Transcript LBSC 708L Session 1

Database Design and MySQL
Session 4
INFM 718N
Web-Enabled Databases
Agenda
• Database design
• MySQL
• Project teams: next steps
• (if we have time) Programming
• Relational normalization
• Structured programming
• Software patterns
• Object-oriented design
• Functional decomposition
Business Interaction
Design
rules
Interface
Design
Client Hardware
Web Browser
Client-side Programming
Interchange Language
Server-side Programming
(PC)
(IE, Firefox)
(JavaScript)
(HTML, XML)
(PHP)
Database
(MySQL)
Server Hardware
(PC, Unix)
Databases
• Database
– Collection of data, organized to support access
– Models some aspects of reality
• DataBase Management System (DBMS)
– Software to create and access databases
• Relational Algebra
– Mathematical theory that supports optimization
Database “Programming”
• Structured Query Language (SQL)
– Consistent, unambiguous interface to any DBMS
– Simple command structure:
• e.g., SELECT last-name FROM students WHERE dept=“CLIS”
– Useful standard for inter-process communications
• Visual programming (e.g., Microsoft Access)
– Unambiguous, and easier to learn than SQL
• Natural language (e.g., interactive voice response system)
– Improves ease of use, but with potential for ambiguity and error
• e.g., Show me the last names of students in CLIS
Getting Started
• What questions must you answer?
• What data is needed to generate the answers?
– Entities
• Attributes of those entities
– Relationships
• Nature of those relationships
• How will the user interact with the system?
– Relating the question to the available data
– Expressing the answer in a useful form
An E-R Example
manage-role
1
student
M
member-of
1
team
1
M
human
creates
implement-role
1
client
1
needs
M
project
d
php-project
ajax-project
E-R Diagrams
• Entities
– Types
• Subtypes (disjoint / overlapping), aggregation
– Attributes
• Mandatory / optional
– Identifier
• Relationships
– Cardinality
– Existence
– Degree
Making Tables from E-R Diagrams
• Pick a primary key for each entity
• Build the tables
– One per entity
– Plus one per M:M relationship
– Choose terse but memorable table and field names
• Check for parsimonious representation
– Relational “normalization”
– Redundant storage of computable values
• Implement using a DBMS
Table-Oriented Lingo
• Field
An “atomic” unit of data
– number, string, true/false, …
• Record
A collection of related fields
• Table
A collection of related records
– Each record is one row in the table
– Each field is one column in the table
• Primary Key The field that identifies a record
– Values of a primary key must be unique
• Database
A collection of tables
Relational Lingo
• Tables represent “relations”
– Course, course description
– Name, email address, department
• Named fields represent “attributes”
• Each row in the table is called a “tuple”
– The order of the rows is not important
• Queries specify desired conditions
– The DBMS then finds data that satisfies them
Visualizing Tables
primary key
Key Lingo
• “Primary Key” uniquely identifies a record
– e.g. student ID in the student table
• “Compound” primary key
– Synthesize a primary key with a combination of fields
– e.g., Student ID + Course ID in the enrollment table
• “Foreign Key” is primary key in the other table
– Note: it need not be unique in this table
Goals of “Normalization”
• Save space
– Save each fact only once
• More rapid updates
– Every fact only needs to be updated once
• More rapid search
– Finding something once is good enough
• Avoid inconsistency
– Changing data once changes it everywhere
Normalization
