MET CS 669 Lab 5: Subqueries
MET CS 669 Database Design and Implementation for Business
Lab 5: Subqueries
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Copyright 2016, 2018-2019 Boston University. All Rights Reserved.
Overview of the Lab
In this lab we learn to work with subqueries, which significantly extend the
expressional power of queries. Through the use of subqueries, a single query can
extract result sets that could not be extracted without subqueries. Subqueries enable
the query creator to ask the database for many complex structures in a single query.
This lab teaches you the mechanics crafting SQL queries that harness the power of
subqueries to handle more complex use cases.
From a technical perspective, together, we will learn:
what correlated and uncorrelated subqueries are and the theory supporting
both.
to use subqueries that return a single value, a list of values, and a table of
values.
to use subqueries that use aggregation.
to address use cases by using uncorrelated subqueries in the column select
list, the where clause, and the from clause.
to address use cases by using correlated subqueries and an EXIST clause in the
WHERE clause.
Lab 5 Explanations Reminder
As a reminder, it is important to read through the Lab 5 Explanation document to
successfully complete this lab, available in the assignment inbox alongside this lab.
The explanation document illustrates how to correctly execute each SQL construct
step-by-step, and explains important theoretical and practical details.
Other Reminders
The examples in this lab will execute in modern versions of Oracle, Microsoft
SQL Server, and PostgreSQL as is.
The screenshots in this lab display execution of SQL in the default SQL clients
supported in the course – Oracle SQL Developer, SQL Server Management
Studio, and pgAdmin – but your screenshots may vary somewhat as different
version of these clients are released.
Don’t forget to commit your changes if you work on the lab in different
sittings, using the “COMMIT” command, so that you do not lose your work.
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Section One – Subqueries
In this lab, you will practice crafting subqueries for the schema illustrated below.
Productproduct_id {pk}product_nameprice_in_us_dollarsSellssells_id {pk}product_id {fk1}store_location_id {fk2}Currencycurrency_id {pk}currency_nameus_dollars_to_currency_ratioStore_locationstore_location_id {pk}store_namecurrency_accepted_id {fk1}uses0..*1..1has1..10..*has1..10..*Shipping_offeringshipping_offering_id {pk}offeringhasOffersoffers_id {pk}store_location_id {fk1}shipping_offering_id {fk2}has1..11..11..*0..*1..1Alternate_Namealtername_name_id {pk}nameproduct_id {fk1}has1..*1..1
This schema’s structure supports basic medical product and currency information for an
international medical supplier, including store locations, the products they sell, shipping
offerings, the currency each location accepts, as well as conversion factors for
converting from U.S. dollars into the accepted currency. Due to the specific and
technical nature of the names of medical products, the supplier also keeps a list of
alternative names for each product that may help customers identify them. This schema
Section Background
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models prices and exchange rates at a specific point in time. While a real-world schema
would make provision for changes to prices and exchange rates over time, the tables
needed to support this have been intentionally excluded from our schema, because
their addition would add unneeded complexity on your journey of learning subqueries,
expressions, and value manipulation. The schema has just the right amount of
complexity for your learning.
The data for the tables is listed below.
Currencies
Name Ratio
British Pound 0.67
Canadian Dollar 1.34
US Dollar 1.00
Euro 0.92
Mexican Peso 16.76
Store Locations
Name Currency
Berlin Extension Euro
Cancun Extension Mexican Peso
London Extension British Pound
New York Extension US Dollar
Toronto Extension Canadian Dollar
Product
Name US Dollar Price
Glucometer $50
Bag Valve Mask $25
Digital Thermometer $250
Electronic Stethoscope $350
Handheld Pulse Oximeter $450
Sells
Store Location Product
Berlin Extension Glucometer
Berlin Extension Bag Valve Mask
Berlin Extension Digital Thermometer
Berlin Extension Handheld Pulse Oximeter
Cancun Extension Bag Valve Mask
Cancun Extension Digital Thermometer
Cancun Extension Handheld Pulse Oximeter
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London Extension Glucometer
London Extension Bag Valve Mask
London Extension Digital Thermometer
London Extension Electronic Stethoscope
London Extension Handheld Pulse Oximeter
New York Extension Glucometer
New York Extension Bag Valve Mask
New York Extension Digital Thermometer
New York Extension Electronic Stethoscope
New York Extension Handheld Pulse Oximeter
Toronto Extension Glucometer
Toronto Extension Bag Valve Mask
Toronto Extension Digital Thermometer
Toronto Extension Electronic Stethoscope
Toronto Extension Handheld Pulse Oximeter
Shipping_offering
Offering
Same Day
Overnight
Two Day
Offers
Store Location Shipping Offering
Berlin Extension Two Day
Cancun Extension Two Day
London Extension Same Day
London Extension Overnight
London Extension Two Day
New York Extension Overnight
New York Extension Two Day
Toronto Extension Two Day
Alternate Names
Name Product
Glucose Meter Glucometer
Blood Glucose Meter Glucometer
Glucose Monitoring System Glucometer
Thermometer Digital Thermometer
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Ambu Bag Bag Valve Mask
Oxygen Bag Valve Mask Oxygen Bag Valve Mask
Cardiology Stethoscope Electronic Stethoscope
Portable Pulse Oximeter Handheld Pulse Oximeter
Handheld Pulse Oximeter System Handheld Pulse Oximeter
The DDL and DML to create and populate the tables in the schema are listed below. You
can copy and paste this into your SQL client to create and populate the tables.
