数据仓库之案例（基础篇）

一、销售案例步骤

（一）ODS层

建立源数据库并生成初始的数据
在Hive中创建源数据过渡区和数据仓库的表
日期维度的数据装载
数据的ETL => 进入dwd层，本案例简单，不需要清洗

（二）DW层

dwd层：ETL清洗，本案例不需要
dws层：建模型+轻聚合，本案例只需要建模型，太简单，不需要聚合。
- 　轻聚合后建模 => 星型模型【注意，是轻聚合后，成为星型模型】

（三）DM层

dm层：-> 宽表
1. 存放在hive -> 太慢！适合复杂计算，用来机器学习/数据挖掘
2. 存放在mysql/oracle等分析型数据库 -> 快！用来数据分析
接口暴露：springboot 暴露接口

数据仓库分层

ODS(operational Date store) 源数据层
DW(Data WareHouse) 数据仓库层
DM(Data Market) 数据集市层

二、数据仓库之构建步骤

（一）ODS层

（1）建立源数据库mysql并生成初始的数据

/*****************************************************
            create database sales_source
******************************************************/
drop database  if exists sales_source;
create database sales_source default charset utf8 collate utf8_general_ci;
use sales_source;

/*****************************************************
            create table 
******************************************************/
-- Table:Customer
drop table if exists Customer;
create table customer(
    customer_number int primary key not null auto_increment,
    customer_name varchar(32) not null,
    customer_street_address varchar(256) not null,
    customer_zip_code int not null,
    customer_city varchar(32) not null,
    customer_state varchar(32) not null
);

-- Table:Product
drop table if exists product;
create table product(
    product_code int primary key not null auto_increment,
    product_name varchar(128) not null,
    product_category varchar(32) not null
);

-- Table:Sales_order
drop table if exists sales_order;
create table sales_order(
    order_number int primary key not null auto_increment,
    customer_number int not null,
    product_code int not null,
    order_date date not null,
    entry_date date not null,
    order_amount int not null
);

-- add constraint
alter table sales_order add constraint fk_cust_order 
    foreign key (customer_number) references customer(customer_number);
alter table sales_order add constraint fk_product_order 
    foreign key (product_code) references product(product_code);

/*************************************************
                insert data
***********************************************/
-- insert customer
insert into customer
(
    customer_name,customer_street_address,customer_zip_code,
    customer_city,customer_state
)
values
(\'Big Customers\',\'7500 Louise Dr.\',17050,\'Mechanicsbrg\',\'PA\'),
(\'Small Stroes\',\'2500 Woodland St.\',17055,\'Pittsubtgh\',\'PA\'),
(\'Medium Retailers\',\'1111 Ritter Rd.\',17055,\'Pittsubtgh\',\'PA\'),
(\'Good Companies\',\'9500 Scott St.\',17050,\'Mechanicsbrg\',\'PA\'),
(\'Wonderful Shops\',\'3333 Rossmoyne Rd.\',17050,\'Mechanicsbrg\',\'PA\'),
(\'Loyal Clients\',\'7070 Ritter Rd.\',17055,\'Mechanicsbrg\',\'PA\');

-- insert product
insert into product (product_name,product_category) values
(\'Hard Disk\',\'Storage\'),
(\'Floppy Driver\',\'Storage\'),
(\'Icd panel\',\'monitor\');
-- insert sales_orders 
-- customer_numer int,product_code int,order_date,entry_date,order_amount
drop procedure if exists proc_generate_saleorder;
delimiter $$
create procedure proc_generate_saleorder()
begin
    -- create temp table 
    drop table if exists temp;
    create table temp as select * from sales_order where 1=0;
    -- declare var 
    set @begin_time := unix_timestamp(\'2018-1-1\');
    set @over_time := unix_timestamp(\'2018-11-23\');
    set @i :=1;
    while @i <= 100000 do
        set @cust_number := floor(1+rand()*6);
        set @product_code := floor(1+rand()*3);
        set @tmp_data := from_unixtime(@begin_time+rand()*(@over_time-@begin_time));
        set @amount := floor(1000+rand()*9000);
        insert into temp values(@i,@cust_number,@product_code,@tmp_data,@tmp_data,@amount);
        set @i := @i+1;
    end while;
    -- clear sales_orders
    truncate table sales_order;
    insert into sales_order select null,customer_number,product_code,order_date,entry_date,order_amount from temp;
    commit;
    drop table temp;
end$$
delimiter ;
call proc_generate_saleorder();

PS:

