基于Hadoop生态圈的数据仓库实践 —— 进阶技术（一）

USE source;

ALTER TABLE customer
  ADD shipping_address VARCHAR(50) AFTER customer_state
, ADD shipping_zip_code INT AFTER shipping_address
, ADD shipping_city VARCHAR(30) AFTER shipping_zip_code
, ADD shipping_state VARCHAR(2) AFTER shipping_city ;

ALTER TABLE sales_order
  ADD order_quantity INT AFTER order_amount ; 

USE rds;

ALTER TABLE customer ADD COLUMNS
 (shipping_address VARCHAR(50) COMMENT ‘shipping_address‘
, shipping_zip_code INT COMMENT ‘shipping_zip_code‘
, shipping_city VARCHAR(30) COMMENT ‘shipping_city‘
, shipping_state VARCHAR(2) COMMENT ‘shipping_state‘) ;

ALTER TABLE sales_order ADD COLUMNS 
 (order_quantity INT COMMENT ‘order_quantity‘) ; 

USE dw;

-- 修改客户维度表
ALTER TABLE customer_dim RENAME TO customer_dim_old;
CREATE TABLE customer_dim (    
    customer_sk INT comment ‘surrogate key‘,    
    customer_number INT comment ‘number‘,    
    customer_name VARCHAR(50) comment ‘name‘,    
    customer_street_address VARCHAR(50) comment ‘address‘,    
    customer_zip_code INT comment ‘zipcode‘,    
    customer_city VARCHAR(30) comment ‘city‘,    
    customer_state VARCHAR(2) comment ‘state‘, 
    shipping_address VARCHAR(50) COMMENT ‘shipping_address‘, 
    shipping_zip_code INT COMMENT ‘shipping_zip_code‘,
    shipping_city VARCHAR(30) COMMENT ‘shipping_city‘, 
    shipping_state VARCHAR(2) COMMENT ‘shipping_state‘,
    version INT comment ‘version‘,  
    effective_date DATE comment ‘effective date‘,    
    expiry_date DATE comment ‘expiry date‘
)  
CLUSTERED BY (customer_sk) INTO 8 BUCKETS    
STORED AS ORC TBLPROPERTIES (‘transactional‘=‘true‘);
INSERT INTO customer_dim 
SELECT customer_sk,
       customer_number,
       customer_name,
       customer_street_address,
       customer_zip_code,
       customer_city,
       customer_state,
       NULL,
       NULL,
       NULL,
       NULL,
       version,
       effective_date,
       expiry_date
  FROM customer_dim_old;
DROP TABLE customer_dim_old;

-- 修改销售订单事实表
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‘,    
    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 *,NULL FROM sales_order_fact_old;
DROP TABLE sales_order_fact_old;

use test;    
drop table if exists t1;
create table t1(c1 int, c2 string)     
clustered by (c1) into 8 buckets     
stored as orc TBLPROPERTIES (‘transactional‘=‘true‘);  
insert into t1 values (1,‘aaa‘); 
alter table t1 add columns (c3 string) ;
update t1 set c2=‘ccc‘ where c1=1;
select * from t1; 

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" --hive-import --hive-table rds.sales_order --incremental append --check-column order_number --last-value $last_value

-- 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;

-- 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,
    date_sk,
    order_amount,
    order_quantity
  FROM
    rds.sales_order a,
    order_dim b,
    customer_dim c,
    product_dim d,
    date_dim e,
    rds.cdc_time f
 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 a.entry_date >= f.last_load AND a.entry_date < f.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;

USE source;  
  
/***   
客户数据的改变如下：  
更新已有八个客户的送货地址  
新增客户9  
***/  
UPDATE customer SET   
  shipping_address = customer_street_address  
, shipping_zip_code = customer_zip_code  
, shipping_city = customer_city  
, shipping_state = customer_state ;  
  
INSERT INTO customer   
(customer_name  
, customer_street_address  
, customer_zip_code  
, customer_city  
, customer_state  
, shipping_address  
, shipping_zip_code  
, shipping_city  
, shipping_state)  
VALUES   
(‘Online Distributors‘  
, ‘2323 Louise Dr.‘  
, 17055  
, ‘Pittsburgh‘  
, ‘PA‘  
, ‘2323 Louise Dr.‘  
, 17055  
, ‘Pittsburgh‘  
, ‘PA‘) ;  

/***    
新增订单日期为2016年7月12日的9条订单。
***/  
SET @start_date := unix_timestamp(‘2016-07-12‘);  
SET @end_date := unix_timestamp(‘2016-07-13‘);  
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 (117, 1, 1, @order_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 (118, 2, 2, @order_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 (119, 3, 3, @order_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 (120, 4, 4, @order_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 (121, 5, 1, @order_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 (122, 6, 2, @order_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 (123, 7, 3, @order_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 (124, 8, 4, @order_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 (125, 9, 1, @order_date, @order_date, @amount, @quantity);  

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

COMMIT ;

./regular_etl.sh 

use dw;
select customer_number no,
       customer_name name,
       shipping_city,
       shipping_zip_code zip,
       shipping_state st,
       version ver,
       effective_date eff,
       expiry_date exp
  from customer_dim;

select order_sk o_sk,
       customer_sk c_sk,
       product_sk p_sk,
       order_date_sk od_sk,
       order_amount amt,
       order_quantity qty
  from sales_order_fact
cluster by o_sk;

select * from rds.cdc_time;

基于Hadoop生态圈的数据仓库实践 —— 进阶技术（一）

原文：http://blog.csdn.net/wzy0623/article/details/51900172