I. OBJECTIVES
1. Understand and become familiar with the SQL Analytical Extensions.
2. Learn to create, use, and maintain materialized views, and their functional equivalents.
3. Effectively apply Advanced Aggregate SQL Operations, such as GROUP BY ROLLUP to solve business intelligence questions and analytical processing problems.

II. PARTS LIST
1. EDUPE-VT Omnymbus Virtual Machine Environment (https://devry.edupe.net:9090/) and/or:
2. MySQL (dev.mysql.com/downloads)

III. PROCEDURE
Scenario and Summary

For the lab this week, we are going to look at how the ROLLUP and CUBE extensions available in SQL can be used to create query result sets that have more than one dimension to them. Both of these extensions are used in conjunction with the GROUP BY clause and allow for a much broader look at the data.
To record your work for this lab use the lab report found at the end of this document. As in your previous labs, you will need to copy/paste your SQL statements and results into this document. Upon completion and prior to the due date, submit this document to the appropriate Dropbox.
iLAB STEPS
STEP 1: Setting Up
For this lab you will be using a different user and set of tables than you have used so far for other labs. To set up your instance you will need to do the following.
The first thing you will do for this lab is to run the following SQL Script. Begin by creating the DBM449Lab6 Schema, and creating any user accounts and privileges you wish to use (at least one).
Run the following script to create and populate a set of tables that will be used for this lab. Instructions for this are outlined in Step 1.
SET @[email protected]@UNIQUE_CHECKS, UNIQUE_CHECKS=0;
SET @[email protected]@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=0;
SET @[email protected]@SQL_MODE, SQL_MODE=’TRADITIONAL,ALLOW_INVALID_DATES’;

USE `DBM449Lab6` ;

— —————————————————–
— Table `DBM449Lab6`.`DISTRICT`
— —————————————————–
DROP TABLE IF EXISTS `DBM449Lab6`.`DISTRICT` ;

CREATE TABLE IF NOT EXISTS `DBM449Lab6`.`DISTRICT` (
`DIST_ID` INT(11) NOT NULL,
`DIST_NAME` VARCHAR(10) NULL DEFAULT NULL,
PRIMARY KEY (`DIST_ID`))
ENGINE = InnoDB;

— —————————————————–
— Table `DBM449Lab6`.`CUSTOMER`
— —————————————————–
DROP TABLE IF EXISTS `DBM449Lab6`.`CUSTOMER` ;

CREATE TABLE IF NOT EXISTS `DBM449Lab6`.`CUSTOMER` (
`CUST_CODE` DECIMAL(10,0) NOT NULL,
`CUST_LNAME` VARCHAR(15) NULL DEFAULT NULL,
`CUST_FNAME` VARCHAR(15) NULL DEFAULT NULL,
`CUST_INITIAL` CHAR(1) NULL DEFAULT NULL,
`CUST_STATE` CHAR(2) NULL DEFAULT NULL,
`DIST_ID` INT(11) NOT NULL,
PRIMARY KEY (`CUST_CODE`),
CONSTRAINT `fk_CUSTOMER_DISTRICT1`
FOREIGN KEY (`DIST_ID`)
REFERENCES `DBM449Lab6`.`DISTRICT` (`DIST_ID`)
ON DELETE NO ACTION
ON UPDATE NO ACTION)
ENGINE = InnoDB;

CREATE INDEX `fk_CUSTOMER_DISTRICT1_idx` ON `DBM449Lab6`.`CUSTOMER` (`DIST_ID` ASC);

— —————————————————–
— Table `DBM449Lab6`.`SUPPLIER`
— —————————————————–
DROP TABLE IF EXISTS `DBM449Lab6`.`SUPPLIER` ;

CREATE TABLE IF NOT EXISTS `DBM449Lab6`.`SUPPLIER` (
`SUP_CODE` INT(11) NOT NULL,
`SUP_NAME` VARCHAR(35) NULL DEFAULT NULL,
`SUP_AREACODE` CHAR(3) NULL DEFAULT NULL,
`SUP_STATE` CHAR(2) NULL DEFAULT NULL,
PRIMARY KEY (`SUP_CODE`))
ENGINE = InnoDB
DEFAULT CHARACTER SET = latin1;