DROP TABLE Sells;
DROP TABLE Offers;
DROP TABLE Store_location;
DROP TABLE Alternate_name;
DROP TABLE Product;
DROP TABLE Currency;
DROP TABLE Shipping_offering;
CREATE TABLE Currency (
currency_id DECIMAL(12) NOT NULL PRIMARY KEY,
currency_name VARCHAR(255) NOT NULL,
us_dollars_to_currency_ratio DECIMAL(12,2) NOT NULL);
CREATE TABLE Store_location (
store_location_id DECIMAL(12) NOT NULL PRIMARY KEY,
store_name VARCHAR(255) NOT NULL,
currency_accepted_id DECIMAL(12) NOT NULL);
CREATE TABLE Product (
product_id DECIMAL(12) NOT NULL PRIMARY KEY,
product_name VARCHAR(255) NOT NULL,
price_in_us_dollars DECIMAL(12,2) NOT NULL);
CREATE TABLE Sells (
sells_id DECIMAL(12) NOT NULL PRIMARY KEY,
product_id DECIMAL(12) NOT NULL,
store_location_id DECIMAL(12) NOT NULL);
CREATE TABLE Shipping_offering (
shipping_offering_id DECIMAL(12) NOT NULL PRIMARY KEY,
offering VARCHAR(255) NOT NULL);
CREATE TABLE Offers (
offers_id DECIMAL(12) NOT NULL PRIMARY KEY,
store_location_id DECIMAL(12) NOT NULL,
shipping_offering_id DECIMAL(12) NOT NULL);
CREATE TABLE Alternate_name (
alternate_name_id DECIMAL(12) NOT NULL PRIMARY KEY,
name VARCHAR(255) NOT NULL,
product_id DECIMAL(12) NOT NULL);
ALTER TABLE Store_location
ADD CONSTRAINT fk_location_to_currency FOREIGN KEY(currency_accepted_id)
REFERENCES Currency(currency_id);
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ALTER TABLE Sells
ADD CONSTRAINT fk_sells_to_product FOREIGN KEY(product_id) REFERENCES
Product(product_id);
ALTER TABLE Sells
ADD CONSTRAINT fk_sells_to_location FOREIGN KEY(store_location_id) REFERENCES
Store_location(store_location_id);
ALTER TABLE Offers
ADD CONSTRAINT fk_offers_to_location FOREIGN KEY(store_location_id) REFERENCES
Store_location(store_location_id);
ALTER TABLE Offers
ADD CONSTRAINT fk_offers_to_offering FOREIGN KEY(shipping_offering_id)
REFERENCES Shipping_offering(shipping_offering_id);
ALTER TABLE Alternate_name
ADD CONSTRAINT fk_name_to_product FOREIGN KEY(product_id)
REFERENCES Product(product_id);
INSERT INTO Currency(currency_id, currency_name, us_dollars_to_currency_ratio)
VALUES(1, 'Britsh Pound', 0.67);
INSERT INTO Currency(currency_id, currency_name, us_dollars_to_currency_ratio)
VALUES(2, 'Canadian Dollar', 1.34);
INSERT INTO Currency(currency_id, currency_name, us_dollars_to_currency_ratio)
VALUES(3, 'US Dollar', 1.00);
INSERT INTO Currency(currency_id, currency_name, us_dollars_to_currency_ratio)
VALUES(4, 'Euro', 0.92);
INSERT INTO Currency(currency_id, currency_name, us_dollars_to_currency_ratio)
VALUES(5, 'Mexican Peso', 16.76);
INSERT INTO Shipping_offering(shipping_offering_id, offering)
VALUES (50, 'Same Day');
INSERT INTO Shipping_offering(shipping_offering_id, offering)
VALUES (51, 'Overnight');
INSERT INTO Shipping_offering(shipping_offering_id, offering)
VALUES (52, 'Two Day');
–Glucometer
INSERT INTO Product(product_id, product_name, price_in_us_dollars)
VALUES(100, 'Glucometer', 50);
INSERT INTO Alternate_name(alternate_name_id, name, product_id)
VALUES(10000, 'Glucose Meter', 100);
INSERT INTO Alternate_name(alternate_name_id, name, product_id)
VALUES(10001, 'Blood Glucose Meter', 100);
INSERT INTO Alternate_name(alternate_name_id, name, product_id)
VALUES(10002, 'Glucose Monitoring System', 100);
–Bag Valve Mask
INSERT INTO Product(product_id, product_name, price_in_us_dollars)
VALUES(101, 'Bag Valve Mask', 25);
INSERT INTO Alternate_name(alternate_name_id, name, product_id)
VALUES(10003, 'Ambu Bag', 101);
INSERT INTO Alternate_name(alternate_name_id, name, product_id)
VALUES(10004, 'Oxygen Bag Valve Mask', 101);
–Digital Thermometer
INSERT INTO Product(product_id, product_name, price_in_us_dollars)
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VALUES(102, 'Digital Thermometer', 250);
INSERT INTO Alternate_name(alternate_name_id, name, product_id)
VALUES(10005, 'Thermometer', 102);
–Electronic Stethoscope
INSERT INTO Product(product_id, product_name, price_in_us_dollars)
VALUES(103, 'Electronic Stethoscope', 350);
INSERT INTO Alternate_name(alternate_name_id, name, product_id)
VALUES(10006, 'Cardiology Stethoscope', 103);
