 1 1 2021 1 \N
2021-01-15 3 5 15 1 1 2021 1 \N
2021-01-16 3 6 16 1 1 2021 0 \N
2021-01-17 3 7 17 1 1 2021 0 \N
2021-01-18 4 1 18 1 1 2021 1 \N
2021-01-19 4 2 19 1 1 2021 1 \N
2021-01-20 4 3 20 1 1 2021 1 \N
2021-01-21 4 4 21 1 1 2021 1 \N
2021-01-22 4 5 22 1 1 2021 1 \N
2021-01-23 4 6 23 1 1 2021 0 \N
2021-01-24 4 7 24 1 1 2021 0 \N
2021-01-25 5 1 25 1 1 2021 1 \N
2021-01-26 5 2 26 1 1 2021 1 \N
2021-01-27 5 3 27 1 1 2021 1 \N
2021-01-28 5 4 28 1 1 2021 1 \N
2021-01-29 5 5 29 1 1 2021 1 \N
2021-01-30 5 6 30 1 1 2021 0 \N
2021-01-31 5 7 31 1 1 2021 0 \N
2021-02-01 6 1 1 2 1 2021 1 \N
2021-02-02 6 2 2 2 1 2021 1 \N
2021-02-03 6 3 3 2 1 2021 1 \N
2021-02-04 6 4 4 2 1 2021 1 \N
2021-02-05 6 5 5 2 1 2021 1 \N
2021-02-06 6 6 6 2 1 2021 0 \N
2021-02-07 6 7 7 2 1 2021 1 \N
2021-02-08 7 1 8 2 1 2021 1 \N
2021-02-09 7 2 9 2 1 2021 1 \N
2021-02-10 7 3 10 2 1 2021 1 \N
2021-02-11 7 4 11 2 1 2021 0 春节
2021-02-12 7 5 12 2 1 2021 0 春节
2021-02-13 7 6 13 2 1 2021 0 春节
2021-02-14 7 7 14 2 1 2021 0 春节
2021-02-15 8 1 15 2 1 2021 0 春节
2021-02-16 8 2 16 2 1 2021 0 春节
2021-02-17 8 3 17 2 1 2021 0 春节
2021-02-18 8 4 18 2 1 2021 1 \N
2021-02-19 8 5 19 2 1 2021 1 \N
2021-02-20 8 6 20 2 1 2021 1 \N
2021-02-21 8 7 21 2 1 2021 0 \N
2021-02-22 9 1 22 2 1 2021 1 \N
2021-02-23 9 2 23 2 1 2021 1 \N
2021-02-24 9 3 24 2 1 2021 1 \N
2021-02-25 9 4 25 2 1 2021 1 \N
2021-02-26 9 5 26 2 1 2021 1 \N
2021-02-27 9 6 27 2 1 2021 0 \N
2021-02-28 9 7 28 2 1 2021 0 \N
2021-03-01 10 1 1 3 1 2021 1 \N
2021-03-02 10 2 2 3 1 2021 1 \N
2021-03-03 10 3 3 3 1 2021 1 \N
2021-03-04 10 4 4 3 1 2021 1 \N
2021-03-05 10 5 5 3 1 2021 1 \N
2021-03-06 10 6 6 3 1 2021 0 \N
2021-03-07 10 7 7 3 1 2021 0 \N
2021-03-08 11 1 8 3 1 2021 1 \N
2021-03-09 11 2 9 3 1 2021 1 \N
2021-03-10 11 3 10 3 1 2021 1 \N
2021-03-11 11 4 11 3 1 2021 1 \N
2021-03-12 11 5 12 3 1 2021 1 \N
2021-03-13 11 6 13 3 1 2021 0 \N
2021-03-14 11 7 14 3 1 2021 0 \N
2021-03-15 12 1 15 3 1 2021 1 \N
2021-03-16 12 2 16 3 1 2021 1 \N
2021-03-17 12 3 17 3 1 2021 1 \N
2021-03-18 12 4 18 3 1 2021 1 \N
2021-03-19 12 5 19 3 1 2021 1 \N
2021-03-20 12 6 20 3 1 2021 0 \N
2021-03-21 12 7 21 3 1 2021 0 \N
2021-03-22 13 1 22 3 1 2021 1 \N
2021-03-23 13 2 23 3 1 2021 1 \N
2021-03-24 13 3 24 3 1 2021 1 \N
2021-03-25 13 4 25 3 1 2021 1 \N
2021-03-26 13 5 26 3 1 2021 1 \N
2021-03-27 13 6 27 3 1 2021 0 \N
2021-03-28 13 7 28 3 1 2021 0 \N
2021-03-29 14 1 29 3 1 2021 1 \N
2021-03-30 14 2 30 3 1 2021 1 \N
2021-03-31 14 3 31 3 1 2021 1 \N
2021-04-01 14 4 1 4 2 2021 1 \N
2021-04-02 14 5 2 4 2 2021 1 \N
2021-04-03 14 6 3 4 2 2021 0 清明节
2021-04-04 14 7 4 4 2 2021 0 清明节
2021-04-05 15 1 5 4 2 2021 0 清明节
2021-04-06 15 2 6 4 2 2021 1 \N
2021-04-07 15 3 7 4 2 2021 1 \N
2021-04-08 15 4 8 4 2 2021 1 \N
2021-04-09 15 5 9 4 2 2021 1 \N
2021-04-10 15 6 10 4 2 2021 0 \N
2021-04-11 15 7 11 4 2 2021 0 \N
2021-04-12 16 1 12 4 2 2021 1 \N
2021-04-13 16 2 13 4 2 2021 1 \N
2021-04-14 16 3 14 4 2 2021 1 \N
2021-04-15 16 4 15 4 2 2021 1 \N
2021-04-16 16 5 16 4 2 2021 1 \N
2021-04-17 16 6 17 4 2 2021 0 \N
2021-04-18 16 7 18 4 2 2021 0 \N
2021-04-19 17 1 19 4 2 2021 1 \N
2021-04-20 17 2 20 4 2 2021 1 \N
2021-04-21 17 3 21 4 2 2021 1 \N