— —————————————————–
— Table `DBM449Lab6`.`PRODUCT`
— —————————————————–
DROP TABLE IF EXISTS `DBM449Lab6`.`PRODUCT` ;

CREATE TABLE IF NOT EXISTS `DBM449Lab6`.`PRODUCT` (
`PROD_CODE` VARCHAR(10) NOT NULL,
`PROD_DESCRIPT` VARCHAR(35) NULL DEFAULT NULL,
`PROD_CATEGORY` VARCHAR(5) NULL DEFAULT NULL,
`SUP_CODE` INT(11) NOT NULL,
PRIMARY KEY (`PROD_CODE`),
CONSTRAINT `fk_PRODUCT_SUPPLIER`
FOREIGN KEY (`SUP_CODE`)
REFERENCES `DBM449Lab6`.`SUPPLIER` (`SUP_CODE`)
ON DELETE NO ACTION
ON UPDATE NO ACTION)
ENGINE = InnoDB;

CREATE INDEX `fk_PRODUCT_SUPPLIER_idx` ON `DBM449Lab6`.`PRODUCT` (`SUP_CODE` ASC);

— —————————————————–
— Table `DBM449Lab6`.`SALES`
— —————————————————–
DROP TABLE IF EXISTS `DBM449Lab6`.`SALES` ;

CREATE TABLE IF NOT EXISTS `DBM449Lab6`.`SALES` (
`TIME_ID` DECIMAL(10,0) NOT NULL DEFAULT ‘0’,
`CUST_CODE` DECIMAL(10,0) NOT NULL DEFAULT ‘0’,
`PROD_CODE` VARCHAR(10) NOT NULL DEFAULT ”,
`SALE_UNITS` DECIMAL(10,0) NULL DEFAULT NULL,
`SALE_PRICE` DECIMAL(10,2) NULL DEFAULT NULL,
PRIMARY KEY (`TIME_ID`, `CUST_CODE`, `PROD_CODE`))
ENGINE = InnoDB;

— —————————————————–
— Table `DBM449Lab6`.`TIME`
— —————————————————–
DROP TABLE IF EXISTS `DBM449Lab6`.`TIME` ;

CREATE TABLE IF NOT EXISTS `DBM449Lab6`.`TIME` (
`TIME_ID` INT(11) NOT NULL,
`TIME_YEAR` INT(11) NULL DEFAULT NULL,
`TIME_MONTH` INT(11) NULL DEFAULT NULL,
`TIME_DAY` INT(11) NULL DEFAULT NULL,
`TIME_QTR` INT(11) NULL DEFAULT NULL,
PRIMARY KEY (`TIME_ID`))
ENGINE = InnoDB
DEFAULT CHARACTER SET = latin1;

SET [email protected]_SQL_MODE;
SET [email protected]_FOREIGN_KEY_CHECKS;
SET [email protected]_UNIQUE_CHECKS;

INSERT INTO SUPPLIER VALUES(31225,’Bryson, Inc.’ ,’615′,’TN’);
INSERT INTO SUPPLIER VALUES(31226,’SuperLoo, Inc.’ ,’904′,’FL’);
INSERT INTO SUPPLIER VALUES(31231,’DE Supply’ ,’615′,’TN’);
INSERT INTO SUPPLIER VALUES(31344,’Gomez Bros.’ ,’615′,’KY’);
INSERT INTO SUPPLIER VALUES(32567,’Dome Supply’ ,’901′,’GA’);
INSERT INTO SUPPLIER VALUES(33119,’Randsets Ltd.’ ,’901′,’GA’);
INSERT INTO SUPPLIER VALUES(44004,’Brackman Bros.’ ,’615′,’TN’);
INSERT INTO SUPPLIER VALUES(44288,’ORDVA, Inc.’ ,’615′,’TN’);
INSERT INTO SUPPLIER VALUES(55443,’BK, Inc.’ ,’904′,’FL’);
INSERT INTO SUPPLIER VALUES(55501,’Damal Supplies’ ,’615′,’TN’);
INSERT INTO SUPPLIER VALUES(55595,’Rubicon Systems’ ,’904′,’FL’);