　　1.为什么要用constraint约束？ 详见 => https://www.cnblogs.com/sabertobih/p/13966709.html

　　2.为什么存储过程中涉及批量插表的时候要用到临时表？

- 已知commit一次是从内存表到物理表的过程，用不用临时表有什么不一样？
- 答：关键在于temp表是新create的表，对于新create的表，insert into是在内存里完成；

（2）在Hive中创建源数据过渡区和数据仓库的表

关于本案例中，hive的主键问题：

>>> 因为数据来源于数据库，天生自带主键当作sk，不用自己生成

>>> 否则，需要使用 row_number/ uuid/ md5生成主键，见 https://www.cnblogs.com/sabertobih/p/14031047.html

inithive.sql => 创建hive表！

drop database if exists ods_sales_source cascade;
create database ods_sales_source;

use ods_sales_source;

drop table if exists ods_product;
create table ods_product(
product_code string,
product_name string,
product_category string,
version string,
ods_start_time string,
ods_end_time string
)
row format delimited fields terminated by \'\u0001\';

drop table if exists ods_customer;
create table ods_customer(
customer_number string,
customer_name string,
customer_street_address string,
customer_zip_code string,
customer_city string,
customer_state string,
version string,
ods_start_time string,
ods_end_time string
)
row format delimited fields terminated by \'\u0001\';

drop table if exists ods_origin_sales_order;
create table ods_origin_sales_order(
order_number string,
customer_number string,
product_code string,
order_date string,
order_amount string
);

drop table if exists ods_dynamic_sales_order;
create table ods_dynamic_sales_order(
order_number string,
customer_number string,
product_code string,
order_date string,
order_amount string
)
partitioned by (ymd string);

import_hive.sh => 直接执行，直接实现从创表到自动从mysql中insert语句到hive

#! /bin/bash
if [ $# = 0 ];then
hive -f /opt/data/inithive.sql
fi

echo "import ods_product...";
#global import product
sqoop import \
    --connect jdbc:mysql://192.168.56.111:3306/sales_source \
    --driver com.mysql.jdbc.Driver \
    --username root \
    --password root \
    --query "select product_code,product_name,product_category,\'1.0\' as version,\'2018-1-1\' as ods_start_time,\'9999-12-31\' as ods_end_time from product where 1=1 and \$CONDITIONS" \
    --target-dir /mytmp/ods/pro \
    --hive-import \
    --hive-database ods_sales_source \
    --hive-table ods_product \
    --hive-overwrite \
    -m 1
    
echo "import ods_customer...";
#global import customer
sqoop import \
    --connect jdbc:mysql://192.168.56.111:3306/sales_source \
    --driver com.mysql.jdbc.Driver \
    --username root \
    --password root \
    --query "select customer_number,customer_name,customer_street_address,customer_zip_code,customer_city,customer_state,\'1.0\' as version,\'2018-1-1\' as ods_start_time,\'9999-12-31\' as ods_end_time from customer where 1=1 and \$CONDITIONS" \
    --hive-import \
    --target-dir /mytmp/ods/cust \
    --hive-database ods_sales_source \
    --hive-table ods_customer \
    --hive-overwrite \
    -m 1

（3）日期维度的数据装载

如何自动导入分区表？

方法一（不推荐）：使用sqoop手动分区，注意sqoop partition不可以带有特殊符号，日期只可以%Y%m%d

echo "import sales_order..."
#increment import sales_order 
#partition 
day=1
md=`date -d \'2018-10-23\' +%j`    
while [ $day -lt $md ]
do
    mdd=`date -d "2018-1-1 +$day day" +%Y%m%d`
    hive -e "use ods_sales_source;alter table ods_start_order add partitioned(ymd=$mdd)"
    sqoop import \
    --connect jdbc:mysql://192.168.56.111:3306/sales_source \
    --driver com.mysql.jdbc.Driver \
    --username root \
    --password root \
    --query "select order_number,customer_number,product_code,order_date,order_amount from sales_order where date_format(order_date,\'%Y%m%d\')=$mdd and \$CONDITIONS" \
    --target-dir /mytmp/so \
    --delete-target-dir
    --hive-import \
    --hive-database ods_sales_source \
    --hive-table ods_sales_order \
    --hive-partition-key "ymd" \
    --hive-partition-value "$mdd" \
    -m 1
    let day=$day +1
done

