基于Hadoop生态圈的数据仓库实践 —— 进阶技术（十）
十、杂项维度
本节讨论杂项维度。简单地说，杂项维度就是一种包含的数据具有很少可能值的维度。例如销售订单，它可能有很多离散数据（yes-no这种类型的值），如
1. 新增销售订单属性杂项维度
给现有的数据仓库新增一个销售订单杂项维度，需要新增一个名为sales_order_attribute_dim的维度表。下图显示了增加杂项维度表后的数据仓库模式（这里只显示了和销售订单属性相关的表）。
新的维度表包括四个yes-no列：verification_ind、credit_check_flag、new_customer_ind和web_order_flag。每个列可以有两个可能值中的一个（Y 或 N），因此sales_order_attribute_dim表最多有16（2^4）行。可以预装载这个维度，并且只需装载一次。
注意，如果知道某种组合是不可能出现的，就不需要装载这种组合。执行下面的脚本修改数据库模式。这个脚本做了四项工作：建立sales_order_attribute_dim表，向表中预装载全部16种可能的数据，给销售订单事实表添加杂项维度代理键，给源数据库里的sales_order表增加对应的四个属性列。
由于有了一个新的维度，必须修改定期装载脚本。下面显示了修改后的regular_etl.sql脚本文件内容。
（1）使用下面的脚本添加八个销售订单。
可以使用下面的分析性查询确认装载正确。该查询分析出检查了信用状态的新用户有所占的比例。
本节讨论杂项维度。简单地说，杂项维度就是一种包含的数据具有很少可能值的维度。例如销售订单，它可能有很多离散数据（yes-no这种类型的值），如
- verification_ind（如果订单已经被审核，值为yes）
- credit_check_flag（表示此订单的客户信用状态是否已经检查）
- new_customer_ind（如果这是新客户的首个订单，值为yes）
- web_order_flag（表示此订单是否是在线下的订单）
1. 新增销售订单属性杂项维度
给现有的数据仓库新增一个销售订单杂项维度，需要新增一个名为sales_order_attribute_dim的维度表。下图显示了增加杂项维度表后的数据仓库模式（这里只显示了和销售订单属性相关的表）。
新的维度表包括四个yes-no列：verification_ind、credit_check_flag、new_customer_ind和web_order_flag。每个列可以有两个可能值中的一个（Y 或 N），因此sales_order_attribute_dim表最多有16（2^4）行。可以预装载这个维度，并且只需装载一次。
注意，如果知道某种组合是不可能出现的，就不需要装载这种组合。执行下面的脚本修改数据库模式。这个脚本做了四项工作：建立sales_order_attribute_dim表，向表中预装载全部16种可能的数据，给销售订单事实表添加杂项维度代理键，给源数据库里的sales_order表增加对应的四个属性列。
USE dw;
-- 建立杂项维度表
CREATE TABLE sales_order_attribute_dim (
sales_order_attribute_sk INT COMMENT 'sales order attribute surrogate key',
verification_ind CHAR(1) COMMENT 'verification index, Y or N',
credit_check_flag CHAR(1) COMMENT 'credit check flag, Y or N',
new_customer_ind CHAR(1) COMMENT 'new customer index, Y or N',
web_order_flag CHAR(1) COMMENT 'web order flag, Y or N',
version int COMMENT 'version',
effective_date DATE COMMENT 'effective date',
expiry_date DATE COMMENT 'expiry date'
)
clustered by (sales_order_attribute_sk) into 8 buckets
stored as orc tblproperties ('transactional'='true');
-- 生成杂项维度数据
INSERT INTO sales_order_attribute_dim VALUES (1, 'Y', 'N', 'N', 'N', 1,'1900-00-00', '2200-01-01');
INSERT INTO sales_order_attribute_dim VALUES (2, 'Y', 'Y', 'N', 'N', 1,'1900-00-00', '2200-01-01');
INSERT INTO sales_order_attribute_dim VALUES (3, 'Y', 'Y', 'Y', 'N', 1,'1900-00-00', '2200-01-01');
INSERT INTO sales_order_attribute_dim VALUES (4, 'Y', 'Y', 'Y', 'Y', 1,'1900-00-00', '2200-01-01');
INSERT INTO sales_order_attribute_dim VALUES (5, 'Y', 'N', 'Y', 'N', 1,'1900-00-00', '2200-01-01');
INSERT INTO sales_order_attribute_dim VALUES (6, 'Y', 'N', 'Y', 'Y', 1,'1900-00-00', '2200-01-01');
INSERT INTO sales_order_attribute_dim VALUES (7, 'Y', 'N', 'N', 'Y', 1,'1900-00-00', '2200-01-01');
INSERT INTO sales_order_attribute_dim VALUES (8, 'Y', 'Y', 'N', 'Y', 1,'1900-00-00', '2200-01-01');
INSERT INTO sales_order_attribute_dim VALUES (9, 'N', 'N', 'N', 'N', 1,'1900-00-00', '2200-01-01');
INSERT INTO sales_order_attribute_dim VALUES (10, 'N', 'Y', 'N', 'N', 1,'1900-00-00', '2200-01-01');
INSERT INTO sales_order_attribute_dim VALUES (11, 'N', 'Y', 'Y', 'N', 1,'1900-00-00', '2200-01-01');
