基于Hadoop生态圈的数据仓库实践 —— 进阶技术（九）
九、退化维度 本节讨论一种称为退化维度的技术。该技术减少维度的数量，简化维度数据仓库模式。简单的模式比复杂的更容易理解，也有更好的查询性能。当一个维度没有数据仓库需要的任何数据时就可以退化此维度，此时需要把退化维度的相关数据迁移到事实表中，然后删除退化的维度。 1. 退化订单维度 本小节说明如何退化订单维度，包括对数据仓库模式和定期装载脚本的修改。使用维度退化技术时你首先要识别数据，分析从来不用的数据列。例如，订单维度的order_number列就可能是这样的一列。但如果用户想看事务的细节，还需要订单号。因此，在退化订单维度前，要把订单号迁移到sales_order_fact表。下图显示了迁移后的模式。
按顺序执行下面的四步退化order_dim维度表： （1）给sales_order_fact表添加order_number列 （2）把order_dim表里的订单号迁移到sales_order_fact表 （3）删除sales_order_fact表里的order_sk列 （4）删除order_dim表 下面的脚本完成所有退化订单维度所需的步骤。
use dw;
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',
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 t2.order_number,
t1.customer_sk,
t1.product_sk,
t1.order_date_sk,
t1.allocate_date_sk,
t1.allocate_quantity,
t1.packing_date_sk,
t1.packing_quantity,
t1.ship_date_sk,
t1.ship_quantity,
t1.receive_date_sk,
t1.receive_quantity,
t1.request_delivery_date_sk,
t1.order_amount,
t1.order_quantity
from sales_order_fact_old t1
inner join order_dim t2 on t1.order_sk = t2.order_sk;
drop table sales_order_fact_old;
drop table order_dim;2. 修改定期装载脚本 退化一个维度后需要做的另一件事就是修改定期装载脚本。修改后的脚本需要把订单号加入到销售订单事实表，而不再需要导入订单维度。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,
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,
rds.cdc_time g
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.entry_date >= g.last_load AND a.entry_date < g.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.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.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.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.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;3. 测试修改后的定期装载 （1）准备测试数据 测试使用具有分配库房、打包、配送和收货里程碑的两个新订单。所以每个订单需要添加五行。下面的脚本向源数据库里的sales_order表新增十行。
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('2016-07-25');
SET @end_date := unix_timestamp('2016-07-26');
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, 131, 1, 1, @order_date, 'N', '2016-08-01', @order_date, @amount, @quantity);
SET @start_date := unix_timestamp('2016-07-25');
SET @end_date := unix_timestamp('2016-07-26');
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, 132, 2, 2, @order_date, 'N', '2016-08-01', @order_date, @amount, @quantity);
SET @start_date := unix_timestamp('2016-07-26');
SET @end_date := unix_timestamp('2016-07-27');
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, 131, 1, 1, @order_date, 'A', '2016-08-01', @order_date, @amount, @quantity);
SET @start_date := unix_timestamp('2016-07-26');
SET @end_date := unix_timestamp('2016-07-27');
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, 132, 2, 2, @order_date, 'A', '2016-08-01', @order_date, @amount, @quantity);
SET @start_date := unix_timestamp('2016-07-27');
SET @end_date := unix_timestamp('2016-07-28');
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, 131, 1, 1, @order_date, 'P', '2016-08-01', @order_date, @amount, @quantity);
SET @start_date := unix_timestamp('2016-07-27');
SET @end_date := unix_timestamp('2016-07-28');
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, 132, 2, 2, @order_date, 'P', '2016-08-01', @order_date, @amount, @quantity);
SET @start_date := unix_timestamp('2016-07-28');
SET @end_date := unix_timestamp('2016-07-29');
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, 131, 1, 1, @order_date, 'S', '2016-08-01', @order_date, @amount, @quantity);
SET @start_date := unix_timestamp('2016-07-28');
SET @end_date := unix_timestamp('2016-07-29');
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, 132, 2, 2, @order_date, 'S', '2016-08-01', @order_date, @amount, @quantity);
SET @start_date := unix_timestamp('2016-07-29');
SET @end_date := unix_timestamp('2016-07-30');
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 (9, 131, 1, 1, @order_date, 'R', '2016-08-01', @order_date, @amount, @quantity);
SET @start_date := unix_timestamp('2016-07-29');
SET @end_date := unix_timestamp('2016-07-30');
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 (10, 132, 2, 2, @order_date, 'R', '2016-08-01', @order_date, @amount, @quantity);
INSERT INTO sales_order
select null,
order_number,
customer_number,
product_code,
status_date,
order_status,
request_delivery_date,
entry_date,
order_amount,
quantity
from temp_sales_order_data t1
order by t1.status_date;
COMMIT ;（2）执行五次定期装载
use rds;
INSERT OVERWRITE TABLE rds.cdc_time SELECT '2016-07-25', '2016-07-26' FROM rds.cdc_time;将regular_etl.sql文件中的SET hivevar:cur_date = CURRENT_DATE();行改为SET hivevar:cur_date = '2016-07-26';
./regular_etl.shuse rds;
INSERT OVERWRITE TABLE rds.cdc_time SELECT '2016-07-26', '2016-07-27' FROM rds.cdc_time;将regular_etl.sql文件中的SET hivevar:cur_date = CURRENT_DATE();行改为SET hivevar:cur_date = '2016-07-27';
./regular_etl.shuse rds;
INSERT OVERWRITE TABLE rds.cdc_time SELECT '2016-07-27', '2016-07-28' FROM rds.cdc_time;将regular_etl.sql文件中的SET hivevar:cur_date = CURRENT_DATE();行改为SET hivevar:cur_date = '2016-07-28';
./regular_etl.shuse rds;
INSERT OVERWRITE TABLE rds.cdc_time SELECT '2016-07-28', '2016-07-29' FROM rds.cdc_time;将regular_etl.sql文件中的SET hivevar:cur_date = CURRENT_DATE();行改为SET hivevar:cur_date = '2016-07-29';
./regular_etl.shuse rds;
INSERT OVERWRITE TABLE rds.cdc_time SELECT '2016-07-29', '2016-07-30' FROM rds.cdc_time;将regular_etl.sql文件中的SET hivevar:cur_date = CURRENT_DATE();行改为SET hivevar:cur_date = '2016-07-30';
./regular_etl.sh查询sales_order_fact表的两条订单。
use dw;
select t1.order_number orn,
t2.order_date od,
t1.order_quantity oq,
t3.allocate_date ad,
t1.allocate_quantity aq,
t4.packing_date pd,
t1.packing_quantity pq,
t5.ship_date sd,
t1.ship_quantity sq,
t6.receive_date rd,
t1.receive_quantity rq
from sales_order_fact t1
inner join order_date_dim t2 on t1.order_date_sk = t2.order_date_sk
left join allocate_date_dim t3 on t1.allocate_date_sk = t3.allocate_date_sk
left join packing_date_dim t4 on t1.packing_date_sk = t4.packing_date_sk
left join ship_date_dim t5 on t1.ship_date_sk = t5.ship_date_sk
left join receive_date_dim t6 on t1.receive_date_sk = t6.receive_date_sk
where t1.order_number IN (131 , 132);查询结果如下图所示。
测试完将regular_etl.sql文件中的SET hivevar:cur_date = CURRENT_DATE();行恢复。
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