2021-04-22 17 4 22 4 2 2021 1 \N
2021-04-23 17 5 23 4 2 2021 1 \N
2021-04-24 17 6 24 4 2 2021 0 \N
2021-04-25 17 7 25 4 2 2021 1 \N
2021-04-26 18 1 26 4 2 2021 1 \N
2021-04-27 18 2 27 4 2 2021 1 \N
2021-04-28 18 3 28 4 2 2021 1 \N
2021-04-29 18 4 29 4 2 2021 1 \N
2021-04-30 18 5 30 4 2 2021 1 \N
2021-05-01 18 6 1 5 2 2021 0 劳动节
2021-05-02 18 7 2 5 2 2021 0 劳动节
2021-05-03 19 1 3 5 2 2021 0 劳动节
2021-05-04 19 2 4 5 2 2021 0 劳动节
2021-05-05 19 3 5 5 2 2021 0 劳动节
2021-05-06 19 4 6 5 2 2021 1 \N
2021-05-07 19 5 7 5 2 2021 1 \N
2021-05-08 19 6 8 5 2 2021 1 \N
2021-05-09 19 7 9 5 2 2021 0 \N
2021-05-10 20 1 10 5 2 2021 1 \N
2021-05-11 20 2 11 5 2 2021 1 \N
2021-05-12 20 3 12 5 2 2021 1 \N
2021-05-13 20 4 13 5 2 2021 1 \N
2021-05-14 20 5 14 5 2 2021 1 \N
2021-05-15 20 6 15 5 2 2021 0 \N
2021-05-16 20 7 16 5 2 2021 0 \N
2021-05-17 21 1 17 5 2 2021 1 \N
2021-05-18 21 2 18 5 2 2021 1 \N
2021-05-19 21 3 19 5 2 2021 1 \N
2021-05-20 21 4 20 5 2 2021 1 \N
2021-05-21 21 5 21 5 2 2021 1 \N
2021-05-22 21 6 22 5 2 2021 0 \N
2021-05-23 21 7 23 5 2 2021 0 \N
2021-05-24 22 1 24 5 2 2021 1 \N
2021-05-25 22 2 25 5 2 2021 1 \N
2021-05-26 22 3 26 5 2 2021 1 \N
2021-05-27 22 4 27 5 2 2021 1 \N
2021-05-28 22 5 28 5 2 2021 1 \N
2021-05-29 22 6 29 5 2 2021 0 \N
2021-05-30 22 7 30 5 2 2021 0 \N
2021-05-31 23 1 31 5 2 2021 1 \N
2021-06-01 23 2 1 6 2 2021 1 \N
2021-06-02 23 3 2 6 2 2021 1 \N
2021-06-03 23 4 3 6 2 2021 1 \N
2021-06-04 23 5 4 6 2 2021 1 \N
2021-06-05 23 6 5 6 2 2021 0 \N
2021-06-06 23 7 6 6 2 2021 0 \N
2021-06-07 24 1 7 6 2 2021 1 \N
2021-06-08 24 2 8 6 2 2021 1 \N
2021-06-09 24 3 9 6 2 2021 1 \N
2021-06-10 24 4 10 6 2 2021 1 \N
2021-06-11 24 5 11 6 2 2021 1 \N
2021-06-12 24 6 12 6 2 2021 0 端午节
2021-06-13 24 7 13 6 2 2021 0 端午节
2021-06-14 25 1 14 6 2 2021 0 端午节
2021-06-15 25 2 15 6 2 2021 1 \N
2021-06-16 25 3 16 6 2 2021 1 \N
2021-06-17 25 4 17 6 2 2021 1 \N
2021-06-18 25 5 18 6 2 2021 1 \N
2021-06-19 25 6 19 6 2 2021 0 \N
2021-06-20 25 7 20 6 2 2021 0 \N
2021-06-21 26 1 21 6 2 2021 1 \N
2021-06-22 26 2 22 6 2 2021 1 \N
2021-06-23 26 3 23 6 2 2021 1 \N
2021-06-24 26 4 24 6 2 2021 1 \N
2021-06-25 26 5 25 6 2 2021 1 \N
2021-06-26 26 6 26 6 2 2021 0 \N
2021-06-27 26 7 27 6 2 2021 0 \N
2021-06-28 27 1 28 6 2 2021 1 \N
2021-06-29 27 2 29 6 2 2021 1 \N
2021-06-30 27 3 30 6 2 2021 1 \N
2021-07-01 27 4 1 7 3 2021 1 \N
2021-07-02 27 5 2 7 3 2021 1 \N
2021-07-03 27 6 3 7 3 2021 0 \N
2021-07-04 27 7 4 7 3 2021 0 \N
2021-07-05 28 1 5 7 3 2021 1 \N
2021-07-06 28 2 6 7 3 2021 1 \N
2021-07-07 28 3 7 7 3 2021 1 \N
2021-07-08 28 4 8 7 3 2021 1 \N
2021-07-09 28 5 9 7 3 2021 1 \N
2021-07-10 28 6 10 7 3 2021 0 \N
2021-07-11 28 7 11 7 3 2021 0 \N
2021-07-12 29 1 12 7 3 2021 1 \N
2021-07-13 29 2 13 7 3 2021 1 \N
2021-07-14 29 3 14 7 3 2021 1 \N
2021-07-15 29 4 15 7 3 2021 1 \N
2021-07-16 29 5 16 7 3 2021 1 \N
2021-07-17 29 6 17 7 3 2021 0 \N
2021-07-18 29 7 18 7 3 2021 0 \N
2021-07-19 30 1 19 7 3 2021 1 \N
2021-07-20 30 2 20 7 3 2021 1 \N
2021-07-21 30 3 21 7 3 2021 1 \N
2021-07-22 30 4 22 7 3 2021 1 \N
2021-07-23 30 5 23 7 3 2021 1 \N
2021-07-24 30 6 24 7 3 2021 0 \N
2021-07-25 30 7 25 7 3 2021 0 \N