–Handheld Pulse Oximeter
INSERT INTO Product(product_id, product_name, price_in_us_dollars)
VALUES(104, 'Handheld Pulse Oximeter', 450);
INSERT INTO Alternate_name(alternate_name_id, name, product_id)
VALUES(10007, 'Portable Pulse Oximeter', 104);
INSERT INTO Alternate_name(alternate_name_id, name, product_id)
VALUES(10008, 'Handheld Pulse Oximeter System', 104);
–Berlin Extension
INSERT INTO Store_location(store_location_id, store_name, currency_accepted_id)
VALUES(10, 'Berlin Extension', 4);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1000, 10, 100);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1001, 10, 101);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1002, 10, 102);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1003, 10, 104);
INSERT INTO Offers(offers_id, store_location_id, shipping_offering_id)
VALUES(150, 10, 52);
–Cancun Extension
INSERT INTO Store_location(store_location_id, store_name, currency_accepted_id)
VALUES(11, 'Cancun Extension', 5);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1004, 11, 101);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1005, 11, 102);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1006, 11, 104);
INSERT INTO Offers(offers_id, store_location_id, shipping_offering_id)
VALUES(151, 11, 52);
–London Extension
INSERT INTO Store_location(store_location_id, store_name, currency_accepted_id)
VALUES(12, 'London Extension', 1);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1007, 12, 100);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1008, 12, 101);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1009, 12, 102);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1010, 12, 103);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1011, 12, 104);
INSERT INTO Offers(offers_id, store_location_id, shipping_offering_id)
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VALUES(152, 12, 50);
INSERT INTO Offers(offers_id, store_location_id, shipping_offering_id)
VALUES(153, 12, 51);
INSERT INTO Offers(offers_id, store_location_id, shipping_offering_id)
VALUES(154, 12, 52);
–New York Extension
INSERT INTO Store_location(store_location_id, store_name, currency_accepted_id)
VALUES(13, 'New York Extension', 3);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1012, 13, 100);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1013, 13, 101);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1014, 13, 102);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1015, 13, 103);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1016, 13, 104);
INSERT INTO Offers(offers_id, store_location_id, shipping_offering_id)
VALUES(155, 13, 51);
INSERT INTO Offers(offers_id, store_location_id, shipping_offering_id)
VALUES(156, 13, 52);
–Toronto Extension
INSERT INTO Store_location(store_location_id, store_name, currency_accepted_id)
VALUES(14, 'Toronto Extension', 2);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1017, 14, 100);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1018, 14, 101);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1019, 14, 102);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1020, 14, 103);
INSERT INTO Sells(sells_id, store_location_id, product_id)
VALUES(1021, 14, 104);
INSERT INTO Offers(offers_id, store_location_id, shipping_offering_id)
VALUES(157, 14, 52);
As a reminder, for each step that requires SQL, make sure to capture a screenshot of the
command and the results of its execution. Further, make sure to eliminate unneeded
columns from the result set, to name your columns something user-friendly and human
readable, and to format any prices as currencies.
1. Create the tables in the schema, including all of their columns, datatypes, and
constraints, and populate the tables with data. You can do so by executing the DDL
and DML above in your SQL client. You only need to capture one or two
Section Steps
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demonstrative screenshots for this step. No need to screenshot execution of every
line of code (that could require dozens of screenshots).
