本文介绍如何在数据仓库中处理迟到事实，特别是针对销售订单数据。通过修改数据模式、定期装载脚本及月底销售订单事实表装载脚本来实现。文章还提供了一个详细的测试案例。
目录
装载日期在生效日期后的事实就是迟到的事实，晚于订单日期进入源数据的销售订单可以看做是一个迟到事实的例子。销售订单被装载进其事实表时，装载的日期晚于销售订单的订单日期，因此是一个迟到的事实。
迟到事实影响周期快照事实表的装载，如“快照”中讨论的 month_end_sales_order_fact 表。例如，2016 年 6 月的销售订单金额月底快照已经计算并存储在 month_end_sales_order_fact 表中，这时一个迟到的 6 月订单在 7 月 10 日被装载，那么 2016 年 6 月的快照金额必须因迟到事实而重新计算。
本篇说明当导入 month_end_sales_order_fact 表时如何处理迟到的销售订单。
一、修改模式
为了知道一个销售订单是否是迟到的，需要把销售订单数据源的登记日期装载进 sales_order_fact 表。因为现在还没有登记日期列，所以需要在事实表上添加此列。使用维度角色扮演技术添加登记日期，在销售订单事实表里添加名为 entry_date_sk 的日期代理键列，并且从日期维度表创建一个叫做 entry_date_dim 的数据库视图。下面的脚本创建 entry_date_dim 视图和销售订单事实表里的 entry_date_sk 代理键列。
USE dw;
CREATE VIEW entry_date_dim (entry_date_sk , entry_date , month_name , month , quarter , year , promo_ind)
AS
SELECT date_sk,
date,
month_name,
month,
quarter,
year,
promo_ind
FROM date_dim;
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',
entry_date_sk int COMMENT 'entry 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 sales_order_fact
select order_number,
customer_sk,
product_sk,
sales_order_attribute_sk,
order_date_sk,
null,
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;二、修改定期装载脚本
创建完 entry_date_dim 视图，并给 sales_order_fact 表添加了 entry_date_sk 列以后，需要修改数据仓库定期装载脚本来包含登记日期。下面显示了修改后的 regular_etl.sql 定期装载脚本，注意 sales_order 源数据表和过渡表已经含有登记日期，只是以前没有将其装载进数据仓库。
-- 设置环境与时间窗口
!run /root/set_time.sql
-- 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product_count_fact表
insert overwrite table product_count_fact
select product_sk,date_sk
from (select a.product_sk product_sk,
a.product_code product_code,
b.date_sk date_sk,
row_number() over (partition by a.product_code order by b.date_sk) rn
from product_dim a,date_dim b
where a.effective_date = b.date) t
where rn = 1;
-- 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,
h.entry_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,
entry_date_dim h,
rds.cdc_time i
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.entry_date) = h.entry_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 >= i.last_load AND a.entry_date < i.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,
t0.entry_date_sk entry_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.entry_date_sk entry_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.entry_date_sk entry_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.entry_date_sk entry_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;三、修改装载周期快照事实表脚本
下面显示了修改后的 month_sum.sql 脚本，它有两部分。第一部分在具有相同产品和月份的现有销售订单行上增加新增的销售金额，第二部分处理没迟到的并且月底订单事实表中不存在销售订单或新增的非迟到的销售订单。
-- 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;
SET hivevar:pre_month_date = add_months(current_date,-1);
drop table if exists tmp;
create table tmp as
select a.order_month_sk order_month_sk,
a.product_sk product_sk,
a.month_order_amount + b.order_amount month_order_amount,
a.month_order_quantity + b.order_quantity month_order_quantity
from month_end_sales_order_fact a,
(select d.month_sk month_sk,
a.product_sk product_sk,
sum(order_amount) order_amount,
sum(order_quantity) order_quantity
from sales_order_fact a,
order_date_dim b,
entry_date_dim c,
month_dim d
where a.order_date_sk = b.order_date_sk
and a.entry_date_sk = c.entry_date_sk
and c.month = month(${hivevar:pre_month_date})
and c.year = year(${hivevar:pre_month_date})
and b.month = d.month
and b.order_date <> c.entry_date
group by d.month_sk , a.product_sk) b
where a.product_sk = b.product_sk
and a.order_month_sk = b.month_sk;
delete from month_end_sales_order_fact
where exists (select 1
from tmp t2
where month_end_sales_order_fact.order_month_sk = t2.order_month_sk
and month_end_sales_order_fact.product_sk = t2.product_sk);
insert into month_end_sales_order_fact select * from tmp;
insert into month_end_sales_order_fact
-- 正常的订单汇总（order_date = entry_date）
select b.month_sk, a.product_sk, sum(order_amount), sum(order_quantity)
from sales_order_fact a,
month_dim b,
order_date_dim d,
entry_date_dim c
where a.order_date_sk = d.order_date_sk
and a.entry_date_sk = c.entry_date_sk
and b.month = d.month
and b.year = d.year
and c.month = month(${hivevar:pre_month_date})
and c.year = year(${hivevar:pre_month_date})