INSERT INTO PRODUCT VALUES(’11QER/31′,’Power painter, 15 psi., 3-nozzle’ ,’CAT1′,55595);
INSERT INTO PRODUCT VALUES(’13-Q2/P2′,’7.25-in. pwr. saw blade’ ,’CAT1′,31344);
INSERT INTO PRODUCT VALUES(’14-Q1/L3′,’9.00-in. pwr. saw blade’ ,’CAT1′,31344);
INSERT INTO PRODUCT VALUES(‘1546-QQ2′,’Hrd. cloth, 1/4-in., 2×50′ ,’CAT2’,33119);
INSERT INTO PRODUCT VALUES(‘1558-QW1′,’Hrd. cloth, 1/2-in., 3×50′ ,’CAT2’,33119);
INSERT INTO PRODUCT VALUES(‘2232/QTY’,’BD jigsaw, 12-in. blade’ ,’CAT2′,44288);
INSERT INTO PRODUCT VALUES(‘2232/QWE’,’BD jigsaw, 8-in. blade’ ,’CAT3′,44288);
INSERT INTO PRODUCT VALUES(‘2238/QPD’,’BD cordless drill, 1/2-in.’ ,’CAT3′,55595);
INSERT INTO PRODUCT VALUES(‘23109-HB’,’Claw hammer’ ,’CAT4′,31225);
INSERT INTO PRODUCT VALUES(‘23114-AA’,’Sledge hammer, 12 lb.’ ,’CAT4′,31225);
INSERT INTO PRODUCT VALUES(‘54778-2T’,’Rat-tail file, 1/8-in. fine’ ,’CAT1′,31344);
INSERT INTO PRODUCT VALUES(’89-WRE-Q’,’Hicut chain saw, 16 in.’ ,’CAT2′,44288);
INSERT INTO PRODUCT VALUES(‘PVC23DRT’,’PVC pipe, 3.5-in., 8-ft’ ,’CAT3′,31225);
INSERT INTO PRODUCT VALUES(‘SM-18277′,’1.25-in. metal screw, 25′ ,’CAT4’,31225);
INSERT INTO PRODUCT VALUES(‘SW-23116′,’2.5-in. wd. screw, 50′ ,’CAT2’,31231);
INSERT INTO PRODUCT VALUES(‘WR3/TT3′ ,’Steel matting, 4”x8”x1/6″, .5″ mesh’,’CAT3′,55595);

INSERT INTO DISTRICT VALUES(1,’NE’);
INSERT INTO DISTRICT VALUES(2,’NW’);
INSERT INTO DISTRICT VALUES(3,’SE’);
INSERT INTO DISTRICT VALUES(4,’SW’);

INSERT INTO CUSTOMER VALUES(110010,’Ramas’ ,’Alfred’,’A’ ,’TN’,3);
INSERT INTO CUSTOMER VALUES(110011,’Dunne’ ,’Leona’ ,’K’ ,’GA’,3);
INSERT INTO CUSTOMER VALUES(110012,’Smith’ ,’Kathy’ ,’W’ ,’NY’,1);
INSERT INTO CUSTOMER VALUES(110013,’Olowski’ ,’Paul’ ,’F’ ,’NJ’,1);
INSERT INTO CUSTOMER VALUES(110014,’Orlando’ ,’Myron’ ,NULL,’CO’,2);
INSERT INTO CUSTOMER VALUES(110015,’O”Brian’,’Amy’ ,’B’ ,’TN’,3);
INSERT INTO CUSTOMER VALUES(110016,’Brown’ ,’James’ ,’G’ ,’GA’,3);
INSERT INTO CUSTOMER VALUES(110017,’Williams’,’George’,NULL,’CA’,4);
INSERT INTO CUSTOMER VALUES(110018,’Farriss’ ,’Anne’ ,’G’ ,’CA’,4);
INSERT INTO CUSTOMER VALUES(110019,’Smith’ ,’Olette’,’K’ ,’CO’,2);

