基于Hadoop生态圈的数据仓库实践 —— 进阶技术（四）

四、角色扮演维度
        当一个事实表多次引用一个维度表时会用到角色扮演维度。例如，一个销售订单有一个是订单日期，还有一个交货日期，这时就需要引用日期维度表两次。
        本节将说明两类角色扮演维度的实现，分别是表别名和数据库视图。这两种都使用了Hive的功能。表别名是在SQL语句里引用维度表多次，每次引用都赋予维度表一个别名。而数据库视图，则是按照事实表需要引用维度表的次数，建立相同数量的视图。
1. 修改数据库模式
        使用下面的脚本修改数据库模式。分别给数据仓库里的事实表sales_order_fact和源数据库中订单销售表sales_order增加request_delivery_date_sk和request_delivery_date列。

-- in hive
USE dw; 

-- sales_order_fact表是ORC格式，增加列需要重建数据
ALTER TABLE sales_order_fact RENAME TO sales_order_fact_old;
CREATE TABLE sales_order_fact (
    order_sk INT comment ‘order surrogate key‘,
    customer_sk INT comment ‘customer surrogate key‘,
    product_sk INT comment ‘product surrogate key‘,
    order_date_sk INT comment ‘date surrogate key‘,
    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_sk) INTO 8 BUCKETS
STORED AS ORC TBLPROPERTIES (‘transactional‘=‘true‘);
INSERT INTO sales_order_fact
SELECT order_sk, customer_sk, product_sk, order_date_sk, NULL, order_amount, order_quantity
  FROM sales_order_fact_old;
DROP TABLE sales_order_fact_old;  

USE rds;
ALTER TABLE sales_order ADD COLUMNS (request_delivery_date DATE COMMENT ‘request delivery date‘) ;  

-- in mysql
USE source;
ALTER TABLE sales_order ADD request_delivery_date DATE AFTER order_date ;

修改后源数据库模式如下图所示。

修改后DW数据库模式如下图所示。

Hive不能像MySQL那样指定新增列的位置，它新增的列都是在表的最后。
2. 重建Sqoop作业
        使用下面的脚本重建Sqoop作业，增加request_delivery_date列。

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, order_date, entry_date, order_amount, order_quantity, request_delivery_date" --hive-import --hive-table rds.sales_order --incremental append --check-column order_number --last-value $last_value

注意columns参数值中列的顺序（MySQL里的source.sales_order）要和rds.sales_order的顺序保持一致。
3. 修改定期装载regular_etl.sql文件
        定期装载HiveQL脚本需要增加对交货日期列的处理，修改后的脚本如下所示。

-- 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order维度
INSERT INTO order_dim
SELECT
    ROW_NUMBER() OVER (ORDER BY t1.order_number) + t2.sk_max,
    t1.order_number,
    t1.version,
    t1.effective_date,
    t1.expiry_date
  FROM
(
SELECT
    order_number order_number,
    1 version,
    order_date effective_date,
    ‘2200-01-01‘ expiry_date
  FROM rds.sales_order, rds.cdc_time
 WHERE entry_date >= last_load AND entry_date < current_load ) t1
CROSS JOIN
(SELECT COALESCE(MAX(order_sk),0) sk_max FROM order_dim) t2;  

-- 装载销售订单事实表
INSERT INTO sales_order_fact
SELECT
    order_sk,
    customer_sk,
    product_sk,
    e.date_sk,
    f.date_sk,
    order_amount,
    order_quantity
  FROM
    rds.sales_order a,
    order_dim b,
    customer_dim c,
    product_dim d,
    date_dim e,
    date_dim f,
    rds.cdc_time g
 WHERE
    a.order_number = b.order_number
AND a.customer_number = c.customer_number
AND a.order_date >= c.effective_date
AND a.order_date < c.expiry_date
AND a.product_code = d.product_code
AND a.order_date >= d.effective_date
AND a.order_date < d.expiry_date
AND to_date(a.order_date) = e.date
AND to_date(a.request_delivery_date) = f.date
AND a.entry_date >= g.last_load AND a.entry_date < g.current_load ;  

-- 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
（1）执行下面的SQL脚本增加三个带有交货日期的销售订单。

USE source;
/***
新增订单日期为2016年7月17日的3条订单。
***/
SET @start_date := unix_timestamp(‘2016-07-17‘);
SET @end_date := unix_timestamp(‘2016-07-18‘);
SET @request_delivery_date := ‘2016-07-20‘;
DROP TABLE IF EXISTS temp_sales_order_data;
CREATE TABLE temp_sales_order_data AS SELECT * FROM sales_order WHERE 1=0;     

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 (126, 1, 1, @order_date, @request_delivery_date, @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 (127, 2, 2, @order_date, @request_delivery_date, @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 (128, 3, 3, @order_date, @request_delivery_date, @order_date, @amount, @quantity);    

INSERT INTO sales_order
SELECT NULL,customer_number,product_code,order_date,request_delivery_date,entry_date,order_amount,order_quantity FROM temp_sales_order_data ORDER BY order_date;      

COMMIT ;

修改后的销售订单源数据如下图所示，最后三条含有交货日期。

（2）修改rds.cdc_time的值

USE rds;
INSERT OVERWRITE TABLE rds.cdc_time SELECT ‘2016-07-17‘, ‘2016-07-17‘ FROM rds.cdc_time;

（3）执行定期装载并查看结果。
        使用下面的命令执行定期装载。

./regular_etl.sh

使用下面的查询验证结果。

use dw;
select a.order_sk, request_delivery_date_sk, c.date
  from sales_order_fact a, date_dim b, date_dim c
 where a.order_date_sk = b.date_sk
   and a.request_delivery_date_sk = c.date_sk ;

查询结果如下图所示，可以看到只有三个新的销售订单具有request_delivery_date_sk值，是2016年7月20日。

5. 使用角色扮演维度查询

-- 使用表别名查询
USE dw;  

SELECT
    order_date_dim.date order_date,
    request_delivery_date_dim.date request_delivery_date,
    SUM(order_amount),
    COUNT(*)
FROM
    sales_order_fact a,
    date_dim order_date_dim,
    date_dim request_delivery_date_dim
WHERE
    a.order_date_sk = order_date_dim.date_sk
        AND a.request_delivery_date_sk = request_delivery_date_dim.date_sk
GROUP BY order_date_dim.date , request_delivery_date_dim.date
CLUSTER BY order_date_dim.date , request_delivery_date_dim.date;

-- 使用视图查询
USE dw;  

CREATE VIEW order_date_dim
(order_date_sk, order_date, month, month_name,  quarter, year, promo_ind)
AS SELECT * FROM date_dim;  

CREATE VIEW request_delivery_date_dim
(request_delivery_date_sk, request_delivery_date, month, month_name, quarter, year, promo_ind)
AS SELECT * FROM date_dim;

SELECT
    order_date,
    request_delivery_date,
    SUM(order_amount),
    COUNT(*)
FROM
    sales_order_fact a,
    order_date_dim b,
    request_delivery_date_dim c
WHERE
    a.order_date_sk = b.order_date_sk
        AND a.request_delivery_date_sk = c.request_delivery_date_sk
GROUP BY order_date , request_delivery_date
CLUSTER BY order_date , request_delivery_date;

上面两个查询的结果相同，如下图所示：