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 hiveUSE dw;修改后源数据库模式如下图所示。
-- 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/sqoopsqoop 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。
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：