INSERT INTO TIME VALUES(201,2009,09,29,3);
INSERT INTO TIME VALUES(202,2009,09,30,3);
INSERT INTO TIME VALUES(203,2009,09,31,3);
INSERT INTO TIME VALUES(206,2009,10,03,4);
INSERT INTO TIME VALUES(207,2009,10,04,4);

INSERT INTO SALES VALUES(201,110014,’13-Q2/P2′,1,14.99);
INSERT INTO SALES VALUES(201,110014,’23109-HB’,1,11.95);
INSERT INTO SALES VALUES(201,110015,’54778-2T’,2,5.99);
INSERT INTO SALES VALUES(201,110015,’2238/QPD’,1,38.95);
INSERT INTO SALES VALUES(202,110016,’1546-QQ2′,1,311.95);
INSERT INTO SALES VALUES(202,110016,’13-Q2/P2′,5,15.99);
INSERT INTO SALES VALUES(202,110017,’54778-2T’,3,5.99);
INSERT INTO SALES VALUES(202,110017,’23109-HB’,2,11.95);
INSERT INTO SALES VALUES(202,110018,’PVC23DRT’,12,5.87);
INSERT INTO SALES VALUES(203,110012,’SM-18277′,3,8.95);
INSERT INTO SALES VALUES(203,110014,’2232/QTY’,1,109.92);
INSERT INTO SALES VALUES(203,110015,’23109-HB’,1,11.95);
INSERT INTO SALES VALUES(203,110015,’89-WRE-Q’,1,258.95);
INSERT INTO SALES VALUES(203,110016,’13-Q2/P2′,2,15.99);
INSERT INTO SALES VALUES(203,110016,’54778-2T’,1,5.99);
INSERT INTO SALES VALUES(203,110016,’PVC23DRT’,5,5.87);
INSERT INTO SALES VALUES(203,110017,’WR3/TT3′,3,111.95);
INSERT INTO SALES VALUES(203,110017,’23109-HB’,1,11.95);
INSERT INTO SALES VALUES(203,110017,’13-Q2/P2′,1,15.99);
INSERT INTO SALES VALUES(203,110018,’23109-HB’,1,11.95);
INSERT INTO SALES VALUES(203,110018,’54778-2T’,2,5.99);
INSERT INTO SALES VALUES(203,110018,’2238/QPD’,1,38.95);
INSERT INTO SALES VALUES(203,110019,’1546-QQ2′,1,311.95);
INSERT INTO SALES VALUES(206,110010,’13-Q2/P2′,5,15.99);
INSERT INTO SALES VALUES(206,110010,’54778-2T’,3,5.99);
INSERT INTO SALES VALUES(206,110010,’23109-HB’,2,11.95);
INSERT INTO SALES VALUES(206,110010,’PVC23DRT’,12,5.87);
INSERT INTO SALES VALUES(206,110011,’SM-18277′,3,8.95);
INSERT INTO SALES VALUES(206,110011,’2232/QTY’,1,109.92);
INSERT INTO SALES VALUES(206,110012,’23109-HB’,1,11.95);
INSERT INTO SALES VALUES(206,110012,’89-WRE-Q’,1,258.95);
INSERT INTO SALES VALUES(207,110013,’13-Q2/P2′,2,15.99);
INSERT INTO SALES VALUES(207,110013,’54778-2T’,1,5.99);
INSERT INTO SALES VALUES(207,110013,’PVC23DRT’,5,5.87);
INSERT INTO SALES VALUES(207,110014,’WR3/TT3′,3,111.95);
INSERT INTO SALES VALUES(207,110015,’23109-HB’,1,11.95);

Once the script has finished running, then issue a SHOW TABLES; sql statement. Make sure that you see the following tables listed.