• 1NF: Single-valued indivisible (atomic) attributes
– Split “Doug Oard” to two attributes as (“Doug”, “Oard”)
– Model M:M implement-role relationship with a table
• 2NF: Attributes depend on complete primary key
– (id, impl-role, name)->(id, name)+(id, impl-role)
• 3NF: Attributes depend directly on primary key
– (id, addr, city, state, zip)->(id, addr, zip)+(zip, city, state)
• 4NF: Divide independent M:M tables
– (id, role, courses) -> (id, role) + (id, courses)
• 5NF: Don’t enumerate derivable combinations
Normalized Table Structure
•
•
•
•
•
•
•
Persons: id, fname, lname, userid, password
Contacts: id, ctype, cstring
Ctlabels: ctype, string
Students: id, team, mrole
Iroles: id, irole
Rlabels: role, string
Projects: team, client, pstring
Referential Integrity
• “Foreign key” values must exist in another table
– If not, those records cannot be joined
• Checked when data added to this table
– MySQL “Error 150”
• Triggers when data deleted/changed in other table
– Specify SET NULL, RESTRICT or CASCADE
Getting started with MySQL
• “root” creates database, grants permissions
– By you on WAMP (mysql –u root –p)
– By Charles Goldman on OTAL
– CREATE DATABASE team1;
– GRANT SELECT, INSERT, UPDATE, DELETE, INDEX, ALTER, CREATE, DROP ON
team1.* TO ‘foo’@’localhost’ IDENTIFIED BY ‘bar’;
– FLUSH PRIVILEGES;
• Start mysql
– Start->Run->cmd for WAMP, ssh for OTAL
– mysql –u foo –p bar [you can cd to your playspace first, but you don’t need to]
• Connect to your database
– USE team1;
Some Useful MySQL Commands
• Looking around
–
–
–
–
SHOW DATABASES;
SHOW TABLES;
DESCRIBE tablename;
SELECT * FROM tablename;
• Optimization
– SHOW TABLE STATUS \G;
• OPTIMIZE TABLE tablename;
– EXPLAIN <SQLquery>;
• ALTER TABLE tablename ADD INDEX fieldname;
Creating Tables
CREATE TABLE contacts (
ckey
MEDIUMINT UNSIGNED NOT NULL AUTO_INCREMENT,
id
MEDIUMINT UNSIGNED NOT NULL,
ctype
SMALLINT UNSIGNED NOT NULL,
cstring VARCHAR(40) NOT NULL,
FOREIGN KEY (id) REFERENCES persons(id) ON DELETE CASCADE,
FOREIGN KEY (ctype) REFERENCES ctlabels(ctype) ON DELETE RESTRICT,
PRIMARY KEY (ckey)
) ENGINE=INNODB;
To delete: DROP TABLE contacts;
Populating Tables
INSERT INTO ctlabels
(string) VALUES
('primary email'),
('alternate email'),
('home phone'),
('cell phone'),
('work phone'),
('AOL IM'),
('Yahoo Chat'),
('MSN Messenger'),
(‘other’);
 To empty a table: DELETE FROM ctlabels;
The SQL SELECT Command
• SELECT (“projection”) chooses columns
– Based on their label
• WHERE (“restriction”) chooses rows
– Based on their contents
• e.g. department ID = “HIST”
• These can be specified together
– SELECT Student ID, Dept WHERE Dept = “History”
WHERE Clause
• Each SELECT contains a single WHERE
• Numeric comparison
<, >, =, <>, …
• e.g., grade<80
• Boolean operations
– e.g., Name = “John” AND Dept <> “HIST”
A Denormalized “Flat File”
Student ID Last Name
1
Arrows
1
Arrows
2
Peters
2
Peters
3
Smith
4
Smith
First Name
John
John
Kathy
Kathy
Chris
John
Department IDDepartmentCourse ID Course description Grades email
EE
EE
lbsc690 Information Technology
90 jarrows@wam
EE
Elec Engin ee750 Communication
95 ja_2002@yahoo
HIST
HIST
lbsc690 Informatino Technology
95 kpeters2@wam
HIST
history
hist405 American History
80 kpeters2@wma
HIST
history
hist405 American History
90 smith2002@glue
CLIS
Info Sci
lbsc690 Information Technology
98 js03@wam
A Normalized Relational Database
Student Table
Student ID
1
2
3
4
Last Name
Arrows
Peters
Smith
Smith
First Name
John
Kathy
Chris
John
Department ID
EE
HIST
HIST
CLIS
Department Table
Course Table
Department ID
EE
HIST
CLIS
Course ID
lbsc690
ee750
hist405
Department