INSERT INTO sales_order_attribute_dim VALUES (12, 'N', 'Y', 'Y', 'Y', 1,'1900-00-00', '2200-01-01');
INSERT INTO sales_order_attribute_dim VALUES (13, 'N', 'N', 'Y', 'N', 1,'1900-00-00', '2200-01-01');
INSERT INTO sales_order_attribute_dim VALUES (14, 'N', 'N', 'Y', 'Y', 1,'1900-00-00', '2200-01-01');
INSERT INTO sales_order_attribute_dim VALUES (15, 'N', 'N', 'N', 'Y', 1,'1900-00-00', '2200-01-01');
INSERT INTO sales_order_attribute_dim VALUES (16, 'N', 'Y', 'N', 'Y', 1,'1900-00-00', '2200-01-01');
-- 建立杂项维度外键
alter table sales_order_fact rename to sales_order_fact_old;
create table sales_order_fact(
order_number int COMMENT 'order number',
customer_sk int COMMENT 'customer surrogate key',
product_sk int COMMENT 'product surrogate key',
sales_order_attribute_sk int COMMENT 'sales order attribute surrogate key',
order_date_sk int COMMENT 'order date surrogate key',
allocate_date_sk int COMMENT 'allocate date surrogate key',
allocate_quantity int COMMENT 'allocate quantity',
packing_date_sk int COMMENT 'packing date surrogate key',
packing_quantity int COMMENT 'packing quantity',
ship_date_sk int COMMENT 'ship date surrogate key',
ship_quantity int COMMENT 'ship quantity',
receive_date_sk int COMMENT 'receive date surrogate key',
receive_quantity int COMMENT 'receive quantity',
request_delivery_date_sk int COMMENT 'request delivery date surrogate key',
order_amount decimal(10,2) COMMENT 'order amount',
order_quantity int COMMENT 'order quantity')
clustered by (order_number) into 8 buckets
stored as orc tblproperties ('transactional'='true');
insert into table sales_order_fact
select order_number,
customer_sk,
product_sk,
null,
order_date_sk,
allocate_date_sk,
allocate_quantity,
packing_date_sk,
packing_quantity,
ship_date_sk,
ship_quantity,
receive_date_sk,
receive_quantity,
request_delivery_date_sk,
order_amount,
order_quantity
from sales_order_fact_old;
drop table sales_order_fact_old;
-- 给源库的销售订单表增加对应的属性
USE source;
ALTER TABLE sales_order
ADD verification_ind CHAR (1) AFTER product_code
, ADD credit_check_flag CHAR (1) AFTER verification_ind
, ADD new_customer_ind CHAR (1) AFTER credit_check_flag
, ADD web_order_flag CHAR (1) AFTER new_customer_ind ;
-- 给销售订单过渡表增加对应的属性
USE rds;
ALTER TABLE sales_order ADD COLUMNS
(
verification_ind CHAR(1) COMMENT 'verification index, Y or N',
credit_check_flag CHAR(1) COMMENT 'credit check flag, Y or N',
new_customer_ind CHAR(1) COMMENT 'new customer index, Y or N',
web_order_flag CHAR(1) COMMENT 'web order flag, Y or N'
) ;
2. 重建Sqoop作业
last_value=`sqoop job --show myjob_incremental_import --meta-connect jdbc:hsqldb:hsql://cdh2:16000/sqoop | grep incremental.last.value | awk '{print $3}'`
sqoop job --delete myjob_incremental_import --meta-connect jdbc:hsqldb:hsql://cdh2:16000/sqoop
sqoop job \
--meta-connect jdbc:hsqldb:hsql://cdh2:16000/sqoop \
--create myjob_incremental_import \
-- \
import \
--connect "jdbc:mysql://cdh1:3306/source?useSSL=false&user=root&password=mypassword" \
--table sales_order \
--columns "order_number, customer_number, product_code, status_date, entry_date, order_amount, quantity, request_delivery_date, order_status, verification_ind, credit_check_flag, new_customer_ind, web_order_flag" \