and d.order_date = c.entry_date
group by b.month_sk, a.product_sk
union all
-- 迟到的事实，但尚不存在汇总数据
select d.month_sk, a.product_sk, sum(order_amount), sum(order_quantity)
from sales_order_fact a,
order_date_dim b,
entry_date_dim c,
month_dim d
where a.order_date_sk = b.order_date_sk
and a.entry_date_sk = c.entry_date_sk
and b.order_date <> c.entry_date
and c.month = month(${hivevar:pre_month_date})
and c.year = year(${hivevar:pre_month_date})
and b.month = d.month
and b.year = d.year
and not exists (select 1
from month_end_sales_order_fact p,
month_dim s
where p.order_month_sk = s.month_sk
and s.month = d.month
and s.year = d.year
and p.product_sk = a.product_sk)
group by d.month_sk , a.product_sk;四、测试
本节说明在执行定期装载脚本前必须的准备步骤。第一步是执行下面的 SQL 语句装载销售订单的登记日期。此 SQL 语句把销售订单的 entry_date_sk 修改为 order_date_sk 值，这些登记日期是后面测试月底快照导入所需要的。
UPDATE dw.sales_order_fact SET entry_date_sk = order_date_sk;在执行定期装载脚本前使用下面的语句查询 month_end_sales_order_fact 表，之后可以对比‘前’（不包含迟到事实）‘后’（包含了迟到事实）的数据，以确认装载的正确性。
use dw;
select year,
month,
product_name,
month_order_amount amt,
month_order_quantity qty
from month_end_sales_order_fact a,
month_dim b,
product_dim c
where a.order_month_sk = b.month_sk
and a.product_sk = c.product_sk
cluster by year , month , product_name;查询结果如下图所示。
为了对比‘前’‘后’日期，执行下面的语句查询 sales_order_fact 表。
use dw;
select a.product_sk,product_name,sum(order_amount)
from sales_order_fact a, product_dim b
where a.product_sk = b.product_sk
group by a.product_sk,product_name
cluster by product_name;查询结果如下图所示。
下一步执行下面的脚本准备销售订单测试数据。此脚本将三个销售订单装载进销售订单源数据，一个是迟到的在 month_end_sales_order_fact 中已存在的产品，一个是迟到的在 month_end_sales_order_fact 中不存在的产品，另一个是非迟到的正常产品。这里需要注意，产品维度是 SCD2 处理的，所以在添加销售订单时，新增订单时间一定要在产品维度的生效与过期时间区间内。
USE source;
-- 迟到已存在
SET @order_date := from_unixtime(unix_timestamp('2016-06-25') + rand() * (unix_timestamp('2016-06-26') - unix_timestamp('2016-06-25')));
SET @entry_date := from_unixtime(unix_timestamp('2016-07-26') + rand() * (unix_timestamp('2016-07-27') - unix_timestamp('2016-07-26')));
SET @amount := floor(1000 + rand() * 9000);
SET @quantity := floor(10 + rand() * 90);
INSERT INTO sales_order VALUES
(null, 141, 6, 2, 'Y', 'Y', 'Y', 'N', @order_date, 'N', '2016-06-30',
@entry_date, @amount, @quantity);
-- 迟到不存在
SET @order_date := from_unixtime(unix_timestamp('2016-06-26') + rand() * (unix_timestamp('2016-06-27') - unix_timestamp('2016-06-26')));
SET @entry_date := from_unixtime(unix_timestamp('2016-07-26') + rand() * (unix_timestamp('2016-07-27') - unix_timestamp('2016-07-26')));
SET @amount := floor(1000 + rand() * 9000);
SET @quantity := floor(10 + rand() * 90);
INSERT INTO sales_order VALUES
(null, 142, 6, 3, 'Y', 'Y', 'Y', 'N', @order_date, 'N', '2016-06-30',
@entry_date, @amount, @quantity);
-- 非迟到
SET @order_date := from_unixtime(unix_timestamp('2016-07-26') + rand() * (unix_timestamp('2016-07-27') - unix_timestamp('2016-07-26')));
SET @amount := floor(1000 + rand() * 9000);
SET @quantity := floor(10 + rand() * 90);
INSERT INTO sales_order VALUES
(null, 143, 12, 4, 'Y', 'N', 'Y', 'N', @order_date, 'N', '2016-07-30',
@order_date, @amount, @quantity);
COMMIT;新增订单数据如下图所示。
在执行新的月底销售订单定期装载脚本前，必须先把两条新的销售订装载进 sales_order_fact 表。使用下面的 SQL 命令修改时间窗口。
INSERT OVERWRITE TABLE rds.cdc_time SELECT '2016-07-26', '2016-07-26' FROM rds.cdc_time;将 set_time.sql 文件中的 SET hivevar:cur_date = CURRENT_DATE(); 行改为 SET hivevar:cur_date = '2016-07-27'; 后执行定期装载。
./regular_etl.sh现在已经准备好运行修改后的月底快照装载，执行下面的命令执行月底销售订单事实表装载脚本导入 2016 年 7 月的快照。
beeline -u jdbc:hive2://cdh2:10000/dw -f month_sum.sql最后，执行相同的查询获取包含了迟到事实月底销售订单数据。
use dw;
select year,
month,
product_name,
month_order_amount amt,
month_order_quantity qty
from month_end_sales_order_fact a,
month_dim b,
product_dim c
where a.order_month_sk = b.month_sk
and a.product_sk = c.product_sk
cluster by year , month , product_name;查询结果如下图所示。
对比‘前’‘后’查询的结果可以看到：
- 2016 年 6 月 Floppy Drive 的销售金额已经从 47810 变为 55060，这是由于迟到的产品销售订单增加了 7250 的销售金额。
- 2016 年 6 月的 LCD Panel（也是迟到的产品）被添加。
- 所有 2016 年 7 月的销售订单被累加，包括刚添加的 Keyboard Drive 销售订单（46082 = 41657 + 4425）。
至此测试完成，将 set_time.sql 文件中的 SET hivevar:cur_date = CURRENT_DATE(); 行还原。
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