2021-07-26 31 1 26 7 3 2021 1 \N
2021-07-27 31 2 27 7 3 2021 1 \N
2021-07-28 31 3 28 7 3 2021 1 \N
2021-07-29 31 4 29 7 3 2021 1 \N
2021-07-30 31 5 30 7 3 2021 1 \N
2021-07-31 31 6 31 7 3 2021 0 \N
2021-08-01 31 7 1 8 3 2021 0 \N
2021-08-02 32 1 2 8 3 2021 1 \N
2021-08-03 32 2 3 8 3 2021 1 \N
2021-08-04 32 3 4 8 3 2021 1 \N
2021-08-05 32 4 5 8 3 2021 1 \N
2021-08-06 32 5 6 8 3 2021 1 \N
2021-08-07 32 6 7 8 3 2021 0 \N
2021-08-08 32 7 8 8 3 2021 0 \N
2021-08-09 33 1 9 8 3 2021 1 \N
2021-08-10 33 2 10 8 3 2021 1 \N
2021-08-11 33 3 11 8 3 2021 1 \N
2021-08-12 33 4 12 8 3 2021 1 \N
2021-08-13 33 5 13 8 3 2021 1 \N
2021-08-14 33 6 14 8 3 2021 0 \N
2021-08-15 33 7 15 8 3 2021 0 \N
2021-08-16 34 1 16 8 3 2021 1 \N
2021-08-17 34 2 17 8 3 2021 1 \N
2021-08-18 34 3 18 8 3 2021 1 \N
2021-08-19 34 4 19 8 3 2021 1 \N
2021-08-20 34 5 20 8 3 2021 1 \N
2021-08-21 34 6 21 8 3 2021 0 \N
2021-08-22 34 7 22 8 3 2021 0 \N
2021-08-23 35 1 23 8 3 2021 1 \N
2021-08-24 35 2 24 8 3 2021 1 \N
2021-08-25 35 3 25 8 3 2021 1 \N
2021-08-26 35 4 26 8 3 2021 1 \N
2021-08-27 35 5 27 8 3 2021 1 \N
2021-08-28 35 6 28 8 3 2021 0 \N
2021-08-29 35 7 29 8 3 2021 0 \N
2021-08-30 36 1 30 8 3 2021 1 \N
2021-08-31 36 2 31 8 3 2021 1 \N
2021-09-01 36 3 1 9 3 2021 1 \N
2021-09-02 36 4 2 9 3 2021 1 \N
2021-09-03 36 5 3 9 3 2021 1 \N
2021-09-04 36 6 4 9 3 2021 0 \N
2021-09-05 36 7 5 9 3 2021 0 \N
2021-09-06 37 1 6 9 3 2021 1 \N
2021-09-07 37 2 7 9 3 2021 1 \N
2021-09-08 37 3 8 9 3 2021 1 \N
2021-09-09 37 4 9 9 3 2021 1 \N
2021-09-10 37 5 10 9 3 2021 1 \N
2021-09-11 37 6 11 9 3 2021 0 \N
2021-09-12 37 7 12 9 3 2021 0 \N
2021-09-13 38 1 13 9 3 2021 1 \N
2021-09-14 38 2 14 9 3 2021 1 \N
2021-09-15 38 3 15 9 3 2021 1 \N
2021-09-16 38 4 16 9 3 2021 1 \N
2021-09-17 38 5 17 9 3 2021 1 \N
2021-09-18 38 6 18 9 3 2021 1 \N
2021-09-19 38 7 19 9 3 2021 0 中秋节
2021-09-20 39 1 20 9 3 2021 0 中秋节
2021-09-21 39 2 21 9 3 2021 0 中秋节
2021-09-22 39 3 22 9 3 2021 1 \N
2021-09-23 39 4 23 9 3 2021 1 \N
2021-09-24 39 5 24 9 3 2021 1 \N
2021-09-25 39 6 25 9 3 2021 0 \N
2021-09-26 39 7 26 9 3 2021 1 \N
2021-09-27 40 1 27 9 3 2021 1 \N
2021-09-28 40 2 28 9 3 2021 1 \N
2021-09-29 40 3 29 9 3 2021 1 \N
2021-09-30 40 4 30 9 3 2021 1 \N
2021-10-01 40 5 1 10 4 2021 0 国庆节
2021-10-02 40 6 2 10 4 2021 0 国庆节
2021-10-03 40 7 3 10 4 2021 0 国庆节
2021-10-04 41 1 4 10 4 2021 0 国庆节
2021-10-05 41 2 5 10 4 2021 0 国庆节
2021-10-06 41 3 6 10 4 2021 0 国庆节
2021-10-07 41 4 7 10 4 2021 0 国庆节
2021-10-08 41 5 8 10 4 2021 1 \N
2021-10-09 41 6 9 10 4 2021 1 \N
2021-10-10 41 7 10 10 4 2021 0 \N
2021-10-11 42 1 11 10 4 2021 1 \N
2021-10-12 42 2 12 10 4 2021 1 \N
2021-10-13 42 3 13 10 4 2021 1 \N
2021-10-14 42 4 14 10 4 2021 1 \N
2021-10-15 42 5 15 10 4 2021 1 \N
2021-10-16 42 6 16 10 4 2021 0 \N
2021-10-17 42 7 17 10 4 2021 0 \N
2021-10-18 43 1 18 10 4 2021 1 \N
2021-10-19 43 2 19 10 4 2021 1 \N
2021-10-20 43 3 20 10 4 2021 1 \N
2021-10-21 43 4 21 10 4 2021 1 \N
2021-10-22 43 5 22 10 4 2021 1 \N
2021-10-23 43 6 23 10 4 2021 0 \N
2021-10-24 43 7 24 10 4 2021 0 \N
2021-10-25 44 1 25 10 4 2021 1 \N
2021-10-26 44 2 26 10 4 2021 1 \N
2021-10-27 44 3 27 10 4 2021 1 \N
2021-10-28 44 4 28 10 4 2021 1 \N