2. Write two queries which together retrieve the price of a digital thermometer in
London. The first query will retrieve the currency ratio for the currency accepted in
London. Your second query will hardcode the currency ratio retrieved in the first
query, in order to determine the price of the thermometer in London. The first query
should be dynamic in that the needed currency should be looked up rather than
hardcoded. That is, the currency should be obtained by looking up the currency the
store location accepts, not hardcoded by manually eyeballing the tables yourself.
3. In step 2, you determined the price of a digital thermometer in London by writing two
queries. For this step, determine the same by writing a single query that contains an
uncorrelated subquery. Explain:
a. how your solution makes use of the uncorrelated subquery to help retrieve the
result
b. how and when the uncorrelated subquery is executed in the context of the outer
query, and
c. the advantages of this solution over your solution for step 2.
4. Imagine a charity in London is hosting a fundraiser to purchase medical supplies for
organizations that provide care to people in impoverished areas. The charity is
targeting both people with average income as well a few wealthier people, and to
this end asks for a selection of products both groups can contribute to purchase.
Specifically, for the average income group, they would like to know what products
cost less than 25 Euros, and for the wealthier group, they would like to know what
products cost more than 300 Euros.
a. Develop a single query to provide them this result, which should contain
uncorrelated subqueries and should list the names of the products as well as their
prices in Euros.
b. Explain how what each subquery does, its role in the overall query, and how the
subqueries were integrated to give the correct results.
Note that the Euro monetary prefix is €.
5. Imagine that Denisha is a traveling doctor who works for an agency that sends her to
various locations throughout the world with very little notice. As a result, she needs
to know about medical supplies that are available in all store locations (not just some
locations). This way, regardless of where she is sent, she knows she can purchase
those products. She is also interested in viewing the alternate names for these
products, so she is absolutely certain what each product is.
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Note: It is important to Denisha that she can purchase the product in any location;
only products sold in all stores should be listed, that is, if a product is sold in some
stores, but not all stores, it should not be listed.
a. Develop a single query to list out these results, making sure to use uncorrelated
subqueries where needed (one subquery will be put into the WHERE clause of the
outer query).
b. Explain how what each subquery does, its role in the overall query, and how the
subqueries were integrated to give the correct results.
In your thinking about how to address this use case, one item should be brought to
your attention – the phrase “all store locations”. By eyeballing the data, you can
determine the number of locations and hardcode that number, which will satisfy
Denisha’s request at this present time; however, as the number of locations change
over time (with stores opening or closing), such hardcoding would fail. It’s better to
dynamically determine the total number of locations in the query itself so that the
results are correct over time.
6. For this problem you will write a single query to address the same use case as in step
5, but change your query so that the main uncorrelated subquery is in the FROM
clause rather than in the WHERE clause. The results should be the same as in step 5,
except of course possibly row ordering which can vary. Explain how you integrated
the subquery into the FROM clause to derive the same results as step 5.
7. For this problem you will write a single query to address the same use case as in step
5, but change your query to use a correlated query combined with an EXISTS clause.
The results should be the same as in step 5, except of course possibly row ordering
which can vary. Explain:
a. how your solution makes use of the correlated subquery and EXISTS clause to help
retrieve the result
b. how and when the correlated subquery is executed in the context of the outer
query.
8. Compare and contrast the construction of the three different queries you developed
in steps 5-7, which all address the same use case. What advantages and
disadvantages do each construction have over the others? Which do you prefer and
why?
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Evaluation
Your lab will be reviewed by your facilitator or instructor with the following criteria and
grade breakdown.
Criterion A B C DFLetter
Grade
Correctness and
Completeness of
Results (70%)
All steps' results are
entirely complete and
correct
About ¾ of the steps'
results are correct and
complete
About half of the steps'
results are correct and
complete
About ¼ of the steps'
results are correct and
complete
Virtually none of the
step's results are correct
and complete
Constitution of
SQL and
Explanations
(30%)
Excellent use and
integration of
appropriate SQL
constructs and
supporting
explanations
Good use and
integration of
appropriate SQL
constructs and
supporting
explanations
Mediocre use and
integration of
appropriate SQL
constructs and
supporting
explanations
Substandard use and
integration of
appropriate SQL
constructs and
supporting
explanations
Virtually all SQL
constructs and supporting
explanations are
unsuitable or improperly
integrated
Assignment Grade:#N/A
The resulting grade is calculated as a weighted average as listed using A+=100, A=96, A-=92, B+=88, B=85, B-=82 etc.
To obtain an A grade for the course, your weighted average should be >=95, A- >=90, B+ >=87, B >= 82, B- >= 80 etc.
Use the Ask the Facilitators Discussion Forum if you have any questions
regarding how to approach this lab. Make sure to include your name in the
filename and submit it in the Assignments section of the course.
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