cdh中业务数仓的搭建
文章目录
- 生成业务数据
- 业务数仓通过sqoop导入HDFS
- ODS层
- 订单表
- 订单详情表
- 商品表
- 用户表
- 商品一级分类表
- 商品二级分类表
- 商品三级分类表
- 支付流水表
- ODS层导入脚本
- DWD层
- 订单表
- 订单详情表
- 用户表
- 支付流水表
- 商品表（增加分类）
- DWD层数据导入脚本
- DWS层之用户行为宽表
- 用户行为宽表
- ADS层（需求：GMV成交总额）
- 建表语句
- MySQL
sql脚本用SQLyog调用不会报错,执行四个sql脚本
生成业务数据
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
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。
查询数据结果
SELECT * from base_category1;
SELECT * from base_category2;
SELECT * from base_category3;
SELECT * from order_info;
SELECT * from order_detail;
SELECT * from sku_info;
SELECT * from user_info;
SELECT * from payment_info;
业务数仓通过sqoop导入HDFS
选择 sqoop1 client --> 选择主机hadoop001
安装完成后
vim sqoop_import.sh
#!/bin/bash
db_date=$2
echo $db_date
db_name=gmall
import_data() {
sqoop import \
--connect jdbc:mysql://hadoop001:3306/$db_name \
--username root \
--password 000000 \
--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
执行
[root@hadoop001 bin]# ./sqoop_import.sh all 2019-02-10
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。
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/'
;
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/'
;
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/'
;
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/'
;
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/'
;
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/'
;
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/'
;
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层导入脚本
[root@hadoop001 bin]# vim ods_db.sh
#!/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='$do_date');
"
hive -e "$sql"
增加脚本执行权限
[root@hadoop001 bin]# chmod 777 ods_db.sh
采用脚本导入数据
[root@hadoop001 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)。
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/'
;
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/'
;
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/'
;
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/'
;
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
[root@hadoop001 bin]# vim dwd_db.sh
#!/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;
"
hive -e "$sql"
[root@hadoop001 bin]# chmod 777 dwd_db.sh
DWS层之用户行为宽表
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。
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");
[root@hadoop001 bin]# vim dws_db_wide.sh
#!/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='$do_date')
select
user_actions.user_id,
sum(user_actions.order_count),
sum(user_actions.order_amount),
sum(user_actions.payment_count),
sum(user_actions.payment_amount)
from
(
select
user_id,
order_count,
order_amount,
0 payment_count,
0 payment_amount
from tmp_order
union all
select
user_id,
0 order_count,
0 order_amount,
payment_count,
payment_amount
from tmp_payment
) user_actions
group by user_id;
"
hive -e "$sql"
chmod 777 dws_db_wide.sh
[root@hadoop001 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/'
;
[root@hadoop001 bin]# vim ads_db_gmv.sh
#!/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 ='$do_date'
group by dt;
"
hive -e "$sql"
[root@hadoop001 bin]# chmod 777 ads_db_gmv.sh
[root@hadoop001 bin]# ./ads_db_gmv.sh 2019-02-10
MySQL
创建表
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;
[root@hadoop001 bin]# vim sqoop_export.sh
#!/bin/bash
db_name=gmall
export_data() {
sqoop export \
--connect "jdbc:mysql://hadoop001:3306/${db_name}?useUnicode=true&characterEncoding=utf-8" \
--username root \
--password 000000 \
--table $1 \
--num-mappers 1 \
--export-dir /warehouse/$db_name/ads/$1 \
--input-fields-terminated-by "\t" \
--update-mode allowinsert \
--update-key "tm_id,category1_id,stat_mn,stat_date" \
--input-null-string '\\N' \
--input-null-non-string '\\N'
}
case $1 in
"ads_gmv_sum_day")
export_data "ads_gmv_sum_day"
;;
"all")
export_data "ads_gmv_sum_day"
;;
esac
[root@hadoop001 bin]# ./sqoop_export.sh all