2021-10-29 44 5 29 10 4 2021 1 \N
2021-10-30 44 6 30 10 4 2021 0 \N
2021-10-31 44 7 31 10 4 2021 0 \N
2021-11-01 45 1 1 11 4 2021 1 \N
2021-11-02 45 2 2 11 4 2021 1 \N
2021-11-03 45 3 3 11 4 2021 1 \N
2021-11-04 45 4 4 11 4 2021 1 \N
2021-11-05 45 5 5 11 4 2021 1 \N
2021-11-06 45 6 6 11 4 2021 0 \N
2021-11-07 45 7 7 11 4 2021 0 \N
2021-11-08 46 1 8 11 4 2021 1 \N
2021-11-09 46 2 9 11 4 2021 1 \N
2021-11-10 46 3 10 11 4 2021 1 \N
2021-11-11 46 4 11 11 4 2021 1 \N
2021-11-12 46 5 12 11 4 2021 1 \N
2021-11-13 46 6 13 11 4 2021 0 \N
2021-11-14 46 7 14 11 4 2021 0 \N
2021-11-15 47 1 15 11 4 2021 1 \N
2021-11-16 47 2 16 11 4 2021 1 \N
2021-11-17 47 3 17 11 4 2021 1 \N
2021-11-18 47 4 18 11 4 2021 1 \N
2021-11-19 47 5 19 11 4 2021 1 \N
2021-11-20 47 6 20 11 4 2021 0 \N
2021-11-21 47 7 21 11 4 2021 0 \N
2021-11-22 48 1 22 11 4 2021 1 \N
2021-11-23 48 2 23 11 4 2021 1 \N
2021-11-24 48 3 24 11 4 2021 1 \N
2021-11-25 48 4 25 11 4 2021 1 \N
2021-11-26 48 5 26 11 4 2021 1 \N
2021-11-27 48 6 27 11 4 2021 0 \N
2021-11-28 48 7 28 11 4 2021 0 \N
2021-11-29 49 1 29 11 4 2021 1 \N
2021-11-30 49 2 30 11 4 2021 1 \N
2021-12-01 49 3 1 12 4 2021 1 \N
2021-12-02 49 4 2 12 4 2021 1 \N
2021-12-03 49 5 3 12 4 2021 1 \N
2021-12-04 49 6 4 12 4 2021 0 \N
2021-12-05 49 7 5 12 4 2021 0 \N
2021-12-06 50 1 6 12 4 2021 1 \N
2021-12-07 50 2 7 12 4 2021 1 \N
2021-12-08 50 3 8 12 4 2021 1 \N
2021-12-09 50 4 9 12 4 2021 1 \N
2021-12-10 50 5 10 12 4 2021 1 \N
2021-12-11 50 6 11 12 4 2021 0 \N
2021-12-12 50 7 12 12 4 2021 0 \N
2021-12-13 51 1 13 12 4 2021 1 \N
2021-12-14 51 2 14 12 4 2021 1 \N
2021-12-15 51 3 15 12 4 2021 1 \N
2021-12-16 51 4 16 12 4 2021 1 \N
2021-12-17 51 5 17 12 4 2021 1 \N
2021-12-18 51 6 18 12 4 2021 0 \N
2021-12-19 51 7 19 12 4 2021 0 \N
2021-12-20 52 1 20 12 4 2021 1 \N
2021-12-21 52 2 21 12 4 2021 1 \N
2021-12-22 52 3 22 12 4 2021 1 \N
2021-12-23 52 4 23 12 4 2021 1 \N
2021-12-24 52 5 24 12 4 2021 1 \N
2021-12-25 52 6 25 12 4 2021 0 \N
2021-12-26 52 7 26 12 4 2021 0 \N
2021-12-27 53 1 27 12 4 2021 1 \N
2021-12-28 53 2 28 12 4 2021 1 \N
2021-12-29 53 3 29 12 4 2021 1 \N
2021-12-30 53 4 30 12 4 2021 1 \N
2021-12-31 53 5 31 12 4 2021 1 \N
3）执行以下语句将其导入时间维度表
insert overwrite table dim_date_info select * from tmp_dim_date_info;
4）检查数据是否导入成功
select * from dim_date_info;
6、用户维度表（拉链表）
（1）拉链表概述
1）什么是拉链表
2）为什么要做拉链表
3）如何使用拉链表
4）拉链表形成过程
（2）制作拉链表
1.建表语句
DROP TABLE IF EXISTS dim_user_info;
CREATE EXTERNAL TABLE dim_user_info(
`id` STRING COMMENT '用户id',
`login_name` STRING COMMENT '用户名称',
`nick_name` STRING COMMENT '用户昵称',
`name` STRING COMMENT '用户姓名',
`phone_num` STRING COMMENT '手机号码',
`email` STRING COMMENT '邮箱',
`user_level` STRING COMMENT '用户等级',
`birthday` STRING COMMENT '生日',
`gender` STRING COMMENT '性别',
`create_time` STRING COMMENT '创建时间',
`operate_time` STRING COMMENT '操作时间',
`start_date` STRING COMMENT '开始日期',
`end_date` STRING COMMENT '结束日期'
) COMMENT '用户表'
PARTITIONED BY (`dt` STRING)
STORED AS PARQUET
LOCATION '/warehouse/gmall/dim/dim_user_info/'
TBLPROPERTIES ("parquet.compression"="lzo");
2.分区规划
3.数据装载
1）首日装载
- 拉链表首日装载，需要进行初始化操作，具体工作为将截止到初始化当日的全部历史用户导入一次性导入到拉链表中。目前的ods_user_info表的第一个分区，即2020-06-14分区中就是全部的历史用户，故将该分区数据进行一定处理后导入拉链表的9999-99-99分区即可。