Electronic Engineering
History
Information Stuides
email
jarrows@wam
kpeters2@wam
smith2002@glue
js03@wam
Course Description
Information Technology
Communication
American History
Enrollment Table
Student ID
1
1
2
2
3
4
Course ID
lbsc690
ee750
lbsc690
hist405
hist405
lbsc690
Grades
90
95
95
80
90
98
Example of Join
Student Table
Student ID
Last Name
1 Arrows
2 Peters
3 Smith
4 Smith
Department Table
First Name
John
Kathy
Chris
John
Department ID
EE
HIST
HIST
CLIS
email
jarrows@wam
kpeters2@wam
smith2002@glue
js03@wam
Department ID
EE
HIST
CLIS
Department
Electronic Engineering
History
Information Stuides
“Joined” Table
Student ID Last Name
1
Arrows
2
Peters
3
Smith
4
Smith
First Name
John
Kathy
Chris
John
Department IDDepartment
EE
Electronic Engineering
HIST
History
HIST
History
CLIS
Information Stuides
email
jarrows@wam
kpeters2@wam
smith2002@glue
js03@wam
Project
New Table
Student ID Last Name
1
Arrows
2
Peters
3
Smith
4
Smith
First Name
John
Kathy
Chris
John
Department IDDepartment
EE
Electronic Engineering
HIST
History
HIST
History
CLIS
Information Stuides
email
jarrows@wam
kpeters2@wam
smith2002@glue
js03@wam
SELECT Student ID, Department
Student ID
1
2
3
4
Department
Electronic Engineering
History
History
Information Stuides
Restrict
New Table
Student ID Last Name
1
Arrows
2
Peters
3
Smith
4
Smith
First Name
John
Kathy
Chris
John
Department IDDepartment
EE
Electronic Engineering
HIST
History
HIST
History
CLIS
Information Stuides
email
jarrows@wam
kpeters2@wam
smith2002@glue
js03@wam
WHERE Department ID = “HIST”
Student ID Last Name
2 Peters
3 Smith
First Name Department IDDepartment
Kathy
HIST
History
Chris
HIST
History
email
kpeters2@wam
smith2002@glue
What are Requirements?
• Attributes
– Appearance
– Concepts (represented by data)
• Behavior
– What it does
– How you control it
– How you observe the results
Who Sets the Requirements?
• People who need the task done (customers)
• People that will operate the system (users)
• People who use the system’s outputs
• People who provide the system’s inputs
• Whoever pays for it (requirements commissioner)
The Requirements Interview
• Focus the discussion on the task
– Look for entities that are mentioned
• Discuss the system’s most important effects
– Displays, reports, data storage
– Learn where the system’s inputs come from
– People, stored data, devices, …
• Note any data that is mentioned
– Try to understand the structure of the data
• Shoot for the big picture, not every detail
First Things First
• Functionality
• Content
• Usability
• Security/Stability
Language Learning
• Learn some words
• Put those words together in simple ways
• Examine to broaden your understanding
• Create to deepen your mastery
• Repeat until fluent
Thinking About PHP
• Local vs. Web-server-based display
• HTML as an indirect display mechanism
• “View Source” for debugging
• Procedural perspective (vs. object-oriented)
Arrays in PHP
• A set of key-element pairs
$days = array(“Jan”->31, “Feb”=>28, …);
$months = explode(“/”, “Jan/Feb/Mar/…/Dec”);
$_POST
• Each element is accessed by the key
– {$days[“Jan”]}
– $months[0];
• Arrays and loops work naturally together
Thinking about Arrays
• Naturally encodes an order among elements
– $days = rksort($days);
• Natural data structure to use with a loop
– Do the same thing to different data
• PHP unifies arrays and hashtables
– Elements may be different types
Functions in PHP
• Declaration
function multiply($a, $b=3){return $a*$b;}
• Invoking a method
$b = multiply($b, 7);
• All variables in a function have only local scope
• Unless declared as global in the function
Why Modularity?