--hive-import \
--hive-table rds.sales_order \
--incremental append \
--check-column id \
--last-value $last_value
3. 修改定期装载脚本
由于有了一个新的维度，必须修改定期装载脚本。下面显示了修改后的regular_etl.sql脚本文件内容。
-- 设置变量以支持事务
set hive.support.concurrency=true;
set hive.exec.dynamic.partition.mode=nonstrict;
set hive.txn.manager=org.apache.hadoop.hive.ql.lockmgr.DbTxnManager;
set hive.compactor.initiator.on=true;
set hive.compactor.worker.threads=1;
USE dw;
-- 设置SCD的生效时间和过期时间
SET hivevar:cur_date = CURRENT_DATE();
SET hivevar:pre_date = DATE_ADD(${hivevar:cur_date},-1);
SET hivevar:max_date = CAST('2200-01-01' AS DATE);
-- 设置CDC的上限时间
INSERT OVERWRITE TABLE rds.cdc_time SELECT last_load, ${hivevar:cur_date} FROM rds.cdc_time;
-- 装载customer维度
-- 设置已删除记录和地址相关列上SCD2的过期，用<=>运算符处理NULL值。
UPDATE customer_dim
SET expiry_date = ${hivevar:pre_date}
WHERE customer_dim.customer_sk IN
(SELECT a.customer_sk
FROM (SELECT customer_sk,
customer_number,
customer_street_address,
customer_zip_code,
customer_city,
customer_state,
shipping_address,
shipping_zip_code,
shipping_city,
shipping_state
FROM customer_dim WHERE expiry_date = ${hivevar:max_date}) a LEFT JOIN
rds.customer b ON a.customer_number = b.customer_number
WHERE b.customer_number IS NULL OR
( !(a.customer_street_address <=> b.customer_street_address)
OR !(a.customer_zip_code <=> b.customer_zip_code)
OR !(a.customer_city <=> b.customer_city)
OR !(a.customer_state <=> b.customer_state)
OR !(a.shipping_address <=> b.shipping_address)
OR !(a.shipping_zip_code <=> b.shipping_zip_code)
OR !(a.shipping_city <=> b.shipping_city)
OR !(a.shipping_state <=> b.shipping_state)
));
-- 处理customer_street_addresses列上SCD2的新增行
INSERT INTO customer_dim
SELECT
ROW_NUMBER() OVER (ORDER BY t1.customer_number) + t2.sk_max,
t1.customer_number,
t1.customer_name,
t1.customer_street_address,
t1.customer_zip_code,
t1.customer_city,
t1.customer_state,
t1.shipping_address,
t1.shipping_zip_code,
t1.shipping_city,
t1.shipping_state,
t1.version,
t1.effective_date,
t1.expiry_date
FROM
(
SELECT
t2.customer_number customer_number,
t2.customer_name customer_name,
t2.customer_street_address customer_street_address,
t2.customer_zip_code customer_zip_code,
t2.customer_city customer_city,
t2.customer_state customer_state,
t2.shipping_address shipping_address,
t2.shipping_zip_code shipping_zip_code,
t2.shipping_city shipping_city,
t2.shipping_state shipping_state,
t1.version + 1 version,
${hivevar:pre_date} effective_date,
${hivevar:max_date} expiry_date
FROM customer_dim t1
INNER JOIN rds.customer t2
ON t1.customer_number = t2.customer_number
AND t1.expiry_date = ${hivevar:pre_date}
LEFT JOIN customer_dim t3
ON t1.customer_number = t3.customer_number
AND t3.expiry_date = ${hivevar:max_date}
WHERE (!(t1.customer_street_address <=> t2.customer_street_address)
OR !(t1.customer_zip_code <=> t2.customer_zip_code)
OR !(t1.customer_city <=> t2.customer_city)
OR !(t1.customer_state <=> t2.customer_state)
OR !(t1.shipping_address <=> t2.shipping_address)
OR !(t1.shipping_zip_code <=> t2.shipping_zip_code)