方法二：使用hive动态分区，先在hive中导入一个全量表，再从全量表==动态分区==>导入分区表

# 全量导入
echo "import ods_origin_sales_order..."
sqoop import \
    --connect jdbc:mysql://192.168.56.111:3306/sales_source \
    --driver com.mysql.jdbc.Driver \
    --username root \
    --password root \
    --query "select order_number,customer_number,product_code,order_date,order_amount from sales_order where \$CONDITIONS" \
    --hive-import \
    --target-dir /mytmp/ods/so \
    --hive-database ods_sales_source \
    --hive-table ods_origin_sales_order \
    --hive-overwrite \
    -m 1
    
echo "import dynamic..."
hive -f /opt/data/dynamic.sql

-- 动态分区自动导入
use ods_sales_source;
set hive.exec.dynamic.partition=true;
set hive.exec.dynamic.partition.mode=nonstrict;
set hive.exec.max.dynamic.partitions.pernode=10000;
set hive.exec.max.dynamic.partitions.pernode.Maximum=10000;
set hive.exec.max.dynamic.partitions=10000;
set hive.exec.max.created.files=10000;
insert into ods_dynamic_sales_order partition(ymd) select order_number,customer_number,product_code,order_date,order_amount,
date_format(order_date,\'yyyyMMdd\') as ymd from ods_origin_sales_order;

（4）数据的ETL

（二）DW层

（1）SQL：建dw层表语句

可以看到只有一张customer表，一张product表，说明给的数据是已经聚合后的！

createdwdinit.sql

-- 建表语句
-- 其中date表是外部表，且以textfile形式存储，方便映射数据
drop database if exists DW_SALES_SOURCE cascade;
create database dw_sales_source;
use dw_sales_source;

drop table if exists dwd_dim_customer;
create table dwd_dim_customer(
customer_sk int,
customer_number int,
customer_name string,
customer_street_address string,
custom_zip_code string,
customer_city string,
customer_state string,
version string,
effectice_date string,
expiry_date string
)
row format delimited fields terminated by \',\'
stored as parquetfile;

drop table if exists dwd_dim_product;
create table dwd_dim_product(
product_sk int,
product_code int,
product_name string,
product_category string,
version string,
effectice_date string,
expiry_date string
)
row format delimited fields terminated by \',\'
stored as parquetfile;

drop table if exists dwd_dim_order;
create table dwd_dim_order(
order_sk int,
order_number int,
version string,
effectice_date string,
expiry_date string
)
row format delimited fields terminated by \',\'
stored as parquetfile;

drop table if exists dwd_dim_date;
create external table dwd_dim_date(
date_sk int,
d_date string,
d_month int,
d_month_name string,
d_quarter int,
d_year int
)
row format delimited fields terminated by \',\'
stored as textfile
location \'/opt/dwdate\';

drop table if exists dwd_fact_sales_order;
create table dwd_fact_sales_order(
order_sk int,
customer_sk int,
product_sk int,
date_sk int,
order_amount float
)
row format delimited fields terminated by \',\'
stored as parquetfile;

（2）SQL：ods层导入dw层数据：customer/product/order

dwd_import.sql

insert into dw_sales_source.dwd_dim_customer
select 
customer_number as customer_sk,
customer_number,
customer_name ,
customer_street_address ,
customer_zip_code ,
customer_city ,
customer_state ,
version ,
ods_start_time as effectice_date,
ods_end_time as expiry_date
from ods_sales_source.ods_customer;
    
insert into dw_sales_source.dwd_dim_product
select
product_code as product_sk,
product_code ,
product_name ,
product_category ,
version,
ods_start_time as effectice_date,
ods_end_time as expiry_date
from
ods_sales_source.ods_product;
    
insert into dw_sales_source.dwd_dim_order
select
order_number as order_sk,
order_number,
\'1.0\' as version,
order_date as effectice_date,
\'9999-12-31\' as expiry_date
from
ods_sales_source.ods_dynamic_sales_order

（3）脚本文件，生成date数据，导入dwd_fact_sales_order表

#!/bin/bash

#创建dw层

echo \'***********************************\'
echo \'create dw layout data table...\'
echo \'***********************************\'
hive -f /opt/data/dw/createdwdinit.sql

echo \'***********************************\'
echo \'import data...\'
echo \'***********************************\'
hive -f /opt/data/dw/dwd_import.sql

echo \'***********************************\'
echo \'generating dwd_date data...\'
echo \'***********************************\'

## hdfs判断是否有文件或目录
targetfilename=/opt/dwdate
hdfs dfs -test -e $targetfilename
if [ $? -eq 0 ] ;then 
    echo \'exist\' 
    hdfs dfs -rm -R $targetfilename
fi