insert overwrite table dim_user_info partition(dt='9999-99-99')
select
id,
login_name,
nick_name,
md5(name),
md5(phone_num),
md5(email),
user_level,
birthday,
gender,
create_time,
operate_time,
'2020-06-14',
'9999-99-99'
from ods_user_info
where dt='2020-06-14';
2）每日装载
（1）实现思路
（2）sql编写
with
tmp as
(
select
old.id old_id,
old.login_name old_login_name,
old.nick_name old_nick_name,
old.name old_name,
old.phone_num old_phone_num,
old.email old_email,
old.user_level old_user_level,
old.birthday old_birthday,
old.gender old_gender,
old.create_time old_create_time,
old.operate_time old_operate_time,
old.start_date old_start_date,
old.end_date old_end_date,
new.id new_id,
new.login_name new_login_name,
new.nick_name new_nick_name,
new.name new_name,
new.phone_num new_phone_num,
new.email new_email,
new.user_level new_user_level,
new.birthday new_birthday,
new.gender new_gender,
new.create_time new_create_time,
new.operate_time new_operate_time,
new.start_date new_start_date,
new.end_date new_end_date
from
(
select
id,
login_name,
nick_name,
name,
phone_num,
email,
user_level,
birthday,
gender,
create_time,
operate_time,
start_date,
end_date
from dim_user_info
where dt='9999-99-99'
)old
full outer join
(
select
id,
login_name,
nick_name,
md5(name) name,
md5(phone_num) phone_num,
md5(email) email,
user_level,
birthday,
gender,
create_time,
operate_time,
'2020-06-15' start_date,
'9999-99-99' end_date
from ods_user_info
where dt='2020-06-15'
)new
on old.id=new.id
)
insert overwrite table dim_user_info partition(dt)
select
nvl(new_id,old_id),
nvl(new_login_name,old_login_name),
nvl(new_nick_name,old_nick_name),
nvl(new_name,old_name),
nvl(new_phone_num,old_phone_num),
nvl(new_email,old_email),
nvl(new_user_level,old_user_level),
nvl(new_birthday,old_birthday),
nvl(new_gender,old_gender),
nvl(new_create_time,old_create_time),
nvl(new_operate_time,old_operate_time),
nvl(new_start_date,old_start_date),
nvl(new_end_date,old_end_date),
nvl(new_end_date,old_end_date) dt
from tmp
union all
select
old_id,
old_login_name,
old_nick_name,
old_name,
old_phone_num,
old_email,
old_user_level,
old_birthday,
old_gender,
old_create_time,
old_operate_time,
old_start_date,
cast(date_add('2020-06-15',-1) as string),
cast(date_add('2020-06-15',-1) as string) dt
from tmp
where new_id is not null and old_id is not null;
7、DIM层首日数据装载脚本
1）编写脚本
（1）在/home/lyh/bin目录下创建脚本ods_to_dim_db_init.sh
[lyh@hadoop102 bin]$ vim ods_to_dim_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
dim_user_info="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
insert overwrite table ${APP}.dim_user_info partition(dt='9999-99-99')
select
id,
login_name,
nick_name,
md5(name),
md5(phone_num),
md5(email),
user_level,
birthday,
gender,
create_time,
operate_time,
'$do_date',
'9999-99-99'
from ${APP}.ods_user_info
where dt='$do_date';
"
dim_sku_info="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
with
sku as
(
select
id,
price,
sku_name,
sku_desc,
weight,
is_sale,
spu_id,
category3_id,
tm_id,
create_time
from ${APP}.ods_sku_info
where dt='$do_date'
),
spu as
(
select
id,
spu_name