OR !(t1.shipping_city <=> t2.shipping_city)
OR !(t1.shipping_state <=> t2.shipping_state)
)
AND t3.customer_sk IS NULL) t1
CROSS JOIN
(SELECT COALESCE(MAX(customer_sk),0) sk_max FROM customer_dim) t2;
-- 处理customer_name列上的SCD1
-- 因为hive的update的set子句还不支持子查询，所以这里使用了一个临时表存储需要更新的记录，用先delete再insert代替update
-- 因为SCD1本身就不保存历史数据，所以这里更新维度表里的所有customer_name改变的记录，而不是仅仅更新当前版本的记录
DROP TABLE IF EXISTS tmp;
CREATE TABLE tmp AS
SELECT
a.customer_sk,
a.customer_number,
b.customer_name,
a.customer_street_address,
a.customer_zip_code,
a.customer_city,
a.customer_state,
a.shipping_address,
a.shipping_zip_code,
a.shipping_city,
a.shipping_state,
a.version,
a.effective_date,
a.expiry_date
FROM customer_dim a, rds.customer b
WHERE a.customer_number = b.customer_number AND !(a.customer_name <=> b.customer_name);
DELETE FROM customer_dim WHERE customer_dim.customer_sk IN (SELECT customer_sk FROM tmp);
INSERT INTO customer_dim SELECT * FROM tmp;
-- 处理新增的customer记录
INSERT INTO customer_dim
SELECT
ROW_NUMBER() OVER (ORDER BY t1.customer_number) + t2.sk_max,
t1.customer_number,
t1.customer_name,
t1.customer_street_address,
t1.customer_zip_code,
t1.customer_city,
t1.customer_state,
t1.shipping_address,
t1.shipping_zip_code,
t1.shipping_city,
t1.shipping_state,
1,
${hivevar:pre_date},
${hivevar:max_date}
FROM
(
SELECT t1.* FROM rds.customer t1 LEFT JOIN customer_dim t2 ON t1.customer_number = t2.customer_number
WHERE t2.customer_sk IS NULL) t1
CROSS JOIN
(SELECT COALESCE(MAX(customer_sk),0) sk_max FROM customer_dim) t2;
-- 重载PA客户维度
TRUNCATE TABLE pa_customer_dim;
INSERT INTO pa_customer_dim
SELECT
customer_sk
, customer_number
, customer_name
, customer_street_address
, customer_zip_code
, customer_city
, customer_state
, shipping_address
, shipping_zip_code
, shipping_city
, shipping_state
, version
, effective_date
, expiry_date
FROM customer_dim
WHERE customer_state = 'PA' ;
-- 装载product维度
-- 设置已删除记录和product_name、product_category列上SCD2的过期
UPDATE product_dim
SET expiry_date = ${hivevar:pre_date}
WHERE product_dim.product_sk IN
(SELECT a.product_sk
FROM (SELECT product_sk,product_code,product_name,product_category
FROM product_dim WHERE expiry_date = ${hivevar:max_date}) a LEFT JOIN
rds.product b ON a.product_code = b.product_code
WHERE b.product_code IS NULL OR (a.product_name <> b.product_name OR a.product_category <> b.product_category));
-- 处理product_name、product_category列上SCD2的新增行
INSERT INTO product_dim
SELECT
ROW_NUMBER() OVER (ORDER BY t1.product_code) + t2.sk_max,
t1.product_code,
t1.product_name,
t1.product_category,
t1.version,
t1.effective_date,
t1.expiry_date
FROM
(
SELECT
t2.product_code product_code,
t2.product_name product_name,
t2.product_category product_category,
t1.version + 1 version,
${hivevar:pre_date} effective_date,
${hivevar:max_date} expiry_date
FROM product_dim t1
INNER JOIN rds.product t2
ON t1.product_code = t2.product_code
AND t1.expiry_date = ${hivevar:pre_date}
LEFT JOIN product_dim t3
ON t1.product_code = t3.product_code
AND t3.expiry_date = ${hivevar:max_date}