STEP 2: Using the ROLLUP Extension

In this section of the lab you are going to create a sales report that will show a supplier code, product code and the total sales for each product based on unit price times a quantity. More importantly the column that shows the total sales will also show a grand total for the supplier as well as a grand total over all (this will be the last row of data shown). To do this you will use the ROLLUP extension as part of the GROUP BY clause in the query. Use aliases for the column names so that the output columns in the result set look like the following.
SUPPLIER CODE PRODUCT TOTAL SALES
31225 23109-HB 119.50
31225 PVC23DRT 199.58
31225 SM-18277 53.70
31225 372.78
31344 13-Q2/P2 254.84
31344 54778-2T 71.88
31344 326.72
33119 1546-QQ2 623.90
33119 623.90
44288 2232/QTY 219.84
44288 89-WRE-Q 517.90
44288 737.74
55595 2238/QPD 77.90
55595 WR3/TT3 671.70
55595 749.60
2810.74

Be sure to copy your SQL code and the result set produced and paste it into the appropriate place in the LAB6_REPORT.

STEP 3: Using the CUBE Extension

In this section of the lab you are going to examine the creation of a sales report that will show a month code, product code and the total sales for each product based on unit price times a quantity. At the time of this writing, MySQL does not implement the CUBE clause, so we will study an example constructed using the ORACLE DBMS. In this report, the column that shows the total sales will also show a subtotal for each month (in this case representing a quarter). Following the monthly totals for each product and the subtotal by month then the report will list a total for each product sold during the period with a grand total for all sales during the period (this will be the last row of data shown). To do this, the CUBE extension is used as part of the GROUP BY clause in the query. Aliases are used for the column names so that the output columns in the result set look like the following.

MONTH PRODUCT TOTAL SALES
———- ———- ———–
9 13-Q2/P2 142.91
9 1546-QQ2 623.9
9 2232/QTY 109.92
9 2238/QPD 77.9
9 23109-HB 71.7
9 54778-2T 47.92
9 89-WRE-Q 258.95
9 PVC23DRT 99.79
9 SM-18277 26.85
9 WR3/TT3 335.85
9 1795.69

MONTH PRODUCT TOTAL SALES
———- ———- ———–
10 13-Q2/P2 111.93
10 2232/QTY 109.92
10 23109-HB 47.8
10 54778-2T 23.96
10 89-WRE-Q 258.95
10 PVC23DRT 99.79
10 SM-18277 26.85
10 WR3/TT3 335.85
10 1015.05
13-Q2/P2 254.84
1546-QQ2 623.9

MONTH PRODUCT TOTAL SALES
———- ———- ———–
2232/QTY 219.84
2238/QPD 77.9
23109-HB 119.5
54778-2T 71.88
89-WRE-Q 517.9
PVC23DRT 199.58
SM-18277 53.7
WR3/TT3 671.7
2810.74

31 rows selected.
Here is the ORACLE PL/SQL query which generated these results. Please study the example carefully.

SQL> SELECT T.TIME_MONTH AS “MONTH”, P.PROD_COD
2 SUM(S.SALE_UNITS*S.SALE_PRICE) AS “TOTAL S
3 FROM SALES S, PRODUCT P, TIME T
4 WHERE S.TIME_ID = T.TIME_ID
5 AND S.PROD_CODE = P.PROD_CODE
6 GROUP BY CUBE (T.TIME_MONTH, P.PROD_CODE)
7 ORDER BY T.TIME_MONTH, P.PROD_CODE;

NOTE: This query will produce the same results using NATURAL JOIN.

SELECT TIME_MONTH AS “MONTH”, PROD_CODE AS “PRODUCT”, SUM(SALE_UNITS*SALE_PRICE) AS “TOTAL SALES”
FROM TIME NATURAL JOIN SALES NATURAL JOIN PRODUCT
GROUP BY CUBE (TIME_MONTH, PROD_CODE)
ORDER BY TIME_MONTH, PROD_CODE;

Notice that this report uses the SALES, PRODUCT and TIME tables. It is also possible to write the query using NATURAL JOIN but some developers may feel more comfortable using a traditional JOIN method that will work just as well. Notice that the grand total amount of 2810.74 is the same total as in step 2.