• Limit complexity
– Extent
– Interaction
– Abstraction
• Minimize duplication
Using PHP with (X)HTML Forms
<form action=“formResponseDemo.php”, method=“post”>
email: <input type=“text”, name=“email”, value=“<?php echo $email ?>”, size=30 />
<input type=“radio”, name=“sure”, value=“yes” /> Yes
<input type=“radio”, name=“sure”, value=“no” /> No
<input type=“submit”, name=“submit”, value=“Submit” />
<input type=“hidden”, name=“submitted”, value=“TRUE” />
</form>
if (isset($_POST[“submitted”])) {
echo “Your email address is $email.”;
} else {
echo “Error: page reached without proper form submission!”;
}
Sources of Complexity
• Syntax
– Learn to read past the syntax to see the ideas
– Copy working examples to get the same effect
• Interaction of data and control structures
– Structured programming
• Modularity
Some Things to Pay Attention To
Syntax
• How layout helps reading
• How variables are named
• How strings are used
• How input is obtained
• How output is created
Structured Programming
• How things are nested
• How arrays are used
Modular Programming
• Functional decomposition
• How functions are invoked
• How arguments work
• How scope is managed
• How errors are handled
• How results are passed
Programming Skills Hierarchy
• Reusing code [run the book’s programs]
• Understanding patterns [read the book]
• Applying patterns [modify programs]
• Coding without patterns [programming]
• Recognizing new patterns
Best Practices
• Design before you build
• Focus your learning
• Program defensively
• Limit complexity
• Debug syntax from the top down
Rapid Prototyping + Waterfall
Update
Requirements
Initial
Requirements
Choose
Functionality
Build
Prototype
Write
Specification
Create
Software
Write
Test Plan
Focus Your Learning
• Find examples that work
– Tutorials, articles, examples
• Cut them down to focus on what you need
– Easiest to learn with throwaway programs
• Once it works, include it in your program
– If it fails, you have a working example to look at
Defensive Programming
• Goal of software is to create desired output
• Programs transform input into output
– Some inputs may yield undesired output
• Methods should enforce input assumptions
– Guards against the user and the programmer!
• Everything should be done inside methods
Limiting Complexity
• Single errors are usually easy to fix
– So avoid introducing multiple errors
• Start with something that works
– Start with an existing program if possible
– If starting from scratch, start small
• Add one new feature
– Preferably isolated in its own method
Types of Errors
• Syntax errors
– Detected at compile time
• Run time exceptions
– Cause system-detected failures at run time
• Logic errors
– Cause unanticipated behavior (detected by you!)
• Design errors
– Fail to meet the need (detected by stakeholders)
Debugging Syntax Errors
• Focus on the first error message
– Fix one thing at a time
• The line number is where it was detected
– It may have been caused much earlier
• Understand the cause of “warnings”
– They may give a clue about later errors
• If all else fails, comment out large code regions
– If it compiles, the error is in the commented part
Run Time Exceptions
• Occur when you try to do the impossible
– Use a null variable, divide by zero, …
• The cause is almost never where the error is
– Why is the variable null?
• Exceptions often indicate a logic error
– Find why it happened, not just a quick fix!
Debugging Run-Time Exceptions
• Run the program to get a stack trace
– Where was this function called from?
• Print variable values before the failure
• Reason backwards to find the cause
– Why do they have these values?
• If necessary, print some values further back
Logic Errors
• Evidenced by inappropriate behavior
• Can’t be automatically detected
– “Inappropriate” is subjective
• Sometimes very hard to detect
– Sometimes dependent on user behavior
– Sometimes (apparently) random
• Cause can be hard to pin down
Debugging Logic Errors
• First, look where the bad data was created
• If that fails, print variables at key locations
– if (DEBUG) echo “\$foobar = $foobar”;
• Examine output for unexpected patterns
• Once found, proceed as for run time errors
– define (“DEBUG”, FALSE); to clean the output
Three Big Ideas
• Functional decomposition
– Outside-in design
• High-level languages
– Structured programming, object-oriented design
• Patterns
– Design patterns, standard algorithms, code reuse
One-Minute Paper
What was the muddiest point in today’s class?
• Be brief!
• No names!