WHERE (t1.product_name <> t2.product_name OR t1.product_category <> t2.product_category) AND t3.product_sk IS NULL) t1
CROSS JOIN
(SELECT COALESCE(MAX(product_sk),0) sk_max FROM product_dim) t2;
-- 处理新增的product记录
INSERT INTO product_dim
SELECT
ROW_NUMBER() OVER (ORDER BY t1.product_code) + t2.sk_max,
t1.product_code,
t1.product_name,
t1.product_category,
1,
${hivevar:pre_date},
${hivevar:max_date}
FROM
(
SELECT t1.* FROM rds.product t1 LEFT JOIN product_dim t2 ON t1.product_code = t2.product_code
WHERE t2.product_sk IS NULL) t1
CROSS JOIN
(SELECT COALESCE(MAX(product_sk),0) sk_max FROM product_dim) t2;
-- 装载销售订单事实表
-- 前一天新增的销售订单
INSERT INTO sales_order_fact
SELECT
a.order_number,
customer_sk,
product_sk,
g.sales_order_attribute_sk,
e.order_date_sk,
null,
null,
null,
null,
null,
null,
null,
null,
f.request_delivery_date_sk,
order_amount,
quantity
FROM
rds.sales_order a,
customer_dim c,
product_dim d,
order_date_dim e,
request_delivery_date_dim f,
sales_order_attribute_dim g,
rds.cdc_time h
WHERE
a.order_status = 'N'
AND a.customer_number = c.customer_number
AND a.status_date >= c.effective_date
AND a.status_date < c.expiry_date
AND a.product_code = d.product_code
AND a.status_date >= d.effective_date
AND a.status_date < d.expiry_date
AND to_date(a.status_date) = e.order_date
AND to_date(a.request_delivery_date) = f.request_delivery_date
AND a.verification_ind = g.verification_ind
AND a.credit_check_flag = g.credit_check_flag
AND a.new_customer_ind = g.new_customer_ind
AND a.web_order_flag = g.web_order_flag
AND a.entry_date >= h.last_load AND a.entry_date < h.current_load ;
-- 处理分配库房、打包、配送和收货四个状态
DROP TABLE IF EXISTS tmp;
CREATE TABLE tmp AS
select t0.order_number order_number,
t0.customer_sk customer_sk,
t0.product_sk product_sk,
t0.sales_order_attribute_sk,
t0.order_date_sk order_date_sk,
t2.allocate_date_sk allocate_date_sk,
t1.quantity allocate_quantity,
t0.packing_date_sk packing_date_sk,
t0.packing_quantity packing_quantity,
t0.ship_date_sk ship_date_sk,
t0.ship_quantity ship_quantity,
t0.receive_date_sk receive_date_sk,
t0.receive_quantity receive_quantity,
t0.request_delivery_date_sk request_delivery_date_sk,
t0.order_amount order_amount,
t0.order_quantity order_quantity
from sales_order_fact t0,
rds.sales_order t1,
allocate_date_dim t2,
rds.cdc_time t4
where t0.order_number = t1.order_number and t1.order_status = 'A'
and to_date(t1.status_date) = t2.allocate_date
and t1.entry_date >= t4.last_load and t1.entry_date < t4.current_load;
DELETE FROM sales_order_fact WHERE sales_order_fact.order_number IN (SELECT order_number FROM tmp);
INSERT INTO sales_order_fact SELECT * FROM tmp;
DROP TABLE IF EXISTS tmp;
CREATE TABLE tmp AS
select t0.order_number order_number,
t0.customer_sk customer_sk,
t0.product_sk product_sk,
t0.sales_order_attribute_sk,
t0.order_date_sk order_date_sk,
t0.allocate_date_sk allocate_date_sk,
t0.allocate_quantity allocate_quantity,
t2.packing_date_sk packing_date_sk,
t1.quantity packing_quantity,
t0.ship_date_sk ship_date_sk,
t0.ship_quantity ship_quantity,
t0.receive_date_sk receive_date_sk,
t0.receive_quantity receive_quantity,