CHECKPOINT QUESTION: Research Data Warehouse Solutions that may be integrated into MySQL enterprise architecture (e.g., PENTAHO). Explain how these solutions may be employed to overcome the current limitations in MySQL support for OLAP functionality. Pay special attention to the reporting and analytic capabilities of these solutions. Based on your analysis and research, do you believe that analytic functions such as GROUP BY CUBE are best implemented in the database (MySQL), the Data Warehouse and Reporting Engine (e.g., PENTAHO), or both. Fully explain your analysis and conclusions.

STEP 4: Materialized Views and a Refresh Procedure

Materialized views (MVs), sometimes referred to as snapshots are a very important aspect of dealing with data when doing data mining or working with a data warehouse. Unlike regular views, a materialized view does not always automatically react to changes made in the base tables of the view. In database systems that directly implement MVs, a Materialized View Log must be created on each base table that will be used in the view. As discussed and explored briefly at the conclusion of Lab 5, MySQL does not directly implement materialized views, but these may be effectively emulated using tables constructed with the same attributes as the view, provided that steps are taken to ensure the automatic update or refresh of the table. For instructional purposes, we will accomplish this by the simple expedient of creating a stored procedure which will drop, recreate, and rebuild the table serving as the view. In our exploration of the concept of the materialized view, we are going to create we are going to use the TIME and the SALES tables.
Because we will emulate an MV in our solution, we will craft a stored procedure to update the MV.
Create and install a stored procedure, named REFRESH_MV_SALESBYMONTH, using the following SQL SELECT statement to guide you. This SELECT statement demonstrates the data to be selected to populate the table. Your stored procedure must: (1) drop the table if it exists; and (2) SELECT INTO the table MV_SALESBYMONTH to create and populate it. Note that, with this logic, no separate logic is needed to first create the view—simply run the stored procedure. The details of the implementation are left to you.
SELECT TIME_YEAR AS “YEAR”, TIME_MONTH AS “MONTH”, PROD_CODE AS “PRODUCT”,
SUM(SALE_UNITS) AS “UNITS SOLD”, SUM(SALE_UNITS*SALE_PRICE) AS “SALES TOTAL”
FROM TIME T, SALES S
WHERE S.TIME_ID = T.TIME_ID
GROUP BY TIME_YEAR, TIME_MONTH, PROD_CODE;
Be sure to copy your SQL code and the result set produced and paste it into the appropriate place in the LAB6_REPORT.

STEP 5: Using the Materialized View
1. First, CALL REFRESH_MV_SALESBYMONTH. This will make sure that the view is created, and currently up-to-date.
2. Run the SQL statement: SELECT * FROM MV_SALESBYMONTH. The output columns from your view should look similar to the following (use aliases to format the column headings) and you should have 18 rows in the result set.
YEAR MONTH PRODUCT CO UNITS SOLD SALES TOTAL

Now we are going to add some data and update the view. Remember, we must manually run the stored procedure to update the view, as it will not (yet!) automatically itself.
1. To begin with, insert the following data into the SALES table—(207, 110016, ‘SM-18277’,1,8.95).
2. CALL the stored procedure to refresh the view.
3. Query the view once again.
You should now see that the row for units sold in month 10 for SM-18277 has increased from 3 to 4 and total sales amount has gone from 26.85 to 35.80.
4. Delete the row you just added to the SALES table, and call the stored procedure to refresh the view, proving that things are up-to-date.
5. Create a Trigger on the SALES table so that any insert automatically fires off the stored procedure to update the view.
6. Test your trigger, by again inserting the following data into the SALES table—(207, 110016, ‘SM-18277’,1,8.95).
7. Query the view once again.
You should now see that the row for units sold in month 10 for SM-18277 has increased from 3 to 4 and total sales amount has gone from 26.85 to 35.80.
CHECKPOINT QUESTION: For instructional purposes and simplicity, we have indulged in the expedient of dropping and recreating the entire MV_SALESBYMONTH table, which now occurs each time a new record is inserted on the SALES table. Would this be unacceptable in practice? Explain why or why not. What other events, and what other tables would require wire-up of triggers in order to ensure that the view is kept up-to-date at all times?
Be sure to copy your SQL code and the result set produced and paste it into the appropriate place in the LAB6_REPORT.