Description
Part 1: Indexes /40
The last thing we’ll be doing with our employee database is adding a few indexes. After adding
each index, run the associated query/queries and record the performance (planning time and
execution time). Also look at the explain plan of the queries. You’ll probably need to rewrite the
queries slightly to fit your database (if there are different columns or tables).
Index 1: Add an index to the employee_histories table first_name and last_name fields.
Index 2: Add an index to the employee_jobs table employee_id and job_id fields.
Index 3: Add an index to the employees table birthdate field.
For each index, answer the following questions:
a. Fill out the tables below describing how adding the index affected the planning and
execution timings.
b. Did adding the index change the explain plans? What changed?
c. Was this what you expected to happen for the timing and the execution plans? What is
a possible reason for this change (or lack of change)?
Index 1
Execution Time Without index With index
Query 1
Query 2
Index 2
Execution Time Without index With index
Query 3
Index 3
Execution Time Without index With index
Query 4
Part 2: Normalization /40
Let’s pretend that the company whose employees we’ve been managing so far is an
engineering firm. The company manages multiple projects at a time, and assigns its employees
to tasks on the different projects. Only one employee can be assigned to a project task. Below is
some un-normalized data used to manage projects in a company. After analyzing this sample
data, structure it in 1
st normal, 2nd normal, and 3
rd normal form one step at a time, showing the
results of each step. So you should have 3 diagram – one for your data in 1st normal, one for 2nd
normal, and one for 3rd normal.
Team
Member
Id
Team
Member
First
Name
Team
Member
Last
Name
Project
Code
Project Name Project
Status
Project
Manager
Task
Number
Task Status
1 John Smith DDL Darren &
Darren Ltd
Active Garth
Butler
10
132
133
134
Resolved
In Progress
Not Started
In Progress
2 Dave Richter DDL
KMI
Darren &
Darren Ltd
Kristen Motors
Inc.
Active
Active
Garth
Butler
Jim David
100
110
10
13
In Progress
Not Started
Not Started
Resolved
3 Janie Klotter KMI Kristen Motors
Inc.
Active Jim David 1
2
15
In Progress
Resolved
Resolved
Part 3: Concurrency /20
1. Scenario – Transaction A and B are being run concurrently in separate sessions.
Below is the initial state of the Accounts table before any transaction is run
Account
Number
Account
Nickname
Account
Balance
1 Chequing 450
2 Chequing 200
Transaction A Transaction B
SET TRANSACTION ISOLATION LEVEL READ
UNCOMMITTED;
BEGIN
SELECT
a.account_number,
a.account_nickname,
a.account_balance
FROM accounts;
UPDATE accounts
SET account_balance = 0
WHERE account_number = 2;
END;
COMMIT;
SET TRANSACTION ISOLATION LEVEL READ
UNCOMMITTED;
BEGIN
SELECT
a.account_number,
a.account_nickname,
a.account_balance
FROM accounts;
UPDATE accounts
SET account_balance = account_balance – 100
WHERE account_number = 1;
UPDATE accounts
SET account_balance = account_balance + 100
WHERE account_number = 2;
END;
COMMIT;
a. What would the Accounts table look like after these transactions are finished?
Account
Number
Account
Nickname
Account
Balance
b. What type(s) of data inconsistency is caused in this case (lost update, dirty read, nonrepeatable read, or phantom read)?
2. Transaction C and D are being run concurrently in separate sessions
Below is the initial state of the Accounts table before any transaction is run:
Account
Number
Account
Nickname
Account
Balance
1 Chequing 450
2 Chequing 200
Transaction C Transaction D
SET TRANSACTION ISOLATION LEVEL READ
COMMITTED;
BEGIN
SELECT
a.account_number,
a.account_nickname,
a.account_balance
FROM accounts;
SELECT
a.account_number,
a.account_nickname,
a.account_balance
FROM accounts;
END;
COMMIT;
SET TRANSACTION ISOLATION LEVEL READ
COMMITTED;
BEGIN
INSERT INTO accounts (account_number,
account_nickname, account_balance)
VALUES(3, ‘Savings’, 50);
UPDATE accounts
SET account_balance = 300
WHERE account_number = 1;
END;
COMMIT;
a. What type(s) of data inconsistency is caused in this case (lost update, dirty read, nonrepeatable read, or phantom read)?
3. Transaction E and F are being run concurrently in separate sessions
Below is the initial state of the Accounts table before any transaction is run:
Account
Number
Account
Nickname
Account
Balance
1 Chequing 450
2 Chequing 200
Transaction E Transaction F
SET TRANSACTION ISOLATION LEVEL UNCOMMITTED
READ;
BEGIN
SELECT
a.account_number,
a.account_nickname,
a.account_balance
FROM accounts;
UPDATE accounts
SET account_balance = 300
WHERE account_number = 1;
SELECT
a.account_number,
a.account_nickname,
a.account_balance
FROM accounts;
END;
ROLLBACK ;
SET TRANSACTION ISOLATION LEVEL UNCOMMITTED
READ;
BEGIN
SELECT
a.account_number,
a.account_nickname,
a.account_balance
FROM accounts;
INSERT INTO accounts (account_number,
account_nickname, account_balance)
VALUES(3, ‘Savings’, 50);
END;
COMMIT;
a. What type(s) of data inconsistency is caused in this case (lost update, dirty read, nonrepeatable read, or phantom read)?