t0.request_delivery_date_sk request_delivery_date_sk,
t0.order_amount order_amount,
t0.order_quantity order_quantity
from sales_order_fact t0,
rds.sales_order t1,
packing_date_dim t2,
rds.cdc_time t4
where t0.order_number = t1.order_number and t1.order_status = 'P'
and to_date(t1.status_date) = t2.packing_date
and t1.entry_date >= t4.last_load and t1.entry_date < t4.current_load;
DELETE FROM sales_order_fact WHERE sales_order_fact.order_number IN (SELECT order_number FROM tmp);
INSERT INTO sales_order_fact SELECT * FROM tmp;
DROP TABLE IF EXISTS tmp;
CREATE TABLE tmp AS
select t0.order_number order_number,
t0.customer_sk customer_sk,
t0.product_sk product_sk,
t0.sales_order_attribute_sk,
t0.order_date_sk order_date_sk,
t0.allocate_date_sk allocate_date_sk,
t0.allocate_quantity allocate_quantity,
t0.packing_date_sk packing_date_sk,
t0.packing_quantity packing_quantity,
t2.ship_date_sk ship_date_sk,
t1.quantity ship_quantity,
t0.receive_date_sk receive_date_sk,
t0.receive_quantity receive_quantity,
t0.request_delivery_date_sk request_delivery_date_sk,
t0.order_amount order_amount,
t0.order_quantity order_quantity
from sales_order_fact t0,
rds.sales_order t1,
ship_date_dim t2,
rds.cdc_time t4
where t0.order_number = t1.order_number and t1.order_status = 'S'
and to_date(t1.status_date) = t2.ship_date
and t1.entry_date >= t4.last_load and t1.entry_date < t4.current_load;
DELETE FROM sales_order_fact WHERE sales_order_fact.order_number IN (SELECT order_number FROM tmp);
INSERT INTO sales_order_fact SELECT * FROM tmp;
DROP TABLE IF EXISTS tmp;
CREATE TABLE tmp AS
select t0.order_number order_number,
t0.customer_sk customer_sk,
t0.product_sk product_sk,
t0.sales_order_attribute_sk,
t0.order_date_sk order_date_sk,
t0.allocate_date_sk allocate_date_sk,
t0.allocate_quantity allocate_quantity,
t0.packing_date_sk packing_date_sk,
t0.packing_quantity packing_quantity,
t0.ship_date_sk ship_date_sk,
t0.ship_quantity ship_quantity,
t2.receive_date_sk receive_date_sk,
t1.quantity receive_quantity,
t0.request_delivery_date_sk request_delivery_date_sk,
t0.order_amount order_amount,
t0.order_quantity order_quantity
from sales_order_fact t0,
rds.sales_order t1,
receive_date_dim t2,
rds.cdc_time t4
where t0.order_number = t1.order_number and t1.order_status = 'R'
and to_date(t1.status_date) = t2.receive_date
and t1.entry_date >= t4.last_load and t1.entry_date < t4.current_load;
DELETE FROM sales_order_fact WHERE sales_order_fact.order_number IN (SELECT order_number FROM tmp);
INSERT INTO sales_order_fact SELECT * FROM tmp;
-- 更新时间戳表的last_load字段
INSERT OVERWRITE TABLE rds.cdc_time SELECT current_load, current_load FROM rds.cdc_time;
4. 测试修改后的定期装载
（1）使用下面的脚本添加八个销售订单。
USE source;
DROP TABLE IF EXISTS temp_sales_order_data;
CREATE TABLE temp_sales_order_data AS SELECT * FROM sales_order WHERE 1=0;
SET @start_date := unix_timestamp('2016-07-31');
SET @end_date := unix_timestamp('2016-08-01');
SET @order_date := from_unixtime(@start_date + rand() * (@end_date - @start_date));
SET @amount := floor(1000 + rand() * 9000);
SET @quantity := floor(10 + rand() * 90);