from ${APP}.ods_spu_info
where dt='$do_date'
),
c3 as
(
select
id,
name,
category2_id
from ${APP}.ods_base_category3
where dt='$do_date'
),
c2 as
(
select
id,
name,
category1_id
from ${APP}.ods_base_category2
where dt='$do_date'
),
c1 as
(
select
id,
name
from ${APP}.ods_base_category1
where dt='$do_date'
),
tm as
(
select
id,
tm_name
from ${APP}.ods_base_trademark
where dt='$do_date'
),
attr as
(
select
sku_id,
collect_set(named_struct('attr_id',attr_id,'value_id',value_id,'attr_name',attr_name,'value_name',value_name)) attrs
from ${APP}.ods_sku_attr_value
where dt='$do_date'
group by sku_id
),
sale_attr as
(
select
sku_id,
collect_set(named_struct('sale_attr_id',sale_attr_id,'sale_attr_value_id',sale_attr_value_id,'sale_attr_name',sale_attr_name,'sale_attr_value_name',sale_attr_value_name)) sale_attrs
from ${APP}.ods_sku_sale_attr_value
where dt='$do_date'
group by sku_id
)
insert overwrite table ${APP}.dim_sku_info partition(dt='$do_date')
select
sku.id,
sku.price,
sku.sku_name,
sku.sku_desc,
sku.weight,
sku.is_sale,
sku.spu_id,
spu.spu_name,
sku.category3_id,
c3.name,
c3.category2_id,
c2.name,
c2.category1_id,
c1.name,
sku.tm_id,
tm.tm_name,
attr.attrs,
sale_attr.sale_attrs,
sku.create_time
from sku
left join spu on sku.spu_id=spu.id
left join c3 on sku.category3_id=c3.id
left join c2 on c3.category2_id=c2.id
left join c1 on c2.category1_id=c1.id
left join tm on sku.tm_id=tm.id
left join attr on sku.id=attr.sku_id
left join sale_attr on sku.id=sale_attr.sku_id;
"
dim_base_province="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
insert overwrite table ${APP}.dim_base_province
select
bp.id,
bp.name,
bp.area_code,
bp.iso_code,
bp.iso_3166_2,
bp.region_id,
br.region_name
from ${APP}.ods_base_province bp
join ${APP}.ods_base_region br on bp.region_id = br.id;
"
dim_coupon_info="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
insert overwrite table ${APP}.dim_coupon_info partition(dt='$do_date')
select
id,
coupon_name,
coupon_type,
condition_amount,
condition_num,
activity_id,
benefit_amount,
benefit_discount,
create_time,
range_type,
limit_num,
taken_count,
start_time,
end_time,
operate_time,
expire_time
from ${APP}.ods_coupon_info
where dt='$do_date';
"
dim_activity_rule_info="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
insert overwrite table ${APP}.dim_activity_rule_info partition(dt='$do_date')
select
ar.id,
ar.activity_id,
ai.activity_name,
ar.activity_type,
ai.start_time,
ai.end_time,
ai.create_time,
ar.condition_amount,
ar.condition_num,
ar.benefit_amount,
ar.benefit_discount,
ar.benefit_level
from
(
select
id,
activity_id,
activity_type,
condition_amount,
condition_num,
benefit_amount,
benefit_discount,
benefit_level
from ${APP}.ods_activity_rule
where dt='$do_date'
)ar
left join
(
select
id,
activity_name,
start_time,
end_time,
create_time
from ${APP}.ods_activity_info
where dt='$do_date'
)ai
on ar.activity_id=ai.id;
"
case $1 in
"dim_user_info"){
hive -e "$dim_user_info"
};;
"dim_sku_info"){
hive -e "$dim_sku_info"
};;
"dim_base_province"){
hive -e "$dim_base_province"
};;
"dim_coupon_info"){
hive -e "$dim_coupon_info"
};;
"dim_activity_rule_info"){