## linux判断是否有文件
filename=/opt/data/tmp
if [ -e $filename ];
then 
    rm -rf $filename
fi
touch $filename

num=0
while(( $num<365 ))
do
    dt=`date -d "2018-01-01 $num days" +%Y-%m-%d`
    mtname=`date -d "${dt}" +%B`
    mt=`date -d "${dt}" +%m`
    if [ $mt -le 3 ];
    then qt=1
    elif [ $mt -le 6 ];
    then qt=2
    elif [ $mt -le 9 ];
    then qt=3
    else qt=4
    fi
    let num=$num+1
    echo "${num},${dt},${dt:5:2},$mtname,$qt,${dt:0:4}" >> $filename
    #hive -e\'insert into dw_sales_source.dwd_dim_date values($num+1,${dt},${dt:5:2},$mtname,$qt,${dt:0:4})\'
done
echo "date data从本地移动到hdfs"
hdfs dfs -rm -R /opt/dwdate
hdfs dfs -mkdir -p /opt/dwdate
hdfs dfs -put /opt/data/tmp /opt/dwdate

echo \'***********************************\'
echo \'import fact_sales_order data...\'
echo \'***********************************\'

hive -e \'insert into dw_sales_source.dwd_fact_sales_order
select
oso.order_number as order_sk,
oso.customer_number as customer_sk,
oso.product_code as product_sk,
dss.date_sk as date_sk,
oso.order_amount as order_amount
from 
　　ods_sales_source.ods_dynamic_sales_order oso 
inner join dw_sales_source.dwd_dim_date dss
on oso.order_date = dss.d_date\'

（三）DM层

（1）如何形成宽表？

① 需求:

>>>

当天-> 顾客，产品，日期，订单个数，当天金额 && 近两天 -> 订单个数，近两天金额

<<<

② 调优见：https://www.cnblogs.com/sabertobih/p/14041854.html

③ 代码：

dm_init.sql

drop database if exists dm_sales_source cascade;
create database dm_sales_source;
use dm_sales_source;

dm_run.sql

drop table if exists dm_sales_source.dm_sales_order_count;
create table dm_sales_source.dm_sales_order_count as 
select 
dss.d_date,d.customer_sk,d.product_sk,
count(d.order_sk) as order_num,
sum(d.order_amount) as order_dailyamount,
sum(sum(d.order_amount)) over(rows between 1 PRECEDING and current row) as recent_amount,
sum(count(d.order_sk)) over(rows between 1 PRECEDING and current row) as recent_num
from 
dw_sales_source.dwd_fact_sales_order d
inner join dw_sales_source.dwd_dim_date dss 
on d.date_sk = dss.date_sk
group by 
dss.d_date,d.customer_sk,d.product_sk
order by dss.d_date

init.sh

#!/bin/bash

hive -f /opt/data/dm/dm_init.sql
hive -f /opt/data/dm/dm_run.sql

（2）sqoop从hdfs导出mysql（如果是orc等压缩格式，老实用Java！）

① sqoop：适用于textffile

如何查看某个table存放在hdfs什么地方？ show create table dm_sales_source.dm_sales_order_count;

!hdfs dfs –text /hive110/warehouse/dm_sales_source.db/dm_sales_order_count/000000_0

mysql中：

drop database if exists dmdb ;
create database dmdb;
use dmdb;
create table dm_sales_order_count(
d_date varchar(20), 
customer_sk int, 
product_sk int, 
order_num int, 
order_dailyamount double, 
recent_amount double, 
recent_num int 
);

然后sqoop从hdfs到mysql

mysql到hive，hive-> hdfs -> mysql，都需要 ‘\001’

sqoop export \
--connect jdbc:mysql://192.168.56.111:3306/dmdb \
--username root \
--password root \
--table dm_sales_order_count \
--export-dir /hive110/warehouse/dm_sales_source.db/dm_sales_order_count \
--input-fields-terminated-by \'\001\' \
-m 1

② Java方法：万能！

见：https://www.cnblogs.com/sabertobih/p/14043929.html

（3）如何暴露接口？

见：https://www.cnblogs.com/sabertobih/p/14043895.html

三、数据仓库之更新数据

本文链接：https://www.cnblogs.com/sabertobih/p/13965010.html

数据仓库之案例（基础篇）

一、销售案例步骤

（一）ODS层

（二）DW层

（三）DM层

数据仓库分层

二、数据仓库之构建步骤

（一）ODS层

（1）建立源数据库mysql并生成初始的数据

（2）在Hive中创建源数据过渡区和数据仓库的表

（3）日期维度的数据装载

（4）数据的ETL

（二）DW层

（1）SQL：建dw层表语句

（2）SQL：ods层导入dw层数据：customer/product/order

（3）脚本文件，生成date数据，导入dwd_fact_sales_order表

（三）DM层

（1）如何形成宽表？

（2）sqoop从hdfs导出mysql（如果是orc等压缩格式，老实用Java！）

① sqoop：适用于textffile

② Java方法：万能！

（3）如何暴露接口？

三、数据仓库之更新数据