INSERT INTO temp_sales_order_data VALUES (1, 133, 1, 1, 'Y', 'Y', 'N', 'Y', @order_date, 'N', '2016-08-05', @order_date, @amount, @quantity);
SET @order_date := from_unixtime(@start_date + rand() * (@end_date - @start_date));
SET @amount := floor(1000 + rand() * 9000);
SET @quantity := floor(10 + rand() * 90);
INSERT INTO temp_sales_order_data VALUES (2, 134, 2, 2, 'N', 'N', 'N', 'N', @order_date, 'N', '2016-08-05', @order_date, @amount, @quantity);
SET @order_date := from_unixtime(@start_date + rand() * (@end_date - @start_date));
SET @amount := floor(1000 + rand() * 9000);
SET @quantity := floor(10 + rand() * 90);
INSERT INTO temp_sales_order_data VALUES (3, 135, 3, 3, 'Y', 'Y', 'N', 'N', @order_date, 'N', '2016-08-05', @order_date, @amount, @quantity);
SET @order_date := from_unixtime(@start_date + rand() * (@end_date - @start_date));
SET @amount := floor(1000 + rand() * 9000);
SET @quantity := floor(10 + rand() * 90);
INSERT INTO temp_sales_order_data VALUES (4, 136, 4, 4, 'Y', 'N', 'N', 'N', @order_date, 'N', '2016-08-05', @order_date, @amount, @quantity);
SET @order_date := from_unixtime(@start_date + rand() * (@end_date - @start_date));
SET @amount := floor(1000 + rand() * 9000);
SET @quantity := floor(10 + rand() * 90);
INSERT INTO temp_sales_order_data VALUES (5, 137, 11, 1, 'N', 'Y', 'Y', 'Y', @order_date, 'N', '2016-08-05', @order_date, @amount, @quantity);
SET @order_date := from_unixtime(@start_date + rand() * (@end_date - @start_date));
SET @amount := floor(1000 + rand() * 9000);
SET @quantity := floor(10 + rand() * 90);
INSERT INTO temp_sales_order_data VALUES (6, 138, 12, 2, 'N', 'Y', 'Y', 'N', @order_date, 'N', '2016-08-05', @order_date, @amount, @quantity);
SET @order_date := from_unixtime(@start_date + rand() * (@end_date - @start_date));
SET @amount := floor(1000 + rand() * 9000);
SET @quantity := floor(10 + rand() * 90);
INSERT INTO temp_sales_order_data VALUES (7, 139, 13, 3, 'Y', 'Y', 'Y', 'N', @order_date, 'N', '2016-08-05', @order_date, @amount, @quantity);
SET @order_date := from_unixtime(@start_date + rand() * (@end_date - @start_date));
SET @amount := floor(1000 + rand() * 9000);
SET @quantity := floor(10 + rand() * 90);
INSERT INTO temp_sales_order_data VALUES (8, 140, 14, 4, 'Y', 'N', 'Y', 'N', @order_date, 'N', '2016-08-05', @order_date, @amount, @quantity);
INSERT INTO sales_order
select null,
@rn:=@rn+1,
customer_number,
product_code,
verification_ind,
credit_check_flag,
new_customer_ind,
web_order_flag,
status_date,
order_status,
request_delivery_date,
entry_date,
order_amount,
quantity
from temp_sales_order_data t1 ,(select @rn:=132) t2
order by t1.status_date;
COMMIT;（2）执行定期装载
./regular_etl.sh（3）验证结果
可以使用下面的分析性查询确认装载正确。该查询分析出检查了信用状态的新用户有所占的比例。
USE dw;
SELECT CONCAT(ROUND(checked / (checked + not_checked) * 100),' % ')
FROM (SELECT sum(case when credit_check_flag='Y' then 1 else 0 end) checked,
sum(case when credit_check_flag='N' then 1 else 0 end) not_checked
FROM sales_order_fact a, sales_order_attribute_dim b
WHERE new_customer_ind = 'Y'
AND a.sales_order_attribute_sk = b.sales_order_attribute_sk) t;
查询结果下图所示。