hive -e "$dim_activity_rule_info"
};;
"all"){
hive -e "$dim_user_info$dim_sku_info$dim_coupon_info$dim_activity_rule_info$dim_base_province"
};;
esac
2）脚本使用
（1）执行脚本
[lyh@hadoop102 bin]$ ods_to_dim_db_init.sh all 2020-06-14
注意：该脚本不包含时间维度表的装载，时间维度表需手动装载数据
（2）查看数据是否导入成功
8、DIM层每日数据装载脚本
1）编写脚本
（1）在/home/lyh/bin目录下创建脚本ods_to_dim_db.sh
[lyh@hadoop102 bin]$ vim ods_to_dim_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
dim_user_info="
set hive.exec.dynamic.partition.mode=nonstrict;
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
with
tmp as
(
select
old.id old_id,
old.login_name old_login_name,
old.nick_name old_nick_name,
old.name old_name,
old.phone_num old_phone_num,
old.email old_email,
old.user_level old_user_level,
old.birthday old_birthday,
old.gender old_gender,
old.create_time old_create_time,
old.operate_time old_operate_time,
old.start_date old_start_date,
old.end_date old_end_date,
new.id new_id,
new.login_name new_login_name,
new.nick_name new_nick_name,
new.name new_name,
new.phone_num new_phone_num,
new.email new_email,
new.user_level new_user_level,
new.birthday new_birthday,
new.gender new_gender,
new.create_time new_create_time,
new.operate_time new_operate_time,
new.start_date new_start_date,
new.end_date new_end_date
from
(
select
id,
login_name,
nick_name,
name,
phone_num,
email,
user_level,
birthday,
gender,
create_time,
operate_time,
start_date,
end_date
from ${APP}.dim_user_info
where dt='9999-99-99'
and start_date<'$do_date'
)old
full outer join
(
select
id,
login_name,
nick_name,
md5(name) name,
md5(phone_num) phone_num,
md5(email) email,
user_level,
birthday,
gender,
create_time,
operate_time,
'$do_date' start_date,
'9999-99-99' end_date
from ${APP}.ods_user_info
where dt='$do_date'
)new
on old.id=new.id
)
insert overwrite table ${APP}.dim_user_info partition(dt)
select
nvl(new_id,old_id),
nvl(new_login_name,old_login_name),
nvl(new_nick_name,old_nick_name),
nvl(new_name,old_name),
nvl(new_phone_num,old_phone_num),
nvl(new_email,old_email),
nvl(new_user_level,old_user_level),
nvl(new_birthday,old_birthday),
nvl(new_gender,old_gender),
nvl(new_create_time,old_create_time),
nvl(new_operate_time,old_operate_time),
nvl(new_start_date,old_start_date),
nvl(new_end_date,old_end_date),
nvl(new_end_date,old_end_date) dt
from tmp
union all
select
old_id,
old_login_name,
old_nick_name,
old_name,
old_phone_num,
old_email,
old_user_level,
old_birthday,
old_gender,
old_create_time,
old_operate_time,
old_start_date,
cast(date_add('$do_date',-1) as string),
cast(date_add('$do_date',-1) as string) dt
from tmp
where new_id is not null and old_id is not null;
"
dim_sku_info="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
with
sku as
(
select
id,
price,
sku_name,
sku_desc,
weight,
is_sale,
spu_id,
category3_id,
tm_id,
create_time
from ${APP}.ods_sku_info
where dt='$do_date'
),
spu as
(
select
id,
spu_name
from ${APP}.ods_spu_info
where dt='$do_date'
),
c3 as
(
select
id,
name,
category2_id
from ${APP}.ods_base_category3
where dt='$do_date'
),
c2 as
(
select
id,
name,
category1_id
from ${APP}.ods_base_category2
where dt='$do_date'
),
c1 as
(
select
id,
name
from ${APP}.ods_base_category1
where dt='$do_date'
),
tm as
(
select
id,
tm_name
from ${APP}.ods_base_trademark
where dt='$do_date'
),
attr as
(
select
sku_id,
collect_set(named_struct('attr_id',attr_id,'value_id',value_id,'attr_name',attr_name,'value_name',value_name)) attrs
from ${APP}.ods_sku_attr_value
where dt='$do_date'
group by sku_id
),
sale_attr as
(
select
sku_id,
collect_set(named_struct('sale_attr_id',sale_attr_id,'sale_attr_value_id',sale_attr_value_id,'sale_attr_name',sale_attr_name,'sale_attr_value_name',sale_attr_value_name)) sale_attrs
from ${APP}.ods_sku_sale_attr_value
where dt='$do_date'
group by sku_id
)
insert overwrite table ${APP}.dim_sku_info partition(dt='$do_date')
select
sku.id,
sku.price,
sku.sku_name,
sku.sku_desc,
sku.weight,
sku.is_sale,
sku.spu_id,
spu.spu_name,
sku.category3_id,
c3.name,
c3.category2_id,
c2.name,
c2.category1_id,
c1.name,
sku.tm_id,
tm.tm_name,
attr.attrs,
sale_attr.sale_attrs,
sku.create_time
from sku
left join spu on sku.spu_id=spu.id
left join c3 on sku.category3_id=c3.id
left join c2 on c3.category2_id=c2.id
left join c1 on c2.category1_id=c1.id
left join tm on sku.tm_id=tm.id
left join attr on sku.id=attr.sku_id
left join sale_attr on sku.id=sale_attr.sku_id;
"
dim_base_province="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
insert overwrite table ${APP}.dim_base_province
select
bp.id,
bp.name,
bp.area_code,
bp.iso_code,
bp.iso_3166_2,
bp.region_id,
bp.name
from ${APP}.ods_base_province bp
join ${APP}.ods_base_region br on bp.region_id = br.id;
"
dim_coupon_info="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
insert overwrite table ${APP}.dim_coupon_info partition(dt='$do_date')
select
id,
coupon_name,
coupon_type,
condition_amount,
condition_num,
activity_id,
benefit_amount,
benefit_discount,
create_time,
range_type,
limit_num,
taken_count,
start_time,
end_time,
operate_time,
expire_time
from ${APP}.ods_coupon_info
where dt='$do_date';
"
dim_activity_rule_info="
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
insert overwrite table ${APP}.dim_activity_rule_info partition(dt='$do_date')
select
ar.id,
ar.activity_id,
ai.activity_name,
ar.activity_type,
ai.start_time,
ai.end_time,
ai.create_time,
ar.condition_amount,
ar.condition_num,
ar.benefit_amount,
ar.benefit_discount,
ar.benefit_level
from
(
select
id,
activity_id,
activity_type,
condition_amount,
condition_num,
benefit_amount,
benefit_discount,
benefit_level
from ${APP}.ods_activity_rule
where dt='$do_date'
)ar
left join
(
select
id,
activity_name,
start_time,
end_time,
create_time
from ${APP}.ods_activity_info
where dt='$do_date'
)ai
on ar.activity_id=ai.id;
"
case $1 in
"dim_user_info"){
hive -e "$dim_user_info"
};;
"dim_sku_info"){
hive -e "$dim_sku_info"
};;
"dim_base_province"){
hive -e "$dim_base_province"
};;
"dim_coupon_info"){
hive -e "$dim_coupon_info"
};;
"dim_activity_rule_info"){
hive -e "$dim_activity_rule_info"
};;
"all"){
hive -e "$dim_user_info$dim_sku_info$dim_coupon_info$dim_activity_rule_info"
};;
esac
（2）增加执行权限
[lyh@hadoop102 bin]$ chmod +x ods_to_dim_db.sh
2）脚本使用
（1）执行脚本
[lyh@hadoop102 bin]$ ods_to_dim_db.sh all 2020-06-14
（2）查看数据是否导入成功